An example of a class in Ruby
Date: May 23rd, 2014
One of the things I got to do in my last career was manufacturing stents. The basics of a stent is to open up a passage way in an artery that has been clogged. Here's a picture of a stent.
At this last job, our company got an order for a shipment of stents and as engineers, it would be our jobs to figure out how to make this shipment of stents. Usually we're given a design (Computer-aided design or CAD file) and we would turn it into something we could manufacture using CNC machines (developing a computer-aided manufacturing, or CAM file). There are tons of other aspects here for the minute details of manufacturing.
One of the things I got to do was create lead times for our customers to see how long it would take to create a shipment of stents given the material type and the amount they wanted. There were a lot more parameters, but to make this example easier, I choose two things everyone could understand. The material for stents remains predominately a metal polymer, although bioabsorbable polymers are starting to come into the market. The amount is usually in large batches in the thousands.
In Ruby, through object-oriented design (OOD), we can create a class with behaviors, otherwise known as methods. A class in Ruby would encompass something tangible, like a gear on a bike, but usually isn't large enough to inclde all components (which is where modules come in). In the book "Practical Object-Oriented Design in Ruby," "A class should do the smallest possible useful thing; that is, it should have a single responsibility." I've created a class called stents and included some behaviors of stent manufacturing. Take a look at the code below.
I've created a class called 'CoronaryStent', which is a type of stent for the coronary arteries (arteries for the heart). For this class, I've initiated two parameters the user must input. These inputs are used to calculate how long it will take to make these stents, using my old job's machinery. These numbers are inflated, and the parameters are not accurate, but this is just to demonstrate how classes work in Ruby (otherwise, I'd be sued for breaking my NDA).
Three of these methods are private because I do not want the user to be able to call these methods. Instead, I've combined them in the method 'manf_duration', because that's what the customer wants. The three methods are 'supplier', 'quantity', and 'total_time'. Each of these methods should do what I want them to. With 'supplier', I want to know how long it will take to actually have the raw tubing from a vendor. In quantity, I want to know how long it will take our normal manufacturing process, in total time including QA, it will take for a specific quantity amount. Lastly, the total_time method just prints out the total time created with the first two methods.
Within a class, we can use instance variables that can be shared between each methods. This is denoted by an '@' symbol before the variable name. Local variables, or variables in a method, can only be called within that method. Local variables can not be called outside the method. Global variables can be called anywhere, within a method or outside of it. They are denoted with a '$' sign before the variable name.
The variable we are manipulating here is '@time' because we want it to increase with each method it goes through.
Lastly, I've included driver code to see how long it will take to manufacture 1,500 nitinol stents through this company. The output should be 2.0 weeks. And in fact, after running through the console, that's what I get.
Cool side note:
Nitinol, short for Nickel titanium, was discovered in 1959 by researchers at the Naval Ordnance Laboratory. Initially, the purpose was for military and defense. Two specific properties that make this material unique is its superelasticity and shape memory property. This material can take lots of bending before it actually makes a dent (meaning you can crumple it and it will turn back into its original form). Shape memory means you can "teach" the piece of nitinol to retain a specific shape, say in the form of a stent. Look it up; science!
Second side thing: When modeling nature or systems, we want to be careful how we're going to define a class, a module, etc. One of the things I want to get into was modeling nature with code. We get close to the realm of philosophy in determining objects and its metaphysical state. Daniel Shiffman, a professor at NYU for their ITP program, wrote a book on this topic using Python. I'm reading through it now and I highly recommend it.


