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intprim
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The ParticleFilter class localizes an interaction in time and space via sequential Monte Carlo sampling. More...
Public Member Functions | |
| def | __init__ (self, basis_model, initial_phase_mean, initial_phase_var, proc_var, initial_ensemble, num_effective_ratio=0.5, time_delta=1.0, cyclical=False) |
| The initialization method for the ParticleFilter. More... | |
| def | get_ensemble_mean (self, ensemble=None) |
| Calculates the unbiased sample mean of the ensemble. More... | |
| def | get_ensemble_covariance (self, ensemble=None) |
| Calculates the unbiased sample covariance of the ensemble. More... | |
| def | get_projected_mean_covariance (self, phase, ensemble=None) |
| Calculates the unbiased sample mean and covariance of the ensemble projected into the observation space. More... | |
| def | h (self, state) |
| The nonlinear observation function \( h(\cdot) \) which maps an input state of dimension \( \mathbb{R}^{B} \) to an output observation of dimension \( \mathbb{R}^D. \) This is simply the dot product of the basis functions to the corresponding basis weights. More... | |
| def | hx (self, output_matrix) |
| This method computes the projection of the ensemble to the observation space. More... | |
| def | systematic_resample (self) |
| A systematic resampling scheme for the ensemble. More... | |
| def | localize (self, measurement, measurement_noise, active_dofs, return_phase_variance=False) |
| This method performs simultaneous localization of both time (phase) and space (basis weights). More... | |
| def | __init__ (self, basis_model, initial_phase_mean, initial_phase_var, proc_var, initial_ensemble, num_effective_ratio=0.5, time_delta=1.0, cyclical=False) |
| The initialization method for the ParticleFilter. More... | |
| def | get_ensemble_mean (self, ensemble=None) |
| Calculates the unbiased sample mean of the ensemble. More... | |
| def | get_ensemble_covariance (self, ensemble=None) |
| Calculates the unbiased sample covariance of the ensemble. More... | |
| def | get_projected_mean_covariance (self, phase, ensemble=None) |
| Calculates the unbiased sample mean and covariance of the ensemble projected into the observation space. More... | |
| def | h (self, state) |
| The nonlinear observation function \( h(\cdot) \) which maps an input state of dimension \( \mathbb{R}^{B} \) to an output observation of dimension \( \mathbb{R}^D. \) This is simply the dot product of the basis functions to the corresponding basis weights. More... | |
| def | hx (self, output_matrix) |
| This method computes the projection of the ensemble to the observation space. More... | |
| def | systematic_resample (self) |
| A systematic resampling scheme for the ensemble. More... | |
| def | localize (self, measurement, measurement_noise, active_dofs, return_phase_variance=False) |
| This method performs simultaneous localization of both time (phase) and space (basis weights). More... | |
| Public Member Functions inherited from intprim.filter.spatiotemporal.nonlinear_system.NonLinearSystem | |
| def | __init__ (self, basis_model, proc_var, time_delta, system_order) |
| Initialization method for NonLinearSystem. More... | |
| def | get_transition_model (self) |
| Gets the transition model for spatiotemporal filtering. More... | |
| def | get_process_noise (self) |
| Gets the process noise for spatiotemporal filtering. More... | |
| def | get_phase_process_noise (self) |
| Gets the process noise specifically for the constant velocity phase system. More... | |
| def | get_measurement_model (self, x) |
| Gets the measurement model for spatiotemporal filtering. More... | |
| def | __init__ (self, basis_model, proc_var, time_delta, system_order) |
| Initialization method for NonLinearSystem. More... | |
| def | get_transition_model (self) |
| Gets the transition model for spatiotemporal filtering. More... | |
| def | get_process_noise (self) |
| Gets the process noise for spatiotemporal filtering. More... | |
| def | get_phase_process_noise (self) |
| Gets the process noise specifically for the constant velocity phase system. More... | |
| def | get_measurement_model (self, x) |
| Gets the measurement model for spatiotemporal filtering. More... | |
Public Attributes | |
| ensemble_size | |
| cyclical | |
| ensemble | |
| weights | |
| num_effective_threshold | |
| Public Attributes inherited from intprim.filter.spatiotemporal.nonlinear_system.NonLinearSystem | |
| system_order | |
| system_size | |
| basis_model | |
| state_dimension | |
| measurement_dimension | |
| proc_var | |
| time_delta | |
| process_noise | |
| transition_model | |
| phase_process_noise | |
| measurement_model_prototype | |
The ParticleFilter class localizes an interaction in time and space via sequential Monte Carlo sampling.
This class is a recursive filter, meaning it maintains state information between successive calls to localize(). As with the other spatiotemporal filters, the EnsembleKalmanFilter's internal state consists of (N+1) dimensions modeling the N-th order phase system plus B dimensions modeling the latent space of the interaction. However, unlike the ExtendedKalmanFilter, this class does not maintain a single explicit state instance. Instead, an internal ensemble of E members is maintained, such that the ensemble matrix dimension is (N+1)+B x E. As a result, no explicit covariance matrix is maintained. Instead, the covariance is modeled implicitly in the ensemble via the sample covariance.
Note: this class is not optimized. In particular, the calculation of the PDF in localize is done individually for each ensemble member which is costly.
References:
Campbell, J., Stepputtis, S., & Ben Amor, H. (2019). Probabilistic Multimodal Modeling for Human-Robot Interaction Tasks.
https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/12-Particle-Filters.ipynb
| def intprim.filter.spatiotemporal.pf.ParticleFilter.__init__ | ( | self, | |
| basis_model, | |||
| initial_phase_mean, | |||
| initial_phase_var, | |||
| proc_var, | |||
| initial_ensemble, | |||
num_effective_ratio = 0.5 , |
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time_delta = 1.0 , |
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cyclical = False |
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| ) |
The initialization method for the ParticleFilter.
Responsible for creating the initial ensemble.
| basis_model | The basis model corresponding to this state space. |
| phase_mean | Vector of dimension N corresponding to the initial mean of the constant velocity phase system. If this is a 0th order system, should contain only [phase_mean]. If this is 1st order, should contain [phase_mean, phase_velocity]. If this is 2nd order, should contain [phase_mean, phase_velocity, phase_acceleration]. Anything above 2nd order is not supported. |
| phase_var | Vector of dimension N corresponding to the diagonal of the initial covariance of the constant velocity phase system. If this is a 0th order system, should contain only [phase_var]. If this is 1st order, should contain [phase_var, phase_velocity_var]. If this is 2nd order, should contain [phase_var, phase_velocity_var, phase_acceleration_var]. Anything above 2nd order is not supported. |
| proc_var | The process noise of the constant velocity phase system. This is a scalar value corresponding to the variance of a piecewise white noise model. |
| initial_ensemble | Matrix of dimension E x B which represents an initial ensemble. Typically, this is just the set of basis weights corresponding to E training demonstrations. |
| num_effective_ratio | If the number of effective samples falls below this ratio, then resampling is performed. Default is 0.5 which means resampling occurs when less than 50% of the samples are ineffective. |
| time_delta | The amount of time that elapses between time steps. This serves as a scaling factor to the constant velocity phase system. In most cases, this should be set to 1.0. |
| cyclical | Indicates whether this is a cyclical primitive. If True, the internal phase state will cycle back to 0 once it exceeds 1, allowing for continuous inference of periodic interactions. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.__init__ | ( | self, | |
| basis_model, | |||
| initial_phase_mean, | |||
| initial_phase_var, | |||
| proc_var, | |||
| initial_ensemble, | |||
num_effective_ratio = 0.5 , |
|||
time_delta = 1.0 , |
|||
cyclical = False |
|||
| ) |
The initialization method for the ParticleFilter.
Responsible for creating the initial ensemble.
| basis_model | The basis model corresponding to this state space. |
| phase_mean | Vector of dimension N corresponding to the initial mean of the constant velocity phase system. If this is a 0th order system, should contain only [phase_mean]. If this is 1st order, should contain [phase_mean, phase_velocity]. If this is 2nd order, should contain [phase_mean, phase_velocity, phase_acceleration]. Anything above 2nd order is not supported. |
| phase_var | Vector of dimension N corresponding to the diagonal of the initial covariance of the constant velocity phase system. If this is a 0th order system, should contain only [phase_var]. If this is 1st order, should contain [phase_var, phase_velocity_var]. If this is 2nd order, should contain [phase_var, phase_velocity_var, phase_acceleration_var]. Anything above 2nd order is not supported. |
| proc_var | The process noise of the constant velocity phase system. This is a scalar value corresponding to the variance of a piecewise white noise model. |
| initial_ensemble | Matrix of dimension E x B which represents an initial ensemble. Typically, this is just the set of basis weights corresponding to E training demonstrations. |
| num_effective_ratio | If the number of effective samples falls below this ratio, then resampling is performed. Default is 0.5 which means resampling occurs when less than 50% of the samples are ineffective. |
| time_delta | The amount of time that elapses between time steps. This serves as a scaling factor to the constant velocity phase system. In most cases, this should be set to 1.0. |
| cyclical | Indicates whether this is a cyclical primitive. If True, the internal phase state will cycle back to 0 once it exceeds 1, allowing for continuous inference of periodic interactions. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.get_ensemble_covariance | ( | self, | |
ensemble = None |
|||
| ) |
Calculates the unbiased sample covariance of the ensemble.
The formula used to compute the covariance is: $$ cov(\boldsymbol{X}) = \frac{1}{E - 1} \boldsymbol{A} \boldsymbol{A}^{T}, \qquad \boldsymbol{A} = \boldsymbol{X} - \bar{\boldsymbol{X}}, $$ where \( cov(\boldsymbol{X}) \in \mathbb{R}^{N+1+B \times N+1+B}. \)
| ensemble | The ensemble from which to calculate the sample covariance. If none, uses the internal state ensemble. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.get_ensemble_covariance | ( | self, | |
ensemble = None |
|||
| ) |
Calculates the unbiased sample covariance of the ensemble.
The formula used to compute the covariance is: $$ cov(\boldsymbol{X}) = \frac{1}{E - 1} \boldsymbol{A} \boldsymbol{A}^{T}, \qquad \boldsymbol{A} = \boldsymbol{X} - \bar{\boldsymbol{X}}, $$ where \( cov(\boldsymbol{X}) \in \mathbb{R}^{N+1+B \times N+1+B}. \)
| ensemble | The ensemble from which to calculate the sample covariance. If none, uses the internal state ensemble. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.get_ensemble_mean | ( | self, | |
ensemble = None |
|||
| ) |
Calculates the unbiased sample mean of the ensemble.
The formula used to compute the mean is: $$ \bar{\boldsymbol{X}} = \frac{1}{E} \sum_{j=1}^{E} \boldsymbol{x}^{j}, $$ where \( \boldsymbol{x}^{j} \in \mathbb{R}^{N+1+B} \) and \( \bar{\boldsymbol{X}} \in \mathbb{R}^{N+1+B}. \)
| ensemble | The ensemble from which to calculate the sample mean. If None, uses the internal state ensemble. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.get_ensemble_mean | ( | self, | |
ensemble = None |
|||
| ) |
Calculates the unbiased sample mean of the ensemble.
The formula used to compute the mean is: $$ \bar{\boldsymbol{X}} = \frac{1}{E} \sum_{j=1}^{E} \boldsymbol{x}^{j}, $$ where \( \boldsymbol{x}^{j} \in \mathbb{R}^{N+1+B} \) and \( \bar{\boldsymbol{X}} \in \mathbb{R}^{N+1+B}. \)
| ensemble | The ensemble from which to calculate the sample mean. If None, uses the internal state ensemble. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.get_projected_mean_covariance | ( | self, | |
| phase, | |||
ensemble = None |
|||
| ) |
Calculates the unbiased sample mean and covariance of the ensemble projected into the observation space.
This is computed by first projecting the entire ensemble to measurement space then calculating the sample covariance. Mathematically, calculating the sample covariance and then projecting it should be equivalent but numerical issues sometimes arise and so this method is preferred. The formula used to compute the covariance is: $$ cov(\boldsymbol{H} \boldsymbol{X}) = \frac{1}{E - 1} (\boldsymbol{H}\boldsymbol{A}) (\boldsymbol{H} \boldsymbol{A}^{T}), \qquad \boldsymbol{H} \boldsymbol{A} = \boldsymbol{X} - \frac{1}{E} \sum_{j=1}^{E}h(\boldsymbol{x}^j), $$ where \( cov(\boldsymbol{H} \boldsymbol{X}) \in \mathbb{R}^{D \times D} \) and \( h(\cdot) \) is the nonlinear observation function.
| phase | The phase value \( \phi \) to use for the projection. |
| ensemble | The ensemble from which to calculate the projected mean and covariance. If none, uses the internal state ensemble. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.get_projected_mean_covariance | ( | self, | |
| phase, | |||
ensemble = None |
|||
| ) |
Calculates the unbiased sample mean and covariance of the ensemble projected into the observation space.
This is computed by first projecting the entire ensemble to measurement space then calculating the sample covariance. Mathematically, calculating the sample covariance and then projecting it should be equivalent but numerical issues sometimes arise and so this method is preferred. The formula used to compute the covariance is: $$ cov(\boldsymbol{H} \boldsymbol{X}) = \frac{1}{E - 1} (\boldsymbol{H}\boldsymbol{A}) (\boldsymbol{H} \boldsymbol{A}^{T}), \qquad \boldsymbol{H} \boldsymbol{A} = \boldsymbol{X} - \frac{1}{E} \sum_{j=1}^{E}h(\boldsymbol{x}^j), $$ where \( cov(\boldsymbol{H} \boldsymbol{X}) \in \mathbb{R}^{D \times D} \) and \( h(\cdot) \) is the nonlinear observation function.
| phase | The phase value \( \phi \) to use for the projection. |
| ensemble | The ensemble from which to calculate the projected mean and covariance. If none, uses the internal state ensemble. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.h | ( | self, | |
| state | |||
| ) |
The nonlinear observation function \( h(\cdot) \) which maps an input state of dimension \( \mathbb{R}^{B} \) to an output observation of dimension \( \mathbb{R}^D. \) This is simply the dot product of the basis functions to the corresponding basis weights.
The nonlinearity comes from the usage of the phase state variable as a parameter to the basis functions.
| state | Vector of dimension N+1+B which is the state to be projected. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.h | ( | self, | |
| state | |||
| ) |
The nonlinear observation function \( h(\cdot) \) which maps an input state of dimension \( \mathbb{R}^{B} \) to an output observation of dimension \( \mathbb{R}^D. \) This is simply the dot product of the basis functions to the corresponding basis weights.
The nonlinearity comes from the usage of the phase state variable as a parameter to the basis functions.
| state | Vector of dimension N+1+B which is the state to be projected. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.hx | ( | self, | |
| output_matrix | |||
| ) |
This method computes the projection of the ensemble to the observation space.
Each member is projected such that the input ensemble is mapped from \( \mathbb{R}^{N+1+B \times E} \) to \( \mathbb{R}^{D \times E} \). The formula used to compute this is: $$ \boldsymbol{H} \boldsymbol{X} = \left[h(\boldsymbol{x}^1), \dots, h(\boldsymbol{x}^E) \right]. $$
| output_matrix | Matrix of dimension D x E in which the results should be stored. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.hx | ( | self, | |
| output_matrix | |||
| ) |
This method computes the projection of the ensemble to the observation space.
Each member is projected such that the input ensemble is mapped from \( \mathbb{R}^{N+1+B \times E} \) to \( \mathbb{R}^{D \times E} \). The formula used to compute this is: $$ \boldsymbol{H} \boldsymbol{X} = \left[h(\boldsymbol{x}^1), \dots, h(\boldsymbol{x}^E) \right]. $$
| output_matrix | Matrix of dimension D x E in which the results should be stored. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.localize | ( | self, | |
| measurement, | |||
| measurement_noise, | |||
| active_dofs, | |||
return_phase_variance = False |
|||
| ) |
This method performs simultaneous localization of both time (phase) and space (basis weights).
This is a recursive call, which means it updates the internal state estimate based on the given observations. For each observation given, two steps are performed recursively: First, the current ensemble is propagated forward in time in what is known as the prediction step. $$ \boldsymbol{x}^j_{t|t-1} = \boldsymbol{G} \boldsymbol{x}^j_{t-1|t-1} + \mathcal{N} \left(0, \boldsymbol{Q}_t\right), \quad 1 \leq j \leq E. $$ Note that in this case, we only apply the dot product to the first N+1 dimensions of the state. This is for computational efficiency as only the constant velocity phase system has a non-zero transition.
Next, we integrate the observations into the current ensemble in the update step. This involves calculating a weight for each ensemble member based on the likelihood of the measurement having been produced. The ensemble is resampled according to a systematic scheme if the number of effective members is less than a threshold.
Lastly, the sample mean and covariance of the ensemble are returned. At the end of both the prediction and update steps the internal phase value is clipped such that it falls within the range [0, 1].
| measurement | Matrix of dimension T x D containing observations, where T is the number of timesteps that have been observed since the last call to localize() and D is the dimension of the measurement space. |
| measurement_noise | Matrix of dimension D x D containing the measurement noise for the given set of measurements. |
| active_dofs | Vector of dimension \( D_o \) containing measurement space indices of the observed degrees of freedom. Note that the measurements will also contain unobserved degrees of freedom, but their values should not be used for inference. |
| return_phase_variance | True if the mean/variance for the phase system should be returned in addition to the basis weights. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.localize | ( | self, | |
| measurement, | |||
| measurement_noise, | |||
| active_dofs, | |||
return_phase_variance = False |
|||
| ) |
This method performs simultaneous localization of both time (phase) and space (basis weights).
This is a recursive call, which means it updates the internal state estimate based on the given observations. For each observation given, two steps are performed recursively: First, the current ensemble is propagated forward in time in what is known as the prediction step. $$ \boldsymbol{x}^j_{t|t-1} = \boldsymbol{G} \boldsymbol{x}^j_{t-1|t-1} + \mathcal{N} \left(0, \boldsymbol{Q}_t\right), \quad 1 \leq j \leq E. $$ Note that in this case, we only apply the dot product to the first N+1 dimensions of the state. This is for computational efficiency as only the constant velocity phase system has a non-zero transition.
Next, we integrate the observations into the current ensemble in the update step. This involves calculating a weight for each ensemble member based on the likelihood of the measurement having been produced. The ensemble is resampled according to a systematic scheme if the number of effective members is less than a threshold.
Lastly, the sample mean and covariance of the ensemble are returned. At the end of both the prediction and update steps the internal phase value is clipped such that it falls within the range [0, 1].
| measurement | Matrix of dimension T x D containing observations, where T is the number of timesteps that have been observed since the last call to localize() and D is the dimension of the measurement space. |
| measurement_noise | Matrix of dimension D x D containing the measurement noise for the given set of measurements. |
| active_dofs | Vector of dimension \( D_o \) containing measurement space indices of the observed degrees of freedom. Note that the measurements will also contain unobserved degrees of freedom, but their values should not be used for inference. |
| return_phase_variance | True if the mean/variance for the phase system should be returned in addition to the basis weights. |
| def intprim.filter.spatiotemporal.pf.ParticleFilter.systematic_resample | ( | self | ) |
A systematic resampling scheme for the ensemble.
Divide the ensemble weights into N regions, then choose a sample from each region such that all samples are exactly 1/N distance apart. Reference: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/12-Particle-Filters.ipynb
| def intprim.filter.spatiotemporal.pf.ParticleFilter.systematic_resample | ( | self | ) |
A systematic resampling scheme for the ensemble.
Divide the ensemble weights into N regions, then choose a sample from each region such that all samples are exactly 1/N distance apart. Reference: https://github.com/rlabbe/Kalman-and-Bayesian-Filters-in-Python/blob/master/12-Particle-Filters.ipynb


Would love to hear your experiences + thoughts about @utrumdotio's platform for reviews + predictions: https://t.co/UIdCojRt2R
Good chance I'll quote you in the upcoming review @DailyChainNews
Comment here or $OOT's discord: https://t.co/eg4E0sUuzg
$KMD $ARRR $VRSC $HUS...
1
Acura22-Sep-19 10:38 PM
@TheHodler yes it works on iOS 13
xeonrider23-Sep-19 08:10 AM
for a DPoW, what are the costs? And do you need this feature to get listed at an exchange?(edited)
Cryptoon23-Sep-19 09:12 AM
@Steve Lee@Audo@PTYX@siu - Chainmakers
Audo23-Sep-19 09:16 AM
@xeonrider, for exteral (non Komodo based) coins the price is roughly 5000 USD/year plus possible security audit and integration costs. For Smart Chains it is half that wihout a need for a security audit nor integration costs.
For more precise pricing plans please contact one of our service providers.
P.S. We don't control the conditions under which various exchanges list coins. However, having dPoW would creatly increase the security and lower the risks of 51% attacks that would cause pain to exchanges.
xeonrider23-Sep-19 09:16 AM
holy shit
so far for my plans
xD
I'll go back to my cave not even have remotely access to that much money
Markus23-Sep-19 09:20 AM
Not sure how 5k is alot if ur coin have a decent marketcap, should be worth protecting
xeonrider23-Sep-19 09:21 AM
5K is a lot for me, not saying it is not worth it. I am just a programmer that does webdev.
Audo23-Sep-19 09:22 AM
if you just want to play around you can launch a smart chain from command line without any protection.
xeonrider23-Sep-19 09:23 AM
I have a plan, but guess I'll have to save bucks for a while. No protection is bad. Just a lone wolf here.(edited)
hyperDEX23-Sep-19 09:24 AM
@xeonrider why not exchange your skills for dpow?
seems like a win win if you have something of value to trade
xeonrider23-Sep-19 09:25 AM
@hyperDEX nah, jobs always go to that place where I have to invest more time than actually saving money.
hyperDEX23-Sep-19 09:25 AM
xeonrider23-Sep-19 09:26 AM
I worked months on a website for a 1000$ deal....  Will play airguitar and hope I get dimes from people just to quit.
TS23-Sep-19 10:02 AM
@TheHodler AtomicDEX works on iOS13 but it's a bit buggy. 0.20 solves these bugs and will be out in 2-3 days(edited)
@Cryptoon (Cryptoon#3362)
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yorklab23-Sep-19 07:21 PM
Yes, that was a good thorough review.
house23-Sep-19 11:14 PM
oh shucks ... thanks guys
was my first go at it ... bit of a test case
Appreciate the tip @Cryptoon mercy buckets
3
hastrup24-Sep-19 05:38 PM
@Audo will there be a forum for this in the future?(edited)
Don’t hijack channels in long one-on-one debates or discussions
we could call a channel long one-on-one debate
Audo24-Sep-19 05:43 PM
yes, we will have a community portal in the future to compliment the Discord's live chat
hastrup24-Sep-19 05:47 PM
@Audo sounds good i find it necessary to have long healthy discussions for an open source project. But i understand that talk-trading is not the best place to do that. so im glad there will be a place for that kind of thing
@Audo i agree on the new guidelines great work
Audo24-Sep-19 05:48 PM
thanks, great to hear!
Would you like to become a community mod?
hastrup24-Sep-19 05:49 PM
@Audo yeahr already fill the form
Audo24-Sep-19 05:49 PM
ah, cool! I'll check
hastrup24-Sep-19 05:49 PM
thx
Audo24-Sep-19 05:50 PM
Awecome, welcome
hastrup24-Sep-19 05:51 PM
@Audo thx
Oszy24-Sep-19 09:16 PM
grats
we can always create sub-channels for long debates that doesn't belong anywhere else
offtopic went kinda crazy lately i see it's now casual
Zatoichi24-Sep-19 10:01 PM
hi, all. I keep my kmd in agama wallet. the newer version. Is this the best way to stake it? Or do I miss something?
Audo24-Sep-19 10:02 PM
You don't need to do anything. Just claim it once a month and you are good. You can claim the rewards with the Verus Agama
1
tomtom25-Sep-19 01:24 AM
how many kmd do i need to get rewards
...and how long does it take
tried the wallet out with some kmd and waiting for some rewards
thanks
hastrup25-Sep-19 04:27 AM
@tomtom you need a utxo of 10 you can make sure that you have a utxo of 10 if you send your balance to yourself. I think It take 1 dopw befor you can get rewards about 10min somone correct me if I'm wrong. but I recommend that you leave it and claim rewards once a month(edited)
1
gcharang25-Sep-19 07:53 AM
@tomtom
You need to have minimum 10 Komodo in your address as a single UTXO that is about 60 minutes (~60 blocks) old and claim once a month
you can check your rewards status here: https://www.atomicexplorer.com/#/rewards
@gcharang ok 60 min i remember that thx for the clarification
2
tomtom25-Sep-19 11:02 AM
thank you
Audo25-Sep-19 12:59 PM
someone called 'Mustafa' sent us a moderator application but forgot to include his discord name, and now I can't reach out to him. If you read this, please PM me.
farl25-Sep-19 01:27 PM
Hi guys. I read about that you are looking for Community Moderators. I would love to help again and filled in the form.
Audo25-Sep-19 01:44 PM
perfect @farl ! I'll add you to the group
Okereke25-Sep-19 01:45 PM
Hi all.
How can I join on the activities here?
I'm new here.
hastrup25-Sep-19 02:02 PM
@Okereke what activities have you in mind? And welcome to komodo :)
farl25-Sep-19 02:09 PM
@Audo thanks
farl25-Sep-19 03:07 PM
@Okereke welcome
Okereke25-Sep-19 03:37 PM
@hastrup anyone I can join komodo to do.
@farl thank you so much.
I'm a writer and researcher.
I started www.blockchaingap.com to help projects, people and firms in research, publications, reviews, etc.
I mean what I can do here too.
farl25-Sep-19 05:53 PM
Where can I find more info about Komodo being quantumresistant?
Unless I read that quantum paper wrong (which is quite possible) they were using a QC to verify that quantum noise was actually randomly distributed, which the verification would take a regular computer a long ass time to verify... that hardly sounds like quantum supremacy to me. Though I guess if you count verifying random noise is random, as a "hard problem" then yea, guess it took the title there(edited)
peddycb26-Sep-19 04:30 PM
hello sell 89.5 DEX for 1750 KMD
if interest then OTC deal on Acura
Oszy26-Sep-19 04:53 PM
Please use #talk-trading@peddycb(edited)
Monstar26-Sep-19 07:52 PM
Hi guys,
I am new here and have just read the "How to make a blockchain" article.
https://komodoplatform.com/how-to-make-a-blockchain/
I have got 2 questions.
"Step 13: Pay For Your Smart Chain.
Now, it’s time to pay for your Smart Chain. You can pay with fiat currency via PayPal or you can pay with cryptocurrency via CoinPayments. Watch the video tutorial here to learn more about paying with cryptocurrency."
- Who does the money for creating the Smart Chain go to?
- How does anyone investing in Komodo coin (like me - small investor) profit from this?
Want to know how to make a blockchain? It's easy with Komodo's Antara Composer. You can customize and launch a Smart Chain in just a few minutes.
Oszy26-Sep-19 08:17 PM
It depends what modules you paid for, explorer an SPV server costs goes to the person that runs the explorers, to cover server costs. same as dPow, it goes to fund dPow itself, DEX listing fee goes to DEX dev fund, and so on
How does anyone investing in Komodo coin (like me - small investor) profit from this? I don't really understand this question, are you asking how do you profit from creating a SmartChain?
There is one thing for sure, the 5% APR reward for holding and using KMD is a nice profit on it's own
ComputerGenie 👴👴👴26-Sep-19 08:23 PM
Alternatively, the the more the platform tech is adopted and utilized is the more likely that the value of your investment will grow(edited)
Monstar27-Sep-19 04:53 AM
@oszy Thanks. The second part of my question was basically meant like "what is the reason/need for someone to buy and use Komodo in creating a his own blockchain"? Do people need to buy Komodo for any eason?
polycryptoblog27-Sep-19 05:17 AM
@Monstar yes for DPoW , and eventually use of sidechains attached to the mainchain to provide other functionality.
Monstar27-Sep-19 05:38 AM
Thanks for the explanation.
Goku27-Sep-19 10:39 AM
Hello
I have a question regarding binance staking
Do the community from komodo think that is good or bad?
Bernz - Selling LABS27-Sep-19 10:41 AM
I'd advise against holding it on Binance unless you're actively trading KMD.. it's nice that rewards are getting distributed to their respective owners however you are still putting your faith in Binance.. Not your keys not your coins! I think it's good for marketing and that's about it
1
Goku27-Sep-19 10:44 AM
Did binance activate komodo staking with the consens of the komodo devs?
Bernz - Selling LABS27-Sep-19 10:45 AM
I believe the Komodo team had been advocating for it
gcharang27-Sep-19 10:47 AM
binance doesn't need any one's permission to distribute coins that they receive because of holding KMD (the active user rewards are enforced by the blockchain and it is not actually staking)
it is good that they are sending the coins to the people who own them, but we don't have any more input than that
@Goku(edited)
Oszy27-Sep-19 10:49 AM
it is not staking and it didn't have to be activated
they were always accumulating rewards, but never distributed it back to the people until now
1
Goku27-Sep-19 10:51 AM
I am not familiar with komodo but if you have governance on chain, does binance not have a big impact on it?
Could it not harm the network?
Oszy27-Sep-19 10:53 AM
It's not different in any way from someone holding 2 million kmd in a wallet, or binance does. rewards will be the same. they never dumped it, they gave it back to us, and will continue to do so.
I see no harm in giving people what's practically theirs.
Your own wallet, or on binance, you get same rewards. Why would you keep it on binance unless you trading actively?
From their perspective they want people to trade on their platform not using them as a wallet, so they don't really have any profit or advantage getting people deposit more and more kmd there.
gcharang27-Sep-19 10:56 AM
the only thing in our ecosystem that comes close to governance is the election of Notary nodes by KMD holders every year
30 Notary nodes stand for election each year
last year the lowest amount of Votes received by an elected candidate was around 1 million
I'am not sure how many KMD, binance controls, but you can estimate the number of candidates they will be able to elect
but the notary system has redundany built in and only used for backing up KMD data to BTC and smart chains to KMD, they can't do any harm
@Goku(edited)


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Have you ever wanted to learn a foriegn yet not so foreign language? Like French or Spanish, the classes you took in high school, you want to make something out of them! Do you not know where to start? Do you want to learn in an encouraging environment full of your peers wanting to learn and improve just like you?! WELL look no further! Welcome to SkyLanguage! Where the sky's the limit on language learning! Participate in an open, friendly forum where you can post, revise and persue your goal to learn a foregin yet not so foregin language!!


And / or
See ssims.co.uk for more info about the author.
Note because this uses Wikipedia, this and any derivatives are licensed under CC Attribution-ShareAlike.


Everyone Is Loving Pokémon ‘Sword And Shield’ But They Love Wooloo Even More
by Michelle RennexThe reception of Pokémon’s newest game, ‘Sword and Shield’ has been overwhelming positive from critics.
Pokémon’s general appeal lives in its ability to let you “tap into a sense of wonder that becomes more and more difficult to access as an adult,” according to Kotaku. “‘Sword and Shield’ does that more successfully than any Pokémon release has in years.”
Casey DeFreitas from IGN called the game “such a complete joy to play” but still recognised that ‘Sword and Shield’ could be improved on. While “better cutscenes, companion Pokemon, [and] the complete Pokédex” could’ve catapulted the game into “true legendary status,” ‘Sword and Shield’ is still more than impressive.
Even with all the drama surrounding the National Dex removal ahead of its release, ‘Sword And Shield’ has still been mostly received positively by the public too. Beyond the “superfans” angered over Game Freak’s game design and graphics, others online are enjoying the game.
In direct response to the negative hashtag #GameFreakLied, fans of the franchise used #ThankYouGameFreak to share stories of positivity around Pokémon. Sharing stories of love, friendship, diversity and self-confidence, fans were thankful for what Game Freak produced in ‘Sword and Shield’ and in Pokémon as a whole.
In regards to the game itself, people are loving the new characters featured in ‘Sword and Shield’ like the feral gremlin-type creature, Perrserker who is an evolution of Meowth.
And the coolest Grookey evolution, Rillaboom who sports a green mullet and definitely passes the vibe check.
But the Pokémon that everyone seems to be most obsessed with is Wooloo, a new, fluffy creature that is literally just a sheep. I’m not really sure what he does beyond being super cute, but I’ll allow it.

And people feel STRONGLY about protecting the little fluff ball.
Beyond Wooloo, did I mention everyone, obviously, loves the fact that you can play catch with the Pokémon?
If you ask me, ‘Sword and Shield’ has to be the year’s best game from that alone.


Hey there
Let's start coding, shall we?
✨ Thanks for using my minimal web boilerplate ✨
Let me know any Suggestion or Question you might have
Let's start coding, shall we?
✨ Thanks for using my minimal web boilerplate ✨
Let me know any Suggestion or Question you might have


The Directory
March 10
Matas tracks
February 10
My live track
Goldfrapp
On the bridge
January 10
Soulhack
Contact
are you want to get listed?
mail@thedirectory.us


§501-174 Power of attorney; registration necessary. Any person may by attorney procure land to be registered and convey or otherwise deal with registered land, but the letters of attorney shall be acknowledged and filed or recorded with the assistant registrar and registered. Any instrument revoking such letters shall be acknowledged and registered in like manner. [L 1903, c 56, §104; RL 1925, §3294; RL 1935, §5092; RL 1945, §12692; RL 1955, §342-92; HRS §501-174; am L 1986, c 246, §24]
Rules of Court
Power of attorney, see RLC rule 64.
Case Notes
Referred to: 50 H. 189, 436 P.2d 207.


#include <uvm_resource_t.h>
Inheritance diagram for uvm::uvm_resource< sc_bv_base >:
Public Member Functions | |
| uvm_resource (const std::string &name, const std::string &scope="*\\.") | |
| Constructor. | |
| ~uvm_resource () | |
| Desstructor. | |
| void | set () |
| Add the resource to the back of the pool. | |
| void | set_override () |
| Add the resource to front the pool. | |
| sc_bv_base | read () |
| Return the object value. | |
| void | write (sc_bv_base value) |
| Modified the stored value of the object. | |
| const std::type_info & | get_value_type_id () |
| Get the type id of the value field. | |
| int | get_nbits () |
| Return number of bits of the resource value. | |
Protected Attributes | |
| sc_bv_base * | m_value |
Definition at line 126 of file uvm_resource_t.h.
| uvm::uvm_resource< sc_bv_base >::uvm_resource | ( | const std::string & | name, | |
| const std::string & | scope = "*\\." | |||
| ) | [inline] |
Constructor.
| name | - Name of resource | |
| scope | - Resource scope |
Definition at line 138 of file uvm_resource_t.h.


A simple visual art portfolio website using Quarto
I recently updated my visual art website. It’s now a very simple portfolio/gallery built using Quarto. This post is a quick run-down of what I did, with notes to my future self. The source code is on github: https://github.com/CrumpLab/things.
What I wanted
I started with a few things I wanted to achieve:
Really simple, clean website design that makes it easy to look at the pictures
Flat structure, minimal clicking
easy to update with new stuff
Some way to tag individual pictures (e.g., different collections of series)
Add more info to describe process behind a particular work
Alt text (still haven’t added this yet, but it’s possible).
Quarto website structure
The folder structure is below. Like all Quarto websites, the general organization of the site is controlled by parameters set in _quarto.yml
.
├── README.md
├── Things.Rproj
├── _quarto.yml
├── about.qmd
├── docs
├── images
│ ├── Colorlands
│ ├── Desertland
│ ├── ...
├── imgs
├── index.qmd
├── make_qmds.R
├── styles.css
└── things
├── Colorlands
├── Desertland ├── ...
The landing page shows a grid of pictures. This behavior is controlled by index.qmd
, which uses the listing option in the top-level YAML.
---
title: "things by Matt Crump"
listing:
contents: things
type: grid
image-height: "100%"
grid-columns: 4
page-size: 20
grid-item-border: false
fields: [image]
sort-ui: [date,categories]
categories: true
sort:
- "date desc"
format:
html:
page-layout: full
---
It’s possible to customize several aspects of the grid, including the number of columns, and the number of images to display before pagination. The contents:
parameter points to the name of a folder things
containing .qmd
for each image. All .qmd
files in the folder are automatically rendered and the image associated with the document is shown in the listing grid. Clicking on a picture in the front page grid links to the individual page for each picture.
A quarto page for each picture
The things
folder contains multiple sub-folders, each with .qmd files for individual works I want listed in the gallery. The sub-folder names the collection or series.
└── things
├── Colorlands
│ ├── Colorland_3.qmd
│ ├── Golden_Pyramid_With_Tunnel_Celebration.qmd │ ├── Meeting_Of_The_Mountains.qmd
An example .qmd
file for an individual piece is below. I’m using the categories feature to code which series the piece is in, and these keywords are displayed on the website to filter all of the pictures really quickly.
---
title: Colorland 3
author: Matt Crump
image: ../../images/Colorlands/Colorland_3.jpeg
description: Digital Work
categories: [Colorlands]
date: 2022-06-10
format:
html:
page-layout: full
---
![](../../images/Colorlands/Colorland_3.jpeg)
Most of the pages have no content except for the picture, but if I ever want to add more, it’s possible to write anything underneath (with all of the options that are available for HTML quarto documents).
Automating qmd generation
One of the tedious aspects of this approach is that each image requires its own qmd
file. That could be a lot of files. Instead of writing them by hand, I scripted this part. Each of the images already had some EXIF metadata that specificied the title and a few other details (like basic description and creation date). I wrote an R script make_qmds.R
that read in each image, extracted the EXIF data, and then wrote a .qmd
file to display the image.
Adding new stuff
To add new stuff I’ll have to pop in an image file in the images
folder, and then write a corresponding .qmd
file in the things folder. It’s not that time consuming for a single image. I have the feeling that I should improve the scripts and turn them into functions. Maybe later.


The pulse is back!
Exams have ended, the next sem has begun. But at this time of the year, there's only one thing on everyone's minds. You guessed right, the most awaited college fest of Pune- Sympulse, this year in its 6th edition, brings to you- A Local Coalesce. What's really in store for the participants? Read on to find out...


| SSasympOff {stats} | R Documentation |
This selfStart
model evaluates an alternative parametrization
of the asymptotic
regression function and the gradient with respect to those parameters.
It has an initial
attribute that creates initial estimates of the parameters
Asym
, lrc
, and c0
.
SSasympOff(input, Asym, lrc, c0)
input |
a numeric vector of values at which to evaluate the model. |
Asym |
a numeric parameter representing the horizontal asymptote on
the right side (very large values of |
lrc |
a numeric parameter representing the natural logarithm of the rate constant. |
c0 |
a numeric parameter representing the |
a numeric vector of the same length as input
. It is the value of
the expression Asym*(1 - exp(-exp(lrc)*(input - c0)))
. If all of
the arguments Asym
, lrc
, and c0
are
names of objects, the gradient matrix with respect to these names is
attached as an attribute named gradient
.
José Pinheiro and Douglas Bates
nls
, selfStart
;
example(SSasympOff)
gives graph showing the SSasympOff
parametrization, where phi_1 is Asymp
,
phi_3 is c0
.
CO2.Qn1 <- CO2[CO2$Plant == "Qn1", ] SSasympOff(CO2.Qn1$conc, 32, -4, 43) # response only Asym <- 32; lrc <- -4; c0 <- 43 SSasympOff(CO2.Qn1$conc, Asym, lrc, c0) # response and gradient getInitial(uptake ~ SSasympOff(conc, Asym, lrc, c0), data = CO2.Qn1) ## Initial values are in fact the converged values fm1 <- nls(uptake ~ SSasympOff(conc, Asym, lrc, c0), data = CO2.Qn1) summary(fm1)


Choose a button too see 10 related GIFs, or type in the name of a sport or athlete, and press enter to create a new button.


{% extends 'admin/master.html' %} {% block body %} {{ super() }}
Bootswatch
This example shows how you can define a bootstrap swatch theme
# set flask admin swatch app.config['FLASK_ADMIN_SWATCH'] = 'cerulean'
Available swatches in http://bootswatch.com/2/
Back


Makro Black Friday 2019 Deals For Gamers
by SillicurMakro is having a big Black Friday 2019 sale that will kick off on 27 November and run until 1 December both in-store and online. We are covering all the deals this year and although there aren’t a tonne of deals for gamers in the Makro Black Friday 2019 sale, you can pick up some gaming hardware or a new TV for your console gaming needs, or maybe a “racing chair” if you are in need of some comfortable seating.
RELATED: Nintendo eShop Cyber Deals Revealed – Up to 70% Off
Some of the prices in the Makro Black Friday 2019 deals list are decent, while others aren’t all that great. We will leave it up to you to decide if the deals are Black Friday worthy or not. Check out all the deals for gamers in the Makro Black Friday 2019 sale according to their brochure below.
- DualShock 4 Controller + 500 Fortnite V-Bucks – R699
- PlayStation 4 Slim 500GB + 3 Games + 90 Days PS+ – R3,999
- PlayStation 4 Slim 1TB + FIFA 20 + Extra Controller – R5,999
- PlayStation 4 Pro 1TB + 2 Games + Extra Controller – R6,999
- Seagate Expansion 2.5″ 2TB Portable HDD – R999
- Xbox One S 1TB Console – R3,299
- Xbox One S 1TB Console + Extra Controller – R4,299
- Samsung 65-inch Smart 4K QLED TV – R14,999
- Sinotec 50-inch UHD Digital LED TV – R3,999
- Samsung 43-inch Smart 4K UHD TV – R4,999
- Hisense 58-inch Smart UHD TV – R6,999
- Samsung 55-inch Smart UHD Curved LED TV – R7,999
- Samsung 55-inch Smart 4K QLED TV – R10,999
- Hisense 55-inch Smart ULED TV – R7,999
- Samsung 58-inch Smart UHD LED TV – R7,999
- Samsung 65-inch Smart UHD LED TV – R10,999
- Hisense 70inch Smart UHD LED TV – R13,999
- Hisense 65-inch Smart ULED TV – R11,999
- Samsung 75-inch Smart UHD LED TV – R19,999
- LG 75-inch Smart UHD LED TV with AI ThinQ – R19,999
- LG 82-inch Smart UHD TV with AI ThinQ – R34,999
- Samsung 75-inch Smart 4K QLED TV – R25,999
- Acer Nitro 5 Intel Core i5 Gaming Laptop – R11,999
- Samsung 24-inch Gaming LED Monitor – R1,899
- Panorama Racing Chair – R1,299
- Silverstone Racing Chair – R1,999
RELATED: BT Games Black Friday 2019 Deals Revealed
What do you think about the Makro Black Friday 2019 sale and the deals for gamers? Let us know in the comment section below.


Traditional HTML with Requests and Responses
Sections
Section
Id
Chart
chart
Alert Count
alertcount
Instance Count
instancecount
Passing Rules
passingrules
Alert Details
alertdetails
Statistics
statistics
Parameters
params
Themes
Theme
Id
Light
light
Dark
dark
Screenshot


Well done, you broke the internet. :(
Just kidding! Only issue is that the page you requested couldn't be found. This could be because
I haven't set the page up yet properly, or, lets be honest, the more likely issue, you went to an
invalid link. Use the navigation buttons to get back to a real webpage.


ethers playground
Sizer Content
LIBRARY
SETTINGS
Experimental
Auto-Completion
Provide an inline popup while typing code making suggestion based on the current identifier-chain.
Pass-Through Provider
Allow access to any
window.ethereum
object via a pass-through provider.
Stored State
Preserve History
Locally store the command history between sessions.
Clear History
Reset Default Settings


When you want more customization than a pre-built theme offers, you can create your own theme file.
TrulyUI also allows you to perform the Runtime swapping of the themes you created. Just specify them in the
setTheme ()
function of the CoreService
service.
We recommend that you use Theme switching in Runtime when you create multiple themes. So your user can switch between them.
A custom theme file does three things:
- It does import builder that will perform the mixin of the color palettes
@import "~truly-ui/css/theming/builders/basic/index";
- Defines a theme data structure as the composition of multiple palettes. The Palette receives the name and its context variables (default, lighter, darker) in number(View the available palettes).
- The object sass is passed to the truly-basic-builder-theme mixin, along with the name that will be the representation of the theme in CoreModule, which will output all of the corresponding styles for the theme.
Example Single Theme
1 - Creates the Theme in style.scss
2 - in CoreModule passes to the theme key
the name of the theme defined in the styles.scss file
Example Multiple Theme
1 - Creates the Theme in style.scss
2 - in CoreModule passes to the theme key
the name of the theme defined in the styles.scss file


The first step is to connect to a MySQL database. Here we see the PHP class description of our database, class videoDB {
, which includes
your username, password, database name, and database host. Other components of the code make the class static, uncloneble, and deserializable.
A new instance of the class is created, instance = new PDO {
,which takes in the driver, password, database, and username. You can see
that $this
precludes the instance in this case, and it references the class code and acts like a local variable. In other tutorials, you may also see the use of
$conn
as the chosen variable name, $this
acts like $conn
in this video database example. The try/catch block encloses the connect code,
which provides an error message to you in the case that they connect to the database.
Here is the code using MySQLi, that accomplishes the same task of connecting to the database. Now we can see the difference of the PDO in contrast to MySQLi in the creation of the private constructor .
Note that in the PDO connection, the PDO::ERRMODE_EXCEPTION
. Exception error mode will send out exceptions as they occur, and has been modifed from the
default, PDO::ERRMODE_SILENT
. Manual error fetching with quieries would need to be carried out with the silent mode option. Lastly, there is an PDO::ERRMODE_WARNING
option with PDO, which sends out an error code and warning message.
PDO::ERRMODE_WARNING
PDO::ERRMODE_SILENT
PDO::ERRMODE_EXCEPTION
Onward to the next section in which we will learn to insert with PDO...


{% blocktrans trimmed %} We, Us, Our Django Girls Foundation, Registered Charity Number 1163260 whose address is United House, North Road, London N7 9DP {% endblocktrans %}
{% blocktrans %}You, Your Are a visitor to the Site{% endblocktrans %}
{% blocktrans %}Site djangogirls.org{% endblocktrans %}
{% trans "This Policy applies to our use of any and all Data collected by us in relation to your use of the Site." %}
{% trans "In this Policy the following terms shall have the following meanings:" %}
{% trans "This Policy applies only to the actions of you and us when using the Site. It does not extend to:" %}
{% trans "Without limitation, all or any of the following Data may be collected by the Site from time to time:" %}
{% trans "We will keep any personal Data you submit for 60 months." %}
{% blocktrans trimmed %} Unless we are obliged or allowed by law, and subject to “Third Party Sites and Services” below, we will not disclose your Data to third parties. This does not include the companies which form part of our group. {% endblocktrans %}
{% blocktrans trimmed %} All personal Data is stored securely in accordance with the principles of the Data Protection Act 1998 and the EU GDPR. {% endblocktrans %}
{% trans "We use your Data so as to give you the best possible service and experience when using the Site." %}
{% trans "Specifically, Data may be used by us for the following reasons:" %}
{% blocktrans trimmed %} We may, from time to time, use other parties for dealing with matters that may include, but are not limited to, payment processing, delivery of purchased items, search engine facilities, advertising and marketing. The providers of such services have access to some of your Data. {% endblocktrans %}
{% blocktrans trimmed %} Any Data used by such parties is used only to the extent required by them to perform the services that we’ve requested. Any use for other purposes is strictly prohibited. {% endblocktrans %}
{% blocktrans trimmed %} Any Data that is processed by third parties will be processed within the terms of this Policy and in accordance with the Data Protection Act 1998 and the EU GDPR. {% endblocktrans %}
{% blocktrans trimmed %} We may, from time to time, provide links to other websites. We have no control over such websites and can’t be responsible for their content. This Policy does not extend to those websites and we suggest that you read the privacy policy or statement on those websites before using them. {% endblocktrans %}
{% blocktrans trimmed %} We may, from time to time, expand or reduce our business and that may mean that the people who control our business change. If we do that your Data will, if relevant, be transferred to the people who then control the business. They will have the same rights and obligations as we have under the terms of this Policy. {% endblocktrans %}
{% trans "If we do transfer Data for this reason we will not tell you in advance." %}
{% trans "If we ask for Data from you, you will be given options to restrict our use of that Data. This may include the following:" %}
{% trans "We do not use any of your data to profile you." %}
{% trans "You may access certain areas of the Site without providing any Data at all but to use all features and functions available on the Site you may be required to submit certain Data." %}
{% trans "You may restrict your internet browser’s use of Cookies – we suggest that you look at the “Tools” setting for your internet browser. See below under “Cookies” for more information." %}
{% trans "You have the right to ask for a copy of any of your personal Data which we hold on payment of a small fee which will not exceed £10." %}
{% trans "Data security is of great importance to us and to protect your Data we have put in place various physical, electronic and managerial procedures to safeguard and secure Data collected via the Site." %}
{% trans "The Site may place and access certain Cookies on your computer. These are first party Cookies and we place them via the Site and only we use them. We use Cookies to improve your experience of using the Site and to improve our range of products and/or services. We choose the Cookies we use carefully and have taken steps to make sure that your privacy is always protected and respected." %}
{% trans "All Cookies used by the Site are used in accordance with current UK and EU Cookie Law." %}
{% trans "Certain features of the Site need Cookies for those features to work. UK and EU Cookie Law deems these Cookies to be “strictly necessary”. In those cases, we don’t ask for your consent to place them but you can still block these Cookies by changing your internet browser’s settings as detailed below." %}
{% trans "The Site uses analytics services provided by Google and others. “Analytics” is a set of tools used to collect and analyse how the Site is used which then helps us understand the needs of our Users. This, in turn, means that we can improve the Site and the products and/or services offered through it." %}
{% trans "You don’t have to let us use these analytic Cookies but when we use them your privacy and safe use of the Site are not at risk." %}
{% trans "The analytics service may place Cookies on your device immediately you visit the Site and it may not be possible to obtain your consent before it does. You can remove these Cookies and prevent future use of them by following the steps set out below:" %}
{% blocktrans trimmed %} We recommend that you make sure that your internet browser is up-to-date and that if you are not sure about anything you look at the help and guidance provided by your internet browser. {% endblocktrans %}
{% blocktrans trimmed %} We have the right to change this Policy as and when we decide from time to time or as may be required by law. Any changes will be immediately posted on the Site and you are deemed to have accepted the terms of this Policy on your first use of the Site following those changes. {% endblocktrans %}


End User Documentation:
· Enter http://www.onlinetryon.com/basic/web/ in your browser. ASIN Analyzer form will be appeared.
· Enter ASIN code as required, including exactly 10 characters from numbers and letters
· Select your desired domain from the combo box and click Analyze
· If your ASIN code is not approved, the alert message will be displayed
· The result will be displayed in the following table


public abstract class NativeObject
extends java.lang.Object
implements java.lang.Cloneable
| Modifier and Type | Field and Description |
|---|---|
protected java.lang.Object |
handleRef
A reference to a "handle".
|
protected int |
id
The ID of the object, usually depends on its type.
|
static int |
INVALID_ID |
protected NativeObjectManager |
objectManager
The object manager to which this NativeObject is registered to.
|
protected static int |
OBJTYPE_AUDIOBUFFER |
protected static int |
OBJTYPE_AUDIOSTREAM |
protected static int |
OBJTYPE_FILTER |
protected static int |
OBJTYPE_FRAMEBUFFER |
protected static int |
OBJTYPE_SHADER |
protected static int |
OBJTYPE_SHADERSOURCE |
protected static int |
OBJTYPE_TEXTURE |
protected static int |
OBJTYPE_VERTEXBUFFER |
protected boolean |
updateNeeded
True if the data represented by this GLObject has been changed
and needs to be updated before used.
|
| Modifier | Constructor and Description |
|---|---|
|
NativeObject()
Creates a new GLObject with the given type.
|
protected |
NativeObject(int id)
Protected constructor that doesn't allocate handle ref.
|
| Modifier and Type | Method and Description |
|---|---|
void |
clearUpdateNeeded()
Internal use only.
|
protected NativeObject |
clone()
This should create a deep clone.
|
abstract NativeObject |
createDestructableClone()
Creates a shallow clone of this GL Object.
|
protected void |
deleteNativeBuffers()
Deletes any associated native
buffers . |
abstract void |
deleteObject(java.lang.Object rendererObject)
Deletes the GL object from the GPU when it is no longer used.
|
void |
dispose()
Reclaims native resources used by this NativeObject.
|
int |
getId() |
abstract long |
getUniqueId()
Returns a unique ID for this NativeObject.
|
boolean |
isUpdateNeeded()
Internal use only.
|
abstract void |
resetObject()
Called when the GL context is restarted to reset all IDs.
|
void |
setId(int id)
Sets the ID of the NativeObject.
|
void |
setUpdateNeeded()
Internal use only.
|
java.lang.String |
toString() |
public static final int INVALID_ID
protected static final int OBJTYPE_VERTEXBUFFER
protected static final int OBJTYPE_TEXTURE
protected static final int OBJTYPE_FRAMEBUFFER
protected static final int OBJTYPE_SHADER
protected static final int OBJTYPE_SHADERSOURCE
protected static final int OBJTYPE_AUDIOBUFFER
protected static final int OBJTYPE_AUDIOSTREAM
protected static final int OBJTYPE_FILTER
protected NativeObjectManager objectManager
protected int id
protected java.lang.Object handleRef
protected boolean updateNeeded
public NativeObject()
type
- The type that the subclass represents.protected NativeObject(int id)
public void setId(int id)
id
- The ID to setpublic int getId()
public void setUpdateNeeded()
public void clearUpdateNeeded()
public boolean isUpdateNeeded()
setUpdateNeeded()
was called before.public java.lang.String toString()
toString
in class java.lang.Object
protected NativeObject clone()
clone
in class java.lang.Object
protected void deleteNativeBuffers()
buffers
.
This is necessary because it is unlikely that native buffers
will be garbage collected naturally (due to how GC works), therefore
the collection must be handled manually.
Only implementations that manage native buffers need to override
this method. Note that the behavior that occurs when a
deleted native buffer is used is not defined, therefore this
method is protectedpublic abstract void resetObject()
public abstract void deleteObject(java.lang.Object rendererObject)
rendererObject
- The renderer to be used to delete the objectpublic abstract NativeObject createDestructableClone()
public abstract long getUniqueId()
public void dispose()
NativeObjectManager.UNSAFE
is
set to true, in that case native buffers are also reclaimed which may
introduce instability.


|
My Project
|
The class for looping through SpriteSheet frames built on ImageBase. More...
#include <animation.h>
Public Member Functions | |
| Animation (SpriteSheet *images, int frame, int *frames, int num_frames, float speed=1) | |
| Constructor of Animation. More... | |
| void | setX (float x) |
| Set the x position of the Animation SpriteSheet. More... | |
| void | setY (float y) |
| Set the y position of the Animation SpriteSheet. More... | |
| float | getX () |
| Get the x position of the Animation SpriteSheet. More... | |
| float | getY () |
| Get the y position of the Animation SpriteSheet. More... | |
| void | setFrame (int frame) |
| Set the frame for the Animation to show. More... | |
| int | getFrame () |
| Get the frame the Animation is showing. More... | |
| void | resetAnimation () |
| set the frame back to 0 More... | |
| void | addTime (float time) |
| add the amount of time passed so that the Animation can update. More... | |
| SpriteSheet * | getSpriteSheet () |
| Get a pointer to the SpriteSheet. More... | |
| SDL_Texture * | getTexture () |
| Get a pointer to the SpriteSheet texture. More... | |
| SDL_Rect * | getDestRect () |
| Get a pointer to the destination rect used for positioning with the renderer. More... | |
| SDL_Rect * | getSrcRect () |
| Get a pointer to the source rect used for getting the frame of the SpriteSheet. More... | |
| Public Member Functions inherited from blackhole::graphics::ImageBase | |
| virtual void | setFlipX (bool flipped) |
| Overridable function for flipping ImageBase across the x plane. More... | |
| virtual void | setFlipY (bool flipped) |
| Overridable function for flipping ImageBase across the y plane. More... | |
| virtual bool | isFlippedX () |
| Overridable function for checking if ImageBase is flipped across the x plane. More... | |
| virtual bool | isFlippedY () |
| Overridable function for checking if ImageBase is flipped across the y plane. More... | |
| virtual void | setTexture (SDL_Texture *tex) |
| Overridable function for setting ImageBase texture generally used in the Camera class. More... | |
| virtual SDL_RendererFlip | getRendererFlip () |
| Overridable function for getting the rendererflip for the ImageBase. Used for rendering. More... | |
| virtual void | setLayer (int layer) |
| Set the layer to be rendered on. 0 - low layer. 100 - high layer. More... | |
| virtual int | getLayer () |
| Get the layer ImageBase is on. More... | |
Additional Inherited Members | |
| Protected Member Functions inherited from blackhole::graphics::ImageBase | |
| void | init (const char *file, SDL_Renderer *renderer) |
| Protected Attributes inherited from blackhole::graphics::ImageBase | |
| SDL_RendererFlip | rendererFlip = SDL_FLIP_NONE |
| int | layer = 0 |
| SDL_Rect | destRect |
| SDL_Texture * | texture |
The class for looping through SpriteSheet frames built on ImageBase.
| blackhole::graphics::Animation::Animation | ( | SpriteSheet * | images, |
| int | frame, | ||
| int * | frames, | ||
| int | num_frames, | ||
| float | speed = 1 |
||
| ) |
Constructor of Animation.
| images | The pointer to the SpriteSheet used for frames |
| frame | The starting frame |
| frames | The order of frames for the animation eg. 0, 2, 1, 2 |
| num_frames | The number of frames in the animation eg. 4 |
| speed | The amount of time between frames |
|
virtual |
add the amount of time passed so that the Animation can update.
| time | The amount of time passed in the frame |
Reimplemented from blackhole::graphics::ImageBase.
|
virtual |
Get a pointer to the destination rect used for positioning with the renderer.
Reimplemented from blackhole::graphics::ImageBase.
| int blackhole::graphics::Animation::getFrame | ( | ) |
Get the frame the Animation is showing.
| SpriteSheet* blackhole::graphics::Animation::getSpriteSheet | ( | ) |
Get a pointer to the SpriteSheet.
|
virtual |
Get a pointer to the source rect used for getting the frame of the SpriteSheet.
Reimplemented from blackhole::graphics::ImageBase.
|
virtual |
Get a pointer to the SpriteSheet texture.
Reimplemented from blackhole::graphics::ImageBase.
|
virtual |
Get the x position of the Animation SpriteSheet.
Reimplemented from blackhole::graphics::ImageBase.
|
virtual |
Get the y position of the Animation SpriteSheet.
Reimplemented from blackhole::graphics::ImageBase.
| void blackhole::graphics::Animation::resetAnimation | ( | ) |
set the frame back to 0
| void blackhole::graphics::Animation::setFrame | ( | int | frame | ) |
Set the frame for the Animation to show.
| frame | The frame you want the Animation to show |
| void blackhole::graphics::Animation::setX | ( | float | x | ) |
Set the x position of the Animation SpriteSheet.
| x | The x position you want the sprite to render on |
| void blackhole::graphics::Animation::setY | ( | float | y | ) |
Set the y position of the Animation SpriteSheet.
| y | the y position you want the sprite to render on |


Provide, as per TEP101, Resource-based access to the Atmega128 ADC via a ReadNow interface. Users of this component must link it to an implementation of Atm128AdcConfig which provides the ADC parameters (channel, etc).


A super secure file storage system. Really! We can't even read what you upload! Maybe the NSA can, but most people can't! Protect your data today.


THE RED TENT A film review by Shane R. Burridge Copyright 1995 Shane R. Burridge
(1971) 121 min
General Umberto Nobile (Peter Finch), an explorer whose airship, the ITALIA, crashed during a 1928 expedition across the Arctic, is visited 40 years later by spirits from his past who have gathered to judge him. These include Hardy Krueger as a mercenary pilot, Sean Connery as Roald Amundsen, and Claudia Cardinale, who looks as if she just dropped by on her way back from the hairdressers (Connery gets top billing but appears in only three scenes). This lengthy pre-credits sequence serves as a framing device for what is essentially a based-on-fact adventure tale that comprises the bulk of the movie. Film concludes with spirits - who are by now recognisable as key characters in the story - delivering their verdict upon Nobile.
Watching this film I can't help think that the producers must have fought the temptation to release two versions - one as it stands, and the other with all the present-day footage removed. This latter cut would certainly concentrate more on the dramatic survival scenes. But the film-makers seem to want to re-open the file on Nobile's story; to exonerate him somehow by presenting perspectives of history as seen then-and-now. Flashback structure seems ill-fitting because events from the past are not told exclusively from Finch's viewpoint, placing viewers in the position of accepting the spirits of the present , who are surely providing him with the full story, as real and not merely conjurations of his guilty subconscious. Why go to all the trouble of providing newsreel archives and authentic locations to establish a realistic atmosphere just to undermine its credibility with a fantasy framework? Fortunately, the arctic landscapes are all too real, and a good anchor back to the world of fact: they are vast, beautiful, and foreboding, where human figures are reduced to black dots on the ice.
Film works best when it shows men grappling against the elements with barely-adequate technology. The scenes with the icebreaker, airplanes, and airship are all convincingly recreated - the initial crash makes for a gripping sequence. Strongest feature of any true-life survival film is that there can be no calculated script decisions over who will live and who will die, so (unless you've read up on your history) there's no sure way to know the outcome. Film's eccentric atmosphere will likewise keep you offguard. Movie buffs may find it quirky to see Krueger and Finch playing a scene together inside airship wreckage in the middle of the Arctic only six years after they were similarly stuck in the middle of the desert in THE FLIGHT OF THE PHOENIX. Question to consider: If Nobile's expedition had been a success, would anybody have bothered to make a film about it?
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
belongs to the author.
Please direct comments/criticisms of the review to relevant newsgroups.
Broken URLs inthe reviews are the responsibility of the author.
The formatting of the review is likely to differ from the original due
to ASCII to HTML conversion.
Related links: index of all rec.arts.movies.reviews reviews


All Classes
Packages
communications
communications.util
compose
composition
delays
dsl
flux
function
primitives
protocols
segmentation
shares
tcp
thread
timeout
udp
xmpp


JPDH exposes Windows performance data helper (PDH) in the Java programming language, and provides some additional features that should have been in PDH but are not, such as the possibility to refer to processes by PID instead of process instance name (process instance name can change while PID can't).
JPDH is orders of magnitude faster than WMI and is a good alternative to the latter when performance is critical.
JPDH has been integrated as an alternative to WMI in the JRDS performance collector, where it provides much faster response times.
Here's JPDH in action.
import com.arkanosis.jpdh.Counter;
import com.arkanosis.jpdh.JPDH;
import com.arkanosis.jpdh.Query;
try (Query query = JPDH.openQuery()) {
Counter diskCounter = query.addCounter("\\PhysicalDisk(_Total)\\Disk Read Bytes/sec");
Counter cpuCounter = query.addCounter("\\PID(8584)\\% User Time");
query.collectData();
System.out.println(diskCounter.getDoubleValue());
System.out.println(cpuCounter.getIntegerValue());
query.removeCounter(diskCounter);
}
With Maven:
<dependency>
<groupId>com.arkanosis</groupId>
<artifactId>jpdh</artifactId>
<version>1.1.4</version>
</dependency>
With Gradle:
compile("com.arkanosis:jpdh:1.1.4")
JPDH is Copyright (c) 2016-2019, Jérémie Roquet. JPDH is provided under the terms of the BSD 2-clause license.


Attn: President/CEO/SIR/MADAM REQUEST FOR URGENT BUSINESS RELATIONSHIP -STRICTLY CONFIDENTIAL Firstly, I must solicit your confidentiality. This is by virtue of it'snature as being utterly "confidential" and "TOP SECRET". Though I know that a transaction of this magnitude will make anyone apprehensive and worried,but I am assuring you that all will be well at the end of the day. A bold step taken shall not be regretted I assure you. I am Mr iyang and I head a seven man tender board in charge of contract awards and payment approvals. I came to know of you in my search for a reliable and reputable person to handle a very confidential business transaction,which involves the transfer of a huge sum of money to a foreign account requiring maximum confidence. My colleagues and I are top official of Federal Government Contract Review and Award Panel. Our duties include evaluation, vetting, and approval for payment of contract jobs done for the Federal Ministry of Aviation(FMA) etc. We are therefore soliciting for your assistance to enable us transfer into your account the said funds. Our country looses a lot of money everyday that is why the international community is very careful and warning their citizens to be careful but I tell you "a trial will convince you".The source of the fund is as follow: During the last military regime here in Nigeria, this committee awarded a contract of US$400 Million to a group of five construction firms on behalf of the Federal Ministry of Aviation (FMA)for the supply and installation of landing and navigational equipment in Lagos and Port Harcourt International Airports. During this process my colleagues and I decided among us to deliberately over inflate the total contract sum of US$420.5 Million with the aim of sharing the remaining sum of US$20.5 million.The government has since approved the sum of US$420.5Million for us as the contract sum, but since the contract is only worth US$400M the remaining US$20.5 Million is what we intend to transfer to a reliable and safe offshore account,we are prohibited to operate foreign account in our names since we are still in Government. Thus, making it impossible for us to acquire the money in our name right now, I have therefore been delegated as a matter of trust by my colleagues to look for an oversea partner into whose account we can transfer the sum of US$20.5Million.My colleagues and I have decided that if you/your Company can as the beneficiary of this funds on our behalf, you or your company will retain 20% of the total amount (US$20.5Million) while 75% will be for us (officials) and the remaining 5% will be used in offsetting all debts/expenses incurred during this transaction We have decided that this transaction can only proceed under the following condition (a) Our conviction of your transparent honesty and that you treat this transaction with utmost secrecy and confidentiality (b) That upon Receipt of the funds you will release the funds as instructed by us after you've removed your share of 20%. Please acknowledge the receipt of this letter using the above e-mail address. I will bring you into the complete of this transaction when I've heard from you. Your urgent response will be highly appreciated as we are already behind schedule for the financial quarter. Please do be informed that his business transaction is 100% legal and completely free from drug or money laundering. Only trust can make the reality of this transaction. Best regards, Mr iyang


Best cookies for you and your family
don't forget to order our other products
Where you can find us
-
Seattle
113 Cherry St, Seattle, WA 98104-2205, US
-
Tokyo
Kyobashi MID Bldg., 13-10, Kyobashi 2-chome, Chuo-ku, Tokyo
-
Dubai
Sheikh Mohamed Bin Rashid Boulevard, Downtown Dubai
-
Paris
172 boulevard Saint Germain, 75006 Paris, France
-
Lima
Calle de la Prosa No. 104, San Borja, Lima 41
always happy to serve you every day from 6am-7pm
feel free to contact us on (06) 582 7051 or hi@salmoncookies.com


Plugin Information |
|---|
View Nested View on the plugin site for more information. |
Older versions of this plugin may not be safe to use. Please review the following warnings before using an older version:
This plugin is up for adoption. Want to help improve this plugin? Click here to learn more!
View type to allow grouping job views into multiple levels instead of one big list of tabs.
This plugin adds a new view type that can be selected when adding job views. This view does not show any jobs, but rather contains another set of views. By default, clicking on the view tab for a nested view shows a list of the subviews it contains (with folder icons). You can also configure a default subview to bypass this intermediate page and jump directly to that default view. Now the view tabs across the top show the views in this nested view, and the job list is for this default subview. This streamlines the navigation between views, but makes it harder to find the Edit View link for the nested view itself. Once a default subview has been assigned, navigate to the edit page by first clicking the plus ("+") icon in the view tabs (for adding a new subview) and then find the Edit View link in the sidepanel.
Note, that starting from 1.10 columns Status and Weather are switched off by default because of issues with performance, but if you'd like to have them, just edit your view and add respective columns.
Change Log
Version 1.14 (November 23, 2013)
- Improve breadcrumb list behavior (JENKINS-19698,JENKINS-20616)
Version 1.13 (November 5, 2013)
- Fixed view results to be gray if no jobs were run in subviews yet (issue #17040)
- Fixed deleting of subviews (it didn't save on disk) (issue #20382)
Version 1.12 (October 30, 2013)
- Fixed view results (aggregating of subviews and jobs) to follow the correct priority - if at least one job is red, then the view will be red (issue #19716)
Version 1.11 (September 25, 2013)
- Fixed creation of subviews if Nested View is the default one (JENKINS-17041)
- Support checking duplicate names with jenkins 1.512 or newer (JENKINS-18053)
Version 1.10 (April 7, 2013)
- Added an ability to remove or add Weather and Status columns which speeds up nested views (JENKINS-17352)
Version 1.9 (August 16, 2012)
- Improved performance and accuracy when computing worst result and health for large views
- add support for hierarchical job model (ex : cloudbees folder plugin)
- display grey icon when no nested content has been built
Version 1.8 (Jul 28, 2011)
- Properly check for duplicate names within the current nested view when creating a new view (in Jenkins 1.416 or newer).
- Ignore disabled project when calculating worst build result in a nested view. (JENKINS-8922)
Version 1.7 (Feb 25, 2011)
- Rerelease 1.6 to properly set required Jenkins version.
Version 1.6 (Feb 16, 2011)
- Show status icon for each view, representing the worst result of any job in that view. (JENKINS-7124)
- Fix link in empty nested view to be a link instead of raw HTML. (JENKINS-7862)
Version 1.5 (Oct 20, 2010)
- Update to work with Hudson 1.381 or higher. (JENKINS-7815)
- Calculate view-health for a nested view by recursing into its subviews. (JENKINS-7125)
- Export nested view details via Remote API. (JENKINS-7489)
- Don't allow setting a default subview for a nested view that is the Hudson default view, as this prevents access to other top level view tabs. (JENKINS-7362)
Version 1.4 (Jul 12, 2010)
- Update to work with Hudson 1.366 or higher.
Version 1.3 (Jun 16, 2010)
- Sort subviews alphabetically. (JENKINS-6757)
- Russian translation.
Version 1.2 (May 27, 2010)
- Add weather icons in nested view. (JENKINS-6235)
Version 1.1 (Mar 22, 2010)
- Spanish translation.
Version 1.0 (Jan 21, 2010)
- Initial release.


This page tracks the new paper links made to my list of SPM 2006 papers.
Page maintained by Ke-Sen Huang.
If you have additions or changes, send an e-mail.
November 29 2009
- Added ACM DOI link for each paper
- Updated some author and paper links
November 30, 2006
August 17, 2006
July 18, 2006
June 24, 2006
June 16, 2006
- Page modified and published
June 15, 2006
- Page created by Roy Walmsley
kesen.huang@gmail.com


Using pgBagder and logsaw for scheduled reports
Hey,
While waiting for next version of pgBadger, here is a tip to create scheduled pgBadger report. For this demo, I’ll suppose:
- we have PostgreSQL’s log files in
/var/log/pgsql
- we want to produce a weekly report using pgBadger with the “postgres” system user…
- …so we keep at least 8 days of log
You will need pgbadger and logsaw. Both tools are under BSD license and pure perl script with no dependencies.
“logsaw” is a tool aimed to parse log files, looking for some regexp-matching lines, printing them to standard output and remembering where it stop last time. At start, it searches for the last line it parsed during its last call, and starts working from there. Yes, for those familiar with “tail_n_mail”, it does the same thing, but without the mail and report processing part. Moreover, it supports rotation and compression of log files (not sure about tail_n_mail). Thanks to this tool, we’ll be able to create new reports from where the last one finished!
We need to create a simple configuration file for logsaw:
There’s three more optional parameters in this configuration file you might want to know:
- if you want to process some particular files, you can use the
LOGFILES
parameter, a regular expression to filter files in theLOGDIR
folder. When not defined, the default empty string means: «take all files in the folder». - if your log files are compressed, you can use the
PAGER
parameter which should be a command that uncompress your log files to standard output, eg.PAGER=zcat -f
. Note that if available, logsaw will use silently IO::Zlib to read your compressed files. - you can filter extracted lines from log files using the
REGEX
parameters. You can add as manyREGEX
parameter than needed, each of them must be a regular expression. When not defined, the default emptyREGEX
array means: «match all the lines». - each time you call logsaw, it saves its states to your configuration file,
adding parameters
FILEID
andOFFSET
.
That’s it for logsaw. See its README files for more details, options and sample configuration file.
Now, the command line to create the report using pgbadger:
You might want to add the -f
option to pgbadger if it doesn’t
guess the log format itself (stderr or syslog or csv).
About creating reports on a weekly basis, let’s say every sunday at 2:10am, using crontab:
Here you go, enjoy :)
That’s why I like UNIX style and spirit commands: simple single-task but powerful and complementary tools.
Wait or help for more nice features in pgBadger !
Cheers !
PS: There’s another tool to deal with log files and reports you might be interested in: “logwatch”
Comments Leave a comment by email or Disqus
Note: I only publish your name/pseudo, mail subject and content. I will NOT publish your email address.
About creating reports with just pgbadger 2.x on a weekly basis, like in your example using crontab:


This help manual is aimed at providing the NGO's administration with a guideline on using the NIMS for visualising and analysing the collected data in an efficient way. The administration can also keep track of the coordinators, projects and their progress over time in a lucid manner.
NIMS is a web-based information management system that will help the NGO in cataloguing their work in various fields in an efficient and hassle-free manner. This will aid the NGO in proper management of its resources and the field data collected with the help of a server-based system. The NGO personnel can analyze and archive data for future use. NIMS also provides a unique feature called social mapping which enables visualization of the different field areas of the NGO and related information. Social maps will give an explicit idea to the NGO about the various ongoing and upcoming tasks in various areas and the area conditions on a visualized map. This information management system will also allow the coordinators of the NGO to better understand the work in progress and tasks at hand in an organized and statistical manner, enabling effective and economical distribution of the NGO's resources.
To start using NIMS, you have to log in with your unique username/email and password by clicking on the "Sign In" button at the top, right side of the login page.
Once you have successfully logged in, you can see the following home page. You can navigate through the web site interface by clicking on the tabs in the navigation bar at the top of the page.
Information collected about a community can be viewed by clicking on that community in the sidebar list of communities on the Communities tab. This tab gives a statistical (pie charts/graphs) as well as general basic information about each community.
On the Coordinator's tab, by clicking on a coordinator in the sidebar list of coordinators, you can view that particular coordinator's performance, work agenda and progress and also their general information and contact details.
On the Projects tab, by dragging the timeline to the left or right, you can view the timelines of various projects undertaken by the NGO. Clicking on a specific project's timebar gives you detailed information about that project.
The Settings tab enables you to change your password, add/edit a coordinator's profile or even change the database by selecting among the choices in the sidebar menu.
To logout, just click on the Logout tab and it will take you to this logout confirmation page:
Q) What is NIMS?
A) A project to enable a NGO to carry out efficient information collection, visualization and interactive mapping in order
to make well informed decisions and give out efficient work results.
Q) Who is behind NIMS?
A) NIMS is being developed by Team 16 -
Aakash Solanki
Lalit Agarwal
Anshul Agrawal
Megha Tak
Parth Manvar
Palashi Vaghela
Surbhi Agarwal
Jesal Janani
Jatan Patel
Q) I would like to see a new feature in NIMS. Can I have any contact details?
A) Send us an email requesting the feature at<EMAIL_ADDRESS>and we might consider adding it.


订单总数
{$order_count}后台用户总数
{$user_count}商品数量
{$goods_count}Total Projects
73Payments
| Products | Agent | Sales | Earnings | Technology |
|---|---|---|---|---|
| Envato | Manoj Kumar | 390 | $400 | PHP |
| Themeforest | John Deo | 70 | $3670 | Java |
| Codecanyon | Yokahona | 150 | $1400 | Angular Js |
| Videohive | Yokahona | 150 | $1400 | Python |
| Themeforest | Yokahona | 150 | $1400 | Dot Net |
| Envato | Yokahona | 150 | $1400 | Angular Js |


LS.Scene Class
The Scene contains all the info about the Scene and nodes
Constructor
LS.Scene
()
Item Index
Methods
- addPreloadResource
- clear
- collectData
- configure
- createAnimation
- filterNodes
- findComponentByUId
- findMaterialByUId
- findNodeComponents
- finish
- generateUniqueNodeName
- getActiveCameras
- getActiveLights
- getAllCameras
- getLayerNames
- getNode
- getNodeByIndex
- getNodeByName
- getNodeByUId
- getNodes
- getPropertyInfo
- getPropertyInfoFromPath
- getPropertyValue
- getResources
- getTime
- init
- instantiate
- load
- loadFromResources
- loadResources
- pause
- recomputeNodesArray
- removePreloadResource
- render
- requestFrame
- Scene.getScriptsList
- serialize
- setFromJSON
- setPropertyValue
- setPropertyValueFromPath
- start
- toPack
- triggerInNodes
- unpause
- update
Methods
addPreloadResource
-
fullpath
Adds a resource that must be loaded when the scene is loaded
Parameters:
-
fullpath
Stringthe name of the resource
clear
()
Clears the scene using the init function and trigger a "clear" LEvent
collectData
-
cameras
This methods crawls the whole tree and collects all the useful info (cameras, lights, render instances, colliders, etc) Mostly rendering stuff but also some collision info. TO DO: refactor this so it doesnt redo the same task in every frame, only if changes are made
Parameters:
-
cameras
Array[optional] an array of cameras in case we want to force some viewpoint
configure
-
scene_info
Configure the Scene using an object (the object can be obtained from the function serialize) Inserts the nodes, configure them, and change the parameters ATTENTION: Destroys all previously existing info
Parameters:
-
scene_info
Objectthe object containing all the info about the nodes and config of the scene
createAnimation
()
LS.Animation
Creates and returns an scene animation track
Returns:
the animation track
filterNodes
-
filter
retrieves a node array filtered by the filter function
Parameters:
-
filter
Functiona callback function that receives every node and must return true or false
Returns:
array containing the nodes that passes the filter
findComponentByUId
-
uid
searches the component with this uid, it iterates through all the nodes and components (slow)
Parameters:
-
uid
Stringuid of the node
Returns:
component or null
findMaterialByUId
-
uid
searches the material with this uid, it iterates through all the nodes (slow)
Parameters:
-
uid
Stringuid of the material
Returns:
Material or null
findNodeComponents
-
type
returns an array with all the components in the scene and scenenodes that matches this class
Parameters:
-
type
String | | Componentthe type of the components to search (could be a string with the name or the class itself)
Returns:
array with the components found
finish
-
dt
stop the scene (triggers an "finish" event)
Parameters:
-
dt
Numberdelta time
generateUniqueNodeName
-
prefix
generate a unique node name given a prefix
Parameters:
-
prefix
Stringthe prefix, if not given then "node" is used
Returns:
a node name that it is not in the scene
getActiveCameras
-
force
Returns an array with all the cameras enabled in the scene
Parameters:
-
force
Boolean[optional] if you want to collect the cameras again, otherwise it returns the last ones collected
Returns:
cameras
getActiveLights
-
force
Returns an array with all the lights enabled in the scene
Parameters:
-
force
Boolean[optional] if you want to collect the lights again, otherwise it returns the last ones collected
Returns:
lights
getAllCameras
()
Array
Returns an array with all the cameras in the scene (even if they are disabled)
Returns:
cameras
getLayerNames
-
layers
returns an array with the name of all the layers given a layers mask
Parameters:
-
layers
Numbera number with the enabled layers in bit mask format, if ommited all layers are returned
Returns:
array of strings with the layer names
getNode
-
name
retrieves a Node based on the name, path ( name|childname|etc ) or uid
Parameters:
-
name
Stringnode name to search
Returns:
the node or null if it didnt find it
getNodeByIndex
-
node
retrieves a Node by its index
Parameters:
-
node
Numberindex
Returns:
returns the node at the 'index' position in the nodes array
getNodeByName
-
name
retrieves a Node that matches that name. It is fast because they are stored in an object. If more than one object has the same name, the first one added to the tree is returned
Parameters:
-
name
Stringname of the node
Returns:
the node or null if it didnt find it
getNodeByUId
-
uid
retrieves a Node based on a given uid. It is fast because they are stored in an object
Parameters:
-
uid
Stringuid of the node
Returns:
the node or null if it didnt find it
getNodes
-
recompute
Returns the array containing all the nodes in the scene
Parameters:
-
recompute
Bool[optional] in case you want to rearrange the nodes
Returns:
array containing every SceneNode in the scene
getPropertyInfo
-
locator
Returns information of a node component property based on the locator of that property Locators are in the form of "{NODE_UID}/{COMPONENT_UID}/{property_name}"
Parameters:
-
locator
Stringlocator of the property
Returns:
object with node, component, name, and value
getPropertyInfoFromPath
-
path
Returns information of a node component property based on the locator of that property Locators are in the form of "{NODE_UID}/{COMPONENT_UID}/{property_name}"
Parameters:
-
path
Array
Returns:
object with node, component, name, and value
getPropertyValue
-
locator
-
value
-
root
Assigns a value to the property of a component in a node based on the locator of that property Locators are in the form of "{NODE_UID}/{COMPONENT_UID}/{property_name}"
Parameters:
-
locator
Stringlocator of the property
-
value
the value to assign
-
root
SceneNode[Optional] if you want to limit the locator to search inside a node
Returns:
the target where the action was performed
getResources
-
resources
-
as_array
-
skip_in_pack
-
skip_local
Returns the resources used by the scene includes the nodes, components, preloads and global_scripts doesn't include external_scripts
Parameters:
-
resources
Object[optional] object with resources
-
as_array
Boolean[optional] returns data in array format instead of object format
-
skip_in_pack
Boolean[optional] skips resources that come from a pack
-
skip_local
Boolean[optional] skips resources whose name starts with ":" (considered local resources)
Returns:
the resources in object format (or if as_array is true, then an array)
getTime
()
Number
returns current scene time (remember that scene time remains freezed if the scene is not playing)
Returns:
scene time in seconds
init
()
Boolean
This initializes the content of the scene. Call it to clear the scene content
Returns:
Returns true on success
instantiate
-
prefab_url
-
position
-
rotation
-
parent
Allows to instantiate a prefab from the fullpath of the resource
Parameters:
-
prefab_url
Stringthe filename to the resource containing the prefab
-
position
Vec3where to instantiate
-
rotation
Quatthe orientation
-
parent
SceneNode[optional] if no parent then scene.root will be used
Returns:
the resulting prefab node
load
-
url
-
[on_complete=null]
-
[on_error=null]
-
[on_progress=null]
-
[on_resources_loaded=null]
Loads a scene from a relative url pointing to a JSON description (or WBIN,ZIP) Warning: this url is not passed through the LS.ResourcesManager so the url is absolute
Parameters:
-
url
Stringwhere the JSON object containing the scene is stored
-
[on_complete=null]
Function optionalthe callback to call when the loading is complete
-
[on_error=null]
Function optionalthe callback to call if there is a loading error
-
[on_progress=null]
Function optionalit is called while loading the scene info (not the associated resources)
-
[on_resources_loaded=null]
Function optionalit is called when all the resources had been loaded
loadFromResources
-
url
-
[on_complete=null]
-
[on_error=null]
-
[on_progress=null]
-
[on_resources_loaded=null]
Loads a scene from a relative url pointing to a JSON description (or WBIN,ZIP) It uses the resources folder as the root folder (in comparison with the regular load function)
Parameters:
-
url
Stringwhere the JSON object containing the scene is stored
-
[on_complete=null]
Function optionalthe callback to call when the loading is complete
-
[on_error=null]
Function optionalthe callback to call if there is a loading error
-
[on_progress=null]
Function optionalit is called while loading the scene info (not the associated resources)
-
[on_resources_loaded=null]
Function optionalit is called when all the resources had been loaded
loadResources
-
on_complete
Loads all the resources of all the nodes in this scene it sends a signal to every node to get all the resources info and load them in bulk using the ResourceManager
Parameters:
-
on_complete
Functioncalled when the load of all the resources is complete
pause
()
pauses the scene (triggers an "pause" event)
recomputeNodesArray
()
all nodes are stored in an array, this function recomputes the array so they are in the right order in case one has changed order
removePreloadResource
-
fullpath
Remove a resource from the list of resources to preload
Parameters:
-
fullpath
Stringthe name of the resource
render
()
renders the scene using the assigned renderer
requestFrame
()
Marks that this scene must be rendered again
Scene.getScriptsList
-
scene
-
allow_local
-
full_paths
Static method, returns a list of all the scripts that must be loaded, in order and with the full path
Parameters:
-
scene
Scene | Objectthe object containing info about the scripts (could be a scene or a JSON object)
-
allow_local
Booleanif we allow local resources
-
full_paths
Booleanif true it will return the full path to every resource
serialize
()
Object
Creates and object containing all the info about the scene and nodes. The oposite of configure. It calls the serialize method in every node
Returns:
return a JS Object with all the scene info
setFromJSON
-
data
-
[on_complete=null]
-
[on_error=null]
-
[on_progress=null]
-
[on_resources_loaded=null]
-
[on_scripts_loaded=null]
Assigns a scene from a JSON description (or WBIN,ZIP)
Parameters:
-
data
StringJSON object containing the scene
-
[on_complete=null]
Function optionalthe callback to call when the scene is ready
-
[on_error=null]
Function optionalthe callback to call if there is a loading error
-
[on_progress=null]
Function optionalit is called while loading the scene info (not the associated resources)
-
[on_resources_loaded=null]
Function optionalit is called when all the resources had been loaded
-
[on_scripts_loaded=null]
Function optionalthe callback to call when the loading is complete but before assigning the scene
setPropertyValue
-
locator
-
value
-
root
Assigns a value to the property of a component in a node based on the locator of that property Locators are in the form of "{NODE_UID}/{COMPONENT_UID}/{property_name}"
Parameters:
-
locator
Stringlocator of the property
-
value
the value to assign
-
root
SceneNode[Optional] if you want to limit the locator to search inside a node
Returns:
the target where the action was performed
setPropertyValueFromPath
-
path
-
value
-
root_node
-
offset
Assigns a value to the property of a component in a node based on the locator that property Locators are in the form of "{NODE_UID}/{COMPONENT_UID}/{property_name}"
Parameters:
-
path
Arraya property locator split by "/"
-
value
the value to assign
-
root_node
SceneNode[optional] the root node where you want to search the locator (this is to limit the locator to a branch of the scene tree)
-
offset
Number[optional] used to avoir generating garbage, instead of slicing the array every time, we pass the array index
Returns:
the target where the action was performed
start
-
dt
start the scene (triggers an "start" event)
Parameters:
-
dt
Numberdelta time
toPack
-
fullpath
-
resources
returns a pack containing all the scene and resources, used to save a scene to harddrive
Parameters:
-
fullpath
Stringa given fullpath name, it will be assigned to the scene with the appropiate extension
-
resources
Array[optional] array with all the resources to add, if no array is given it will get the active resources in this scene
Returns:
the pack
triggerInNodes
-
event_type
-
data
triggers an event to all nodes in the scene this is slow if the scene has too many nodes, thats why we use bindings
Parameters:
-
event_type
Stringevent type name
-
data
Objectdata to send associated to the event
unpause
()
unpauses the scene (triggers an "unpause" event)
update
-
dt
updates the scene (it handles variable update and fixedUpdate)
Parameters:
-
dt
Numberdelta time in seconds
Events
afterUpdate
Fired after updating the scene
beforeLoad
Fired before loading scene
change
Fired when something changes in the scene
Event Payload:
-
scene_info
Objectcontains all the info to do the configuration
clear
Fired when the whole scene is cleared
configure
Fired after the scene has been configured
Event Payload:
-
scene_info
Objectcontains all the info to do the configuration
fixedUpdate
Fired while updating but using a fixed timestep (1/60)
Event Payload:
-
dt
Number
load
Fired when the scene has been loaded but before the resources
loadCompleted
Fired after all resources have been loaded
serialize
Fired after the scene has been serialized to an object
Event Payload:
-
object
Objectto store the persistent info
update
Fired while updating
Event Payload:
-
dt
Number


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