Context
Game Jam entry for LudamDare 45 Game Jam
Role
Unity C# Programmer
Jam
Chris Wong
Tamiko Tadeo
Kenny Cheung
Danielle Ragas
Nancy Chen
Josephine Ryu
Finished Raccoon Bikini Game is a Unity puzzle game built by a team of artists and programmers for the Ludamdare 45 Game Jam. The jam lasted for 72 hours and 2611 games were submitted. The theme was “Start with nothing”.
Please try our game: Finished Raccoon Bikini Game
We came up with the idea by a cascade of memes and inside jokes from past projects that eventually merged and became something that had possibility for gameplay dynamics between mechanics. When we step back and took a good look, we thought
to ourselves, how did it go so wrong in such a short time.
To match the theme, we started brainstorming gameplay mechanics that starts with nothing that players can build upon. We landed on the idea for building a level out of nothing, with constraints that makes both building the level itself and
controlling the character to clear it engaging. Why Bikini and Racoon? The answer to that might be found at the end of the internet.
An inventory of objects is provided to players including platforms, bikinis and the racoon. Players are able to drag the objects onto the play area to composite the level. Player can play and test out their composition anytime and retry
infinitely.
The puzzle aspect comes from the constraints on how players can use the objects. Some objects are wrapped around a colour outline, which indicates that the objects can only be dragged onto area on the screen with the same colour. The
coloured area, objects provided and their colour is predefined and allow us to define variety of creative puzzles.
Another mechanic is that objects sometimes comes in bundle of multiple objects. Players do not have the power to change the layout, and are only able to drag the entire cluster into the play area. This allow us to create level with forced
structure that lead players to solve the puzzle in a specific way.
My main role is to implement interative features such as drag and drop for items, coloured area boundary detection.
One of the challenge came surprising when I was implementing the coloured area boundary detection. Unity has provided easy detection methods for when two objects touches each other. But it does not support detection for when two a sprite is
fully covered in another. I had to research and came up with the solution.
The surprise comes from how there is not many solution suggested in this topic. I then turn to my teammates for help. We discussed a few possibilities, such as using Raycast, or SpriteRenderer.isVisible, but none of these solutions are able
to achieve detection of sprite fully in range. Finally, we came up with a custom and not very efficient way to solve it.
We built 4 small Trigger Collider to the 4 corners of the item If the 4 corner colliders are all within the area of the coloured area, that indicates the object is fully covered.
Upon more research after the Jam, I found the bounds.extents field from colliders. It will provide us the width and height of the object. We would be able to perform simple checks with the width and height information, which would be more
efficient than having 4 small colliders on each object.
Other than programming, I am also dedicated to making interesting level. A 72 hours jam left us very little time left to design levels after the code implementation. Despite the time constraint, we managed to build 20 levels and ordered
them in difficult.
The 20 levels includes tutorial levels, which gradually teaches game mechanics to players one at a time. We intend to improve the player experience of the game by providing a smooth difficulty ramp. After the tutorial levels, the player
should have learned all the techniques required to clear the game. For example, we teach players that colour outlined objects can only be dragged onto coloured area by a simple level, while another level teaches player that a floating
platform’s motion can be reversed if it hits a box.
After the game is submitted, I regret how one of my level turned out. Level 13 provides players a few simple elements to place, and tight coloured area that constraints their position.
The solution is shown in the second image. In order to pass the level, both of the ladders are required to be placed at the very edge of the coloured area, players also have to hold down their right arrow key when starting the game to
maximize their speed to the right in order for the raccoon to reach the ladder.
The solution requires precise and careful control of the character and placement of the ladder, which derived from the rest of the levels in game, where mainly prerequisite planning and intelligent interaction between elements are craved
from players. The level is the only action packed level that becomes out of place.
If I have the chance and time, I would definitely have discarded the level and rethink the aspect I should challenge the players with.
I have gathered experience in collaborating with other programmers under tight time constraints. The ability to clearly articulate ideas is as crucial as building clean code that smoothly merges with features built by others.
I have also learned my lesson in design that is applicable to many media. It is important to understand what the end-users expect from our product. In this case, a smart puzzle instead of an action-game level is desired.
Please try our game: Finished Raccoon Bikini Game


On week 8, I used the Tajima Sai, a commercial embrodiery machine, to sew a hat. I put the word "ICE" on the hat. Before sewing, I put backings both inside and outside the hat so that the machine has a place to pull its needles without damaging the hat itself. I put hard backings inside the hat; and soft backings outside the hat. The soft backing dissolves in water, so the outside of the hat would look perfectly clean and normal after I use water to wipe the soft backing off. I used the "Hoop Master" to fix the hat to make sure that it wouldn't get pulled around by the needle when the machine was sewing.
I also made a pillow case using three pieces of patterned clothes using the Viking Emrald 118 03 Sewer. First, I cut off pieces of different size from the clothes I chose and ironed them. I pinned them together and sewed them. Then I repeated the same process until all the edges were sewed together. Finally, I turned the pillow case inside out, ironed it, and sewed it one more time to ensure its durability.
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Also, TonyL has offered to help with the TUI!
Hi @CrisF I'm making a separate simple TUI for oracles (most of the functionality was implemented in gateways TUI, so just split things mostly) and want to add support of files upload/download (as binary) and text publish/read. If some automation of external data publishing on oracle needed - I'm glad to help and implement it in current TUI. I'm very sorry, but not understood from pad you've shared what actually needs to be implemented


