JYTHON TUTORIAL PDF
This two-part tutorial will introduce you to the Jython scripting language, and provide you with enough knowledge introduction to object-oriented programming in Jython. From there, we'll heading graphics, and two PDF files . Our ability to. in-depth introduction to object-oriented programming with Jython. We'll also discuss This tutorial is designed as a progressive introduction to Jython. If you have not completed heading graphics, and two PDF files. Our ability to generate. Jython is the JVM implementation of the Python programming language. This is an introductory tutorial, which covers the basics of Jython and.
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Jython Tutorial in PDF - Learn Jython in simple and easy steps starting from basic to advanced concepts with examples including Overview, Installation. Chapter Databases and Jython: Object Relational Mapping and For this tutorial we will also make use of the Postgresql database. Jython. Vesa-Matti Hartikainen [email protected] myavr.info Jython runtime for running Python scripts. Interactive shell. Compiler Jython W ebapp Tutorial.
Note that in the above example the indentation of the fac function is critical. You'll learn more about this requirement later in the tutorial see Blocks. If Jython accepted only command-line input it wouldn't be all that useful; thus, it also accepts source files.
Jython source files end in the extension. A Jython file must contain a sequence of Jython statements. To display expressions, you must place them in a print statement. Thus, the sequence from the previous section could be coded in a source file as follows:. The above code would produce the same output as the examples in Using Jython as a command-line interpreter.
In fact, the statements could have been entered interactively with the addition of a blank line after the fac function and would result in the same output. As shown in the previous section, we use the print statement to print expressions. The statement has the following forms:. The print statement above can also contain a list of expressions separated by commas.
Each such expression is output with a space automatically added between them. So that print "Hello", "Goodbye" outputs Hello Goodbye. If a print statement ends in comma, no new-line is output.
The line print by itself outputs a new-line. In Jython, the quintessential example program -- Hello World -- is a single-line file say, hello.
To run the program you would enter the command: Note that the. The jython command has several options. See the Jython home page in Related topics for more information. Jython source files can contain more than a sequence of statements to execute.
They can also contain function see Jython functions and class definitions we'll talk more about class definitions in Part 2 of this tutorial. In fact, Jython source files can be modules more on these later, in Modules and packages that may not be used directly but instead imported by other programs. A single source file can perform both roles.
Consider this variant of the file in the previous section:. Again, running this file results in the same output as before. But if the file were imported into another program that only wanted to reuse the fac function, then none of the statements under the if see The if statement test would be executed. This feature can be used to create a test case for each module. Jython source files can be compiled to Java source code which is automatically compiled into byte-code to produce standalone class or Java Archive Files JAR files.
This step is necessary to create Jython code that is called directly from the Java platform, such as when creating an applet or a servlet.
Jython Tutorial in PDF
It is also useful to provide Jython applications without releasing the Jython source. For more details on using jythonc see the Jython home page Related topics. We'll use the factor. To compile it, use the command:.
If there are no errors, Java class files factor. You'll find the actual generated Java source code in the download. To run this now Java application use the command:. Note that the output is identical to that generated by using the factor. Unlike the Java language, Jython sees everything, including all data and code, as an object. This means you can manipulate these objects using Jython code, making reflective and functional programming very easy to do in Jython. See Appendix G: Jython types summary for more information.
Some select types, such as numbers and strings, are more conveniently considered as values, not objects. Jython supports this notion as well. Note that unlike in the Java language, all types are comparable.
In general, if the types of the operands do not match, the result is unequal. The less-than or greater-than relations on complex types are consistent but arbitrary. Jython has no separate boolean type.
All the other types described in the following sections can be used as booleans. For numeric types, zero is considered to be false and all other values true.
For structured types that is, sequences and maps , an empty structure is considered to be false and others true. The None value is always false. Numbers are immutable that is, unchangeable after creation objects treated as values. Jython supports three numeric types, as follows:. For numeric types, the operands are promoted to the next higher type. For the int , long , float , and complex conversion functions, x may be a string or any number. We'll run an example to demonstrate the functions in the math module from the previous section.
See The import statement and Formatting strings and values for more information. Frequently, you will need to create collections of other data items. Jython supports two major collection types. The most basic is the sequence type which is an ordered collection of items.
Sequences support several subtypes such as strings, lists, and tuples. The other is the map type. Maps support associative lookup via a key value. You'll learn about both types in this section. A sequence is an ordered collection of items. All sequences are zero-indexed, which means the first element is element zero 0. Indices are consecutive that is, 0, 1, 2, 3, Thus sequences are similar to C and Java arrays. All sequences support indexing or subscripting to select sub-elements. If x is a sequence then the expression x[n] selects the nth value of the sequence.
Mutable sequences such as lists support indexing on assignment, which causes elements to be replaced. Sequences support an extension of indexing, called slicing , which selects a range of elements.
For example, x[1: Like indexing, slicing can be used on assignment to replace multiple elements. In Jython, a sequence is an abstract concept, in that you do not create sequences directly, only instances of subtypes derived from sequences. Any sequence subtype has all the functions described for sequences.
The many valid forms of slicing are summarized below. Assume x is a sequence containing 10 elements indexes 0 through 9.
Jython supports several operations between sequences x and y , as summarized below:. As I mentioned earlier, a sequence in Jython is an abstract concept, in that you do not create sequences directly, only instances of subtypes derived from sequences.
There are several sequences subtypes, as follows:. A string is an immutable sequence of characters treated as a value. As such, strings support all of the immutable sequence functions and operators that result in a new string. For example, "abcdef"[1: For more information on string functions see Appendix B: String methods.
Jython does not have a character type. Characters are represented by strings of length one that is, one character. Strings literals are defined by the use of single or triple quoting.
Strings defined using single quotes cannot span lines while strings using triple quotes can. A string may be enclosed in double quotes " or single ones '. See Appendix A: Escape characters for more on this.
Note that the next-to-last example shows a raw string. In raw strings the backslash characters are taken literally that is, there is no need to double the backslash to get a backslash character. This raw form is especially useful for strings rich in escapes, such as regular expressions.
We'll talk more about regular expressions in Part 2 of this tutorial. The last example shows a Unicode string and how to create Unicode escaped values. Note that all strings are stored using Unicode character values as provided by the JVM ; this format just lets you enter Unicode character values. This makes it easy to enter long strings and to mix quote types in a single string.
For example the sequential literals here:. Triple quoting is used to enter long strings that include new-lines. They can also be used to enter short single-line strings that mix quote types. For example, the following is one long multi-line string:. While this is a short mixed-quote string: The set value is usually a single value, a tuple of values, or a dictionary of values.
