Note
Go to the end to download the full example code. or to run this example in your browser via Binder
Decoding of a dataset after GLM fit for signal extraction¶
Full step-by-step example of fitting a GLM to perform a decoding experiment. In this decoding analysis, we will be doing a one-vs-all classification. 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
t_r = 2.5
[get_dataset_dir] Dataset found in /home/runner/nilearn_data/haxby2001
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"].to_numpy()
# Record these as an array of runs
runs = behavioral["chunks"].to_numpy()
unique_runs = behavioral["chunks"].unique()
# fMRI data: a unique file for each run
func_filename = haxby_dataset.func[0]
Build a proper event structure for each run¶
events = {}
# events will take the form of a dictionary of Dataframes, one per run
for run in unique_runs:
# get the condition label per run
conditions_run = conditions[runs == run]
# get the number of scans per run, then the corresponding
# vector of frame times
n_scans = len(conditions_run)
frame_times = t_r * np.arange(n_scans)
# each event last the full TR
duration = t_r * np.ones(n_scans)
# Define the events object
events_ = pd.DataFrame(
{
"onset": frame_times,
"trial_type": conditions_run,
"duration": duration,
}
)
# remove the rest condition and insert into the dictionary
events[run] = events_[events_.trial_type != "rest"]
Instantiate and run FirstLevelModel¶
We generate a list of z-maps together with their run and condition index
z_maps = []
conditions_label = []
run_label = []
# Instantiate the glm
from nilearn.glm.first_level import FirstLevelModel
glm = FirstLevelModel(
t_r=t_r,
mask_img=haxby_dataset.mask,
high_pass=0.008,
smoothing_fwhm=4,
memory="nilearn_cache",
)
Run the GLM on data from each run¶
events[run].trial_type.unique()
from nilearn.image import index_img
for run in unique_runs:
# grab the fmri data for that particular run
fmri_run = index_img(func_filename, runs == run)
# fit the GLM
glm.fit(fmri_run, events=events[run])
# set up contrasts: one per condition
conditions = events[run].trial_type.unique()
for condition_ in conditions:
z_maps.append(glm.compute_contrast(condition_))
conditions_label.append(condition_)
run_label.append(run)
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0917f1b80>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>, { '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 - 1.3s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.575977, ..., 0.481522]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.00779 , ..., 0.924282]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.349128, ..., -1.50145 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.902378, ..., -0.291882]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.624816, ..., -1.682436]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.838935, ..., -1.528093]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.06136 , ..., -0.289828]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.983539, ..., 0.291568]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5697e80>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b5af4d90>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>, { '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 - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.940878, ..., -0.740886]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.56852 , ..., -1.374435]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.141601, ..., -1.208286]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.922613, ..., 2.268412]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.904959, ..., -1.104956]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.242388, ..., 1.270843]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.197666, ..., -1.944003]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.6546 , ..., -0.644223]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a6a0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b4d44a00>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>, { '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 - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.441012, ..., -0.384872]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.457669, ..., 1.351326]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.697867, ..., 0.601435]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.763713, ..., 0.209919]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.447911, ..., -1.13823 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.162822, ..., -2.114926]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.785086, ..., -0.260924]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.456513, ..., 0.23571 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5b32fd0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b505d730>, <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>, { '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 - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.424686, ..., -1.190333]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.365571, ..., -0.28288 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.288251, ..., 0.627725]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.327741, ..., -0.321921]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.152188, ..., -0.366314]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.629164, ..., -0.735212]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.873351, ..., -0.14857 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.061121, ..., -0.426454]), <nibabel.nifti1.Nifti1Image object at 0x7ff09050da60>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b52c41c0>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>, { '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 - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.09849 , ..., -0.026579]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.329567, ..., -0.355602]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.134325, ..., 0.590796]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.998496, ..., -0.158513]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.0983 , ..., 0.684567]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.003411, ..., -1.001089]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.28481 , ..., -0.796836]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.349998, ..., 1.77109 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5936880>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b591c2b0>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>, { '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 - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.773093, ..., 1.506396]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.945765, ..., 0.964351]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.489424, ..., 1.36385 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.60046 , ..., 1.292458]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.115105, ..., 0.360045]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.341327, ..., -0.115557]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.0463 , ..., -0.011997]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.304337, ..., -0.465647]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5af45b0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff088ee3f10>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>, { '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 - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.091521, ..., -0.624162]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.375261, ..., -0.645241]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.852582, ..., 0.144768]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.861249, ..., -1.104223]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.147067, ..., 1.938447]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.043015, ..., 0.397187]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.518244, ..., -0.247356]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.292525e-03, ..., -1.546823e+00]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6cac0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0a9e08e20>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>, { '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. ],
...,
[-0.352245, ..., 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([-0.011367, ..., -0.044286]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 1.378178, ..., -0.435127]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.249686, ..., 1.006757]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.627259, ..., -0.703344]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.673 , ..., 0.634889]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.170346, ..., -0.003907]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.74249 , ..., 2.416095]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([3.813068, ..., 0.397476]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b5ba2dc0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0b5a5a550>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>, { '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. ],
...,
[-0.352245, ..., 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([-1.400515, ..., 2.602952]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.497881, ..., 0.140209]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.499281, ..., -0.49038 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.038187, ..., 0.57069 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.622503, ..., 1.679756]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.211798, ..., -1.574836]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.143717, ..., -0.364448]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.156681, ..., -1.209168]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b509a7c0>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0902a1d00>, <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>, { '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([[-5.223948, ..., -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 - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.00555 , ..., 1.955666]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.488905, ..., 1.063614]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.126069, ..., 0.347685]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.147577, ..., -0.722242]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.137613, ..., 1.219668]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.30493 , ..., 1.036247]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.64672 , ..., 0.256269]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.01524, ..., 0.32111]), <nibabel.nifti1.Nifti1Image object at 0x7ff0905b6d90>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff0a6b35160>, <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>, { '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 - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.497712, ..., -3.241636]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-2.884836, ..., -2.579124]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.361796, ..., -2.404684]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.15611 , ..., 0.717663]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.124367, ..., 1.85051 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.322726, ..., 0.187135]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.943745, ..., -1.223389]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.298717, ..., -1.02394 ]), <nibabel.nifti1.Nifti1Image object at 0x7ff0b4d6c370>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker._filter_and_mask...