In the last few years, satellite remote sensing has produced vast amounts of observation data from a wide variety of sources. A vast array of different sensor types allows for the observation of different terrestrial, oceanic and atmospheric geophysical and geobiochemical parameters at different spatio-temporal resolutions, which amounts to a huge quantity of greatly under-exploited data. In this regard, huge exploitation potential exists within these datasets, particularly for data mining and machine learning approaches, which have proved to be useful for a wide variety of applications, ranging from high-resolution reconstruction to the spatio-temporal analysis and segmentation of system dynamics. To fully exploit this potential, however, a better understanding, representation and modeling of the interactions between interest variables and the processes behind these interactions is needed. Improved comprehension of variable relationships would lead to improved quality, power and accuracy of geophysical and geobiochemical process models and representations, by tackling shortcomings associated both with the available datasets and with current models and representations. In this context, this thesis project has two major objectives:
Variable relationship characterization is a classical problem in signal and image processing, for which several approaches have been proposed (cf. [6, 22, 28]). From a methodological point of view, we aim at identifying linear or non-linear transfer function dictionaries capable of accurately summarizing and representing the relationship between two interest variables. To do so, we will exploit non-negative and sparse decomposition formulations for source separation. From a mathematical point of view, the classical blind source separation problem (cf. [22]) is generally stated as the observation-based decomposition of a signal or image yn into K additive components associated with different processes or sources:
|
| (1) |
where yn ∈ ℝI, coefficient αnk ∈ ℝ quantifies the contribution of component bk ∈ ℝI, which corresponds to the
k-th reference signal or image and ωn ∈ ℝI is a multivariate white Gaussian noise process.
In this context, non-negative and sparse formulations (cf. [8, 19, 21, 27]) will constraint the problem to allow
for the development of more suitable models and to ensure the identifiability of the model from a set of
observations of signals or images {y}n.
Our objective is to extend model (1), and its non-negative/sparse constrained variants, to the identification and
separation of linear or non-linear relationships between two variables xn and yn from a representative set of joint
observations {x,y}n. The core hypothesis here lies in the validity of the following model:
|
| (2) |
where xn ∈ ℝJ, yn ∈ ℝI, coefficient αnk ∈ ℝ quantifies the amplitude of the relationship fθk(xn) ∈ ℝI for
observations xn and yn, which corresponds to the k-th element of the linear or non-linear transfer
function dictionary, and ωn ∈ ℝI is a multivariate random noise process that characterizes global
incertitude.
Each transfer function fθk is characterized by a set of parameters θk, which we aim to identify along with the
mixing coefficients αnk.
With respect to model (2), our main objectives are:
As previously stated, the amount of multi-source ocean remote sensing data available has experienced
considerable growth in the last few decades, which has been a key factor for more thorough analysis possibilities
leading to a better understanding of upper ocean dynamics, ocean circulation and atmosphere-ocean
interactions.
In particular, a considerable amount of effort has been put into understanding the relationships and interactions
between different oceanic physical quantities (cf. [3, 4, 10, 12, 20, 26]). In [28], the authors show that
surface currents can be directly retrieved from SSH (Sea Surface Height) fields using the geostrophy
balance. Additionally, the existence of a sea surface dynamical mode, referred to as the SQG mode
(Surface Quasi-Geostrophy), characterized by a linear transfer function between ST (Sea Surface
Temperature) and SSH, has been exhibited both from theoretical and observation-driven studies
(cf. [10, 11, 13, 15, 16, 17, 18, 28]). Indeed, recent work points out that upper ocean dynamics
may be characterized by local SSH-SST linear relationships that correspond precisely to fractional
Laplacian operators (cf. [10, 13, 16, 17, 18, 28]), and uses this characterization to draw the following
conclusions:
These findings motivated the development of new statistical observation-driven methods to combine available
SSH information with other remote sensing data, such as microwave SST, or in situ data (cf. [3, 5, 28, 30]).
Whereas [9, 14] investigated Fourier-based representations of linear SSH-SST couplings, Tandeo et al. (cf. [28])
hypothesize that upper ocean dynamics may be predicted from surface density horizontal variations possibly
dominated by SST fields. They exploit this idea to explore linear transfer functions between SSH and SST by
introducing a multi-modal decomposition, using a latent class regression model, such that local
SSH-SST relationships are described by one among a predefined number of possible linear transfer
functions.
An important limitation of latent class regression models is that they can only account for a finite set of linear
transfer functions. By contrast, the SQG mode is characterized by a single class of linear transfer functions,
namely the fractional Laplacian operator γΔ1∕2, but involves a free positive scalar parameter γ (cf.
[11, 18, 28]), which relates to local geophysical features (e.g., mixed layer depth). As a result,
SQG-like upper ocean dynamics, characterized by continuously-varying γ parameters, may not be well
represented by latent class regression models. Such considerations lead us to consider a non-negative
linear version of model (2) (linear transfer functions fθk, non-negative mixing coefficients αnk),
where the relationship between SST and SSH is a non-negative linear combination of K linear
regressions. An observation-wise adjustable amplitude parameter α ∈ ℝ+ is used to take into account
local variations in the strength of the relationships, in a manner similar to coefficient γ in SQG
dynamics.
It should be noted that the considered non-negative decomposition suggests that the dynamical modes in play do
not exclude each other but are rather superimposed. This is significantly different from the basis assumption
made by the latent class regression model developed in [28], which assumes that only one mode is active at any
space-time location. Whereas previous works focused either on spatially homogeneous linear SSH-SST couplings
(cf. [9, 14]) or on a finite set of linear SSH-SST transfer functions (cf. [28]), we consider here a richer
representation, accounting for all mixing possibilities between a finite set of families of linear SSH-SST
relationships.
To our knowledge, this thesis topic is the first study to exhibit, from an observation-driven analysis, the
continuous superimposition of different dynamical modes associated with different types of SSH-SST
transfer functions. We further stress that multi-modal approaches are necessary, as a single linear
transfer function does not suffice to capture the complex non-stationary space-time variability of
mesoscale upper ocean dynamics. Previous work rather explored regional mean transfer functions (cf.
[9, 14]) or latent class mixture models (cf. [28]), even though obtained results truly support the
existence of superimposed dynamical modes, rather than mutually exclusive ones as assumed in
[28].
The separation and identification of contributions associated with different types of sources or processes from
multiple observations is a general problem in signal and image processing (cf. [22]). Significant advances in blind
source separation have been reported in the last decade with the introduction of non-negative and sparse
formulations (cf. [8, 19, 21, 27]). In the context introduced by the classical blind source separation problem
formulated in (1), non-negativity constraints are used to develop part-based representations that have
multiple applications, ranging from learning parts of faces or semantic features of text (cf. [19]) to
audio blind source separation of convolutive mixtures (cf. [8]). Sparsity constraints, on the other
hand, allow for dimensionality reduction and simpler representations of high-dimensional data (cf.
[29]), thus leading to the development of simpler models and representations that are easier to
understand.
Inspired by latent class regression models (cf. [6]) applications to satellite-derived remote sensing oceanic data,
as in [28], we introduce a non-negative linear version of model (2) (linear transfer functions fθk, non-negative
mixing coefficients αnk), and develop algorithms for model parameter estimation. Such a model allows us to
address decomposition problems involving mixed linear contributions, thus generalizing linear mixture problems
involving linear regressions and latent class regression models. In particular, the non-negativity constraint
provides the mean for distinguishing the form of the linear relationships between variables xn and yn from the
magnitude of these relationships.
Compared to state-of-the-art models like classical regression and latent class regression models, the key features
of the proposed model are two-fold: first, it accounts for possibly varying magnitudes of the linear relationships;
second, it explicitly evaluates the relative importance of different linear relationships. This is of wide
interest for a variety applications such as transfer function identification, regime-switching dynamics,
etc.
Following this developments, an alternative reformulation of the problem, based on the estimation of locally
linear operators representing the relationships between each observation pair (xn,yn), and the subsequent
decomposition of such regressions using dictionary learning approaches, is considered. We explore such
formulation to address robustness issues with the original model, and to allow for the seamless integration of
different constraints by selecting appropriate dictionary learning approaches, such as sparsity by means of KSVD
(cf. [1, 24]), which has already been used extensively for a wide variety of applications, including analysis
sparse model learning (cf. [23]), transformation learning (cf. [7]) and face recognition (cf. [31]), and
exists in several variants, including a non-negative variant (cf. [2]) and a double spase variant (cf.
[25]).
Finally, even though our original motivation was to overcome some drawbacks of classical models for physical
oceanography applications, the proposed model and algorithms proved to have much greater capabilities.
They constitute, in fact, a general tool for the analysis of variable relationships, i.e., for analyzing
and synthesizing variable correlation. Such a tool would allow for the analysis, segmentation and
reconstruction of the relationship between any two interest variables in any given setting, thus allowing
for a deeper insight into the nature of said relationship and the processes and phenomena behind
it.
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[3] M. M. Ali, D. Swain, and R. A. Weller. Estimation of ocean subsurface thermal structure from surface parameters: A neural network approach. Geophysical Research Letters, 31(20):n/a–n/a, 2004. L20308.
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[8] C. F\E9votte, N. Bertin, and J. Durrieu. Nonnegative matrix factorization with the itakura-saito divergence: With application to music analysis. Neural Comput., 21(3):793–830, Mar. 2009.
[9] C. Gonz\E1lez-Haro and J. Isern-Fontanet. Global ocean current reconstruction from altimetric and microwave SST measurements. Journal of Geophysical Research: Oceans, 119(6):3378–3391, 2014.
[10] U. Hausmann and A. Czaja. The observed signature of mesoscale eddies in sea surface temperature and the associated heat transport. Deep Sea Research Part I: Oceanographic Research Papers, 70:60 – 72, 2012.
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[19] D. Lee and H. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 401(6755):788–791, Oct. 1999.
[20] E. W. Leuliette and J. M. Wahr. Coupled pattern analysis of sea surface temperature and TOPEX/Poseidon sea surface height. Journal of Physical Oceanography, 29(4):599–611, 2016/01/22 1999.
[21] A. Ozerov and C. Fevotte. Multichannel nonnegative matrix factorization in convolutive mixtures for audio source separation. Audio, Speech, and Language Processing, IEEE Transactions on, 18(3):550–563, March 2010.
[22] M. Pal, R. Roy, J. Basu, and M. Bepari. Blind source separation: A review and analysis. In Oriental COCOSDA held jointly with 2013 Conference on Asian Spoken Language Research and Evaluation (O-COCOSDA/CASLRE), 2013 International Conference, pages 1–5, Nov 2013.
[23] R. Rubinstein, T. Peleg, and M. Elad. Analysis k-svd: A dictionary-learning algorithm for the analysis sparse model. IEEE Transactions on Signal Processing, 61(3):661–677, Feb 2013.
[24] R. Rubinstein, M. Zibulevsky, and M. Elad. Efficient implementation of the k-svd algorithm using batch orthogonal matching pursuit.
[25] R. Rubinstein, M. Zibulevsky, and M. Elad. Double sparsity: Learning sparse dictionaries for sparse signal approximation. IEEE Transactions on Signal Processing, 58(3):1553–1564, March 2010.