Tuples are immutable lists of any type. Once created they cannot be changed. Tuples can be of any length and can contain any type of object. Some examples are shown here:. Note that although a tuple is immutable, the elements in it may not be. In particular, nested lists see Lists and maps see Maps and dictionaries can be changed. To implement iteration see the The for statement Jython uses immutable sequences of increasing integers. These sequences are called ranges.
Ranges are easily created by two built-in functions:. Ranges run from start defaults to 0 , up to but not including end , stepping by inc defaults to 1. For example:. Lists are mutable sequences of any type. They can grow or shrink in length and elements in the list can be replaced or removed. Lists can be of any length and can contain any type of object. For more information on list functions see Appendix C: List methods.
Some examples are shown below. Using list x to create a stack, remove items with x. Using list x to create a queue, remove items with x. To add elements to the list use x. Lists can also be created via an advanced notation, called list comprehensions. List comprehensions are lists combined with for and if statements to create the elements of the list. For more information see The for statement and The if statement.
Some example list comprehensions follow:. Mapping types support a mutable set of key-value pairs called items. Maps are distinct from sequences although they support many similar operations. They are similar to sequences in that they are abstract; you work only with map subtypes, of which the most commonly used type is the dictionary. For more information on map functions see Appendix D: Map methods. Maps support associative lookup via the key value.
A key can be any immutable type. Keys must be immutable as they are hashed see Appendix E: Built-in functions and the hash value must stay stable.
Common key types are numbers, strings, and tuples with immutable elements. Values may be of any type including None. If m is a map, function len m returns the number of items in the map. Maps, like sequences, support subscripting, but by key instead of index. As shown in Formatting strings and values , dictionaries are convenient for format mapping. As explained in the introduction, Jython programs are simply text files. These files contain statements that are interpreted as they are input after a quick parsing for syntax errors.
Other files can be effectively included into Jython programs by use of the import see Modules and packages and exec statements see Dynamic code evaluation. The following example shows a function fac that has a documentation comment and two remarks. It also demonstrates how to access the documentation comment programmatically. As you likely have gathered from the previous sections, Jython has a simple syntax.
It more closely resembles English than languages like C and Java language. In particular, each source line is generally a single statement. Except for expression and assignment statements, each statement is introduced by a keyword name, such as if or for. You may have blank or remark lines between any statements.
You don't need to end each line with a semicolon but you may do so if desired. If you wish to include multiple statements per line, then a semicolon is needed to separate statements. If required, statements may continue beyond one line.
You may continue any line by ending it with the backslash character, as shown below:. Here's an example:. Identifiers are used to name variables, functions, and classes, and also as keywords.
Identifiers can be of any length. They may contain any combination of letters, decimal digits, and the underscore.
A Fiji Scripting Tutorial
Jython also has several reserved words or keywords which cannot be used as variable, function, or class names. They fall under the following categories:. Note that keywords can be used in special circumstances, such as names of methods. For instance, you might use a keyword to call a Java method with the same name as a Jython keyword.
Improper keyword use will generally cause a SyntaxError. Blocks or suites are groups of statements that are used where single statements are expected.
All statements that can take a block of statements as a target introduce the block with the colon character. The following statements or statement clauses can take a block as their target: Either a single statement or small group of statements, separated by semicolons, may follow the colon on the same line, or a block may follow the statement indented on subsequent lines. I highly recommend that you use spaces to indent. Using tabs can cause problems when moving between systems or editors with different tab stops.
Do not mix tabs and spaces in the same source file. By convention, four spaces are used per level. All the lines in the outermost block of a module must start at column one; otherwise, a SyntaxError is created. Unlike with C and the Java language, in Jython curly braces are not used to delimit blocks; indentation is used instead.
For example. The block that is the body of the for-loop is indicated by the indented code. All lines in the body except for comments must be indented to the same position. The same loop could be written as:. In general, variables are visible in the scope of the block they are declared in and in any function see Jython functions defined in that scope. Variables can be declared only once per scope; subsequent use re-binds that variable.
Jython is distinguished from typical languages in its ability to dynamically create code and then execute it. For example, in a calculator application, the user can enter an expression in text form and Jython can directly execute the expression assuming it follows Jython source rules. Below are some sample expressions to evaluate using the code above and the results of those evaluations:. The eval function is used to execute an expression that returns a value. The exec statement is used to evaluate a code block one or more statements that does not return a value.
It takes a file, a string often read from a file , or a function as its source operand. The execfile function executes a code block from a file. In effect it runs a subprogram. All three forms optionally take two dictionaries that define the global and local namespaces.
See Visibility and scopes for more details on namespaces. If these dictionaries are omitted, the current local namespace as provided by the locals function and the current global namespace as provided by the globals function are used. More details on the use of the eval function and exec statement are available in the Python Library Reference see Related topics. Jython breaks programs down into separate files, called modules. Modules are reused by importing them into your code.
Jython provides many modules for you to reuse see Appendix F: Jython library summary. Jython also allows you to reuse any Java class and API. It is necessary to import a module when the importing program or module needs to use some or all of the definitions in the imported module.
Jython packages are conceptually hierarchically structured sets of modules. Modules and packages enable reuse of the extensive standard Jython and Java libraries. You can also create modules and packages for reuse in you own Jython applications. For more information on the available Jython modules see Appendix F: For more information on the available Java libraries visit the Sun Microsystems' Java technology home page in Related topics.
The import statement executes another file and adds some or all of the names bound in it to the current namespace see Visibility and scopes. The current namespace will generally be the global namespace in the importing file. All statements, including assignments, in the module are executed. The import statement comes in several forms:.
The module value names a Jython. The name value selects specific names from the module. Module names are case sensitive. These arguments can be repeated. The optional alias value allows imported objects to be renamed.
To import a module or package, Jython must be able to find the associated source. Jython uses the python. You can use any text editor to add to or update the registry file in the Jython home directory usually c: For more information, see the Jython registry in Related topics or the registry file itself. By default, Jython will search the directory containing the executing source file; thus, modules located in the same directory as the importing Jython program can be found.
Frequently the current directory is also on the path. Simply enter the following command to examine the current search paths:. On my machine, when running in the C: Unlike in the Java language, the import statement is executable and is not a compiler directive in Jython.