_filter_and_mask(<nibabel.nifti1.Nifti1Image object at 0x7ff091819cd0>, <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>, { '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 - 1.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.028052, ..., -1.076144]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.07071 , ..., 1.117228]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([1.278396, ..., 0.666023]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-0.606351, ..., -0.186859]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([ 0.228201, ..., -0.299271]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0.760997, ..., 0.931553]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.005349, ..., 0.835504]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________unmask - 0.1s, 0.0min
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([-1.093039, ..., -0.791928]), <nibabel.nifti1.Nifti1Image object at 0x7ff0a699c700>)
___________________________________________________________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, copy_header=True)
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 |
| high_pass (Hz) | 0.01 |
| hrf_model | glover |
| noise_model | ar1 |
| 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 | 0.001 |
| Threshold (computed) | 3.291 |
| Cluster size threshold (voxels) | 0 |
| Minimum distance (mm) | 8.0 |
| Cluster ID | X | Y | Z | Peak Stat | Cluster Size (mm3) |
|---|---|---|---|---|---|
| 1 | -54.25 | 13.12 | 5.62 | 6.49 | 98 |
| 2 | 29.75 | -20.62 | -24.38 | 6.49 | 836 |
| 2a | 12.25 | -20.62 | -31.88 | 4.58 | |
| 3 | 26.25 | -20.62 | -13.12 | 6.13 | 295 |
| 4 | -36.75 | -46.88 | -46.88 | 6.11 | 147 |
| 5 | 43.75 | -50.62 | -5.62 | 6.03 | 1476 |
| 5a | 47.25 | -43.12 | -1.88 | 4.74 | |
| 5b | 40.25 | -28.12 | -1.88 | 3.99 | |
| 6 | -1.75 | -39.38 | 13.12 | 5.95 | 147 |
| 7 | 33.25 | -43.12 | -43.12 | 5.94 | 1181 |
| 7a | 26.25 | -35.62 | -43.12 | 5.67 | |
| 8 | 22.75 | -54.38 | -39.38 | 5.91 | 885 |
| 8a | 8.75 | -58.12 | -39.38 | 4.15 | |
| 9 | -43.75 | 28.12 | -9.38 | 5.90 | 196 |
| 10 | -12.25 | -24.38 | -76.88 | 5.86 | 196 |
| 11 | -19.25 | -24.38 | -43.12 | 5.80 | 590 |
| 11a | -19.25 | -31.88 | -39.38 | 5.31 | |
| 12 | 26.25 | -46.88 | -46.88 | 5.75 | 147 |
| 13 | -57.75 | -39.38 | 9.38 | 5.74 | 246 |
| 14 | 33.25 | 5.62 | -13.12 | 5.69 | 442 |
| 14a | 26.25 | 9.38 | -16.88 | 5.46 | |
| 15 | 22.75 | -46.88 | -61.88 | 5.68 | 442 |
| 16 | 19.25 | -76.88 | -13.12 | 5.60 | 590 |
| 17 | 22.75 | -43.12 | 9.38 | 5.57 | 196 |
| 18 | -29.75 | 9.38 | -28.12 | 5.56 | 246 |
| 19 | 26.25 | -69.38 | -16.88 | 5.49 | 689 |
| 19a | 36.75 | -65.62 | -13.12 | 4.91 | |
| 19b | 33.25 | -58.12 | -16.88 | 4.13 | |
| 20 | 47.25 | -24.38 | -13.12 | 5.47 | 3248 |
| 20a | 40.25 | -35.62 | -24.38 | 5.23 | |
| 20b | 33.25 | -50.62 | -20.62 | 4.80 | |
| 20c | 43.75 | -16.88 | -13.12 | 4.67 | |
| 21 | 1.75 | 50.62 | -16.88 | 5.46 | 295 |
| 22 | -26.25 | -16.88 | -24.38 | 5.38 | 147 |
| 23 | 12.25 | -84.38 | -28.12 | 5.31 | 98 |
| 24 | -29.75 | -43.12 | -50.62 | 5.30 | 442 |
| 25 | 1.75 | -43.12 | -1.88 | 5.29 | 98 |
| 26 | -15.75 | -28.12 | -43.12 | 5.24 | 49 |
| 27 | 12.25 | -13.12 | -28.12 | 5.23 | 98 |
| 28 | -47.25 | -13.12 | 13.12 | 5.21 | 295 |
| 29 | -19.25 | -50.62 | -39.38 | 5.19 | 639 |
| 29a | -8.75 | -35.62 | -39.38 | 4.36 | |
| 29b | -12.25 | -46.88 | -39.38 | 3.96 | |
| 30 | -40.25 | -35.62 | -35.62 | 5.17 | 344 |
| 31 | 12.25 | -28.12 | -73.12 | 5.16 | 147 |
| 32 | 8.75 | -50.62 | -35.62 | 5.13 | 590 |
| 33 | 29.75 | 1.88 | -9.38 | 5.11 | 98 |
| 34 | 19.25 | -31.88 | 16.88 | 5.10 | 49 |
| 35 | -1.75 | -50.62 | -39.38 | 5.08 | 98 |
| 36 | -50.75 | 35.62 | -16.88 | 5.08 | 196 |
