trekker¶
Synopsis¶
diffusion MRI based fiber tracking using parallel transport tractography
Usage¶
trekker [ options ]
Description¶
Trekker expects at least the following options to be specified:
-fod: Trekker generates streamlines from a fiber orientation distribution (FOD) image provided by the user with this option.
-seed_image or -seed_coordinates: Trekker expects the user to provide seeds using either of these options.
-seed_count or -seed_countPerVoxel or -timeLimit: If a seed image is provided, the user can provide the number of streamlines to be generated either with the option -seed_count or -seed_countPerVoxel. If -seed_coordinates options is used, trekker will generate one streamline for each specified coordinate. Alternatively the -timeLimit option can be used to stop tracking after a certain amount of time.
-output: Lastly the user has to provide the output path with -output followed by the path and name of the output file. trekker’s output file format is *.vtk. An additional .json formatted metadata is also saved.
All unspecified parameters will be set to trekker’s default values.
# Example 1
./trekker -fod FOD.nii.gz \
-seed_image SEED.nii.gz \
-seed_count 1000 \
-output OUTPUT.vtk
# Example 2
./trekker -fod FOD.nii.gz \
-seed_coordinates COORDINATES \
-output OUTPUT.vtk
# Example 3
./trekker -fod FOD.nii.gz \
-seed_image SEED.nii.gz \
-timeLimit 15 \ # time limit is in minutes
-output OUTPUT.vtk
Options¶
Tracking options¶
-fod <nifti image of spherical harmonic coefficients> or -fod <nifti image of spherical function values> <list of spherical coordinates> <either “sym” or “asym”> Starting with version 0.5, trekker supports both symmetric and asymmetric FODs that can be represented with even or odd ordered spherical harmonics.
There are two ways to provide the input FOD image:
1. as spherical harmonic coefficients
The FOD image stores each coefficient as a separate volume with respect to spherical harmonic degree, l and m, as shown in the tables below. Based on the number of volumes in the image, trekker automatically identifies if the FOD is symmetric or asymmetric.
vol1 |
vol2 |
vol3 |
vol4 |
vol5 |
vol6 |
vol7 |
vol8 |
vol9 |
… |
|
l |
0 |
2 |
2 |
2 |
2 |
2 |
4 |
4 |
4 |
… |
m |
0 |
-2 |
-1 |
0 |
1 |
2 |
-4 |
-3 |
-2 |
… |
vol1 |
vol2 |
vol3 |
vol4 |
vol5 |
vol6 |
vol7 |
vol8 |
vol9 |
… |
|
l |
0 |
1 |
1 |
1 |
2 |
2 |
2 |
2 |
2 |
… |
m |
0 |
-1 |
0 |
1 |
-2 |
-1 |
0 |
1 |
2 |
… |
2. as a spherical function defined on provided vertices of a sphere
In this case, FOD image stores a spherical function at each voxel. Volume n of the FOD image corresponds to the value of the spherical function at the coordinates provided in line n of the vertices of the sphere that should be provided after the image. Additionally user should specify if the function is symmetric or asymmetric.
# Example 4
./trekker -fod FOD_spherical_function_values.nii.gz FOD_spherical_function_domain.txt asym \
-seed_image SEED.nii.gz \
-seed_count 1000 \
-output OUTPUT.vtk
-orderOfDirections <one of the 48 direction options> Switches direction of FOD. By default XYZ is assumed (no change). A lower case can be used to switch the direction, e.g., xYZ switches the sign of the first component. The order of components can be changed as well, e.g., yXz switches the sign of the second component while replacing it with the first, at the same time the sign of the last component is also changed.
# Example 5
./trekker -fod FOD.nii.gz \
-orderOfDirections Zyx \
-seed_image SEED.nii.gz \
-seed_count 1000 \
-output OUTPUT.vtk
-dontDiscretizeFod Turns of FOD discretization. For faster tracking, trekker by default will discretize the FOD on a sphere (using 2076 points for symmetric functions and 1004 points for asymmetric functions).
-stepSize <positive real number> Step size. Default=1/40 x the smallest of the FOD voxel dimensions.
-minRadiusOfCurvature <positive real number> Minimum radius of curvature. Default=1/2 x the smallest of the FOD voxel dimensions.
-minFODamp <positive real number> Threshold for minimum data support. For PTT algorithm this is minimum FOD threshold. Default=0.05.
-dataSupportExponent <positive real number> Data support to the power dataSupportExponent is used for rejection sampling. Default=1.
-minLength <positive real number> Minimum length of the tracks. Default=0.
-maxLength <positive real number> Maximum length of the tracks. Default=infinite.
-atMaxLength <either “stop” or “discard”> If set to “stop”, when maxLength is reached the propagation stops and the streamline is written in the output. If set to “discard”, the streamline is not written in the output. Default=discard.
-writeInterval <positive integer> Because stepSize might be very small, it might not be desirable to save each step of propagation in the output. writeInterval enables skipping of a number of steps before saving them. This parameter does not change the propagation algorithm, internally trekker always walks with the specified -stepSize. -writeInterval by Default: is set, so that the distance between the output tracks is 0.5 x the smallest of the FOD voxel dimensions.
-directionality <either “one_sided” or “two_sided”> If set to “two_sided”, tracking is done along both directions starting from the seed point. If set to “one_sided”, tracking is only done along the other way. Default=two_sided.
-maxEstInterval <positive integer> For random sampling, an estimate for the maximum posterior probability is needed. If set to n, this estimate is computed at every n th step. Default=1.
-initMaxEstTrials <positive integer> Number of trials done for the estimation of maximum posterior probability during initialization. Default: is set adaptively based on previous trials.
-propMaxEstTrials <positive integer> Number of trials done for the estimation of maximum posterior probability during propagation. Default: is set adaptively based on previous trials.
-maxSamplingPerStep <positive integer> Maximum number of random sampling to select an acceptable candidate for propagation. Tracking stops if a suitable candidate cannot be found after this many attempts. Default: 1000.
-useBestAtInit If used, during initialization, random sampling is skipped and the candidate that has the largest data support is used for propagation. Default: off
# Example 6
./trekker -fod FOD.nii.gz \
-initMaxEstTrials 500 \
-useBestAtInit \ # for initialization, out of 500 candidates, the one with the highest data support will be used
-seed_image SEED.nii.gz \
-seed_count 1000 \
-output OUTPUT.vtk
Probe options¶
Trekker uses probes for estimating future propagation steps. A probe is a short, cylinderical model of the connecting segment.
-probeLength <positive real number> Length of the probe. Default=1/4 x the smallest of the FOD voxel dimensions.
-probeRadius <positive real number> Radius of the probe. Default=0.
-probeCount <integer between 1 and 100> probeCount determines the number of parallel lines used to model the cylinder. Default: 1 if probeRadius=0, 4 if probeRadius≠0.
-probeQuality <integer between 1 and 100> This parameter sets the number of segments to split the cylinder along the length of the probe. Default=4.
-ignoreWeakLinks <positive real number> If the individual data support (FOD amplitude along the direction of the segment) for any of the points in the probe is lower than what is specified with this parameter, this candidate probe is ignored. Default: 0.
Seeding options¶
-seed_image <nifti image> {optional:labelNo} Path to seed image and optionally a label no. If label is not specified all voxels with values larger than zero are used to seed points. Accepted file extensions are: nii, nii.gz.
-seed_count <positive integer> Number of seeds to randomly sample from the seed image. Trekker tries to generate a single streamline from each seed. For that it tries what is set by seed_maxTrials. If an acceptable streamline cannot be generated then it is skipped. If this happens, there will be less streamlines in the output tractogram than what is defined with seed_count.
-seed_countPerVoxel <positive integer> Number of seeds to randomly sample per each voxel of the seed image.
-seed_coordinates <coordinate list file> {optional:initial direction list file} Path to an ASCII formatted list of the physical locations of the seed coordinates. This text file for the coordinates can optionally be followed with another ASCII formatted list that specifies the initial directions for propagation. Each of the listed seeds is used to generate a single streamline, seed_count and seed_countPerVoxels options do not have any affect when seed_coordinates is used. Example for the contents of a COORDINATES file is given below:
0 0 0
0 0 0
0 0 0
1 1 1
1 1 1
0.121323 0.21323 0.123213123
1.231232.1.2313213 2.5545435
12.2323213<PHONE_NUMBER><PHONE_NUMBER>
# Example 7
./trekker -fod FOD.nii.gz \
-seed_coordinates COORDINATES \
-output OUTPUT.vtk
# Example 8
./trekker -fod FOD.nii.gz \
-seed_coordinates COORDINATES DIRECTIONS \
-output OUTPUT.vtk
-seed_maxTrials <positive integer> Sets the maximum number of attempts to generate streamline from the seed point. Default=100000.
Output options¶
-output <vtk file> Output file path. Trekker saves tractograms in .vtk format. An additional output file with .json extension is also saved which contains metadata about tracking parameters and information about the results.
-enableOutputOverwrite If used, output file will be overwritten in the case that it already exists in the specified path. Default=off.
-writeSeedCoordinates If used, seed coordinates of each streamline is written in the output.
-dontWriteSegmentsInSeedROI If used, seeding is done using the provided seed image but streamlines are saved starting from the surface of the seed ROI. The seed coordinates with this option are also written starting from the seed surface.
