Merge pull request #146 from GloryFish/structuring-edits

Structuring edits

docs/writing/structure.rst

@@ -8,49 +8,49 @@ Structuring your project properly is extremely important.
 Structure is Key
 ----------------
 
-Thanks to the way imports and module are handled in Python, it is
+Thanks to the way imports and modules are handled in Python, it is
 relatively easy to structure a python project. Easy, here, means
-actually that you have not many constraints and that the module
+that you do not have many constraints and that the module
-importing model is easy grasp. Therefore, you are left with the
+importing model is easy to grasp. Therefore, you are left with the
-pure architectural task of drawing the different parts of your
+pure architectural task of crafting the different parts of your
 project and their interactions.
 
-Easy structuration of a project means it is also easy
+Easy structuring of a project means it is also easy
-to do it poorly. Some signs of a poorly structured projects
+to do it poorly. Some signs of a poorly structured project
 include:
 
-- Multiple and messy circular dependencies: if your classes
+- Multiple and messy circular dependencies: If your classes
   Table and Chair in furn.py need to import Carpenter from workers.py
-  to answer to a question such as table.isdoneby(),
+  to answer a question such as table.isdoneby(),
-  and if convertly the class Carpenter need to import Table and Chair,
+  and if conversely the class Carpenter needs to import Table and Chair,
-  for example to answer to carpenter.whatdo(), then you
+  to answer the question carpenter.whatdo(), then you
-  have a circular dependency, and will have to resort to
+  have a circular dependency. In this case you will have to resort to
   fragile hacks such has using import statements inside
   methods or functions.
 
-- Hidden coupling. Each and every change in Table implementation
+- Hidden coupling: Each and every change in Table's implementation
   breaks 20 tests in unrelated test cases because it breaks Carpenter's code,
   which requires very careful surgery to adapt the change. This means
   you have too many assumptions about Table in Carpenter's code or the
   reverse.
 
-- Heavy usage of global state or context: Instead of explicitely
+- Heavy usage of global state or context: Instead of explicitly
   passing ``(height, width, type, wood)`` to each other, Table
   and Carpenter rely on global variables that can be modified
-  and are modified on the fly by different agent. You need to
+  and are modified on the fly by different agents. You need to
-  scrutinize all access to this global variables to understand why
+  scrutinize all access to these global variables to understand why
-  a rectangular table became a sqaure, and discover that a remote
+  a rectangular table became a square, and discover that remote
   template code is also modifying this context, messing with
   table dimensions.
 
 - Spaghetti code: Multiple pages of nested if clauses and for loops
   with a lot of copy-pasted procedural code and no
   proper segmentation are known as spaghetti code. Python's 
-  meaningful indentation (one of its most controversial feature) make
+  meaningful indentation (one of its most controversial features) make
   it very hard to maintain this kind of code. So the good news is that
   you might not see too much of it.
 
-- Ravioli code is more likely in Python: it consists of hundreds of
+- Ravioli code is more likely in Python: It consists of hundreds of
   similar little pieces of logic, often classes or objects, without
   proper structure. If you never can remember if you have to use
   FurnitureTable, AssetTable or Table, or even TableNew for your
@@ -60,53 +60,53 @@ include:
 Modules
 -------
 
-Python modules are one of the main abstraction layer available and probably the
+Python modules are one of the main abstraction layers available and probably the
 most natural one. Abstraction layers allow separating code into parts holding
-related data and functionalities.
+related data and functionality.
 
 For example, a layer of a project can handle interfacing with user actions,
 while another would handle low-level manipulation of data. The most natural way
-to separate these two layers is to regroup all interfacing functionalities
+to separate these two layers is to regroup all interfacing functionality
 in one file, and all low-level operations in another file. In this case,
-the interface file need to import the low-level file. This is done with the
+the interface file needs to import the low-level file. This is done with the
 `import` and `from ... import` statements.
 
-As soon as you use `import` statements you use modules, either builtin modules
+As soon as you use `import` statements you use modules. These can be either built-in
-such as `os` and `sys`, or third-party modules you have installed in your
+modules such as `os` and `sys`, third-party modules you have installed in your
-environment, or project's internal modules.
+environment, or your project's internal modules.
 
 Nothing special is required for a Python file to be a module, but the import
-mechanism need to be understood in order to use this concept properly and avoid
+mechanism needs to be understood in order to use this concept properly and avoid
 some issues.
 
 Concretely, the `import modu` statement will look for the proper file, which is
 `modu.py` in the same directory as the caller if it exists.  If it is not
-found, the Python interpreter with search for `modu.py` in the "path"
+found, the Python interpreter will search for `modu.py` in the "path"
 recursively and raise an ImportError exception if it is not found.
 
 Once `modu.py` is found, the Python interpreter will execute the module in an
 isolated scope. Any top-level statement in `modu.py` will be executed,
-including other imports if any. Function and classes definitions are stored in
+including other imports if any. Function and class definitions are stored in
 the module's dictionary.
 
-Then modules variables, functions and classes will be available to the caller
+Then, the module's variables, functions, and classes will be available to the caller
 through the module's namespace, a central concept in programming that is
 particularly helpful and powerful in Python.
 
-In many languages, a `include file` directive is used by the preprocessor to
+In many languages, an `include file` directive is used by the preprocessor to
-take all code found in the file and 'copy' it in the caller's code. It is
+take all code found in the file and 'copy' it into the caller's code. It is
 different in Python: the included code is isolated in a module namespace, which
 means that you generally don't have to worry that the included code could have
-unwanted effect, eg override an existing function with the same name.
+unwanted effects, e.g. override an existing function with the same name.
 
 It is possible to simulate the more standard behavior by using a special syntax
 of the import statement: `from modu import *`. This is generally considered bad
-practice, **using import * makes code harder to read and dependencies less
+practice. **Using `import *` makes code harder to read and makes dependencies less
-compartimented**.
+compartmentalized**.
 
 Using `from modu import func` is a way to pinpoint the function you want to
-import and put it is the global namespace. While much less harmful than `import
+import and put it in the global namespace. While much less harmful than `import
-*` because it shows explicitely what is imported in the global namespace, it's
+*` because it shows explicitly what is imported in the global namespace, its
 advantage over a simpler `import modu` is only that it will save some typing.
 
 **Very bad**
@@ -134,13 +134,13 @@ advantage over a simpler `import modu` is only that it will save some typing.
     [...]
     x = modu.sqrt(4)  # sqrt is visibly part of modu's namespace
 
-As said in the section about style, readability is one of the main feature of
+As said in the section about style, readability is one of the main features of
 Python. Readability means to avoid useless boilerplate text and clutter,
 therefore some efforts are spent trying to achieve a certain level of brevity.
-But terseness and obscurity are the limits where brevity should stop: being
+But terseness and obscurity are the limits where brevity should stop. Being
-able to tell immediately from where comes a class or a function, as in the
+able to tell immediately where a class or function comes from, as in the
-`modu.func` idiom, improves greatly code readability and understandability in
+`modu.func` idiom, greatly improves code readability and understandability in
-most cases but the simplest single file projects.
+all but the simplest single file projects.
 
 
 Packages
@@ -149,35 +149,35 @@ Packages
 Python provides a very straightforward packaging system, which is simply an
 extension of the module mechanism to a directory.
 
-Any directory with a __init__.py file is considered a Python package. The
+Any directory with an __init__.py file is considered a Python package. The
 different modules in the package are imported in a similar manner as plain
-modules, will a special behavior for the __init__.py file, that is used to
+modules, but with a special behavior for the __init__.py file, which is used to
 gather all package-wide definitions.
 
 A file modu.py in the directory pack/ is imported with the statement `import
-pack.modu`. This statement will look for a __init__.py file in `pack`, execute
+pack.modu`. This statement will look for an __init__.py file in `pack`, execute
-all its top-level statements. Then it will look for a file `pack/modu.py` and
+all of its top-level statements. Then it will look for a file `pack/modu.py` and
-execute all its top-level statements. After these operations, any variable,
+execute all of its top-level statements. After these operations, any variable,
-function or class defined in modu.py is available in pack.modu namespace.
+function, or class defined in modu.py is available in the pack.modu namespace.
 
-A commonly seen issue is to add too many code and functions in __init__.py
+A commonly seen issue is to add too much code to __init__.py
 files. When the project complexity grows, there may be sub-packages and
-sub-sub-packages in a deep directory structure, and then, import a single item
+sub-sub-packages in a deep directory structure, and then, importing a single item
-from a sub-sub-package will require to execute all __init__.py file met while
+from a sub-sub-package will require executing all __init__.py files met while
-descending the tree.
+traversing the tree.
 
-Leaving a __init__.py file empty is considered normal and even a good pratice,
+Leaving an __init__.py file empty is considered normal and even a good practice,
 if the package's modules and sub-packages do not need to share any code.
 
 Lastly, a convenient syntax is available for importing deeply nested packages:
-`import very.deep.module as mod` allow to use `mod` in place of the verbose
+`import very.deep.module as mod`. This allows you to use `mod` in place of the verbose
-repetition of `very.deep.module` in front of each calls to module items.
+repetition of `very.deep.module`.
 
 Object-oriented programming
 ---------------------------
 
-Python is sometime described as an object-oriented programming language. This
+Python is sometimes described as an object-oriented programming language. This
-can be somewhat misleading and need to be clarified.
+can be somewhat misleading and needs to be clarified.
 
 In Python, everything is an object, and can be handled as such. This is what is
 meant when we say that, for example, functions are first-class objects.
@@ -188,46 +188,46 @@ object-oriented language.
 
 However, unlike Java, Python do not impose object-oriented programming as the
 main programming paradigm. It is perfectly viable for a Python project to not
-be object-oriented, ie. to use no or very few class definitions, class
+be object-oriented, i.e. to use no or very few class definitions, class
-inheritance, and any other mechanism that are specific to object-oriented
+inheritance, or any other mechanisms that are specific to object-oriented
 programming.
 
 Moreover, as seen in the modules_ section, the way Python handles modules and
-namespaces gives directly to the developer a natural way to ensure
+namespaces gives the developer a natural way to ensure
 encapsulation and separation of abstraction layers, both being the most common
 reasons to use object-orientation. Therefore, Python programmers have more
 latitude to not use object-orientation, when it is not required by the business
-model to be constructed.
+model.
 
-There are some reasons to avoid unnecessary object-orientation. Definining
+There are some reasons to avoid unnecessary object-orientation. Defining
 custom classes is useful when we want to glue together some state and some
-functionality.  The problem, as pointed out by the discussions about functional
+functionality. The problem, as pointed out by the discussions about functional
 programming, comes from the "state" part of the equation.
 
-In some architectures, typically web applications, instances of Python
+In some architectures, typically web applications, multiple instances of Python
-processes are spawned simultaneously to answer to external requests that can
+processes are spawned to respond to external requests that can
-happen at the same time. In this case, holding some state into instanciated
+happen at the same time. In this case, holding some state into instantiated
 objects, which means keeping some static information about the world, is prone
-to concurrency problems or race-conditions: between the initialization of the
+to concurrency problems or race-conditions. Sometime between the initialization of the
 state of an object, usually done with the __init__() method, and the actual use
-of the object state through one of its method, the world may have changed, and
+of the object state through one of its methods, the world may have changed, and
 the retained state may be outdated. For example, a request may load an item in
 memory and mark it as read by a user. If another request requires the deletion
-of this item at the same, it may happen that the deletion actually occur after
+of this item at the same, it may happen that the deletion actually occurs after
 the first process loaded the item, and then we have to mark as read a deleted
 object.
 
 This and other issues led to the idea that using stateless functions is a
 better programming paradigm.
 
-Another way to say the same thing is to propose to use functions and procedures
+Another way to say the same thing is to suggest using functions and procedures
-with as few implicit context and side-effects as possible.  A function's
+with as few implicit contexts and side-effects as possible. A function's
-implicit context is decelable when the function body refers to some global
+implicit context is made up of any of the global variables or items in the persistence layer
-variables or fetches data from the persistence layer. Side-effects are the
+that are accessed from within the function. Side-effects are the changes that a function makes
-opposite: if a function body modifies the global context or save or delete data
+to it's implicit context. If a function saves or deletes data in a global variable or
-on the persistence layer, it is said to have side-effect.
+in the persistence layer, it is said to have a side-effect.
 
-Isolating carefully functions with context and side-effects from functions with
+Carefully isolating functions with context and side-effects from functions with
 logic (called pure functions) allow the following benefits:
 
 - Pure functions are more likely to be deterministic: given a fixed input,
@@ -239,7 +239,7 @@ logic (called pure functions) allow the following benefits:
 - Pure functions are easier to test with unit-tests: There is less
   need for complex context setup and data cleaning afterwards.
 
-- Pure functions are easier to manipulate, decorate_, pass-around.
+- Pure functions are easier to manipulate, decorate_, and pass-around.
 
 In summary, pure functions, without any context or side-effects, are more
 efficient building blocks than classes and objects for some architectures.
@@ -253,12 +253,12 @@ relatively long life of their own in the computer's memory.
 Decorators
 ----------
 
-Python language provides a simple yet powerful syntax called 'decorators'.
+The Python language provides a simple yet powerful syntax called 'decorators'.
 A decorator is a function or a class that wraps (or decorate) a function
 or a method. The 'decorated' function or method will replace the original
-'undecorated' function or method. Because function are first-class objects
+'undecorated' function or method. Because functions are first-class objects
 in Python it can be done 'manually' but using the @decorator syntax is
-clearer and thus prefered.
+clearer and thus preferred.
 