| 37 | -26.25 | -54.38 | 46.88 | 5.06 | 98 |
| 38 | -54.25 | -31.88 | -13.12 | 5.06 | 49 |
| 39 | -8.75 | -46.88 | -65.62 | 5.05 | 295 |
| 40 | 1.75 | -58.12 | -24.38 | 5.05 | 98 |
| 41 | -33.25 | 13.12 | 28.12 | 5.05 | 196 |
| 42 | -12.25 | 5.62 | -16.88 | 5.03 | 49 |
| 43 | 12.25 | -58.12 | -28.12 | 5.03 | 246 |
| 44 | 19.25 | -43.12 | -20.62 | 5.02 | 49 |
| 45 | -26.25 | -20.62 | -13.12 | 5.02 | 98 |
| 46 | -47.25 | -39.38 | -20.62 | 5.00 | 1082 |
| 46a | -50.75 | -39.38 | -9.38 | 4.39 | |
| 47 | -40.25 | -61.88 | -16.88 | 4.99 | 295 |
| 48 | 5.25 | -61.88 | -20.62 | 4.97 | 246 |
| 49 | -61.25 | -5.62 | 20.62 | 4.96 | 98 |
| 50 | 40.25 | 16.88 | -20.62 | 4.96 | 49 |
| 51 | -26.25 | -54.38 | -46.88 | 4.95 | 49 |
| 52 | -22.75 | -58.12 | -46.88 | 4.95 | 49 |
| 53 | 22.75 | 31.88 | 9.38 | 4.93 | 49 |
| 54 | 8.75 | -16.88 | 46.88 | 4.93 | 49 |
| 55 | -43.75 | 50.62 | 16.88 | 4.90 | 49 |
| 56 | 22.75 | -20.62 | -35.62 | 4.90 | 98 |
| 57 | -29.75 | -84.38 | 1.88 | 4.89 | 442 |
| 57a | -36.75 | -76.88 | 1.88 | 4.46 | |
| 58 | -40.25 | -20.62 | -31.88 | 4.83 | 49 |
| 59 | -36.75 | 58.12 | 35.62 | 4.83 | 49 |
| 60 | 19.25 | 35.62 | 28.12 | 4.82 | 49 |
| 61 | -1.75 | -46.88 | -24.38 | 4.81 | 147 |
| 62 | 33.25 | -9.38 | -58.12 | 4.80 | 49 |
| 63 | -29.75 | -58.12 | -9.38 | 4.80 | 49 |
| 64 | -36.75 | -39.38 | 31.88 | 4.80 | 344 |
| 65 | -15.75 | -31.88 | -61.88 | 4.80 | 49 |
| 66 | -40.25 | 24.38 | -9.38 | 4.78 | 49 |
| 67 | 33.25 | 31.88 | -9.38 | 4.77 | 98 |
| 68 | -1.75 | -46.88 | 1.88 | 4.76 | 98 |
| 69 | 1.75 | -1.88 | 31.88 | 4.76 | 98 |
| 70 | -40.25 | -28.12 | -20.62 | 4.75 | 49 |
| 71 | -33.25 | -13.12 | -13.12 | 4.75 | 344 |
| 72 | -15.75 | -1.88 | -69.38 | 4.75 | 49 |
| 73 | -47.25 | 24.38 | 20.62 | 4.75 | 196 |
| 74 | 26.25 | 31.88 | -31.88 | 4.75 | 147 |
| 75 | -12.25 | -58.12 | -16.88 | 4.74 | 49 |
| 76 | -12.25 | -5.62 | 28.12 | 4.74 | 246 |
| 77 | 36.75 | -61.88 | -5.62 | 4.74 | 98 |
| 78 | -33.25 | -73.12 | 31.88 | 4.72 | 49 |
| 79 | 12.25 | 43.12 | -16.88 | 4.72 | 49 |
| 80 | -1.75 | -9.38 | -20.62 | 4.71 | 49 |
| 81 | -47.25 | -54.38 | -9.38 | 4.69 | 295 |
| 82 | 8.75 | -91.88 | 5.62 | 4.68 | 49 |
| 83 | -50.75 | 9.38 | 9.38 | 4.67 | 49 |
| 84 | 12.25 | -54.38 | -43.12 | 4.67 | 98 |
| 85 | 8.75 | -20.62 | -13.12 | 4.67 | 147 |
| 86 | -22.75 | 16.88 | 5.62 | 4.66 | 98 |
| 87 | 29.75 | -50.62 | -50.62 | 4.66 | 196 |
| 88 | -5.25 | -13.12 | -69.38 | 4.66 | 147 |
| 89 | 19.25 | -35.62 | 69.38 | 4.65 | 49 |
| 90 | -15.75 | -39.38 | -50.62 | 4.64 | 98 |
| 91 | 22.75 | -35.62 | 54.38 | 4.64 | 295 |
| 91a | 26.25 | -43.12 | 54.38 | 4.06 | |
| 92 | -47.25 | -9.38 | 5.62 | 4.63 | 246 |
| 93 | -1.75 | -54.38 | -9.38 | 4.62 | 98 |
| 94 | 19.25 | -39.38 | -13.12 | 4.61 | 49 |
| 95 | 33.25 | 9.38 | -24.38 | 4.61 | 98 |
| 96 | 22.75 | 46.88 | -16.88 | 4.61 | 98 |
| 97 | 22.75 | -16.88 | -9.38 | 4.61 | 98 |
| 98 | -36.75 | 20.62 | 35.62 | 4.60 | 344 |
| 99 | -43.75 | 24.38 | -1.88 | 4.59 | 196 |
| 100 | 15.75 | 50.62 | 58.12 | 4.59 | 98 |
| 101 | 19.25 | 16.88 | 28.12 | 4.59 | 49 |
| 102 | -40.25 | -65.62 | -5.62 | 4.59 | 98 |
| 103 | -29.75 | -80.62 | -9.38 | 4.58 | 49 |
| 104 | -15.75 | -76.88 | 28.12 | 4.57 | 196 |
| 105 | -57.75 | -16.88 | 1.88 | 4.57 | 49 |
| 106 | 22.75 | 9.38 | 16.88 | 4.57 | 246 |
| 107 | -1.75 | 13.12 | -20.62 | 4.57 | 98 |
| 108 | 40.25 | -35.62 | -43.12 | 4.56 | 49 |
| 109 | -5.25 | -31.88 | 58.12 | 4.55 | 49 |
| 110 | 12.25 | 24.38 | 61.88 | 4.55 | 98 |
| 111 | 26.25 | 28.12 | -20.62 | 4.55 | 196 |
| 112 | -57.75 | 5.62 | 13.12 | 4.55 | 492 |
| 112a | -61.25 | -1.88 | 9.38 | 4.19 | |
| 112b | -57.75 | -9.38 | 9.38 | 4.13 | |
| 113 | 19.25 | -1.88 | 28.12 | 4.54 | 49 |
| 114 | 5.25 | -76.88 | 24.38 | 4.54 | 49 |
| 115 | 33.25 | 20.62 | -20.62 | 4.54 | 98 |