-writeColors If used, direction coded colors are written for each segment in the output tractogram. Default=off. (If this is enabled, you can visualize the tracks in the familiar tractography RGB type coloring in Paraview. For that load the .vtk file in Paraview, choose “colors” as the coloring option and make sure that “Map Scalars” option is not selected.)
-writeFODamp, -writeTangents, -writek1axes, -writek2axes, -writek1s, -writek2s, -writeCurvatures, -writeLikelihoods options can be used separately to write these values for each segment in the output. Likelihoods are the computed data supports.
Pathway options¶
Starting from the seed point, trekker first propagates along one direction to find one end of the connection. Propagation is then done towards the opposition direction from the seed to find the other end of the connection. (This “two_sided” tracking is the default option in trekker and can be changed to “one_sided” using the -directionality option.) The two different sides of propagation are named as A and B in trekker.
Trekker allows two options to define rules for pathways:
Rules can be defined for each propagation direction separately, using options -pathway_A= and -pathway_B=.
Rules can be defined for either propagation direction, using options starting with -pathway=. These options define rules for “A or B” not for “A and B”.
Notes:
Trekker accepts multiple pathway rules which are either defined using option 1 or 2, i.e.: if a pathway rule is defined using -pathway=, all other rules have to start with -pathway=. Or, if a pathway rule is defined using -pathway_A=, all other rules have to start either with -pathway_A= or -pathway_B=.
Rules for -pathway_A= and -pathway_B= are considered separate. Therefore they are treated independent of each other. Rules can be defined using only -pathway_A= or -pathway_B=.
Users do not need to think about which side trekker considers to be side A or B. The decision is done automatically based on the first rule that is satisfied. For example, consider the case -seed_image THAL -pathway_A=require_entry GRAYMATTER -pathway_B=stop_at_exit THAL. Here, starting from the seed point, trekker propagates towards one direction which will soon exit THAL. But because this is a stopping condition for side B, propagation will stop here and this part will be considered as side B. Trekker then will return back to seed point and start propagating towards the other side which is from now on considered as side A.
Trekker allows the specification of the following pathway rules:
=require_entry
=stop_at_entry
=require_exit
=stop_at_exit
=discard_if_enters
=discard_if_exits
=discard_if_ends_inside
-pathway=require_entry <nifti image> {optional:labelNo} All tracks are required to enter the ROI present in the specified image.
-pathway_A=require_entry <nifti image> {optional:labelNo} One side of all tracks are required to enter the ROI present in the specified image.
-pathway_B=require_entry <nifti image> {optional:labelNo} The other side of all tracks are required to enter the ROI present in the specified image.
Example: The following will generate connections from left precentral gyrus (Left_PCG) back to Left_PCG.
# Example 9
./trekker -fod FOD.nii.gz \
-seed_image WHITEMATTER.nii.gz \
-seed_count 1000 \
-pathway_A=require_entry Left_PCG.nii.gz \
-pathway_B=require_entry Left_PCG.nii.gz \
-output OUTPUT.vtk
-pathway=stop_at_entry <nifti image> {optional:labelNo} All tracks will stop propagating if they enter the ROI present in the specified image.
-pathway_A=stop_at_entry <nifti image> {optional:labelNo} One side of all tracks will stop propagating if they enter the ROI present in the specified image.
-pathway_B=stop_at_entry <nifti image> {optional:labelNo} The other side of all tracks will stop propagating if they enter the ROI present in the specified image.
Example: The following will generate connections from left precentral gyrus (Left_PCG) back to Left_PCG. Tracking stops at both ends when they enter the Left_PCG ROI.
# Example 10
./trekker -fod FOD.nii.gz \
-seed_image WHITEMATTER.nii.gz \
-seed_count 1000 \
-pathway_A=require_entry Left_PCG.nii.gz \
-pathway_A=stop_at_entry Left_PCG.nii.gz \
-pathway_B=require_entry Left_PCG.nii.gz \
-pathway_B=stop_at_entry Left_PCG.nii.gz \
-output OUTPUT.vtk
-pathway=require_exit <nifti image> {optional:labelNo} All tracks are required to enter and exit the ROI present in the specified image.
-pathway_A=require_exit <nifti image> {optional:labelNo} One side of all tracks are required to enter and exit the ROI present in the specified image.
-pathway_B=require_exit <nifti image> {optional:labelNo} The other side of all tracks are required to enter and exit the ROI present in the specified image.
Example: At least one end of each streamline will end outside white matter.
# Example 11
./trekker -fod FOD.nii.gz \
-seed_image WHITEMATTER.nii.gz \
-seed_count 1000 \
-pathway=require_exit WHITEMATTER.nii.gz \
-output OUTPUT.vtk
Example: All streamline end points will be outside the white matter.
# Example 12
./trekker -fod FOD.nii.gz \
-seed_image WHITEMATTER.nii.gz \
-seed_count 1000 \
-pathway_A=require_exit WHITEMATTER.nii.gz \
-pathway_B=require_exit WHITEMATTER.nii.gz \
-output OUTPUT.vtk
-pathway=stop_at_exit <nifti image> {optional:labelNo} All tracks that enter the ROI present in the specified image will stop propagating if they exit the ROI.
-pathway_A=stop_at_exit <nifti image> {optional:labelNo} One side of all tracks that enter the ROI present in the specified image will stop propagating if they exit the ROI.
-pathway_B=stop_at_exit <nifti image> {optional:labelNo} The other side of all tracks that enter the ROI present in the specified image will stop propagating if they exit the ROI.
Example: None of the streamlines will terminate inside the white matter and all streamlines will stop if they reach the end of gray matter.
# Example 13
./trekker -fod FOD.nii.gz \
-seed_image WHITEMATTER.nii.gz \
-seed_count 1000 \
-pathway_A=discard_if_ends_inside WHITEMATTER.nii.gz \
-pathway_A=stop_at_exit GRAYMATTER.nii.gz \
-pathway_B=discard_if_ends_inside WHITEMATTER.nii.gz \
-pathway_B=stop_at_exit GRAYMATTER.nii.gz \
-output OUTPUT.vtk
-pathway=discard_if_enters <nifti image> {optional:labelNo} Tracks are not allowed to enter the ROI present in the specified image.
-pathway_A=discard_if_enters <nifti image> {optional:labelNo} One side of tracks are not allowed to enter the ROI present in the specified image.
-pathway_B=discard_if_enters <nifti image> {optional:labelNo} The other side of tracks are not allowed to enter the ROI present in the specified image.
Example: None of the streamlines generated by the following example ends inside the white matter or projects to CSF.
# Example 14
./trekker -fod FOD.nii.gz \
-seed_image WHITEMATTER.nii.gz \
-seed_count 1000 \
-pathway=discard_if_ends_inside WHITEMATTER.nii.gz \
-pathway=discard_if_enters CSF.nii.gz \
-output OUTPUT.vtk
-pathway=discard_if_exits <nifti image> {optional:labelNo} Tracks are allowed to enter but not to exit the ROI present in the specified image.
-pathway_A=discard_if_exits <nifti image> {optional:labelNo} One side of tracks are allowed to enter but not to exit the ROI present in the specified image.
-pathway_B=discard_if_exits <nifti image> {optional:labelNo} The other side of tracks are allowed to enter but not to exit the ROI present in the specified image.
Example: All connections within hippocampus. Nothing projects inside or outside.
# Example 15
./trekker -fod FOD.nii.gz \
-seed_image HIPPOCAMPUS.nii.gz \
-seed_count 1000 \
-pathway_A=discard_if_exits HIPPOCAMPUS.nii.gz \
-pathway_B=discard_if_exits HIPPOCAMPUS.nii.gz \
-output OUTPUT.vtk
-pathway=discard_if_ends_inside <nifti image> {optional:labelNo} Tracks are not allowed to stop inside the ROI present in the specified image.
-pathway_A=discard_if_ends_inside <nifti image> {optional:labelNo} One side of tracks are not allowed to stop inside the ROI present in the specified image.
-pathway_B=discard_if_ends_inside <nifti image> {optional:labelNo} The other side of tracks are not allowed to stop inside the ROI present in the specified image.
Example: None of the streamlines will terminate inside the white matter.
# Example 16
./trekker -fod FOD.nii.gz \
-seed_image WHITEMATTER.nii.gz \
-seed_count 1000 \
-pathway=discard_if_ends_inside WHITEMATTER.nii.gz \
-output OUTPUT.vtk
-pathway=satisfy_requirements_in_order If enabled all pathway requirements are going to be satisfied in the order that they are input to trekker. All pathway options should be defined for pathway_A/pathway_B in order to use this option.
Example: Only generates those streamlines that go from the start of ROI_1 till the end of ROI_5 in order where ROI_3 is the seed.
# Example 17
./trekker -fod FOD.nii.gz \
-seed_image ROI_3.nii.gz \
-seed_count 1000 \
-pathway=satisfy_requirements_in_order \
-pathway_A=require_entry ROI_4.nii.gz \
-pathway_A=require_exit ROI_5.nii.gz \
-pathway_B=require_entry ROI_2.nii.gz \
-pathway_B=require_exit ROI_1.nii.gz \
-output OUTPUT.vtk
Other options¶
-numberOfThreads <positive integer> Number of parallel threads to run during tracking. Default=number of CPU cores of the local processor
-timeLimit <positive real number> Sets the maximum allowed duration in minutes for trekker to continue tracking. If any of the -seed_coordinates, -seed_count or -seed_countPerVoxel options are used together with -timeLimit, tracking stops either when the required number of streamlines are reconstructed or the time limit is hit, whichever comes first. When tracking stops due to time limit, all the streamlines that are computed so far are written to the disk.
-verboseLevel <integer between 0 and 4> Determines the level of information displayed on the terminal. Verbose level must be between 0-4. No information is displayed at level 0. Default=1.
-help Displays this manual.


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Find a list of servers on this page.
You can add as many servers as you want them separated by commas.
For example: sbl-xbl.spamhaus.org , bsb.spamlookup.net
Do not forget to click Save to save your changes.


Type Variations
Being Strong And Independent Doesn’t Mean You Do Everything On Your Own
Holly Riordan
NORMAL TYPE
Normal Type 1
Normal Type 2
Normal Type 3
Normal Type 4
Normal Type 5
EXPRESSIVE TYPE
Expressive Type 1
Expressive Type 2
Expressive Type 3
Expressive Type 4
Expressive Type 5


The skies darken. Clouds roll in. Ye cross the Burnside Bridge without incident. There is a crazed looking man in a toga on the East side ranting about gravy. He is pointing at you and looking wide-eyed.


Multi-Wave Model¶
The following examples demonstrate the multi-wave capability of PRIME.
Two Waves¶
The first example considers daily confirmed case data in the state of New Mexico up to August 26th, 2020, shown in Fig. 8.
We use the two-wave model to fit this since there are two large peaks in new cases in early May and late July 2020.
Problem Setup¶
While the two wave model can be run independently it is highly recommended to make use of one-wave model results to help compute prior distributions for the first wave in the two-wave model. This is done to i) make use of all available information and ii) limit the parameter range for the early wave parameters to enhance the robustness of the multi-wave model.
The priors for the first wave parameters \(t_0\), \(N_1\), \(k_1\), \(\theta_1\) can be then be estimated by the mean and variance of the one-wave MCMC chain. This gives us an informed guess for part of the parameters, limiting the region of parameter space that an MCMC approach needs to explore. A smaller parameter range increases the chance that MCMC is able to find find regions of high likelihood \(p\left(\boldsymbol{y} | \Theta \right)\), making the model more robust. The second wave prior distributions should be chosen to include values that approximate the case data well; these distributions may need to be selected by trial and error, although \(\Delta t_2\) can be estimated by visually inspecting raw daily new case data.
This example uses Gaussian prior distributions for \(t_0\) and \(\Delta t_2\) and uniform prior distributions for all other model parameters. For Gaussian priors the mean and variance are estimated from the one-wave chain. For uniform priors the minimum and maximum parameter are set equal to \(\mu_{chain} \pm 3 \sigma_{chain}\), where \(\mu_{chain}\) and \(\sigma_{chain}\) are the mean and standard deviation computed from the one-wave chain. Note that positive parameters \(N_1,k_1,\theta_1\) are restricted to positive values; that is, the lower bound is the maximum of zero and \(\mu_{chain} - 3 \sigma_{chain}\).
The json file for a two-wave model can be produced from a one-wave model by running the script “prime_compute_prior_from_mcmc.py” in the directory containing the one-wave model json script and HDF5 file with MCMC chain data. The arguments for this script in this case are the name of json file used to run the one-wave model and the model type for the desired json file, in this case “twoWave”. For this example, we use the results from example 1 to generate the two-wave json file (included in the “JSON Input Files” section at the end of this example).
It is recommended to experiment with the prior distributions for the 2nd wave parameters \(\Delta t_2\), \(N_2\), \(k_2\), and \(\theta_2\). The time between the first and second wave, \(\Delta t_2\), can be estimated by the number of days between the first large increase in cases and the second large increase. For this example, we use a wide prior distribution for \(\Delta t_2\), centered on 60 days with a standard deviation of 15 days.
The two-wave model is also run with the “prime_run.py” script.
New Case Forecast Results¶
The two-wave forecast results are also computed by running the “prime_compute_epi_inf_curves.py” in the same directory as the run. The postprocessing sections of the json script are the same for multiple waves. Please refer to example 1 for more details on postprocessing.
Three Waves¶
The first example considers daily confirmed case data in the state of New Mexico up to November 10th, 2020, shown in Fig. 10.
We use the three-wave model to fit this since there are three large peaks in new cases in early May, late July 2020, and cases are rising rapidly in November 2020.
Problem Setup¶
Like the two-wave model, the three-wave model can be run independently but it is highly recommended to make use of two-wave model results to help compute prior distributions for the first and second waves in the three-wave model. The priors for the parameters \(t_0\), \(N_1\), \(k_1\), \(\theta_1\), \(\Delta t_2\), \(N_2\), \(k_2\), and \(\theta_2\) are estimated from the two-wave model MCMC chain.
The json file for a three-wave model can be produced from a two-wave model by running the script “prime_compute_prior_from_mcmc.py” in the directory containing the two-wave model json script and HDF5 file with MCMC chain data. The arguments for this script in this case are the name of json file used to run the two-wave model and the model type for the desired json file, in this case “threeWave”. For this example, we use the two-wave results from the previous section to generate the three-wave json file (included in the “JSON Input Files” section at the end of this example).
It is recommended to experiment with the prior distributions for the 3rd wave parameters \(\Delta t_3\), \(N_3\), \(k_3\), and \(\theta_3\). The time between the first and third wave, \(\Delta t_3\), can be estimated by the number of days between the first large increase in cases and the third large increase. For this example, we use a wide prior distribution for \(\Delta t_3\), centered on 180 days with a standard deviation of 15 days.
The three-wave model is also run with the “prime_run.py” script.
New Case Forecast Results¶
The three-wave forecast results are also computed by running the “prime_compute_epi_inf_curves.py” in the same directory as the run. The postprocessing sections of the json script are the same for multiple waves. Please refer to example 1 for more details on postprocessing.
JSON Input Files¶
Two Wave¶
{
"regioninfo": {
"regionname": "NM",
"fchain": "NM_mcmc.h5",
"day0": "2020-03-01",
"running_avg_obs": 7
},
"mcmcopts": {
"model_type": "twoWave",
"error_model_type": "addMult",
"logfile": "logmcmcNM_2wave.txt",
"nsteps": 1000000,
"nfinal": 10000000,
"useconv": 1,
"incubation_type": "uncertain",
"gamma": 0.7,
"spllo": [
-2.877357932637136,
0.009575447192753876,
3.4902877959833143,
12.321758685351922,
20,
0.0002,
0.1,
0.1,
0.0,
-20
],
"splhi": [
2.969027281659369,
0.01839093251179052,
5.360610614202396,
26.17367878217896,
100,
0.5,
30.0,
400.0,
10.0,
1.0
],
"cini": [
0.04583467451111642,
0.013983189852272197,
4.425449205092855,
19.247718733765442,
60,
0.02,
6.0,
20.2,
3.0,
0.1
],
"cvini": [
0.9494505576095775,
2.158688372504182e-06,
0.09716965123197129,
5.3298802880244684,
225,
0.001,
0.01,
0.01,
0.01,
0.01
]
},
"bayesmod": {
"prior_types": ["g","u","u","u","g","u","u","u","u","u"],
"prior_info": [
[0.04583467451111642,0.9743975357160841],[0,1],[0,1],[0,1],
[60,15.0],[0,1],[0,1],[0,1],
[0,1],[0,1]
]
},
"incopts": {
"incubation_median": 5.1,
"incubation_sigma": 0.418,
"incubation_025": 2.2,
"incubation_975": 11.5
},
"ppopts": {
"nstart": 100000,
"nsamples": 10000,
"days_extra": 10,
"runmodel": 1,
"postpred": 1,
"quantile_newcases": [0.025,0.25,0.5,0.75,0.975],
"linetype_newcases": ["b--","g-","r-","g-","b--"],
"linewidth_newcases": [3,2,3,2,3],
"fillbetw_newcases": [[0.25,0.5,"g",0.4],[0.5,0.75,"g",0.4]],
"xylim_newcases": ["2020-03-01","2020-04-15",0,500],
"xylbl_newcases": ["Date",16,"Reported New Cases on Date",16],
"xyticklbl_newcases": [14,14],
"newcases": ["ko",6],
"figtype": "pdf",
"fpredout": "NM_epidemic_curve",
"fout_newcases": "NM_epidemic_curve"
},
"infopts": {
"inftype": "gamma",
"ndays": 180,
"runmodel": 1,
"postpred": 1,
"quantile_inf": [0.025,0.25,0.5,0.75,0.975],
"linetype_inf": ["b--","g-","r-","g-","b--"],
"linewidth_inf": [3,2,3,2,3],
"fillbetw_inf": [[0.25,0.5,"g",0.4],[0.5,0.75,"g",0.4]],
"xylim_inf": ["2020-03-01","2020-05-01",10,1000],
"xylbl_inf": ["Date",16,"Infection Rate [ppl/day]",16],
"xyticklbl_inf": [14,14],
"newcases": ["ko",6],
"figtype": "pdf",
"finfout": "NM_infection_curve",
"fout_inf": "NM_infection_curve"
},
"csvout": {
"nskip": 100,
"finfcurve": "NM_infection_curve",
"fnewcases": "NM_epidemic_curve",
"qlist": [0.025,0.25,0.5,0.75,0.975]
}
}
Three Wave¶
{
"regioninfo": {
"regionname": "NM",
"fchain": "NM_mcmc.h5",
"day0": "2020-03-01",
"running_avg_obs": 7
},
"mcmcopts": {
"model_type": "threeWave",
"error_model_type": "addMult",
"logfile": "logmcmcNM_3wave.txt",
"method": "am",
"nsteps": 1000000,
"nfinal": 10000000,
"useconv": 1,
"incubation_type": "uncertain",
"gamma": 0.7,
"spllo": [
-1.5198886917920054,
0.01343778201509116,
3.715378521549199,
17.57101548608055,
70.81826941872282,
0.010771637229469904,
0.0,
4.976510035388319,
140,
0.0002,
0.1,
0.1,
0.0,
-20
],
"splhi": [
1.8197861405425901,
0.016193383448404937,
4.6929341387263,
24.574886774303263,
112.57412043153732,
0.013487998537736372,
11.208390968975433,
15.442199070301406,
220,
0.5,
30.0,
400.0,
10.0,
1.0
],
"cini": [
0.1499487243752924,
0.014815582731748048,
4.204156330137749,
21.072951130191907,
91.69619492513007,
0.012129817883603138,
5.552822284750233,
10.209354552844863,
180,
0.02,
6.0,
20.2,
3.0,
0.1
],
"cvini": [
0.30981744404803085,
2.1092609053558147e-07,
0.02654486068540284,
1.3626170283886236,
48.43197482789893,
2.0496163214019776e-07,
3.5539396824431955,
3.0425179715416664,
225,
0.001,
0.01,
0.01,
0.01,
0.01
]
},
"bayesmod": {
"prior_types": [
"g","u","u","u",
"g","u","u","u",
"g","u","u","u",
"u","u"
],
"prior_info": [
[0.1499487243752924,0.5566124720557659],[0,1],[0,1],[0,1],
[91.69619492513007,6.959308502135749],[0,1],[0,1],[0,1],
[180,15.0],[0,1],[0,1],[0,1],
[0,1],[0,1]
]
},
"incopts": {
"incubation_median": 5.1,
"incubation_sigma": 0.418,
"incubation_025": 2.2,
"incubation_975": 11.5
},
"ppopts": {
"nstart": 100000,
"nsamples": 10000,
"days_extra": 10,
"runmodel": 1,
"postpred": 1,
"quantile_newcases": [0.025,0.25,0.5,0.75,0.975],
"linetype_newcases": ["b--","g-","r-","g-","b--"],
"linewidth_newcases": [3,2,3,2,3],
"fillbetw_newcases": [[0.25,0.5,"g",0.4],[0.5,0.75,"g",0.4]],
"xylim_newcases": ["2020-03-01","2020-04-15",0,500],
"xylbl_newcases": ["Date",16,"Reported New Cases on Date",16],
"xyticklbl_newcases": [14,14],
"newcases": ["ko",6],
"figtype": "pdf",
"fpredout": "NM_epidemic_curve",
"fout_newcases": "NM_epidemic_curve"
},
"infopts": {
"inftype": "gamma",
"ndays": 180,
"runmodel": 1,
"postpred": 1,
"quantile_inf": [0.025,0.25,0.5,0.75,0.975],
"linetype_inf": ["b--","g-","r-","g-","b--"],
"linewidth_inf": [3,2,3,2,3],
"fillbetw_inf": [[0.25,0.5,"g",0.4],[0.5,0.75,"g",0.4]],
"xylim_inf": ["2020-03-01","2020-05-01",10,1000],
"xylbl_inf": ["Date",16,"Infection Rate [ppl/day]",16],
"xyticklbl_inf": [14,14],
"newcases": ["ko",6],
"figtype": "pdf",
"finfout": "NM_infection_curve",
"fout_inf": "NM_infection_curve"
},
"csvout": {
"nskip": 100,
"finfcurve": "NM_infection_curve",
"fnewcases": "NM_epidemic_curve",
"qlist": [0.025,0.25,0.5,0.75,0.975]
}
}


- UKPRN:12345678
Adult Education Budget 2018 to 2019
Year total: £4,725,425
Download this breakdown (PDF, 285KB)
Adult Education Budget Grant
| Baseline | £4,494,746 | |
| August 2018 to March 2019 | £2,815,958 | |
| April 2019 to July 2019 | £1,678,788 | |
| 2018 to 2019 allocation | £4,494,746 |
Adult Education Budget contract for service
| Baseline | £230,679 | |
| August 2018 to March 2019 | £154,833 | |
| April 2019 to July 2019 | £75,846 | |
| 2018 to 2019 allocation | £230,679 | |


Codehaus XFire
DocumentationQuicklinksDevelopers
Sponsors |
Creating a serviceSteps
Assuming that you know how to do steps 1 and 2, lets pick up at step 3. Create your service class: package org.codehaus.xfire.xmlbeans; import net.webservicex.GetWeatherByZipCodeDocument; import net.webservicex.GetWeatherByZipCodeResponseDocument; import net.webservicex.WeatherForecasts; public class WeatherService { public GetWeatherByZipCodeResponseDocument GetWeatherByZipCode( GetWeatherByZipCodeDocument body ) { GetWeatherByZipCodeResponseDocument res = GetWeatherByZipCodeResponseDocument.Factory.newInstance(); WeatherForecasts weather = res.addNewGetWeatherByZipCodeResponse().addNewGetWeatherByZipCodeResult(); weather.setLatitude(1); weather.setLongitude(1); weather.setPlaceName("Grand Rapids, MI"); return res; } } Notice that we used the "Document" Types, not the "GetWeatherByZipCode" class. After you've created your service class you simply need to register it: XFire xfire = XFireFactory.newInstance().getXFire();
XmlBeansServiceFactory factory = new XmlBeansServiceFactory(xfire.getTransportManager());
Service service = factory.create(WeatherService.class);
xfire.getServiceRegistry().register(service);
XFire will now expose your service as the "WeatherService" and will generate WSDL for you automatically! XMLBeans with JSR 181You may use your XMLBeans service with JSR 181 annotations. You need to set up a special service factory to create your service though: import org.codehaus.xfire.aegis.AegisBindingProvider; import org.codehaus.xfire.xmlbeans.XmlBeansWSDLBuilderFactory; AnnotationServiceFactory asf = new AnnotationServiceFactory(); // Set up the XMLBeans binding AegisBindingProvider provider = new AegisBindingProvider(new XmlBeansTypeRegistry()); asf.setBindingProvider(provider); // Tell XFire to use the schemas from XMLBeans in the WSDL asf.setWsdlBuilderFactory(new XmlBeansWSDLBuilderFactory()); Then you can create your service: Service service = asf.create(MyService.class); Problems with XMLBeans (XMLBeans Namespace Hack)Due to some bugs in how XMLBeans handles StAX streams you may need to enable the "xmlbeans namespace hack". This just tells xfire to use the XMLBeans DOM to write to the outgoing xml stream instead of using the XMLBeans XMLStreamReader. You will need to enable this when: To enable the fix simply do: import org.codehaus.xfire.service.Service; import org.codehaus.xfire.xmlbeans.XmlBeansType; Service service = ...; service.setProperty(XmlBeansType.XMLBEANS_NAMESPACE_HACK, "true"); |


... so that, for example, you can rewrite the way 'Monk, Devout' takes its skills and feats?
Someone mentioned it to me, but I'd never heard of anything like it before.
Edited By Avendesora84 on 01/24/05 15:22
Maybe you should post this question to one of the design/builder forums. I think most people here are strictly players, not designers or hak makers.
K. _________________ Check out the Epic Character Builders Guild Click Here Test your new builds in this mod: Click Here
The answer lies in the packages.2da. When you look into that file, it will give you the files that is related to this specific build, then you open the other files that is specified in there.
If you need more information, feel free to ask then.
Anuis _________________ - Epic Character Builders Guild


Compound Interest
Enter your initial investment amount:
Enter amount to be added monthly:
Enter the annual rate of interest:
Enter investment period (in months):


England bring in keeper Henderson for injured Heaton, Barkley out
England have called up Sheffield United goalkeeper Dean Henderson for the Euro 2020 qualifiers against Montenegro and Kosovo after Aston Villa's Tom Heaton pulled out with injury, the FA said on Sunday.
LONDON: England have called up Sheffield United goalkeeper Dean Henderson for the Euro 2020 qualifiers against Montenegro and Kosovo after Aston Villa's Tom Heaton pulled out with injury, the FA said on Sunday.
Heaton sustained a calf injury in training on Saturday which prevented him playing in Sunday's 2-1 Premier League defeat at local rivals Wolverhampton Wanderers, with Jed Steer replacing him in goal.
Henderson, on loan from Manchester United, leads the English top flight with five clean sheets this season.
Chelsea midfielder Ross Barkley has also been ruled out through injury but England manager Gareth Southgate did not name a replacement. Barkley has been sidelined since Oct. 19 after suffering an ankle problem against Newcastle United.
England face Montenegro at Wembley on Thursday in the country's 1,000th international fixture, before visiting Kosovo on Nov. 17. They need one point from their remaining games to guarantee their place in next year's finals.
(Reporting by Rohith Nair in Bengaluru; Editing by Ken Ferris)


Test Plot JSON converter
Convert
Convert gasmallen as objects, not polygons
...
Input JSON:
Run
npm run start-elements
in the
lcms-plot
project to see the map with the result.


THESIS
SOIL DESCRIPTIONS AND SIMULATION MODEL OF
POTASSIUM CYCLING IN COLORADO FORESTS
Submitted by
Robert George Woodmansee
In partial fulfillment of the requirements for the Degree of Doctor of Philosophy Colorado State University Fort Collins, Colorado
August, 1972


| 20 | (A) | One thaym that velanye to venge þat his veynes braste |
| 20 | (C) | On hem þat vylonye to venge . þat his veynes brusten |
| 20 | (D) | On hem þat vilany to venge | þat his veynes brostyn |
| 20 | (E) | that vylienye to venge . but mekeliche it suffrede . |
| (Ex) | ---- no text ---- | |
| 20 | (L) | On hem þe vyleny to venge þat his veynys brosten |
| 20 | (P) | On þaim vylany to venge . þat his vaynes brasten |
| 20 | (U) | On hem that vilenye to venge / that his veynes brusten |
| (V) | ---- no text ---- |


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Gecode::Iter::Ranges::RangeListIter Class Reference
[Range iterators]
Iterator over range lists. More...
#include <ranges-list.hpp>
Classes | |
| class | RangeList |
| Range list class. More... | |
| class | RLIO |
| Shared object for allocation. More... | |
Public Member Functions | |
| ~RangeListIter (void) | |
| Destructor. | |
Protected Member Functions | |
| void | set (RangeList *l) |
| Set range lists. | |
| RangeList * | get (void) const |
| Get head of current range list. | |
| RangeList * | range (int min, int max, RangeList *&f) |
| Create new range possibly from freelist f and init. | |
| RangeList * | range (int min, int max) |
| Create new range possibly and init. | |
| template<class I > | |
| RangeList * | range (I &i, RangeList *&f) |
| Create new range possibly from freelist f and init. | |
| template<class I > | |
| RangeList * | range (I &i) |
| Create new range possibly and init. | |
| template<class I > | |
| RangeList * | copy (I &i) |
| Copy the iterator i to a range list. | |
Protected Attributes | |
| RLIO * | rlio |
| Reference to shared object. | |
| RangeList * | h |
| Head of range list. | |
| RangeList * | c |
| Current list element. | |
Constructors and initialization | |
|
| |
| RangeListIter (void) | |
| Default constructor. | |
| RangeListIter (const RangeListIter &i) | |
| Copy constructor. | |
| RangeListIter (Region &r) | |
| Initialize. | |
| void | init (Region &r) |
| Initialize. | |
| RangeListIter & | operator= (const RangeListIter &i) |
| Assignment operator. | |
Iteration control | |
|
| |
| bool | operator() (void) const |
| Test whether iterator is still at a range or done. | |
| void | operator++ (void) |
| Move iterator to next range (if possible). | |
| void | reset (void) |
| Reset iterator to start. | |
Range access | |
|
| |
| int | min (void) const |
| Return smallest value of range. | |
| int | max (void) const |
| Return largest value of range. | |
| unsigned int | width (void) const |
| Return width of range (distance between minimum and maximum). |
Detailed Description
Iterator over range lists.
Definition at line 45 of file ranges-list.hpp.
Constructor & Destructor Documentation
| Gecode::Iter::Ranges::RangeListIter::RangeListIter | ( | void | ) | [inline] |
Default constructor.
Definition at line 132 of file ranges-list.hpp.
| Gecode::Iter::Ranges::RangeListIter::RangeListIter | ( | const RangeListIter & | i | ) | [inline] |
Copy constructor.
Definition at line 146 of file ranges-list.hpp.
| Gecode::Iter::Ranges::RangeListIter::RangeListIter | ( | Region & | r | ) | [inline] |
Initialize.
Definition at line 136 of file ranges-list.hpp.
| Gecode::Iter::Ranges::RangeListIter::~RangeListIter | ( | void | ) | [inline] |
Destructor.
Definition at line 169 of file ranges-list.hpp.
Member Function Documentation
| void Gecode::Iter::Ranges::RangeListIter::set | ( | RangeList * | l | ) | [inline, protected] |
Set range lists.
Definition at line 179 of file ranges-list.hpp.
| RangeListIter::RangeList * Gecode::Iter::Ranges::RangeListIter::get | ( | void | ) | const [inline, protected] |
Get head of current range list.
Definition at line 184 of file ranges-list.hpp.
| RangeListIter::RangeList * Gecode::Iter::Ranges::RangeListIter::range | ( | int | min, | |
| int | max, | |||
| RangeList *& | f | |||
| ) | [inline, protected] |
Create new range possibly from freelist f and init.
Definition at line 189 of file ranges-list.hpp.
| RangeListIter::RangeList * Gecode::Iter::Ranges::RangeListIter::range | ( | int | min, | |
| int | max | |||
| ) | [inline, protected] |
Create new range possibly and init.
Definition at line 202 of file ranges-list.hpp.
| RangeListIter::RangeList * Gecode::Iter::Ranges::RangeListIter::range | ( | I & | i, | |
| RangeList *& | f | |||
| ) | [inline, protected] |
Create new range possibly from freelist f and init.
Definition at line 210 of file ranges-list.hpp.
| RangeListIter::RangeList * Gecode::Iter::Ranges::RangeListIter::range | ( | I & | i | ) | [inline, protected] |
Create new range possibly and init.
Definition at line 216 of file ranges-list.hpp.
| RangeListIter::RangeList * Gecode::Iter::Ranges::RangeListIter::copy | ( | I & | i | ) | [inline, protected] |
Copy the iterator i to a range list.
Definition at line 222 of file ranges-list.hpp.
| void Gecode::Iter::Ranges::RangeListIter::init | ( | Region & | r | ) | [inline] |
Initialize.
Definition at line 140 of file ranges-list.hpp.
| RangeListIter & Gecode::Iter::Ranges::RangeListIter::operator= | ( | const RangeListIter & | i | ) | [inline] |
Assignment operator.
Definition at line 153 of file ranges-list.hpp.
| bool Gecode::Iter::Ranges::RangeListIter::operator() | ( | void | ) | const [inline] |
Test whether iterator is still at a range or done.
Definition at line 234 of file ranges-list.hpp.
| void Gecode::Iter::Ranges::RangeListIter::operator++ | ( | void | ) | [inline] |
Move iterator to next range (if possible).
Definition at line 239 of file ranges-list.hpp.
| void Gecode::Iter::Ranges::RangeListIter::reset | ( | void | ) | [inline] |
Reset iterator to start.
Definition at line 244 of file ranges-list.hpp.
| int Gecode::Iter::Ranges::RangeListIter::min | ( | void | ) | const [inline] |
Return smallest value of range.
Definition at line 249 of file ranges-list.hpp.
| int Gecode::Iter::Ranges::RangeListIter::max | ( | void | ) | const [inline] |
Return largest value of range.
Definition at line 253 of file ranges-list.hpp.
| unsigned int Gecode::Iter::Ranges::RangeListIter::width | ( | void | ) | const [inline] |
Return width of range (distance between minimum and maximum).
Definition at line 257 of file ranges-list.hpp.
Member Data Documentation
RLIO* Gecode::Iter::Ranges::RangeListIter::rlio [protected] |
Reference to shared object.
Definition at line 64 of file ranges-list.hpp.
RangeList* Gecode::Iter::Ranges::RangeListIter::h [protected] |
Head of range list.
Definition at line 66 of file ranges-list.hpp.
RangeList* Gecode::Iter::Ranges::RangeListIter::c [protected] |
Current list element.
Definition at line 68 of file ranges-list.hpp.
The documentation for this class was generated from the following file:
- gecode/iter/ranges-list.hpp (Revision: 14967)


Personal development plan / assessment - Ani Hakobyan
Review your past performance to identify your strengths and areas for development. Think about feedback you have received regarding
your performance and abilities. Consider what knowledge, skills, behaviours, and abilities you have excelled or struggled with in your career.
Identifying your strengths and areas for development will help you prioritise your development interests and determine your career goals and
development plan.
Strengths and interests
- What are the greatest strengths I bring to my work?
- What is most important to me professionally?
- (status, expertise, title, pay, work/life balance, etc)
- What type of work is most rewarding for me to do?
Growth areas
- What are my development opportunities?
- What skills or competencies do I need to improve in my current role?
- What must I develop to get to the next level of performance, job level or
- new role?
Short term (1-2 years)
- Where do I want to be in the next 1-2 years?
- Is there something I'd like to do more/less frequently in my current role?
- How can I prepare for more responsibility?
Long term (3+ years)
- Where do I want to be in 3-5 years?
- What skills, knowledge, or expertise do I need to develop in the next 3-5 years?
- What are things I'd like to do more/less frequently in my future roles?
Manager’s comments


Players
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Name
Action
{{playerItem.name}}


boolean equals( String strA, String strB ) { if ( strA.length() == 0 && strB.length() == 0 ) return true; else if ( strA.length() == 0 && strB.length() != 0 ) return false; else if ( strA.length() != 0 && strB.length() == 0 ) return false; else if ( strA.charAt(0) != strB.charAt(0) ) return false; else return equals( strA.substring(1), strB.substring(1) ); }
You have reached the end this chapter. You may wish to review the following. Click on a subject that interests you to go to where it was discussed.


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Left MSD, Mean Naples, Nestlé Normal ROC Results SIRS SVT Statistical Studies The addition, additional analyze available. better brachycephalic breed, certain chambers class confirm contrast controlled deposition develop diffuse dog, dysfunction erythrocyte established events evidence exam fluid found. genes gibsoni help hospitalization incidence indicate length location mechanisms membrane needed novel outcome. pathological pattern performed. physiological points practice prescription profile quantitative recorded. reduce results, sHA samples. screening searched shelter species. successful syringes technique thyroxine transmission vaccination vincristine week ± (.% (range: (‐). .%; .(.‐.); ALT ATE Australia Chronic Compared Division EER EHPSS ENT EPSS Echocardiographic Edinburgh, Finally, Fisher's Free French IBD IBW III Infectious LA:Ao Laboratories, Milano, Moreover, North Novara, Ozzano PLE Plasma RETIC‐HGB Retrospective SPECT Spain Spec Survival Swiss TLR, UPEC Vallonea. 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Kai Liège, Mann‐Whitney Milan, Minnesota, PRBC Patients Pimobendan Protein SID Spaniels Specialists, TCC TTKG Total Use Using Vétérinaire abundance accurate acids activities addition air alternative apical approximately bactibilia bias category characterize characterized circulating cleaning combined commercially comparable compliance complications composition congenital consisted conventional cross‐sectional d'Alfort, days decision demonstrated demonstrates diarrhea differ dimethylarginine distributed dogs) drugs eBAT electrolytes enrolled. excluded. experienced extrahepatic follow functional gas general glomerulonephritis groups, growth however, hypercortisolism, hyperkalemic hypotension immediately immune‐mediated immunoassay improved independent influenced infusion initiation insufficiency lacking. lipase local lymph mainly majority mass medication medicine, method. models modified murmurs narrowing neoplasia obese objectives occur occurring occurs opinion optimal outflow particular peripheral platelet presenting procedure project protocols px; reaction reconditum recordings recruited relation represent sedentary sepsis septal sequence sequencing short showing shows signalment, signs. sinonasal stored surplus survival. symptomatic taken test). ticks ultrasonography units uptake urethral used. volumes weeks widely {font-family ‐hydroxyvitamin (. ± .) 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Kruskal‐Wallis LPS Laboklin Leipzig, Life London, MSD MVD Munich, Naples Nationale Padua, SDMARR Schuller Significant Spillmann Thus, T‐W Veggiano, Zürich, [(OH)D [‐] ability able accurately achieved acidosis acquired agents aggregates alterations alveolar associations atrioventricular biological bla blinded bronchial care cases); categories cathelicidin cavity changed characteristics chemiluminescent clinic clinical, clinico‐pathological collection compared. comparisons concentration. concerning contrast, correction cultures date decreases dialysis diosmectite discriminating disorders disturbances edema elevation entire equilibrium estimates exosomal experimental fan folate font-size fractional future generated genome grading grant histopathology history, hypercobalaminaemia hypothyroidism hypovolemic identified. immunodeficiency immunosuppressant‐responsive improvement interaction intermediate kit kittens lead leptin levels. limit line linear linked locations longitudinal lumen mRNA major measures met metaplasia minimal mucosa neutered observed. older outcomes owned pacemaker pancreatitis. period. pet play position predisposition preliminary preparation prevalent providing published pug quantification quantified randomized readings relevant reliable remodelling reoccurrence repeated resulting reverse room selection sensitive serial series spayed spectrometry spp staff status. submitted subnormal suggested superior susceptible swabs systole therapy, thorax tissues titre titres transverse trend t‐test uAPR unilateral university unrelated urea variation variety views vitamin worse minutes %), > >% (.%:.%), (CKD) (ES (G) (HR (MMVD) (M‐W, (NLR) (P < .), (including (one (only (sensitivity .%). .[., . ml/kg . years .‐., = .); AA‐amyloid AKI Ames, Assessment BLUE Baron Bartonella Benvenuti Bottero Burmese B‐cell C.R. CHF. CVBD Cambridge, Cancer Canin Care Cavalier Centre Charles Cocker Correlation Council DM, DOX ELISA ET‐ Endoscopic Enterobacteriaceae Escherichia Europe FCV‐VSD FPV Finland Foundation, Frequency German Groups HD, HOCM High Inflammatory Interestingly, Investigation Iowa Jaffey June King Laboratory, Lisbon, Lorenz Madrid, Marchetti NAG NTI Napoli, Overall PET POCUS Parma, PhD Platelet P < .) 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Jack Japan Kissingen, Kleiter LA/Ao LPPs LPs Lactate Las Legnaro Leishmaniasis Lidbury Lippi, Long‐term M.A. M.P. M.V. MCT MRCoNS MRI MSCs MSD. Manchester, March Mhc: Midwestern Morris Nantes, Nasal Neutrophil‐to‐lymphocyte Nevertheless, Newmarket, Novartis Odds Organoids PASP_C PASP_D PBGM PCR, PDH Palmas Passavin Pathology, Patterns Paul, Perugia, Pet Pharmacology Pierini Piero Pietra Poser Potential Products Prognostic Public Pugs P < .). P‐value RESEARCH RSS Radiation Raleigh, Ratio Reference Referrals, Re‐irradiation Rome, Roslin, Ruiz S.P. Safety Sante Sharp Shepherd Shorthair South Spaniel Spearman's St. Studi Studies, Sweden, Syrjä Taiwan, Thirty Toxicology, Translational Trust, Twenty‐five Twenty‐two UP/C ≥ . Uppsala, Usefulness Utrecht Ventricular Vetsuisse, Westbrook, Wilcoxon World Youden [FENa% [‐]), abnormalities, abundant account adding adequate admission. agree airways alanine aldosterone allows analyses. anatomical and, animals, annular antigen apparent applying area, asymmetry atrium atrophy, attenuation aureus available, avoid azotemia, babesiosis base biogenic biplane blood. bodyweight, bold breed‐specific cats: cats; caudal causing cells, chain challenging characteristic chest chi‐square classic clear clotrimazole coagulation collapsibility composed comprehensive comprised conclude confirming consequence consequences correct corrected corresponding cortisol, cough courier; cross‐over culture. cutoff cycles data, data. date, days. delayed delivered delivery dell'Emilia, depending design detailed detected. diet, diet. diphenhydramine diseased disorders. disseminated distance distinct dogs), domestic doxorubicin duodenal dysbiosis. d‐S echocardiography. electrocardiographic electrolyte em; endemic endocrine endogenous endoscopy enhancement epicardial erythropoiesis estimate examinations exclusively expected extensive extent external factor. factors, factors. faeces failing favourable feces femoral fibrosis field filtration financially fish fluorescence focusing follow‐up. form formed formula free‐roaming fulfilled genera generally gland's global glycemic haemodialysis haemolytic health. heart. hemoglobin hospitals hyperplasia hyperplastic hypoferremia hypointense hypoxia hypoxic identifying idiopathic immunoblotting immunological impaired improve improves included: indexes indicates indicator indirect inducing infantum infections, intact intensity interactions interpreted interventricular intranasal invasive investigators involves it. kept knowledge laboratorial laboratories lactate later lean led lemon lesion leukocyte levels, lipid liquid load loop low‐dose lymphoma, lymphoma. malign managed material mean ± SD) mechanism meeting member metabolome metastases. methemoglobinemia miRNA miR‐ middle monoplane mucocele mucous multidrug multivariate myelosupressive nadirs nebulized neck neoplasms nonsurvivors non‐parametric non‐thyroidal normality normalize noted observers obtained. old. online option origin. packed parasites particularly patent pathogenic pathogens patient. people. peptides performance per‐endoscopic phase placebo plane pools poorly portal positivity potentiated predictor premedication present, prevent pre‐excitation pre‐operative procedures products program promote proportional‐hazards proposed prospective, protein/creatinine proteins, proteinuric proteomes proven pt;} pugs radiographic random range, rapidly rare rate. rates reactions reason receives recurrence reflux refractometer regionally regression. reproductive requires research. reservoir resolved respectively), retrieved reversible sCr safe sampled score. screened segments separate separation sepsis. sera setting settings severely significant, simultaneous site sole specifically stable stages. standards strains subcutaneous subsequent sufficient supporting surface surgery, surgically survived syncope system, table teaching tertiary testing. tests. texture timing tissue. training trials tumors. turbulence uncommon unfamiliar uremic urethra usefulness usually vaccinated varied vascular vein vincristine, visualization volume, vomiting, warranted well. white Škor αB‐crystallin U/. mL days). mL min years. ‐hours −/Na −°C


Africa
Asia and the Pacific
Europe and the Americas
Middle East
ICRC presence
Civilians
Economic security
Water and Habitat activities
Primary health care : - Health centres supported
People deprived of their freedom
Assistance
Wounded and sick
Physical rehabilitation : - Centres supported
Hospitals and first aid : - Hospitals supported
Protection
Civilians: restoring family links
People deprived of their freedom: visits
Expenditure in KChf


| predict.qda {MASS} | R Documentation |
Classify multivariate observations in conjunction with qda
## S3 method for class 'qda' predict(object, newdata, prior = object$prior, method = c("plug-in", "predictive", "debiased", "looCV"), ...)
object |
object of class |
newdata |
data frame of cases to be classified or, if |
prior |
The prior probabilities of the classes, by default the proportions in the
training set or what was set in the call to |
method |
This determines how the parameter estimation is handled. With |
... |
arguments based from or to other methods |
This function is a method for the generic function
predict()
for class "qda"
.
It can be invoked by calling predict(x)
for an
object x
of the appropriate class, or directly by
calling predict.qda(x)
regardless of the
class of the object.
Missing values in newdata
are handled by returning NA
if the
quadratic discriminants cannot be evaluated. If newdata
is omitted and
the na.action
of the fit omitted cases, these will be omitted on the
prediction.
a list with components
class |
The MAP classification (a factor) |
posterior |
posterior probabilities for the classes |
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Ripley, B. D. (1996) Pattern Recognition and Neural Networks. Cambridge University Press.
tr <- sample(1:50, 25) train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]) test <- rbind(iris3[-tr,,1], iris3[-tr,,2], iris3[-tr,,3]) cl <- factor(c(rep("s",25), rep("c",25), rep("v",25))) zq <- qda(train, cl) predict(zq, test)$class


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Carefully crafted and beautiful landing page.
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Once upon a time all the Rivers combined to protest against the action of the Sea in making their waters salt. “When we come to you,” said they to the Sea.
John Doe
CEO, The Rivers Company
A shoe is not only a design, but it's a part of your body language, the way you walk. The way you're going to move is quite dictated by your shoes.
Dean Winchester
UX-Designer, Google Inc.
Once upon a time all the Rivers combined to protest against the action of the Sea in making their waters salt. “When we come to you,” said they to the Sea.
John Doe
CEO, The Rivers Company
A shoe is not only a design, but it's a part of your body language, the way you walk. The way you're going to move is quite dictated by your shoes.
Dean Winchester
UX-Designer, Google Inc.
Once upon a time all the Rivers combined to protest against the action of the Sea in making their waters salt. “When we come to you,” said they to the Sea.
John Doe
CEO, The Rivers Company
A shoe is not only a design, but it's a part of your body language, the way you walk. The way you're going to move is quite dictated by your shoes.
Dean Winchester
UX-Designer, Google Inc.
Once upon a time all the Rivers combined to protest against the action of the Sea in making their waters salt. “When we come to you,” said they to the Sea.
John Doe
CEO, The Rivers Company
A shoe is not only a design, but it's a part of your body language, the way you walk. The way you're going to move is quite dictated by your shoes.
Dean Winchester
UX-Designer, Google Inc.


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TESTSASLAUTHD
Section: Maintenance Commands (8)Updated: 14 October 2006
Index Return to Main Contents
NAME
testsaslauthd - test utility for the SASL authentication serverSYNOPSIS
testsaslauthd [ -r realm ] [ -s servicename ] [ -f socket path ] [ -R repeatnum ]DESCRIPTION
This manual page documents briefly the testsaslauthd command.SEE ALSO
saslauthd(8).AUTHOR
testsaslauthd was written by Carnegie Mellon University.This manual page was written by Roberto C. Sanchez<EMAIL_ADDRESS>for the Debian project (but may be used by others).
Index
This document was created by man2html, using the manual pages.
Time: 05:34:30 GMT, December 24, 2015


Hi, I am a post-graduate student from the University of Edinburgh. With a strong curiosity, I would like to roam widely from Psychology, NeuroScience to computer science. Those interdisciplinary pieces of knowledge benefit me a lot, allowing me to view and analyze the world in multiple ways.
I have infinite curiosity about technology,
so I have been teaching myself programming.
Data analysist 07/2019-present
Though self-learning and join a group of fabulous people in the university, I am able to build some websites, mainly responsible for the font-end and a little back-end.
Psychology have changed my life! By reading from those classic textbooks and delibarate practicing, I swithed my lens towards the world. I reborn as a super confident and happy person. All right, I amdit I may not be happy all the time, but I feel way more happier that what I was before!!!!
GPA: 3.23
Major：Psychology
GPA: 3.23
Apart from being a web developer, I enjoy my time being indoors.
I follow a number of sci-fi and fantasy genre movies and television shows, and I spend a large amount of my free time exploring the latest technology advancements in the front-end web development world.
When I am outdoors, I like to wander around the museums or do some aerobic exercise.


Defined in generated file: python/ops/gen_array_ops.py
Copy a tensor setting everything outside a central band in each innermost matrix
tf.compat.v1.linalg.band_part
tf.compat.v1.matrix_band_part
tf.compat.v2.linalg.band_part
tf.linalg.band_part( input, num_lower, num_upper, name=None )
to zero.
The band
part is computed as follows:
Assume input
has k
dimensions [I, J, K, ..., M, N]
, then the output is a
tensor with the same shape where
band[i, j, k, ..., m, n] = in_band(m, n) * input[i, j, k, ..., m, n]
.
The indicator function
in_band(m, n) = (num_lower < 0 || (m-n) <= num_lower)) &&
(num_upper < 0 || (n-m) <= num_upper)
.
# if 'input' is [[ 0, 1, 2, 3] [-1, 0, 1, 2] [-2, -1, 0, 1] [-3, -2, -1, 0]], tf.matrix_band_part(input, 1, -1) ==> [[ 0, 1, 2, 3] [-1, 0, 1, 2] [ 0, -1, 0, 1] [ 0, 0, -1, 0]], tf.matrix_band_part(input, 2, 1) ==> [[ 0, 1, 0, 0] [-1, 0, 1, 0] [-2, -1, 0, 1] [ 0, -2, -1, 0]]
tf.matrix_band_part(input, 0, -1) ==> Upper triangular part. tf.matrix_band_part(input, -1, 0) ==> Lower triangular part. tf.matrix_band_part(input, 0, 0) ==> Diagonal.
input
: A Tensor
. Rank k
tensor.num_lower
: A Tensor
. Must be one of the following types: int32
, int64
.
0-D tensor. Number of subdiagonals to keep. If negative, keep entire
lower triangle.num_upper
: A Tensor
. Must have the same type as num_lower
.
0-D tensor. Number of superdiagonals to keep. If negative, keep
entire upper triangle.name
: A name for the operation (optional).A Tensor
. Has the same type as input
.


"The Patriot" -- Life, Liberty and Happiness by Homer Yen (c) 2000
"The Patriot," a powerful and impressive looking period piece set during the American Revolution, tells the story of a family being torn apart by war and a father torn between duty to his family and duty to his country. Full of stirring moments, it is a sweeping saga that showcases beautifully filmed and brilliantly choreographed battles, a patriotic soundtrack that combines the reverie of fife and drums with majestic orchestrations, and utterly unbridled flag-waving jingoism. I can barely contain my enthusiasm and am saluting as I write this.
In 1776, the American colonists have come to a critical juncture. Fed up with British rule, revolutionaries charge that "if principles dictate independence, then war is the only way." However, countless lives will be lost, though the sacrifice in the name of freedom is worthwhile. Benjamin Martin (Mel Gibson) hopes that instead of voting for war, America might adopt a more diplomatic course of action. Benjamin was once a brilliant military tactician and warrior, but is now the loving parent of seven. He doesn't wish to leave them fatherless. "I am a parent," he says. "I don't have the luxury of principles."
Although Benjamin remains adamant about his pacifist stance, his eldest son, Gabriel (Heath Ledger) is compelled by the crusade. Against his father's wishes, he enlists, grateful to serve the cause of liberty. But he is horrified to see the disorganization of the American army. The Red Coats are an impressive force who marches into battle with an imposing cadence. Going muzzle-to-muzzle against them in an open field is madness. The American front lines continually collapse, leading to swift defeats. General Cornwallis of the British Army states, "those rustics are so inept that it takes the honor out of victory."
Eventually, the battle winds up right on Benjamin's front lawn. His political stance is shattered when he encounters the ruthless Colonel Tavington (Jason Isaacs). Gabriel is captured; their plantation home is burned to the ground. Tavington doesn't abide by the rules of war and displays a brutality that is horrifying. He shoots innocent children, mercilessly kills prisoners-of-war, and even locks dozens of people inside a church and then instructs his men to burn it down. This is the kind of villain that elicits boos from the audience. It's impossible not to hate him, and the audience will not find satisfaction unless he dies by the hand of Benjamin.
We increasingly cheer for our pacifist-turned-revolutionary hero, as he becomes something of a colonial Robin Hood. Recruiting men throughout the countryside, he employs guerrilla tactics and mounts a series of successful skirmishes. In addition to his superb military skills, he is also adept at humiliating the enemy. We find much levity in watching him make a mockery of General Cornwallis. Yet like the American army, who is outmanned and outmatched, so too is Benjamin. As the battle wears on, personal losses begin to mount.
However, the colonists' and Benjamin's fighting spirit never dies. Even in the darkest of moments, there is time for a miraculous turnaround. This leads to a final, glorious engagement between the Colonial/British forces and between Benjamin Martin and Colonel Tavington. An American victory may turn the tide of war. And for our hero, payback may be close at hand. This sequence, with flying cannonballs, thousands of soldiers fighting, and its swings in momentum are nothing less than riveting.
"The Patriot" packs as much raw power and energy as any fireworks display and is certainly one of the best films of the year.  As far as summer movies go, this film is as good as it gets, featuring bold emotions, an epic storyline, incredibly visualized battle sequences and a monumental dramatic arc. To miss this film would be an act of treason.
Grade: A S: 1 out of 3 L: 0 out of 3 V: 3 out of 3
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B.C. Government Bootstrap Theme
The new home for Bootstrap v4 B.C. Government look-and-feel. Very much under construction. A simple demonstration of all common styles.
The new home for Bootstrap v4 B.C. Government look-and-feel. Very much under construction. A simple demonstration of all common styles.


Facebook Hubspot
Connector setup
Login with Facebook
Click on a page name to make the app subscribe to the events from that page
[[ item.name ]]
Page id is:
[[ pageId ]]
Your access token is:
[[ token ]]


DataAccessFunctions
class DataAccessFunctions extends AbstractSmartyPlugin
Implementation of data access to main Thelia objects (users, cart, etc.)
Traits
Methods
Get a function or block parameter value, and normalize it, trimming balnks and making it lowercase
Get a function or block parameter value
Define the various smarty plugins hendled by this class
search if cart already exists in session. If not try to create a new one or duplicate an old one.
No description
Provides access to the current logged administrator attributes using the accessors.
Provides access to the current logged customer attributes throught the accessor
No description
No description
No description
No description
No description
currency global data
No description
No description
No description
Lang global data
No description
No description
Return the event dispatcher,
Details
in
AbstractSmartyPlugin at line 56
mixed
getNormalizedParam(
array $params,
mixed $name,
mixed $default = null)
Get a function or block parameter value, and normalize it, trimming balnks and making it lowercase
in
AbstractSmartyPlugin at line 72
mixed
getParam(
array $params,
mixed $name,
mixed $default = null)
Get a function or block parameter value
at line 483
an
getPluginDescriptors()
Define the various smarty plugins hendled by this class
in
CartTrait at line 42
Cart
getCart(
EventDispatcherInterface $dispatcher,
Request $request)
search if cart already exists in session. If not try to create a new one or duplicate an old one.
at line 57
__construct(
Request $request,
SecurityContext $securityContext,
TaxEngine $taxEngine,
ParserContext $parserContext,
ContainerAwareEventDispatcher $dispatcher)
at line 73
string
adminDataAccess(
array $params,
unknown $smarty)
Provides access to the current logged administrator attributes using the accessors.
at line 85
string
customerDataAccess(
array $params,
unknown $smarty)
Provides access to the current logged customer attributes throught the accessor
at line 90
productDataAccess($params, $smarty)
at line 103
categoryDataAccess($params, $smarty)
at line 116
contentDataAccess($params, $smarty)
at line 129
folderDataAccess($params, $smarty)
at line 142
brandDataAccess($params, $smarty)
at line 161
currencyDataAccess($params, $smarty)
currency global data
at line 173
countryDataAccess($params, $smarty)
at line 183
cartDataAccess($params, $smarty)
at line 222
orderDataAccess($params, $smarty)
at line 254
string
langDataAccess($params, $smarty)
Lang global data
at line 259
configDataAccess($params, $smarty)
at line 270
statsAccess($params, $smarty)
at line 508
EventDispatcher
getDispatcher()
Return the event dispatcher,


Preparation time
less than 30 mins
Cooking time
1 to 2 hours
Serves
Serves 4
Preheat the oven to 150C/300F/Gas 2.
Scatter the cloves of garlic (still in their skins) into a roasting tin. Scatter half of the rosemary and half of the blood orange on top.
Insert the remaining orange and rosemary into the cavity of the chicken and gently slide slices of the butter under the skin, being careful not to pierce it.
Place the chicken breast-side up on top of the garlic in the roasting tin.
Pour over half a bottle of Cava and cover with aluminium foil.
Transfer to the oven and roast for 1½ hours.
After 1½ hours cooking time, carefully remove the foil and, using tongs, turn the chicken upside down and baste with the meaty juices from the tin.
Cook the chicken for a further 20 minutes to brown the underside, before turning the chicken back to the original position.
Baste the chicken again. Turn the oven up to 180C/350F/Gas 4 and cook until the chicken is browned all over - you will know when the chicken is cooked as the juices will run clear when the chicken is pierced with a skewer.
When the chicken is cooked, remove the chicken from the tin and leave to rest in a warm place.
For the gravy, remove the garlic skins, rosemary and blood oranges with a slotted spoon and discard - if any soft garlic clove remains in the skins, carefully slip the soft garlic clove out of the peel and into a clean pan. Pour the cooking juices into the pan along with all the soft garlic cloves.
Add half a bottle of Cava to the pan. Simmer over a medium heat. When the volume begins to diminish, add white wine to taste (up to a bottle - though you may want to drink a glass or two from the bottle) and sprinkle in the tarragon. Leave the gravy to simmer gently until reduced by half. Just before serving, whisk in a knob of butter.
Meanwhile, carve the chicken into generous portions.
Serve the chicken with the gravy.


GDB Examples¶
In this section we have a few useful gdb commands.
Starting GDB¶
Once at the gdb prompt, VPP can be started by running the following commands:
(gdb) run -c /etc/vpp/startup.conf
Starting program: /scratch/vpp-master/build-root/install-vpp_debug-native/vpp/bin/vpp -c /etc/vpp/startup.conf
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib/x86_64-linux-gnu/libthread_db.so.1".
vlib_plugin_early_init:361: plugin path /scratch/vpp-master/build-root/install-vpp_debug-native/vpp/lib/vpp_plugins:/scratch/vpp-master/build-root/install-vpp_debug-native/vpp/lib/vpp_plugins
....
Backtrace¶
If you encounter errors when running VPP, such as VPP terminating due to a segfault or abort signal, then you can run the VPP debug binary and then execute backtrace or bt.
(gdb) bt
#0 ip4_icmp_input (vm=0x7ffff7b89a40 <vlib_global_main>, node=0x7fffb6bb6900, frame=0x7fffb6725ac0) at /scratch/vpp-master/build-data/../src/vnet/ip/icmp4.c:187
#1 0x00007ffff78da4be in dispatch_node (vm=0x7ffff7b89a40 <vlib_global_main>, node=0x7fffb6bb 6900, type=VLIB_NODE_TYPE_INTERNAL, dispatch_state=VLIB_NODE_STATE_POLLING, frame=0x7fffb6725ac0, last_time_stamp=10581236529 65565) at /scratch/vpp-master/build-data/../src/vlib/main.c:988
#2 0x00007ffff78daa77 in dispatch_pending_node (vm=0x7ffff7b89a40 <vlib_global_main>, pending_frame_index=6, last_time_stamp=1058123652965565) at /scratch/vpp-master/build-data/../src/vlib/main.c:1138
....
Get to the GDB prompt¶
When VPP is running, you can get to the command prompt by pressing CTRL+C.
Breakpoints¶
When at the GDB prompt, set a breakpoint by running the commands below:
(gdb) break ip4_icmp_input
Breakpoint 4 at 0x7ffff6b9c00b: file /scratch/vpp-master/build-data/../src/vnet/ip/icmp4.c, line 142.
List the breakpoints already set:
(gdb) i b
Num Type Disp Enb Address What
1 breakpoint keep y 0x00007ffff6b9c00b in ip4_icmp_input at /scratch/vpp-master/build-data/../src/vnet/ip/icmp4.c:142
breakpoint already hit 3 times
2 breakpoint keep y 0x00007ffff6b9c00b in ip4_icmp_input at /scratch/vpp-master/build-data/../src/vnet/ip/icmp4.c:142
3 breakpoint keep y 0x00007ffff640f646 in tw_timer_expire_timers_internal_1t_3w_1024sl_ov
at /scratch/vpp-master/build-data/../src/vppinfra/tw_timer_template.c:775
Delete a breakpoint:
(gdb) del 2
(gdb) i b
Num Type Disp Enb Address What
1 breakpoint keep y 0x00007ffff6b9c00b in ip4_icmp_input at /scratch/vpp-master/build-data/../src/vnet/ip/icmp4.c:142
breakpoint already hit 3 times
3 breakpoint keep y 0x00007ffff640f646 in tw_timer_expire_timers_internal_1t_3w_1024sl_ov
at /scratch/vpp-master/build-data/../src/vppinfra/tw_timer_template.c:775
Step/Next/List¶
Step through the code using (s)tep into, (n)ext, and list some lines before and after where you are with list.
Thread 1 "vpp_main" hit Breakpoint 1, ip4_icmp_input (vm=0x7ffff7b89a40 <vlib_global_main>, node=0x7fffb6bb6900, frame=0x7fffb6709480)
at /scratch/jdenisco/vpp-master/build-data/../src/vnet/ip/icmp4.c:142
142 {
(gdb) n
143 icmp4_main_t *im = &icmp4_main;
(
(gdb) list
202 vlib_put_next_frame (vm, node, next, n_left_to_next);
203 }
204
205 return frame->n_vectors;
206 }
207
208 /* *INDENT-OFF* */
209 VLIB_REGISTER_NODE (ip4_icmp_input_node,static) = {
210 .function = ip4_icmp_input,
211 .name = "ip4-icmp-input",
Examining Data and packets¶
To look at data and packets use e(x)amine or (p)rint.
For example in this code look at the ip packet:
(gdb) p/x *ip0
$3 = {{ip_version_and_header_length = 0x45, tos = 0x0, length = 0x5400,
fragment_id = 0x7049, flags_and_fragment_offset = 0x40, ttl = 0x40, protocol = 0x1,
checksum = 0x2ddd, {{src_address = {data = {0xa, 0x0, 0x0, 0x2},
data_u32 = 0x200000a, as_u8 = {0xa, 0x0, 0x0, 0x2}, as_u16 = {0xa, 0x200},
as_u32 = 0x200000a}, dst_address = {data = {0xa, 0x0, 0x0, 0xa}, data_u32 = 0xa00000a,
as_u8 = {0xa, 0x0, 0x0, 0xa}, as_u16 = {0xa, 0xa00}, as_u32 = 0xa00000a}},
address_pair = {src = {data = {0xa, 0x0, 0x0, 0x2}, data_u32 = 0x200000a,
as_u8 = {0xa, 0x0, 0x0, 0x2}, as_u16 = {0xa, 0x200}, as_u32 = 0x200000a},
dst = {data = {0xa, 0x0, 0x0, 0xa}, data_u32 = 0xa00000a, as_u8 = {0xa, 0x0, 0x0, 0xa},
as_u16 = {0xa, 0xa00}, as_u32 = 0xa00000a}}}}, {checksum_data_64 =
{0x40704954000045, 0x200000a2ddd0140}, checksum_data_64_32 = {0xa00000a}},
{checksum_data_32 = {0x54000045, 0x407049, 0x2ddd0140, 0x200000a, 0xa00000a}}}
Then the icmp header
(gdb) p/x *icmp0
$4 = {type = 0x8, code = 0x0, checksum = 0xf148}
Then look at the actual bytes:
(gdb) x/50w ip0
0x7fde9953510e: 0x54000045 0x00407049 0x2ddd0140 0x0200000a
0x7fde9953511e: 0x0a00000a 0xf1480008 0x03000554 0x5b6b2e8a
0x7fde9953512e: 0x00000000 0x000ca99a 0x00000000 0x13121110
0x7fde9953513e: 0x17161514 0x1b1a1918 0x1f1e1d1c 0x23222120


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Murrieta, CA
GameCloud Studios is looking for rock star senior software developers for its Murrieta, CA studio. Applicants should have at least five (5) years commercial experience developing apps and/or games, at least one shipped commercial title built in Unity, a great understanding of Computer Science, and be comfortable tackling complex problems in C#, or similar languages.
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Email<EMAIL_ADDRESS>Mail: 25026 Las Brisas Rd., Ste. A, Murrieta, CA 92562
© Copyright 2014 GameCloud Studios, Inc. All Rights Reserved.


Restricted Python Sandbox Playground
# Example program to run in sandbox def hello(): print("Hello world!")
Function name:
Run!