[26] M. Saraceno, C. Provost, and A. R. Piola. On the relationship between satellite-retrieved surface temperature fronts and chlorophyll a in the western south atlantic. Journal of Geophysical Research: Oceans, 110(C11):n/a–n/a, 2005. C11016.
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Normalizing Flows and Autoregressive Flows
This is a note that I took when I read the MAF paper, Normalizing Flows Tutorials by Eric Jang and its variations by Adam Kosiorek
Machine learning is all about probability. To train a model, we typically tune its parameters to maximize the probability of the training dataset under the model. To do so, we have to assume some probability distribution as the output of our model. The most common one, Gaussian distribution, can be problematic, as the true probability density function ( ) of real data is often far from Gaussian. If we use the Gaussian as likelihood for image-generation models, we end up with blurry reconstructions, as proved in Variational Autoencoders (VAEs). We can circumvent this issue by adversarial training, which is an example of likelihood-free inference, but this approach has its own issues.
It is fact that Gaussians are also used, and often prove too simple. Fortunately, we can often take a simple probability distribution, take a sample from it and then transform the sample. This is equivalent to change of variables in probability distributions and, if the transformation meets some mild conditions, can result in a very complex of the transformed variable. Danilo Rezende formalized this in his paper on Normalizing Flows (NF). NFs are usually used to parametrize the approximate posterior in VAEs but can also be applied for the likelihood function.
Change of Variables
Let’s build up some intuition by examining linear transformations of D random variables. Suppose is Uniform . Let random variable be a simple affine transformation of the underlying “source distribution” . What this means is that a sample from can be converted into a sample from by simply applying the function to it.
Becasue of the linear transformation, the spanning of transforms from . Since the probability mass must integrate to for both distributions, we have density
Let’s zoom in on a particular and an infinitesimally nearby point , then applying to them takes us to the pair .
That being said, to preserve the total probability ( ), the change of along must be equivalent to the change of along :
Then, we have the density after transformation
To make it numerical stabler, we often take .
Now, let’s look at multivariate case, specifically with 2
variabels.
We have a square from four initial vertices . After appling the matrix transformation , they are taken into a parallelogram and each vertice is sent to respectively.
Thus, the area changes to . It’s important to notice that is nothing but the determinant of the linear transformation.
Mathematically,
Notice: As far, we just explore linear transformation, which is the simplest case. Non-linear transformation is essentially the same but just need to meet one condition: bijection (linear transformation is for sure invertible).
In Danilo’s paper, let be a random variable and an invertible smooth mapping. We can use to transform . The resulting random variable has the following probability distribution:
We can apply a series of invertible mappings and obtain a normalizing flow.
This series of transformations can transform a simple probability distribution (e.g. Gaussian) into a complicated multi-modal one. To be of practical use, however, we can consider only transformations whose determinants of Jacobians are easy to compute. The original paper considered two simple family of transformations, named planar and radial flows.
- Planar Flow
with , and h an element-wise non-linearity. Let . Then determinant can be easily computed as
We can compute the logdet-Jacobian term in time using the matrix determinant lemma:
We conclude that the density obtained by transforming an arbitraty initial density through the sequence of maps is implicitly given by:
- Radial Flow
with
This family allows for a linear-time computation of the determinant.
Discussion
Here is the result of two families on Gaussian and Uniform distribution.
These simple flows are useful only for low dimensional spaces, since each transformation affects only a small volume in the original space. As the volume of the space grows exponentially with the number of dimensions , we need a lot of layers in a high-dimensional space.
Another way to understand the need for many layers is to look at the form of the mappings. Each mapping behaves as a hidden layer of a neural network with one hidden unit and a skip connection. Since a single hidden unit is not very expressive, we need a lot of transformations. Recently introduced Sylvester Normalizing Flows overcome the single-hidden-unit issue of these simple flows.
Autoregressive Flows
Enhancing expressivity of normalizing flows is not easy, since we are constrained by functions, whose Jacobians are easy to compute. It turns out, though, that we can combine it with autoregressive model. We introduce dependencies between different dimensions of the latent variable, and still end up with a tractable Jacobian. Namely, if after a transformation, the dimension of the resulting variable depends only on demension of the input variable, then the Jacobian of this transformation is triangular. As we know, a determinant of a triangular matrix is equal to the product of the terms on the diagonal. More formally, let be the Jacobian of the mapping , then
Here are four flows that use the above concept, albeit in different ways, and arrive at mappings with very different properties.
- Real Non-Volume Preserving Flows (R-NVP)
R-NVPs are arguably the least expressive but the most generally applicable of the three. Let , element-wise multiplication and and two mappings (through neural networks) (Note that is not the sigmoid function). What’s called Coupling layer that transforms a density to another density is defined as:
It is an autoregressive transformation, although not as general as equation allows. It copies the first dimensions, while shifting and scaling all the remaining ones. The first part of the Jacobian(up to dimension ) is just an identity matrix, while the second part is lower-triangular with on the diagonal. Hence, the determinant of the Jacobian is
R-NVPs are particularly attractive because both sampling and evaluating probability of some external sample are very efficient. Computational complexity of both operations is, in fact, exactly the same. This allows to use R-NVPs as a parametrization of an approximate posterior in VAEs, but also as the output likelihood (in VAEs or general regression models). To see this, first note that we can compute all elements of and in parallel, since all inputs ( ) are available. We can therefore compute in a single forward pass. Next, note that the inverse transformation has the following form, with all divisions done element-wise,
Note that and are usually implemented as Neural networks, which are generally not invertible. Thanks to equation , however, they do not have to be invertible for the whole R-NVP transformation to be invertible. In original paper, the authors apply stacking coupling layers of this mapping and permute the ordering of copied dimensions. This way, variables that are just copied in one step, are to be transformed in the following step.
Autoregressive models as normalizing flows
We can be even more expressive than R-NVPs. Consider an autoregressive model whose conditionals are parameterize as single Gaussian. That is, the conditional is given by
where and are unconstraied scalar functions(through neural nets) that compute the mean and log standard deviation of the conditional given all previous variables. Thus,we can generate data from the above model using the following recursion:
where and is the vecotr of random numbes the model uses internally to generate data.
provides an alternative characterization of the autoregressive model as a transformation from the space of random numbers to the space of data . That is, we can express the model as . Given a datapoint , the random numbers that were used to generate it are obtained by:
Due to the autoregressive structure, the Jacobian of is triangular by design, hence its absolute determinant can be easily obtained as follows:
- Masked Autoregressive Flow (MAF)
MAF directly uses equations and to transform as random variable. It is named Masked because it used Masked Autoencoder for distribution Estimation (MADE) as a building block. Specifically, we choose to implement the set of functions with masking. The benefit of using maskng is that it enables transforming from data to random numbers and thus calcupating in one forward pass through the flow, thus eliminating the need for sequential recursion in . In practice, we adds flexibility to this model by stacking multiple instances of the model into a deeper flow.
- Inverse Autoregressive Flow (IAF)
Like MAF, IAF is a normalizing flow which uses MADE as its component layer. Each layer of IAF is defined by the following resursion:
Similar to MAF, functions are computed using a MADE with Gaussian conditionals. The difference is architectural: in MAF and are directly computed from previous data variables , whereas in IAF and are directly computed from precious random numbers
where and are unconstraied scalar functions(through neural nets) that compute the mean and log standard deviation of the conditional given all previous variables.
MAF v.s IAF
It’s important to know the trade-off of MAF and IAF as they present different consequences. MAF is capable of calculating the density of any datapoint in one pass through the model, however samping from it requires performing D sequential passes(D is the dimensionality of ). In contrast, IAF can generate samples and calculate their density with one pass, however calculating the density of externally provided datapint requires D passes to find the random number associated with . Hence, the design choice depends on the intended usage. IAF is suitable as a recognition model for stochastic variational inference, where it only ever needs to calculate the density of its own samples. In contrast, MAF is more suitable for density estimation, because each example requires only one pass through the model.
Neural Autoregressive Flows (NAF)
This is a relatively state-of-the-art work by Huang, et al. which substantially improve the above-mentioned autoregressive flows in terms of expressiveness.
Let’s recall what we got so far from IAF and MAF. Our initial motivation is that autoregressive model can be combined with normalizing flows to produce good results in density estimation and that different ways to combine these two, as shown in IAF and MAF, give us different results, depending the usage case. However, both IAF and MAF limit themsleves with affine transformation throughout normalizing flows for computational simplicity. For instance, .
One problem of this restriction is that because of this restriction, we are limiting ourselves in a small family of functions, and thus a small famil of densities. However, we observed that for computational simplicity, we just need to satisfy two requirements:
- transforming functions are invertible w.r.t
- is cheap to compute
This raised the possibility to using a more poweful transformation in order to increase the expressivity of the flow.
We define an autoregressive conditioner c, and an invertible transformer , and then the function that transform to is defined as:
In NAF, we replace the affine transformer with a neural network, and yields a more rich family of distributions with only a minor increae in computation and memory requirements:
where in deep neural network, is input, is output and its weights and biases are given by the output of
Now the question is, how to make sure that the two requirements for computational simplicity are satisfied?
In original paper, the author makes the network to be monotonic by forcing strictly positive weights and strictly monotonic activation functions for . Strictly monotonic NAF will thus be invertible (as per requirement ). Meanwhile, and gradients w.r.t to parameters can be computed efficiently via backpropagation (as per requirement 2).
Two neural architectures for is proposed performing well: deep sigmoidal flows (DSF) and deep dense sigmoidal flows (DDSF)
Summary
In this article, I attempt to understand a) what is normalizing flows, b) what is the mathematical background of this normallizing flows, c) R-NVP that utilizes deep normalizing flows, d) two generalization of R-NVP–MAF and IAF–that use autoregressive models as normalizing flows, called autoregressive flows.
Moreover, I demonstrate the pros and cons of R-NVP, MAF, and IAF, depending on the intention of usage:
-
MAF can calculate the density in a single pass so it’s useful for explicit density estimation. However, it takes D passes to generate the data, thus not suitable for variational inference.
-
IAF, on the other hand, can generate data in single pass but is slow in computing density
-
R-NVP, is a special case of MAF but the benefits of it is that it can both generate data and estimate densities with one forward pass only.