Let us analyze the problem by splitting up the correlation term into partial sums of the form: $$ f_d = \frac{1}{n-d}\sum_{k=1}^{n-d}(x_k - \bar x_n)(x_{k+d} - \bar x_n) $$ The correlation term of the error can now be rewritten in terms of \( f_d \) $$ \frac{2}{n}\sum_{k < l} (x_k - \bar x_n)(x_l - \bar x_n) = 2\sum_{d=1}^{n-1} f_d $$ The value of \( f_d \) reflects the correlation between measurements separated by the distance \( d \) in the sample samples. Notice that for \( d=0 \), \( f \) is just the sample variance, \( \mathrm{var}(x) \). If we divide \( f_d \) by \( \mathrm{var}(x) \), we arrive at the so called autocorrelation function $$ \kappa_d = \frac{f_d}{\mathrm{var}(x)} $$ which gives us a useful measure of pairwise correlations starting always at \( 1 \) for \( d=0 \).
The sample error can now be written in terms of the autocorrelation function: $$ \begin{align} \mathrm{err}_X^2 &= \frac{1}{n}\mathrm{var}(x)+\frac{2}{n}\cdot\mathrm{var}(x)\sum_{d=1}^{n-1} \frac{f_d}{\mathrm{var}(x)}\nonumber\\ &=& \left(1+2\sum_{d=1}^{n-1}\kappa_d\right)\frac{1}{n}\mathrm{var}(x)\nonumber\\ &=\frac{\tau}{n}\cdot\mathrm{var}(x) \label{_auto1} \end{align} $$ and we see that \( \mathrm{err}_X \) can be expressed in terms the uncorrelated sample variance times a correction factor \( \tau \) which accounts for the correlation between measurements. We call this correction factor the autocorrelation time: $$ \begin{equation} \tau = 1+2\sum_{d=1}^{n-1}\kappa_d \label{eq:autocorrelation_time} \end{equation} $$
For a correlation free experiment, \( \tau \) equals 1.
We can interpret a sequential correlation as an effective reduction of the number of measurements by a factor \( \tau \). The effective number of measurements becomes: $$ n_\mathrm{eff} = \frac{n}{\tau} $$ To neglect the autocorrelation time \( \tau \) will always cause our simple uncorrelated estimate of \( \mathrm{err}_X^2\approx \mathrm{var}(x)/n \) to be less than the true sample error. The estimate of the error will be too good. On the other hand, the calculation of the full autocorrelation time poses an efficiency problem if the set of measurements is very large.
The so-called time-displacement autocorrelation \( \phi(t) \) for a quantity \( \mathbf{M} \) is given by $$ \phi(t) = \int dt' \left[\mathbf{M}(t')-\langle \mathbf{M} \rangle\right]\left[\mathbf{M}(t'+t)-\langle \mathbf{M} \rangle\right], $$ which can be rewritten as $$ \phi(t) = \int dt' \left[\mathbf{M}(t')\mathbf{M}(t'+t)-\langle \mathbf{M} \rangle^2\right], $$ where \( \langle \mathbf{M} \rangle \) is the average value and \( \mathbf{M}(t) \) its instantaneous value. We can discretize this function as follows, where we used our set of computed values \( \mathbf{M}(t) \) for a set of discretized times (our Monte Carlo cycles corresponding to moving all electrons?) $$ \phi(t) = \frac{1}{t_{\mathrm{max}}-t}\sum_{t'=0}^{t_{\mathrm{max}}-t}\mathbf{M}(t')\mathbf{M}(t'+t) -\frac{1}{t_{\mathrm{max}}-t}\sum_{t'=0}^{t_{\mathrm{max}}-t}\mathbf{M}(t')\times \frac{1}{t_{\mathrm{max}}-t}\sum_{t'=0}^{t_{\mathrm{max}}-t}\mathbf{M}(t'+t). \label{eq:phitf} $$
One should be careful with times close to \( t_{\mathrm{max}} \), the upper limit of the sums becomes small and we end up integrating over a rather small time interval. This means that the statistical error in \( \phi(t) \) due to the random nature of the fluctuations in \( \mathbf{M}(t) \) can become large.
One should therefore choose \( t \ll t_{\mathrm{max}} \).
Note that the variable \( \mathbf{M} \) can be any expectation values of interest.
The time-correlation function gives a measure of the correlation between the various values of the variable at a time \( t' \) and a time \( t'+t \). If we multiply the values of \( \mathbf{M} \) at these two different times, we will get a positive contribution if they are fluctuating in the same direction, or a negative value if they fluctuate in the opposite direction. If we then integrate over time, or use the discretized version of, the time correlation function \( \phi(t) \) should take a non-zero value if the fluctuations are correlated, else it should gradually go to zero. For times a long way apart the different values of \( \mathbf{M} \) are most likely uncorrelated and \( \phi(t) \) should be zero.
We can derive the correlation time by observing that our Metropolis algorithm is based on a random walk in the space of all possible spin configurations. Our probability distribution function \( \mathbf{\hat{w}}(t) \) after a given number of time steps \( t \) could be written as $$ \mathbf{\hat{w}}(t) = \mathbf{\hat{W}^t\hat{w}}(0), $$ with \( \mathbf{\hat{w}}(0) \) the distribution at \( t=0 \) and \( \mathbf{\hat{W}} \) representing the transition probability matrix. We can always expand \( \mathbf{\hat{w}}(0) \) in terms of the right eigenvectors of \( \mathbf{\hat{v}} \) of \( \mathbf{\hat{W}} \) as $$ \mathbf{\hat{w}}(0) = \sum_i\alpha_i\mathbf{\hat{v}}_i, $$ resulting in $$ \mathbf{\hat{w}}(t) = \mathbf{\hat{W}}^t\mathbf{\hat{w}}(0)=\mathbf{\hat{W}}^t\sum_i\alpha_i\mathbf{\hat{v}}_i= \sum_i\lambda_i^t\alpha_i\mathbf{\hat{v}}_i, $$ with \( \lambda_i \) the \( i^{\mathrm{th}} \) eigenvalue corresponding to the eigenvector \( \mathbf{\hat{v}}_i \).
If we assume that \( \lambda_0 \) is the largest eigenvector we see that in the limit \( t\rightarrow \infty \), \( \mathbf{\hat{w}}(t) \) becomes proportional to the corresponding eigenvector \( \mathbf{\hat{v}}_0 \). This is our steady state or final distribution.
We can relate this property to an observable like the mean energy. With the probabilty \( \mathbf{\hat{w}}(t) \) (which in our case is the squared trial wave function) we can write the expectation values as $$ \langle \mathbf{M}(t) \rangle = \sum_{\mu} \mathbf{\hat{w}}(t)_{\mu}\mathbf{M}_{\mu}, $$ or as the scalar of a vector product $$ \langle \mathbf{M}(t) \rangle = \mathbf{\hat{w}}(t)\mathbf{m}, $$ with \( \mathbf{m} \) being the vector whose elements are the values of \( \mathbf{M}_{\mu} \) in its various microstates \( \mu \).
We rewrite this relation as $$ \langle \mathbf{M}(t) \rangle = \mathbf{\hat{w}}(t)\mathbf{m}=\sum_i\lambda_i^t\alpha_i\mathbf{\hat{v}}_i\mathbf{m}_i. $$ If we define \( m_i=\mathbf{\hat{v}}_i\mathbf{m}_i \) as the expectation value of \( \mathbf{M} \) in the \( i^{\mathrm{th}} \) eigenstate we can rewrite the last equation as $$ \langle \mathbf{M}(t) \rangle = \sum_i\lambda_i^t\alpha_im_i. $$ Since we have that in the limit \( t\rightarrow \infty \) the mean value is dominated by the the largest eigenvalue \( \lambda_0 \), we can rewrite the last equation as $$ \langle \mathbf{M}(t) \rangle = \langle \mathbf{M}(\infty) \rangle+\sum_{i\ne 0}\lambda_i^t\alpha_im_i. $$ We define the quantity $$ \tau_i=-\frac{1}{log\lambda_i}, $$ and rewrite the last expectation value as $$ \langle \mathbf{M}(t) \rangle = \langle \mathbf{M}(\infty) \rangle+\sum_{i\ne 0}\alpha_im_ie^{-t/\tau_i}. \label{eq:finalmeanm} $$
The quantities \( \tau_i \) are the correlation times for the system. They control also the auto-correlation function discussed above. The longest correlation time is obviously given by the second largest eigenvalue \( \tau_1 \), which normally defines the correlation time discussed above. For large times, this is the only correlation time that survives. If higher eigenvalues of the transition matrix are well separated from \( \lambda_1 \) and we simulate long enough, \( \tau_1 \) may well define the correlation time. In other cases we may not be able to extract a reliable result for \( \tau_1 \). Coming back to the time correlation function \( \phi(t) \) we can present a more general definition in terms of the mean magnetizations $ \langle \mathbf{M}(t) \rangle$. Recalling that the mean value is equal to $ \langle \mathbf{M}(\infty) \rangle$ we arrive at the expectation values $$ \phi(t) =\langle \mathbf{M}(0)-\mathbf{M}(\infty)\rangle \langle \mathbf{M}(t)-\mathbf{M}(\infty)\rangle, $$ resulting in $$ \phi(t) =\sum_{i,j\ne 0}m_i\alpha_im_j\alpha_je^{-t/\tau_i}, $$ which is appropriate for all times.
If the correlation function decays exponentially $$ \phi (t) \sim \exp{(-t/\tau)}$$ then the exponential correlation time can be computed as the average $$ \tau_{\mathrm{exp}} = -\langle \frac{t}{log|\frac{\phi(t)}{\phi(0)}|} \rangle. $$ If the decay is exponential, then $$ \int_0^{\infty} dt \phi(t) = \int_0^{\infty} dt \phi(0)\exp{(-t/\tau)} = \tau \phi(0),$$ which suggests another measure of correlation $$ \tau_{\mathrm{int}} = \sum_k \frac{\phi(k)}{\phi(0)}, $$ called the integrated correlation time.
Two famous resampling methods are the independent bootstrap and the jackknife.
The jackknife is a special case of the independent bootstrap. Still, the jackknife was made popular prior to the independent bootstrap. And as the popularity of the independent bootstrap soared, new variants, such as the dependent bootstrap.
The Jackknife and independent bootstrap work for independent, identically distributed random variables. If these conditions are not satisfied, the methods will fail. Yet, it should be said that if the data are independent, identically distributed, and we only want to estimate the variance of \( \overline{X} \) (which often is the case), then there is no need for bootstrapping.
The Jackknife works by making many replicas of the estimator \( \widehat{\theta} \). The jackknife is a resampling method, we explained that this happens by scrambling the data in some way. When using the jackknife, this is done by systematically leaving out one observation from the vector of observed values \( \hat{x} = (x_1,x_2,\cdots,X_n) \). Let \( \hat{x}_i \) denote the vector $$ \hat{x}_i = (x_1,x_2,\cdots,x_{i-1},x_{i+1},\cdots,x_n), $$
which equals the vector \( \hat{x} \) with the exception that observation number \( i \) is left out. Using this notation, define \( \widehat{\theta}_i \) to be the estimator \( \widehat{\theta} \) computed using \( \vec{X}_i \).
To get an estimate for the bias and standard error of \( \widehat{\theta} \), use the following estimators for each component of \( \widehat{\theta} \) $$ \widehat{\mathrm{Bias}}(\widehat \theta,\theta) = (n-1)\left( - \widehat{\theta} + \frac{1}{n}\sum_{i=1}^{n} \widehat \theta_i \right) \qquad \text{and} \qquad \widehat{\sigma}^2_{\widehat{\theta} } = \frac{n-1}{n}\sum_{i=1}^{n}( \widehat{\theta}_i - \frac{1}{n}\sum_{j=1}^{n}\widehat \theta_j )^2. $$
from numpy import *
from numpy.random import randint, randn
from time import time
def jackknife(data, stat):
n = len(data);t = zeros(n); inds = arange(n); t0 = time()
## 'jackknifing' by leaving out an observation for each i
for i in range(n):
t[i] = stat(delete(data,i) )
# analysis
print("Runtime: %g sec" % (time()-t0)); print("Jackknife Statistics :")
print("original bias std. error")
print("%8g %14g %15g" % (stat(data),(n-1)*mean(t)/n, (n*var(t))**.5))
return t
# Returns mean of data samples
def stat(data):
return mean(data)
mu, sigma = 100, 15
datapoints = 10000
x = mu + sigma*random.randn(datapoints)
# jackknife returns the data sample
t = jackknife(x, stat)
Bootstrapping is a nonparametric approach to statistical inference that substitutes computation for more traditional distributional assumptions and asymptotic results. Bootstrapping offers a number of advantages:
Since \( \widehat{\theta} = \widehat{\theta}(\hat{X}) \) is a function of random variables, \( \widehat{\theta} \) itself must be a random variable. Thus it has a pdf, call this function \( p(\hat{t}) \). The aim of the bootstrap is to estimate \( p(\hat{t}) \) by the relative frequency of \( \widehat{\theta} \). You can think of this as using a histogram in the place of \( p(\hat{t}) \). If the relative frequency closely resembles \( p(\vec{t}) \), then using numerics, it is straight forward to estimate all the interesting parameters of \( p(\hat{t}) \) using point estimators.
In the case that \( \widehat{\theta} \) has more than one component, and the components are independent, we use the same estimator on each component separately. If the probability density function of \( X_i \), \( p(x) \), had been known, then it would have been straight forward to do this by:
But unless there is enough information available about the process that generated \( X_1,X_2,\cdots,X_n \), \( p(x) \) is in general unknown. Therefore, Efron in 1979 asked the question: What if we replace \( p(x) \) by the relative frequency of the observation \( X_i \); if we draw observations in accordance with the relative frequency of the observations, will we obtain the same result in some asymptotic sense? The answer is yes.
Instead of generating the histogram for the relative frequency of the observation \( X_i \), just draw the values \( (X_1^*,X_2^*,\cdots,X_n^*) \) with replacement from the vector \( \hat{X} \).
The independent bootstrap works like this:
The following code starts with a Gaussian distribution with mean value \( \mu =100 \) and variance \( \sigma=15 \). We use this to generate the data used in the bootstrap analysis. The bootstrap analysis returns a data set after a given number of bootstrap operations (as many as we have data points). This data set consists of estimated mean values for each bootstrap operation. The histogram generated by the bootstrap method shows that the distribution for these mean values is also a Gaussian, centered around the mean value \( \mu=100 \) but with standard deviation \( \sigma/\sqrt{n} \), where \( n \) is the number of bootstrap samples (in this case the same as the number of original data points). The value of the standard deviation is what we expect from the central limit theorem.
%matplotlib inline
from numpy import *
from numpy.random import randint, randn
from time import time
from scipy.stats import norm
import matplotlib.pyplot as plt
# Returns mean of bootstrap samples
def stat(data):
return mean(data)
# Bootstrap algorithm
def bootstrap(data, statistic, R):
t = zeros(R); n = len(data); inds = arange(n); t0 = time()
# non-parametric bootstrap
for i in range(R):
t[i] = statistic(data[randint(0,n,n)])
# analysis
print("Runtime: %g sec" % (time()-t0)); print("Bootstrap Statistics :")
print("original bias std. error")
print("%8g %8g %14g %15g" % (statistic(data), std(data),\
mean(t), \
std(t)))
return t
mu, sigma = 100, 15
datapoints = 10000
x = mu + sigma*random.randn(datapoints)
# bootstrap returns the data sample t = bootstrap(x, stat, datapoints)
# the histogram of the bootstrapped data
t = bootstrap(x, stat, datapoints)
# the histogram of the bootstrapped data
n, binsboot, patches = plt.hist(t, bins=50, density='true',histtype='bar', color='red', alpha=0.75)
# add a 'best fit' line
y = norm.pdf( binsboot, mean(t), std(t))
lt = plt.plot(binsboot, y, 'r--', linewidth=1)
plt.xlabel('Smarts')
plt.ylabel('Probability')
plt.axis([99.5, 100.6, 0, 3.0])
plt.grid(True)
plt.show()
The blocking method was made popular by Flyvbjerg and Pedersen (1989) and has become one of the standard ways to estimate \( V(\widehat{\theta}) \) for exactly one \( \widehat{\theta} \), namely \( \widehat{\theta} = \overline{X} \).
Assume \( n = 2^d \) for some integer \( d>1 \) and \( X_1,X_2,\cdots, X_n \) is a stationary time series to begin with. Moreover, assume that the time series is asymptotically uncorrelated. We switch to vector notation by arranging \( X_1,X_2,\cdots,X_n \) in an \( n \)-tuple. Define: $$ \begin{align*} \hat{X} = (X_1,X_2,\cdots,X_n). \end{align*} $$
The strength of the blocking method is when the number of observations, \( n \) is large. For large \( n \), the complexity of dependent bootstrapping scales poorly, but the blocking method does not, moreover, it becomes more accurate the larger \( n \) is.
The quantity \( \vec{X}_k \) is subject to \( k \) blocking transformations. We now have \( d \) vectors \( \vec{X}_0, \vec{X}_1,\cdots,\vec X_{d-1} \) containing the subsequent averages of observations. It turns out that if the components of \( \vec{X} \) is a stationary time series, then the components of \( \vec{X}_i \) is a stationary time series for all \( 0 \leq i \leq d-1 \)
We can then compute the autocovariance, the variance, sample mean, and number of observations for each \( i \). Let \( \gamma_i, \sigma_i^2, \overline{X}_i \) denote the autocovariance, variance and average of the elements of \( \vec{X}_i \) and let \( n_i \) be the number of elements of \( \vec{X}_i \). It follows by induction that \( n_i = n/2^i \).
Using the definition of the blocking transformation and the distributive property of the covariance, it is clear that since \( h =|i-j| \) we can define $$ \begin{align} \gamma_{k+1}(h) &= cov\left( ({X}_{k+1})_{i}, ({X}_{k+1})_{j} \right) \nonumber \\ &= \frac{1}{4}cov\left( ({X}_{k})_{2i-1} + ({X}_{k})_{2i}, ({X}_{k})_{2j-1} + ({X}_{k})_{2j} \right) \nonumber \\ &= \frac{1}{2}\gamma_{k}(2h) + \frac{1}{2}\gamma_k(2h+1) \hspace{0.1cm} \mathrm{h = 0} \label{_auto3}\\ &=\frac{1}{4}\gamma_k(2h-1) + \frac{1}{2}\gamma_k(2h) + \frac{1}{4}\gamma_k(2h+1) \quad \mathrm{else} \label{_auto4} \end{align} $$
The quantity \( \hat{X} \) is asymptotic uncorrelated by assumption, \( \hat{X}_k \) is also asymptotic uncorrelated. Let's turn our attention to the variance of the sample mean \( V(\overline{X}) \).
We can then wrap up $$ \begin{align} n_{j+1} \overline{X}_{j+1} &= \sum_{i=1}^{n_{j+1}} (\hat{X}_{j+1})_i = \frac{1}{2}\sum_{i=1}^{n_{j}/2} (\hat{X}_{j})_{2i-1} + (\hat{X}_{j})_{2i} \nonumber \\ &= \frac{1}{2}\left[ (\hat{X}_j)_1 + (\hat{X}_j)_2 + \cdots + (\hat{X}_j)_{n_j} \right] = \underbrace{\frac{n_j}{2}}_{=n_{j+1}} \overline{X}_j = n_{j+1}\overline{X}_j. \label{_auto7} \end{align} $$ By repeated use of this equation we get \( V(\overline{X}_i) = V(\overline{X}_0) = V(\overline{X}) \) for all \( 0 \leq i \leq d-1 \). This has the consequence that $$ \begin{align} V(\overline{X}) = \frac{\sigma_k^2}{n_k} + e_k \qquad \text{for all} \qquad 0 \leq k \leq d-1. \label{eq:convergence} \end{align} $$
Flyvbjerg and Petersen demonstrated that the sequence \( \{e_k\}_{k=0}^{d-1} \) is decreasing, and conjecture that the term \( e_k \) can be made as small as we would like by making \( k \) (and hence \( d \)) sufficiently large. The sequence is decreasing (Master of Science thesis by Marius Jonsson, UiO 2018). It means we can apply blocking transformations until \( e_k \) is sufficiently small, and then estimate \( V(\overline{X}) \) by \( \widehat{\sigma}^2_k/n_k \).
For an elegant solution and proof of the blocking method, see the recent article of Marius Jonsson (former MSc student of the Computational Physics group).