 .. code-block:: Python
 
@@ -277,8 +277,8 @@ clearer and thus prefered.
     # bar() is decorated
 
 Using this mechanism is useful for separating concerns and avoiding
-external un-related logic to 'pollute' the core logic of the function
+external un-related logic 'polluting' the core logic of the function
-or method. A good example of a functionality that is better handled
+or method. A good example of a piece of functionality that is better handled
 with decoration is memoization or caching: you want to store the results of an
 expensive function in a table and use them directly instead of recomputing
 them when they have already been computed. This is clearly not part
@@ -290,18 +290,18 @@ Dynamic typing
 Python is said to be dynamically typed, which means that variables
 do not have a fixed type. In fact, in Python, variables are very
 different from what they are in many other languages, specifically
-strongly-typed languages: variables are not a segment of the computer's
+strongly-typed languages. Variables are not a segment of the computer's
-memory where some value ir written, they are 'tags' or 'names' pointing
+memory where some value is written, they are 'tags' or 'names' pointing
 to objects. It is therefore possible for the variable 'a' to be set to
 the value 1, then to the value 'a string', then to a function.
 
-The dynanic typing of Python is often considered as a weakness, and indeed
+The dynamic typing of Python is often considered to be a weakness, and indeed
-it can lead to complexities and to hard-to-debug code, where something
+it can lead to complexities and hard-to-debug code. Something
 named 'a' can be set to many different things, and the developer or the
-maintainer need to track this name in the code to make sure it has not
+maintainer needs to track this name in the code to make sure it has not
 been set to a completely unrelated object.
 
-Some guidelines allow to avoid this issue:
+Some guidelines help to avoid this issue:
 
 - Avoid using variables for different things.
 
@@ -323,9 +323,8 @@ Some guidelines allow to avoid this issue:
     def func()
         pass  # Do something
 
-Using short functions or methods helps writing good code for many
+Using short functions or methods helps reduce the risk
-reasons, one being that their local scope is clearer, and the risk
+of using the same name for two unrelated things.
-of using the same name for two unrelated things is lowered.
 
 It is better to use different names even for things that are related,
 when they have a different type:
@@ -340,14 +339,14 @@ when they have a different type:
 
 There is no efficiency gain when reusing names: the assignments
 will have to create new objects anyway. However, when the complexity
-grows are each assignment are separated by other lines of code, including
+grows and each assignment is separated by other lines of code, including
-'if' branches and loops, it becomes harder to acertain which type is the
+'if' branches and loops, it becomes harder to ascertain what a given
-variable at hand.
+variable's type is.
 
-Some coding practices, like functional programming, even recommend to never re-assign a variable, which
+Some coding practices, like functional programming, recommend never reassigning a variable.
-is done in Java with the keyword final. Python do not have such a keyword,
+In Java this is done with the `final` keyword. Python does not have a `final` keyword
-and it would be against its philosophy anyway, but it may be a good
+and it would be against its philosophy anyway. However, it may be a good
-discipline to avoid setting more than once any variable, and it helps
+discipline to avoid assigning to a variable more than once, and it helps
 in grasping the concept of mutable and immutable types.
 
 Mutable and immutable types
@@ -357,12 +356,12 @@ Python has two kinds of built-in or user-defined types.
 
 Mutable types are those that allow in-place modification
 of the content. Typical mutables are lists and dictionaries:
-All lists have muting methods, like append() or pop(), and
+All lists have mutating methods, like append() or pop(), and
-can be modified in place. Same for dictionaries.
+can be modified in place. The same goes for dictionaries.
 
 Immutable types provide no method for changing their content.
 For instance, the variable x set to the integer 6 has no "increment" method. If you
-want to computed x + 1, you have to create another integer and give it
+want to compute x + 1, you have to create another integer and give it
 a name.
 
 .. code-block:: python
@@ -386,8 +385,8 @@ For example, the immutable equivalent of a list is the tuple, created
 with ``(1, 2)``. This tuple is a pair that cannot be changed in-place,
 and can be used as a key for a dictionary.
 
-One particularity of Python that can surprise in the beginning is that
+One peculiarity of Python that can surprise beginners is that
-string are immutable. This means that when constructing a string from
+strings are immutable. This means that when constructing a string from
 its parts, it is much more efficient to accumulate the parts in a list,
 which is mutable, and then glue ('join') the parts together when the
 full string is needed.