Tellurium User Group
Please post your questions to
Tellurium User Group
:
http://groups.google.com/group/tellurium-users
.


Company profile - Canadian Company Capabilities
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Gentherm Global Power Technologies Inc., (Formerly Global Thermoelectric)
Legal Name:
Gentherm Global Power Technologies Inc., (Formerly Global Thermoelectric)
Operating Name:
Gentherm Global Power Technologies, (Formerly Global Thermoelectric)
Mailing Address:
16-7875 57 St SECALGARY, Alberta
T2C 5K7
Location Address:
16-7875 57 St SECALGARY, Alberta
T2C 5K7
Tel.:<PHONE_NUMBER>
Tel.:<PHONE_NUMBER>
Fax:<PHONE_NUMBER>
Logo
Company Profile
Providing power solutions around the world for unique remote industrial sites is typical for GPT.
Gentherm Global Power Technologies (GPT) isn’t constrained by the limits of what is typically done. For over 40 years, GPT played off the grid in some of the most remote and challenging environments found in the world … and off of it.*
What is ‘typically done’ doesn’t work when you are off the grid and in extreme environments; this is where GPT excels.
Our people thrive on challenge. They offer innovative approaches in unique situations;
• Creating solutions that are simple to use
• Environmentally friendly
• Requiring minimal maintenance
• Around the world, with local service support … if it’s ever needed
You can count on us as we specialize in reliable remote, low maintenance, turnkey systems & power solutions.
*Original TEG was designed for NASA and used on the moon
Full profile
Full profile
Contact information
Company description
Product / Service / Licensing
DETAILED PRODUCT DESCRIPTION
GLOBAL THERMOELECTRIC generators are available in power ranges
1 to 5000 Watts, configured to suit your needs, available in all
commonly required DC voltages. The generators are solid state
with no moving parts to wear or break. Applications include
SCADA/RTU, cathodic protection, radio and telecommunications, gas
monitoring, automation systems and telemetry.
Model 5030 21 Watts
Model 5060 60 Watts
Model 5120 120 Watts
Model 8550 550 Watts
Model 1120 110 Watts
Model 5220 220 Watts
The generators are certified for use in Class I, Div.II, Group D
environments. Harmonized Commodity Code 8502.39.00
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety.
Model 1500 500 Watts
Model 5030 21 Watts
Model 5050 50 Watts*
Model 5060 60 Watts
Model 5100 100 Watts*
Model 5120 120 Watts
Model 8550 480 Watts
Model 1120 110 Watts
Model 5220 195 Watts
*For release in Alberta, Canada and the Middle East only.
The generators are certified for use in Class I, Div.II, Group D
environments. Harmonized Commodity Code 8502.39.00
Though the initial thermoelectric generator (TEG) was developed for a NASA 1975 Apollo program to generate power on the moon, the technology has continued to evolve. TEGs now offers remote sites the power solutions needed in some of the harshest environmental conditions on earth. They have the ability to run on various fuels including solar and be integrated into total site power solutions.
Gentherm Global Power Technologies (GPT) continues to develop new solutions through in-house design and testing. R&D is done by qualified engineers and technicians who enthusiastically respond to unique industrial site power challenges to design the needed solutions.
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety.
GPT isn’t constrained by the limits of what is typically done. For over 40 years, GPT played off the grid in some of the most remote and challenging environments found in the world … and off of it.*
What is ‘typically done’ doesn’t work when you are off the grid and in extreme environments; this is where GPT excels.
Our people thrive on challenge. They offer innovative approaches in unique situations;
• Creating solutions that are simple to use
• Environmentally friendly
• Requiring minimal maintenance
• Around the world, with local service support … if it’s ever needed
You can count on us as we specialize in reliable remote, low maintenance, turnkey systems & power solutions.
Gentherm Global Power Technologies (GPT) Thermoelectric Generators (TEGs)
GPT thermoelectric generators are available in power ranges 5 to 5,000 Watts, configured to suit your needs, integrated with alternate systems, available in all commonly required DC voltages. The generators are solid state with no moving parts to wear or break.
TEGs require minimal maintenance as they are a solid state design. Recommended maintenance of one to two hours per year is all that is required to check the power output and ensure a clean fuel supply by cleaning and/or changing the orifice and fuel filter. Consumables for recommended maintenance are typically less than one percent of the capital cost per year.
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety
• Creating solutions that are simple to use,
• Environmentally friendly,
• Requiring minimal maintenance,
• Around the world, with local service support … if it’s ever needed.
You can count on us as we specialize in reliable remote, low maintenance, turnkey systems & power solutions.
Solutions include thermoelectric generators we manufacture, SolarHybrid systems, cathodic protection and fully enclosed integrated site solutions.
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety.
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety.
2540013965430
6150219203242
2590-Miscellaneous Vehicular Components
4420-Heat Exchangers and Steam Condensers
6115-Generators and Generators Sets, Electrical
Market profile
Industry sector market interests:
- Construction
- Environment
- Fishery
- Forestry
- Information Technology and Telecommunications
- Manufacturing
- Mining/Petroleum/Gas
- Aerospace
- Defence
- Electrical Equipment
Geographic markets:
Export experience:
- Algeria
- Argentina
- Australia
- Austria
- Bangladesh
- Brazil
- China
- Denmark
- Finland
- France
- Germany
- Iceland
- Indonesia
- Iran, Islamic Republic of
- Iraq
- Japan
- Kuwait
- Libyan Arab Jamahiriya
- Malaysia
- New Zealand
- Norway
- Pakistan
- Russian Federation
- Saudi Arabia
- Sweden
- United Kingdom
- United States
- Alaska
- Arizona
- Arkansas
- California
- Colorado
- Delaware
- Idaho
- Louisiana
- Montana
- New Mexico
- Oklahoma
- Oregon
- Tennessee
- Texas
- Utah
- Washington
- Wyoming
Actively pursuing:
- Mexico
Third-Party Information Liability Disclaimer
Some of the information on this Web page has been provided by external sources. The Government of Canada is not responsible for the accuracy, reliability or currency of the information supplied by external sources. Users wishing to rely upon this information should consult directly with the source of the information. Content provided by external sources is not subject to official languages, privacy and accessibility requirements.
Contacts
Contact information
Description
Company description
Products, services and licensing
Product / Service / Licensing
DETAILED PRODUCT DESCRIPTION
GLOBAL THERMOELECTRIC generators are available in power ranges
1 to 5000 Watts, configured to suit your needs, available in all
commonly required DC voltages. The generators are solid state
with no moving parts to wear or break. Applications include
SCADA/RTU, cathodic protection, radio and telecommunications, gas
monitoring, automation systems and telemetry.
Model 5030 21 Watts
Model 5060 60 Watts
Model 5120 120 Watts
Model 8550 550 Watts
Model 1120 110 Watts
Model 5220 220 Watts
The generators are certified for use in Class I, Div.II, Group D
environments. Harmonized Commodity Code 8502.39.00
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety.
Model 1500 500 Watts
Model 5030 21 Watts
Model 5050 50 Watts*
Model 5060 60 Watts
Model 5100 100 Watts*
Model 5120 120 Watts
Model 8550 480 Watts
Model 1120 110 Watts
Model 5220 195 Watts
*For release in Alberta, Canada and the Middle East only.
The generators are certified for use in Class I, Div.II, Group D
environments. Harmonized Commodity Code 8502.39.00
Though the initial thermoelectric generator (TEG) was developed for a NASA 1975 Apollo program to generate power on the moon, the technology has continued to evolve. TEGs now offers remote sites the power solutions needed in some of the harshest environmental conditions on earth. They have the ability to run on various fuels including solar and be integrated into total site power solutions.
Gentherm Global Power Technologies (GPT) continues to develop new solutions through in-house design and testing. R&D is done by qualified engineers and technicians who enthusiastically respond to unique industrial site power challenges to design the needed solutions.
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety.
GPT isn’t constrained by the limits of what is typically done. For over 40 years, GPT played off the grid in some of the most remote and challenging environments found in the world … and off of it.*
What is ‘typically done’ doesn’t work when you are off the grid and in extreme environments; this is where GPT excels.
Our people thrive on challenge. They offer innovative approaches in unique situations;
• Creating solutions that are simple to use
• Environmentally friendly
• Requiring minimal maintenance
• Around the world, with local service support … if it’s ever needed
You can count on us as we specialize in reliable remote, low maintenance, turnkey systems & power solutions.
Gentherm Global Power Technologies (GPT) Thermoelectric Generators (TEGs)
GPT thermoelectric generators are available in power ranges 5 to 5,000 Watts, configured to suit your needs, integrated with alternate systems, available in all commonly required DC voltages. The generators are solid state with no moving parts to wear or break.
TEGs require minimal maintenance as they are a solid state design. Recommended maintenance of one to two hours per year is all that is required to check the power output and ensure a clean fuel supply by cleaning and/or changing the orifice and fuel filter. Consumables for recommended maintenance are typically less than one percent of the capital cost per year.
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety
• Creating solutions that are simple to use,
• Environmentally friendly,
• Requiring minimal maintenance,
• Around the world, with local service support … if it’s ever needed.
You can count on us as we specialize in reliable remote, low maintenance, turnkey systems & power solutions.
Solutions include thermoelectric generators we manufacture, SolarHybrid systems, cathodic protection and fully enclosed integrated site solutions.
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety.
Applications include: Upstream Gas Wellheads (onshore), Natural Gas Offshore wellhead Platforms, Gas Pipelines, Liquids Pipeline and Telecom. Power applications include: SCADA, cathodic protection, valve control, instrumentation and other applications that require a consistent reliable power source to maintain site function and safety.
2540013965430
6150219203242
2590-Miscellaneous Vehicular Components
4420-Heat Exchangers and Steam Condensers
6115-Generators and Generators Sets, Electrical
Market
Market profile
Industry sector market interests:
- Construction
- Environment
- Fishery
- Forestry
- Information Technology and Telecommunications
- Manufacturing
- Mining/Petroleum/Gas
- Aerospace
- Defence
- Electrical Equipment
Geographic markets:
Export experience:
- Algeria
- Argentina
- Australia
- Austria
- Bangladesh
- Brazil
- China
- Denmark
- Finland
- France
- Germany
- Iceland
- Indonesia
- Iran, Islamic Republic of
- Iraq
- Japan
- Kuwait
- Libyan Arab Jamahiriya
- Malaysia
- New Zealand
- Norway
- Pakistan
- Russian Federation
- Saudi Arabia
- Sweden
- United Kingdom
- United States
- Alaska
- Arizona
- Arkansas
- California
- Colorado
- Delaware
- Idaho
- Louisiana
- Montana
- New Mexico
- Oklahoma
- Oregon
- Tennessee
- Texas
- Utah
- Washington
- Wyoming
Actively pursuing:
- Mexico


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Each CSS challenge will show you the amazing capabilities of CSS styling.
Click the link down below to advance to the next challenge.


"x"
"1" "MORI,H, MORI,Y, SUGIE,S, YOSHIMI,N, TAKAHASHI,M, NII,H, YAMAZAKI,H,TOYOSHI,K AND WILLIAMS,GM, GENOTOXICITY OF A VARIETY OF AZOBENZENE ANDAMINOAZOBENZENE COMPOUNDS IN THE HEPATOCYTE/DNA REPAIR TEST AND __ THESALMONELLA/MUTAGENICITY TEST, CANCER RES. 46(4, PT. 1):1654-1658,1986"


We're just a group of Ruby enthusiasts, who like to get together to talk, share, and learn more about Ruby.
If you're a new Ruby developer looking to learn the ropes, a grizzled veteran wanting to share your experience, or you just want to hang out because you love Ruby, come join us.