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Accept connection
Make connection
Watch the console - all the interesting stuff happens there.


What is the posterior probability of each genotype?
Error rate:
B-allele frequency prior:
Depth of Coverage:


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Advanced interpolation modules using polynomial and statistically based kriging methods in 3D.
Input Ports
Name Type Description in_fld Mesh+Node_Data 3D scatter points Parameters
Name Type Description nx int number of grid nodes in x direction ny int number of grid nodes in y direction nz int number of grid nodes in z direction radius float search radius Output Ports
Name Type Description zest float[][][] estmated values at grid nodes zstd float[][][] standard deviation at grid nodes
Krig - Interpolation from 3D scatter points and
data values to a Kriging 3D grid.
Estimates a random function of 3 variables at all grid nodes belonging to a 3D regular grid using the method of Kriging. The random function estimate and standard deviation arrays are calculated at all grid nodes. The semi-variogram function "gam" is supplied in this project and may be configured as needed with re-compilation.
in_fld
3D scatter points which combine a mesh and some node data. A Node_Data object represents the values of some dataset at the node points of a field, while the Mesh represents the locations of that field's nodes in space.
zest
3D float array. This array contains the estimated values at the grid nodes.
zstd
3D float array. This array contains the standard deviations t the grid nodes.
The Krig2D macro uses the XuKrigging3D low-level module. This modules uses the XuKriging3DCalc and other functions of the the Toolmaster agX library.
Two example applications are applied, KrigEg0 and KrigEg1. The KrigEg0 application demonstrates how 3D scatter points can be interpolated into uniform 3D grid. KrigEg1 is similar to KrigEg0, but adds a crop macro to show the boundary of 3D grid.
xp_comm_proj/krig/krigmods.v contains the V definition of the agxint,XuKrigging2D and XuKrigging3D modules.
xp_comm_proj/krig/krigmacs.v contains the V definitions of the Krig2D,Interp2D and Krig macros.
xp_comm_proj/krig/krigapps.v contains the V definition of the KrigApps example application.
Mark Lambert, Roger Fleuty, Ian CuringtonAdvanced Visual Systems, Inc.
Ma YingLiang, Manchester Visualization Centre Andrew Dodd, International AVS Centre


| JavaScript | HTML | CSS |
| Python | C# | Java |
| React | Angular | .NET MVC |
| IntelliJ | Visual Stuido/Code | Azure Dev Ops |
| Linux | Windows | Mac OS |
Create and deliver lesson plans for a full-stack driven web development curriculum. Provide individual feedback and hands-on assistance to a maxim of 25 students on coding assignments. Develop full-stack web applications for instruction purposes utilizing technologies outlined in the curriculum
Contribute to the development of the Omega web application primarily using Angular 6 and C#.Completed UI screens for the Omega Web Application.Work on an Agile team to complete assigned tasks each sprint.Utilize Azure DevOps for source control and communication of tasks between team members.
Assisted 21 students during instruction to resolve code issues and maintain a fast paced learning environment.Graded students' daily assignments and weekly projects and provided feedback to improve scores and skill level.Engaged with 12 Code School alumni to ensure continued success and involvement in the tech community in Memphis.
CodeCrew is a hands-on class designed to train individuals to be entry-level software developers within a six-month course. Students work in a small classroom setting and use real-word technologies to learn the fundamentals of coding, app development, the leadership, and the life skills needed for a successful career and job market competitiveness. Learn More!
I earned 85 credits toward a degree in architecture at the University of Tennessee in Knoxville and withdrew with a 3.7 GPA. My time at UTK was a great opportunity to learn critical time and project management skills. It was also a way for me to cultivate the creative thinking that ultimately makes me a better programmer!


Three of a family killed in accident
by Staff ReporterThree members of a family were killed in an accident near Attur on Sunday.
The deceased were identified as Chandra (40), her daughter Nithya (18) and son Sakthivel (16).
According to police, the three were riding triples on a two-wheeler near Veerakanur when the vehicle collided with a private bus heading to Tiruchi from Gengavelli. All three died on the spot.
Gengavelli police rushed to the spot and recovered the bodies. The driver of the bus surrendered at the station.
Namakkal
In Namakkal, two died when a truck rammed a parked truck on Salem-Coimbatore Bypass Road. According to police, the truck was moving towards Tiruppur from Salem when it hit the stationary truck near Kumarapalayam. Driver Nandhakumar (20) and his friend Gokulkrishnan (18) died and Kumar, who was travelling with them, suffered grievous injuries. He was rushed to a government hospital nearby.
Kumarapalayam police have registered a case and are investigating.


Who are you?
Hi! I'm Tim Mazyrko, a twenty year old college student from North NJ (about 25 minutes away from
The Big Apple). You're currently looking at my personal portfolio website, created as an
exercise in using the Bootstrap framework.
What do you do?
I'm currently an incoming Junior at the New Jersey Institute of Technology. My primary course of
study is Information Technology with a specialization in Network and Information Security, but
I'm also minoring in Web and Mobile Development! Visit my About page
for more.
What about your skills?
I may still be a college student, but I already have quite a bit of experience with IT in the
finance sector. I've worked several times with Access Industries, and I even had my own project
at Baron Capital where I implemented LogPoint SIEM. Otherwise, I also have experience in
engineering, AV systems, and programming.


Multiple methods for testing directories:
gobuster
wfuzz
Testing comment pages: sql
Testing POST page: parameth for 'common' parameters'
Multiple wordlists: dir list 2.3 medium did not find '.git'
.git directory for source!
Also git.canape.htb, so sub domains!
Virtual host routing, another reason to try to send full name instead of ip address for websites
git log to check commit histories, look for interesting things
See pickle is being used, so we can exploit that
couchdb enumeration
curl <IP_ADDRESS>:5984
<CouchDB welcome banner>
see release is 2.0.0
API is available?
curl <IP_ADDRESS>:5984/_all_dbs
curl <IP_ADDRESS>:5984/_all_docs
curl <IP_ADDRESS>:5984/simpsons
curl <IP_ADDRESS>:5984/passwords <- not authorized
couchdb create user RCE (creates an admin user)
curl --user 'ippsec:PleaseShareTheVideo' <IP_ADDRESS>:5984/passwords
curl --user 'ippsec:PleaseShareTheVideo' <IP_ADDRESS>:5984/passwords/_all_docs
sudo pip, malicious package install for root
mkdir ippsec
cd ippsec
vi setup.py
<python rev shell here>
#run sudo pip install on the current working directory. it will look for setup.py files
now sudo pip install .


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Christians in medieval Iberia had their own unique religious practices until the end of the 11th century. About 40 manuscripts and fragments preserve the texts and melodies that they used in their worship. And that means that the fragment you see here is a rare and precious witness to a cultural practice that was almost entirely suppressed after the year 1080 AD.
The parts written in large text are the prayers, the readings and the hymns. The parts written in small text are the more complicated chants, and space is left above each line of text for the melody to be written. At some point, our fragment got detached from the rest of its manuscript, which might have had 200 or more pages, and just this one page survives, by pure luck.
You can click the green button to find out more about: how the manuscript was used; what it is like now; and some of the odd places that manuscripts have been found. When you have finished, you will be able to return to this presentation.
The text is in Latin. The handwriting might be unfamiliar to you, but once you learn the different shapes of the letters, it is easy to read. Although this is just a small fragment, we can tell the liturgical day that it’s providing material for, and the different components of the liturgy. The chant texts are drawn from the bible and, together with the prayers, they offer the congregation a particular theological experience of the liturgical day.
In the musical notation, each sign goes up and down on the page as the melody goes up and down, but there is no information about precise pitch or intervals. The medieval singers reading the manuscript already knew the melodies, and used the notation as a reminder.
Even in a small fragment like this, we have identified dozens of different notational shapes. Although the shapes do not tell us exactly how these melodies sounded, we can identify how they divided the texts into phrases. We have learned to identify the different melodic patterns that appear at the ends of sentences. We have also learned how the melody paces the text. Some syllables were stretched with lots of notes. Other syllables had only one or a few notes, and were more similar to speech.
The chant you can see is preserved in a fragment held in the biblioteca nacional in Madrid. In this chant, most syllables have up to four notes each, but as we approach a phrase division, there’s usually a syllable which is stretched out, with more like 5 or 6 notes each. In the middle of the chant, there are three, very short, phrases. Then after that broken up part of the text, there is one really long phrase, where they work all the way through “that I took evil away from you”, and as you can hear as I say it, that emphasises the flow of the text. Then, near the end, the word “creabo” (“I will create”), has 16 notes on it. In that context, this is a real musical highpoint, and it means that it gives the listeners a chance to meditate on that really important word for longer than any other moment.


{% load staticfiles %}
As we have entry rules, we also need some rules for exiting the market. The vast majority of traders, either use indicators to determine the exit moment, or set a fixed Stop Loss and Take Profit. We will call the latter kind of exit a 'horizontal exit' and we will explain why in brief moments. We will use instead another kind of exit that we will call a 'vertical exit'.
They are the most common ones. In a chart, they look like this:
We call them horizonal exits because in a chart they look as two horizontal lines that limit the price at which an asset can arrive. Once the price touches one of these lines, a market exit automatically triggers, either with benefits (Take Profit) or losses (Stop Loss).
For a lot of traders this is the regular way of exiting the market. Nevertheless, these kind of exits create a statistical drawback because they are two more parameters that need to be optimized, contributing to 'overoptimization'. That's why we substitute them with a vertical exit. You can read more about 'overoptimization' in the appendix.
Avanti’s strategy can also be called a timed exit. It is simply the substitution of the traditional Stop Loss and Take Profit for a unique exit after a certain amount of periods since entering the market. That means that after a prestablished amount of time you exit the market, either with profits or losses.
It is called a vertical exit because it looks like this in a chart:
Using a vertical exit doesn't mean that you will not use a Stop Loss. But you will put it very far, so it only is triggered in extraordinary moments where an unexpected news moves the market in an abrupt way. This kind of unpredictable movements in the price are called 'Black Swans'.
You may think that a vertical exit is very risky. But anyone with a bit of experience in the markets know that when a 'Black Swan' appears the Stop Loss doesn't helps at all. The volatility and the slippage is so big that the Stop Loss may activate hundred of pips or ticks away from the price the trader set. For that reason, the wiser thing to do is to know from the beginning that this situations could happen and therefore be prepared trading moderated amounts of money and only trusting the Stop Loss in extraordinary cases.
Another advantage of the vertical exits is a better backtesting precision because is easier to consider the slippage.


