Glossary

Key Points

Programming Style	Follow standard Python style in your code. Use docstrings to provide builtin help.
Errors and Exceptions	Tracebacks can look intimidating, but they give us a lot of useful information about what went wrong in our program, including where the error occurred and what type of error it was. An error having to do with the ‘grammar’ or syntax of the program is called a `SyntaxError`. If the issue has to do with how the code is indented, then it will be called an `IndentationError`. A `NameError` will occur when trying to use a variable that does not exist. Possible causes are that a variable definition is missing, a variable reference differs from its definition in spelling or capitalization, or the code contains a string that is missing quotes around it. Containers like lists and strings will generate errors if you try to access items in them that do not exist. This type of error is called an `IndexError`. Trying to read a file that does not exist will give you an `FileNotFoundError`. Trying to read a file that is open for writing, or writing to a file that is open for reading, will give you an `IOError`.
Defensive Programming	Program defensively, i.e., assume that errors are going to arise, and write code to detect them when they do. Put assertions in programs to check their state as they run, and to help readers understand how those programs are supposed to work. Use preconditions to check that the inputs to a function are safe to use. Use postconditions to check that the output from a function is safe to use. Write tests before writing code in order to help determine exactly what that code is supposed to do.
Debugging	Know what code is supposed to do before trying to debug it. Make it fail every time. Make it fail fast. Change one thing at a time, and for a reason. Keep track of what you’ve done. Be humble.
Timing and Speeding Up Your Programs	Use magic commands `%time` and `%timeit` to speed-test your code in Notebooks Numpy arrays and functions (ufuncs) are much faster than using lists and loops. You can make code more efficient for handling arrays (if not faster) using vectorization.
Working with Numpy Arrays	Numpy arrays can be created from lists using `numpy.array` or via other numpy functions. Like lists, numpy arrays are indexed in row-major order, with the last index read out fastest. Numpy arrays can be edited and selected from using indexing and slicing, or have elements appended, inserted or deleted using using `numpy.append`, `numpy.insert` or `numpy.delete`. Numpy arrays must be copied using `numpy.copy` or by operating on the array so that it isn’t changed, not using `=` which simply assigns another label to the same array, as for lists. Use `numpy.reshape`, `numpy.transpose` (or `.T`) to reshape arrays, and `numpy.ravel` to flatten them to a single dimension. Various `numpy` `stack` functions can be used to combine arrays. `numpy.genfromtxt` can read data into structured numpy arrays. Columns must be referred to using the field name given to that column when the data is read in. Conditional statements can be used to select elements from arrays with the same shape, e.g. that correspond to the same data set.
Array Calculations with Numpy	Numpy ufuncs operate element-wise (item by item) on an array. Common mathematical operators applied to numpy arrays act as wrappers for fast array calculations. Binary ufuncs operate on two arrays: if the arrays have different shapes which are compatible, the operation uses broadcasting rules. Many operations and numerical methods (such as random number generation) can be carried out with numpy functions. Arrays can be masked to allow unwanted elements (e.g. with `nan` values) to be ignored in array calculations using special masked array ufuncs. Define your own functions that carry out complex array operations by combining different numpy functions.
Numerical Methods with Scipy	Scipy sub-packages need to be individually loaded - `import scipy` and then referring to the package name is not sufficient. Instead use, e.g. `from scipy import fft`. Specific functions can also be loaded separately such as `from scipy.interpolate import interp1d`. For model fitting when errors are normally distributed you can use `scipy.optimize.curve_fit`. For more general function minimization use `scipy.optimize.minimize` Be careful with how Scipy’s Fast Fourier Transform results are ordered in the output arrays. Always be careful to read the documentation for any Scipy sub-packages and functions to see how they work and what is assumed.
Introduction to Astropy	Astropy includes the core packages plus coordinated sub-packages and affiliated sub-packages (which need to be installed separately). The `astropy.units` sub-package enables calculations to be carried out using self-consistent physical units. `astropy.constants` enables calculations using physical constants using a whole range of physical units when combined with the `units` sub-package. `astropy.cosmology` allows calculations of fundamental cosmological quantities such as the cosmological age or luminosity distance, for a specified cosmological model. `astropy.coordinates` and `astropy.time`, provide a number of functions that can be combined to determine when a given target object can best be observed from a given location.
Working with FITS Data	FITS files can be read in and explored using the `astropy.io.fits` sub-package. The `open` command is used to open a datafile. FITS files consist of one or more Header Data Units (HDUs) which include a header and possibly data, in the form of a table or image. The structure can be accessed using the `.info()` method Headers contain sets of keyword/value pairs (like a dictionary) and optional comments, which describe the metadata for the data set, accessible using the `.header['KEYWORD']` method. Tables and images can be accessed using the `.data` method, which assigns table data to a structured array, while image data is assigned to an n-dimensional array which may be plotted with e.g. matplotlib’s `imshow` function.

additive color model: A way to represent colors as the sum of contributions from primary colors such as red, green, and blue.
argument: A value given to a function or program when it runs. The term is often used interchangeably (and inconsistently) with parameter.
assertion: An expression which is supposed to be true at a particular point in a program. Programmers typically put assertions in their code to check for errors; if the assertion fails (i.e., if the expression evaluates as false), the program halts and produces an error message. See also: invariant, precondition, postcondition.
assign: To give a value a name by associating a variable with it.
body: (of a function): the statements that are executed when a function runs.
call stack: A data structure inside a running program that keeps track of active function calls.
case-insensitive: Treating text as if upper and lower case characters of the same letter were the same. See also: case-sensitive.
case-sensitive: Treating text as if upper and lower case characters of the same letter are different. See also: case-insensitive.
comment: A remark in a program that is intended to help human readers understand what is going on, but is ignored by the computer. Comments in Python, R, and the Unix shell start with a # character and run to the end of the line; comments in SQL start with --, and other languages have other conventions.
compose: To apply one function to the result of another, such as f(g(x)).
conditional statement: A statement in a program that might or might not be executed depending on whether a test is true or false.
comma-separated values: (CSV) A common textual representation for tables in which the values in each row are separated by commas.
default value: A value to use for a parameter if nothing is specified explicitly.
defensive programming: The practice of writing programs that check their own operation to catch errors as early as possible.
delimiter: A character or characters used to separate individual values, such as the commas between columns in a CSV file.
docstring: Short for “documentation string”, this refers to textual documentation embedded in Python programs. Unlike comments, docstrings are preserved in the running program and can be examined in interactive sessions.
documentation: Human-language text written to explain what software does, how it works, or how to use it.
dotted notation: A two-part notation used in many programming languages in which thing.component refers to the component belonging to thing.
empty string: A character string containing no characters, often thought of as the “zero” of text.
encapsulation: The practice of hiding something’s implementation details so that the rest of a program can worry about what it does rather than how it does it.
floating-point number: A number containing a fractional part and an exponent. See also: integer.
for loop: A loop that is executed once for each value in some kind of set, list, or range. See also: while loop.
function: A named group of instructions that is executed when the function’s name is used in the code. Occurrence of a function name in the code is a function call. Functions may process input arguments and return the result back. Functions may also be used for logically grouping together pieces of code. In such cases, they don’t need to return any meaningful value and can be written without the return statement completely. Such functions return a special value None, which is a way of saying “nothing” in Python.
function call: A use of a function in another piece of software.
immutable: Unchangeable. The value of immutable data cannot be altered after it has been created. See also: mutable.
import: To load a library into a program.
in-place operators: An operator such as += that provides a shorthand notation for the common case in which the variable being assigned to is also an operand on the right hand side of the assignment. For example, the statement x += 3 means the same thing as x = x + 3.
index: A subscript that specifies the location of a single value in a collection, such as a single pixel in an image.
inner loop: A loop that is inside another loop. See also: outer loop.
integer: A whole number, such as -12343. See also: floating-point number.
invariant: An expression whose value doesn’t change during the execution of a program, typically used in an assertion. See also: precondition, postcondition.
library: A family of code units (functions, classes, variables) that implement a set of related tasks.
loop variable: The variable that keeps track of the progress of the loop.
member: A variable contained within an object.
method: A function which is tied to a particular object. Each of an object’s methods typically implements one of the things it can do, or one of the questions it can answer.
mutable: Changeable. The value of mutable data can be altered after it has been created. See immutable.”
notebook: Interactive computational environment accessed via your web browser, in which you can write and execute Python code and combine it with explanatory text, mathematics and visualizations. Examples are IPython or Jupyter notebooks.
object: A collection of conceptually related variables (members) and functions using those variables (methods).
outer loop: A loop that contains another loop. See also: inner loop.
parameter: A variable named in the function’s declaration that is used to hold a value passed into the call. The term is often used interchangeably (and inconsistently) with argument.
pipe: A connection from the output of one program to the input of another. When two or more programs are connected in this way, they are called a “pipeline”.
postcondition: A condition that a function (or other block of code) guarantees is true once it has finished running. Postconditions are often represented using assertions.
precondition: A condition that must be true in order for a function (or other block of code) to run correctly.
regression: To re-introduce a bug that was once fixed.
return statement: A statement that causes a function to stop executing and return a value to its caller immediately.
RGB: An additive model that represents colors as combinations of red, green, and blue. Each color’s value is typically in the range 0..255 (i.e., a one-byte integer).
sequence: A collection of information that is presented in a specific order. For example, in Python, a string is a sequence of characters, while a list is a sequence of any variable.
shape: An array’s dimensions, represented as a vector. For example, a 5×3 array’s shape is (5,3).
silent failure: Failing without producing any warning messages. Silent failures are hard to detect and debug.
slice: A regular subsequence of a larger sequence, such as the first five elements or every second element.
stack frame: A data structure that provides storage for a function’s local variables. Each time a function is called, a new stack frame is created and put on the top of the call stack. When the function returns, the stack frame is discarded.
standard input: A process’s default input stream. In interactive command-line applications, it is typically connected to the keyboard; in a pipe, it receives data from the standard output of the preceding process.
standard output: A process’s default output stream. In interactive command-line applications, data sent to standard output is displayed on the screen; in a pipe, it is passed to the standard input of the next process.
string: Short for “character string”, a sequence of zero or more characters.
syntax: The rules that define how code must be written for a computer to understand.
syntax error: A programming error that occurs when statements are in an order or contain characters not expected by the programming language.
test oracle: A program, device, data set, or human being against which the results of a test can be compared.
test-driven development: The practice of writing unit tests before writing the code they test.
traceback: The sequence of function calls that led to an error.
tuple: An immutable sequence of values.
type: The classification of something in a program (for example, the contents of a variable) as a kind of number (e.g. floating-point, integer), string, or something else.
type of error: Indicates the nature of an error in a program. For example, in Python, an IOError to problems with file input/output. See also: syntax error.
variable: A value that has a name associated with it.
while loop: A loop that keeps executing as long as some condition is true. See also: for loop.

Programming for Astronomy and Astrophysics 2: Programming Skills, Arrays and Scientific Libraries: Glossary

Key Points

Glossary