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Why?
-
We make numerous downstream calls to DES APIs that have varied responses. And other services in general that we may wish to stub out.
-
Our frontend handles these responses differently and so we have a number of test scenarios we want to prove.
-
We wanted to be able to add new test scenarios (test data) quickly and efficiently; without needing to change any code.
dynamic stub
-
We wanted to be able to add entirely new endpoints to the dynamic stub quickly and efficiently; without needing to change any code.
super dynamic stub
How?
- Team came together to whiteboard ideas/design
- Decided to keep our scope small (only supports GET requests atm)
- Agreed the structure of the MongoDB repositories we would need
- Schema Repository (to tell the service what endpoints are available to stub)
- Data Repository (to store the request to match and the response to give)
MongoDB Document Structure
Schema Repository
- Endpoint Name: Uniquely identify the endpoint. We've been using the DES API name.
- Url Regex: Regex definition of the URL to match against when adding responses to the stub for this endpoint.
- Http Method: Http Method of the stubbed endpoint. (at the moment, only supports GET).
- Response Schema: Json Schema Definition for the expected response format for the endpoint.
MongoDB Document Structure
Data Repository
- Endpoint Name: Uniquely identify the endpoint. Must match an endpoint that has been stubbed.
- Url Regex: URL to stub. Must match the URL regex for the endpoint.
- Http Method: Http Method of the stubbed endpoint. (at the moment, only supports GET).
- Response Status: Response Status to be returned when called.
- Response Body: Json Response Body to be returned when called. Can be empty.
High Level Design
High Level Design
High Level Design
What's Next?
- Automatically Retrieve Active endpoints that can be stubbed.
- Add support for additional Http Methods
(e.g. POST, PUT et al.) and understand what that
means for design of the:
- Schema Repository
- Data Repository
- Explore whether we can make this a Generic stub for any team to use (without a need for theirown stub microservice)
- Unit and Component Tests. Originally started as a PoC. Highr quality code base
- Improved Error Handling
- Promote contribution from the communities


Hello visitor!
We are a small new company.
This project starts from @CodeDies and first thing is just have fun
& Upload it just as one simple html page.
On the continue @skino0 Design to improve her friend's idea.
Future is unknown but present looks so awesome.
Hope to enjoy their work &
Support us !!


Change Home Sites Dialog
This dialog allows you to edit the home sites of all player factions.
- Map View
- Faction List
- Selected Site
- Drag the vertical splitter between Map View and Faction List to adjust their respective share of the current dialog width.
- Choose OK or press Enter when the focus is not on another button to accept any changes made and dismiss the dialog.
- Choose Cancel or press Escape to discard any changes made and dismiss the dialog. Closing the window using X (Close) or Alt+F4 has the same effect.
- Choose Help or press F1 to show this help page.
Remarks
1. You cannot assign the same home site to more than one faction.
2. You may define home sites that are initially unowned, or not owned by the faction whose home they are. In that case, you may want to ensure that the map site can be captured, or that the rule script transfers ownership of the site to the proper faction.
3. Changing home sites marks the Areas section as changed, not the Factions section, because all map locations are stored with the Areas section.
The left side of this dialog is taken up by a map view. The site corresponding to the first entry in the Faction List is initially centered and selected.
- Highlighted sites indicate faction home sites. Each highlighted site corresponds to one entry in the Faction List.
- Left-click on a site to select it. If the site is highlighted, its corresponding entry is also selected in the Faction List.
- Double-click on a site that is not highlighted to set its coordinates as the new home site of the selected faction in the Faction List, as with the Set As Home Site button. The site is now highlighted.
This list shows all player factions and their current home sites.
- Column Faction shows the unique internal identifiers of all factions currently defined on the Factions Page. The list entries appear in the same order as on that page.
- Column Home shows the map coordinates of the current home site of each faction. The default is (–,–) for all factions, indicating that no home site is defined.
- Left-click on an entry to select it. The corresponding site, if any, is also selected in the Map View, and scrolled into view if necessary.
- Double-click on a selected entry to re-select the corresponding site in the Map View.
This box shows information on the selected site, if any. Click on a site in the Map View to select it. You can also click on its corresponding entry in the Faction List.
- Site (in the box title) shows the map coordinates of the selected site.
- Owner shows the unique internal identifiers of the faction that owns the selected site, or “(unowned)” if the site has no owner.
- Units Can Capture shows a check mark if the selected site contains at least one terrain with the “Capture” ability.
- Choose Clear Home Site (Alt+C) to clear the current home site of the selected faction in the Faction List. This button appears only when the selected site is already in the list.
- Choose Set As Home Site (Alt+H) to set the selected site as the new home site of the selected faction in the Faction List. This button appears only when the selected site is not in the list.


Autor: Andy Weir
In 2035, the crew of NASA's Ares 3 mission have arrived at Acidalia Planitia for a planned month-long stay on Mars. After only six sols, an intense dust and wind storm threatens to topple their Mars Ascent Vehicle (MAV), which would trap them on the planet. During the hurried evacuation, an antenna tears loose and impales astronaut Mark Watney, a botanist and engineer, also disabling his spacesuit radio. He is flung out of sight by the wind and presumed dead. As the MAV teeters dangerously, mission commander Melissa Lewis has no choice but to take off without completing the search for Watney.
However, Watney is not dead. His injury proves relatively minor, but with no long-range radio, he cannot communicate with anyone. He must rely on his own resourcefulness to survive. He begins a log of his experiences. His philosophy is to "work the problem", solving each challenge in turn as it confronts him. With food a critical, though not immediate, problem, he begins growing potatoes in the crew's Martian habitat (the Hab), and burns hydrazine to generate water for the plants.


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This page describes how to setup a working Python development environment that can be used in developing Neutron on Ubuntu, Fedora or Mac OS X. These instructions assume you’re already familiar with Git and Gerrit, which is a code repository mirror and code review toolset , however if you aren’t please see this Git tutorial for an introduction to using Git and this wiki page for a tutorial on using Gerrit and Git for code contribution to Openstack projects.
Following these instructions will allow you to run the Neutron unit tests. If you want to be able to run Neutron in a full OpenStack environment, you can use the excellent DevStack project to do so. There is a wiki page that describes setting up Neutron using DevStack.
Grab the code from GitHub:
git clone git://git.openstack.org/openstack/neutron.git
cd neutron
The unit tests (neutron/test/unit/) are meant to cover as much code as possible and should be executed without the service running. They are designed to test the various pieces of the neutron tree to make sure any new changes don’t break existing functionality.
The functional tests (neutron/tests/functional/) are intended to validate actual system interaction. Mocks should be used sparingly, if at all. Care should be taken to ensure that existing system resources are not modified and that resources created in tests are properly cleaned up.
It is expected that any new changes that are proposed for merge come with tests for that feature or code area. Ideally any bugs fixes that are submitted also have tests to prove that they stay fixed! In addition, before proposing for merge, all of the current tests should be passing.
Testing OpenStack projects, including Neutron, is made easier with DevStack.
Create a machine (such as a VM or Vagrant box) running a distribution supported by DevStack and install DevStack there. For example, there is a Vagrant script for DevStack at https://github.com/bcwaldon/vagrant_devstack.
Note
If you prefer not to use DevStack, you can still check out source code on your local machine and develop from there.
There are three mechanisms for running tests: run_tests.sh, tox, and nose. Before submitting a patch for review you should always ensure all test pass; a tox run is triggered by the jenkins gate executed on gerrit for each patch pushed for review.
With these mechanisms you can either run the tests in the standard environment or create a virtual environment to run them in.
By default after running all of the tests, any pep8 errors found in the tree will be reported.
You can use the run_tests.sh script in the root source directory to execute tests in a virtualenv:
./run_tests -V
You can use nose to run individual tests, as well as use for debugging portions of your code:
source .venv/bin/activate
pip install nose
nosetests
There are disadvantages to running Nose - the tests are run sequentially, so race condition bugs will not be triggered, and the full test suite will take significantly longer than tox & testr. The upside is that testr has some rough edges when it comes to diagnosing errors and failures, and there is no easy way to set a breakpoint in the Neutron code, and enter an interactive debugging session while using testr.
Neutron, like other OpenStack projects, uses tox for managing the virtual environments for running test cases. It uses Testr for managing the running of the test cases.
Tox handles the creation of a series of virtualenvs that target specific versions of Python (2.6, 2.7, 3.3, etc).
Testr handles the parallel execution of series of test cases as well as the tracking of long-running tests and other things.
Running unit tests is as easy as executing this in the root directory of the Neutron source code:
tox
To run functional tests that do not require sudo privileges or specific-system dependencies:
tox -e functional
To run all the functional tests in an environment that has been configured by devstack to support sudo and system-specific dependencies:
tox -e dsvm-functional
For more information on the standard Tox-based test infrastructure used by OpenStack and how to do some common test/debugging procedures with Testr, see this wiki page:
For running individual test modules or cases, you just need to pass the dot-separated path to the module you want as an argument to it.
For executing a specific test case, specify the name of the test case class separating it from the module path with a colon.
For example, the following would run only the JSONV2TestCase tests from neutron/tests/unit/test_api_v2.py:
$ ./run_tests.sh neutron.tests.unit.test_api_v2.JSONV2TestCase
or:
$ tox -e py27 neutron.tests.unit.test_api_v2.JSONV2TestCase
Neutron has a fast growing code base and there is plenty of areas that need to be covered by unit and functional tests.
To get a grasp of the areas where tests are needed, you can check current coverage by running:
$ ./run_tests.sh -c
By default, calls to pdb.set_trace() will be ignored when tests are run. For pdb statements to work, invoke run_tests as follows:
$ ./run_tests.sh -d [test module path]
It’s possible to debug tests in a tox environment:
$ tox -e venv -- python -m testtools.run [test module path]
Tox-created virtual environments (venv’s) can also be activated after a tox run and reused for debugging:
$ tox -e venv
$ . .tox/venv/bin/activate
$ python -m testtools.run [test module path]
Tox packages and installs the neutron source tree in a given venv on every invocation, but if modifications need to be made between invocation (e.g. adding more pdb statements), it is recommended that the source tree be installed in the venv in editable mode:
# run this only after activating the venv
$ pip install --editable .
Editable mode ensures that changes made to the source tree are automatically reflected in the venv, and that such changes are not overwritten during the next tox run.
Setting OS_POST_MORTEM_DEBUG=1 in the shell environment will ensure that pdb.post_mortem() will be invoked on test failure:
$ OS_POST_MORTEM_DEBUG=1 ./run_tests.sh -d [test module path]


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Altra Super Three
Tic-Tac-Toe
Hi, Welcome to this tic-tac-toe game! The game includes automatic scoring. Lets enjoy this game!
Name:
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| Name | Created By | Created At | Runtime | Last Executed At | Update Schedule |
|---|---|---|---|---|---|
| {{query.name}} Unpublished | {{query.user.name}} | {{query.created_at | dateTime}} | {{query.runtime | durationHumanize}} | {{query.retrieved_at | dateTime}} | {{query.schedule | scheduleHumanize}} |


hello, world
welcome to my first webpage!
Here are some things I'm going to learn about coding
check out moringa school.It is great place to learn coding
here is a link to my my favourite things


{% load static %} library_main_page welcome to my music library this is a music library used for downloading best musics in the world {% if songs_list %} music_name musician_name album_name album_cover {% for songs in songs_list %} {{ songs.music_name }} {{ songs.musician }} {{ songs.album }} {% endfor %} {% else %} No song is available. {% endif %} Written by : Mohamad-Aref-Shahidani Visit us at<EMAIL_ADDRESS>Isfahan,Iran
this is a music library used for downloading best musics in the world
No song is available.


A TOC (Table of Contents) widget for ESRI ArcGIS Server JavaScript API. The namespace is agsjs
For a description and examples of how to use this library, check out the how-to.
This class is a Table Of Content widget.
| Constructor | Description |
|---|---|
TOC(opts:ags.TOCOptions, srcNodeRef:DOMNode|id) |
Create a Table Of Contents (TOC) |
This is an object literal that specify the options for each map rootLayer layer.
| Properties | Type | Description |
|---|---|---|
collapsed |
Boolean |
whether to collapsed the rootLayer layer at beginning. default is false, which means expand if visible, collapse if not. |
layer |
Layer |
ArcGIS Server layer. |
noLegend |
Boolean |
whether to skip the legend, and only display layers. default is false. |
slider |
Boolean |
whether to show slider for each rootLayer to adjust transparency. default is false. |
title |
string |
title. optional. If not specified, rootLayer name is used. |
This is an object literal that specify the option to construct a TOC
. There is no constructor for this class. Instead, this class is instantiated as a javascript object literal.
| Properties | Type | Description |
|---|---|---|
indentSize |
Number |
indent size of tree nodes. default to 18. |
layerInfos |
Object[] |
a subset of layers in the map to show in TOC. each object is a TOCLayerInfo |
map |
esri.Map |
the map instance. required. |


{% load static %} {% load rest_framework %}
All the scheduled events to signUp
Scheduled Events
All events - Times are shown in JST
Event Name
Event Location
Start Time
End Time
Details
{% for event in events %}
{{ event.name }}
{{ event.location }}
{{ event.start_time }}
{{ event.end_time }}
{{ event.description }}
{% endfor %}


| Methods | ||
|---|---|---|
public
|
__construct(Application $application, MiddlewareRepository $subscriber)
|
# |
public
|
resolve(MiddlewareCollector $collection): ArrayBag
|
# |
protected
|
dispatch(string|array $middleware): ResponseInterface|Closure|bool
|
# |
protected
|
handle(array $middlewares): void
|
# |
protected
|
resolveGeneralMiddlewares(array $middlewares): void
|
# |
protected
|
resolveGroupMiddlewares(array $middlewares): void
|
# |
protected
|
resolveSingleMiddlewares(array $middlewares): void
|
# |
protected
|
resolveCustomMiddlewares(array $middlewares): void
|
# |
| Properties | |||
|---|---|---|---|
public
|
MiddlewareRepository
|
$subscriber
|
# |
public
|
Application
|
$app
|
# |
public
|
ArrayBag
|
$hooks
|
# |


/*
Copyright 2016 Google Inc. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
*/
import Handler from './handler';
import {isInstance} from '../../../../lib/assert';
/**
* An implementation of a [cache-first](/web/fundamentals/instant-and-offline/offline-cookbook/#cache-falling-back-to-network)
* request strategy.
*
* The important thing to note with this caching strategy is that once a
* response is cached, it will not be updated. This is useful for assets
* that are revisioned since it caches the asset long term and doesn't waste
* the user's data.
*
* @example
* // Set up a route to match any requests made for URLs that end in .txt.
* // The requests are handled with a cache-first strategy.
* const route = new workbox.routing.RegExpRoute({
* regExp: /\.txt$/,
* handler: new workbox.runtimeCaching.CacheFirst(),
* });
*
* const router = new workbox.routing.Router();
* router.registerRoute({route});
*
* @memberof module:workbox-runtime-caching
* @extends module:workbox-runtime-caching.Handler
*/
class CacheFirst extends Handler {
/**
* The handle method will be called by the
* {@link module:workbox-routing.Route|Route} class when a route matches a
* request.
*
* @param {Object} input
* @param {FetchEvent} input.event The event that triggered the service
* worker's fetch handler.
* @return {Promise.<Response>} The response, either from the cache,
* or if that isn't available, the request will be made on the
* the network and the result will be cached for future use.
*/
async handle({event} = {}) {
isInstance({event}, FetchEvent);
const cachedResponse = await this.requestWrapper.match({
request: event.request,
});
return cachedResponse || await this.requestWrapper.fetchAndCache({
request: event.request,
waitOnCache: this.waitOnCache,
});
}
}
export default CacheFirst;


Open Source Computer Vision LibrayFrequently Asked Questions |
A1: Send email to<EMAIL_ADDRESS>Subject: BUG <....your title...>
A2: Send email to<EMAIL_ADDRESS>A3: Send email to<EMAIL_ADDRESS>after you are a member and select your logon, you can read the web group at http://groups.yahoo.com/group/OpenCV **.
A4: To get the messages real time, or once a day as a daily digest, you can go to http://groups.yahoo.com/mygroups ** and choose your setting from the pull down list to the right of OpenCV.
A5: For the Image Processing Library, all support questions should go through:
http://support.intel.com/support/performancetools/support.htm (for release libraries)
https://premier.intel.com/scripts-quad/welcomeplsb.asp (for beta libraries)
A1: You need the direct show SDK for streams.h. You do not need DirectX* SDK for NT* 4.0 to compile and run DirectShow filters. You need only DirectX Media* SDK (version 5.x or higher), that can be downloaded from http://microsoft.com/directx/homeuser/downloads/default.asp ** This SDK can be used under Win98, NT 4.0 or Win2000 without any problem. Do not forget to put paths to the include subfolder and classes\base (in case of DirectX Media 5.x or 6.x) or samples\Multimedia\DirectShow\BaseClasses (in case of DirectX 8.x) subfolder to include paths in DevStudio* (they should go first in the list!!!) and lib subfolder to lib paths in DevStudio.
If you have DirectX 8.x, you should build base C++ library for Direct Show by yourself. In DevStudio open baseclasses.dsw workspace from samples\Multimedia\DirectShow\BaseClasses subfolder and build both release and debug configurations of the library. Finally, copy resulting strmbase.lib and strmbasd.lib libraries to lib subfolder of DirectX 8.x or OpenCV installation folder. That's it.
A2: Do not forget to put paths to the include subfolder and classes\base subfolder to include paths in DevStudio (they should go first in the list!!!) and lib subfolder to lib paths in DevStudio. Do not forget to build DirectShow C++ libraries. See the previous question as well.
A3: look at http://web2.airmail.net/mprater/101/ ** There are a lot of USB camera (often called WebCams) links there.
A4: You can get the output pins to appear by selecting "refresh" from the graphedt.exe** menu.
A5: Set the video format with the property page of the output pin of the capture source. Just click the right mouse button in GraphEdit on the output pin to display the property page. But make sure you do that before connecting the pin to other pins.
A6: IPL library has a bug in it, it uses a DLL switcher so that IPL.DLL replaces itself with the processor appropriate DLL (such as IPLA6.DLL for a PIII). The problem is, the DLL switcher looks only in the system path, not in the current directory. Thus, if the IPL DLL directory isn't in the system path, or you have a different version of IPL somewhere in front of the current directory in the system path, this error will occur. Solution: Get rid of other versions of IPL and change the system path to point to the correct IPL directory or move the IPL DLLs to a directory (system, system32) that is in the system path.
A7: Call up a command line prompt, cd to the directory the filter is in, run regsvr32 <filtername.ax>. Obviously, this will only work if regsvr32 is in your system path.
A8: Almost always means that some DLL the filter depends on is not being found in the local directory or the system path. If you have MSVC++* installed, you have a copy of a program called "depends.exe". Run this program on the offending filter. Missing DLLs will appear highlighted. Find these DLLs, move them to the local directory or system path and try regsvr32 again. Viola.
A9: It's the debug version of the library. Using MSVC++: Either: Under the Project menu, select Settings. Then select the Link tab and change the library to use to cv.lib; Or: Build a debug version of the library -- Under the build menu select "Set Active Configuration", click on "Debug" and the build all to build a debug version of cv.lib named cvd.lib. Another method is to just rename cv.lib to cvd.lib ... but you won't be able to debug.
A1: USB on Linux is just now maturing, but is doing so very quickly. The best site to visit is (URL:http://www.linux-usb.org/ ** ). Of specific interest is the link (URL:http://www.qbik.ch/usb/devices/ **), which has a video section. For the Matrox MeteorII, there appears to be a driver for the Linux4Video2 system, which is a more advanced video system being developed in parallel with the Linux4Video(1), included in the kernel. Details at (URL:http://www.thedirks.org/v4l2/ **). See the "People" section for the driver. A downside with V4L2 is that it's not yet working under 2.3.x or 2.4.x of the Linux kernel.


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{% extends 'layout.html' %} {% block title %}Unable to sign out{% endblock %} {% block content %}
Your account currently has a client certificate registered and your browser is providing that client certificate.
To sign out you must either remove the client certificate from your account or your browser.
{% endblock %}


Spark Release 3.2.1
Spark 3.2.1 is a maintenance release containing stability fixes. This release is based on the branch-3.2 maintenance branch of Spark. We strongly recommend all 3.2 users to upgrade to this stable release.
Notable changes
- [SPARK-30789]: Support IGNORE/RESPECT NULLS for LEAD/LAG/NTH_VALUE/FIRST_VALUE/LAST_VALUE
- [SPARK-33277]: Python/Pandas UDF right after off-heap vectorized reader could cause executor crash.
- [SPARK-34399]: Add file commit time to metrics and shown in SQL Tab UI
- [SPARK-35714]: Bug fix for deadlock during the executor shutdown
- [SPARK-36754]: array_intersect should handle Double.NaN and Float.NaN
- [SPARK-37001]: Disable two level of map for final hash aggregation by default
- [SPARK-37023]: Avoid fetching merge status when shuffleMergeEnabled is false for a shuffleDependency during retry
- [SPARK-37088]: Python UDF after off-heap vectorized reader can cause crash due to use-after-free in writer thread
- [SPARK-37202]: Temp view didn’t collect temp function that registered with catalog API
- [SPARK-37208]: Support mapping Spark gpu/fpga resource types to custom YARN resource type
- [SPARK-37214]: Fail query analysis earlier with invalid identifiers
- [SPARK-37392]: Fix the performance bug when inferring constraints for Generate
- [SPARK-37695]: Skip diagnosis ob merged blocks from push-based shuffle
- [SPARK-37705]: Write session time zone in the Parquet file metadata so that rebase can use it instead of JVM timezone
- [SPARK-37957]: Deterministic flag is not handled for V2 functions
Dependency Changes
While being a maintence release we did still upgrade some dependencies in this release they are:
You can consult JIRA for the detailed changes.
We would like to acknowledge all community members for contributing patches to this release.
Spark News Archive


|
PyFlow
|
Classes | |
| class | asynchronous |
| class | CircularBuffer |
| Circular buffer container class. More... | |
| class | DataTypes |
| Data types identifires. More... | |
| class | Direction |
| Direction identifires. More... | |
| class | NodeTypes |
| Determines wheter it is callable node or pure. More... | |
| class | PinDirection |
| Determines wheter it is input pin or output. More... | |
| class | PinSelectionGroup |
| Used in PyFlow.AbstractGraph.NodeBase.getPinByName for optimization purposes. More... | |
Functions | |
| def | lerp (start, end, alpha) |
| Performs a linear interpolation. More... | |
| def | clamp (n, vmin, vmax) |
| Computes the value of the first specified argument clamped to a range defined by the second and third specified arguments. More... | |
| def | roundup (x, to) |
| Rounding up to sertain value.Used in grid snapping. More... | |
| def | pinAffects (affects_pin, affected_pin) |
| This function for establish dependencies bitween pins. More... | |
| def | cycle_check (src, dst) |
| Check for cycle connected nodes. More... | |
| def | push (start_from) |
| marks dirty all ports from start to the right this part of graph will be recomputed every tick More... | |
| def | clearLayout (layout) |
| This function clears property view's layout. More... | |
| def | memoize (foo) |
| Decorator from Python decorator library More... | |
| def | getDataTypeName (inValue) |
| Returns string representation of the data type identifier See DataTypes. More... | |
Variables | |
| float | FLOAT_SINGLE_STEP = 0.01 |
| determines step for all floating point input widgets More... | |
| int | FLOAT_DECIMALS = 10 |
| determines floating precision More... | |
| float | FLOAT_RANGE_MIN = 0.1 + (-sys.maxint - 1.0) |
| determines floating minimum value More... | |
| float | FLOAT_RANGE_MAX = sys.maxint + 0.1 |
| determines floating maximum value More... | |
| int | INT_RANGE_MIN = -sys.maxint + 0 |
| determines int minimum value More... | |
| int | INT_RANGE_MAX = sys.maxint + 0 |
| def PyFlow.Core.AGraphCommon.clamp | ( | n, | |
| vmin, | |||
| vmax | |||
| ) |
Computes the value of the first specified argument clamped to a range defined by the second and third specified arguments.
| [in] | n | |
| [in] | vmin | |
| [in] | vmax |
| def PyFlow.Core.AGraphCommon.clearLayout | ( | layout | ) |
This function clears property view's layout.
| [in] | layout | QLayout class |
| def PyFlow.Core.AGraphCommon.cycle_check | ( | src, | |
| dst | |||
| ) |
Check for cycle connected nodes.
| [in] | src | pin |
| [in] | dst | pin |
| def PyFlow.Core.AGraphCommon.getDataTypeName | ( | inValue | ) |
Returns string representation of the data type identifier See DataTypes.
| [in] | data | type identifier (int) |
| def PyFlow.Core.AGraphCommon.lerp | ( | start, | |
| end, | |||
| alpha | |||
| ) |
Performs a linear interpolation.
| [in] | start | the value to interpolate from |
| [in] | end | the value to interpolate to |
| [in] | alpha | how far to interpolate |
| def PyFlow.Core.AGraphCommon.memoize | ( | foo | ) |
Decorator from Python decorator library
| [in] | foo | function to memorize |
| def PyFlow.Core.AGraphCommon.pinAffects | ( | affects_pin, | |
| affected_pin | |||
| ) |
This function for establish dependencies bitween pins.
| [in] | affects_pin | this pin affects other pins |
| [in] | affected_pin | this pin affected by other pin |
| def PyFlow.Core.AGraphCommon.push | ( | start_from | ) |
marks dirty all ports from start to the right this part of graph will be recomputed every tick
| [in] | start_from | pin from which recursion begins |
| def PyFlow.Core.AGraphCommon.roundup | ( | x, | |
| to | |||
| ) |
Rounding up to sertain value.Used in grid snapping.
| [in] | x | value to round |
| [in] | to | value x will be rounded to |
| int PyFlow.Core.AGraphCommon.FLOAT_DECIMALS = 10 |
determines floating precision
| float PyFlow.Core.AGraphCommon.FLOAT_RANGE_MAX = sys.maxint + 0.1 |
determines floating maximum value
| float PyFlow.Core.AGraphCommon.FLOAT_RANGE_MIN = 0.1 + (-sys.maxint - 1.0) |
determines floating minimum value
| float PyFlow.Core.AGraphCommon.FLOAT_SINGLE_STEP = 0.01 |
determines step for all floating point input widgets
| int PyFlow.Core.AGraphCommon.INT_RANGE_MAX = sys.maxint + 0 |
| int PyFlow.Core.AGraphCommon.INT_RANGE_MIN = -sys.maxint + 0 |
determines int minimum value


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I am Mr. Joel quite new to GitHub and have knowledge in WordPress Content Management System. I am very interested on learning a lot about both. I am planning to start a new project soon hope so. Although I am not new using WordPress. I already have some using this great tool. Not new developer though. But now I am looking for other tools that are cheaper for the end user and WordPress is what suites my needs it is a great framework too.
Apart from being a freelance web design and development, I enjoy most of my time in learning adobe photoshop and illustrator.


#{extends 'main.html' /}
#{set title: "dbtest - Speed Results for " + test.inserts + " inserts" /}
Speed Results - ${test.inserts} inserts
The results for the test of ${test.inserts} inserts is detailed below.
MongoHQ
Average Time: ${test.mongoAvg} inserts/sec
Xeround
Average Time: ${test.xeAvg} inserts/sec
#{if test.xeAvg < test.mongoAvg}
MongoHQ is the winner here.
#{/if}
#{else}
Xeround is the winner here.
#{/else}
Speed Results - Query
The results for the query test are detailed below.
MongoHQ
Elapsed Time: ${test.mongoTimeQ} seconds
Xeround
Elapsed Time: ${test.xeTimeQ} seconds
#{if test.xeTimeQ > test.mongoTimeQ}
MongoHQ is the winner here.
#{/if}
#{else}
Xeround is the winner here.
#{/else}


Music Mixer
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Own Music
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types of music


{% trans "new arrival" %}
PAVSHOP shares its interior styling solutions to make your house a home. Start decorating today with the latest range of PAVSHOP home décor and homewares.{{i.information|truncatechars:30}}
{% trans "popular products" %}
Discover the perfect look for casual days or special occasions with the new popular collection of clothes.  Donec faucibus maximus vehicula. Sed feugiat, tellus vel tristique posuere, diam-
December 27Donec commo is vulputate
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Donec faucibus maximus vehicula. Sed feugiat, tellus vel tristique posuere, diam
By Admin -
December 09Donec commo is vulputate
Lorem ipsum dolor sit amet, consectetur adipiscing elit. Donec faucibus maximus vehicula. Sed feugiat, tellus vel tristique posuere, diam
By Admin
{% trans "about PAVSHOP" %}
PAVSHOP has been an online sales site with active sales in Newyork for more than 10 years. The site offers its customers all kind of categories like clothes, cosmetics, furniture, gadgets, shoes, bags, home decore etc.


a block of code, loaded on request, providing data types and procedures
Basically, a library is a set of functions and/or structures to be used by applications. For example, if a programmer wishes to use more complex mathematical functions in C like sin he would have to use the library libm where this and other functions are defined. To do so, he can link the library in the compilation stage, using the -l option with the name of the library (without the lib prefix and the extension: in this case, -lm).
A shared library opposes to a static one in the sense that the library is only loaded at runtime and not in the compilation stage. So, while in static libraries the program carries the library code, when linked to a shared one the library code is not present and is only loaded into memory when needed.
One of the named advantages of shared libraries, and also another characteristic that differs them from static ones, is the fact that the system has, at most, one copy of the library's code per library. When loading, the system first checks if there is already a copy of the library on memory. The code is only copied if there is none, while as in static the code is included in every compiled program to which the library is linked.
So, if the program doesn't carry the library's code, when is it loaded? There are two alternatives:
automatically at the start of the process and, thus, only unloaded at the end: the library must be linked at compilation stage;
by request of the program itself (using, for example, dlopen()): the code can be loaded and unloaded in the middle of the process or not even loaded at all; furthermore, the library is not linked at compilation stage;
A small example at the end of this section will illustrate these concepts.
But why use a shared library instead of a static one? And why use demand loading instead of automatic loading or the other way around? Shared libraries bring a number of advantages...
libraries are only loaded at run time (thus, the possibility to change the library without having to recompile the end application);
lighter applications and saving of space (since the application doesn't carry the library's code);
In particular, demand loading of a shared library can be very useful
as stated before it is the application itself which requests the loading: the code can be loaded and unloaded at any given stage or not loaded at all; this is particularly useful when the program may use resources from several libraries but not necessarily at that moment, thus, avoiding that dozens of libraries that are not being used would be wasting memory;
since the library is not linked at compilation stage the application doesn't have to know it beforehand: for example, all the libraries can be put in a certain directory where the application will fetch them; this enables that resources can be added or changed without touching the application itself;
But they have a cost. Also, some problems may arise and should be taken in account.
First, errors only show at runtime. This makes the debugging more difficult but also imposes some care on moving libraries and changing them;
Creating a shared library is more complex and requires a few number of flags and details. However, this can be easily overcome (see Libtool);
The dynamic loading obliges the code to be Platform Independent Code(PIC). This makes the library heavier so, although applications are lighter, space is only actually saved if there is more than one application linked or there is demand loading;
The resources that demand loading enables (see above) also bring dangerous situations:
What happens if the program doesn't find the library expected? The error can occur at anytime of the process life and not at the beginning as in automatic loading;
How does the program know which interface the library provides? If it is defined beforehand, how to ally versioning and heterogeneity of libraries with this prior knowledge without having to touch the application?
Developing a shared library, as is, is generally more difficult than developing a static one. The complexity increases in a great amount when trying to make it platform independent and easily built (a good user interface). There are, however, some very powerful utilities that not only simplify this procedure but are also greatly recommended since they produce a standard, compliant and robust final product. Their use is quite straightforward and apply to static ones as well.
These two utilities combined are especially adequate for projects intended to be easily developed, used and installed in different platforms, helping to provide:
platform independence and/or ability to check for the features needed by the package;
a custom and straightforward user interface when installing the package;
an easy maintenance of Makefile (thus, helping compilation)
To configure the software package, a configure script is generated from a file called configure.in created by the developer. This file should contain invocations to Autoconf macros that test the system features needed by the package. Autoscan can be used to help write this file.
Automake assumes that Autoconf is being used and is used to produce Makefile.in files from Makefile.am files created by the developer. When the configure script is run these files will originate Makefile's. These Makefile.am files consist mainly of a series of make macro definitions.
The final user will only have to type
$ ./configure $ make $ make install |
Autoconf and Automake manuals in the GNU documentation provide a very good explanation on how to use these tools ( see Links and further information)
Libtool is a tool for developing libraries that provides the complete functionality of a host type in a generic interface but hiding the confusing and complex details. It can be used together with Autoconf and Automake.
Some points about libtool:
Libtool is based on the paradigm that " libraries are programs with multiple entry points, and more formally defined interfaces."; in fact, the rules to create a library look much like a compilation of a program;
By default, libtool tries to produce both a static and shared library; if the system doesn't permit shared libraries, it produces a static version;
When creating the library, wrappers are used (for extra meta-data, since the meta-data stored varies with the platforms) as well as temporary directories for storing the libraries.
Libtool provides several facilities:
Creating object files (including PIC)
Linking libraries
Linking executables
Debugging executables
Installing libraries
Installing executables
In A small example, it's illustrated the use of libtool to create a library.
GNU documentation has also a very good, step by step, manual of libtool (see Links and further information)
To illustrate, here is the development of a very small library, libgarfield.so, from the files eating.c and sleeping.c with the header file garfield.h. An executable called garfield is created by linking main.c to libgarfield.la (which is a wrapper to libgarfield.so).
Autoconf and automake are not used here, only libtool. It is presented the steps for the most common actions; see the libtool manual for the explanation of these steps and other possible actions that can be performed (see Links and further information).
Creating object files. Note that besides the normal object files it is also created .lo files which are PIC object files needed when creating the shared library.
$ gcc -g -O -c main.c $ libtool gcc -g -O -c eating.c mkdir .libs gcc -g -O -c eating.c -fPIC -DPIC -o .libs/eating.lo 5 gcc -g -O -c eating.c -o eating.o >/dev/null 2>&1 mv -f .libs/eating.lo eating.lo $ libtool gcc -g -O -c sleeping.c rm -f .libs/sleeping.lo gcc -g -O -c sleeping.c -fPIC -DPIC -o .libs/sleeping.lo 10 gcc -g -O -c sleeping.c -o sleeping.o >/dev/null 2>&1 mv -f .libs/sleeping.lo sleeping.lo |
Linking libraries. The libraries are put in the .libs/ directory and libgarfield.la is created in place which, as was said, is a wrapper for the shared library.
$ libtool gcc -g -O -o libgarfield.la eating.lo sleeping.lo \ -rpath /usr/local/lib rm -fr .libs/libgarfield.la .libs/libgarfield.* .libs/libgarfield.* gcc -shared eating.lo sleeping.lo -lc -Wl,-soname -Wl,libgarfield.so.0 -o .libs/libgarfield.so.0.0.0 5 (cd .libs && rm -f libgarfield.so.0 && ln -s libgarfield.so.0.0.0 libgarfield.so.0) (cd .libs && rm -f libgarfield.so && ln -s libgarfield.so.0.0.0 libgarfield.so) ar cru .libs/libgarfield.a eating.o sleeping.o ranlib .libs/libgarfield.a creating libgarfield.la 10 (cd .libs && rm -f libgarfield.la && ln -s ../libgarfield.la libgarfield.la) |
Linking the executable. The executable is put inside .libs/ and a wrapper script is created in place.
$ libtool gcc -g -O -o garfield main.o libgarfield.la gcc -g -O -o .libs/garfield main.o .libs/libgarfield.so -Wl,--rpath -Wl,/usr/local/lib creating garfield |
Installing libraries. libgarfield.la is also installed with the static and shared libraries. All the necessary operations are usually done (like, for example, calling ldconfig) but running libtool -n --finish libdir will give further hints on what to do (in this example, the installation command already gives this information).
$ libtool install -c libgarfield.la /usr/local/lib/libgarfield.la install -c .libs/libgarfield.so.0.0.0 /usr/local/lib/libgarfield.so.0.0.0 (cd /usr/local/lib && rm -f libgarfield.so.0 && ln -s libgarfield.so.0.0.0 libgarfield.so.0) (cd /usr/local/lib && rm -f libgarfield.so && ln -s libgarfield.so.0.0.0 libgarfield.so) 5 install -c .libs/libgarfield.lai /usr/local/lib/libgarfield.la install -c .libs/libgarfield.a /usr/local/lib/libgarfield.a ranlib /usr/local/lib/libgarfield.a chmod 644 /usr/local/lib/libgarfield.a PATH="$PATH:/sbin" ldconfig -n /usr/local/lib 10 ---------------------------------------------------------------------- Libraries have been installed in: /usr/local/lib If you ever happen to want to link against installed libraries 15 in a given directory, LIBDIR, you must either use libtool, and specify the full pathname of the library, or use `-LLIBDIR' flag during linking and do at least one of the following: - add LIBDIR to the `LD_LIBRARY_PATH' environment variable during execution 20 - add LIBDIR to the `LD_RUN_PATH' environment variable during linking - use the `-Wl,--rpath -Wl,LIBDIR' linker flag - have your system administrator add LIBDIR to `/etc/ld.so.conf' 25 See any operating system documentation about shared libraries for more information, such as the ld(1) and ld.so(8) manual pages. ---------------------------------------------------------------------- |
Other actions like debugging are also easily done and take advantage of the wrapper scripts.
Autoconf and it's manual can be found at http://www.gnu.org/software/autoconf/autoconf.html.
Automake and it's manual can be found at http://www.gnu.org/software/automake/automake.html.
Libtool's homepage is located at http://www.gnu.org/software/libtool/libtool.html.
The man pages of dlopen , ld, ldconfig also provide useful information.
General description and implementation information on shared libraries can be found in Building and Using Static and Shared "C" Libraries and Shared Libraries (Section 4).
In the GLib Reference Manual, there is a section describing the gmodule library, Dynamic Loading of Modules.


My name is Larissa Manlosa. I am a senior at Eastlake High School doing Running Start Classes at Bellevue College. After high school, I will be moving to Pullman to attend Washington State University, studying computer science. I enjoy reading, listening to music, and getting inspired. I currently work as a hostess at the Sammamish Cafe, but I'd love to become a Web Developer. My goals are to learn the basics of coding to begin developing my skills to get an early start for the Computer Science Program and to use independently.