Thus, imports do not need to be done at the start of a module; just sometime before the imported symbols are used. In fact importing can be done conditionally, as in the following example. When you import modules, all values assigned or functions created in the module are usually available for reference by the module importer. You can prevent this by altering the code within the module.
A similar strategy can be used at the module directory level. Using the os. For example, to compile a Java program you could use. Regardless of how much care a programmer takes in designing and testing his or her code, unexpected errors, or exceptions , can occur.
Jython provides excellent support for recovering from these errors,. Exceptions are generally subclasses of the Jython type exceptions.
Intro to Jython, Part 1: Java programming made easier
Exception or the Java class java. For more information see The Jython exception hierarchy or the Python Library Reference see Related topics for a link.
This hierarchy is a subset of the Python Library Reference see Related topics. These exceptions may be subclassed. These handlers are defined by the try-except-else statement, which has the following form:.
The except clause may be repeated with different type values. If so, the exceptions either must not overlap hierarchically that is, be siblings or they must be ordered from child to root exceptions. The optional type value is an exception type either a subclass of exceptions.
Exception or java. If type is missing, then the except clause catches all Jython and Java exceptions. The optional var value receives the actual exception object. If var is missing, then the exception object is not directly accessible. The else clause is optional. It is executed only if no exception occurs. If an exception occurs in the try clause, the clause is exited and the first matching except clause if any is entered.
If no exception matches, the block containing the try-except-else is exited and the exception is re-raised. If an exception is raised in the except or else clause, the clause will exit and the new exception will be processed in the containing block. To access information about an exception, you may use the value provided in the except clause as described previously or the sys.
For example, you can use the following function, in which type is the class of the exception, value is the exception object use str value to get the message , and traceback is the execution trace back, which is a linked list of execution stack frames. More details on the exceptions and trace backs is available in the Python Reference Manual see Related topics. Any code in the finally clause is guaranteed to be executed once the try clause is entered, even if it is exited via a return statement see The return statement or an exception.
The try-finally statement has the following forms:. Note that try-except-else statements may nest in try-finally statements and vice versa. Here is an example of using both try-except and try-finally statements together. Exceptions are generated by called functions or built-in services. You can also generate one by using the raise statement. The raise statement has the following forms:.
Jython has a number of statements that perform computation or control program flow, including the expression , assignment , pass , if , while , for , break , continues , and del statements. You'll learn about these procedural statements in the sections that follow. The pass statement is used where a Jython statement is required syntactically but when no action is required programmatically. The statement has the following form:. In Jython, any expression can serve as a statement; the resulting value is simply discarded.
Most often any such expression statement calls a function or method discussed further in Part 2. For example, the following code invokes three functions in sequence:.
Jython expressions consist of any valid combination of the operators described in Summary of operator precedence. Jython operator precedence is summarized in the table below. Use parentheses to change the order or to improve readability. Unless otherwise noted, within the same precedence level operations are evaluated left-to-right.
Higher priority operations are at the top of the list. The assignment statement is used to bind or re-bind a value to a variable. If not already defined, binding creates the variable and assigns it the value. The field is nothing else than a variable, which, for a given image instance, points to a specific value. For example, the "title" field points to "boats. In python, accessing fields of an instance is straightforward: In the Fiji API documentation , if you don't see a specific field like width in a particular class, but there is a getWidth method, then from python they are one and the same.
This dictionary also called map or table in other programming languages then lets us ask it for a specific image type such as ImagePlus. GRAY8 , and we get back the corresponding text, such as "8-bit". You may have realized by now that the ImagePlus.
What is the image type? It's the kind of pixel data that the image holds. It could be numbers from 0 to what fits in an 8-bit range , or from 0 to values that fit in a bit range , or could be three channels of 8-bit values an RGB image , or floating-point values bit. The table of values versus colors is limited to , and hence these images may not look very well.
These images are meant for display in the web in ". For example, in a "green" look-up table on an 8-bit image, values of zero are black, values of are darkish green, and the maximum value of is fully pure green. The ImageStatistics class offers a convenient getStatistics static method. A static method is a function, in this case of the ImageStatistics namespace, that is unrelated to a class instance.
Java confuses namespaces with class names. Notice how we import the ImageStatistics namespace as "IS", i. The options variable is the bitwise-or combination of three different static fields of the ImageStatistics class. The final options is an integer that has specific bits set that indicate mean, median and min and max values. Remember that in a computer, an integer number is a set of bits, such as In this example, we'd say that the first and the fourth bits are set.
Interpreting this sequence of 0 and 1 in binary gives the integer number in decimal. Now, how about obtaining statistics for a lot of images? From a list of images in a folder, we would have to: Load each image Get statistics for it So we define a folder that contains our images, and we loop the list of filenames that it has.
For every filename that ends with ". See also the python documentation page on control flow, with explanations on the keywords if, else and elif , the for loop keyword and the break and continue keywords, defining a function with def , functions with variable number of arguments , anonymous functions with the keyword lambda , and guidelines on coding style. Iterating pixels is considered a low-level operation that you would seldom, if ever, have to do.
But just so you can do it when you need to, here are various ways to iterate all pixels in an image. The last should be your preferred method. There's the least opportunity for introducting an error, and it is very concise. Regarding the example given, keep in mind: That the pixels variable points to an array of pixels, which can be any of byte, short, float, or int for RGB images, with the 3 color channels channels bit-packed.
That the example method for finding out the minimum value would NOT work for RGB images, because they have the three 8-bit color channels packed into a single integer value. For an RGB image, you'd want to ask which pixel is the least bright.
Or compute the minimum for one of its color channels, which you get with the method ip. Minimum is: Ultimately all operations that involve iterating a list or a collection of elements can be done with the for looping construct. But in almost all occasions the for is not the best choice, neither regarding performance nor in clarity or conciseness. The latter is important to minimize the amount of errors that we may possibly introduce without noticing.
There are three kinds of operations to perform on lists or collections: We show them here along with the equivalent for loop. A map operation takes a list of length N and returns another list also of length N , with the results of applying a function that takes a single argument to every element of the original list. With the for loop, we have to create first a list explictly and then append one by one every image.
With list comprehension , the list is created directly and the logic of what goes in it is placed inside the square brackets--but it is still a loop. That is, it is still a sequential, unparallelizable operation. With the map , we obtain the list automatically by executing the function WM. While this is a trivial example, suppose you were executing a complex operation on every element of a list or an array.
If you were to redefine the map function to work in parallel, suddenly any map operation in your program will run faster, without you having to modify a single line of tested code! A filter operation takes a list of length N and returns a shorter list, with anywhere from 0 to N elements.