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{% extends 'security/public_base.html' %} {% block content %} {% from "security/_macros.html" import render_field_with_errors, render_field %} {% include "security/_messages.html" %}
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{{extend 'user_admin_layout.html'}} {{block sub_menu}} {{sidemenu('sidemenu','user_admin/ldap_list')}} {{end}} {{block content_tool}} {{end}} {{block content_main}} {{use 'jqcookie'}} {{use 'bootstraptable', extensions = ["cookie"]}}
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Before Change
// This is because of a MonitoredTrainingSession "bug".
// When ending training it saves a checkpoint as the next step.
// That causes that we are one step ahead when loading it.
self.assertEqual(step, 5)
if __name__ == "__main__":
tf.test.main()
After Change
)
// Get tensor from graph and run it in session
w_tensor = tf.get_default_graph().get_tensor_by_name(
"mockfasterrcnn/w:0"
)
w_numpy = new_session.run(w_tensor)
// Assert we correctly loaded the weight
self.assertArrayNear(w_numpy, [2.5, 3.0], err=0.01)


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Heart Disease Predictor
Gender repersented as (0=female,1=male)
Resting Blood Pressure (restbp)(90 - 200 mmHg)
Thalium Stress test(thalach) this is the maxium HR achived, during the test(70 - 230)
Major Vessels Coloured by Fluoroscopy (ca) (0 - 3)
Chest Pain Type (cp) (1=typical angina, 2=atypical angina, 3=non-angina, 4=asymptomatic angina)
Peak Exercise ST Segment (oldpeak)(0=flat or downsloping, 1=upsloping)
Thalium Test (thal)(0=normal or fixed defect, 1=reversible defect)


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Happy birthday to my lovely wife Amber. I'm so incredibly blessed to have you in my life.
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Happy birthday to my lovely wife Amber. I'm so incredibly blessed to have you in my life.
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Mike Croucher and Paul Richmond
EPSRC Research Software Engineering Fellows
Web: http://rse.shef.ac.uk2 Research Software Engineering Fellows in Sheffield
First Fellowship of its kind
Rosie Staniforth, biochemist, University of Sheffield
Good practice for research software
Mike Croucher and Paul Richmond
EPSRC Research Software Engineering Fellows
Web: http://rse.shef.ac.uk


Welcome to FBChan!
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Dark Side of the Rainbow
Tech Used:
Features:
- Login
- Admin Dashboard
- Manage Users and Albums
- Simple Search
- Encrypted Authentication
To-do:
- Registration
- Unit Testing
- User Settings
- Shop
- Advanced search
- Integrate AngularJS with REST endpoints
- Link Spotify for users to preview album music
- More Roles


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This application is designed to help you select the perfect programming language to learn. Simply fill out the following form and hit SUBMIT to see your results.
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halo¶
This module is for calculating intensity profiles for dust scattering halos from cosmological (z > 0) point sources.
Classes¶
The Halo object is used for both Galactic and extragalactic sources and contains the attribute htype, which can be filled with one of two objects: either CosmHalo (for extragalactic sources) or GalHalo (for Galactic sources, see galhalo).
-
class
halo.
Halo
(E0, alpha=array([ 5., 10., 15., 20., 25., 30., 35., 40., 45., 50., 55., 60., 65., 70., 75., 80., 85., 90., 95., 100.]), rad=<dust.Grain object>, scatm=<sigma_scat.Scatmodel object>)¶ - ATTRIBUTEShtype : abstract class containing information about the halo calculationE0 : float : observed energy [keV]alpha : np.array : observed angle [arcsec]rad : dust.Grain OR dust.Dustdist : grain size distributiondist : dust.Dustspectrum : initially NONE, stored from calculationscatm : ss.Scatmodel : scattering model usedintensity : np.array : fractional intensity [arcsec^-2]FUNCTIONSecf(theta, nth=500)theta : float : Value for which to compute enclosed fraction (arcseconds)nth : int (500) : Number of angles to use in calculationreturns the enclosed fraction for the halo surface brightnessprofile, via integral(theta,2pi*theta*halo)/tau.
Object definition for the halo type CosmHalo
-
class
halo.
CosmHalo
(zs=None, zg=None, cosm=None, igmtype=None)¶ An htype class for storing halo properties
ATTRIBUTESzs : float : redshift of X-ray sourcezg : float : redshift of an IGM screencosm : cosmo.Cosmology objectigmtype : labels the type of IGM scattering calculation : ‘Uniform’ or ‘Screen’
Functions¶
Functions within this module modify the Halo object by updating the htype, intensity, and any other related attributes.
-
halo.
UniformIGM
(halo, zs=4.0, cosm=<cosmology.Cosmology object>, nz=500)¶ Calculates the intensity of a scattering halo from intergalactic dust that is uniformly distributed along the line of sight.
MODIFIEShalo.htype, halo.dist, halo.intensity, halo.tauxINPUThalo : Halo objectzs : float : redshift of sourcecosm : cosmo.Cosmologynz : int : number of z-values to use in integration
-
halo.
ScreenIGM
(halo, zs=2.0, zg=1.0, md=1.5e-05, cosm=<cosmology.Cosmology object>)¶ Calculates the intensity of a scattering halo from intergalactic dust that is situated in an infinitesimally thin screen somewhere along the line of sight.
MODIFIEShalo.htype, halo.dist, halo.intensity, halo.tauxINPUTShalo : Halo objectzs : float : redshift of sourcezg : float : redshift of screenmd : float : mass density of dust to use in screen [g cm^-2]cosm : cosmo.Cosmology


| Top |
Functions
| NcmMPIJobMCMC * | ncm_mpi_job_mcmc_new () |
| NcmMPIJobMCMC * | ncm_mpi_job_mcmc_ref () |
| void | ncm_mpi_job_mcmc_free () |
| void | ncm_mpi_job_mcmc_clear () |
Properties
| NcmFit * | fit | Read / Write / Construct Only |
| NcmObjArray * | function-array | Read / Write / Construct Only |
Functions
ncm_mpi_job_mcmc_new ()
NcmMPIJobMCMC * ncm_mpi_job_mcmc_new (NcmFit *fit
,NcmObjArray *func_oa
);
Creates a new NcmMPIJobMCMC object.
ncm_mpi_job_mcmc_ref ()
NcmMPIJobMCMC *
ncm_mpi_job_mcmc_ref (NcmMPIJobMCMC *mjmcmc
);
Increase the reference of mjmcmc
by one.
ncm_mpi_job_mcmc_free ()
void
ncm_mpi_job_mcmc_free (NcmMPIJobMCMC *mjmcmc
);
Decrease the reference count of mjmcmc
by one.
ncm_mpi_job_mcmc_clear ()
void
ncm_mpi_job_mcmc_clear (NcmMPIJobMCMC **mjmcmc
);
Decrease the reference count of mjmcmc
by one, and sets the pointer *mjmcmc
to
NULL.
Property Details
The “fit”
property
“fit” NcmFit *
Fit object.
Owner: NcmMPIJobMCMC
Flags: Read / Write / Construct Only
The “function-array”
property
“function-array” NcmObjArray *
Functions array.
Owner: NcmMPIJobMCMC
Flags: Read / Write / Construct Only


10.2 Interface for Heaps
void heap_init(Heap *heap, int (*compare)(const void *key1, const void *key2)
void (*destroy)(void *data));
Return Value
None.
Description
Initializes the heap specified by
heap. This operation must be called for a heap
before the heap can be used with any other operation. The
compare argument is a function used by various
heap operations to compare nodes when fixing the heap. This
function should return 1 if key1 >
key2, if key1 =
key2, and -1 if key1 <
key2 for a top-heavy heap. For a bottom-heavy
heap, compare should reverse the cases that
return 1 and -1. The destroy argument provides
a way to free dynamically allocated data when heap_destroy is called. For example,
if the heap contains data dynamically allocated using malloc, destroy
should be set to free to free
the data as the heap is destroyed. For structured data
containing several dynamically allocated members,
destroy should be set to a user-defined
function that calls free for
each dynamically allocated member as well as for the structure
itself. For a heap containing data that should not be freed,
destroy should be set to NULL.
Complexity
O (1)
void heap_destroy(Heap *heap);
Return Value
None.
Description
Destroys the heap specified by
heap. No other operations are permitted after
calling heap_destroy unless
heap_init is called again. The
heap_destroy operation removes
all nodes from a heap and calls the function passed as
destroy to heap_init once for each node as it is
removed, provided destroy was not set to
NULL.
Complexity
O (n), where n is the number of nodes in the
heap.
int heap_insert(Heap *heap, const void *data);
Return Value
0if inserting the node is successful, or -1
otherwise.
Description
Inserts a node into the heap specified by
heap. The new node contains a pointer to
data, so the memory referenced by
data should remain valid as long as the node
remains in the heap. It is the responsibility of the caller to
manage the storage associated with data.
Complexity
O (lg n), where n is the number of nodes in the
heap.
int heap_extract(Heap *heap, void **data);
Return Value
0if extracting the node is successful, or -1
otherwise.
Description
Extracts the node at the top of the heap
specified by heap. Upon return,
data points to the data stored in the node
that was extracted. It is the responsibility of the caller to
manage the storage associated with the data.
Complexity
O (lg n), where n is the number of nodes in the
heap.
int heap_size(const Heap *heap);
Return Value
Number of nodes in the heap.
Description
Macro that evaluates to the number of nodes
in the heap specified by heap.
Complexity
O (1)
|


{{ partial "footer_mathjax" . }}
{{ $jsgistembed := resources.Get "js/gist-embed.min.js" }}
{{ $jsprism := resources.Get "js/prism.js" }}
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|
ProEventos
Material do curso
|
Intermediates the communications between the API and the Persistence layers for records on the Lotes table. More...
Public Member Functions | |
| Task | AddLote (int eventoId, LoteDto model) |
| Uses ProEventos.Persistence.GeneralPersistence.SaveChangesAsync to generate a new Lotes record More... | |
| Task< LoteDto[]> | SaveLotes (int eventoId, LoteDto[] models) |
| Uses ProEventos.Persistence.GeneralPersistence.SaveChangesAsync to save new and existing Lote records More... | |
| Task< bool > | DeleteLote (int eventoId, int loteId) |
| Uses ProEventos.Persistence.GeneralPersistence.Delete<T> to delete existing Lote records More... | |
| Task< LoteDto[]> | GetLotesByEventoIdAsync (int eventoId) |
| Uses ProEventos.Persistence.LotePersistence.GetLotesByEventoIdAsync to get an array of all Lotes relative to the given Evento Id More... | |
| Task< LoteDto > | GetLoteByEventoIdAsync (int eventoId, int loteId) |
| Uses ProEventos.Persistence.LotePersistence.GetLoteByEventoIdAsync to get a single Lote associated with the given Evento More... | |
Intermediates the communications between the API and the Persistence layers for records on the Lotes table.
Definition at line 10 of file ILoteService.cs.
| Task ProEventos.Application.Contratos.ILoteService.AddLote | ( | int | eventoId, |
| LoteDto | model | ||
| ) |
Uses ProEventos.Persistence.GeneralPersistence.SaveChangesAsync to generate a new Lotes record
| eventoId | The Evento ID that the Lotes should associated with |
| model | An object of type ProEventos.Application.DTOs.LoteDto to be stored |
Implemented in ProEventos.Application.LoteService.
| Task<bool> ProEventos.Application.Contratos.ILoteService.DeleteLote | ( | int | eventoId, |
| int | loteId | ||
| ) |
Uses ProEventos.Persistence.GeneralPersistence.Delete<T> to delete existing Lote records
| eventoId | The Evento ID with which the records of the Lote array should be associated with it |
| loteId | The ID of the desired Lote |
Implemented in ProEventos.Application.LoteService.
| Task<LoteDto> ProEventos.Application.Contratos.ILoteService.GetLoteByEventoIdAsync | ( | int | eventoId, |
| int | loteId | ||
| ) |
Uses ProEventos.Persistence.LotePersistence.GetLoteByEventoIdAsync to get a single Lote associated with the given Evento
| eventoId | The Evento ID with which the records of the Lote array should be associated with it |
| loteId | The ID of the desired Lote |
Implemented in ProEventos.Application.LoteService.
| Task<LoteDto[]> ProEventos.Application.Contratos.ILoteService.GetLotesByEventoIdAsync | ( | int | eventoId | ) |
Uses ProEventos.Persistence.LotePersistence.GetLotesByEventoIdAsync to get an array of all Lotes relative to the given Evento Id
| eventoId | The Evento ID with which the records of the Lote array should be associated with it |
Implemented in ProEventos.Application.LoteService.
| Task<LoteDto[]> ProEventos.Application.Contratos.ILoteService.SaveLotes | ( | int | eventoId, |
| LoteDto[] | models | ||
| ) |
Uses ProEventos.Persistence.GeneralPersistence.SaveChangesAsync to save new and existing Lote records
| eventoId | The Evento ID with which the records of the Lote array should be associated with it |
| models | An array of objects with type ProEventos.Application.DTOs.LoteDto |
Implemented in ProEventos.Application.LoteService.


04 Feb 2021 - Sparisoma Viridi
Yi Xing, Jie Zheng, Jindong Li, Yixin Cao, Wei Pan, Jie Zhang, Yujie Wang, “X-Ray Tomography Investigation of Cyclically Sheared Granular Materials”, Physical Review Letters [Phys. Rev. Lett.], vol. 126, no. 4, p. 048002, Jan 2021, url https://doi.org/10.1103/PhysRevLett.126.048002.
Experiment setup is given in follwong Fig. 1.
Figure 1 (a) Schematic of the experimental setup: granular packing is sheared by the motor on the linear stage, and the macroscopic shear force is measured by the force sensor. (b) Evolutions of the global volume fraction ϕ and shear force F within one cycle (ensemble averaged over 12 realizations).


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| Column 1 | Column 2 | Column 3 | Column 4 | zero | one | two |
|---|---|---|---|
| three four |
five | six seven eight |
nine ten |
| eleven | twelve | ||
| thirteen fourteen fifteen |
sixteen seventeen eighteen |
nineteen twenty |
twenty-one twenty-two twenty-three |
| twenty-four twenty-five twenty-six |
twenty-seven twenty-eight twenty-nine |
thirty thirty-one thirty-two |
|
| thirty-three | thirty-four thirty-five thirty-six |
thirty-seven | |
| thirty-eight thirty-nine |
forty forty-one |
forty-two forty-three forty-four |
|
| forty-five | forty-six | forty-seven forty-eight forty-nine |
fifty fifty-one fifty-two |
| fifty-three fifty-four |
fifty-five | ||
| fifty-six fifty-seven fifty-eight |
fifty-nine sixty sixty-one |
||
| sixty-two sixty-three |
sixty-four sixty-five sixty-six |
sixty-seven sixty-eight |
|
| sixty-nine seventy seventy-one |
seventy-two seventy-three |
seventy-four seventy-five |
seventy-six seventy-seven seventy-eight |
| seventy-nine | eighty eighty-one eighty-two |
eighty-three eighty-four eighty-five |
eighty-six |
| eighty-seven eighty-eight eighty-nine |
ninety ninety-one ninety-two |
ninety-three | ninety-four ninety-five ninety-six |
| ninety-seven | ninety-eight ninety-nine |
||
| one-hundred one-hundred-one |
one-hundred-two one-hundred-three |
one-hundred-four one-hundred-five |
|
| one-hundred-six | one-hundred-seven one-hundred-eight one-hundred-nine |
||
| one-hundred-ten one-hundred-eleven |
one-hundred-twelve one-hundred-thirteen |
one-hundred-fourteen one-hundred-fifteen |
|
| one-hundred-sixteen | one-hundred-seventeen | one-hundred-eighteen one-hundred-nineteen |
one-hundred-twenty one-hundred-twenty-one |
| one-hundred-twenty-two one-hundred-twenty-three |
one-hundred-twenty-four one-hundred-twenty-five one-hundred-twenty-six |
one-hundred-twenty-seven | |
| one-hundred-twenty-eight | one-hundred-twenty-nine one-hundred-thirty |
one-hundred-thirty-one one-hundred-thirty-two |
|
| one-hundred-thirty-three one-hundred-thirty-four |
one-hundred-thirty-five one-hundred-thirty-six |
one-hundred-thirty-seven | one-hundred-thirty-eight one-hundred-thirty-nine |
| one-hundred-forty one-hundred-forty-one one-hundred-forty-two |
one-hundred-forty-three one-hundred-forty-four one-hundred-forty-five |
one-hundred-forty-six one-hundred-forty-seven one-hundred-forty-eight |
|
| one-hundred-forty-nine one-hundred-fifty |
one-hundred-fifty-one | one-hundred-fifty-two one-hundred-fifty-three |
|
| one-hundred-fifty-four one-hundred-fifty-five one-hundred-fifty-six |
one-hundred-fifty-seven one-hundred-fifty-eight |
one-hundred-fifty-nine one-hundred-sixty one-hundred-sixty-one |
one-hundred-sixty-two |
| one-hundred-sixty-three one-hundred-sixty-four |
one-hundred-sixty-five one-hundred-sixty-six |
one-hundred-sixty-seven | |
| one-hundred-sixty-eight | one-hundred-sixty-nine one-hundred-seventy |
one-hundred-seventy-one one-hundred-seventy-two one-hundred-seventy-three |
one-hundred-seventy-four one-hundred-seventy-five one-hundred-seventy-six |
| one-hundred-seventy-seven one-hundred-seventy-eight |
one-hundred-seventy-nine one-hundred-eighty |
one-hundred-eighty-one one-hundred-eighty-two |
one-hundred-eighty-three |
| one-hundred-eighty-four one-hundred-eighty-five |
one-hundred-eighty-six one-hundred-eighty-seven |
one-hundred-eighty-eight one-hundred-eighty-nine |
one-hundred-ninety one-hundred-ninety-one |
| one-hundred-ninety-two one-hundred-ninety-three one-hundred-ninety-four |
one-hundred-ninety-five | one-hundred-ninety-six | one-hundred-ninety-seven one-hundred-ninety-eight |
| one-hundred-ninety-nine | two-hundred two-hundred-one two-hundred-two |
two-hundred-three | two-hundred-four two-hundred-five |
| two-hundred-six two-hundred-seven |
two-hundred-eight two-hundred-nine two-hundred-ten |
two-hundred-eleven two-hundred-twelve |
|
| two-hundred-thirteen | two-hundred-fourteen two-hundred-fifteen two-hundred-sixteen |
two-hundred-seventeen | two-hundred-eighteen two-hundred-nineteen two-hundred-twenty |
| two-hundred-twenty-one | two-hundred-twenty-two | two-hundred-twenty-three two-hundred-twenty-four two-hundred-twenty-five |
|
| two-hundred-twenty-six two-hundred-twenty-seven two-hundred-twenty-eight |
two-hundred-twenty-nine two-hundred-thirty |
two-hundred-thirty-one two-hundred-thirty-two |
two-hundred-thirty-three |
| two-hundred-thirty-four two-hundred-thirty-five |
two-hundred-thirty-six two-hundred-thirty-seven |
two-hundred-thirty-eight two-hundred-thirty-nine |
|
| two-hundred-forty two-hundred-forty-one |
two-hundred-forty-two | two-hundred-forty-three | |
| two-hundred-forty-four two-hundred-forty-five |
two-hundred-forty-six | two-hundred-forty-seven two-hundred-forty-eight |
two-hundred-forty-nine two-hundred-fifty two-hundred-fifty-one |
| two-hundred-fifty-two two-hundred-fifty-three |
two-hundred-fifty-four | two-hundred-fifty-five two-hundred-fifty-six two-hundred-fifty-seven |
|
| two-hundred-fifty-eight | two-hundred-fifty-nine two-hundred-sixty |
two-hundred-sixty-one two-hundred-sixty-two two-hundred-sixty-three |
|
| two-hundred-sixty-four | two-hundred-sixty-five two-hundred-sixty-six |
two-hundred-sixty-seven two-hundred-sixty-eight |
|
| two-hundred-sixty-nine | two-hundred-seventy two-hundred-seventy-one two-hundred-seventy-two |
two-hundred-seventy-three two-hundred-seventy-four |
|
| two-hundred-seventy-five | two-hundred-seventy-six two-hundred-seventy-seven |
two-hundred-seventy-eight two-hundred-seventy-nine two-hundred-eighty |
two-hundred-eighty-one two-hundred-eighty-two two-hundred-eighty-three |
| two-hundred-eighty-four two-hundred-eighty-five two-hundred-eighty-six |
two-hundred-eighty-seven | two-hundred-eighty-eight two-hundred-eighty-nine |
|
| two-hundred-ninety | two-hundred-ninety-one two-hundred-ninety-two |
||
| two-hundred-ninety-three two-hundred-ninety-four two-hundred-ninety-five |
two-hundred-ninety-six two-hundred-ninety-seven two-hundred-ninety-eight |
two-hundred-ninety-nine three-hundred |
|
| three-hundred-one three-hundred-two three-hundred-three |
three-hundred-four three-hundred-five three-hundred-six |
three-hundred-seven three-hundred-eight three-hundred-nine |
three-hundred-ten |
| three-hundred-eleven | three-hundred-twelve three-hundred-thirteen |
three-hundred-fourteen three-hundred-fifteen three-hundred-sixteen |
|
| three-hundred-seventeen three-hundred-eighteen three-hundred-nineteen |
three-hundred-twenty | three-hundred-twenty-one three-hundred-twenty-two three-hundred-twenty-three |
|
| three-hundred-twenty-four three-hundred-twenty-five three-hundred-twenty-six |
three-hundred-twenty-seven three-hundred-twenty-eight three-hundred-twenty-nine |
three-hundred-thirty | three-hundred-thirty-one three-hundred-thirty-two three-hundred-thirty-three |
| three-hundred-thirty-four three-hundred-thirty-five three-hundred-thirty-six |
three-hundred-thirty-seven | three-hundred-thirty-eight three-hundred-thirty-nine |
|
| three-hundred-forty three-hundred-forty-one |
three-hundred-forty-two | ||
| three-hundred-forty-three three-hundred-forty-four three-hundred-forty-five |
three-hundred-forty-six | three-hundred-forty-seven three-hundred-forty-eight |
|
| three-hundred-forty-nine | three-hundred-fifty | three-hundred-fifty-one three-hundred-fifty-two |
|
| three-hundred-fifty-three | three-hundred-fifty-four | ||
| three-hundred-fifty-five | three-hundred-fifty-six | three-hundred-fifty-seven | |
| three-hundred-fifty-eight three-hundred-fifty-nine |
three-hundred-sixty three-hundred-sixty-one three-hundred-sixty-two |
||
| three-hundred-sixty-three three-hundred-sixty-four |
three-hundred-sixty-five | three-hundred-sixty-six three-hundred-sixty-seven |
|
| three-hundred-sixty-eight | three-hundred-sixty-nine three-hundred-seventy three-hundred-seventy-one |
three-hundred-seventy-two three-hundred-seventy-three |
|
| three-hundred-seventy-four | three-hundred-seventy-five three-hundred-seventy-six three-hundred-seventy-seven |
||
| three-hundred-seventy-eight three-hundred-seventy-nine three-hundred-eighty |
|||
| three-hundred-eighty-one three-hundred-eighty-two |
three-hundred-eighty-three | three-hundred-eighty-four three-hundred-eighty-five three-hundred-eighty-six |
three-hundred-eighty-seven three-hundred-eighty-eight three-hundred-eighty-nine |
| three-hundred-ninety | three-hundred-ninety-one three-hundred-ninety-two |
three-hundred-ninety-three | three-hundred-ninety-four three-hundred-ninety-five |
| three-hundred-ninety-six three-hundred-ninety-seven |
three-hundred-ninety-eight | three-hundred-ninety-nine four-hundred four-hundred-one |
four-hundred-two |
| four-hundred-three | four-hundred-four | four-hundred-five four-hundred-six four-hundred-seven |
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| four-hundred-eight four-hundred-nine four-hundred-ten |
|||
| four-hundred-eleven four-hundred-twelve |
four-hundred-thirteen | four-hundred-fourteen four-hundred-fifteen |
|
| four-hundred-sixteen four-hundred-seventeen four-hundred-eighteen |
four-hundred-nineteen four-hundred-twenty four-hundred-twenty-one |
four-hundred-twenty-two | |
| four-hundred-twenty-three | four-hundred-twenty-four four-hundred-twenty-five |
four-hundred-twenty-six four-hundred-twenty-seven |
four-hundred-twenty-eight four-hundred-twenty-nine |
| four-hundred-thirty | four-hundred-thirty-one four-hundred-thirty-two |
||
| four-hundred-thirty-three | four-hundred-thirty-four four-hundred-thirty-five four-hundred-thirty-six |
four-hundred-thirty-seven | |
| four-hundred-thirty-eight | four-hundred-thirty-nine four-hundred-forty |
||
| four-hundred-forty-one four-hundred-forty-two four-hundred-forty-three |
four-hundred-forty-four four-hundred-forty-five four-hundred-forty-six |
||
| four-hundred-forty-seven | four-hundred-forty-eight four-hundred-forty-nine |
||
| four-hundred-fifty | four-hundred-fifty-one | four-hundred-fifty-two four-hundred-fifty-three |
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| four-hundred-fifty-four | four-hundred-fifty-five four-hundred-fifty-six |
four-hundred-fifty-seven four-hundred-fifty-eight |
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| four-hundred-fifty-nine four-hundred-sixty four-hundred-sixty-one |
|||
| four-hundred-sixty-two four-hundred-sixty-three four-hundred-sixty-four |
four-hundred-sixty-five | four-hundred-sixty-six four-hundred-sixty-seven four-hundred-sixty-eight |
|
| four-hundred-sixty-nine four-hundred-seventy |
four-hundred-seventy-one | four-hundred-seventy-two four-hundred-seventy-three |
|
| four-hundred-seventy-four four-hundred-seventy-five |
four-hundred-seventy-six | four-hundred-seventy-seven four-hundred-seventy-eight |
four-hundred-seventy-nine four-hundred-eighty four-hundred-eighty-one |
| four-hundred-eighty-two four-hundred-eighty-three four-hundred-eighty-four |
four-hundred-eighty-five | four-hundred-eighty-six four-hundred-eighty-seven |
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| four-hundred-eighty-eight four-hundred-eighty-nine |
four-hundred-ninety four-hundred-ninety-one |
four-hundred-ninety-two | four-hundred-ninety-three four-hundred-ninety-four |
| four-hundred-ninety-five | four-hundred-ninety-six four-hundred-ninety-seven |
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| four-hundred-ninety-eight four-hundred-ninety-nine five-hundred |
five-hundred-one five-hundred-two |
five-hundred-three five-hundred-four |
|
| five-hundred-five five-hundred-six |
five-hundred-seven | ||
| five-hundred-eight five-hundred-nine five-hundred-ten |
five-hundred-eleven five-hundred-twelve five-hundred-thirteen |
five-hundred-fourteen | |
| five-hundred-fifteen five-hundred-sixteen |
five-hundred-seventeen five-hundred-eighteen five-hundred-nineteen |
five-hundred-twenty five-hundred-twenty-one five-hundred-twenty-two |
five-hundred-twenty-three five-hundred-twenty-four five-hundred-twenty-five |
| five-hundred-twenty-six | five-hundred-twenty-seven five-hundred-twenty-eight five-hundred-twenty-nine |
||
| five-hundred-thirty five-hundred-thirty-one |
five-hundred-thirty-two | five-hundred-thirty-three | |
| five-hundred-thirty-four five-hundred-thirty-five |
five-hundred-thirty-six five-hundred-thirty-seven |
five-hundred-thirty-eight five-hundred-thirty-nine |
five-hundred-forty five-hundred-forty-one |
| five-hundred-forty-two five-hundred-forty-three five-hundred-forty-four |
five-hundred-forty-five five-hundred-forty-six five-hundred-forty-seven |
||
| five-hundred-forty-eight | five-hundred-forty-nine five-hundred-fifty five-hundred-fifty-one |
||
| five-hundred-fifty-two five-hundred-fifty-three five-hundred-fifty-four |
five-hundred-fifty-five five-hundred-fifty-six |
five-hundred-fifty-seven five-hundred-fifty-eight |
five-hundred-fifty-nine five-hundred-sixty five-hundred-sixty-one |
| five-hundred-sixty-two | five-hundred-sixty-three five-hundred-sixty-four five-hundred-sixty-five |
five-hundred-sixty-six five-hundred-sixty-seven |
|
| five-hundred-sixty-eight | five-hundred-sixty-nine | five-hundred-seventy five-hundred-seventy-one five-hundred-seventy-two |
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| five-hundred-seventy-three five-hundred-seventy-four |
five-hundred-seventy-five | ||
| five-hundred-seventy-six | five-hundred-seventy-seven five-hundred-seventy-eight |
||
| five-hundred-seventy-nine | five-hundred-eighty five-hundred-eighty-one |
five-hundred-eighty-two five-hundred-eighty-three |
five-hundred-eighty-four five-hundred-eighty-five |
| five-hundred-eighty-six five-hundred-eighty-seven five-hundred-eighty-eight |
five-hundred-eighty-nine five-hundred-ninety |
||
| five-hundred-ninety-one | five-hundred-ninety-two five-hundred-ninety-three |
five-hundred-ninety-four five-hundred-ninety-five |
five-hundred-ninety-six five-hundred-ninety-seven |
| five-hundred-ninety-eight five-hundred-ninety-nine six-hundred |
six-hundred-one six-hundred-two |
six-hundred-three six-hundred-four six-hundred-five |
six-hundred-six six-hundred-seven six-hundred-eight |
| six-hundred-nine six-hundred-ten |
six-hundred-eleven | six-hundred-twelve six-hundred-thirteen six-hundred-fourteen |
|
| six-hundred-fifteen | six-hundred-sixteen | six-hundred-seventeen six-hundred-eighteen |
|
| six-hundred-nineteen six-hundred-twenty |
six-hundred-twenty-one six-hundred-twenty-two six-hundred-twenty-three |
six-hundred-twenty-four six-hundred-twenty-five |
six-hundred-twenty-six six-hundred-twenty-seven |
| six-hundred-twenty-eight six-hundred-twenty-nine six-hundred-thirty |
six-hundred-thirty-one six-hundred-thirty-two |
six-hundred-thirty-three six-hundred-thirty-four |
|
| six-hundred-thirty-five six-hundred-thirty-six |
six-hundred-thirty-seven | six-hundred-thirty-eight six-hundred-thirty-nine |
|
| six-hundred-forty six-hundred-forty-one six-hundred-forty-two |
six-hundred-forty-three | six-hundred-forty-four | six-hundred-forty-five six-hundred-forty-six six-hundred-forty-seven |
| six-hundred-forty-eight six-hundred-forty-nine six-hundred-fifty |
six-hundred-fifty-one six-hundred-fifty-two |
six-hundred-fifty-three six-hundred-fifty-four six-hundred-fifty-five |
six-hundred-fifty-six |
| six-hundred-fifty-seven six-hundred-fifty-eight |
six-hundred-fifty-nine six-hundred-sixty six-hundred-sixty-one |
six-hundred-sixty-two six-hundred-sixty-three six-hundred-sixty-four |
six-hundred-sixty-five |
| six-hundred-sixty-six six-hundred-sixty-seven |
six-hundred-sixty-eight six-hundred-sixty-nine six-hundred-seventy |
six-hundred-seventy-one six-hundred-seventy-two six-hundred-seventy-three |
|
| six-hundred-seventy-four six-hundred-seventy-five |
six-hundred-seventy-six six-hundred-seventy-seven |
six-hundred-seventy-eight six-hundred-seventy-nine six-hundred-eighty |
six-hundred-eighty-one six-hundred-eighty-two |
| six-hundred-eighty-three six-hundred-eighty-four |
six-hundred-eighty-five | six-hundred-eighty-six six-hundred-eighty-seven six-hundred-eighty-eight |
|
| six-hundred-eighty-nine six-hundred-ninety |
six-hundred-ninety-one six-hundred-ninety-two six-hundred-ninety-three |
||
| six-hundred-ninety-four six-hundred-ninety-five six-hundred-ninety-six |
six-hundred-ninety-seven six-hundred-ninety-eight |
six-hundred-ninety-nine seven-hundred seven-hundred-one |
|
| seven-hundred-two seven-hundred-three |
seven-hundred-four seven-hundred-five seven-hundred-six |
||
| seven-hundred-seven | seven-hundred-eight seven-hundred-nine seven-hundred-ten |
seven-hundred-eleven seven-hundred-twelve seven-hundred-thirteen |
seven-hundred-fourteen seven-hundred-fifteen |
| seven-hundred-sixteen | seven-hundred-seventeen seven-hundred-eighteen |
seven-hundred-nineteen seven-hundred-twenty |
seven-hundred-twenty-one seven-hundred-twenty-two |
| seven-hundred-twenty-three seven-hundred-twenty-four |
seven-hundred-twenty-five | seven-hundred-twenty-six seven-hundred-twenty-seven seven-hundred-twenty-eight |
|
| seven-hundred-twenty-nine seven-hundred-thirty |
seven-hundred-thirty-one | seven-hundred-thirty-two seven-hundred-thirty-three |
seven-hundred-thirty-four seven-hundred-thirty-five seven-hundred-thirty-six |
| seven-hundred-thirty-seven seven-hundred-thirty-eight seven-hundred-thirty-nine |
seven-hundred-forty seven-hundred-forty-one seven-hundred-forty-two |
seven-hundred-forty-three seven-hundred-forty-four seven-hundred-forty-five |
seven-hundred-forty-six |
| seven-hundred-forty-seven seven-hundred-forty-eight |
seven-hundred-forty-nine seven-hundred-fifty |
seven-hundred-fifty-one seven-hundred-fifty-two seven-hundred-fifty-three |
|
| seven-hundred-fifty-four seven-hundred-fifty-five |
seven-hundred-fifty-six | seven-hundred-fifty-seven seven-hundred-fifty-eight seven-hundred-fifty-nine |
|
| seven-hundred-sixty seven-hundred-sixty-one seven-hundred-sixty-two |
seven-hundred-sixty-three seven-hundred-sixty-four |
||
| seven-hundred-sixty-five seven-hundred-sixty-six |
seven-hundred-sixty-seven seven-hundred-sixty-eight seven-hundred-sixty-nine |
seven-hundred-seventy seven-hundred-seventy-one |
|
| seven-hundred-seventy-two seven-hundred-seventy-three seven-hundred-seventy-four |
seven-hundred-seventy-five seven-hundred-seventy-six seven-hundred-seventy-seven |
seven-hundred-seventy-eight | |
| seven-hundred-seventy-nine | seven-hundred-eighty seven-hundred-eighty-one |
||
| seven-hundred-eighty-two | seven-hundred-eighty-three seven-hundred-eighty-four seven-hundred-eighty-five |
seven-hundred-eighty-six seven-hundred-eighty-seven |
|
| seven-hundred-eighty-eight | seven-hundred-eighty-nine seven-hundred-ninety seven-hundred-ninety-one |
seven-hundred-ninety-two seven-hundred-ninety-three |
seven-hundred-ninety-four seven-hundred-ninety-five seven-hundred-ninety-six |
| seven-hundred-ninety-seven seven-hundred-ninety-eight |
seven-hundred-ninety-nine eight-hundred |
||
| eight-hundred-one eight-hundred-two |
eight-hundred-three eight-hundred-four eight-hundred-five |
||
| eight-hundred-six | eight-hundred-seven | eight-hundred-eight | |
| eight-hundred-nine | eight-hundred-ten | eight-hundred-eleven eight-hundred-twelve eight-hundred-thirteen |
|
| eight-hundred-fourteen eight-hundred-fifteen |
eight-hundred-sixteen eight-hundred-seventeen |
||
| eight-hundred-eighteen eight-hundred-nineteen |
eight-hundred-twenty eight-hundred-twenty-one |
eight-hundred-twenty-two eight-hundred-twenty-three |
eight-hundred-twenty-four |
| eight-hundred-twenty-five eight-hundred-twenty-six |
eight-hundred-twenty-seven | ||
| eight-hundred-twenty-eight | eight-hundred-twenty-nine eight-hundred-thirty |
eight-hundred-thirty-one eight-hundred-thirty-two |
|
| eight-hundred-thirty-three | eight-hundred-thirty-four eight-hundred-thirty-five eight-hundred-thirty-six |
eight-hundred-thirty-seven eight-hundred-thirty-eight |
eight-hundred-thirty-nine |
| eight-hundred-forty eight-hundred-forty-one eight-hundred-forty-two |
eight-hundred-forty-three eight-hundred-forty-four eight-hundred-forty-five |
eight-hundred-forty-six eight-hundred-forty-seven eight-hundred-forty-eight |
|
| eight-hundred-forty-nine eight-hundred-fifty |
|||
| eight-hundred-fifty-one eight-hundred-fifty-two |
eight-hundred-fifty-three eight-hundred-fifty-four |
eight-hundred-fifty-five eight-hundred-fifty-six |
eight-hundred-fifty-seven |
| eight-hundred-fifty-eight eight-hundred-fifty-nine eight-hundred-sixty |
eight-hundred-sixty-one | ||
| eight-hundred-sixty-two eight-hundred-sixty-three eight-hundred-sixty-four |
eight-hundred-sixty-five eight-hundred-sixty-six |
eight-hundred-sixty-seven | eight-hundred-sixty-eight eight-hundred-sixty-nine eight-hundred-seventy |
| eight-hundred-seventy-one | eight-hundred-seventy-two | ||
| eight-hundred-seventy-three eight-hundred-seventy-four eight-hundred-seventy-five |
eight-hundred-seventy-six | eight-hundred-seventy-seven | eight-hundred-seventy-eight eight-hundred-seventy-nine eight-hundred-eighty |
| eight-hundred-eighty-one eight-hundred-eighty-two eight-hundred-eighty-three |
eight-hundred-eighty-four eight-hundred-eighty-five eight-hundred-eighty-six |
eight-hundred-eighty-seven | |
| eight-hundred-eighty-eight | |||
| eight-hundred-eighty-nine eight-hundred-ninety |
eight-hundred-ninety-one | eight-hundred-ninety-two eight-hundred-ninety-three eight-hundred-ninety-four |
|
| eight-hundred-ninety-five eight-hundred-ninety-six eight-hundred-ninety-seven |
|||
| eight-hundred-ninety-eight eight-hundred-ninety-nine nine-hundred |
nine-hundred-one nine-hundred-two |
||
| nine-hundred-three nine-hundred-four |
|||
| nine-hundred-five | nine-hundred-six | nine-hundred-seven nine-hundred-eight nine-hundred-nine |
nine-hundred-ten nine-hundred-eleven nine-hundred-twelve |
| nine-hundred-thirteen nine-hundred-fourteen |
nine-hundred-fifteen nine-hundred-sixteen nine-hundred-seventeen |
||
| nine-hundred-eighteen nine-hundred-nineteen |
nine-hundred-twenty nine-hundred-twenty-one nine-hundred-twenty-two |
||
| nine-hundred-twenty-three nine-hundred-twenty-four nine-hundred-twenty-five |
nine-hundred-twenty-six nine-hundred-twenty-seven nine-hundred-twenty-eight |
nine-hundred-twenty-nine nine-hundred-thirty |
nine-hundred-thirty-one nine-hundred-thirty-two |
| nine-hundred-thirty-three nine-hundred-thirty-four |
nine-hundred-thirty-five nine-hundred-thirty-six |
nine-hundred-thirty-seven | |
| nine-hundred-thirty-eight | nine-hundred-thirty-nine | nine-hundred-forty nine-hundred-forty-one |
|
| nine-hundred-forty-two | nine-hundred-forty-three | nine-hundred-forty-four | nine-hundred-forty-five |
| nine-hundred-forty-six nine-hundred-forty-seven nine-hundred-forty-eight |
nine-hundred-forty-nine nine-hundred-fifty |
||
| nine-hundred-fifty-one nine-hundred-fifty-two |
|||
| nine-hundred-fifty-three nine-hundred-fifty-four nine-hundred-fifty-five |
nine-hundred-fifty-six nine-hundred-fifty-seven nine-hundred-fifty-eight |
nine-hundred-fifty-nine nine-hundred-sixty |
|
| nine-hundred-sixty-one nine-hundred-sixty-two nine-hundred-sixty-three |
nine-hundred-sixty-four nine-hundred-sixty-five |
nine-hundred-sixty-six nine-hundred-sixty-seven nine-hundred-sixty-eight |
|
| nine-hundred-sixty-nine nine-hundred-seventy nine-hundred-seventy-one |
nine-hundred-seventy-two | ||
| nine-hundred-seventy-three nine-hundred-seventy-four |
nine-hundred-seventy-five | nine-hundred-seventy-six nine-hundred-seventy-seven nine-hundred-seventy-eight |
nine-hundred-seventy-nine nine-hundred-eighty nine-hundred-eighty-one |
| nine-hundred-eighty-two nine-hundred-eighty-three nine-hundred-eighty-four |
nine-hundred-eighty-five nine-hundred-eighty-six |
||
| nine-hundred-eighty-seven nine-hundred-eighty-eight nine-hundred-eighty-nine |
nine-hundred-ninety nine-hundred-ninety-one nine-hundred-ninety-two |
||
| nine-hundred-ninety-three nine-hundred-ninety-four nine-hundred-ninety-five |
nine-hundred-ninety-six nine-hundred-ninety-seven nine-hundred-ninety-eight |
nine-hundred-ninety-nine one-thousand one-thousand-one |


In this blog I will be writing about problem solving. With every agonizingly frustrating day that passes its become exceedingly apparent
that this is probably the most important attribute youll need to enhance to make it in this industry. Every facet of this initial fondations
course has some form of problem solving involved. From the basic page layouts to styling to the much more involved interactivity that is
utilised through Javscript you're making decisions that have problems needing to be resolved.
Rubber ducky was the first method I learnt through
the course which is simplifying the problem and trying to relay it in a way that a complete learner could understand.
This method has helped me out tremendously especially during the freecode camp portions and also later in the Kata sections.
During kata however I feel the method that I used the most as it was usually the first thing to try if i wasnt initially sure about what to do was to 'Try Something'.
Seems simple but because we had the benifit of the pre-written asserts it meant that we'd get feedback from the error codes on what was expected.
- Pseudocode:
- Try Something:
- Rubber Ducky:
- Reading Error Messages:
- Console.logging:
- Googling:
- Asking Peers:
- Asking Coaches:
- Reflection:
These are the main methods used so far. Console logging and Reading the Error Messages are two that im still getting my head around.
Trying to work out exactly what im looking at but the rest im feeling pretty confident with. Asking Peers and Coaches was hard at first due to not wanting
to feel dumb but its a natural part of the learning process so I knew it was necessary overcome that fear.
I was sceptical at the start of the course just how much help Reflection was going to be. My mind has changed on this.
The value of being able to think back on what you were trying to achieve and then what you did to get there and then further more seeing what
others did helps to imprint on what the best option would be in the future.


--- layout: main title: Hackference 2015 - Sponsor Us TODO: all of the page plz ---
Hackference is three unique days of learning, collaboration and creating, taking place in two venues across central Birmingham. 250 Conference attendees will be enjoying talks focusing on new technology, creating amazing experiences - watching and participating in workshops, and 150 Hackathon attendees will be part of one of the biggest hackathons in the United Kingdom.
October 2nd
{% include conference-map.html %}October 3rd - October 4th
{% include hackathon-map.html %}We're lucky at Hackference to have a diverse mixture of attendees - previous event's have had plenty of student, startup, corporate and freelance attendees from all over the skill spectrum. Although we love local developers and designers, we are even more lucky to have such a large number of them travel in from across the country.
The majority of our speakers are renowed on a national level - and some, international - and travel to us from all across the world. They are active on social media, and usually have a very large following as they are considered trendsetters, influencers and brand promoters.
Supporting Hackference is a truely amazing way to get your company exposed to highly skilled and motivated developers and designers who are on the leading-edge of technology. In addition to helping us cover the costs of organising such an event - you are also helping us exhibit the brilliant, and sometimes, overlooked Birmingham technology scene. With your support, it mean's we can provide a fantastic event to our attendees.
As the bare minimum everyone that sponsors is listed on our sponsors page with logo and a page explaining who you are and why you are supporting Hackference 2015. Also, a thank you will be sent out from our social media accounts and a mention in our periodic e-mail.
We have plenty of other things that you can sponsor, either adding in or swapping with items in the packages.
If you have any questions, or specific goals or requirements, please don't hesitate to get in contact; we're always open to ideas and will try to accommodate as much as possilbe.
You can contact us via:


INNOCENCE ---------
Five decades ago, Andreas (Charles Tingwell) and Claire (Julia Blake) shared a passionate romance in Belgium, following World War II, then went their separate ways. Now, a half-century later, they discover that they live in the same Australian city and it soon becomes evident that their love of so long ago has not faded. Although Andreas is a widower, Claire is still married and is torn between her loyalty to husband John (Terry Norris) and the passion she feels for her former lover in "Innocence."
LAURA:
When widower Andreas (Charles 'Bud' Tingwell, "The Castle") discovers his first love resides in the same Australian city, he writes Claire (Julia Blake, "Hotel de Love") a sentimental letter, begging for a meeting. Claire, married for forty-five years to John (Terry Norris, husband of costar Julia Blake), guiltily meets Andreas for dinner, but states that while it was nice seeing him, there's no reason to stir up old feelings. But Andreas persists in pitching woo and Claire finds herself falling in love again in "Innocence."
Writer/director Paul Cox, who explored middle-aged romance in "Lonely Hearts" and dying with dignity in "A Woman's Tale," combines those themes in this septuagenarian love story. Claire, whose marriage has been sexless for twenty years, initially opts for keeping the current peace. When Andreas is asked to be present at the disinterment of his wife's grave, he asks Claire to come for moral support. She's so moved by his distress and his appeal to stay the night with him, that she decides to celebrate life. Claire also decides that she's too old to lie.
John, a man who hasn't voiced his true feelings in decades, is dumbstruck by his wife's confession of infidelity. He calls in their son David (Robert Menzies), a doctor, hoping for a physical or mental analysis that will explain things. He tries to physically restrain Claire from going out, then tries to woo her back. His delighted surprise at hearing her tell an off-color joke is both touching in the depth of feeling Norris brings to the scene and telling in how little he really knows the woman he's spent most of his life with. Norris gives a powerful and layered performance.
The film belongs to Julia Blake, however, a truly beautiful woman who glows in her rediscovered love. Blake conveys the heartbreak of breaking another's heart while simultaneously expressing the joy of living to the fullest. Tingwell makes Andreas the more Bohemian, worldly choice next to the staid John, but his performance has just a touch of self congratulation about it. Claire tells Andreas, whose strict father came between them all those years ago, that she doesn't think he fought hard enough for her then and Tingwell's portray of Andreas keeps him slightly suspect the second time around.
Cox has written some beautiful dialogue, from reflections on the definition of a memory to Andreas' humorous baiting of a hospital chaplain, although he occasionally meanders into overt melodrama. Reflections are featured visually
throughout the film as well, from flashbacks of the young lovers seen in the windows of a passing train to Claire's peering into a mirror (where her younger self dissolves into her present). Overall, however, the film has the look of a British TV production.
"Innocence" stumbles occasionally, but it's refreshing to see senior citizens treated as sexual beings with needs and desires and the ability to light up a room with their presence. You may never see your grandparents in quite the same light again.
B-
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{% extends 'layouts/main.html' %} {% block title %}Pairs{% endblock %} {% block content %}
Check the boxes next to the pairs you'd like to approve, then click the 'Make Pairs' button.
Click the arrows next to each column to sort pairs; click the ≡ icon to search for terms.
{% endblock %}


