Note
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Decoding of a dataset after GLM fit for signal extraction#
Full step-by-step example of fitting a GLM to perform a decoding experiment. We use the data from one subject of the Haxby dataset.
More specifically:
Download the Haxby dataset.
Extract the information to generate a glm representing the blocks of stimuli.
Analyze the decoding performance using a classifier.
Fetch example Haxby dataset#
We download the Haxby dataset This is a study of visual object category representation
# By default 2nd subject will be fetched
import numpy as np
import pandas as pd
from nilearn import datasets
haxby_dataset = datasets.fetch_haxby()
# repetition has to be known
TR = 2.5
Load the behavioral data#
# Load target information as string and give a numerical identifier to each
behavioral = pd.read_csv(haxby_dataset.session_target[0], sep=" ")
conditions = behavioral["labels"].values
# Record these as an array of sessions
sessions = behavioral["chunks"].values
unique_sessions = behavioral["chunks"].unique()
# fMRI data: a unique file for each session
func_filename = haxby_dataset.func[0]
Build a proper event structure for each session#
events = {}
# events will take the form of a dictionary of Dataframes, one per session
for session in unique_sessions:
# get the condition label per session
conditions_session = conditions[sessions == session]
# get the number of scans per session, then the corresponding
# vector of frame times
n_scans = len(conditions_session)
frame_times = TR * np.arange(n_scans)
# each event last the full TR
duration = TR * np.ones(n_scans)
# Define the events object
events_ = pd.DataFrame(
{
"onset": frame_times,
"trial_type": conditions_session,
"duration": duration,
}
)
# remove the rest condition and insert into the dictionary
events[session] = events_[events_.trial_type != "rest"]
Instantiate and run FirstLevelModel#
We generate a list of z-maps together with their session and condition index
z_maps = []
conditions_label = []
session_label = []
# Instantiate the glm
from nilearn.glm.first_level import FirstLevelModel
glm = FirstLevelModel(
t_r=TR,
mask_img=haxby_dataset.mask,
high_pass=0.008,
smoothing_fwhm=4,
memory="nilearn_cache",
)
Run the GLM on data from each session#
events[session].trial_type.unique()
from nilearn.image import index_img
for session in unique_sessions:
# grab the fmri data for that particular session
fmri_session = index_img(func_filename, sessions == session)
# fit the GLM
glm.fit(fmri_session, events=events[session])
# set up contrasts: one per condition
conditions = events[session].trial_type.unique()
for condition_ in conditions:
z_maps.append(glm.compute_contrast(condition_))
conditions_label.append(condition_)
session_label.append(session)
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1df57f7290>, <nibabel.nifti1.Nifti1Image object at 0x7f1df57f4680>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.114769, ..., -2.149296],
...,
[ 2.367151, ..., 0.779998]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.44475 , ..., 0.379275]), <nibabel.nifti1.Nifti1Image object at 0x7f1df57f4680>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.013524, ..., 0.844135]), <nibabel.nifti1.Nifti1Image object at 0x7f1df57f4680>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.217486, ..., -1.430348]), <nibabel.nifti1.Nifti1Image object at 0x7f1df57f4680>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.106474, ..., -0.182434]), <nibabel.nifti1.Nifti1Image object at 0x7f1df57f4680>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.747494, ..., -1.660679]), <nibabel.nifti1.Nifti1Image object at 0x7f1df57f4680>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.080159, ..., -1.32614 ]), <nibabel.nifti1.Nifti1Image object at 0x7f1df57f4680>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.253894, ..., -0.452682]), <nibabel.nifti1.Nifti1Image object at 0x7f1df57f4680>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.240914, ..., 0.244136]), <nibabel.nifti1.Nifti1Image object at 0x7f1df57f4680>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1dfec91ca0>, <nibabel.nifti1.Nifti1Image object at 0x7f1dfedf5b50>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 12.660587, ..., -13.536042],
...,
[ -3.254408, ..., -33.842804]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.871458, ..., -0.990755]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfedf5b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.627194, ..., -1.290147]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfedf5b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.153013, ..., -1.320123]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfedf5b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.15748 , ..., 2.082416]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfedf5b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.997775, ..., -0.754066]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfedf5b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.109412, ..., 1.330079]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfedf5b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.030863, ..., -1.731439]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfedf5b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.559734, ..., -0.720924]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfedf5b50>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1dde4017c0>, <nibabel.nifti1.Nifti1Image object at 0x7f1dde400830>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 5.205584, ..., 26.587189],
...,
[-6.836576, ..., 10.676956]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.695564, ..., -0.455092]), <nibabel.nifti1.Nifti1Image object at 0x7f1dde400830>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.457214, ..., 1.537178]), <nibabel.nifti1.Nifti1Image object at 0x7f1dde400830>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.803925, ..., 0.570463]), <nibabel.nifti1.Nifti1Image object at 0x7f1dde400830>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.614932, ..., 0.232909]), <nibabel.nifti1.Nifti1Image object at 0x7f1dde400830>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.527175, ..., -1.062723]), <nibabel.nifti1.Nifti1Image object at 0x7f1dde400830>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.126756, ..., -2.274819]), <nibabel.nifti1.Nifti1Image object at 0x7f1dde400830>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.635166, ..., -0.395548]), <nibabel.nifti1.Nifti1Image object at 0x7f1dde400830>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.250365, ..., 0.364311]), <nibabel.nifti1.Nifti1Image object at 0x7f1dde400830>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1dfedf7500>, <nibabel.nifti1.Nifti1Image object at 0x7f1df40f30e0>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-2.026206, ..., 5.974948],
...,
[ 2.616334, ..., 0.104535]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.503142, ..., -1.639351]), <nibabel.nifti1.Nifti1Image object at 0x7f1df40f30e0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.271132, ..., -0.047089]), <nibabel.nifti1.Nifti1Image object at 0x7f1df40f30e0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.318104, ..., 0.724813]), <nibabel.nifti1.Nifti1Image object at 0x7f1df40f30e0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.073279, ..., -0.316956]), <nibabel.nifti1.Nifti1Image object at 0x7f1df40f30e0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.380183, ..., -0.690685]), <nibabel.nifti1.Nifti1Image object at 0x7f1df40f30e0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.631912, ..., -0.753286]), <nibabel.nifti1.Nifti1Image object at 0x7f1df40f30e0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.155784, ..., -0.065658]), <nibabel.nifti1.Nifti1Image object at 0x7f1df40f30e0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.186135, ..., -0.69267 ]), <nibabel.nifti1.Nifti1Image object at 0x7f1df40f30e0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1dfed5d6a0>, <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[ 53.033577, ..., -55.45955 ],
...,
[-51.57195 , ..., -55.994713]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.158342, ..., -0.068131]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.396497, ..., -0.424937]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.091867, ..., 0.463109]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.054041, ..., -0.122921]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.025223, ..., 0.562991]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.001653, ..., -0.968729]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.274665, ..., -0.667 ]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.564329, ..., 1.496068]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1de699a780>, <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-27.150482, ..., -5.81308 ],
...,
[-30.204891, ..., 7.417917]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.987059, ..., 1.41717 ]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.24774 , ..., 0.674399]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.731234, ..., 1.341998]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.714869, ..., 1.182988]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.222674, ..., 0.480354]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.366899, ..., -0.091153]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.2708 , ..., -0.247146]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.251249, ..., -0.413063]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1df5ea4ad0>, <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[129.51173 , ..., -15.279282],
...,
[-18.911755, ..., 21.839058]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.290412, ..., -0.609221]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.090297, ..., -0.822602]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.747918, ..., -0.108861]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.095788, ..., -1.376995]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.807425, ..., 1.826947]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.017351, ..., 0.622242]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.625042, ..., -0.231224]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.056424, ..., -1.672737]), <nibabel.nifti1.Nifti1Image object at 0x7f1df50eccb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1df5ea4ec0>, <nibabel.nifti1.Nifti1Image object at 0x7f1df5133560>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-15.915996, ..., 22.07737 ],
...,
[-16.981215, ..., 3.372383]], dtype=float32),
array([[ 0. , ..., 1. ],
...,
[-0.200737, ..., 1. ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.008536, ..., -0.066075]), <nibabel.nifti1.Nifti1Image object at 0x7f1df5133560>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.168487, ..., -0.636238]), <nibabel.nifti1.Nifti1Image object at 0x7f1df5133560>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.145684, ..., 0.932773]), <nibabel.nifti1.Nifti1Image object at 0x7f1df5133560>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.580823, ..., -0.455655]), <nibabel.nifti1.Nifti1Image object at 0x7f1df5133560>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.685537, ..., 0.715791]), <nibabel.nifti1.Nifti1Image object at 0x7f1df5133560>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.245273, ..., -0.099707]), <nibabel.nifti1.Nifti1Image object at 0x7f1df5133560>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.79538 , ..., 1.913842]), <nibabel.nifti1.Nifti1Image object at 0x7f1df5133560>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.519925, ..., 0.629218]), <nibabel.nifti1.Nifti1Image object at 0x7f1df5133560>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1df57bfb60>, <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-0.292987, ..., 18.392956],
...,
[-3.935719, ..., 0.602484]], dtype=float32),
array([[ 0. , ..., 1. ],
...,
[-0.200737, ..., 1. ]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.425611, ..., 2.348025]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.31867 , ..., 0.408223]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.534932, ..., -0.150519]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.149007, ..., 0.640215]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.640699, ..., 1.50369 ]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.246384, ..., -1.346316]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.162243, ..., -0.519251]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.203695, ..., -1.335337]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe6f4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1df5e7d1f0>, <nibabel.nifti1.Nifti1Image object at 0x7f1ddccb4cb0>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-5.223948, ..., -5.959582],
...,
[-7.677519, ..., 16.024363]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.034515, ..., 1.612397]), <nibabel.nifti1.Nifti1Image object at 0x7f1ddccb4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.62798 , ..., 1.160445]), <nibabel.nifti1.Nifti1Image object at 0x7f1ddccb4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.506632, ..., 0.459388]), <nibabel.nifti1.Nifti1Image object at 0x7f1ddccb4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.261016, ..., -0.747236]), <nibabel.nifti1.Nifti1Image object at 0x7f1ddccb4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.231796, ..., 1.098904]), <nibabel.nifti1.Nifti1Image object at 0x7f1ddccb4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.148582, ..., 0.999934]), <nibabel.nifti1.Nifti1Image object at 0x7f1ddccb4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.548262, ..., 0.09934 ]), <nibabel.nifti1.Nifti1Image object at 0x7f1ddccb4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.222824, ..., 0.318977]), <nibabel.nifti1.Nifti1Image object at 0x7f1ddccb4cb0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1df4fbc740>, <nibabel.nifti1.Nifti1Image object at 0x7f1df4fbd160>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-19.66533 , ..., -6.299562],
...,
[-24.647343, ..., 2.331865]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 1.0s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.175763, ..., -3.429485]), <nibabel.nifti1.Nifti1Image object at 0x7f1df4fbd160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-3.146358, ..., -2.947626]), <nibabel.nifti1.Nifti1Image object at 0x7f1df4fbd160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.806852, ..., -2.720554]), <nibabel.nifti1.Nifti1Image object at 0x7f1df4fbd160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.008926, ..., 0.4544 ]), <nibabel.nifti1.Nifti1Image object at 0x7f1df4fbd160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.279543, ..., 1.828183]), <nibabel.nifti1.Nifti1Image object at 0x7f1df4fbd160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.463642, ..., 0.26599 ]), <nibabel.nifti1.Nifti1Image object at 0x7f1df4fbd160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.058735, ..., -1.191442]), <nibabel.nifti1.Nifti1Image object at 0x7f1df4fbd160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.390268, ..., -1.112207]), <nibabel.nifti1.Nifti1Image object at 0x7f1df4fbd160>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7f1df5e683e0>, <nibabel.nifti1.Nifti1Image object at 0x7f1dfe8daea0>, { 'clean_kwargs': {},
'detrend': False,
'dtype': None,
'high_pass': None,
'high_variance_confounds': False,
'low_pass': None,
'reports': True,
'runs': None,
'smoothing_fwhm': 4,
'standardize': False,
'standardize_confounds': True,
't_r': 2.5,
'target_affine': None,
'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=0, confounds=None, sample_mask=None, copy=True, dtype=None)
__________________________________________________filter_and_mask - 0.8s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.glm.first_level.first_level.run_glm...
run_glm(array([[-1.095605, ..., 16.449202],
...,
[ 2.59974 , ..., -2.179998]], dtype=float32),
array([[0., ..., 1.],
...,
[0., ..., 1.]]), noise_model='ar1', bins=100, n_jobs=1, random_state=None)
__________________________________________________________run_glm - 0.9s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.340751, ..., -1.056108]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe8daea0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.043261, ..., 1.144442]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe8daea0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.517954, ..., 0.611394]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe8daea0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-5.797134e-01, ..., 4.317655e-06]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe8daea0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.398581, ..., -0.488427]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe8daea0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.714396, ..., 0.869941]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe8daea0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.924894, ..., 0.723724]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe8daea0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.145297, ..., -0.821272]), <nibabel.nifti1.Nifti1Image object at 0x7f1dfe8daea0>)
___________________________________________________________unmask - 0.1s, 0.0min
Generating a report#
Since we have already computed the FirstLevelModel and have the contrast, we can quickly create a summary report.
from nilearn.image import mean_img
from nilearn.reporting import make_glm_report
mean_img_ = mean_img(func_filename)
report = make_glm_report(
glm,
contrasts=conditions,
bg_img=mean_img_,
)
report # This report can be viewed in a notebook
Statistical Report for bottle, cat, chair, face, house, scissors, scrambledpix, shoe
First Level Model
Model details:
| drift_model | cosine |
|---|---|
| drift_order | 1 |
| fir_delays | [0] |
| high_pass (Hz) | 0.01 |
| hrf_model | glover |
| noise_model | ar1 |
| scaling_axis | 0 |
| signal_scaling | 0 |
| slice_time_ref | 0.0 |
| smoothing_fwhm | 4 |
| standardize | False |
| subject_label | None |
| t_r (s) | 2.5 |
| target_affine | None |
| target_shape | None |
Design Matrix:
Contrasts
Mask
Stat Maps with Cluster Tables
bottle
Contrast Plot
Cluster Table
| Height control | fpr |
|---|---|
| α | 0.0 |
| Threshold (computed) | 3.29 |
| Cluster size threshold (voxels) | 0 |
| Minimum distance (mm) | 8.0 |
| Cluster ID | X | Y | Z | Peak Stat | Cluster Size (mm3) |
|---|---|---|---|---|---|
| 1 | -36.75 | -46.88 | -46.88 | 6.63 | 147 |
| 2 | -54.25 | 13.12 | 5.62 | 6.62 | 98 |
| 3 | 29.75 | -20.62 | -24.38 | 6.33 | 393 |
| 4 | -1.75 | -39.38 | 13.12 | 6.33 | 196 |
| 5 | 33.25 | -43.12 | -43.12 | 6.21 | 2264 |
| 5a | 22.75 | -31.88 | -39.38 | 5.95 | |
| 5b | 8.75 | -50.62 | -35.62 | 5.77 | |
| 6 | -43.75 | 28.12 | -9.38 | 6.16 | 196 |
| 7 | -57.75 | -39.38 | 9.38 | 6.12 | 246 |
| 8 | -19.25 | -24.38 | -43.12 | 6.04 | 541 |
| 8a | -19.25 | -31.88 | -39.38 | 5.59 | |
| 9 | 26.25 | -20.62 | -13.12 | 5.94 | 344 |
| 10 | -40.25 | -35.62 | -35.62 | 5.92 | 344 |
| 11 | 26.25 | -46.88 | -46.88 | 5.86 | 147 |
| 12 | 22.75 | -46.88 | -61.88 | 5.83 | 492 |
| 12a | 22.75 | -58.12 | -61.88 | 5.39 | |
| 13 | 19.25 | -54.38 | -43.12 | 5.81 | 1132 |
| 14 | -12.25 | -24.38 | -76.88 | 5.71 | 196 |
| 15 | 19.25 | -76.88 | -13.12 | 5.70 | 639 |
| 16 | 43.75 | -50.62 | -5.62 | 5.64 | 984 |
| 16a | 47.25 | -43.12 | -1.88 | 4.69 | |
| 17 | 22.75 | -43.12 | 9.38 | 5.64 | 246 |
| 18 | -26.25 | -16.88 | -24.38 | 5.63 | 147 |
| 19 | 12.25 | -28.12 | -73.12 | 5.62 | 246 |
| 20 | -1.75 | -50.62 | -39.38 | 5.61 | 147 |
| 21 | 40.25 | 16.88 | -20.62 | 5.59 | 49 |
| 22 | -29.75 | 9.38 | -28.12 | 5.53 | 246 |
| 23 | 26.25 | -69.38 | -16.88 | 5.50 | 787 |
| 23a | 36.75 | -65.62 | -13.12 | 4.90 | |
| 24 | 1.75 | 50.62 | -16.88 | 5.49 | 344 |
| 25 | -50.75 | 35.62 | -16.88 | 5.48 | 196 |
| 26 | -15.75 | -28.12 | -43.12 | 5.48 | 49 |
| 27 | 33.25 | 5.62 | -13.12 | 5.47 | 492 |
| 27a | 26.25 | 9.38 | -16.88 | 5.44 | |
| 28 | 12.25 | -58.12 | -28.12 | 5.46 | 393 |
| 29 | 12.25 | -84.38 | -28.12 | 5.44 | 98 |
| 30 | -47.25 | -13.12 | 13.12 | 5.42 | 295 |
| 31 | -19.25 | -50.62 | -39.38 | 5.37 | 590 |
| 31a | -8.75 | -35.62 | -39.38 | 4.34 | |
| 31b | -12.25 | -46.88 | -39.38 | 3.83 | |
| 32 | -33.25 | -73.12 | 31.88 | 5.34 | 49 |
| 33 | -29.75 | -43.12 | -50.62 | 5.31 | 393 |
| 34 | -54.25 | -31.88 | -13.12 | 5.31 | 49 |
| 35 | 1.75 | -43.12 | -1.88 | 5.28 | 147 |
| 36 | 12.25 | -13.12 | -28.12 | 5.25 | 98 |
| 37 | -12.25 | 5.62 | -16.88 | 5.25 | 49 |
| 38 | 29.75 | 1.88 | -9.38 | 5.23 | 98 |
| 39 | -57.75 | -16.88 | 1.88 | 5.23 | 98 |
| 40 | 5.25 | -61.88 | -20.62 | 5.22 | 295 |
| 41 | -47.25 | -39.38 | -20.62 | 5.17 | 1132 |
| 41a | -50.75 | -39.38 | -9.38 | 4.55 | |
| 42 | 40.25 | -35.62 | -24.38 | 5.16 | 2214 |
| 42a | 47.25 | -24.38 | -13.12 | 5.04 | |
| 42b | 47.25 | -35.62 | -20.62 | 4.36 | |
| 43 | -29.75 | -84.38 | 1.88 | 5.16 | 442 |
| 43a | -36.75 | -76.88 | 1.88 | 4.69 | |
| 44 | 22.75 | -20.62 | -35.62 | 5.09 | 98 |
| 45 | 19.25 | -43.12 | -20.62 | 5.08 | 147 |
| 46 | -47.25 | -9.38 | 5.62 | 5.06 | 295 |
| 47 | 5.25 | -76.88 | 24.38 | 5.05 | 49 |
| 48 | -33.25 | 13.12 | 28.12 | 5.04 | 246 |
| 49 | -40.25 | 24.38 | -9.38 | 5.04 | 49 |
| 50 | 33.25 | 20.62 | -20.62 | 5.03 | 98 |
| 51 | 8.75 | -91.88 | 5.62 | 5.03 | 49 |
| 52 | -26.25 | -54.38 | -46.88 | 5.02 | 147 |
| 53 | 26.25 | 31.88 | -31.88 | 5.01 | 147 |
| 54 | -26.25 | -20.62 | -13.12 | 5.00 | 98 |
| 55 | -61.25 | -5.62 | 20.62 | 5.00 | 344 |
| 56 | 33.25 | 9.38 | -24.38 | 5.00 | 98 |
| 57 | 1.75 | -58.12 | -24.38 | 4.99 | 98 |
| 58 | -40.25 | -39.38 | 31.88 | 4.97 | 344 |
| 58a | -29.75 | -39.38 | 31.88 | 4.29 | |
| 59 | -47.25 | -61.88 | -5.62 | 4.96 | 49 |
| 60 | 29.75 | -50.62 | -50.62 | 4.95 | 196 |
| 61 | -15.75 | -76.88 | 28.12 | 4.94 | 295 |
| 62 | -8.75 | -46.88 | -65.62 | 4.94 | 246 |
| 63 | -43.75 | 50.62 | 16.88 | 4.93 | 49 |
| 64 | 22.75 | -20.62 | -28.12 | 4.90 | 344 |
| 64a | 12.25 | -20.62 | -31.88 | 4.58 | |
| 65 | -19.25 | -58.12 | -9.38 | 4.89 | 98 |
| 66 | -54.25 | -46.88 | -5.62 | 4.88 | 98 |
| 67 | 33.25 | -50.62 | -20.62 | 4.88 | 541 |
| 67a | 29.75 | -43.12 | -20.62 | 3.73 | |
| 68 | -40.25 | -61.88 | -16.88 | 4.87 | 295 |
| 69 | -50.75 | -50.62 | 20.62 | 4.87 | 98 |
| 70 | 19.25 | 35.62 | 28.12 | 4.87 | 49 |
| 71 | 33.25 | 31.88 | -9.38 | 4.86 | 98 |
| 72 | -40.25 | -20.62 | -31.88 | 4.80 | 49 |
| 73 | -47.25 | -54.38 | -9.38 | 4.80 | 344 |
| 74 | 19.25 | -31.88 | 16.88 | 4.80 | 49 |
| 75 | -15.75 | -50.62 | -1.88 | 4.79 | 49 |
| 76 | 22.75 | -16.88 | -9.38 | 4.77 | 98 |
| 77 | -15.75 | -1.88 | -69.38 | 4.76 | 49 |
| 78 | -26.25 | -54.38 | 46.88 | 4.76 | 98 |
| 79 | -12.25 | -58.12 | -16.88 | 4.75 | 49 |
| 80 | -29.75 | -80.62 | -24.38 | 4.72 | 49 |
| 81 | 40.25 | 31.88 | 13.12 | 4.72 | 49 |
| 82 | -50.75 | 16.88 | -1.88 | 4.72 | 49 |
| 83 | -47.25 | -28.12 | 13.12 | 4.71 | 98 |
| 84 | 22.75 | -35.62 | 54.38 | 4.70 | 295 |
| 85 | -15.75 | 16.88 | 43.12 | 4.70 | 147 |
| 86 | -22.75 | 1.88 | -1.88 | 4.70 | 49 |
| 87 | 8.75 | -16.88 | 46.88 | 4.70 | 49 |
| 88 | -1.75 | -46.88 | 1.88 | 4.69 | 98 |
| 89 | 36.75 | 28.12 | -35.62 | 4.68 | 49 |
| 90 | -40.25 | -28.12 | -20.62 | 4.68 | 49 |
| 91 | -15.75 | -31.88 | -61.88 | 4.68 | 49 |
| 92 | -12.25 | -58.12 | -46.88 | 4.67 | 49 |
| 93 | -5.25 | -13.12 | -69.38 | 4.65 | 49 |
| 94 | 19.25 | -1.88 | 28.12 | 4.65 | 49 |
| 95 | 36.75 | -61.88 | -5.62 | 4.64 | 98 |
| 96 | -29.75 | 13.12 | -13.12 | 4.64 | 295 |
| 96a | -26.25 | 5.62 | -13.12 | 3.88 | |
| 97 | -29.75 | -58.12 | -9.38 | 4.63 | 49 |
| 98 | -1.75 | -9.38 | -20.62 | 4.63 | 49 |
| 99 | -50.75 | 9.38 | 9.38 | 4.62 | 49 |
| 100 | -40.25 | -65.62 | -5.62 | 4.62 | 98 |
| 101 | -1.75 | -58.12 | -54.38 | 4.62 | 49 |
| 102 | -5.25 | -31.88 | 58.12 | 4.62 | 49 |
| 103 | 33.25 | -9.38 | -58.12 | 4.62 | 98 |
| 104 | 12.25 | -9.38 | 13.12 | 4.61 | 49 |
| 105 | 26.25 | 28.12 | -20.62 | 4.61 | 196 |
| 106 | 50.75 | 1.88 | 43.12 | 4.61 | 98 |
| 107 | 19.25 | -65.62 | -24.38 | 4.61 | 196 |
| 108 | 8.75 | -20.62 | -13.12 | 4.60 | 147 |
| 109 | -15.75 | 9.38 | -20.62 | 4.60 | 98 |
| 110 | -47.25 | -28.12 | 5.62 | 4.59 | 49 |
| 111 | -15.75 | -39.38 | -50.62 | 4.59 | 49 |
| 112 | -15.75 | -16.88 | 24.38 | 4.59 | 98 |
| 113 | -36.75 | 58.12 | 35.62 | 4.57 | 49 |
| 114 | 15.75 | -24.38 | -35.62 | 4.55 | 344 |
| 115 | -47.25 | -24.38 | -16.88 | 4.55 | 147 |
| 116 | -33.25 | -13.12 | -13.12 | 4.55 | 344 |
| 117 | 19.25 | -39.38 | -13.12 | 4.54 | 49 |
| 118 | -26.25 | 13.12 | 46.88 | 4.54 | 49 |
| 119 | 19.25 | -35.62 | 69.38 | 4.53 | 49 |
| 120 | 12.25 | 43.12 | -16.88 | 4.52 | 49 |
| 121 | -1.75 | -54.38 | -9.38 | 4.52 | 98 |
| 122 | -47.25 | -28.12 | 43.12 | 4.52 | 49 |
| 123 | -47.25 | 24.38 | 20.62 | 4.51 | 196 |
| 124 | 5.25 | -73.12 | 16.88 | 4.51 | 344 |
| 124a | -1.75 | -65.62 | 16.88 | 4.21 | |
| 125 | -15.75 | -9.38 | 28.12 | 4.51 | 344 |
| 126 | 12.25 | -80.62 | 13.12 | 4.51 | 147 |
| 127 | -12.25 | -13.12 | -46.88 | 4.50 | 98 |
| 128 | 12.25 | 20.62 | 35.62 | 4.50 | 49 |
| 129 | -36.75 | 43.12 | 24.38 | 4.49 | 98 |
| 130 | 1.75 | 13.12 | 9.38 | 4.48 | 49 |
| 131 | -1.75 | -46.88 | -24.38 | 4.48 | 147 |
| 132 | -47.25 | 35.62 | 16.88 | 4.48 | 196 |
| 133 | -1.75 | -9.38 | -46.88 | 4.47 | 196 |
| 134 | -1.75 | -50.62 | -1.88 | 4.46 | 49 |
| 135 | 22.75 | 31.88 | 9.38 | 4.45 | 49 |
| 136 | -29.75 | -80.62 | -9.38 | 4.45 | 98 |
| 137 | 15.75 | -1.88 | -16.88 | 4.45 | 196 |
| 138 | -40.25 | -46.88 | 1.88 | 4.44 | 147 |
| 139 | 40.25 | 5.62 | -24.38 | 4.44 | 49 |
| 140 | 47.25 | -54.38 | -16.88 | 4.44 | 49 |
| 141 | -57.75 | 5.62 | 13.12 | 4.43 | 98 |
| 142 | 50.75 | -5.62 | 5.62 | 4.43 | 49 |
| 143 | -50.75 | -5.62 | -5.62 | 4.42 | 49 |
| 144 | 68.25 | -1.88 | -9.38 | 4.42 | 196 |
| 144a | 68.25 | -13.12 | -9.38 | 4.41 | |
| 145 | 33.25 | 24.38 | 39.38 | 4.42 | 49 |
| 146 | -57.75 | -9.38 | 9.38 | 4.42 | 344 |
| 146a | -61.25 | -1.88 | 9.38 | 4.25 | |
| 147 | -36.75 | 20.62 | 35.62 | 4.42 | 295 |
| 148 | 54.25 | -24.38 | -5.62 | 4.42 | 344 |
| 149 | -43.75 | 43.12 | -28.12 | 4.41 | 147 |
| 150 | -1.75 | -1.88 | 1.88 | 4.41 | 246 |
| 151 | 40.25 | 35.62 | -1.88 | 4.41 | 49 |
| 152 | -61.25 | -20.62 | 13.12 | 4.41 | 49 |
| 153 | 19.25 | 16.88 | 28.12 | 4.40 | 49 |
| 154 | 40.25 | -35.62 | -43.12 | 4.40 | 49 |
| 155 | -50.75 | -16.88 | 24.38 | 4.39 | 49 |
| 156 | 15.75 | 9.38 | 43.12 | 4.39 | 98 |
| 157 | -22.75 | 16.88 | 5.62 | 4.39 | 98 |
| 158 | -1.75 | 58.12 | 13.12 | 4.39 | 49 |
| 159 | 8.75 | 16.88 | -20.62 | 4.38 | 49 |
| 160 | -43.75 | 24.38 | -1.88 | 4.38 | 196 |
| 161 | -50.75 | -20.62 | 9.38 | 4.38 | 98 |
| 162 | -8.75 | -46.88 | -31.88 | 4.37 | 49 |
| 163 | -15.75 | 13.12 | 50.62 | 4.36 | 147 |
| 164 | -8.75 | 9.38 | 24.38 | 4.36 | 246 |
| 165 | -40.25 | 5.62 | -28.12 | 4.36 | 147 |
| 166 | 29.75 | -24.38 | -16.88 | 4.36 | 49 |
| 167 | 50.75 | 43.12 | -5.62 | 4.36 | 49 |
| 168 | -47.25 | 16.88 | 5.62 | 4.35 | 98 |
| 169 | -47.25 | 20.62 | -13.12 | 4.35 | 49 |
| 170 | 40.25 | 9.38 | -20.62 | 4.35 | 98 |
| 171 | -22.75 | -54.38 | -61.88 | 4.35 | 147 |
| 172 | -15.75 | 24.38 | -9.38 | 4.34 | 49 |
| 173 | 36.75 | -73.12 | 5.62 | 4.34 | 98 |
| 174 | -36.75 | 28.12 | 31.88 | 4.34 | 246 |
| 175 | 12.25 | 50.62 | -1.88 | 4.34 | 98 |
| 176 | -22.75 | -9.38 | 13.12 | 4.34 | 49 |
| 177 | -47.25 | -58.12 | 13.12 | 4.33 | 98 |
| 178 | 22.75 | 46.88 | -16.88 | 4.33 | 49 |
| 179 | -15.75 | -1.88 | 16.88 | 4.33 | 49 |
| 180 | -5.25 | -24.38 | -20.62 | 4.33 | 196 |
| 181 | 19.25 | -69.38 | -13.12 | 4.32 | 98 |
| 182 | 33.25 | -24.38 | -13.12 | 4.32 | 49 |
| 183 | -15.75 | -54.38 | 43.12 | 4.32 | 49 |
| 184 | 5.25 | -35.62 | -58.12 | 4.31 | 98 |
| 185 | -22.75 | -69.38 | -13.12 | 4.31 | 196 |
| 186 | 36.75 | -31.88 | -9.38 | 4.31 | 295 |
| 187 | 1.75 | -76.88 | 35.62 | 4.31 | 49 |
| 188 | 36.75 | -54.38 | -20.62 | 4.31 | 49 |
| 189 | 5.25 | -88.12 | -1.88 | 4.31 | 49 |
| 190 | -1.75 | -54.38 | -28.12 | 4.30 | 98 |
| 191 | -1.75 | 13.12 | -20.62 | 4.30 | 98 |
| 192 | 22.75 | 16.88 | 5.62 | 4.29 | 147 |
| 193 | 1.75 | -5.62 | -13.12 | 4.29 | 49 |
| 194 | 22.75 | -28.12 | 69.38 | 4.29 | 98 |
| 195 | -15.75 | -73.12 | -13.12 | 4.29 | 98 |
| 196 | 1.75 | -5.62 | -54.38 | 4.29 | 49 |
| 197 | 36.75 | 43.12 | -28.12 | 4.28 | 49 |
| 198 | -22.75 | -76.88 | -16.88 | 4.28 | 49 |
| 199 | 54.25 | -20.62 | 39.38 | 4.28 | 49 |
| 200 | -15.75 | -20.62 | -58.12 | 4.27 | 98 |
| 201 | 22.75 | 9.38 | 16.88 | 4.26 | 246 |
| 202 | 19.25 | -9.38 | 20.62 | 4.26 | 49 |
| 203 | -50.75 | 9.38 | -16.88 | 4.26 | 246 |
| 204 | -5.25 | -46.88 | -16.88 | 4.26 | 738 |
| 204a | 12.25 | -39.38 | -13.12 | 3.93 | |
| 205 | 15.75 | 50.62 | 58.12 | 4.25 | 49 |
| 206 | -33.25 | 13.12 | 13.12 | 4.25 | 196 |
| 207 | -19.25 | -20.62 | -35.62 | 4.24 | 98 |
| 208 | 33.25 | -50.62 | -28.12 | 4.24 | 49 |
| 209 | -43.75 | 16.88 | 58.12 | 4.24 | 49 |
| 210 | -1.75 | -39.38 | 1.88 | 4.23 | 246 |
| 211 | -19.25 | 61.88 | 5.62 | 4.23 | 49 |
| 212 | 15.75 | -1.88 | -1.88 | 4.23 | 49 |
| 213 | 47.25 | -54.38 | -1.88 | 4.23 | 147 |
| 214 | -1.75 | 1.88 | 13.12 | 4.23 | 49 |
| 215 | 5.25 | -58.12 | -54.38 | 4.23 | 49 |
| 216 | 12.25 | 24.38 | 61.88 | 4.21 | 98 |
| 217 | 54.25 | -9.38 | 20.62 | 4.21 | 196 |
| 218 | 29.75 | -28.12 | -24.38 | 4.21 | 49 |
| 219 | -1.75 | -24.38 | 9.38 | 4.21 | 49 |
| 220 | 8.75 | -50.62 | -20.62 | 4.21 | 98 |
| 221 | -47.25 | -65.62 | 1.88 | 4.20 | 196 |
| 222 | 19.25 | -9.38 | 5.62 | 4.20 | 49 |
| 223 | 57.75 | 35.62 | 28.12 | 4.20 | 98 |
| 224 | 19.25 | 65.62 | 9.38 | 4.19 | 49 |
| 225 | -22.75 | 31.88 | -24.38 | 4.19 | 98 |
| 226 | -57.75 | -31.88 | -5.62 | 4.19 | 49 |
| 227 | 1.75 | -1.88 | 31.88 | 4.19 | 98 |
| 228 | -50.75 | 39.38 | 9.38 | 4.19 | 49 |
| 229 | 5.25 | -43.12 | -9.38 | 4.19 | 49 |
| 230 | -8.75 | 84.38 | 20.62 | 4.18 | 147 |
| 231 | -5.25 | 31.88 | -20.62 | 4.18 | 147 |
| 232 | 36.75 | 5.62 | 35.62 | 4.18 | 147 |
| 233 | -54.25 | 1.88 | 13.12 | 4.18 | 49 |
| 234 | -22.75 | -50.62 | 54.38 | 4.17 | 49 |
| 235 | 12.25 | 1.88 | 28.12 | 4.16 | 147 |
| 236 | -47.25 | -28.12 | 28.12 | 4.16 | 49 |
| 237 | 19.25 | 16.88 | 13.12 | 4.16 | 98 |
| 238 | -36.75 | 43.12 | 1.88 | 4.16 | 147 |
| 239 | -54.25 | -20.62 | -24.38 | 4.16 | 49 |
| 240 | -12.25 | 54.38 | -9.38 | 4.16 | 98 |
| 241 | -15.75 | -76.88 | 20.62 | 4.15 | 49 |
| 242 | 12.25 | 5.62 | 43.12 | 4.15 | 49 |
| 243 | -26.25 | 1.88 | 43.12 | 4.15 | 98 |
| 244 | -19.25 | -76.88 | -28.12 | 4.15 | 147 |
| 245 | 33.25 | 5.62 | 28.12 | 4.14 | 49 |
| 246 | 40.25 | -58.12 | 5.62 | 4.14 | 98 |
| 247 | -8.75 | 5.62 | -24.38 | 4.14 | 49 |
| 248 | 15.75 | -35.62 | 31.88 | 4.14 | 98 |
| 249 | -50.75 | 16.88 | -13.12 | 4.14 | 147 |
| 250 | -1.75 | -9.38 | -5.62 | 4.13 | 49 |
| 251 | 12.25 | -65.62 | -13.12 | 4.13 | 196 |
| 252 | 8.75 | 20.62 | -24.38 | 4.13 | 49 |
| 253 | -26.25 | 50.62 | 5.62 | 4.12 | 147 |
| 254 | 12.25 | -9.38 | 5.62 | 4.12 | 49 |
| 255 | 15.75 | -35.62 | 73.12 | 4.12 | 196 |
| 256 | -33.25 | -69.38 | 39.38 | 4.12 | 49 |
| 257 | 19.25 | -1.88 | 43.12 | 4.11 | 98 |
| 258 | 15.75 | -88.12 | 5.62 | 4.11 | 98 |
| 259 | 36.75 | 50.62 | 1.88 | 4.11 | 98 |
| 260 | -8.75 | -46.88 | -24.38 | 4.11 | 49 |
| 261 | 12.25 | 61.88 | 5.62 | 4.11 | 49 |
| 262 | -22.75 | -1.88 | -20.62 | 4.10 | 393 |
| 263 | -33.25 | -31.88 | -65.62 | 4.10 | 98 |
| 264 | -47.25 | 46.88 | 20.62 | 4.10 | 49 |
| 265 | 19.25 | -13.12 | -28.12 | 4.09 | 98 |
| 266 | 26.25 | -61.88 | 31.88 | 4.09 | 49 |
| 267 | 8.75 | 76.88 | 35.62 | 4.08 | 246 |
| 268 | -47.25 | -43.12 | 9.38 | 4.08 | 147 |
| 269 | -19.25 | -50.62 | 16.88 | 4.08 | 49 |
| 270 | 50.75 | -54.38 | -9.38 | 4.08 | 49 |
| 271 | 40.25 | -43.12 | -28.12 | 4.08 | 49 |
| 272 | 22.75 | -69.38 | 1.88 | 4.08 | 147 |
| 273 | -29.75 | 20.62 | -24.38 | 4.07 | 49 |
| 274 | 15.75 | -73.12 | -31.88 | 4.07 | 49 |
| 275 | 29.75 | -31.88 | 39.38 | 4.07 | 246 |
| 276 | -26.25 | -73.12 | -35.62 | 4.07 | 98 |
| 277 | -15.75 | 20.62 | -43.12 | 4.07 | 98 |
| 278 | 40.25 | -20.62 | 20.62 | 4.06 | 49 |
| 279 | -15.75 | -46.88 | -5.62 | 4.06 | 98 |
| 280 | -50.75 | 13.12 | -5.62 | 4.06 | 49 |
| 281 | 36.75 | -39.38 | -69.38 | 4.05 | 49 |
| 282 | 12.25 | -46.88 | -46.88 | 4.05 | 49 |
| 283 | -26.25 | -28.12 | -31.88 | 4.04 | 49 |
| 284 | 19.25 | 13.12 | -1.88 | 4.04 | 98 |
| 285 | -36.75 | 16.88 | 50.62 | 4.04 | 147 |
| 286 | 8.75 | 13.12 | 50.62 | 4.03 | 98 |
| 287 | 26.25 | 1.88 | 13.12 | 4.02 | 98 |
| 288 | 5.25 | -80.62 | -1.88 | 4.01 | 49 |
| 289 | 26.25 | -5.62 | -1.88 | 4.01 | 49 |
| 290 | -54.25 | -20.62 | -9.38 | 4.01 | 49 |
| 291 | -43.75 | -65.62 | 24.38 | 4.01 | 49 |
| 292 | 19.25 | 28.12 | 31.88 | 4.01 | 49 |
| 293 | 43.75 | -69.38 | -1.88 | 4.01 | 98 |
| 294 | 29.75 | -58.12 | -46.88 | 4.00 | 49 |
| 295 | 5.25 | -28.12 | -9.38 | 4.00 | 49 |
| 296 | 19.25 | -24.38 | 16.88 | 4.00 | 49 |
| 297 | -57.75 | -46.88 | -1.88 | 3.99 | 246 |
| 297a | -54.25 | -39.38 | -1.88 | 3.69 | |
| 298 | 22.75 | -61.88 | -46.88 | 3.99 | 147 |
| 299 | 12.25 | 24.38 | -39.38 | 3.99 | 147 |
| 300 | 22.75 | 5.62 | -1.88 | 3.99 | 49 |
| 301 | 29.75 | -43.12 | -58.12 | 3.99 | 49 |
| 302 | 19.25 | -84.38 | -20.62 | 3.98 | 49 |
| 303 | -33.25 | 39.38 | 54.38 | 3.98 | 49 |
| 304 | -8.75 | -39.38 | -31.88 | 3.98 | 49 |
| 305 | -40.25 | -58.12 | 9.38 | 3.97 | 98 |
| 306 | -36.75 | -31.88 | -46.88 | 3.97 | 49 |
| 307 | 1.75 | 24.38 | 31.88 | 3.97 | 49 |
| 308 | 40.25 | -31.88 | 54.38 | 3.97 | 98 |
| 309 | 8.75 | -43.12 | -35.62 | 3.96 | 98 |
| 310 | -29.75 | 24.38 | 13.12 | 3.96 | 98 |
| 311 | 5.25 | -61.88 | 54.38 | 3.96 | 196 |
| 312 | 26.25 | -20.62 | -65.62 | 3.96 | 98 |
| 313 | 33.25 | 43.12 | 24.38 | 3.95 | 49 |
| 314 | -15.75 | -5.62 | 61.88 | 3.95 | 147 |
| 315 | -64.75 | -13.12 | -24.38 | 3.95 | 49 |
| 316 | -40.25 | -16.88 | -50.62 | 3.94 | 49 |
| 317 | 1.75 | -20.62 | -35.62 | 3.94 | 49 |
| 318 | 12.25 | 69.38 | 5.62 | 3.94 | 49 |
| 319 | 47.25 | 20.62 | 5.62 | 3.94 | 49 |
| 320 | 26.25 | -80.62 | -9.38 | 3.94 | 49 |
| 321 | 43.75 | -39.38 | -1.88 | 3.93 | 196 |
| 322 | -33.25 | 1.88 | -16.88 | 3.92 | 147 |
| 323 | -19.25 | 1.88 | 58.12 | 3.92 | 49 |
| 324 | -47.25 | -13.12 | 35.62 | 3.92 | 49 |
| 325 | -12.25 | 46.88 | 28.12 | 3.92 | 49 |
| 326 | -5.25 | -84.38 | -20.62 | 3.91 | 49 |
| 327 | -47.25 | -39.38 | 16.88 | 3.91 | 49 |
| 328 | -8.75 | 39.38 | 5.62 | 3.91 | 49 |
| 329 | -15.75 | -16.88 | -43.12 | 3.91 | 49 |
| 330 | 64.75 | -43.12 | 5.62 | 3.91 | 49 |
| 331 | -19.25 | -13.12 | 61.88 | 3.91 | 49 |
| 332 | 61.25 | -31.88 | 39.38 | 3.91 | 49 |
| 333 | 8.75 | 46.88 | -5.62 | 3.91 | 49 |
| 334 | -12.25 | -28.12 | -50.62 | 3.91 | 196 |
| 335 | -29.75 | -1.88 | -24.38 | 3.91 | 98 |
| 336 | 36.75 | -73.12 | -9.38 | 3.90 | 49 |
| 337 | 8.75 | -24.38 | 9.38 | 3.90 | 49 |
| 338 | 19.25 | 54.38 | 1.88 | 3.90 | 49 |
| 339 | 22.75 | 13.12 | 35.62 | 3.90 | 196 |
| 340 | 1.75 | -58.12 | 9.38 | 3.90 | 49 |
| 341 | 54.25 | 1.88 | 35.62 | 3.90 | 49 |
| 342 | 15.75 | -20.62 | 73.12 | 3.90 | 98 |
| 343 | 40.25 | -28.12 | -1.88 | 3.90 | 49 |
| 344 | -40.25 | 16.88 | 24.38 | 3.90 | 98 |
| 345 | 26.25 | 24.38 | 13.12 | 3.89 | 98 |
| 346 | -29.75 | -65.62 | -20.62 | 3.89 | 49 |
| 347 | -54.25 | 13.12 | 20.62 | 3.89 | 147 |
| 348 | 33.25 | -28.12 | 65.62 | 3.89 | 49 |
| 349 | 26.25 | -9.38 | 13.12 | 3.89 | 147 |
| 350 | 19.25 | 35.62 | 1.88 | 3.89 | 98 |
| 351 | -47.25 | -13.12 | -54.38 | 3.89 | 98 |
| 352 | 12.25 | -9.38 | 31.88 | 3.88 | 98 |
| 353 | 47.25 | 13.12 | -20.62 | 3.88 | 49 |
| 354 | 57.75 | -39.38 | 5.62 | 3.88 | 49 |
| 355 | -54.25 | -28.12 | -16.88 | 3.88 | 49 |
| 356 | 19.25 | -13.12 | 28.12 | 3.88 | 49 |
| 357 | 15.75 | 88.12 | 9.38 | 3.88 | 98 |
| 358 | 5.25 | -28.12 | 73.12 | 3.87 | 49 |
| 359 | -50.75 | 28.12 | 46.88 | 3.87 | 49 |
| 360 | 8.75 | -73.12 | 35.62 | 3.86 | 49 |
| 361 | -15.75 | -50.62 | -31.88 | 3.86 | 196 |
| 362 | -33.25 | 35.62 | -24.38 | 3.86 | 98 |
| 363 | 29.75 | -31.88 | -31.88 | 3.86 | 49 |
| 364 | 12.25 | -1.88 | -69.38 | 3.86 | 98 |
| 365 | -22.75 | 13.12 | -24.38 | 3.86 | 49 |
| 366 | -19.25 | 65.62 | 39.38 | 3.85 | 49 |
| 367 | -61.25 | -16.88 | 9.38 | 3.85 | 49 |
| 368 | 12.25 | -54.38 | -54.38 | 3.85 | 49 |
| 369 | -47.25 | -58.12 | -28.12 | 3.84 | 49 |
| 370 | -33.25 | -16.88 | -5.62 | 3.84 | 49 |
| 371 | -15.75 | -1.88 | -31.88 | 3.84 | 49 |
| 372 | -12.25 | -20.62 | -61.88 | 3.84 | 49 |
| 373 | -12.25 | 20.62 | 73.12 | 3.84 | 196 |
| 373a | -15.75 | 13.12 | 73.12 | 3.74 | |
| 374 | -12.25 | -20.62 | -46.88 | 3.83 | 49 |
| 375 | -54.25 | -24.38 | -39.38 | 3.83 | 98 |
| 376 | 33.25 | 43.12 | 35.62 | 3.83 | 49 |
| 377 | 50.75 | -13.12 | 5.62 | 3.83 | 49 |
| 378 | -1.75 | -43.12 | -31.88 | 3.83 | 98 |
| 379 | 19.25 | -58.12 | -13.12 | 3.82 | 49 |
| 380 | -15.75 | -73.12 | 39.38 | 3.82 | 98 |
| 381 | 1.75 | -31.88 | 1.88 | 3.82 | 49 |
| 382 | -15.75 | 5.62 | 65.62 | 3.82 | 98 |
| 383 | -1.75 | -16.88 | -28.12 | 3.82 | 49 |
| 384 | -12.25 | 54.38 | 54.38 | 3.81 | 98 |
| 385 | 57.75 | -20.62 | 28.12 | 3.80 | 49 |
| 386 | -5.25 | 46.88 | 54.38 | 3.80 | 98 |
| 387 | -8.75 | -54.38 | -31.88 | 3.80 | 49 |
| 388 | 19.25 | -9.38 | -5.62 | 3.80 | 49 |
| 389 | -33.25 | -35.62 | -9.38 | 3.80 | 98 |
| 390 | 15.75 | -20.62 | -20.62 | 3.79 | 49 |
| 391 | -15.75 | -43.12 | -43.12 | 3.79 | 98 |
| 392 | 33.25 | -9.38 | -9.38 | 3.79 | 49 |
| 393 | -36.75 | -69.38 | 5.62 | 3.79 | 49 |
| 394 | 26.25 | 20.62 | -24.38 | 3.79 | 98 |
| 395 | 43.75 | 31.88 | 5.62 | 3.78 | 49 |
| 396 | 47.25 | 16.88 | -39.38 | 3.78 | 98 |
| 397 | -26.25 | -31.88 | 16.88 | 3.78 | 49 |
| 398 | 26.25 | -20.62 | 20.62 | 3.77 | 49 |
| 399 | 19.25 | -16.88 | 16.88 | 3.76 | 49 |
| 400 | 1.75 | 65.62 | -16.88 | 3.76 | 98 |
| 401 | -57.75 | -1.88 | -13.12 | 3.76 | 98 |
| 402 | 12.25 | -9.38 | 50.62 | 3.75 | 49 |
| 403 | 26.25 | -20.62 | 65.62 | 3.75 | 49 |
| 404 | 36.75 | -54.38 | -28.12 | 3.75 | 49 |
| 405 | -1.75 | -28.12 | 58.12 | 3.75 | 49 |
| 406 | -1.75 | 76.88 | 5.62 | 3.75 | 98 |
| 407 | 1.75 | -35.62 | -61.88 | 3.75 | 98 |
| 408 | 22.75 | 46.88 | 13.12 | 3.75 | 49 |
| 409 | -26.25 | -35.62 | -9.38 | 3.75 | 49 |
| 410 | -57.75 | -31.88 | 24.38 | 3.75 | 98 |
| 411 | 22.75 | -54.38 | 61.88 | 3.74 | 49 |
| 412 | -47.25 | -1.88 | -20.62 | 3.73 | 49 |
| 413 | -33.25 | 65.62 | 20.62 | 3.73 | 98 |
| 414 | 40.25 | -35.62 | 50.62 | 3.73 | 49 |
| 415 | -54.25 | 46.88 | 9.38 | 3.73 | 98 |
| 416 | 5.25 | -61.88 | -46.88 | 3.72 | 49 |
| 417 | -1.75 | -43.12 | -9.38 | 3.72 | 49 |
| 418 | 26.25 | -65.62 | 46.88 | 3.72 | 49 |
| 419 | 29.75 | 16.88 | 35.62 | 3.72 | 49 |
| 420 | 50.75 | 50.62 | -1.88 | 3.72 | 49 |
| 421 | 22.75 | -76.88 | -35.62 | 3.72 | 98 |
| 422 | -19.25 | -61.88 | 20.62 | 3.72 | 49 |
| 423 | 29.75 | -16.88 | -61.88 | 3.72 | 49 |
| 424 | 47.25 | 13.12 | 5.62 | 3.72 | 98 |
| 425 | 19.25 | -84.38 | 24.38 | 3.72 | 49 |
| 426 | 43.75 | -5.62 | -13.12 | 3.71 | 49 |
| 427 | -47.25 | 1.88 | -28.12 | 3.71 | 49 |
| 428 | 26.25 | -80.62 | 1.88 | 3.71 | 49 |
| 429 | 40.25 | 28.12 | -39.38 | 3.71 | 49 |
| 430 | 22.75 | -35.62 | -20.62 | 3.71 | 196 |
| 431 | -1.75 | -28.12 | 20.62 | 3.71 | 98 |
| 432 | 1.75 | 9.38 | -16.88 | 3.71 | 49 |
| 433 | 19.25 | -50.62 | -73.12 | 3.70 | 49 |
| 434 | 19.25 | -31.88 | -16.88 | 3.70 | 49 |
| 435 | -1.75 | -46.88 | -65.62 | 3.70 | 49 |
| 436 | 29.75 | -50.62 | 54.38 | 3.70 | 147 |
| 437 | -22.75 | -80.62 | 1.88 | 3.70 | 49 |
| 438 | 26.25 | 13.12 | 9.38 | 3.70 | 49 |
| 439 | 15.75 | -46.88 | 20.62 | 3.70 | 49 |
| 440 | 29.75 | 65.62 | 43.12 | 3.70 | 49 |
| 441 | 50.75 | -50.62 | 9.38 | 3.69 | 49 |
| 442 | 15.75 | -73.12 | -20.62 | 3.69 | 49 |
| 443 | -22.75 | -5.62 | -1.88 | 3.69 | 49 |
| 444 | -26.25 | -35.62 | 54.38 | 3.69 | 49 |
| 445 | 12.25 | -35.62 | 9.38 | 3.69 | 49 |
| 446 | -54.25 | -9.38 | 20.62 | 3.69 | 49 |
| 447 | -54.25 | -20.62 | -50.62 | 3.69 | 49 |
| 448 | 40.25 | -9.38 | -31.88 | 3.69 | 49 |
| 449 | -36.75 | 61.88 | 16.88 | 3.69 | 98 |
| 450 | 15.75 | -16.88 | -58.12 | 3.69 | 49 |
| 451 | -47.25 | -61.88 | 20.62 | 3.69 | 49 |
| 452 | 61.25 | 1.88 | 24.38 | 3.68 | 49 |
| 453 | 33.25 | -43.12 | -24.38 | 3.68 | 49 |
| 454 | -1.75 | -16.88 | 24.38 | 3.68 | 49 |
| 455 | -29.75 | -43.12 | 58.12 | 3.68 | 98 |
| 456 | -19.25 | -65.62 | -20.62 | 3.68 | 49 |
| 457 | -26.25 | -76.88 | 35.62 | 3.68 | 49 |
| 458 | -33.25 | -35.62 | 46.88 | 3.68 | 49 |
| 459 | 15.75 | -16.88 | 5.62 | 3.68 | 49 |
| 460 | 43.75 | 24.38 | -5.62 | 3.68 | 49 |
| 461 | -47.25 | 16.88 | -24.38 | 3.68 | 49 |
| 462 | 33.25 | -35.62 | -5.62 | 3.68 | 49 |
| 463 | -8.75 | -65.62 | -43.12 | 3.67 | 49 |
| 464 | 1.75 | 16.88 | -39.38 | 3.67 | 98 |
| 465 | 1.75 | -58.12 | -13.12 | 3.66 | 49 |
| 466 | 26.25 | 1.88 | -13.12 | 3.66 | 49 |
| 467 | 19.25 | -9.38 | 35.62 | 3.66 | 49 |
| 468 | 36.75 | -24.38 | -31.88 | 3.66 | 49 |
| 469 | -40.25 | 35.62 | 31.88 | 3.66 | 98 |
| 470 | 36.75 | 20.62 | 31.88 | 3.65 | 49 |
| 471 | -29.75 | 35.62 | -1.88 | 3.65 | 49 |
| 472 | -40.25 | -69.38 | 31.88 | 3.65 | 49 |
| 473 | -43.75 | 50.62 | 28.12 | 3.65 | 49 |
| 474 | 36.75 | 50.62 | -16.88 | 3.65 | 49 |
| 475 | 22.75 | -24.38 | 1.88 | 3.65 | 49 |
| 476 | 68.25 | -31.88 | 9.38 | 3.65 | 147 |
| 477 | 5.25 | 5.62 | -69.38 | 3.65 | 49 |
| 478 | 33.25 | -13.12 | 65.62 | 3.65 | 49 |
| 479 | -15.75 | -43.12 | 1.88 | 3.64 | 49 |
| 480 | -12.25 | -9.38 | -24.38 | 3.64 | 49 |
| 481 | 47.25 | 43.12 | 9.38 | 3.64 | 49 |
| 482 | -47.25 | 46.88 | 28.12 | 3.64 | 49 |
| 483 | 33.25 | 43.12 | -16.88 | 3.64 | 49 |
| 484 | 33.25 | -24.38 | -58.12 | 3.64 | 49 |
| 485 | -29.75 | -46.88 | -13.12 | 3.64 | 49 |
| 486 | 47.25 | 31.88 | -24.38 | 3.64 | 49 |
| 487 | 33.25 | 50.62 | 24.38 | 3.63 | 98 |
| 488 | -29.75 | 35.62 | -9.38 | 3.63 | 49 |
| 489 | 50.75 | -43.12 | 9.38 | 3.63 | 49 |
| 490 | 26.25 | -58.12 | -13.12 | 3.63 | 98 |
| 491 | 22.75 | -5.62 | 20.62 | 3.63 | 49 |
| 492 | -47.25 | 43.12 | 9.38 | 3.63 | 49 |
| 493 | -68.25 | -24.38 | 1.88 | 3.63 | 98 |
| 494 | -29.75 | -1.88 | -1.88 | 3.63 | 49 |
| 495 | 47.25 | 16.88 | 39.38 | 3.63 | 49 |
| 496 | -1.75 | 20.62 | -16.88 | 3.63 | 98 |
| 497 | 19.25 | 43.12 | -5.62 | 3.62 | 49 |
| 498 | -54.25 | -35.62 | -5.62 | 3.62 | 49 |
| 499 | 29.75 | -16.88 | 58.12 | 3.62 | 49 |
| 500 | -54.25 | -9.38 | -5.62 | 3.61 | 98 |
| 501 | -1.75 | -9.38 | -28.12 | 3.61 | 49 |
| 502 | 29.75 | -1.88 | -20.62 | 3.61 | 49 |
| 503 | -36.75 | -13.12 | 9.38 | 3.61 | 49 |
| 504 | 36.75 | 35.62 | 16.88 | 3.61 | 196 |
| 505 | 36.75 | 1.88 | 20.62 | 3.61 | 49 |
| 506 | 33.25 | -39.38 | -58.12 | 3.61 | 49 |
| 507 | 22.75 | -16.88 | 43.12 | 3.60 | 49 |
| 508 | -8.75 | -9.38 | -20.62 | 3.60 | 49 |
| 509 | 19.25 | 20.62 | 20.62 | 3.60 | 49 |
| 510 | 19.25 | -28.12 | -24.38 | 3.59 | 49 |
| 511 | -12.25 | 16.88 | 20.62 | 3.59 | 49 |
| 512 | -1.75 | -9.38 | 31.88 | 3.59 | 49 |
| 513 | -29.75 | 16.88 | -5.62 | 3.59 | 49 |
| 514 | -33.25 | -43.12 | -28.12 | 3.59 | 98 |
| 515 | 50.75 | -16.88 | 28.12 | 3.58 | 49 |
| 516 | -36.75 | 16.88 | 1.88 | 3.58 | 49 |
| 517 | 1.75 | -84.38 | -16.88 | 3.58 | 49 |
| 518 | -47.25 | 20.62 | 46.88 | 3.58 | 49 |
| 519 | 26.25 | -16.88 | -58.12 | 3.58 | 49 |
| 520 | -33.25 | 46.88 | 35.62 | 3.58 | 49 |
| 521 | 15.75 | 1.88 | 35.62 | 3.58 | 49 |
| 522 | -5.25 | -43.12 | 69.38 | 3.57 | 49 |
| 523 | 8.75 | 28.12 | -24.38 | 3.57 | 49 |
| 524 | 15.75 | 20.62 | 43.12 | 3.57 | 49 |
| 525 | -29.75 | -20.62 | -69.38 | 3.57 | 49 |
| 526 | 26.25 | 5.62 | 5.62 | 3.56 | 98 |
| 527 | 5.25 | -9.38 | -20.62 | 3.56 | 49 |
| 528 | -12.25 | 88.12 | 16.88 | 3.56 | 49 |
| 529 | 15.75 | 16.88 | -16.88 | 3.56 | 49 |
| 530 | -12.25 | -39.38 | -5.62 | 3.56 | 49 |
| 531 | 19.25 | 9.38 | 20.62 | 3.56 | 49 |
| 532 | 1.75 | -16.88 | -24.38 | 3.56 | 49 |
| 533 | -19.25 | 24.38 | 31.88 | 3.56 | 49 |
| 534 | -5.25 | -54.38 | -54.38 | 3.56 | 49 |
| 535 | -47.25 | 9.38 | -9.38 | 3.56 | 49 |
| 536 | -33.25 | -1.88 | 13.12 | 3.55 | 49 |
| 537 | -50.75 | 5.62 | -9.38 | 3.55 | 49 |
| 538 | 22.75 | 73.12 | 5.62 | 3.55 | 98 |
| 539 | -15.75 | -5.62 | 1.88 | 3.55 | 98 |
| 540 | -5.25 | -13.12 | -61.88 | 3.55 | 49 |
| 541 | 36.75 | -31.88 | 13.12 | 3.55 | 49 |
| 542 | -12.25 | 5.62 | 35.62 | 3.54 | 147 |
| 543 | -19.25 | 43.12 | -1.88 | 3.54 | 49 |
| 544 | 47.25 | 28.12 | -39.38 | 3.54 | 49 |
| 545 | -22.75 | -20.62 | -31.88 | 3.54 | 49 |
| 546 | -50.75 | -58.12 | 1.88 | 3.54 | 49 |
| 547 | 19.25 | -46.88 | 31.88 | 3.54 | 49 |
| 548 | -26.25 | -61.88 | 35.62 | 3.53 | 98 |
| 549 | 47.25 | -35.62 | 16.88 | 3.53 | 49 |
| 550 | 15.75 | -5.62 | 28.12 | 3.53 | 49 |
| 551 | 12.25 | -1.88 | 24.38 | 3.53 | 49 |
| 552 | 61.25 | -35.62 | 35.62 | 3.52 | 49 |
| 553 | 5.25 | -16.88 | -20.62 | 3.52 | 49 |
| 554 | -15.75 | -69.38 | 28.12 | 3.52 | 49 |
| 555 | -5.25 | 43.12 | -16.88 | 3.52 | 49 |
| 556 | 15.75 | -13.12 | -73.12 | 3.51 | 49 |
| 557 | -36.75 | -16.88 | -46.88 | 3.51 | 49 |
| 558 | 43.75 | 9.38 | -39.38 | 3.51 | 49 |
| 559 | 5.25 | -73.12 | -16.88 | 3.51 | 49 |
| 560 | -36.75 | 61.88 | 9.38 | 3.51 | 49 |
| 561 | -57.75 | -24.38 | 5.62 | 3.51 | 49 |
| 562 | -8.75 | -54.38 | 9.38 | 3.50 | 98 |
| 563 | 12.25 | -84.38 | -5.62 | 3.50 | 49 |
| 564 | 33.25 | 50.62 | 50.62 | 3.50 | 49 |
| 565 | -29.75 | -80.62 | 20.62 | 3.50 | 49 |
| 566 | 1.75 | -65.62 | -43.12 | 3.50 | 49 |
| 567 | -5.25 | -20.62 | 1.88 | 3.50 | 49 |
| 568 | 12.25 | -9.38 | -24.38 | 3.50 | 49 |
| 569 | 40.25 | -31.88 | -58.12 | 3.50 | 98 |
| 570 | -1.75 | 80.62 | 1.88 | 3.49 | 49 |
| 571 | -47.25 | -24.38 | -5.62 | 3.49 | 49 |
| 572 | 54.25 | -16.88 | -1.88 | 3.49 | 98 |
| 573 | 47.25 | 5.62 | 31.88 | 3.49 | 49 |
| 574 | -1.75 | 9.38 | 9.38 | 3.49 | 98 |
| 575 | -12.25 | -76.88 | -9.38 | 3.49 | 49 |
| 576 | 68.25 | -28.12 | 16.88 | 3.49 | 49 |
| 577 | -8.75 | 43.12 | 24.38 | 3.49 | 49 |
| 578 | 8.75 | -76.88 | 9.38 | 3.49 | 49 |
| 579 | -29.75 | 24.38 | 5.62 | 3.49 | 49 |
| 580 | 22.75 | 24.38 | 46.88 | 3.49 | 49 |
| 581 | 12.25 | -9.38 | 43.12 | 3.49 | 49 |
| 582 | 22.75 | -1.88 | 5.62 | 3.48 | 49 |
| 583 | 29.75 | -39.38 | -54.38 | 3.48 | 49 |
| 584 | -33.25 | -58.12 | -1.88 | 3.48 | 49 |
| 585 | -33.25 | -43.12 | -61.88 | 3.48 | 49 |
| 586 | -43.75 | 13.12 | 16.88 | 3.48 | 98 |
| 587 | -19.25 | -84.38 | 1.88 | 3.48 | 49 |
| 588 | -15.75 | -50.62 | 39.38 | 3.48 | 49 |
| 589 | 40.25 | -35.62 | 39.38 | 3.48 | 98 |
| 590 | 12.25 | -65.62 | 1.88 | 3.48 | 49 |
| 591 | -33.25 | 1.88 | 31.88 | 3.48 | 49 |
| 592 | 33.25 | 5.62 | 50.62 | 3.47 | 49 |
| 593 | 22.75 | -9.38 | 43.12 | 3.47 | 49 |
| 594 | -1.75 | -1.88 | -5.62 | 3.47 | 49 |
| 595 | -15.75 | -31.88 | -16.88 | 3.47 | 49 |
| 596 | 26.25 | 31.88 | 43.12 | 3.46 | 49 |
| 597 | 40.25 | -5.62 | 46.88 | 3.46 | 49 |
| 598 | -19.25 | 13.12 | 28.12 | 3.46 | 49 |
| 599 | 19.25 | -31.88 | -54.38 | 3.46 | 49 |
| 600 | 47.25 | -35.62 | -9.38 | 3.46 | 49 |
| 601 | 40.25 | -39.38 | -13.12 | 3.45 | 49 |
| 602 | -5.25 | 1.88 | -58.12 | 3.45 | 49 |
| 603 | 12.25 | -35.62 | -16.88 | 3.45 | 49 |
| 604 | -19.25 | -50.62 | -46.88 | 3.45 | 49 |
| 605 | 12.25 | 16.88 | -50.62 | 3.45 | 49 |
| 606 | 1.75 | -1.88 | -20.62 | 3.45 | 49 |
| 607 | -12.25 | -16.88 | -65.62 | 3.45 | 49 |
| 608 | -19.25 | -43.12 | 31.88 | 3.45 | 49 |
| 609 | -1.75 | -35.62 | -13.12 | 3.45 | 49 |
| 610 | -19.25 | -61.88 | -13.12 | 3.44 | 49 |
| 611 | -1.75 | -69.38 | -13.12 | 3.44 | 49 |
| 612 | -8.75 | 54.38 | 43.12 | 3.44 | 49 |
| 613 | 22.75 | 39.38 | 20.62 | 3.44 | 49 |
| 614 | -29.75 | 9.38 | 61.88 | 3.44 | 49 |
| 615 | -40.25 | -50.62 | 28.12 | 3.43 | 49 |
| 616 | 1.75 | 84.38 | 1.88 | 3.43 | 49 |
| 617 | 57.75 | -13.12 | 16.88 | 3.43 | 49 |
| 618 | -15.75 | -16.88 | -28.12 | 3.43 | 98 |
| 619 | 54.25 | -16.88 | 35.62 | 3.43 | 98 |
| 620 | -1.75 | 24.38 | -39.38 | 3.43 | 49 |
| 621 | -33.25 | 73.12 | 20.62 | 3.43 | 49 |
| 622 | 54.25 | -5.62 | 13.12 | 3.43 | 49 |
| 623 | 26.25 | -39.38 | 35.62 | 3.43 | 49 |
| 624 | -36.75 | 35.62 | -13.12 | 3.43 | 49 |
| 625 | -50.75 | -9.38 | -13.12 | 3.42 | 49 |
| 626 | 50.75 | 39.38 | -9.38 | 3.42 | 49 |
| 627 | 40.25 | 13.12 | -46.88 | 3.42 | 49 |
| 628 | -12.25 | -13.12 | -73.12 | 3.42 | 98 |
| 629 | -12.25 | -5.62 | -20.62 | 3.42 | 49 |
| 630 | 43.75 | -9.38 | 46.88 | 3.42 | 49 |
| 631 | 47.25 | -20.62 | 20.62 | 3.42 | 49 |
| 632 | 33.25 | -20.62 | -9.38 | 3.42 | 49 |
| 633 | -50.75 | -1.88 | 16.88 | 3.41 | 98 |
| 634 | 26.25 | 16.88 | -20.62 | 3.41 | 49 |
| 635 | 22.75 | 20.62 | 16.88 | 3.41 | 49 |
| 636 | 12.25 | -35.62 | 65.62 | 3.41 | 49 |
| 637 | 15.75 | 28.12 | -5.62 | 3.41 | 49 |
| 638 | -50.75 | 13.12 | -39.38 | 3.41 | 49 |
| 639 | -54.25 | 24.38 | 24.38 | 3.41 | 49 |
| 640 | -64.75 | -35.62 | -1.88 | 3.41 | 49 |
| 641 | -1.75 | -9.38 | 69.38 | 3.41 | 49 |
| 642 | -40.25 | -5.62 | 35.62 | 3.40 | 49 |
| 643 | -19.25 | -9.38 | -65.62 | 3.40 | 49 |
| 644 | -29.75 | -1.88 | -46.88 | 3.40 | 49 |
| 645 | 54.25 | 1.88 | 9.38 | 3.40 | 49 |
| 646 | 29.75 | 35.62 | 39.38 | 3.40 | 49 |
| 647 | 19.25 | -5.62 | 13.12 | 3.39 | 49 |
| 648 | 5.25 | -61.88 | -35.62 | 3.39 | 49 |
| 649 | -22.75 | 43.12 | -28.12 | 3.39 | 49 |
| 650 | 19.25 | -61.88 | -20.62 | 3.39 | 49 |
| 651 | 12.25 | -35.62 | -61.88 | 3.39 | 49 |
| 652 | -29.75 | -24.38 | -9.38 | 3.38 | 49 |
| 653 | 47.25 | 39.38 | 13.12 | 3.38 | 98 |
| 654 | -57.75 | 1.88 | 20.62 | 3.38 | 49 |
| 655 | -1.75 | 5.62 | -39.38 | 3.38 | 49 |
| 656 | -40.25 | 39.38 | 43.12 | 3.37 | 98 |
| 657 | -36.75 | -69.38 | 13.12 | 3.37 | 49 |
| 658 | 12.25 | 20.62 | -46.88 | 3.37 | 49 |
| 659 | -19.25 | -16.88 | 9.38 | 3.37 | 49 |
| 660 | 33.25 | -28.12 | 58.12 | 3.37 | 49 |
| 661 | 50.75 | -20.62 | -43.12 | 3.37 | 49 |
| 662 | -5.25 | 1.88 | 1.88 | 3.37 | 49 |
| 663 | 40.25 | -69.38 | 9.38 | 3.37 | 98 |
| 664 | 8.75 | -65.62 | 20.62 | 3.36 | 49 |
| 665 | -12.25 | -20.62 | -13.12 | 3.36 | 49 |
| 666 | -5.25 | -61.88 | 20.62 | 3.36 | 49 |
| 667 | -26.25 | -31.88 | -58.12 | 3.36 | 49 |
| 668 | 15.75 | 35.62 | 5.62 | 3.36 | 49 |
| 669 | 12.25 | -69.38 | 5.62 | 3.36 | 49 |
| 670 | 1.75 | 65.62 | -9.38 | 3.36 | 49 |
| 671 | -12.25 | -39.38 | 5.62 | 3.36 | 49 |
| 672 | -43.75 | -16.88 | 13.12 | 3.36 | 49 |
| 673 | 47.25 | -54.38 | 13.12 | 3.36 | 49 |
| 674 | 12.25 | -50.62 | 39.38 | 3.36 | 49 |
| 675 | 22.75 | -73.12 | -5.62 | 3.35 | 49 |
| 676 | -36.75 | -35.62 | 39.38 | 3.35 | 49 |
| 677 | 1.75 | 1.88 | -16.88 | 3.35 | 49 |
| 678 | 22.75 | -50.62 | 35.62 | 3.35 | 49 |
| 679 | -22.75 | 20.62 | -1.88 | 3.35 | 49 |
| 680 | -50.75 | 39.38 | 16.88 | 3.35 | 49 |
| 681 | -36.75 | 9.38 | 54.38 | 3.35 | 49 |
| 682 | -8.75 | 39.38 | -28.12 | 3.35 | 49 |
| 683 | 1.75 | -69.38 | 46.88 | 3.35 | 49 |
| 684 | -1.75 | -16.88 | -58.12 | 3.35 | 49 |
| 685 | 5.25 | 1.88 | 13.12 | 3.35 | 49 |
| 686 | -47.25 | -9.38 | 43.12 | 3.35 | 49 |
| 687 | -1.75 | -5.62 | -73.12 | 3.35 | 49 |
| 688 | 22.75 | -13.12 | 73.12 | 3.35 | 49 |
| 689 | -15.75 | 35.62 | 5.62 | 3.34 | 49 |
| 690 | 36.75 | 24.38 | -13.12 | 3.34 | 49 |
| 691 | 54.25 | -28.12 | 24.38 | 3.34 | 49 |
| 692 | 54.25 | -28.12 | 31.88 | 3.34 | 49 |
| 693 | -5.25 | -31.88 | 1.88 | 3.34 | 49 |
| 694 | -22.75 | 20.62 | -46.88 | 3.34 | 49 |
| 695 | -5.25 | -61.88 | 54.38 | 3.34 | 49 |
| 696 | 40.25 | -76.88 | -9.38 | 3.34 | 49 |
| 697 | -26.25 | -24.38 | -5.62 | 3.34 | 49 |
| 698 | -36.75 | -46.88 | 9.38 | 3.34 | 49 |
| 699 | -22.75 | 1.88 | 28.12 | 3.34 | 49 |
| 700 | 1.75 | 28.12 | -5.62 | 3.34 | 49 |
| 701 | -47.25 | -50.62 | 5.62 | 3.33 | 49 |
| 702 | 5.25 | 46.88 | 13.12 | 3.33 | 49 |
| 703 | 22.75 | 54.38 | 5.62 | 3.33 | 49 |
| 704 | 5.25 | -16.88 | 69.38 | 3.33 | 49 |
| 705 | -22.75 | 24.38 | 20.62 | 3.33 | 49 |
| 706 | 54.25 | 1.88 | -31.88 | 3.33 | 49 |
| 707 | 19.25 | -69.38 | 20.62 | 3.33 | 49 |
| 708 | 26.25 | -80.62 | -28.12 | 3.33 | 49 |
| 709 | -50.75 | -35.62 | -54.38 | 3.33 | 49 |
| 710 | 15.75 | -31.88 | -50.62 | 3.33 | 49 |
| 711 | 29.75 | -9.38 | 9.38 | 3.33 | 49 |
| 712 | -40.25 | -46.88 | -5.62 | 3.32 | 49 |
| 713 | 5.25 | -50.62 | -5.62 | 3.32 | 98 |
| 714 | -8.75 | -13.12 | 5.62 | 3.32 | 49 |
| 715 | 12.25 | -58.12 | 46.88 | 3.32 | 49 |
| 716 | -19.25 | -65.62 | 9.38 | 3.32 | 49 |
| 717 | 26.25 | -54.38 | 31.88 | 3.32 | 49 |
| 718 | 26.25 | -43.12 | 5.62 | 3.32 | 49 |
| 719 | 29.75 | -1.88 | -54.38 | 3.32 | 49 |
| 720 | -36.75 | -31.88 | 20.62 | 3.32 | 49 |
| 721 | 15.75 | 46.88 | 46.88 | 3.32 | 49 |
| 722 | 29.75 | 43.12 | 16.88 | 3.31 | 49 |
| 723 | 15.75 | -16.88 | 54.38 | 3.31 | 49 |
| 724 | 50.75 | -61.88 | -13.12 | 3.31 | 49 |
| 725 | -15.75 | -46.88 | -24.38 | 3.31 | 49 |
| 726 | 33.25 | -76.88 | -28.12 | 3.31 | 49 |
| 727 | -50.75 | -16.88 | -9.38 | 3.31 | 49 |
| 728 | -40.25 | -65.62 | -31.88 | 3.31 | 49 |
| 729 | 33.25 | -35.62 | -35.62 | 3.31 | 49 |
| 730 | 33.25 | 31.88 | -28.12 | 3.31 | 49 |
| 731 | 40.25 | -9.38 | -13.12 | 3.31 | 49 |
| 732 | -29.75 | -46.88 | -28.12 | 3.30 | 49 |
| 733 | 36.75 | -46.88 | 9.38 | 3.30 | 49 |
| 734 | -15.75 | -69.38 | -43.12 | 3.30 | 49 |
| 735 | 47.25 | 5.62 | -1.88 | 3.30 | 49 |
| 736 | -68.25 | -24.38 | 13.12 | 3.30 | 49 |
| 737 | -22.75 | -28.12 | -65.62 | 3.30 | 49 |
| 738 | -26.25 | -35.62 | -43.12 | 3.30 | 49 |
| 739 | 22.75 | 9.38 | 46.88 | 3.30 | 49 |
| 740 | 8.75 | 13.12 | 16.88 | 3.30 | 49 |
| 741 | 40.25 | -31.88 | -69.38 | 3.30 | 49 |
| 742 | 8.75 | 24.38 | 39.38 | 3.30 | 49 |
| 743 | -12.25 | -54.38 | -9.38 | 3.29 | 49 |
| 744 | -26.25 | -5.62 | 65.62 | 3.29 | 49 |
| 745 | -1.75 | -76.88 | 1.88 | 3.29 | 49 |
| 746 | -64.75 | 20.62 | 5.62 | 3.29 | 49 |