Only those elements of the original list that pass a test are placed in the new, returned list. For example, suppose you want to find the subset of opened images in Fiji whose title match a specific criterium. With the for loop, we have to create a new list first, and then append elements to that list as we iterate the list of images. The second variant of the for loop uses list comprehension. The code is reduced to a single short line, which is readable, but is still a python loop with potentially lower performance.
With the filter operation, we get the potentially shorter list automatically. The code is a single short line, instead of 4 lines! A reduce operation takes a list of length N and returns a single value. This value is composed from applying a function that takes two arguments to the first two elements of the list, then to the result of that and the next element, etc.
Optionally an initial value may be provided, so that the cycle starts with that value and the first element of the list. For example, suppose you want to find the largest image, by area, from the list of all opened images in Fiji.
With the for loop, we have to we have to keep track of which was the largest area in a pair of temporary variables. And even check whether the stored largest image is null! We could have initizalized the largestArea variable to the first element of the list, and then start looping at the second element by slicing the first element off the list with "for imp in imps[1: With the reduce operation, we don't need any temporary variables. All we need is to define a helper function which could have been an anonymous lambda function, but we defined it explicitly for extra clarity and reusability.
First we obtain the minimum pixel value, using the reduce method explained just above. Then we subtract this minimum value to every pixel. We have two ways to do it: In place, by iterating the pixel array C-style and setting a new value to each pixel: On a new list: We map in other words, we apply this function to every pixel in the pixels array, returning a new list of pixels with the results.
With the first method, since the pixels array was already a copy notice we called convertToFloat on the ImageProcessor , we can use it to create a new ImagePlus with it without any unintended consequences. With the second method, the new list of pixels must be given to a new FloatProcessor instance, and with it, a new ImagePlus is created, of the same dimensions as the original.
Suppose you want to analyze a subset of pixels. For example, find out how many pixels have a value over a certain threshold. The reduce built-in function is made just for that. It takes a function with two arguments the running count and the next pixel ; the list or array of pixels; and an initial value in this case, zero for the first argument the "count' , and will return a single value the total count. In this example, we computed first the mean pixel intensity, and then filtered all pixels for those whose value is above the mean.
Notice that we compute the mean by using the convenient built-in function sum , which is able to add all numbers contained in any kind of collection be it a list, a native array, a set of unique elements, or the keys of a dictionary. We could imitate the built-in sum function with reduce lambda s, x: Notice we are using anonymous functions again that is, functions that lack a name , declared in place with the lambda keyword.
A function defined with def would do just fine as well. Another useful application of filtering pixels by their value: The filter built-in function is made just for that. The indices of the pixels whose value is above the mean are collected in a list named "above", which is created by filtering the indices of all pixels provided by the built-in function xrange.
The filtering is done by an anonymous function declared with lambda , with a single argument: The second method computes the X and Y coordinates of the center of mass with a single line of code for each. Notice that both lines are nearly identical, differing only in the body of the function mapped to the "above" list containing the indices of the pixels whose value is above the mean.
While, in this case, the method is less performant due to repeated iteration of the list "above", the code is shorter, easier to read, and with far less opportunities for introducing errors. If the actual computation was far more expensive than the simple calculation of the coordinates of a pixel given its index in the array of pixels, this method would pay off for its clarity. The third method pushes the functional approach too far.
While written in a single line, that doesn't mean it is clearer to read: Notice that the reduce is invoked with three arguments, the third one being the list [0, 0] containing the initialization values of the sums. Avoid writing code like this. Notice as well that, by creating a new list at every iteration step, this method is the least performant of all.
The fourth method is a clean up of the third method. Notice that we import the partial function from the functools package.
With it, we are able to create a version of the "accum" helper function that has a frozen "width" argument also known as currying a function. In this way, the "accum" function is seen by the reduce as a two-argument function which is what reduce needs here. While we regain the performance of the for loop, notice that now the code is just as long as with the for loop. The purpose of writing this example is to illustrate how one can write python code that doesn't use temporary variables, these generally being potential points of error in a computer program.
It is always better to write lots of small functions that are easy to read, easy to test, free of side effects, documented, and that then can be used to assemble our program. Here is an example plugin run programmatically: The median filter, along with the mean, minimum, maximum, variance, remove outliers and despeckle menu commands, are implemented in the RankFilters class.
A new instance of RankFilters is created notice the " " after "RankFilters" , and we call its method rank with the ImageProcessor, the radius, and the desired filter flag as arguments. With the result, we create a new ImagePlus and we show it. Here is a simple method to find out, via the Command Finder: Type "FFT". A bunch of FFT-related commands are listed. Click on the "Show full information" checkbox at the bottom. Read, next to each listed command, the plugin class that implements it.
Notice that the plugin class comes with some text. For example: FFT "inverse" ] The above two commands are implemented by a single plugin ij. FFT whose run method accepts, like all PlugIn, a text string specifying the action: The first part of the information shows where in the menus you will find the command.
In this case, under menu "Process", submenu "FFT". Once you have found the PlugIn class that implements a specific command, you may want to use that class directly. The information is either in the online java documentation or in the source code.
How to find these? To do that, we'll use the Macro Recorder. Make sure that an image is open. Open the "Plugins - Macros - Record Set the desired radius, and push "OK". Look into the Recorder window: That is valid macro code, that ImageJ can execute. The first part is the command "Median If there were more parameters, they would be separated by spaces.
We can use these macro recordings to create jython code that executes a given plugin on a given image. Here is an example. Very simple! The IJ namespace has a function, run , that accepts an ImagePlus as first argument, then the name of the command to run, and then the macro-ready list of arguments that the command requires. When executing this script, no dialogs are shown!
Behind the curtains, ImageJ is placing the right parameters in the right places, making it all just work. The pixels array: Where primitive is one of byte, short, int, or float. The ImageProcessor subclass instance that holds the pixels array. The ImagePlus instance that holds the ImageProcessor instance.
In the example, we create an empty array of floats see creating native arrays , and fill it in with random float values. Then we give it to a FloatProcessor instance, which is then wrapped by an ImagePlus instance. To fill a region of interest in an image, we could iterate the pixels, find the pixels that lay within the bounds of interest, and set their values to a specified value.
But that tedious and error prone. In this example, we create an image filled with white noise like before, and then define a rectangular region of interest in it, which is filled with a value of 2. The white noise is drawn from a random distribution whose values range from 0 to 1. When filling an area of the FloatProcessor with a value of 2. The area with 2.