| 116 | -15.75 | 16.88 | 43.12 | 4.53 | 147 |
| 117 | -47.25 | -28.12 | 5.62 | 4.51 | 49 |
| 118 | -29.75 | 13.12 | -13.12 | 4.51 | 295 |
| 118a | -26.25 | 5.62 | -13.12 | 4.20 | |
| 119 | 12.25 | -80.62 | 13.12 | 4.51 | 98 |
| 120 | -8.75 | 84.38 | 20.62 | 4.50 | 147 |
| 121 | 15.75 | -35.62 | 73.12 | 4.50 | 196 |
| 122 | -15.75 | -16.88 | 24.38 | 4.49 | 98 |
| 123 | -15.75 | 9.38 | -20.62 | 4.49 | 98 |
| 124 | 22.75 | -28.12 | 69.38 | 4.49 | 98 |
| 125 | 12.25 | -9.38 | 13.12 | 4.49 | 98 |
| 126 | 5.25 | -43.12 | -9.38 | 4.49 | 49 |
| 127 | -1.75 | -9.38 | -46.88 | 4.49 | 196 |
| 128 | -12.25 | 9.38 | 24.38 | 4.48 | 295 |
| 129 | -26.25 | 13.12 | 46.88 | 4.48 | 49 |
| 130 | -12.25 | -13.12 | -46.88 | 4.48 | 98 |
| 131 | -1.75 | -50.62 | -1.88 | 4.47 | 49 |
| 132 | 1.75 | 24.38 | 31.88 | 4.47 | 49 |
| 133 | -5.25 | -24.38 | -20.62 | 4.45 | 147 |
| 134 | 8.75 | 16.88 | -20.62 | 4.45 | 49 |
| 135 | 15.75 | -24.38 | -35.62 | 4.45 | 344 |
| 136 | -22.75 | 31.88 | -24.38 | 4.45 | 98 |
| 137 | -47.25 | -28.12 | 43.12 | 4.44 | 49 |
| 138 | -33.25 | 13.12 | 13.12 | 4.44 | 246 |
| 139 | -47.25 | -61.88 | -5.62 | 4.44 | 49 |
| 140 | -50.75 | 39.38 | 9.38 | 4.44 | 49 |
| 141 | -50.75 | -20.62 | 9.38 | 4.43 | 98 |
| 142 | 33.25 | -24.38 | -13.12 | 4.43 | 147 |
| 143 | -15.75 | -20.62 | -58.12 | 4.43 | 98 |
| 144 | -54.25 | -46.88 | -5.62 | 4.42 | 98 |
| 145 | 40.25 | 31.88 | 13.12 | 4.42 | 49 |
| 146 | -43.75 | 16.88 | 58.12 | 4.42 | 49 |
| 147 | -47.25 | -24.38 | -16.88 | 4.41 | 196 |
| 148 | -12.25 | -58.12 | -46.88 | 4.40 | 49 |
| 149 | -22.75 | -9.38 | 13.12 | 4.40 | 49 |
| 150 | 22.75 | 16.88 | 5.62 | 4.39 | 196 |
| 151 | 50.75 | 1.88 | 43.12 | 4.39 | 98 |
| 152 | -1.75 | -39.38 | 1.88 | 4.39 | 295 |
| 153 | -50.75 | -16.88 | 24.38 | 4.38 | 49 |
| 154 | 36.75 | 50.62 | 1.88 | 4.38 | 98 |
| 155 | -1.75 | -54.38 | -28.12 | 4.36 | 98 |
| 156 | 22.75 | 13.12 | 35.62 | 4.36 | 344 |
| 156a | 12.25 | 20.62 | 35.62 | 4.14 | |
| 157 | -36.75 | 16.88 | 50.62 | 4.36 | 147 |
| 158 | 36.75 | 43.12 | -28.12 | 4.35 | 49 |
| 159 | 33.25 | 24.38 | 39.38 | 4.35 | 49 |
| 160 | 36.75 | 5.62 | 35.62 | 4.34 | 147 |
| 161 | 29.75 | -31.88 | 39.38 | 4.34 | 442 |
| 161a | 40.25 | -35.62 | 39.38 | 3.94 | |
| 162 | -15.75 | -50.62 | -1.88 | 4.33 | 49 |
| 163 | -5.25 | -46.88 | -16.88 | 4.33 | 689 |
| 164 | -1.75 | -65.62 | 16.88 | 4.33 | 344 |
| 164a | 5.25 | -73.12 | 16.88 | 4.13 | |
| 165 | -47.25 | 35.62 | 16.88 | 4.33 | 295 |
| 166 | -36.75 | 43.12 | 24.38 | 4.32 | 98 |
| 167 | -54.25 | -20.62 | -9.38 | 4.32 | 49 |
| 168 | -15.75 | 20.62 | -46.88 | 4.32 | 98 |
| 169 | 47.25 | -54.38 | -16.88 | 4.31 | 49 |
| 170 | -50.75 | -50.62 | 20.62 | 4.31 | 49 |
| 171 | -22.75 | 1.88 | -1.88 | 4.31 | 49 |
| 172 | -26.25 | 1.88 | 43.12 | 4.31 | 98 |
| 173 | -15.75 | -54.38 | 43.12 | 4.31 | 49 |
| 174 | 8.75 | 13.12 | 50.62 | 4.30 | 98 |
| 175 | -15.75 | -73.12 | -13.12 | 4.30 | 49 |
| 176 | 54.25 | -24.38 | -5.62 | 4.29 | 196 |
| 177 | -15.75 | -1.88 | 16.88 | 4.29 | 49 |
| 178 | -43.75 | 43.12 | -28.12 | 4.28 | 98 |
| 179 | 68.25 | -1.88 | -9.38 | 4.28 | 49 |
| 180 | -50.75 | 16.88 | -1.88 | 4.28 | 49 |
| 181 | 19.25 | -69.38 | -13.12 | 4.28 | 98 |
| 182 | 12.25 | 50.62 | -1.88 | 4.28 | 98 |
| 183 | 19.25 | -9.38 | 20.62 | 4.28 | 49 |
| 184 | -19.25 | 1.88 | 58.12 | 4.27 | 49 |
| 185 | -29.75 | -80.62 | -24.38 | 4.27 | 49 |
| 186 | 19.25 | -1.88 | 43.12 | 4.26 | 98 |
| 187 | -19.25 | -58.12 | -9.38 | 4.26 | 49 |
| 188 | 68.25 | -13.12 | -9.38 | 4.25 | 98 |
| 189 | 19.25 | -65.62 | -24.38 | 4.25 | 147 |
| 190 | -5.25 | 31.88 | -20.62 | 4.25 | 147 |
| 191 | -15.75 | -46.88 | -5.62 | 4.25 | 98 |
| 192 | 54.25 | -9.38 | 20.62 | 4.25 | 246 |
| 193 | -47.25 | -58.12 | 13.12 | 4.25 | 49 |
