int add(int a, int b);
28 August 2021
Java 8 brought lambdas to the Java language, which is a corner stone of functional programming. Although, the functional programming features of Java out of the box are limited, some interesting concepts can be implemented using it. This post is about applying function parameters partially.
Partial application refers to the process of fixing some arguments of a function and producing a function of lower arity - actually, the arity is reduced by the number of fixed arguments. To not become to theoretical and I’ll give an example: The function
int add(int a, int b);
adds two integer numbers. If we fix an argument applying the integer number 3
,
we get the function (in pseudo-code)
partial(add, 3) => int add3(int a)
which returns the sum of 3
and the given integer parameter. While the function
add
does have the arity 2
, the arity of add3
is 1, which is the arity of
function add
reduced by the number of fixed arguments.
| Partial application is sometimes incorrectly called currying, which is a related, but distinct concept. |
Functions in Java are represented by objects implementing a functional interface.
In the following the Function
and BiFunction
interfaces are used to
demonstrate partial application - but the same holds for other functional
interfaces like Supplier
. Given a BiFunction
corresponding to the
introductory example:
BiFunction<Integer, Integer, Integer> add = (a, b) -> a + b;
To partially apply the function, we need to fix an argument and produce a function which takes one argument. This can be accomplished by:
public static <T, U, R> Function<U, R> partial(BiFunction<T, U, R> f, T x) { return (y) -> f.apply(x, y); }
The function takes the BiFunction
plus an argument and returns a Function
.
The logic of the created Function
calls the given BiFunction
applying
one fixed and one call argument. The partial
function is used as follows:
Function<Integer, Integer> add3 = partial(add, 3); Assertions.assertEquals(6, add3.apply(3));
The partial
function implements a higher-order functions because
it takes a function as argument
and returns a function as its result
Cool, how lambdas makes functions to first-class citizen of the Java language. But, let’s take the next step.
The add
function example works fine, because it doesn’t matter which
of the two arguments get fixed. More precisely, the add
function is
commutative, i.e. it does not matter in which order the arguments are provided,
the result will be the same.
If we define a function divide
as follows
BiFunction<Integer, Integer, Integer> divide = (a, b) -> a / b;
the order of the arguments provided actually matters, i.e. the function is non-commutative.
The first idea to solve this is to provide two functions, one partially applies the first argument, the second partially applies the second argument:
public static <T, U, R> Function<U, R> partialFirst(BiFunction<T, U, R> f, T x) { return (y) -> f.apply(x, y); }
public static <T, U, R> Function<T, R> partialSecond(BiFunction<T, U, R> f, U x) { return (y) -> f.apply(y, x); }
These two functions can then used to fix the first or second argument respectively:
Function<Integer, Integer> divideFourBy = partialFirst(divide, 4); Assertions.assertEquals(2, divideFourBy.apply(2));
Function<Integer, Integer> divideByTwo = partialSecond(divide, 2); Assertions.assertEquals(2, divideByTwo.apply(4));
The interface of partialFirst
and partialSecond
doesn’t look very nice
and concise, because you need to select the right function when applying the
function arguments partially. This is even more true for functions with higher
arity - we’ll come to such functions in a second. From an API point of view, it
would be nicer to have just one overloaded partial
function, to which you
provide all arguments, some fixed, others denoted with placeholders. Let’s
first define a placeholder class and instance:
static class Matches { public static Matches _any; }
This placeholder is then used when defining the overloaded partial
function:
public static <T, U, R> Function<U, R> partial(BiFunction<T, U, R> f, T x, Matches m) { return (y) -> f.apply(x, y); }
public static <T, U, R> Function<T, R> partial(BiFunction<T, U, R> f, Matches m, U x) { return (y) -> f.apply(y, x); }
When statically importing the placeholder instance Matcher._any
the partial
application looks like:
Function<Integer, Integer> divideByFour = partial(divide, _any, 4); Assertions.assertEquals(2, divideByFour.apply(8));
Function<Integer, Integer> divideSixBy = assume(divide, 6, _any); Assertions.assertEquals(3, divideSixBy.apply(2));
The overloaded partial
function feels more concise on usage, because one
don’t need to worry about the name of the function for partial application
and can provide the all arguments of the original function in the same order.
The next step would be to apply this pattern to functions of higher arity. Functions taking three arguments and map those arguments to one result are usually called tri-function and can be defined in Java as:
@FunctionalInterface public interface TriFunction<T, U, V, R> { R apply(T t, U u, V v); }
| Increasing the arity would result in QuadFunction (4 arguments), QuintFunction (5), SextFunction (6), SeptFunction (7), OctFunction(8) and so on. |
The definition of partial applications to generate bi-functions out of tri-functions are straight forward:
public static <T, U, V, R> BiFunction<U, V, R> partial(TriFunction<T, U, V, R> f, T a1, Matches m2, Matches m3) { return (x, y) -> f.apply(a1, x, y); }
public static <T, U, V, R> BiFunction<T, V, R> partial(TriFunction<T, U, V, R> f, Matches m1, U a2, Matches m3) { return (x, y) -> f.apply(x, a2, y); }
public static <T, U, V, R> BiFunction<T, U, R> partial(TriFunction<T, U, V, R> f, Matches m1, Matches m2, V a3) { return (x, y) -> f.apply(x, y, a3); }
The partial
function for tri-function arguments can be used very the same way
as we’ve done for bi-functions:
TriFunction<Integer, String, List<String>, String> formatter = (indent, delimiter, strings) -> " ".repeat(indent) + strings.stream() .collect(joining(delimiter)); BiFunction<Integer, List<String>, String> commaSeparatingFormatter = partial(formatter, _any, ",", _any); Assertions.assertEquals(" one,two,three", commaSeparatingFormatter.apply(3, Arrays.asList("one", "two", "three")));
Because the tri-function’s partial
return a bi-function, for which partial
functions are already defined, the calls can be cascaded to partially apply
even more arguments:
Function<List<String>, String> indentThreeCommaSeparatingFormatter = partial( partial(formatter, _any, ",", _any), 3, ._any);
Assertions.assertEquals(" one,two,three", indentThreeCommaSeparatingFormatter.apply(Arrays.asList("one", "two", "three")));
The implementation of partial application for functions with higher arity is basically the same as for bi- or tri-function.
Lambdas introduced functional programming features into the Java ecosystem. With the exception of processing of streams functional programming techniques seem to be less used by Java programmers, maybe because of the imperative and object oriented history of Java. Nevertheless, adapting functional programming style can be beneficial in Java, too. The partial application of functions shown in this Blog post demonstrated the power of the functional programming style in Java.