Then we iterate its slices. Each slice is a ColorProcessor: Each integer is represented by 4 bytes, and the lower 3 bytes represent, respectively, the intensity values for red, green and blue. The upper most byte is usually reserved for alpha the inverse of transparency , but ImageJ ignores it. Dealing with low-level details like that is not necessary. The ColorProcessor has a method, toFloat , that can give us a FloatProcessor for a specific color channel.
Red is 0, green is 1, and blue is 2. Representing the color channel in floats is most convenient for further processing of the pixel values--won't overflow like a byte would. In this example, all we do is collect each slice into a list of slices we named greens. Then we add all the slices to a new ImageStack , and pass it to a new ImagePlus. Then we invoke the "Green" command on that ImagePlus instance, so that a linear green look-up table is assigned to it.
And we show it. Suppose we want to analyze each color channel independently: So we convert the RGB stack to a hyperstack with two separate channels, where each channel slice is a bit FloatProcessor. The first step is to create a new ImageStack instance, to hold all the slices that we'll need: We ignore the blue channel which is empty in the "Fly brain" image , so we end up creating twice as many slices as we had in the RGB stack.
The final step is to open the hyperstack. For that: We assign the new stack2 to a new ImagePlus , imp2. We set the same calibration microns per pixel that the original image has. We tell it how to interpret its image stack: We pass the imp2 to a new CompositeImage , comp , indicating how we want it displayed: With CompositeImage. We show the comp , which will open a stack window with two slides: Open the "Image - Color - Channels Tool" and you'll see that the Composite image is set to show only the red channel--try checking the second channel as well.
The script takes an opened, virtual hyperstack as input, and registers in 3D every time frame to the previous one, using phase correlation, correcting any translations on the X,Y,Z axis.
The script is useful for correcting sample drift under the microscope in long 4D time series. See DirectoryChooser. See OpenDialog and SaveDialog. There are more possibilities, but these are the basics. See GenericDialog. All plugins that use a GenericDialog are automatable. Remember how above we run a command on an image? When the names in the dialog fields match the names in the macro string, the dialog is fed in the values automatically.
If a dialog field doesn't have a match, it takes the default value as defined in the dialog declaration. If a plugin was using a dialog like the one we built here, we would run it automatically like this:.
Chances are, if you are scripting, it's because there's a task that has to be repeated many times over as many images. Above , we showed how to iterate over a list of files using os. Here, we will take two directories, a directory from which images are read sourceDir and another one into which modified images are written into, or saved targetDir.
There are two strategies for iterating images inside a directory: With os. But if we wanted to also look into files within a nested directory, we would have to first find out whether a file is a directory with os.
This makes for cumbersome code, needing if and else statements and a helper function processDirectory so that we can invoke it recursively on nested directories. The directories loop variable we ignore here, for we don't need them: The elegance of os.
For every file that we come across using any of the two file system traversing strategies, we could directly do something with it, or delegate to a helper function, named here loadProcessAndSave , which takes two arguments: It loads the image, invokes the function given as argument, and then saves the result in the targetDir.
The actual work happens in the function normalizeContrast , which implements the operation that we want to apply to every image. The NormalizeLocalContrast plugin see documentation on the algorithm is useful for e. The NormalizeLocalContrast plugin uses the integral image technique also known as summed-area table which computes a value for each pixel on the basis of its neighoring pixels a window or arbitrary size centered on the pixel while only iterating over each pixel twice: A naive approach would revisit pixels many times, as a function of the dimension of the window around any one pixel, because two consecutive pixels share a lot of neighbors when the window is large.
By not revisiting pixels many times, the integral image approach is much faster because it performs less operations and revisits memory locations less times. The trade-off is in the shape of the window around every pixel: Using a square is not an impediment for performing complex computations such as very fast approximations of Gabor filters for detecting e.
The NormalizeLocalContrast plugin can correct for background illumination issues quite well, and very fast. To explore the parameters, first load a single image and find out which window size gives the desired output, having ticked the "preview" checkbox.
The plugin can be invoked from "Plugins - Integral image filters - Normalize local contrast". Using either of the two strategies for traversing directories, we'll load a bunch of images from a source directory sourceDir , then apply the local contrast normalization, and save the result in the target directory targetDir.
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In the except code block, notice that any file path that failed is printed out. Notice that we pass the normalizeContrast function as an argument to loadProcessAndSave: The actual code for batch processing, therefore, consists of a mere 3 lines in strategy 2 to visit all files, and a helper function loadProcessAndSave to robustly execute the desired operation on every image.
ImageJ owes much of its success to the VirtualStack: What it stores is the recipe for generating each slice. The original VirtualStack loaded on demand each individual slice from a file that encoded a 2D image. For all purposes, a VirtualStack operates like a fully memory-resident ImageStack.
The extraordinary ability to load image stacks larger than the available computer memory is wonderful, with only a trade-off in speed: Batch processing is one of the many uses of the VirtualStack.
From "File - Open - Image Sequence", choose a folder and a file name pattern, and load the whole folder of 2D images as a VirtualStack.
Programmatically, a VirtualStack can be created among other ways by providing the width and height, and the path to the directory containing the images. We define the function dimensionsOf to read the width and height from the header of an image file. The BioFormats library is very powerful, and among its features it offers a ChannelSeparator , which, despite its odd name it has other capabilities not relevant here , is capable of parsing image file headers without actually reading the whole image.
While we could have also simply typed in the numbers for the width, height , or loaded the whole first image to find them out via getWidth , getHeight on the ImagePlus , now you know how to extract the width, height from the header of an image file. Then the function tiffImageFilenames returns a generator , which is essentially a list that is constructed one item at a time on the fly using the yield built-in keyword.
Here, we yield only filenames under sourceDir that end in ". Importantly, now sorting matters, as we are to display the images sequentially in the stack: Also note we call lower on the filename to obtain an all-lowercase version, so that we can handle both ". TIF" and ". The string returned by lower is used only for the if test and discarded immediately. The VirtualStack can then be constructed with the width, height , a null ColorModel given here as None ; will be found out later , and the sourceDir.
All we've done so far is construct the VirtualStack. We can now wrap it with an ImagePlus just like before we wrapped an ImageProcessor and show it. Importantly, a VirtualStack has no permanence: With the VirtualStack now loaded, we can use it as the way to convert image file paths into images, process them, and save them into a targetDir. That's exactly what is done here. First, we define the targetDir , import the necessary classes, iterate over each slice notice slices are indexed starting from one, not from zero , and directly apply the NormalizeLocalContrast to the ImageProcessor of every slice.