If I could sum up the best programmers I’ve worked with in a sentence, it would be:
They know what to build and they know how build it
The later is almost trivial to achieve compared to the former, but without both, you won’t be doing your best work.
I’ve only recently come to understand how equally important both building “software right” and building the “right software” are. As a young engineer, working as a consultant, at the bottom of an org-chart, I was given things to build so I built them. “Knowing how to build” was all it seemed I needed to know. Fortunately, getting good at knowing how to build software is straightforward (if time consuming). It just takes experience and practice, and there are myriad tools, techniques, and other resources to help.
Knowing what to build, however, is pretty difficult. Solving problems is often easier than knowing which ones need solving.
What problem are we solving?
Knowing what software to build requires specialized knowledge beyond software development. Generically, you have to understand what problem exists that needs solving. You then have to evaluate all the ways in which that problem could be solved (with or without software). And then you have to weigh those solutions against a variety of constraints: time, money, chance of success, cost of failure, etc.
Unlike building software, the processing of deciding what to build is not that generalizable. You have to have specific knowledge of the domain in question as well as access to data about the organization you’re working for. My (limited) understanding of the utility industry while working at Opower in no way prepared me for working at Stitch Fix, where I must understand retail, logistics, and…women’s fashion.
Enter the product manager
The way most teams deal with balancing what to build against how to build it is to separate duties. On one side, you have developers who are great at building software. On the other are product managers/analysts/whatevers that understand the domain as well as the specific business. The two teams are intended to work together: product managers identify the right problems to solve, and developers write software to solve them.
This is a nice idea, but it has practical problems. The product manager is rarely technical enough to understand software at a detailed level. Even when they are (for example, a former developer who moved into product management), they have no real skin in the game to actually develop the solution. Whatever decisions they make, it’s the developers that are subject to them.
The developers, lacking an understanding of the business, won’t propose solutions that prioritize solving the underlying problem, but instead prioritize their own team or work. Depending on the developers, you might get solutions that:
- are easy to build and maintain, but don’t really solve the problem.
- are more interesting to work on than an optimal solution.
- use new technology, putting the solution at risk, AKA “Résumé-Driven-Development”
I’ve done all of these things myself, and seen talented programmers to the same.
Without a firm understanding of the purpose of the software, can anyone blame them?
When developers complain about being treated as replaceable parts, this is precisely why that treatment happens. Developers proposing solutions without a solid understanding of the problem being solved are not acting like partners; they are acting like ticket-takers. It’s demoralizing to be a ticket-taker, and you aren’t going to do your best work this way.
Instead of acting like machines that ingest software and produce source code, developers need to act like partners.
Partners
With a product person lacking necessary technical skills and responsibility to properly balance technical solutions, and a development team lacking an understanding of the problem being solved, someone has to meet in the middle.
Although acquiring technical proficiency is straightforward, it’s incredibly time-consuming. There’s no reasonable way a product manager can gain a deep enough knowledge of programming to help. But, understanding the problems being solved, why they are important, and how to know when they’ve been solved is much simpler. This is what the developers should be doing.
Which brings me back to the best developers I’ve worked with. Although technically skilled, they weren’t necessarily programming wizards. What made them effective was that they took the time to understand the problem being solved. They took the time to understand what measurable impact a solution might have, and why it was important. They learned about the specifics of the business where they worked.
They were doing their best work. They were engaged with what they were doing. They delivered results and could point out how what they did had a positive impact on the organization.
When the developers understand why they are doing what they’re doing and have a way to know that what they did achieved its goals, everyone benefits. The organization gets more optimal solutions, and the development team is much happier. Instead of acting like (and being treated like) ticket-takers, the developers are treated as partners. And partners are trusted.
Instead of pressured to deliver something quickly, a trusted team can have honest conversations about delivery schedules. A trusted team doesn’t have to justify their every technical decision to someone that can’t understand it. A trusted team doesn’t spend a lot of time getting “permission” to work a particular way. A trusted team spends most of its time building great software that solves real problems and has a measurable impact. Who wouldn’t want to work on that team?
This is a massive payoff for spending time understanding the team’s place in an organization. Gaining said understanding, however, can be tricky.
Understanding
To get an understanding of the problems your team exists to solve, you need to connect what your team does directly to the goals of the organization. Ideally, you can point to the features you build, and show how they affect the data used by the company’s decision-makers.
This may be impossible at your current job. It is likely difficult. If your company culture can withstand it, start asking questions. Get curious about what you’re doing and why. Talk to the people involved about how they work and how they make decisions. If your company culture makes this difficult, or you don’t find any answers, start polishing your résumé. You are not positioned to do your best work.
If you can’t (or don’t want to) hone this skill where you’re at, my recommendation is to find a product company that sells its product directly to the product’s users. The business model of such a company is very easy to understand. Everything such a company does can be traced to reducing cost or increasing/protecting revenue. All you need to learn is the specifics of the company’s line of business.
I’m not saying that you can’t do your best work at an enterprise software company, a non-profit, or as a consultant. I’m just saying that if you want to start getting good at knowing what software to build, you want to start off simple, and a business model based on direct sales tends to be pretty simple.
Whatever you do, start asking yourself (and others), why you are building what you are building. What problem does it solve? What other solutions were evaluated? Be curious, and you’ll start doing your best work.


SDL RPC Request for expanded information about a supported system/HMI capability
@since SDL 4.5
Convenience init
- (nonnull instancetype)initWithType:(nonnull SDLSystemCapabilityType)type;
init(type: SDLSystemCapabilityType)
The type of system capability
A SDLSystemCapabilityType object
Convenience init
- (nonnull instancetype)initWithType:(nonnull SDLSystemCapabilityType)type
subscribe:(BOOL)subscribe;
init(type: SDLSystemCapabilityType, subscribe: Bool)
The type of system capability
Whether or not to subscribe to updates of the supplied service capability type
A SDLSystemCapabilityType object
The type of system capability to get more information on
SDLSystemCapabilityType, Required
@property (nonatomic, strong) SDLSystemCapabilityType _Nonnull systemCapabilityType;
var systemCapabilityType: SDLSystemCapabilityType { get set }
Flag to subscribe to updates of the supplied service capability type. If true, the requester will be subscribed. If false, the requester will not be subscribed and be removed as a subscriber if it was previously subscribed.
Boolean, Optional
@property (nonatomic, strong, nullable) NSNumber<SDLBool> *subscribe;
var subscribe: (NSNumber & SDLBool)? { get set }


CHAOS SWORDS
Go full screen
Player 1: Movement - Cursor Attack - Space Player 2: Movement - NumPad (8654) Attack - Enter


Hi, I’m Hallessandro, a Brazilian guy that works as software developer and like to write posts about technology and play video games in the free time. Above you will found the links for all my social network, but if you want, you can also add me on the PSN for play some games, my nickname is MrDvilla.
Technologist in Systems For Internet by the Federal Institute of Education, Science and Technology of the Rio Grande do Norte - Brazil.
I start my professional carrear in techology in 2017, working as interniship in two different companies. The first was the ESIG Software e Consultoria, where I worked as Support and Customer Service.
The second company where I worked in 2017 was RN Tecnologia, where I worked professionally for the first time as developer. On this company I worked as a Full Stack Developer in development of a medical system, developed with Angular 4 for the frontend, Spring Boot for the backend, Java for the Android app and Ionic 3 for the iOS app.
In this position, in addition to what I said above I also worked with PostgreSQL and Firebase.
From 2018 until now, I work in the ESIG Software e Consultoria, in a full time position of software developer. In this position I already worked in two different teams with two different stack of technology and focus.
The first team that I worked at ESIG, gives me the oportunity of work with Java, JSF, Hibernate, Jasper Reports and others technologies linked to the Java ecosystem, besides GIT, SVN, Jenkins and PostgreSQL.
The second team that I worked at ESIG is the QuarkClinic team, responsible by a clinical system used for a large numbers of clinics in some states of Brazil. At this position I working with AngularJS, Groovy, Grails, Java and PostgreSQL.
Besides this, I have experience with Python, Django, JavaScript, Node, Electron and Meteor. Although I have worked more with Java, my prefered language is JavaScript.
My first language is Portuguese, because I’m from Brazil, but we can talk in English and maybe in French, even though my French is not yet at a very advanced level.


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Target Audience
The target audience of the Hurricane Parrot site is described below:
- WHO: It can be used by anyone who is looking to find out about the weather. It can be used by anyone who has access to the internet. Among other people, it could be used especially used by: Agriculturalists, policy makers, private sector.
- WHERE: Visitors are from the state of Idaho, and from around the World.
- WHEN: At any time of the day.
- WHY: Because they are planning their day. People who visit this site can be locals, or anyone from outside of Idaho who wants to know the weather forecast for any given time of the month.
Personas
The creation of a representative user based on available data and user interviews. Though the personal details of the persona may be fiction, the information used to create the user type is not.
This section requires some research about the website visitors by looking into the purpose and the industry, visiting similar sites and preforming surveys to discover what scenarios are relevant to the visitor. Develop two personas (Links to an external site.) to describe the target audience for the weather site. This section should describe the target audience being judicious about the selection of the most plausible demographics.
Some visitor demographic information to consider is age, income, education, experience with the topic and family status. Additional audience information would include where, when and how the visitor will be accessing the site. What device will they be using?
Demographics: The common variables that are gathered in demographic research include age, sex, income level, race, employment, location, homeownership, and level of education.
Devon S. Bass Morrison
- Occupation: Farmer.
- Demographics and Education: 60 years old male. High income level. Owner of his own farmland. Uncomplet college level.
- Goals and motivations for using the site: He knows weather forecasting is not fortune-telling, he knows it is a science. He has save to his business several thousands of dollars on money.
- Social: Married man, socially very active. He likes to work the land by himself. Member of a big family. The farmland he owns is worked by him and two of his sons. They have their own corporation with 50 workers.
- Quote: "The work we do requires we keep on eye on the future. One never knows what the weather and the Nation's economy is preparing for the future. It is always good idea to keeps some savings and stores.
Leila M. Huff
- Occupation: High school teacher.
- Demographics and Education: 25 years old female. Middle income level. Independent worker. College level.
- Goals and motivations for using the site: She works on the field of Science teaching biology. When doing so, she prefers to take her entire classes every to weeks in a trip to the countryside.
- Social: Very active woman, teaching in 3 different highschools. She also liker going out every weekend with her boyfriend and friends.
- Quote: "I'm a very passionate person about what I do, and because of that I want teenagers in my classes to feel the same. I started loving Biology in a school trip to the mountains."
Scenarios
Scenarios describe the visitors purpose in visiting and using the site. Questions are made, of which the site content can provide the answers.
- How will be the weather this morning/afternoon/evening?
- Is going to snow/rain this weekend? I'm planning to have a barbecue with my friends.
- How's going to be the weather next week on Idaho? I'm plannig to spend some days there.
- I'm planning on seeding the land next week. Are we going to have the best weather conditions?