| 194 | 50.75 | -54.38 | -9.38 | 4.23 | 49 |
| 195 | -50.75 | -5.62 | -5.62 | 4.23 | 49 |
| 196 | -22.75 | -69.38 | -13.12 | 4.23 | 147 |
| 197 | 19.25 | 16.88 | 13.12 | 4.23 | 147 |
| 198 | 15.75 | 9.38 | 43.12 | 4.22 | 98 |
| 199 | 1.75 | 13.12 | 9.38 | 4.22 | 49 |
| 200 | 12.25 | -54.38 | -54.38 | 4.22 | 49 |
| 201 | 15.75 | -88.12 | 5.62 | 4.21 | 98 |
| 202 | -40.25 | 5.62 | -28.12 | 4.21 | 196 |
| 203 | 40.25 | -31.88 | 54.38 | 4.21 | 98 |
| 204 | -1.75 | -1.88 | 1.88 | 4.20 | 246 |
| 205 | -1.75 | 1.88 | 13.12 | 4.20 | 49 |
| 206 | 12.25 | -9.38 | 5.62 | 4.20 | 49 |
| 207 | 12.25 | 24.38 | -39.38 | 4.19 | 196 |
| 208 | 50.75 | 43.12 | -5.62 | 4.19 | 49 |
| 209 | -1.75 | -58.12 | -54.38 | 4.19 | 49 |
| 210 | -47.25 | -28.12 | 13.12 | 4.18 | 98 |
| 211 | -26.25 | 50.62 | 5.62 | 4.18 | 147 |
| 212 | 15.75 | -1.88 | -16.88 | 4.17 | 196 |
| 213 | 54.25 | 1.88 | 35.62 | 4.17 | 98 |
| 214 | -47.25 | -43.12 | 9.38 | 4.17 | 147 |
| 215 | 40.25 | 5.62 | -24.38 | 4.17 | 49 |
| 216 | 40.25 | 35.62 | -1.88 | 4.17 | 49 |
| 217 | 22.75 | 46.88 | 13.12 | 4.16 | 49 |
| 218 | 50.75 | -5.62 | 5.62 | 4.16 | 49 |
| 219 | -40.25 | -58.12 | 9.38 | 4.16 | 98 |
| 220 | -26.25 | -31.88 | 16.88 | 4.16 | 49 |
| 221 | -22.75 | -54.38 | -61.88 | 4.16 | 98 |
| 222 | -12.25 | 20.62 | 73.12 | 4.15 | 246 |
| 223 | -36.75 | 43.12 | 1.88 | 4.15 | 196 |
| 224 | 19.25 | -24.38 | 16.88 | 4.15 | 49 |
| 225 | 8.75 | 46.88 | -5.62 | 4.14 | 49 |
| 226 | 19.25 | -9.38 | 5.62 | 4.14 | 98 |
| 227 | -26.25 | -28.12 | -31.88 | 4.13 | 49 |
| 228 | 54.25 | -20.62 | 39.38 | 4.13 | 49 |
| 229 | 36.75 | -35.62 | -9.38 | 4.13 | 295 |
| 230 | -36.75 | -69.38 | 5.62 | 4.13 | 49 |
| 231 | -19.25 | 61.88 | 5.62 | 4.13 | 49 |
| 232 | 26.25 | -61.88 | 31.88 | 4.13 | 49 |
| 233 | 5.25 | -28.12 | -9.38 | 4.13 | 147 |
| 234 | -54.25 | 1.88 | 13.12 | 4.13 | 49 |
| 235 | -50.75 | 13.12 | -5.62 | 4.12 | 49 |
| 236 | -8.75 | -46.88 | -31.88 | 4.12 | 49 |
| 237 | -15.75 | 13.12 | 50.62 | 4.12 | 49 |
| 238 | -40.25 | -46.88 | 1.88 | 4.10 | 49 |
| 239 | -33.25 | -35.62 | -65.62 | 4.10 | 98 |
| 240 | 12.25 | 80.62 | 35.62 | 4.10 | 147 |
| 241 | 29.75 | -31.88 | -31.88 | 4.10 | 49 |
| 242 | -33.25 | 1.88 | -16.88 | 4.10 | 147 |
| 243 | -29.75 | -1.88 | -24.38 | 4.09 | 98 |
| 244 | -54.25 | 46.88 | 9.38 | 4.09 | 98 |
| 245 | -36.75 | -50.62 | 1.88 | 4.08 | 49 |
| 246 | -43.75 | -65.62 | 24.38 | 4.08 | 49 |
| 247 | 1.75 | -5.62 | -13.12 | 4.08 | 49 |
| 248 | 1.75 | -5.62 | -54.38 | 4.07 | 49 |
| 249 | 19.25 | 13.12 | -1.88 | 4.07 | 98 |
| 250 | -61.25 | -20.62 | 13.12 | 4.07 | 49 |
| 251 | 22.75 | -35.62 | -20.62 | 4.06 | 147 |
| 252 | 29.75 | -43.12 | -58.12 | 4.06 | 49 |
| 253 | 26.25 | 24.38 | 13.12 | 4.06 | 147 |
| 254 | 29.75 | -28.12 | -24.38 | 4.06 | 49 |
| 255 | -8.75 | 5.62 | -24.38 | 4.06 | 49 |
| 256 | -15.75 | 24.38 | -9.38 | 4.05 | 49 |
| 257 | -15.75 | -1.88 | -31.88 | 4.05 | 49 |
| 258 | 8.75 | 20.62 | -24.38 | 4.05 | 49 |
| 259 | -22.75 | -1.88 | -20.62 | 4.05 | 393 |
| 260 | 26.25 | 1.88 | 13.12 | 4.04 | 147 |
| 261 | -33.25 | 28.12 | 31.88 | 4.04 | 246 |
| 262 | 29.75 | -58.12 | -46.88 | 4.04 | 49 |
| 263 | 5.25 | -61.88 | 54.38 | 4.03 | 196 |
| 264 | -54.25 | 13.12 | 20.62 | 4.03 | 196 |
| 265 | 40.25 | 9.38 | -20.62 | 4.02 | 98 |
| 266 | 12.25 | 5.62 | 43.12 | 4.02 | 98 |
| 267 | -54.25 | 5.62 | -16.88 | 4.01 | 196 |
| 268 | -33.25 | -35.62 | -9.38 | 4.01 | 98 |
| 269 | -26.25 | -73.12 | -35.62 | 4.01 | 49 |
| 270 | 26.25 | -9.38 | 13.12 | 4.01 | 246 |
| 271 | 47.25 | 43.12 | 9.38 | 4.00 | 49 |
| 272 | 1.75 | -76.88 | 35.62 | 4.00 | 49 |
| 273 | -33.25 | -69.38 | 39.38 | 4.00 | 49 |
| 274 | -15.75 | -43.12 | -43.12 | 4.00 | 98 |
| 275 | -47.25 | -65.62 | 1.88 | 4.00 | 196 |
| 276 | -12.25 | 46.88 | 28.12 | 4.00 | 49 |
| 277 | -1.75 | -24.38 | 9.38 | 3.99 | 49 |
| 278 | -1.75 | 58.12 | 13.12 | 3.99 | 49 |
| 279 | -8.75 | -46.88 | -24.38 | 3.99 | 49 |
| 280 | -19.25 | 13.12 | 28.12 | 3.99 | 49 |
| 281 | 29.75 | 65.62 | 43.12 | 3.98 | 49 |
| 282 | 40.25 | -20.62 | 20.62 | 3.98 | 49 |
| 283 | 36.75 | 28.12 | -35.62 | 3.97 | 49 |
| 284 | -57.75 | -31.88 | -5.62 | 3.97 | 49 |
| 285 | -19.25 | 24.38 | 31.88 | 3.97 | 49 |
| 286 | -50.75 | 28.12 | 46.88 | 3.97 | 98 |
| 287 | 61.25 | -31.88 | 39.38 | 3.97 | 49 |
| 288 | -47.25 | 20.62 | -13.12 | 3.96 | 49 |
| 289 | 47.25 | 16.88 | -39.38 | 3.96 | 98 |
| 290 | 47.25 | -54.38 | -1.88 | 3.96 | 147 |
| 291 | 64.75 | -43.12 | 5.62 | 3.95 | 49 |
| 292 | -15.75 | -31.88 | -16.88 | 3.95 | 49 |
| 293 | 8.75 | 76.88 | 35.62 | 3.95 | 49 |
| 294 | 5.25 | -88.12 | -1.88 | 3.95 | 49 |
| 295 | 22.75 | -69.38 | 1.88 | 3.95 | 147 |
| 296 | 12.25 | 9.38 | 28.12 | 3.94 | 147 |
| 297 | 36.75 | -73.12 | 5.62 | 3.94 | 98 |
| 298 | 33.25 | 43.12 | 35.62 | 3.94 | 49 |
| 299 | 33.25 | 43.12 | 24.38 | 3.93 | 49 |
| 300 | 26.25 | -20.62 | -65.62 | 3.93 | 196 |
| 301 | -22.75 | -76.88 | -16.88 | 3.93 | 49 |
| 302 | 54.25 | -28.12 | -9.38 | 3.93 | 98 |
| 303 | 12.25 | -9.38 | 50.62 | 3.92 | 49 |
| 304 | -12.25 | -28.12 | -50.62 | 3.92 | 196 |
| 305 | 15.75 | -35.62 | 31.88 | 3.92 | 98 |
| 306 | 15.75 | -1.88 | -1.88 | 3.92 | 49 |
| 307 | 5.25 | -28.12 | 73.12 | 3.92 | 49 |
| 308 | -26.25 | -61.88 | 35.62 | 3.92 | 98 |
| 309 | 5.25 | -58.12 | -54.38 | 3.92 | 49 |
| 310 | -19.25 | -76.88 | -28.12 | 3.91 | 98 |
| 311 | 19.25 | -76.88 | -35.62 | 3.91 | 98 |
| 312 | 22.75 | 5.62 | -1.88 | 3.91 | 49 |
| 313 | -54.25 | -20.62 | -24.38 | 3.91 | 49 |
| 314 | -1.75 | -9.38 | -5.62 | 3.91 | 49 |
| 315 | 15.75 | -20.62 | 73.12 | 3.91 | 98 |
| 316 | 19.25 | 65.62 | 9.38 | 3.91 | 49 |
| 317 | -64.75 | -13.12 | -24.38 | 3.91 | 49 |
| 318 | -15.75 | -5.62 | 61.88 | 3.91 | 147 |
| 319 | -26.25 | 24.38 | 13.12 | 3.90 | 98 |
| 320 | -19.25 | -13.12 | 61.88 | 3.90 | 49 |
| 321 | -22.75 | 13.12 | -24.38 | 3.90 | 49 |
| 322 | 19.25 | 54.38 | 1.88 | 3.90 | 147 |
| 323 | -15.75 | 54.38 | 54.38 | 3.90 | 98 |
| 324 | 1.75 | -31.88 | 1.88 | 3.89 | 49 |
| 325 | -33.25 | 39.38 | 54.38 | 3.89 | 49 |
| 326 | -50.75 | 16.88 | -16.88 | 3.89 | 147 |
| 327 | 33.25 | 50.62 | 50.62 | 3.89 | 49 |
| 328 | 5.25 | -35.62 | -58.12 | 3.89 | 98 |
| 329 | -47.25 | -28.12 | 28.12 | 3.89 | 49 |
| 330 | 12.25 | 69.38 | 5.62 | 3.89 | 49 |
| 331 | 5.25 | -50.62 | -20.62 | 3.88 | 98 |
| 332 | 15.75 | 88.12 | 9.38 | 3.88 | 49 |
| 333 | -19.25 | -50.62 | 16.88 | 3.88 | 49 |
| 334 | -47.25 | 16.88 | 5.62 | 3.88 | 49 |
| 335 | -47.25 | -13.12 | -54.38 | 3.88 | 98 |
| 336 | -1.75 | -16.88 | -28.12 | 3.87 | 49 |
| 337 | 33.25 | -28.12 | 65.62 | 3.87 | 98 |
| 338 | 19.25 | 35.62 | 1.88 | 3.87 | 98 |
| 339 | 33.25 | 5.62 | 28.12 | 3.86 | 49 |
| 340 | -47.25 | 16.88 | -24.38 | 3.86 | 49 |
| 341 | -54.25 | -24.38 | -39.38 | 3.86 | 98 |
| 342 | 22.75 | -16.88 | 43.12 | 3.86 | 49 |
| 343 | -47.25 | 20.62 | 46.88 | 3.86 | 49 |
| 344 | 57.75 | -20.62 | 28.12 | 3.85 | 49 |
| 345 | 36.75 | 1.88 | 20.62 | 3.85 | 98 |
| 346 | -47.25 | -39.38 | 16.88 | 3.85 | 49 |
| 347 | 50.75 | -50.62 | 9.38 | 3.85 | 98 |
| 348 | -54.25 | -20.62 | -50.62 | 3.85 | 49 |
| 349 | 36.75 | -39.38 | -69.38 | 3.85 | 49 |
| 350 | 15.75 | -73.12 | -31.88 | 3.85 | 49 |
| 351 | -26.25 | -35.62 | -9.38 | 3.85 | 49 |
| 352 | 19.25 | 20.62 | 20.62 | 3.84 | 49 |
| 353 | -19.25 | -20.62 | -35.62 | 3.84 | 98 |
| 354 | 12.25 | -9.38 | 31.88 | 3.83 | 147 |
| 355 | -33.25 | -35.62 | 46.88 | 3.83 | 49 |
| 356 | 1.75 | 16.88 | -39.38 | 3.83 | 49 |
| 357 | -29.75 | -46.88 | -13.12 | 3.82 | 49 |
| 358 | 26.25 | -65.62 | 46.88 | 3.82 | 49 |
| 359 | 1.75 | -20.62 | -35.62 | 3.82 | 49 |
| 360 | 26.25 | 5.62 | 5.62 | 3.82 | 98 |
| 361 | -54.25 | -39.38 | 1.88 | 3.81 | 246 |
| 361a | -57.75 | -46.88 | -1.88 | 3.78 | |
| 362 | -40.25 | 16.88 | 24.38 | 3.81 | 98 |
| 363 | -1.75 | -43.12 | -9.38 | 3.81 | 49 |
| 364 | -5.25 | -84.38 | -20.62 | 3.81 | 49 |
| 365 | 33.25 | -35.62 | -5.62 | 3.80 | 49 |
| 366 | 1.75 | -35.62 | -61.88 | 3.78 | 49 |
| 367 | -1.75 | -28.12 | 20.62 | 3.78 | 98 |
| 368 | 1.75 | -58.12 | -13.12 | 3.78 | 49 |
| 369 | -36.75 | 61.88 | 16.88 | 3.78 | 98 |
| 370 | 12.25 | -46.88 | -46.88 | 3.78 | 49 |
| 371 | -12.25 | -20.62 | -46.88 | 3.78 | 49 |
| 372 | 29.75 | 16.88 | 35.62 | 3.78 | 49 |
| 373 | 26.25 | 13.12 | 9.38 | 3.77 | 98 |
| 374 | 15.75 | -20.62 | -20.62 | 3.77 | 49 |
| 375 | -33.25 | 35.62 | -24.38 | 3.77 | 49 |
| 376 | 8.75 | -24.38 | 9.38 | 3.77 | 49 |
| 377 | -22.75 | -50.62 | 54.38 | 3.76 | 49 |
| 378 | 36.75 | -73.12 | -9.38 | 3.76 | 49 |
| 379 | 8.75 | -73.12 | 35.62 | 3.76 | 49 |
| 380 | -1.75 | -16.88 | 24.38 | 3.76 | 49 |
| 381 | -15.75 | -16.88 | -43.12 | 3.76 | 49 |
| 382 | 22.75 | -73.12 | -5.62 | 3.76 | 49 |
| 383 | 12.25 | 16.88 | -50.62 | 3.76 | 49 |
| 384 | -12.25 | 54.38 | -9.38 | 3.75 | 49 |
| 385 | 8.75 | -43.12 | -35.62 | 3.75 | 49 |
| 386 | 22.75 | 24.38 | 43.12 | 3.75 | 98 |
| 387 | 19.25 | 43.12 | -5.62 | 3.75 | 49 |
| 388 | -12.25 | -39.38 | -5.62 | 3.75 | 147 |
| 389 | 5.25 | -46.88 | -39.38 | 3.75 | 49 |
| 390 | 43.75 | -5.62 | -13.12 | 3.74 | 49 |
| 391 | 47.25 | 13.12 | -20.62 | 3.74 | 49 |
| 392 | -54.25 | -9.38 | 20.62 | 3.74 | 49 |
| 393 | 12.25 | -35.62 | 9.38 | 3.74 | 49 |
| 394 | -57.75 | -1.88 | -13.12 | 3.74 | 98 |
| 395 | -33.25 | -43.12 | -28.12 | 3.74 | 98 |
| 396 | 47.25 | 13.12 | 5.62 | 3.74 | 49 |
| 397 | 1.75 | -84.38 | -16.88 | 3.74 | 49 |
| 398 | 40.25 | 28.12 | -39.38 | 3.74 | 49 |
| 399 | 33.25 | 50.62 | 24.38 | 3.74 | 98 |
| 400 | -1.75 | -9.38 | 69.38 | 3.74 | 49 |
| 401 | 19.25 | -16.88 | 16.88 | 3.74 | 49 |
| 402 | -33.25 | 65.62 | 20.62 | 3.73 | 98 |
| 403 | -29.75 | 16.88 | -5.62 | 3.73 | 49 |
| 404 | 22.75 | 46.88 | 5.62 | 3.73 | 49 |
| 405 | 1.75 | -16.88 | -24.38 | 3.73 | 49 |
| 406 | 12.25 | -65.62 | -13.12 | 3.73 | 49 |
| 407 | 22.75 | 16.88 | 50.62 | 3.73 | 49 |
| 408 | 19.25 | -50.62 | -73.12 | 3.73 | 49 |
| 409 | -33.25 | -43.12 | 58.12 | 3.72 | 98 |
| 410 | -47.25 | -58.12 | -28.12 | 3.72 | 49 |
| 411 | 19.25 | 9.38 | 20.62 | 3.72 | 49 |
| 412 | -5.25 | -43.12 | 69.38 | 3.72 | 98 |
| 413 | -8.75 | 39.38 | 5.62 | 3.72 | 49 |
| 414 | 26.25 | 20.62 | -24.38 | 3.71 | 98 |
| 415 | -29.75 | 35.62 | -1.88 | 3.71 | 49 |
| 416 | 33.25 | 43.12 | -16.88 | 3.71 | 49 |
| 417 | 40.25 | -58.12 | 5.62 | 3.71 | 98 |
| 418 | 40.25 | -5.62 | 46.88 | 3.70 | 147 |
| 419 | 33.25 | -9.38 | -9.38 | 3.70 | 49 |
| 420 | -68.25 | -24.38 | 13.12 | 3.70 | 49 |
| 421 | -15.75 | -76.88 | 20.62 | 3.70 | 49 |
| 422 | 43.75 | 31.88 | 5.62 | 3.70 | 49 |
| 423 | 1.75 | 65.62 | -16.88 | 3.70 | 98 |
| 424 | 26.25 | -20.62 | 20.62 | 3.70 | 49 |
| 425 | 47.25 | 20.62 | 5.62 | 3.70 | 49 |
| 426 | -29.75 | 20.62 | -24.38 | 3.70 | 49 |
| 427 | -47.25 | 46.88 | 20.62 | 3.70 | 49 |
| 428 | 15.75 | 1.88 | 35.62 | 3.69 | 49 |
| 429 | 40.25 | -43.12 | -28.12 | 3.69 | 49 |
| 430 | -40.25 | 39.38 | 43.12 | 3.69 | 147 |
| 431 | -54.25 | -35.62 | -5.62 | 3.69 | 49 |
| 432 | -12.25 | -20.62 | -61.88 | 3.69 | 49 |
| 433 | -50.75 | 13.12 | -39.38 | 3.68 | 49 |
| 434 | 5.25 | 1.88 | 13.12 | 3.68 | 98 |
| 435 | 43.75 | -69.38 | -1.88 | 3.68 | 49 |
| 436 | -22.75 | -80.62 | 1.88 | 3.68 | 49 |
| 437 | -36.75 | -31.88 | -46.88 | 3.68 | 49 |
| 438 | 26.25 | -39.38 | 35.62 | 3.68 | 49 |
| 439 | 8.75 | 28.12 | -24.38 | 3.67 | 49 |
| 440 | 15.75 | -5.62 | 28.12 | 3.67 | 49 |
| 441 | -15.75 | 5.62 | 65.62 | 3.67 | 98 |
| 442 | 5.25 | -65.62 | -13.12 | 3.67 | 98 |
| 443 | 54.25 | -5.62 | 13.12 | 3.67 | 49 |
| 444 | -33.25 | 46.88 | 35.62 | 3.67 | 49 |
| 445 | 33.25 | -20.62 | -9.38 | 3.67 | 49 |
| 446 | 36.75 | -31.88 | 13.12 | 3.66 | 49 |
| 447 | 36.75 | 35.62 | 16.88 | 3.66 | 196 |
| 448 | -36.75 | -35.62 | 39.38 | 3.66 | 49 |
| 449 | 1.75 | 9.38 | -16.88 | 3.65 | 49 |
| 450 | -47.25 | -13.12 | 35.62 | 3.65 | 49 |
| 451 | -50.75 | 5.62 | -9.38 | 3.65 | 49 |
| 452 | 19.25 | -9.38 | 35.62 | 3.65 | 49 |
| 453 | -5.25 | -20.62 | 1.88 | 3.65 | 49 |
| 454 | 57.75 | -39.38 | 5.62 | 3.64 | 49 |
| 455 | -36.75 | 16.88 | 1.88 | 3.64 | 49 |
| 456 | -36.75 | -13.12 | 9.38 | 3.64 | 49 |
| 457 | -1.75 | 24.38 | -39.38 | 3.64 | 98 |
| 458 | -8.75 | -39.38 | -31.88 | 3.64 | 49 |
| 459 | 12.25 | 20.62 | -46.88 | 3.64 | 49 |
| 460 | 12.25 | -1.88 | -69.38 | 3.64 | 98 |
| 461 | -1.75 | -9.38 | 31.88 | 3.64 | 98 |
| 462 | -26.25 | -39.38 | -43.12 | 3.64 | 98 |
| 463 | 33.25 | -13.12 | 65.62 | 3.63 | 98 |
| 464 | -15.75 | -50.62 | -31.88 | 3.63 | 196 |
| 465 | -40.25 | -16.88 | -50.62 | 3.63 | 49 |
| 466 | 29.75 | -16.88 | 58.12 | 3.62 | 49 |
| 467 | 43.75 | 24.38 | -5.62 | 3.62 | 49 |
| 468 | -1.75 | -43.12 | -31.88 | 3.62 | 49 |
| 469 | -8.75 | 46.88 | 24.38 | 3.62 | 98 |
| 470 | 19.25 | -61.88 | 35.62 | 3.61 | 49 |
| 471 | -12.25 | -16.88 | -65.62 | 3.61 | 49 |
| 472 | 50.75 | 50.62 | -1.88 | 3.61 | 49 |
| 473 | 33.25 | -50.62 | -28.12 | 3.61 | 49 |
| 474 | -8.75 | 39.38 | -28.12 | 3.61 | 49 |
| 475 | -5.25 | -31.88 | 1.88 | 3.61 | 49 |
| 476 | 22.75 | -5.62 | 20.62 | 3.61 | 49 |
| 477 | 50.75 | -43.12 | 9.38 | 3.61 | 49 |
| 478 | -54.25 | -28.12 | -16.88 | 3.61 | 49 |
| 479 | -12.25 | 16.88 | 20.62 | 3.61 | 98 |
| 480 | -22.75 | -5.62 | -1.88 | 3.60 | 49 |
| 481 | 29.75 | 35.62 | 39.38 | 3.60 | 49 |
| 482 | -47.25 | -24.38 | -5.62 | 3.60 | 49 |
| 483 | 12.25 | 61.88 | 5.62 | 3.60 | 49 |
| 484 | -43.75 | 43.12 | 5.62 | 3.59 | 49 |
| 485 | 33.25 | -28.12 | 58.12 | 3.59 | 49 |
| 486 | 19.25 | 28.12 | 31.88 | 3.59 | 49 |
| 487 | -19.25 | -61.88 | 20.62 | 3.59 | 49 |
| 488 | 57.75 | 35.62 | 28.12 | 3.58 | 98 |
| 489 | 33.25 | -35.62 | -35.62 | 3.58 | 49 |
| 490 | -19.25 | -9.38 | -65.62 | 3.58 | 49 |
| 491 | -40.25 | -46.88 | -5.62 | 3.58 | 49 |
| 492 | 5.25 | -80.62 | -1.88 | 3.58 | 49 |
| 493 | -15.75 | -5.62 | 1.88 | 3.58 | 98 |
| 494 | 26.25 | -61.88 | -16.88 | 3.58 | 98 |
| 495 | 29.75 | -1.88 | -20.62 | 3.58 | 49 |
| 496 | 5.25 | 5.62 | -69.38 | 3.57 | 49 |
| 497 | 5.25 | -9.38 | -20.62 | 3.57 | 49 |
| 498 | 15.75 | -13.12 | -73.12 | 3.57 | 49 |
| 499 | -57.75 | -9.38 | 16.88 | 3.57 | 98 |
| 500 | -1.75 | 1.88 | 69.38 | 3.56 | 49 |
| 501 | -43.75 | 13.12 | 13.12 | 3.56 | 98 |
| 502 | 26.25 | 31.88 | 43.12 | 3.56 | 49 |
| 503 | -33.25 | 73.12 | 20.62 | 3.56 | 49 |
| 504 | 36.75 | 20.62 | 31.88 | 3.56 | 49 |
| 505 | -12.25 | 13.12 | 58.12 | 3.56 | 49 |
| 506 | -43.75 | 58.12 | 20.62 | 3.56 | 49 |
| 507 | -29.75 | -24.38 | -9.38 | 3.55 | 49 |
| 508 | -33.25 | -16.88 | -5.62 | 3.55 | 49 |
| 509 | -12.25 | 5.62 | 35.62 | 3.55 | 147 |
| 510 | 19.25 | -31.88 | -16.88 | 3.55 | 49 |
| 511 | -61.25 | -16.88 | 9.38 | 3.55 | 49 |
| 512 | -54.25 | -9.38 | -5.62 | 3.55 | 98 |
| 513 | 50.75 | -13.12 | 5.62 | 3.54 | 49 |
| 514 | -26.25 | -35.62 | 54.38 | 3.54 | 49 |
| 515 | 26.25 | -54.38 | -13.12 | 3.54 | 98 |
| 516 | -1.75 | 46.88 | 54.38 | 3.54 | 49 |
| 517 | 47.25 | 28.12 | -39.38 | 3.54 | 49 |
| 518 | -29.75 | 43.12 | 39.38 | 3.54 | 49 |
| 519 | -47.25 | 43.12 | 9.38 | 3.54 | 49 |
| 520 | 57.75 | -13.12 | 16.88 | 3.54 | 49 |
| 521 | -40.25 | 13.12 | -43.12 | 3.53 | 49 |
| 522 | 22.75 | 69.38 | 5.62 |