Notice that nothing here is actually specific of virtual stacks. Any normal stack can be processed in exactly the same way. We could now open a second VirtualStack listing not the original images in sourceDir , but the processed images in targetDir. I leave this as an exercise for the reader. What we could do instead is filter images after these are loaded, but before they are used to render slices of the VirtualStack.
To this end, we will create here your first python class: The keyword class is used. A class has an opening declaration that includes the name FilterVirtualStack and, in parentheses, zero or more superclasses or interfaces separated by commas here, only the superclass VirtualStack.
Notice the first argument of every function, self: You could name it "this" instead of "self" if you wanted, it doesn't matter, except it is convention in python to use the word "self". Here, the body of the function has 3 statements: Before we did this after creating the VirtualStack; here, for convenience, we do it already within the constructor.
The next and last method to implement is getProcessor. This is the key method: Whatever modifications we do to it, will appear in the data. So the method loads the appropriate image from disk at filepath , gets its processor named ip as is convention , then retrives the parameters for the NormalizeLocalContrast plugin from the self. Finally, it returns the ip. Once the class is defined, we declare the parameters for the filtering plugin in the params dictionary, which we then use to construct the FilterVirtualStack together with the sourceDir from which to retrieve the image files, and the width, height that, here, I hard-coded, but we could have discovered them from e.
We construct an ImagePlus and show it. Now you may ask: Only one image is retrieved at a time, and, if you were to run "File - Save As - Image Sequence", the original images would be saved into the directory of your choice, in the format and filename pattern of your choice, transformed i.
The critical advantage of this approach is two-fold: Second, if you run the script from an interactive session e. Save the script in Fiji's plugins folder or a subfolder, with: The script will appear as a regular menu command under "Plugins", and you'll be able to run it from the Command Finder.
Where is the plugins folder? Go to the "Applications" folder in the Finder. Calling java classes and methods for jython is seamless: But there is a subtle difference when calling java methods that expect native arrays.
Jython will automatically present a jython list as a native array to the java method that expects it. But as read-only! In this example, we create an AffineTransform that specifies a translation. Then we give it: A 2D point defined as a list of 2 numbers: A 2D point defined as a native float array of 2 numbers: The ability to pass jython lists as native arrays to java methods is extremely convenient, and we have used it in the example above to pass a list of strings to the GenericDialog addChoice method.
The package array contains two functions: The type of array is specified by the first argument. For primitive types char, short, int, float, long, double , use a single character in quotes. See the list of all possible characters. Manipulating arrays is done in the same way that you would do in java. See lines in the example. In some ways, arrays behave very much like lists, and offer functions like extend to grow the array using elements from an iterable like a list, tuple, or generator , append, pop, insert, reverse, index and others like tolist, tostring, fromstring, fromlist.
See also the documentation on how to create multidimensional native arrays with Jython. In addition to the array package, jython provides the jarray package see documentation. The difference between the two is unclear; the major visible difference is the order of arguments when invoking their homonimous functions zeros and array: Perhaps the only relevant difference is that the array package supports more types of arrays such as unsigned int, etc.
Imglib is a general-purpose software library for n-dimensional data processing, mostly oriented towards images.
Scripting with Imglib greatly simplifies operations on images of different types 8-bit, bit, color images, etc. Scripting in imglib is based around the Compute function, which composes images, functions and numbers into output images.
The script. There are three kinds of operations, each in its own package: These functions are composable: These math functions accept any possible pair of: The functions to extract channels or specific color spaces are composable with mathematical functions.
For example, to subtract one color channel from another. These color functions are composable with math functions. Some change the dimensions of an image. These algorithm functions all return images, or what is the same, they are the result images of applying the function to the input image. For example, the DoGPeak , which finds intensity peaks in the image by difference of Gaussian, returns a list of the coordinates of the found peaks.
These analysis functions are all collections of the results. In the example, we start by opening an image from the sample image collection of ImageJ. Then, since we are lacking a flatfield image, we simulate one. We could do it using a median filter with a very large radius, but that it's too expensive to compute just for this example. Instead, we scale down the image, apply a Gauss to the scaled down image, and then resample the result up to the original image dimensions.
Then we do the math for flat-field correction: Subtract the brighfield from the image. The brighfield is an image taken in the same conditions as the data image, but without the specimen: Subtract the darkfield from the image. The darkfield could represent the thermal noise in the camera chip. Divide 1 by 2. Multiply 3 by the mean intensity of the original image. With imglib, all the above operations happen in a pixel-by-pixel basis, and are computed as fast or faster than if you had manually hand-coded every operation.
And multithreaded! In the examples above we have already used the Red and Green functions. There's also Blue , Alpha , and a generic Channel that takes the channel index as argument--where red is 3, green is 2, blue is 1, and alpha is 4 these numbers are related to the byte order in the 4-byte that makes up a bit integer. These arguments can be images, other functions, or numbers--for example, all pixels of a channel would have the value maximum intensity.
In the example, we create a new RGBA image that takes the Gaussian of the red channel, the value 40 for all pixels of the green channel, and the dithered image of the blue channel.
Notice that the Dither function returns 0 or 1 values for each pixel, hence we multiply them by to make them full intensity of blue in the RGBA image. In the second example, we extract the HSB channels from the clown image.
To the Hue channel which is expressed in the range [0, 1] , we add 0. We've shifted the hue around a bit. To understand how the hue values work by flooring the float value and subtracting that from it , see this page. In the third example, we apply a gamma correction to an RGB confocal stack. To correct the gamma, we must first extract each color channel from the image, and then apply the gamma to each channel independently.
In this example we use a gamma of 0. Of course you could apply different gamma values to each channel, or apply it only to specific channels. Notice how we use asImage instead of Compute.
The result is the same; the former is syntactic sugar of the latter. Find cells in an 3D image stack by Difference of Gaussian, count them, and show them in 3D as spheres. First we define the cell diameter that we are looking for 5 microns; measure it with a line ROI over the image and the minimum voxel intensity that will care about in this case, anything under a value of 40 will be ignored. And we load the image of interest: Then we scale down the image to make it isotropic: The peaks are each a float array that specifies its coordinate.
With these, we create Point3f instances, which we transport back to calibrated image coordinates. For a high-level introduction to ImgLib2, see: Pietzsch et al. ImgLib2-generic image processing in Java. A paper that introduces ImgLib2 and provides numerous examples in its supplemental data.