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Deep Learning in the Weeds
Can we distinguish a weed from a crop seedling? Many weeds and crop seedlings look similar, so sometimes it can be hard to differentiate the two. Although they look alike when they are young, they will eventually grow into totally different plants. This challenge presents itself in many real-world situations. For example, Charlock is an agricultural weed and an invasive species in some areas outside its native range, while Shepherds Purse, considered an herbal medicine, has seedlings of very similar shape to Charlock (See Fig1). Thus, farmers need to differentiate each type of seedling to successfully cultivate without damaging their plants. The goal of this project is to build a model to effectively classify several species of seedlings. This is based on a Kaggle project called “Plant Seedlings Classification” (https://www.kaggle.com/c/plant-seedlings-classification). The dataset contains 4,750 training images and 794 test images, each of which belongs to one of twelve species at several different growth stages. I aim to train several deep learning models using Python/Keras, and test how accurately they can estimate the correct species.
Problem Statement
Why is this project meaningful? As described earlier, many types of seedlings look very similar, so even an experienced gardener can have difficulty differentiating weeds from the seedlings of more helpful plants. Moreover, as each species includes different growth stages, the character of each seedling could change depending on their stage of growth. Furthermore, I will use low resolution images, which are more likely representative of real life, and because of their low resolution it is not always easy for even an experienced human to identify the correct seedling species.
This problem is related to a computer vision challenge, so I will leverage a Convolutional Neural Network (CNN) to train a model to categorize each seedling in the dataset. CNNs are particularly powerful for training on multi-dimensional data, such as images. They are designed to handle 2D spatial information in images particularly well; unlike a regular neural network, CNNs use locally-connected layers and do not use vectors for their hidden layers. To do this, I need to implement models with multiple trials with different CNN layers, such as convolution and pooling layers by using different optimizing parameters and dropout. Furthermore, I will compare my models from scratch with models using the popular technique of transfer learning with Xception, which has been established to help save time when training neural networks on image classification problems. Finally, I will determine the best model to predict seeding species using mean F-score as the evaluation metric, and I will elaborate on it in the following section.
Analysis
Data Exploration
The dataset contains 4,750 training images and 794 test images, each of which belongs to one of twelve species at several different growth stages. I obtained the dataset through a Kaggle competition, but originally the dataset was from the Aarhus University Department of Engineering Signal Processing Group. The training images include the label of seedling species, while the test images do not include labels. I am using only the 4,750 training images to train a model and validate and test the model accuracy.
The input images are all square, but they have a wide range of pixel widths and heights, with some less than 100x100 pixels and some over 1000x1000 pixels, and each image has RGB colors. The figure on left (Fig2) shows the distribution of pixel widths (also equal to the height distribution) for all input images. I will need to rescale the input images to a uniform size to input into my neural network. A smaller uniform size makes the most sense, considering that many images are ~100 pixels on a side, and it’s easier to reduce the sizes of images than to reliably scale up the smaller images to a larger size. Also, using a smaller uniform size will help to save computational power. Therefore, I chose to rescale all images to the minimum size of the input data, 47 x 47.
The input images include twelve seedling species from the following: Black-grass, Charlock, Cleavers, Common Chickweed, Common wheat, Fat Hen, Loose Silky-bent, Maize, Scentless Mayweed, Shepherds Purse, Small-flowered Cranesbill, Sugar beet. The table below gives the total number of each species, which range from 221 - 654.
I split the 4,750 images into three different datasets: training set ( 80%), validation set (10%), and a test set (10%). The training set is used to train the model and the validation set is used to adjust weight parameters to check the loss function after training the model. Finally, the test set serves to check the model accuracy using our scoring metrics. The images below are examples of each seedling of the input images.
As seen above (Fig3), the input images only include soil, gravel, and measure tape, except seedlings. So, filtering with other objects might not matter much. However, some seedlings, such as Black-grass, have pretty narrow leaves, so it might not be easy to train on those images. Also, some photos are blurry, so it could be hard to predict “by eye” as well. As mentioned earlier, as part of the pre-processing I rescale all input images to the minimum size of the input images, 47 x 47, which helps to save computational time.
Algorithms and Techniques
Why CNN? Neural Networks are designed to mimic the process of learning from the human brain. They can be comprised of multiple layers, each containing many nodes to simulate neurons. When training a neural network, the weights applied to each node are adjusted. A regular neural network connects every node to every other node in each successive layer, making them fully connected. To process an image with a regular neural network, the image must first be transformed into a vector. This transformation is not optimal for analyzing 2-D images because it uses a vector for each layer. On the other hand, CNNs are especially powerful when we must train on multi-dimensional data, such as images, because layers of a CNN have neurons arranged in 3 dimensions. CNNs consist of locally connected layers, which use far fewer weights compared to the densly connected layers of a regular neural network. The locally connected layers efficiently prevent overfitting and allow us to easily understand the image data because they naturally handle two dimensional patterns. You can read more details in the "Step 2-2. Define the CNN model architecture" in the previous blog, dog breed classifications.I will use a CNN to train a model using Keras in Python to categorize each seedling in the dataset. Keras is a deep learning library in Python, allowing for easy and fast prototyping. Keras allows one to build a model with a linear stack of layers, so I can quickly build a basic CNN structure with multiple layers and see how it performs. I will start with a basic CNN structure with three convolutional layers and pooling layers. Also, it will have a fully-connected layer with a softmax activation function before the output layer. Once the basic CNN is set up, I will improve the model in several ways such as:
- 1) adding more CNN layers
- 2) differentsizeoffilters
- 3) different optimization functions (SGD, Adam)
- 4) image augmentation
Finally, I will compare the results of the CNN models with the results I obtain using transfer learning, by building CNNs on top of the popular models, Xception.
Benchmark
For a benchmark model, I will use the simple CNN model described above with three convolutional layers and max-pooling layers. I tested this simple CNN on the input dataset, and I achieved a mean F-score of 0.40, and a prediction accuracy of 0.4. This accuracy is much better than random chance (1/12), but still not impressive. The aim of this project is to find a model with significantly better mean F-score and accuracy.
Methodology
Data Preprocessing
The data preprocessing function includes three processes as follows: First, the images are reshaped to have the same width and hight (47 pixels by 47 pixels). Second, I convert the image data to Numpy arrays with (number of images, 3, 47, 47). Also, I convert the pixel values to float32 format and normalize all of the arrays. In the case of the label dataset, I simply convert 1-dimensional class arrays to 12-dimensional class matrices. All codes for these are available in my github.
Implementation
Next, I start with a basic CNN structure with three convolutional layers and Max Pooling layers. Also, it will have fully connected layer with activation function (Relu) before the output layer. Finally, I added Global Average Pooling layers and a fully connected layer to predict the probabilities for each species. The code for this basic CNN structure is below:
A callback is a set of functions to be applied at given stages of the training procedure. The code for my callback function implementation is below. For fitting a model, I used 10 epochs and 20 for the batch size.
The mean F1 score of this first basic CNN is approximately 0.34, and accuracy is 0.4. I will use this as my benchmark model.
Refinement
Once the basic CNN is set up, I improved upon the model in several ways such as: (1) adding more CNN layers, (2) different size of filters, (3) different optimization functions (SGD, Adam), and (4) Image augmentation.
(1) I designed four different models shown in Fig8. One of the biggest challenges I faced was designing different models to test, because there are so many free parameters. I looked to other models people have used for inspiration in designing these four models in Fig8. For compiling these models, I used ‘categorical_crossentropy' as loss function and ‘Adam’ as optimizer with the default values of the parameters (e.g., learning rate=0.001). Then, I iterated each model with 20 epochs with batch_size=20 with training set and validation set. Another difficult challenge was determining a reasonable number of epochs to train these models, because I needed to find the right balance between saving time and getting reliable estimates for the performance of these models.
The following table gives the results of each model. The accuracy and mean F-1 score values are calculated with the test set, which I did not use to train models. The running time is estimated by my macbook, which has 2.9 GHz Intel Core i5.
As seen above, Model 3 has the best result, with Accuracy=0.83 and mean F-1 score=0.81 with test data. However, when I compare the running time, Model 1 and Model 2 have much better time efficiency. Thus, I picked two models, Model 1 (best time efficiency with the second best mean F-1) and Model 3 (best mean F-1 value) to use in the next trial to refine my process.
(2) With Model 1 and Model 3, I trained them with a different filter size (3 x 3) compared to the original filter size (2 x 2). The table below compares the previous results to the new trials, with a larger filter size, for the two models I’m testing.
Overall, we can see there is no improvement for both models. So, I decided to use the first filter size (2 x 2) hereafter for my convolutional layers.
(3) I have used the Adam optimization algorithm with the default value for the learning rate. I decided to test my models using a range of learning rates: 0.01, 0.0005, and 0.0001. Also, I tried to use the Root Mean Square Propagation (RMSProps) as an alternative optimization algorithm. The results of these tests are in the table below.
Overall, the default value of the learning rate for Adam optimization function seems to be the best choice. The lower learning rates, like 0.0001, did not work well because it is too slow, and so it requires many more epochs to train the model. Considering the time efficiency, I concluded that a lower learning rate is not appropriate for the models. Also, I tried using a larger learning rate than 0.001 with the Adam algorithm, but most of them did not work well. So, I left those models off the table above. On the other hand, RMSProps works pretty well, but using Adam optimization function seems to have a slightly better result. Therefore, I decided to use an Adam optimization algorithm and used the Keras default values (e.g., lr=0.001).
Finally, I added Image Augmentation, which is the process of taking images that are already in a training dataset and manipulating them to create many altered versions of the same images. For example, we can have more images with different rotation angles or vertically/horizontally flipped versions of the original images. This provides more images to train on, which could help a model to better generalize its training. The function ImageDataGenerator() in Keras generates batches of the input images with many different variations depending on its parameters. You can read more details here. .flow() and .fit_generator() are used to fit the model in batches with data augmentation. We can use these at the same time, as the code above shows. I only applied the horizontal_flip and vertical_flip options. This is my last refinement factor, so I trained the models with many epochs (30 - 50).
I took the best Model 1 in the table above, and the best Model 3 above, and then trained those models on an additional 30 epochs using image augmentation. The results of these tests are given in the table below.
Model 1 results in a better accuracy and higher mean f1 score (even with less time). Therefore, I decided to use Model 1 with RMSProps + Image Augmentation. Having settled on a model, I next decided to train it using larger input image sizes ( 256 x 256) with 50 epochs. At the same time, for comparison I used a pre-trained Xception model with fully-connected layers added (to do transfer learning). As with the model above, I first trained the model with Xception without image augmentation, and then trained it with image augmentation. The table below has the results using Xception. It seems surprising that the Xception model performed slightly worse than the Model 1 results above, but this was mainly due to a limitation in my CPU power. The training of the Xception model, which took 200 min, only got through five epochs. If I had more time or a more powerful processor, I would have achieved better results through transfer learning. However, given my computational resources, I would choose to use Model 1 above to train a model for this project.
Result
The Model 1 (+RMSprops optimization and Image Augmentation) is my final chosen CNN model made from scratch. The code for the model is saved on my Github page (Here).
Model Evaluation and Validation
The table in the previous section has my final accuracy (0.94) and mean F-1 value (0.93) of the model using the test sets. The result is a vast improvement over the first CNN model that I tried, especially considering that the total run time is less than 30 min on my macbook. The inclusion of image augmentation in my final model allows that model to better generalize the training dataset, and therefore to achieve better accuracy in the test dataset.
Justification
- Benchmark model : mean F1 score is 0.34, and accuracy is 0.4
- Final model : mean F1 score is 0.93, and accuracy is 0.94
The comparison between my bench mark model and the final model shows that I have more than doubled my accuracy, and the F1 score is nearly triple.
Above I plot the confusion matrix to more closely analyze the results of the model. The x-axis is the predicted class and y-axis is the true class for 10% seedings of overall sample (the test sample). The diagonal numbers correspond to the cases where the predicted class is matched with the true classes. The sum of each row gives the TNs (True Negatives), which are called “Negative” although the seedlings are truly identified. The sum of each column gives the FPs (False Positives), which although they are called “Positive”, the seedlings are falsely identified. Thus, Precision and Recall are each 0.93. Based on the definition of mean F-1 score I mentioned in the previous section, I derive 0.93 as mean F-1 score. The equations used to explicitly calculate these are given later in this section.
In order to check the result in detail, I compared TP, FP, TN, Precision, and Recall for each seeding species. The table below shows the results. As you can see, most of the seedling species are relatively well-recognized by the model, with the obvious exception being BG (which is often mis-identified as LSB).
Here, I use the micro-average mean F-1 score, which is given by:
- Precision = (TP1+TP2+…+TP12) / (TP1+TP2+……+TP12 + FP1 + FP2 +…. +FP12)
- Recall = (TP1+TP2+…+TP12) / (TP1+TP2+…+TP12 + FN1 + FN2 +…. +FN12)
Thus, precision & recall are 0.93, and F-1 score is the same.
Summary and Future work
End-to-End Summary
I tested a variety of different CNN models, using different numbers of filters, sizes of filters, optimization functions, and with or without image augmentation. I also used a pre-trained model (based on Xception) as a comparison to my CNN built from scratch, and I found the results to be slightly better with the pre-trained model. Both of my refined models have significantly better results when compared to the first basic model with three simple convolutional layers. I verified the test score using the leaderboard on Kaggle, so I got the result of 0.93 using their test data.
The figure above shows the F-1 score vs. accuracy for all of the models tested in this project. Different colored symbols indicate the different model tested. This figure highlights the significant improvements made with the variation in the models that were tested. Furthermore, the linear trend results from the fact that F-1 score correlates with accuracy.
Reflection
One of the biggest challenges for me in the project was the sheer number of free parameters available when building a CNN from scratch. It was hard for me to even know where to start in testing a set of models, and the fact that training each one takes at least 20 minutes only added to the difficulty. However, I came away from this process with a much greater understanding of what each of these components of a CNN does, and the effects they have on the computational time and on the final qualities of the models. So my take-away from this project will certainly be that the process of testing out all of these models was really valuable for me. I also have a greater appreciation of the value of using Cloud Computing (e.g., AWS) with GPU processors, which would allow me to more rapidly prototype model variations to speed up this process in the future.
Improvement
(1) When examining the confusion matrix above, the most obvious flaw is the mis-identification between Black-grass (BG) and Loose silky-bent (LSB). The precision and recall for Black-grass is the worst overall, since a large fraction of the BG are considered by the model to be LSB. On the other hand, the precision and recall for LSB are not bad, because I believe the number of input images for LSB could be enough to train the model to predict with at least a 90% accuracy. Thus, the most important step I would take to improve my model would be to include more images, especially for the BG class. At the same time, if we want to use this model to categorize BG, we need to accept that there will be a significant number of FPs and TNs (and a lower precision and recall). If you look back at Fig3, which shows example images of each seedling species, it’s apparent that the features of BG are not very easy to see, and that visually they look very similar to the LSB seedlings. This no doubt contributes to the problems that the model has with identifying them.
Cloud : Everything in this project was executed on my laptop, which is not the most efficient way to train models such as these. In the future, I will use GPU computing on an Amazon Web Services EC2 instance, which can be extremely helpful because it will allow me to train more models, and with a greater number of epochs, thanks to a large improvement in speed. On AWS, I could also use larger image input sizes than 47x47. Also, I could test many pre-trained models with different parameters, which could also lead me find a better overall model.
Reference
- https://www.kaggle.com/c/plant-seedlings-classification
- https://www.tensorflow.org/tutorials/layers
- https://machinelearningmastery.com/adam-optimization-algorithm-for-deep-learning/
- https://towardsdatascience.com/image-augmentation-for-deep-learning-using-keras-and-histogram-equalization-9329f6ae5085
- https://www.kaggle.com/gaborfodor/seedlings-pretrained-keras-models
- https://keras.io/applications/
- http://cs231n.github.io/convolutional-networks/#overview


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Tablelist supports interactive cell editing with the aid of the Tk core entry, text, spinbox, and checkbutton widgets. These widgets are automatically registered for cell editing, hence the only action needed for using one of them for editing the cells of a given column is as follows:
Use the tablelist widget's columnconfigure
subcommand to set the given column's -editable
option to true
and its -editwindow
option to
entry
, text
,
spinbox
, or checkbutton
,
respectively. (These options are supported at cell level, too, with the
aid of the cellconfigure
subcommand.) Since the default value of the
-editwindow
column configuration option is
entry
, it is not necessary to set it explicitly if the
editing should take place with the aid of an embedded entry widget.
tablelist::tablelist
command for details on the editing
process.-padx
and
-pady
options set to 2
, its
-wrap
option set to none
, and its
initial height set to the number of lines contained in it. There is,
however, an exception from this rule: If the -wrap
option of the cell's
column was set to true and Tk version 8.5 or higher is being used, then the
text widget's -wrap
option will be set to
word
and its initial height will equal the number of
display lines (taking into account the line wraps) contained in
it. You can use the script corresponding to the -editstartcommand
tablelist configuration option to override the initial settings according
to your needs.
-wrap
option was set to
word
or char
(either by Tablelist
or from within the above-mentioned script) and Tk version 8.5 or higher is
being used, then, whenever its width changes (e.g., due to interactive
column resizing), its height will be set automatically to the number of
display lines contained in it. (The number of display lines is
retrieved with the aid of the
count -displaylines
text widget subcommand,
introduced in Tk 8.5.)package require Wcb
or
package require wcb
) then the text widget's
height will be updated automatically whenever text is inserted into or
deleted from it, which makes the editing much more user-friendly.
This is achieved by using an appropriately defined
after-insert
and after-delete
callback for the edit window. You can use the script corresponding to
the -editstartcommand
tablelist configuration option to define further callbacks for the text
widget.  (The above-mentioned callback is created via
wcb::cbappend
, after returning from that
script.)Tab
key is reserved for navigation between the editable
cells, but the user can insert a tabulator character into the text widget
by pressing Control-i
.Return
and KP_Enter
keys insert a newline
character into the text widget. Control-j
can also be
used for inserting a newline. Control-Return
and
Control-KP_Enter
terminate the editing and destroy the edit
window.Control-Home
and Control-End
have their
well-known text widget-specific bindings, just like Meta-<
and Meta->
if tk_strictMotif
is
false. Again, this is different from the behavior of the other
widgets used for interactive cell editing. For jumping into the
first/last editable cell, the user can press
Alt-Home
/Alt-End
or
Meta-Home
/Meta-End
(Command-Home
/Command-End
on Mac OS Classic and
Mac OS X Aqua).-state
option set to
normal
, which makes the widget editable. You can
use the script corresponding to the -editstartcommand
tablelist configuration option to set the state of the spinbox to
readonly
or define validations for it, as well as for
setting its (range of) values and its -wrap
option.-borderwidth
,
-font
, -padx
,
-pady
, and -variable
options.
In an X11 environment it will be created with explicitly set values for its
-borderwidth
, -indicatoron
,
-image
, -selectimage
,
-selectcolor
, and -variable
options. You can use the script corresponding to the
-editstartcommand
tablelist configuration option to set any other configuration options, like
-offvalue
and -onvalue
, according
to the internal values of the cells. Since the default values
of the -offvalue
and -onvalue
checkbutton options are 0
and 1
, you don't need
to change these options if the cells have the same internal values
0
and 1
.


Different Kinds of Nouns
Nouns are words used to name people, places, things, animals, ideas, and events - Rome, clothes, peace, success, etc.
Nouns are classified as
- Common Noun - this is the general term for anything - table, chair, airplane, belief.
- Proper Noun - this is a specific name of anything - Roger, Philippines, Bible.
Six special classes of nouns:
- concrete
- abstract
- mass
- count
- compound
- collective
Properties of nouns:
- person
- number
- gender
- case
In a sentence, a noun can be used as any of the following:
Subject - what is being talked about
Maria is happy.
Predicate Nominative - same thing as the subject; it is placed after the verb
The prince became a beggar.
In a sentence, a noun can be used as any of the following:
Direct Object - receiver of the action which/who is not the same as the subject
The chef prepared a banquet.
Indirect Object - tells to/for whom or to/for what something was done
Mother bought Nancy a bag.
In a sentence, a noun can be used as any of the following:
Object of the Preposition - answers the question what/where/whom after the preposition
The bus came from Manila.
Appositive - another name for the same person or thing represented by the subject
Baguio, the summer capital of the Philippines, is our next destination.
In a sentence, a noun can be used as any of the following:
Objective Complement - added to the direct object to complete the meaning of the verb
We made Lea the manager.
Direct Address - word by which a person is addressed; it is not the subject
Kiara, the room has to be cleaned.


--- subject: Cover Letter ---
Thanks for making the time to meet with me last week; it was great speaking with you. I wanted to touch bases quickly on the opportunity you mentioned to work with Disney and the Void during the school year. I'm very interested in the work that Disney has been doing with the Void. It sounds like your next steps are data related which plays well with my background. I'd love to move forward with this and see how I can continue contributing to the work Disney is doing. Let me know what it looks like on your end.
Thanks,
Data Scientist | MBA Candidate | Connecting Business and Statistics
University of Utah, David Eccles School of Business


{% extends "../layout.html" %} {% block page_title %} Find a career {% endblock %} {% block content %} {% set serviceName = 'Find a career' %} {% set bannerStyle = 'BETA-style' %} {% set filterName = 'Training routes' %} {% include "../includes/survey.html" %} {% include "../includes/global_header_bar.html" %} {% include "../includes/breadcrumb_filters.html" %} {{filterName}} Select an appropriate training level Step 5 of 5 Apprenticeships Other ways of learning (example college or university) Not applicable Continue {% endblock %}


IM is pretty fast. However, as with any framework, abstraction often comes as a trade-off with performance. You pay a few CPU cycles for short, pretty and maintainable code.
Please feel free to use IM where ever it will result in shorter, more readable code. In most cases performance is important, but will not require you to sqeeze out every CPU cycle possibe. In very few cases you need to write framework-less JavaScript code to get things really fast. In most cases you will not notice the overhead of a framework.
That said, just know that:


sgfcharm
/
onion.w4v3xrmknycexlsd.lib.sgfcharm
/
SgfController
/
InteractionMode
/
FREE_PLAY
FREE_PLAY
FREE_PLAY
(source)
Let the user execute all moves.


list itemsub-t tesub-t!
03/08/2020
- list item 1
- list item 2
- list item 3
- sub-list item 1
- sub-sub-list item 1
- sub-sub-list item 2
- sub-list item 3
- numbered list item 1
- numbered sub-list item 1
- numbered sub-list item 2
- numbered sub-list item 3
- numbered list item 2
- i don't even know how deeply nested this is
- sub-list item 4
- list item 4
paragraph
- another list
- this time it's a numbered list
- with an unnumbered sub-list
- second sub-list item
debug goes brrr


Alice
Alice
Flower
Post
Alice
Bob
Selena
Joye
Jeff
Alice
Alice
Flower
Post
Bob
Bob
Nice
Add a comment
Post
Selena
Selena
Best place
Add a comment
Post
Joye
Joye
Beautiful day
Add a comment
Post
Jeff
Jeff
What a day !


Quote: Posted 08/22/04 20:30:03 (GMT) -- divine warri0r
hi if u hav any ideas of an epic charcter build thats draconic epic build or a pure good character build or it would meen alot to me if u had any ideas of a draconic pure good character build
post in ur ideas here
-THANK YOU-


it costs you nothing
Free for commercial and personal use.
You can easily edit its contents and code it for yourself or your client!
Lorem ipsum dolor sit amet consectetur, adipisicing elit. Illum vitae delectus totam. Corporis, dolorem quos reiciendis velit facilis possimus iure distinctio blanditiis aperiam, earum quam voluptatibus necessitatibus accusamus dicta cupiditate.
Lorem ipsum dolor sit amet consectetur adipisicing elit. Dolorum est tenetur exercitationem vel, error vero ipsum modi odit repellat dicta?
I love people who make me laugh. I honestly think it's the thing I like the most, to laugh. It cures a multitude of ills. It's probably the most important thing in a person.
Waking up to see another day is a blessing. Don't take today for granted. Make it count and be happy you are alive.

Sometimes you will never know the value of a moment until it becomes a memory.
It is health that is the real wealth and not pieces of gold and silver


Corinna in the Perl Core!
It’s been a years-long, painful process, but with the release of Perl v.5.38,0 , the first bits of Corinna have been added to the Perl core. For those who have not been following along, Corinna is a project to add a new object system to the Perl core. Note that it’s not taking anything away from Perl; it’s adding a core object system for better memory consumption, performance, and elegance.
If you don’t have v5.38.0, you can still install Feature::Compat::Class on Perl v5.14.0 and above and get the same features.
The MVP for the Perl core
is in seven stages, three of which are done and one of which (convenience
attributes) is started (there is a :param
attribute for field
).
- Classes (done)
- Inheritance (done)
- Roles
- Convenience attributes
- Field initializer blocks (done)
- MOP
- Method modifiers
We need at least six of those to be implemented to really have something decent out of the box, but the MOP, while important, is not something that most OOP developers reach for on a regular basis.
You can read what’s currently implemented at perldoc
perlclass
, but here’s a 2D point
class using the existing features. We’ll make the default of the point be the
graph origin.
class Point::2D {
field $x :param = 0;
field $y :param = 0;
field $orig_x = $x;
field $orig_y = $y;
method x () { $x }
method y () { $y }
method reset () {
$x = $orig_x;
$y = $orig_y;
}
method move ($dx, $dy) {
$x += $dx;
$y += $dy;
}
method description () {
return "($x, $y)";
}
}
my $point = Point::2D->new( x => 10, y => 20 );
$point->move( 5, 5 );
say $point->description; # (15, 25)
When the full Corinna is implemented, the method x
and method y
will be
replaced with:
field $x :param :reader = 0;
field $y :param :reader = 0;
The Teams
Here’s a partial list of the main contributors and/or influencers to Corinna:
- Damian Conway
- Dan Book (Grinnz)
- Graham Knop (haarg)
- Harald Jörg
- Matt Trout
- Paul Evans
- Sawyer X
- Stevan Little
Note that Corinna was largely inspired by the work of Stevan Little. Damian Conway was also a huge contributor, but he was mostly behind the scenes sharing email with me and posting to blogs.perl.org ;
Sawyer X’s main contribution was being the pumpking who encouraged me to stop trying to implement Corinna, but instead focusing on building something great without worrying about the current limitations of Perl.
Paul Evans is doing the actual implementation work and he’s doing a fantastic job.
The others (not to slight them, I can’t remember all the details of a multi-year project) were also huge contributors. They helped suss out design flaws, called me on my bullshit, and helped me understand the limitations of what we were trying to accomplish.
It’s also worth noting that due to this team effort, Corinna is not what I wanted. I wanted more. Much more. But it wasn’t just about trimming Corinna down to what P5P would accept, it was also vetoing many of my ideas about what I thought should be in Corinna. I was wrong about a lot of things and I’m sure I’m right about about some others, but the team effort is what made Corinna what it is today and I’m immensely grateful for that.
Other Languages
In the process of developing the Corinna MVP, many other languages were looked at. I was particularly curious about Beta , but there was nothing that fit. I also looked at Smalltalk, Java, Raku, Python, Eiffel, and Ruby (and probably others I’m forgetting right now). There are many interesting ideas, but given that we were trying to get something that fit Perl, the biggest influences were Raku, Moo/se, and yes, Java. Primarily, I was interested in protected methods and inner classes, but the former seemed problematic and the latter wasn’t something appropriate for the MVP. However, reading about OOP design (so many examples are in Java), helped quite a bit with Corinna.
What We’re Losing
There are, of course, trade-offs in something this huge.
You Can’t Violate Encapsulation
The biggest concern amongst developers is that we can no longer violate encapsulation (well, you can via the MOP, but it will be more work, as it should be. Emergency patches sometimes require violating encapsulation).
However, I don’t know many developers who can define encapsulation. People keep saying it’s about hiding state, but that’s not true. It’s about hiding state and process because the two are intimately linked in classes. If you can change the state without going through the interface, you have no guarantees the process is correct. That’s a bad thing.
Fortunately, this has been well-understood for decades, so there’s a wealth of information about proper OOP design on how handle this. I expect there will be changes to Corinna to help with this, but we don’t know what they are yet.
No Multiple Inheritance
To my surprise, no one on the design team strongly argued against this change. There were discussions, but nothing too serious. There are many OOP languages which do not allow multiple inheritance and it’s not a big deal. In fact, there are some OOP language which don’t allow inheritance at all.
Most languages which forbid multiple inheritance offer some sort of alternative. In our case, it’s roles.
No Inheriting from Blessed Classes
Your new Corinna class cannot inherit from anything not declared as a class
.
This was a slightly contentious issue, but there are simply too many
differences. Corinna doesn’t allow you to “reach inside” and manipulate the
data. Corinna is single-inheritance, so it’s unclear how that would have worked
with classes allowing multiple inheritance. And ultimately, we’d like Corinna to
have the option of having a different UNIVERSAL::
base class, offering
different capabilities.
This, of course, means that developers will have to understand “composition over inheritance.” This is a good thing.
What We’re Gaining
Object-oriented programming will be “first class” in Perl, not just a hacked on solution. Methods and subroutines will no longer be the same thing. We’ll have private methods. We already have a much more concise, and clear way of writing classes. Companies using the Object::Pad prototype have reported a 10% reduction in CPU usage and that’s without optimization. We have have potential for a much more efficient implementation of objects than we’ve had before.
The Principle of Parsimony
Given all of the above, I have confession to make about the new class
syntax
for Perl: we made a mistake. It’s a whopping huge mistake.
The problem is that we don’t know what that mistake is. There’s probably more than one of them.
In the README of the Corinna repository is a section entitled The Principle of Parsimony , it states:
Many things in the proposal are deliberately restrictive, such as Corinna only allowing single inheritance. This is to allow Corinna to be cautious in not promising too much. If we later find this too restrictive, we can allow multiple inheritance. However, if we start with multiple inheritance and discover we don’t need or want multiple inheritance, we would break existing code by taking it away.
Any proposals to change the RFC must consider the principle of parsimony.
The design team really should have highlighted this more because it’s the
single most important thing (outside of the new class
syntax itself).
Most people seem happy with the changes. They’re new. They’re exciting. They’re a step towards a new future for the language.
Some people are unhappy. Some are unhappy for reasons that are, well, silly. Case in point: I’ve had a developer angrily tell me they’re nearing retirement and they don’t want to keep up with a bunch of changes in the Perl language (more than one, but the other wasn’t quite so strident in this position).
Some are unhappy because Corinna didn’t go far enough.
Some are unhappy because Corinna has gone too far.
Others are unhappy because « insert reason here ».
I get that. I really do.
So why doesn’t Corinna have multiple inheritance? Because if it did and it’s a mistake, we can’t easily walk it back without breaking existing code.
So why doesn’t Corinna allow inheriting from non-Corinna objects? Because if it did and it’s a mistake, we can’t easily walk it back without breaking existing code.
So why doesn’t Corinna have native support for field
(attribute) builders
like Moo/se? Because if it did and it’s a mistake, we can’t easily walk it back
without breaking existing code.
The counter-argument is that the new class
syntax is experimental and we
should have that freedom. My counter-counter-argument is that I know of
large code-bases which haven’t upgraded past 5.26 because 5.28
swapped the order of attributes and signatures
bodies .
Companies can and do rely on experimental features.
The design team agreed (generally) to follow the Principle of Parsimony to ensure we didn’t paint ourselves into corners more than we already had. We expect that in the real-world, people are going to make compelling arguments for changes based on limitations they discover. We know mistakes are going to be found. We’d like to see if those are mistakes in our design, or mistakes in how people (ab)use OOP code in Perl.
I might add that the Principle of Parsimony hasn’t been an absolute, even in the design stage. For example, originally Corinna required a postfix block:
class Foo {
...
}
The extremely tight scoping of the postfix block would make it much harder for Corinna keywords to bleed into an unwanted scope. I argued for that, but I was overruled. The postfix block is not required.
So please keep this in mind about future discussions of the new class
syntax.
We made mistakes.  We worked very hard to try to ensure that fixing those mistakes means extending Corinna, not breaking it.
Conclusion
Corinna, for now, is experimental and incomplete. The biggest obstacle for its
adoption is likely to be the fact that developers are going to have to learn new
ways of programming. The most annoying might be that field
variables aren’t
available in subclasses (encapsulation!), so you’ll find it convenient to make a
public accessor to fetch this data. There’s a similar issue with roles. The Java
notion of protected
methods is not available.
There are ways we could potentially address these issues, but we need to see what people are doing with Corinna and what their real pain points are.
Fortunately, the Object::Pad
project shows that the new approach plays quite
well with existing code, so we are pretty sure we have the basics right, but we
don’t know what we don’t know.
Interesting times are ahead.


--- title: Login/Unlock layout: component contributors: - name: Justin Sanny github: jsannyjr ---
The gateway to knowledge at your fingertips, the Login/Unlock screen for Windows 7 and OS X is the gate to the wonders of computers.
The Login/Unlock screen is possibly the most common attritube to any computer, Windows, Mac, Linux, etc. While the aesthetics may change from platform to platform, the same idea and function is prevalent in all platforms. Each screen has a number of possible users that can be accessed via passwords. Both screens have the options to shut down or restart the system as well.
Both platforms do not vary when it comes to login. Each requires the user to pick a domain and enter the domain's password. For Windows 7, if the wrong password is typed in, the login screen doesn't change and asks the user for another password. Depending on the settings, the user may allow for hints or, if installed, use the fingerprint scanner.
For OS X, if the user types in the wrong password, the domain image will vibrate and ask for the password again. If the user gets the password wrong three times, OS X will go into sleep mode. The user may change these options such as adding more tries for the password or adding hints for the correct password. In addition if the caps lock is on for either platform, their will be a notifcation. Windows 7 will specifically tell you on the screen while OS X will show a greyed out image. Both platforms provide buttons to enter the password after it has been typed. (See Button page for more information about buttons).
The Login/Unlock screens act simliar for both Windows and OS X but are aesthetically different.
The Login/Unlock screen is used by almost every interface available today and uses all five metrics to an extent. The usability metrics that affect this the most are Learnability, Efficency, Error. Users must be able to recognize and understand how to login into his or her computer or else will not be able to use it (Learnability). Login/Unlock must be efficient so the user does not have to wait to use the computer (Efficency). Users should never make an error of using the wrong domain or typing in the wrong password. This would delay and dissatisfy the user (Errors).
Satisfaction and Memorability affect Login/Unlock screens the least. To login or unlock the screen should be quick, lack of errors, and efficient which leads to satisfaction (Satisfaction). The user must memorize which domain goes with which password so as to not make errors (Errors).
Overall, the Login/Unlock screen is rarely used outside of security measures and relies mostly on Learnability, Efficency, and Errors.
For Windows 7, some computer have the option for a fingerprint scanner. A fingerprint scanner allows the user to suplement their fingerprint as a password by swiping their selected finger onto the scanner. As such, both the fingerprint and password for each user can be used to unlock the computer.
OS X does not have this feature. Also, across all platforms and websites, the same steps are taken except that some websites require a security question to login.
A picture of the scanner can be found below.
The Unlock/Login screen allows selection and provides an easy, spaced out layout for the different domains. It becomes easy to navigate and zooms in when a domain is selected. At the Unlock window, it provides examples and intuitive instructions.
Windows 7's design uses colors effectively by invoking the pleasing aeshtic of their backgrounds, following their guidelines. It provides easy to read icons that make using the Login/Unlock screen easy to navigate.
OS X's design follows uses clear and precise languge as well as clear UI content so the screen isn't cluttered or hard to follow. The guidelines are specifically followed by making the interface the main point instead of aesthetic.
When the button is pressed, it provides feedback by comprehensively showing that the computer is being unlocked by taking away the unlock screen. It replaces the screen with a different screen and unlocks the computer if its works otherwise it will return to the unlock screen.
David Bradley helped build IBM’s new personal computer. Yet, when the computer encountered a glitch, then they had to manually restart the system. So Bradley created a keyboard shortcut ctrl + alt + del that triggered a system reset.Full history of ctrl+alt+del can be found here.
The reason why those three keys were chosen was due to their placement since it wasn’t likely for all three keys to be pressed at the same time on accident.
Although Bradley had created the shortcut to save time and help his fellow programmers, it was never intended for the customers. However, it wasn’t until much later on that Bradley’s shortcut became well-known.
Apple Login Screen Instructions
Finger Print Scanner Image
History of Ctrl + Alt + Del
OS X Login Image
OS X Unlock Image
State Diagram
Windows Guidelines
Windows Login Image
Windows Login Screen Instructions
Windows Unlock Image
Written by Justin Sanny


9.4 Binary Tree Example: Expression
Processing
One intuitive way to process arithmetic
expressions with a computer is using an expression tree. An expression tree
is a binary tree consisting of nodes containing two types of
objects: operators and terminal values. Operators are objects that have
operands; terminal values are objects that have no
operands.
The idea behind an expression tree is simple:
the subtrees rooted at the children of each node are the
operands of the operator stored in the parent (see Figure
9.5). Operands may be terminal values, or they may be
other expressions themselves. Expressions are expanded in
subtrees; terminal values reside in leaf nodes. One of the
nice things about this idea is how easily an expression tree
allows us to translate an expression into one of three common
representations: prefix, infix, and postfix. To obtain these
representations, we simply traverse the tree using a preorder, inorder, or postorder
traversal.
Traversing the tree in Figure
9.5 in preorder, for example, yields the prefix expression
x / - 74 10 32 + 23 17. To evaluate a prefix expression, we
apply each operator to the two operands that immediately
follow it. Thus, the prefix expression just given is evaluated
as:
( x ( / ( - 74 10 ) 32 ) ( + 23 17 ) ) =
80
Infix expressions
are the expressions we are most familiar with from
mathematics, but they are not well suited to processing by a
computer. If we traverse the tree of Figure
9.5 using an inorder traversal, we get the infix
expression 74 - 10 / 32 x 23 + 17. Notice that one of the
difficulties with infix expressions is that they do not
inherently identify in which order operations should be
performed, whereas prefix and postfix expressions do. However,
we can remedy this situation in an infix expression by
parenthesizing each part of the expression as we traverse it
in the tree. Fully parenthesized, the previous infix
expression is evaluated as:
( ( ( 74 - 10 ) / 32 ) x ( 23 + 17 ) ) =
80
Postfix expressions
are well suited to processing by a computer. If we traverse
the tree of Figure
9.5 in postorder, we get the postfix expression 74 10 - 32
/ 23 17 + x. To evaluate a postfix expression, we apply each
operator to the two operands immediately preceding it. Thus,
the postfix expression just given is evaluated as:
( ( ( 74 10 - ) 32 /) ( 23 17 + ) x ) =
80
One reason postfix expressions are well
suited to computers is that they are easy to evaluate with an
abstract
stack machine, an abstraction used by compilers and
hand-held calculators. To process a postfix expression using
an abstract stack machine, we proceed as follows. First, we
move from left to right through the expression, pushing values
onto the stack until an operator is encountered. Next, the
operands required by the operator are popped, the operator is
applied to them, and the result is pushed back on the stack.
This procedure is repeated until the entire expression has
been processed, at which point the value of the expression is
the lone item remaining on the stack (see Figure
9.6).
Example
9.3 illustrates how to produce the prefix, infix, and
postfix representations of an expression stored in an
expression tree. For this, three functions are provided, preorder,
inorder, and postorder, which traverse a binary tree in
preorder, inorder, and postorder, respectively. Each function
accepts two arguments: node and
list.
To begin a traversal, we set
node to the root node of the expression tree
we wish to traverse. Successive recursive calls set
node to the node at the top of the subtree
about to be traversed. On the initial call to each function,
we also pass into list an empty linked list
already initialized with list_init. For each of the
traversals, nodes are placed into the list in the order they
are encountered. When the initial call in the recursion
returns, list contains the preorder, inorder,
or postorder listing of the nodes, as appropriate. Notice how a recursive implementation of
these traversals nicely models the definitions presented
earlier in the chapter.
Example 9.3.
Implementation of Functions for Traversing a Binary Tree /*****************************************************************************
* *
* ------------------------------ traverse.c ------------------------------ *
* *
*****************************************************************************/
#include "list.h"
#include "traverse.h"
/*****************************************************************************
* *
* ------------------------------- preorder ------------------------------- *
* *
*****************************************************************************/
int preorder(const BiTreeNode *node, List *list) {
/*****************************************************************************
* *
* Load the list with a preorder listing of the tree. *
* *
*****************************************************************************/
if (!bitree_is_eob(node)) {
if (list_ins_next(list, list_tail(list), bitree_data(node)) != 0)
return -1;
if (!bitree_is_eob(bitree_left(node)))
if (preorder(bitree_left(node), list) != 0)
return -1;
if (!bitree_is_eob(bitree_right(node)))
if (preorder(bitree_right(node), list) != 0)
return -1;
}
return 0;
}
/*****************************************************************************
* *
* -------------------------------- inorder ------------------------------- *
* *
*****************************************************************************/
int inorder(const BiTreeNode *node, List *list) {
/*****************************************************************************
* *
* Load the list with an inorder listing of the tree. *
* *
*****************************************************************************/
if (!bitree_is_eob(node)) {
if (!bitree_is_eob(bitree_left(node)))
if (inorder(bitree_left(node), list) != 0)
return -1;
if (list_ins_next(list, list_tail(list), bitree_data(node)) != 0)
return -1;
if (!bitree_is_eob(bitree_right(node)))
if (inorder(bitree_right(node), list) != 0)
return -1;
}
return 0;
}
/*****************************************************************************
* *
* ------------------------------- postorder ------------------------------ *
* *
*****************************************************************************/
int postorder(const BiTreeNode *node, List *list) {
/*****************************************************************************
* *
* Load the list with a postorder listing of the tree. *
* *
*****************************************************************************/
if (!bitree_is_eob(node)) {
if (!bitree_is_eob(bitree_left(node)))
if (postorder(bitree_left(node), list) != 0)
return -1;
if (!bitree_is_eob(bitree_right(node)))
if (postorder(bitree_right(node), list) != 0)
return -1;
if (list_ins_next(list, list_tail(list), bitree_data(node)) != 0)
return -1;
}
return 0;
} |


CHARLIE CHAN'S SECRET (director: Gordon Wiles; screenwriters: from an original story by Robert Ellis and Helen Logan/Joseph Hoffman; cinematographer: Rudolph Maté; editor: Nick De Maggio; cast: Warner Oland (Charlie Chan), Rosina Lawrence (Alice Lowell), Charles Quigley (Dick Williams), Henrietta Crosman (Henrietta Lowell), Edward Trevor (Fred Gage), Astrid Allwyn (Janice Gage), Herbert Mundin (Baxter), Jonathan Hale (Warren T. Phelps), Egon Brecher (Ulrich), Gloria Roy (Carlotta), Ivan Miller (Inspector Morton), Arthur Edmund Carewe (Professor Bowan), Jerry Miley (Allen Colby); Runtime: 72; 20th Century Fox; 1936)
Reviewed by Dennis Schwartz
Charlie Chan (Oland) is asked by an old friend, Mrs. Henrietta Lowell (Crosman), to solve the disappearance and then the murder of an heir. Allen Colby is the true heir of an estate, but was lost track of when he joined the French Foreign Legion and became a war prisoner. Allen doesn't believe in psychics like his father does and the two quarreled about this, which caused the rift between them. Upon his father's death he returns by ship to claim his inheritance, but his vessel sinks near Honolulu and he's presumed dead. A diver retrieves his briefcase, and Charlie discovers notes that say attempts were made on his life. If Allen returns to claim the will, everyone else will in it get cut off.
Somehow Allen is alive, and is seen climbing over the wall of the Colby House when the unfriendly caretaker Ulrich (Egon) doesn't answer the door. Once inside, he is killed when a knife is thrown into his back while he's correcting the hands of a fast clock.
When Charlie arrives at Mrs. Lowell's house in San Francisco he thinks Allen might be alive, but is in danger. He attends a seance arranged by Mrs. Lowell--a believer in psychics. While using her Ouija board with her butler Baxter (Mundin), she senses that Allen is dead. The seance will be attended by all those who are in the will and will take place in the Colby House where a Professor Bowen (Carewe), who is Mrs. Lowell's spiritual adviser, and Carlotta (Roy), who is a medium, will try to reach the spirit of Allen Colby.
In the seance, the medium Carlotta says she feels the presence of Allen Colby and suddenly his face appears and his dead body falls inside their room.
The other guests present are all suspects: Alice Lowell (Lawrence) is Mrs Lowell's sweet but not wealthy daughter.  Mrs Lowell's other daughter, who seems bitter, Janice Gage (Allwyn), is there with her husband Fred Gage (Trevor). He handles Mrs. Lowell's business affairs. Mr. Phelps (Hale) is the executor of the estate, and he will be discharged of that duty if Allen is alive. He has also recently sustained heavy losses in the stock market. Ulrich, the other suspect, resents Allen because his daughter was engaged to him, and when he ditched her she committed suicide. He blames Allen for his daughter's death.
Charlie stays overnight with the spooked Baxter in the Colby House, as he cunningly searches for clues. He soon finds that Bowan is a phony, using x-ray machines and other devices to fool the clients. There's also an attempt on Mrs. Lowell's life, as Charlie now gets the police to protect her.
Charlie sets a trap and calls on Inspector Morton (Miller) for assistance, as he suspects Mrs. Lowell is in further danger. He uses a dummy of her, which the killer shoots at with a high-powered rifle from a nearby bell tower. With the killer thinking he got rid of her, Charlie then gathers all the suspects into Colby House for another seance. Here, he cleverly gets the killer to reveal himself as he uses the psychic's phony methods to reproduce Mrs. Lowell's face on a mirror. When the killer tries to throw a knife at that figure in the darkened room, he is trapped because the knife had graphite on it and it rubs off on his hands.
This Charlie Chan episode lacked humor, as there was no Number One Son present. But despite being a bit dry, it applied the usual formula methods and proved to be entertaining. It was also difficult to guess who the killer was.
REVIEWED ON 8/15/2001 GRADE: C+
Dennis Schwartz: "Ozus' World Movie Reviews"
http://www.sover.net/~ozus
<EMAIL_ADDRESS>© ALL RIGHTS RESERVED DENNIS SCHWARTZ
========== X-RAMR-ID: 29160 X-Language: en X-RT-ReviewID: 243876 X-RT-TitleID: 1003923 X-RT-SourceID: 873 X-RT-AuthorID: 1315 X-RT-RatingText: C+
The review above was posted to the
rec.arts.movies.reviews newsgroup (de.rec.film.kritiken for German reviews).
The Internet Movie Database accepts no responsibility for the contents of the
review and has no editorial control. Unless stated otherwise, the copyright
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Related links: index of all rec.arts.movies.reviews reviews


Staff Number at Present(highlights low staff)
Filter By Store
| Store | Staff Type | Staff Number |
|---|---|---|
| Store 1 | Sales Person | 10 |
| Store 1 | Floor Managers | 2 |
| Store 1 | Cleaning Staff | 12 |
| Store 2 | Sales Person | 0 |
| Store 2 | Floor Managers | 2 |
| Store 2 | Cleaning Staff | 12 |
Staff Attendence
Time Range
Filter By
| Store | Employee Id | Employee Name | Attendence |
|---|---|---|---|
| Store 1 | S102 | ABC | 26 |
| Store 1 | S122 | BCD | 23 |
| Store 2 | S212 | NSA | 11 |
| Store 2 | S222 | BCD | 23 |
Staff Punctuality
Time Range
Filter By
| Store | Employee Id | Employee Name | Average Delay(minutes) |
|---|---|---|---|
| Store 2 | S212 | NSA | 30 |
| Store 1 | S102 | ABC | 8 |
| Store 1 | S122 | BCD | 2 |
| Store 2 | S222 | BCD | 1 |


Description
This game is played on a vertical board wich has seven hollow columns and six rows. Each column has a hole in the upper part of the board, where pieces are introduced. There is a window for every square, so that pieces can be seen from both sides.
In short, it´s a vertical board with 42 windows distributed in 6 rows and 7 columns.
Both players have a set of 21 thin pieces (like coins); each of them uses a different colour. The board is empty at the start of the game.
Objective
The aim for both players is to make a straight line of four own pieces; the line can be vertical, horizontal or diagonal.
How the game goes on
Moves are made alternatively, one by turn. Player has first move for first game, then game loser makes first move on the next game.
Moves entails in placing new pieces on the board; pieces slide downwards from upper holes, falling down to the last row or piling up on the last piece introduced in the same column. So, in every turn the introduced piece may be placed at most on seven different squares.
The winner is the first player who gets a straight line made with four own pieces and no gaps between them.


--- title: "Recent Autotunes" layout: single permalink: /tunes share: true header: overlay_color: "#824700" addl_headers: | ---
With dRonin's advanced autotuning feature, a well tuned machine is easy. To begin check out the getting started guide.
| Board | Response Time | City | Country | Submitted |
|---|---|---|---|---|
| {{ 't.Board' | angular }} | {{ 't.Tau * 1000 | number : 1' | angular }} ms | {{ 't.City | titleCase' | angular }} | {{ 't.Country | uppercase' | angular }} | {{ 't.Timestamp | relDate' | angular }} |
{{ 'error' | angular }}


 Quality within pertinence are what make an experience feeling great, when you are transfers some thought onto a friend's brain it must be fluid and seamless, Vortex's focus is to make this experience so great that the "bandwidth" must be the highest possible, just like your internet connection. In short terms, we're giving you power trough control and making this experience being very efficient and enjoyable by essence.
Vortex give you the full control and protection over your private communications and strongly protect your privacy for now but also for many years because it use the greatest technological advancements available in the cryptography and blockchain technological domains so you can take the power to feel safe and avoid a lot of communications-related issues.
Details make perfection, and perfection is not a detail.
― Leonardo da Vinci


--- layout: post title: Parachute Advansed body lotion tags: - Parachute status: publish type: post published: true meta: _edit_last: '2' _aioseop_keywords: Parachute Advansed body lotion _aioseop_title: Parachute Advansed body lotion wp_plus_one_redirect: '' _graphene_slider_img: global _graphene_slider_imgurl: '' _graphene_show_addthis: global _graphene_post_date_display: global al2fb_facebook_link_id: '218440691518395_343374829024980' al2fb_facebook_link_time: '2012-01-12T05:23:19+00:00' al2fb_facebook_link_picture: post=http://missenchanted.com/wp-content/uploads/2012/01/IMG_2728.jpg _yoast_wpseo_linkdex: '0' _thumbnail_id: '2409' _yoast_wpseo_focuskw: '' _yoast_wpseo_title: '' _yoast_wpseo_metadesc: '' _yoast_wpseo_metakeywords: '' _yoast_wpseo_meta-robots-noindex: '0' _yoast_wpseo_meta-robots-nofollow: '0' _yoast_wpseo_meta-robots-adv: none _yoast_wpseo_sitemap-include: ! '-' _yoast_wpseo_sitemap-prio: ! '-' _yoast_wpseo_canonical: '' _yoast_wpseo_redirect: '' ---
Parachute Advansed body lotion, the only moisturizer with 100% Natural Moisturizers and goodness of coconut milk that makes skin delightfully soft and smooth in just 7 days. Enriched with the goodness of coconut milk, it works in a superior manner to actually penetrate deep into skin and nourish it from within – leaving your skin feeling soft, glowing and supple, absolutely irresistible to touch!
Pamper yourself for the first time ever with the nourishment of coconut milk, as it moisturizes and hydrates the skin like no other ingredient! As the coconut- based moisturizers are similar to the skin’s natural oils, they penetrate the skin deep within and lock in moisture, doubling moisture levels in just 2 hours. The result is soft, delightful skin you’ve always dreamt of. So, treat your skin to the luxury of pure coconut milk with Parachute Advansed body lotion and gain soft, glowing and supple skin in just 7 days! Parachute Advansed Body Lotion Variant Details|
Product |
Pack Size |
MRP (Inclusive of all taxes) |
|
Parachute Advansed Body Lotion |
250ml |
Rs 99/- |
|
100ml |
Rs 49 |
|
|
40ml |
Rs 22 |
|
|
20ml |
Rs 10 |


As much as the current US diet avoids carbs and other grains, it is one of our largest segments of food produced across cereals, wheats, rice, and potatoes
Considering the large proportion of meat in the american diet, the US doesn't produce nearly as much meat compared to other products, as we see Pig, Chicken, and Meat lower on the chart
The US produces are large volume of Fruits and Vegetables. Too bad our children (and most of our adults) don't really like them. (Probably because they don't come with a combo meal).
The US is 3rd in eating its own food. China, India, Brazil, and Russia all eat large portions
The china market is the top market at 20-30% larger than the US for US-foods
The US produces are large volume of Fruits and Vegetables. Too bad our children (and most of our adults) don't really like them. (Probably because they don't come with a combo meal).
China eats 3000 times more US food than Malta, big suprise no?


See all your debts and all your friends debts towards you.

All your debts are kept safe in your phone.


DrawingContext
Undocumented
-
The current line width for path painting of the page. Must be nonegative. Default value is 1.
Declaration
Swift
public var lineWidth: Float
-
The dash pattern for lines in the page.
Declaration
Swift
public var dashStyle: DashStyle
-
The shape to be used at the ends of lines. Default value is
LineCap.buttEnd
.Declaration
Swift
public var lineCap: LineCap
-
The line join style in the page. Default value is
LineJoin.miter
.Declaration
Swift
public var lineJoin: LineJoin
-
The miter limit for the joins of connected lines. Minimum value is 1. Default value is 10.
Declaration
Swift
public var miterLimit: Float
-
The current value of the page’s stroking color space.
Declaration
Swift
public var strokingColorSpace: PDFColorSpace
-
The current value of the page’s filling color space.
Declaration
Swift
public var fillingColorSpace: PDFColorSpace
-
The current value of the page’s stroking color. Default is RGB black.
Declaration
Swift
public var strokeColor: Color
-
The current value of the page’s filling color. Default is RGB black.
Declaration
Swift
public var fillColor: Color
-
Sets the clipping area for drawing.
Important
Graphics parameters that are set inside the
drawInsideClippingArea
closure do not persist outside the call of that closure. I. e. if, for example, the context’s fill color had been black and then was set to red inside thedrawInsideClippingArea
closure, after the closure returns it is black again.Declaration
Swift
public func clip(to path: Path, evenOddRule: Bool = false, _ drawInsideClippingArea: (Void) -> Void)
Parameters
path
The path that constraints the clipping area. Must be closed.
evenOddRule
If
true
, uses even-odd rule for specifying a clipping area. Otherwise uses nonzero winding number rule.drawInsideClippingArea
All that is drawn inside this closure is clipped to the provided
path
. -
Fills the
path
.Declaration
Swift
public func fill(_ path: Path, evenOddRule: Bool = false, stroke: Bool = false)
Parameters
path
The path to fill.
evenOddRule
If specified
true
, fills the path using the even-odd rule. Otherwise fills it using the nonzero winding number rule. Default value isfalse
.stroke
If specified
true
, also paints the path itself. Default value isfalse
. -
Paints the
path
.Declaration
Swift
public func stroke(_ path: Path)
Parameters
path
The path to paint.
-
Tha current font of the context.
Declaration
Swift
public var font: Font
-
The maximum size of a font that can be set.
Declaration
Swift
public var maximumFontSize: Float
-
The size of the current font of the context. Valid values are between 0 and
maximumFontSize
. Setting an invalid value makes mo change. Default value is 11.Declaration
Swift
public var fontSize: Float
-
The encoding to use for a text. If the encoding cannot be used with the specified font, does nothing.
Declaration
Swift
public var encoding: Encoding
-
Gets the width of the text in the current fontsize, character spacing and word spacing. If the text is multiline, returns the width of the longest line.
Declaration
Swift
public func textWidth(for text: String) -> Float
Parameters
text
The text to get width of.
Return Value
The width of the text in current fontsize, character spacing and word spacing.
-
Gets the bounding box of the text in the current font size and leading. Text can be multiline.
Declaration
Parameters
text
The text to get the bounding box of.
position
The assumed position of the text.
Return Value
The bounding box of the text.
-
The vertical ascent of the current font in the current font size.
Declaration
Swift
public var fontAscent: Float
-
The vertical descent of the current font in the current font size. This value is negative.
Declaration
Swift
public var fontDescent: Float
-
The height of lowercase letters reach based on height of lowercase x in the current font and font size; does not include ascenders or descenders.
Declaration
Swift
public var fontXHeight: Float
-
The height of a capital letter in the current font and font size measured from the baseline.
Declaration
Swift
public var fontCapHeight: Float
-
Text leading (line spacing) for text showing. Default value is 11.
Declaration
Swift
public var textLeading: Float
-
Prints the text at the specified position on the page. You can use
\n
to print multiline text.Declaration
Swift
public func show(text: String, atPosition position: Point)
Parameters
text
The text to print.
position
The position to show the text at.
-
Prints the text at the specified position on the page. You can use
\n
to print multiline text.Declaration
Swift
public func show(text: String, atX x: Float, y: Float)
Parameters
text
The text to print.
x
x coordinate of the position to show the text at.
y
y coordinate of the position to show the text at.


Comics
Top 40 | Checklist | Want list
The art of reading, enjoying and collecting comics was introduced to me by father. Shazam from the fourties to the sevenites and Marvel Spectacular starring Thor was the foundation for my core collecting habit. This is a small look into a lifetime of collecting comics. Entered with Classic Marvel, fell in love with the gimmick cover age, ground zero for Image, shy away during the over speculation era. Today the MCU brought me back, now I spend my days relearning Marvel 616 lore and also branching into outlaw and indie comics.
Explore


Class FluentListAssert
- java.lang.Object
-
- org.assertj.core.api.AbstractAssert<SELF,ACTUAL>
-
- io.fluentlenium.assertj.custom.FluentListAssert
-
- All Implemented Interfaces:
org.assertj.core.api.Assert<FluentListAssert,FluentList>
,org.assertj.core.api.Descriptable<FluentListAssert>
,org.assertj.core.api.ExtensionPoints<FluentListAssert,FluentList>
,AttributeAssert<org.assertj.core.api.ListAssert<String>>
,FluentAssert
,ListAttributeAssert
,ListStateAssert
public class FluentListAssert extends org.assertj.core.api.AbstractAssert<SELF,ACTUAL> implements ListStateAssert, ListAttributeAssert
Default implementation forFluentList
assertions.
-
-
Constructor Summary
Constructors Constructor Description FluentListAssert(FluentList<? extends FluentWebElement> actual)
-
Method Summary
All Methods Instance Methods Concrete Methods Modifier and Type Method Description org.assertj.core.api.ListAssert<String>
hasAttribute(String attribute)
Checks if at least one of the elements in the list of elements has the given property.FluentListAssert
hasAttributeValue(String attribute, String value)
Checks if the element, or at least one element in a list of elements, has property with the exact given value.FluentListAssert
hasClass(String classToFind)
Checks if the element, or at least one element in a list of elements, has the class.FluentListAssert
hasClasses(String... classesToFind)
Checks if the element, or at least one element in a list of elements, has all of the argument classes.FluentListAssert
hasDimension(org.openqa.selenium.Dimension dimension)
Checks if the element, or at least one element in a list of elements, has given dimension.FluentListAssert
hasId(String idToFind)
Checks if the element, or at least one element in a list of elements, has the given idFluentListAssert
hasName(String name)
Checks if the element, or at least one element in a list of elements, has given name (in its 'name' attribute).FluentListAssert
hasNotAttribute(String attribute)
Checks if the element doesn't have, or no element in a list of elements has, the given property.FluentListAssert
hasNotClass(String htmlClass)
Checks if the element does not contain, or none of the elements in a list of elements contain the classFluentListAssert
hasNotClasses(String... htmlClasses)
Checks if the element does not contain, or none of the elements in a list of elements contain any of the classes.FluentListAssert
hasNotText(String textToFind)
Checks if the element does not contain, or none of the elements in a list of elements contain the text.FluentListAssert
hasNotTextContaining(String textToFind)
Checks if the element does not contain, or none of the elements in a list of elements contain the text.FluentListSizeBuilder
hasSize()
Creates aFluentListSizeBuilder
object from which further size related assertion methods may be called.FluentListAssert
hasSize(int expectedSize)
Checks whether the list size is the same as the expected one in the argument.FluentListAssert
hasTagName(String tagName)
Checks if the element, or at least one element in a list of elements, has given tag.FluentListAssert
hasText(String textToFind)
Checks if the element, or at least one element in a list of elements, contain the text.FluentListAssert
hasTextContaining(String textToFind)
Checks if the element, or at least one element in a list of elements, contain the text.FluentListAssert
hasTextMatching(String regexToBeMatched)
Checks if the element, or at least one element in a list of elements, matches the given regex.FluentListAssert
hasValue(String value)
Checks if the element, or at least one element in a list of elements has given value (in its 'value' attribute).FluentListAssert
isEmpty()
Checks if the list is empty.FluentListAssert
isNotEmpty()
Checks if the list is not empty.-
Methods inherited from class org.assertj.core.api.AbstractAssert
asInstanceOf, asList, assertionError, asString, describedAs, descriptionText, doesNotHave, doesNotHaveSameClassAs, equals, extracting, extracting, failWithActualExpectedAndMessage, failWithMessage, getWritableAssertionInfo, has, hashCode, hasSameClassAs, hasSameHashCodeAs, hasToString, inBinary, inHexadecimal, is, isEqualTo, isExactlyInstanceOf, isIn, isIn, isInstanceOf, isInstanceOfAny, isInstanceOfSatisfying, isNot, isNotEqualTo, isNotExactlyInstanceOf, isNotIn, isNotIn, isNotInstanceOf, isNotInstanceOfAny, isNotNull, isNotOfAnyClassIn, isNotSameAs, isNull, isOfAnyClassIn, isSameAs, matches, matches, newListAssertInstance, overridingErrorMessage, overridingErrorMessage, satisfies, satisfies, satisfiesAnyOf, satisfiesAnyOf, satisfiesAnyOf, setCustomRepresentation, setDescriptionConsumer, setPrintAssertionsDescription, throwAssertionError, usingComparator, usingComparator, usingDefaultComparator, usingRecursiveComparison, usingRecursiveComparison, withFailMessage, withFailMessage, withRepresentation, withThreadDumpOnError
-
-
-
-
Constructor Detail
-
FluentListAssert
public FluentListAssert(FluentList<? extends FluentWebElement> actual)
-
-
Method Detail
-
isEmpty
public FluentListAssert isEmpty()
Description copied from interface:ListStateAssert
Checks if the list is empty.Example:
assertThat(elementList).isEmpty();
- Specified by:
isEmpty
in interfaceListStateAssert
- Returns:
- this assertion object
-
isNotEmpty
public FluentListAssert isNotEmpty()
Description copied from interface:ListStateAssert
Checks if the list is not empty.Example:
assertThat(elementList).isNotEmpty();
- Specified by:
isNotEmpty
in interfaceListStateAssert
- Returns:
- this assertion object
-
hasSize
public FluentListAssert hasSize(int expectedSize)
Description copied from interface:ListStateAssert
Checks whether the list size is the same as the expected one in the argument.Example:
assertThat(elementList).hasSize(5);
- Specified by:
hasSize
in interfaceListStateAssert
- Parameters:
expectedSize
- expected size- Returns:
- this assertion object
-
hasSize
public FluentListSizeBuilder hasSize()
Description copied from interface:ListStateAssert
Creates aFluentListSizeBuilder
object from which further size related assertion methods may be called.This method doesn't do any assertion, only creates the new object.
Example:
assertThat(elementList).hasSize().greaterThanOrEqualTo(5);
- Specified by:
hasSize
in interfaceListStateAssert
- Returns:
- a new ist size builder
-
hasText
public FluentListAssert hasText(String textToFind)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, contain the text.Example:
For a
FluentWebElement
it can be:assertThat(element).hasText("magnificent");
which passes when the element contains (but is not necessarily exactly equal to) the argument text.NOTE: currently both this method and
FluentAssert.hasTextContaining(String)
validate text containment. If you want to validate containment please useFluentAssert.hasTextContaining(String)
, as this method will be updated in a future release to validate equality of text(s).- Specified by:
hasText
in interfaceFluentAssert
- Parameters:
textToFind
- text to find- Returns:
this
assertion object.
-
hasTextContaining
public FluentListAssert hasTextContaining(String textToFind)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, contain the text.Example:
For a
FluentWebElement
it can be:assertThat(element).hasTextContaining("magnificent");
which passes when the element contains (but is not necessarily exactly equal to) the argument text.NOTE: currently both
FluentAssert.hasText(String)
and this method validate text containment. If you want to validate containment please use this method, asFluentAssert.hasText(String)
will be updated in a future release to validate equality of text(s).- Specified by:
hasTextContaining
in interfaceFluentAssert
- Parameters:
textToFind
- text to find- Returns:
this
assertion object.
-
hasTextMatching
public FluentListAssert hasTextMatching(String regexToBeMatched)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, matches the given regex.Example:
For a
FluentWebElement
it can be:assertThat(element).hasTextMatching(".*magnificent$");
- Specified by:
hasTextMatching
in interfaceFluentAssert
- Parameters:
regexToBeMatched
- regex to be matched- Returns:
this
assertion object.
-
hasNotText
public FluentListAssert hasNotText(String textToFind)
Description copied from interface:FluentAssert
Checks if the element does not contain, or none of the elements in a list of elements contain the text.Example:
For a
FluentWebElement
it can be:assertThat(element).hasNotText("magnificent");
which passes when the element text doesn't contains (and not when it is not equal to) to the argument text.NOTE: currently both this method and
FluentAssert.hasNotTextContaining(String)
validate text containment. If you want to validate containment please useFluentAssert.hasNotTextContaining(String)
, as this method will be updated in a future release to validate equality of text(s).- Specified by:
hasNotText
in interfaceFluentAssert
- Parameters:
textToFind
- text to find- Returns:
this
assertion object.
-
hasNotTextContaining
public FluentListAssert hasNotTextContaining(String textToFind)
Description copied from interface:FluentAssert
Checks if the element does not contain, or none of the elements in a list of elements contain the text.Example:
For a
FluentWebElement
it can be:assertThat(element).hasNotText("magnificent");
which passes when the element text doesn't contains (and not when it is not equal to) to the argument text.NOTE: currently both
FluentAssert.hasNotText(String)
and this method validate text containment. If you want to validate containment please use this method, asFluentAssert.hasNotText(String)
will be updated in a future release to validate equality of text(s).- Specified by:
hasNotTextContaining
in interfaceFluentAssert
- Parameters:
textToFind
- text to find- Returns:
this
assertion object.
-
hasId
public FluentListAssert hasId(String idToFind)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, has the given idExample:
For a
FluentWebElement
it can be:assertThat(element).hasId("marked");
which passes when the element text is equal to the argumentidToFind
.- Specified by:
hasId
in interfaceFluentAssert
- Parameters:
idToFind
- id to find- Returns:
this
assertion object.
-
hasClass
public FluentListAssert hasClass(String classToFind)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, has the class.Example:
For a
FluentWebElement
it can be:assertThat(element).hasClass("marked");
which passes when the element class attribute (handled as a list of class values) contains the argumentclassToFind
.- Specified by:
hasClass
in interfaceFluentAssert
- Parameters:
classToFind
- class to find- Returns:
this
assertion object.
-
hasClasses
public FluentListAssert hasClasses(String... classesToFind)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, has all of the argument classes.Example:
For a
FluentWebElement
it can be:assertThat(element).hasClasses("marked", "as", "great");
which passes when the element class attribute (handled as a list of class values) contains all of the argumentclassesToFind
.- Specified by:
hasClasses
in interfaceFluentAssert
- Parameters:
classesToFind
- classes to find- Returns:
this
assertion object.
-
hasNotClass
public FluentListAssert hasNotClass(String htmlClass)
Description copied from interface:FluentAssert
Checks if the element does not contain, or none of the elements in a list of elements contain the classIt passes assertion both when the class attribute is present but doesn't contain the argument class, and when the class attribute is not present at all.
Example:
For a
FluentWebElement
it can be:assertThat(element).hasNotClass("marked");
- Specified by:
hasNotClass
in interfaceFluentAssert
- Parameters:
htmlClass
- class to find the absence of- Returns:
this
assertion object.
-
hasNotClasses
public FluentListAssert hasNotClasses(String... htmlClasses)
Description copied from interface:FluentAssert
Checks if the element does not contain, or none of the elements in a list of elements contain any of the classes.Example:
For a
FluentWebElement
it can be:assertThat(element).hasNotClasses("marked", "as", "great");
- Specified by:
hasNotClasses
in interfaceFluentAssert
- Parameters:
htmlClasses
- classes to find the absence of- Returns:
this
assertion object.
-
hasValue
public FluentListAssert hasValue(String value)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements has given value (in its 'value' attribute).Example:
For a
FluentWebElement
it can be:assertThat(element).hasValue("John Smith");
which passes when the element's value is equal to the argument value.- Specified by:
hasValue
in interfaceFluentAssert
- Parameters:
value
- value to find- Returns:
this
assertion object.
-
hasName
public FluentListAssert hasName(String name)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, has given name (in its 'name' attribute).Example:
For a
FluentWebElement
it can be:assertThat(element).hasName("John Smith");
which passes when the element's value is equal to the argument name.- Specified by:
hasName
in interfaceFluentAssert
- Parameters:
name
- name to find- Returns:
this
assertion object.
-
hasTagName
public FluentListAssert hasTagName(String tagName)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, has given tag.Example:
For a
FluentWebElement
it can be:assertThat(element).hasTagName("div");
which passes when the element's value is equal to the argument tag name.- Specified by:
hasTagName
in interfaceFluentAssert
- Parameters:
tagName
- tag name to find- Returns:
this
assertion object.
-
hasDimension
public FluentListAssert hasDimension(org.openqa.selenium.Dimension dimension)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, has given dimension.Example:
For a
FluentWebElement
it can be:assertThat(element).hasDimension(fluentWebElement.getDimension());
which passes when the element's dimension is equal to the argument dimension.- Specified by:
hasDimension
in interfaceFluentAssert
- Parameters:
dimension
- dimension to find- Returns:
this
assertion object.
-
hasAttributeValue
public FluentListAssert hasAttributeValue(String attribute, String value)
Description copied from interface:FluentAssert
Checks if the element, or at least one element in a list of elements, has property with the exact given value.Example:
For a
FluentWebElement
it can be:assertThat(element).hasAttributeValue("href", "https://fluentlenium.io");
- Specified by:
hasAttributeValue
in interfaceFluentAssert
- Parameters:
attribute
- attribute to findvalue
- property value to match with actual- Returns:
this
assertion object.
-
hasAttribute
public org.assertj.core.api.ListAssert<String> hasAttribute(String attribute)
Description copied from interface:ListAttributeAssert
Checks if at least one of the elements in the list of elements has the given property.It allows users to do not just presence validation but to apply chained List assertions to further validate the attribute values.
Examples:
Validating the presence of an attribute on a
FluentList
:assertThat(fluentList).hasAttribute("class");
Validating both the presence of an attribute on a fluent list, and the value of that attribute:
assertThat(element).hasAttribute("class").containsAnyOf("some-class", "other-class");
- Specified by:
hasAttribute
in interfaceAttributeAssert<org.assertj.core.api.ListAssert<String>>
- Specified by:
hasAttribute
in interfaceListAttributeAssert
- Parameters:
attribute
- the attribute to find- Returns:
- a new
ListAssert
object with the actual attribute values
-
hasNotAttribute
public FluentListAssert hasNotAttribute(String attribute)
Description copied from interface:AttributeAssert
Checks if the element doesn't have, or no element in a list of elements has, the given property.- Specified by:
hasNotAttribute
in interfaceAttributeAssert<org.assertj.core.api.ListAssert<String>>
- Parameters:
attribute
- attribute to find the absence of- Returns:
this
assertion object.
-
-


Over 30 years of experience delivering freights over the sea, give you a high level of service and reliablity. Our strong connections with all players in the field will ensure that you will get the most efficient delivery service. In addition, we are a certified shipping agency of Marguisa.
Air cargo service can be arranged to all worldwide destinations. Due to the quicker service, and virtually no limits with regards to the weight or size of cargo, you will find that shipping cargo by this method will be slightly more expensive; however, it is the perfect solution for urgent shipments, or cargo that is time sensitive.
MG Line Trade and Logistic Solutions is a private company which was founded on 2008 by Moshe Gabbai. MG Line gathers his 30 years of experience in areas all across the logistic chain. Moshe has been working and managing export / import divisions and business development divisions in various shipping companies, and also was an active partner in various cooperations.
MG Line is part of one of the Israeli leading logistic groups, containing also Peltransport, Oshfir, AllWays Gateport and Sonigo. These cooperations extend MG Line's reachability to all needed services.
Hi,
Have you ever thought about how the glass or computer on your desk have got there? Did you know that a
complex logistic process allowed it to be at your service?
If you'll take a high-level look on the shipping logistics field, you can see that it's a super simple process - move something from A to B. But if you drill down, you discover a fascinating world, that even after 30 years of experience can still make me feel in love with this profession.
MG Line Trade and Logistic Solutions was founded in order to be different and special among all other competitors worldwide. Only because we are a small and agile company, we distinguish ourselves by using out of the box thinking, and a little extra Israeli Chutzpa :) We don't use the words "Can't", "No", "Don't", and believe that our service and professionality are the core foundations for pleased customers.
Sincerely yours,
Moshe Gabbai, CEO
70, Weizman Street
Kfar-Saba 4425036 Israel
<EMAIL_ADDRESS>Tel: +972 9 835 4928
Fax: +972 3 915 8843
Operation Branches:
Tel-Aviv (TLV) Air Port
Ashdod (ILASH) Sea Port
Haifa (ILHFA) Sea Port


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