The github source code repository for ImgLib2. The ImgLib2 code examples in the ImageJ wiki. ImgLib2 is a powerful library with a number of key concepts for high-performance, memory-efficient image processing. One such concept is that of a view of an image. Then, we view the image as an infinite image, using the Views. An infinite image cannot be visualized in full. Therefore, we apply the Views. Importantly, no pixel data was duplicated at any step. The Views concept enables us to define transformations to the image that are then concatenated and finally used to render the final image.
And furthermore, thanks to ImgLib2's underlying dimension-independent, data source-independent, and image type-independent model, this code applies to any image of any type and dimensions: ImgLib2 is a very powerful library. There are multiple strategies for filling in the space beyond an image boundaries. Above, we used Views. But there are several variants, including View.
See Views for details and for more. In this example, we use Views. The various extended views each have their purpose. Extending enables, for example, to avoid writing in special purpose code for e. The pixels on the border or near the border depending on the size of the window would need to be special cased.
Instead, with extended views, you can specify what data should be present beyond the border a constant value, a mirror reflection of the image , and reduce enormously the complexity of your code.
You could also use them like ROIs regions of interest: Views simplify programming for image processing a lot.
First we load ImageJ's "embryos" example image, which is RGB, and convert it to 8-bit bit or bit would work just fine. Then we wrap it as an ImgLib2 image, and acquire a mirroring infinite view of the image which is suitable for computing Gaussians. The key parameters are the sigmaLarger and sigmaSmaller , which define the sigmas of the two Gaussians that will be subtracted one from the other.
The minPeakValue acts as a filter for noisy detections. The calibration would be useful in e. For visual validation, we read out the detected peaks as a PointRoi that we set on the imp , the original ImagePlus with the embryos see image below with a PointRoi point on each embryo. Then, we set out to measure a small interval around each detected peak each embryo.
For this, we use the sigmaSmaller , which is half of the radius of an embryo determined empirically by using a line ROI over embryos and pushing 'm' to measure them , so that we define a 2d box around the peak, with a side twice that of sigmaSmaller plus one. Picking and measuring areas with Views. To sum the pixel intensity values within the interval, we use Views. Otherwise, the interval, which is a RandomAccessibleInterval , would yield its pixel values only if we gave it each pixel coordinate to be measured.
Then, we iterate each small view, obtaining a t a Type instance for every pixel, which in ImgLib2 is one of the key design features that enables so much indirection without sacrificing performance. To the t Type , which is a subclass of NumericType , we ask it to yield an integer with t. Python's built-in sum function adds up all the values of the generator no list is created. Listing measurements with a Results Table. Finally, the peak X,Y coordinates and the sum of pixel values within the interval are added to an ImageJ ResultsTable.
Generative image: Here, I am showing how to express images whose underlying data is not the typical array of pixels, but rather, each pixel value is chosen as a function of the spatial coordinate.
The underlying pixel data is just the function. In this example, a white pixel is returned when the pixel falls within a radius of the detected embryo, and a black pixel otherwise, for the background. You may ask yourself what is the point of this simulated object segmentation. It is merely to illustrate how these function-based images can be created. Practical uses will come later. First, we detect embryos using the Difference of Gaussian approach used above, with the DogDetection class. From this, we obtain the centers of all detected embryos, in floating-point coordinates.
Second, we define a value for the inside of the embryo white , and another for the outside black, the background. Then we specify the radius that we want to paint with the inside value around the center coordinate of every detected embryo.
And crucially, we construct a KDTree , which is a data structure for fast spatial queries of coordinates. Here we use the kdtree to swiftly find, for every pixel in the final image, the nearest embryo center point. Then, we define our "image". In quotes, because it is not an image. What we define is a method to obtain pixel values at arbitrary spatial coordinates, returning either inside white or outside black depending on the position in space for which we request a value.
To this end, we define a new class Circles that is a RealRandomAccess , and, to avoid having to implement all the necessary methods of the RealRandomAccess interface, we extend the RealPoint class too, because it already implements pretty much everything we need except the critical get method from the Sampler interface. In other words, the only practical difference between a RealPoint and a RealRandomAccess is that the latter also implements the Sampler interface for requesting values.
On the basis of the result--comparing with the radius --either the inside or the outside is returned. All that remains now is using the Circles RealRandomAccess as the data provider for a RealRandomAccessible that we name CircleData , which is still in real coordinates and unbounded.
So we view it in a rasterized way, to be able to iterate it with integer coordinates--like the pixels of an image--, and define its bounds to be those of the original image img containing the embryos that is, img here can be used because it implements Interval and happens to have exactly the dimensions we want.
The "pixels" never exist in memory until they are written to the final image that is visualized. In the Results Table window, choose "File - Save Doesn't get any easier than this!
In the absence of a Results Table, we can use python's built-in csv library. First, we define two functions to provide the data peakData and the helper function centerAt , so that for simplicity and clarity we separate getting the peak data from writing the CSV. To get the peak data, we define the function peakData that does the same as was done above in a for loop: The helper function centerAt returns two copied arrays with the two arrays minC, maxC that delimit the region of interest around the peak translated to the peak.
Then, we write the CSV file one row at a time. We open the file within python's with statement, which ensures that, even if an error was to come up, the file handle will be closed, properly releasing system resources. The csv. Notice the arguments provided to csv.
The first row is the header, containing the titles of each column in the CSV file. Then each data row is written by providing writerow with the list of column entries to write: For completeness, I am showing here how to read the CSV file back into, in this example, a PointRoi , using the complementary function csv.
Note that numeric values are read in as strings, and must be transformed into floating-point numbers using the built-in function float.
In this example, we will use ImgLib2 's RealViews namespace to transform images with affine transforms: Let's introduce the concept of a View in ImgLib2: Meaning, the underlying pixel array is not duplicated, with merely a transformation of some sort being applied to the pixels on the fly as these are requested. Views can be concatenated.
Here we use: Returns images of the RealRandomAccessible type, suitable for transformations. Operates on images that are RealRandomAccessible , such as those returned by Views.
This is what we use to "crop" or to select a specific field of view. If the field of view includes regions outside the originally wrapped image, then it'd better be "filled in" with a Views. While the reasons that led to split the functionality into two separate namespaces the Views and the RealViews don't matter, the basic heuristic when looking up for a View method is that we'll use Views when the interval is defined that is, the image data is known to exist within a specific range between 0 and width, height, depth, etc.
In the end, we call ImageJFunctions. No data has been duplicated at any step! At any time, use e. Then, either look it up in the ImgLib2's github repositories or in Google, or perhaps sufficiently, use print dir imgR to list all its accessible methods. While the code in this example applies to images of any number of dimensions 2D, 3D, 4D and type 8-bit, bit, bit, others , here we scale by a factor of two the boats example ImageJ image. Now we continue with a rotation around the Z axis rotation in XY by 30 degrees.
Remember, this code applies to images of any number of dimensions: The rotation must be defined as the values of a matrix that describes an affine transform. For convenience, I use here the java. AffineTransform aliased as Affine2D to obtain the values of the rotation transform. The matrix has to have one more column than rows, with the last column defining the translation. The last row would be all zeros and a 1. Notice that the rest of the diagonal of the matrix is filled with 1.
Then we view the rotated image as an ImagePlus that wraps a VirtualStack just like above. Of course, the rotated image is cropped: Below, we instead view an enlarged interval that fully contains the rotated image. In this particular example the effect is not very visible because the MRI stack of a human head has black corners.
To reveal the issue, I draw a white line along the borders beforehand by pushing 'a' to select all with a rectangular ROI, then choosing white color for the foreground color, and then pushing 'd' to draw it, confirming the dialog to draw in every section.
To read out the values of the transformation matrix that specifies the rotation, print it: Or pretty-print it with pprint , which requires turning the inner arrays into lists for nicer printing. The third column contains the translation values corresponding to a rotation specified relative to the center of the image.
While you could always write in the matrix by hand, it is better to use libraries like, for 2D, the java. AffineTransform and its methods such as getRotateInstance. For 3D rotations and affine transformations in general, use e. Transform3D and e. An ARGB image is a hack: Processing directly the pixel array, made of integers, makes no sense at all.
Prior to any processing, color channels must be separated. For reference, the alpha channel is in the upper byte index 0 , the red in the 2nd index 1 , the green in the 3rd index 2 and blue in the lowest byte, the 4th index 3.
In ImgLib2, rather than copying a color channel into a new image with a new array of bytes, we acquire a View of its channels: If the ImagePlus is not backed by a ColorProcessor , it will throw an error. Here, we use Converters. The channels image is equivalent to ImageJ's CompositeImage , in that each channel can be processed independently. To read out a single channel, e. Or, as we illustrate here, use Views.
Of course, this code runs on 2D images e. In neuroscience, we can observe the activity of neurons in a circuit by expressing, for example, a calcium sensor in every neuron of interest, generally using viruses as delivery vectors for mammals, birds and reptiles, or genetic constructs for the fruit fly Drosophila , the nematode C. Here is a copy of the first 10 time points to try out the scripts below.
For testing, I used only the first two of these ten. GCaMP time series data comes in many forms. Here, I am using a series of 3D volumes, each volume saved a single, separate file, representing a single time point of the neuronal activity data.
The file format, KLB, is a compressed open source format for which a library exists klb-bdv in Fiji: Opening KLB-formatted stacks is easy: Given that the library is optional, I wrapped it in a try statement to warn the user about is absence if so. Each KLB stack file is compressed, so its size in disk can be misleading: The decompression process is also costly.
Therefore, we need a way to minimize the number of times we load each stack. To this end, I define a cache strategy known as memoization: To prevent filling up all RAM, we can define a maximum amount of items to store using the keyword argument maxsize , which defaults here to 30 but we set to be Which ones should be thrown out first?
The specific implementation here is an LRU: This is crucial not just for cache correctness, but also for good performance of e. Read more about thread concurrency and synchronization in jython. Note that in python 3 not available from java so far , we could merely decorate the openStack function with functools. Representing the whole 4D series as an ImgLib2 image.
To ease processing the 4D series we take full advantage of the ImgLib2 library capabilities to abstract over data sources and type, and represent the whole data set as a single image, vol4d. We accomplish this feat by using a LazyCellImg: First, we define dimensions of the data, by reading the first stack. We assume that all stacks--one per time point--have the same spatial dimensions.
Then, we define the dimensions of vol4d: Then, we define the CellGrid: Basically, the grid here is a simple linear arrangement--in time--of individual 3D stacks. Then we define how each Cell of the grid is loaded: The method TimePointGet.
Finally we define vol4d as a LazyCellImg , taking as argument the grid, the type in this case, an implicit UnsignedShortType since KLB data is in bit , and the cell loader. Note that via the Converters we could be loading e. Above, we memoized the loading of image volumes from disk as a way to avoid doing so repeatedly. Loaded images were stored in an OrderedDict , from which we could tell which images had been accessed least recently by removing and reinserting images anytime we accessed them , and get rid of the eldest when the maximum number of images was reached.
This approach has its drawbacks: We can do much better. First, to ease the management of least accessed images, we'll use a LinkedHashMap data structure, which is a dictionary that "mantains a doubly-linked list running through all its entries.
The advantage is that we can tell its constructor to keep this linked list relative to the order in which entries were accesssed rather than added , which is great for an LRU cache LRU means "Least Recently Used" , and furthermore, it offers the method removeEldestEntry to, upon inserting an entry, also remove the entry that was accessed least recently when e.
Second, we overcome the two problems of 1 having to define a maximum number of entries and 2 not knowing how much memory we can use, by storing each image wrapped in a SoftReference. Any images not referred to anywhere else in our program will be available for the automatic java garbage collector to remove to clear up memory for other uses. When that happens, accessing the entry in the cache will return an empty reference, and then we merely reload the image and store it again.
Despite this safety mechanism, it is still sensible to define a maximum number of images to attempt to store; but this time our LRU cache is not commited to keeping them around.
With the vol4d variable now describing our entire 4D data set, we proceed to visualize it. There are multiple ways to do so. By creating both the CompositeImage and VirtualStack by hand, which affords more flexibility: We could also insert slices as desired to e.
To accomplish this, we require a fast way to copy pixels from a hyperslice of the 4D volume obtained via Views. I am using here a trick that shouldn't be used much: This is done via the Weaver. The Weaver. Despite the ugly type casts, at runtime these are erased and therefore the code will perform just as fast as more modern java code with generics.Final remarks: For convenience, I use here the java.
If the actual computation was far more expensive than the simple calculation of the coordinates of a pixel given its index in the array of pixels, this method would pay off for its clarity.
I leave this as an exercise for the reader. The Jython interpreter converts Jython source into an internal form for more efficient processing. Then we invoke the function "save" on it, which will open a file dialog.
The darkfield could represent the thermal noise in the camera chip. We initialize it with a fixed number of threads: