Imports, Namespaces, and References

References I had a decent idea of how importing and namespaces worked, but it wasn’t until I started setting up unit tests that everything clicked. False assumptions I had about namespaces and imports caused test failures. It forced me to do things the right way, so that I was changing the right objects instead of creating objects in random places. I learned how most things are references, and when you think of variables that way, namespaces and imports make a lot of sense.

Some basics

When you write a module, you start defining things: functions, variables, classes, etc.. When you define something, Python creates an object in memory. The variable name you use is how you refer to that chunk of memory. Everything is references behind the scenes.

A namespace is a container for these references. It maps variable names to addresses in memory. Any module you write has its own namespace. When you define variable mylist in your module, you are defining mylist in your module’s namespace. Variables defined in other modules, or I should say in other modules’ namespaces, are not accessible in your module – unless you import them.

You will work with open source libraries, and to unlock their power you need to access their functions and classes which are in the library’s namespace. You gain access though importing. At a high level Python imports modules by doing the following:

  1. See if the module name is in sys.modules – this like a cache of imported modules.
  2. If not, look for the file that matches the module name
  3. Run the module being imported. This setups up all global variables, including variables for classes & functions
  4. Put the loaded module into sys.modules
  5. Setup references so you can access imports – where the fun happens.

I want to explain these in a little more detail as well as how they affect modifying variables inside of an import. I will go through the steps out of order because step 1, loading from the cache, doesn’t mean much except after step 4, putting a module into the cache.

Step 2: Finding what to import

Finding the modules is pretty useful and interesting, but does not have a lot to do with references so I am skipping the details here. Also, the Python docs outline this clearly and succulently:

https://docs.python.org/3.3/tutorial/modules.html#the-module-search-path

Step 3: Executing Code

After finding the file, Python loads and executes it. To give a concrete example, I have 2 simple modules:

cars.py

car_list = [1]

def myfun():
    return car_list

print "Importing..."

dealer.py

import cars

If you run cardealer.py, you see the following:

> python cardealer.py
Importing...

So by loading and executing I mean Python runs everything at the top level. That includes setting variables, defining functions, and running any other code, such as the print statement in cars.

It also does this in each module’s own namespace. Defining car_list in cars basically adds a pointer in cars to this new list. It does not affect dealer, which could have its own, completely independent variable named car_list. After the import is done, dealer will also not have direct access to car_list. cars has the reference/pointer to that object in memory, and the only way dealer can access the list is to go through cars: cars.car_list.

Step 4 & Step 1

Caching takes place after loading the module. Python stores the module in a module class object, which it puts into the sys.modules dictionary. The key is usually the name of the module, with 1 exception. If the module is the the one you are directly running, its key is ‘main‘. (Aside: this is why we use if name == ‘main‘).

What’s cool is that you can access modules through sys.modules. So back to the simple module. After importing it, you can access the list as cars.car_list. You can also access it through sys.modules:

>>> import sys
>>> sys.modules['cars'].car_list
[1]

The caching in sys.modules has many effects. Importing a cached module means it doesn’t get executed again. Reimporting something is cheap! It also means that all the classes, functions, and variables defined at the top level are only defined once, and statements only execute once. The print statement in cars won’t print if it is imported again, or imported anywhere else in the same process.

Step 5: Reference Setup

This is where the most interesting stuff happens. Depending on how you import something, references are setup in your module’s namespace to let you access what you imported. There are a few variations, and I have some code that shows what Python effectively does. Remember, the modules are already loaded into sys.modules at this point.

  1. import cars

    cars= sys.modules['cars']
  2. import cars as vehicles

    vehicles= sys.modules['cars']
  3. from cars import car_list

    car_list = sys.modules['cars'].car_list

No matter how you import something, code from a module always runs in its own namespace. So when cars looks up a variable, it looks it up in cars’s own namespace, not who imported.

Mutability

Imported variables behave is the same way as regular variables, which depends on mutability.

Remember, when Python creates an object, it puts it somewhere in memory. For mutable objects (dict, list, most other objects), when you make a change to that object, you change the value in memory. For immutable objects (int, float, str, tuple), you can never change the value in memory. For immutable objects you can reassign the variable to point to a new value, but you cannot change the original value in memory.

To illustrate this I can use the id function which prints an address in memory for a variable. I modify the value of an int and a list.

Immutable:

>>> a = 1
>>> id(a)
140619792058920
>>> a += 1
>>> id(a)
140619792058896

Mutable

>>> b = [1]
>>> id(b)
4444200832
>>> b += [2]
>>> id(b)
4444200832

When I change the value of a, the immutable integer, the id changes. But when I do the same for a list, the id remains the same.

There is a sneaky catch though! Many times when you assign a variable, even a mutable one, you change the reference.

>>> b = [1]
>>> id(b)
4320637944
>>> b = b + [2]
>>> id(b)
4320647936

Behind the scene, Python is creating a new list, and changing where the b reference is pointing. The difference can be subtle between this case and the += case above. I wouldn’t worry about it much, it’s never affected me, but it’s good to be aware.

Modifying Imports

So how can I change values in imported modules? And how does mutability affect imports?

The way you access, and sometimes change, variables in imported modules may, or may not, have an effect on the module. This is where unit tests taught me a lot, since my tests imported modules and tried to set variables in order to setup certain test cases. I learned it all comes down to references. There are a few key rules.

Modules execute their code in their own namespace

When a function in cars tries to use car_list, it looks up car_list in cars namespace. That means:

The module is only affected if:

  • you update the object data behind that reference, or
  • **you change where the reference points in the module’s namespace **

There are some simple ways to update what’s behind the reference – which works no matter how you import. Let’s run this code:

tester_1.py

import cars
from cars import car_list, myfun
# update data through the cars module
cars.car_list.append(2)
print "updated through cars:", cars.myfun()
# update through my namespace
car_list.append(3)
print "updated imported var:", myfun()

This outputs:

> python tester_1.py
Importing...
updated through cars: [1, 2]
updated imported var: [1, 2, 3]

It’s trickier to change cars’s reference, which does depends on how you import:

tester_2.py

import cars
from cars import car_list, myfun
# change referece through the cars module
cars.car_list = [2]
print "changed cars reference    :", cars.myfun()
# the imported variable still points to the original value
print "test original import      :", cars_list
# changing the imported variable will not affect the "real" value
# so this should print the same as the first call
car_list = [3]
print "changed imported reference:", myfun()

This outputs:

> python tester_2.py
changed cars reference    : [2]
test original import      : [1]
changed imported reference: [2]

In both cases, we are creating a new list, and changing what the variables are referencing. But remember, myfun is in cars, and gets variables from the cars namespace. In the first case we are going to the cars namespace, and setting its car_list to this new list. When myfun runs, it gets the reference we just updated and returns our new list.

When we from cars import car_list we create a car_list in the tester_2 namespace which points to the same list cars has. When cars’s reference is set to [2], tester_2′s reference stays unchanged, which is why it keeps the value [1]. When we update car_list in tester_2′s namespace, it only changes where tester_2 is pointing. It is not changing where cars points (and not changing the value in memory where cars points), so it has no effect on how the cars module runs. So when we check myfun, it returns [2].

Final thoughts

When you run into a problem where you set a variable in one module, then access it though another, knowing what’s going on behind the scenes helps make sense of it all. With imports it’s easy to figure out what’s going on when you realize that variables are pointers to objects in memory. If your code is not doing what you expect when setting variables, then something is pointing to the wrong object. Knowing how Python works behind the scenes with namespaces and imports will help you figure out what’s going on. It’s really not bad once you realize it’s all just references.

Using Angular Rapydly

RapydScript with AngularJS I’ve been asked a few times if RapydScript works with Angular. The answer is the same as with any other JavaScript framework/library/etc. – of course! RapydScript takes the same approach to compilation/abstraction as CoffeeScript, so there really isn’t anything JavaScript can do that RapydScript can’t. Salvatore has already proven this with multiple demos using frameworks I haven’t even thought of. I urge any developer with similar questions to just try it out, it’s easier (and more fun) to spend an hour building an app than hesitating about it for weeks.

With that said, I decided to put together this tutorial by cloning the 5 examples shown here via RapydScript. I suggest you read the original article before this post. I should also mention that I myself have not used AngularJS before, and decided to learn it through these examples as well. I was a bit disappointed to see that a big chunk of AngularJS is handled magically from their html templates rather than JavaScript itself. This means that there is very little actual JavaScript (and hence RapydScript) used. As far as html itself, you could either write it directly, or use RapydML or Jade (both will have similar syntax). For the purposes of this post, I will leave css code alone, although you can easily convert it to sass as well. Let’s get started.

Setup (Only Needed for RapydML)

Looking at the first example, we immediately notice Django-like variables in the html code. That’s a sign that we may want to use template engines in RapydML. So let’s slap one together. Glancing through the examples we see that most AngularJS variables follow this format:

{{var}}

Doesn’t look like they’re doing anything fancy, so let’s create a very simple template engine in a new pyml file:

angular = TemplateEngine('{%s}')
angular.js = create('{%s}')

I should also mention that you can use AngularJS from RapydML directly, without creating a template engine, I chose to do so for clarity. But nothing prevents you from writing this:

div:
    'This is an angular var: {{myvar}}'

Example 1 (Navigation Menu)

This one doesn’t require any JavaScript. We’ll just need to slap together a RapydML template, leveraging the 2-line template engine we just wrote:

import angular

div(id="main", ng-app):
    nav(class="angular.js(active)", ng-click="\$event.preventDefault()"):
        for $name in [home, projects, services, contact]:
            a(href='#', class="$name", ng-click="active='$name'"):
                python.str.title('$name')

    p(ng-hide='active'):
        'Please click a menu item'
    p(ng-show='active'):
        'You choose'
        b:
            "angular.js(active)"

That’s it, compile it and plug into AngularJS. You will need the most recent version of RapydML, older versions did not handle template engine calls within strings, the new one handles them in double-quoted strings and ignores them in single-quoted strings.

Example 2 (Inline Editor)

As before, we start with the RapydML code:

import angular

div(id='main', ng-app, ng-controller='InlineEditorController', ng-click='hideTooltip()'):
    div(class='tooltip', ng-click='\$event.stopPropagation()', ng-show='showtooltip'):
        input(type='text', ng-model='value')
    p(ng-click='toggleTooltip(\$event)'):
        "angular.js(value)"

Next, let’s add some RapydScript:

def InlineEditorController($scope):
    $scope.showtooltip = False
    $scope.value = 'Edit me.'

    $scope.hideTooltip = def():
        $scope.showtooltip = False

    $scope.toggleTooltip = def(e):
        e.stopPropagation()
        $scope.showtooltip = not $scope.showtooltip

Once again, after compiling the first block of code via RapydML and second via RapydScript, we’ll have a fully-working example.

Example 3 (Order Form)

In this example, we see a new way of hooking into AngularJS. Note the {active: service.active} class tag. We could either reference is in the code verbatim (since it is quoted), or add a new method to the angular template engine for consistency:

angular.active = create('active: %s')

We can now continue with the 3rd example, first the RapydML code:

import angular

form(ng-app, ng-controller='OrderFormController'):
    h1:
        'Services'
    ul:
        li(ng-repeat='service in services', ng-click='toggleActive(service)', ng-class="angular.active(service.active)")
        "angular.js(service.name)"
        span:
            "angular.js(service.price | currency)"

    div(class='total'):
        'Total: '
        span:
            "angular.js(total() | currency)"

Finally, let’s complement it with RapydScript:

def OrderFormController($scope):
    $scope.services = [
        {
            name: 'Web Development',
            price: 300,
            active: True
        }, {
            name: 'Design',
            price: 400,
            active: False
        }, {
            name: 'Integration',
            price: 250,
            active: False
        }, {
            name: 'Training',
            price: 220,
            active: False
        }
    ]

    $scope.toggleActive = def(s):
        s.active = not s.active

    $scope.total = def():
        total = 0
        angular.forEach($scope,services, def(s):
            nonlocal total
            if s.active:
                total += s.price
        )
        return total

As usual, looks similar to the JavaScript version. RapydScript did prevent us from shooting ourselves in the foot by accidentally declaring total as global, something you’d need to be careful about in native JavaScript version.

Example 4 (Instant Search)

For this example, I’ll actually show alternative RapydML code, that doesn’t use our template engine:

div(ng-app='instantSearch', ng-controller='InstantSearchController'):
    div(class='bar'):
        input(type='text', ng-model='searchString', placeholder='Enter your search terms')

    ul:
        li(ng-repeat='i in items | searchFor:searchString')
            a(href='{{i.url}}'):
                img(ng-src='{{i.image}}')
            p:
                '{{i.title}}'

That was even easier than the template engine version, I think I’ll stick to this for my next example as well. Now the RapydScript portion:

app = angular.module('instantSearch', [])
app.filter('searchFor', def():
    return def(arr, searchString):
        if not searchString:
            return arr

        result = []
        searchString = searchString.toLowerCase()
        angular.forEach(arr, def(item):
            if item.title.toLowerCase().indexOf(searchString) != -1:
                result.push(item)
        )
        return result
)

def InstantSearchController($scope):
    $scope.items = [
        {
            url: 'http://tutorialzine.com/2013/07/50-must-have-plugins-for-extending-twitter-bootstrap/',
            title: '50 Must-have plugins for extending Twitter Bootstrap',
            image: 'http://cdn.tutorialzine.com/wp-content/uploads/2013/07/featured_4-100x100.jpg'
        }, {
            url: 'http://tutorialzine.com/2013/08/simple-registration-system-php-mysql/',
            title: 'Making a Super Simple Registration System With PHP and MySQL',
            image: 'http://cdn.tutorialzine.com/wp-content/uploads/2013/08/simple_registration_system-100x100.jpg'
        }, {
            url: 'http://tutorialzine.com/2013/08/slideout-footer-css/',
            title: 'Create a slide-out footer with this neat z-index trick',
            image: 'http://cdn.tutorialzine.com/wp-content/uploads/2013/08/slide-out-footer-100x100.jpg'
        }, {
            url: 'http://tutorialzine.com/2013/06/digital-clock/',
            title: 'How to Make a Digital Clock with jQuery and CSS3',
            image: 'http://cdn.tutorialzine.com/wp-content/uploads/2013/06/digital_clock-100x100.jpg'
        }, {
            url: 'http://tutorialzine.com/2013/05/diagonal-fade-gallery/',
            title: 'Smooth Diagonal Fade Gallery with CSS3 Transitions',
            image: 'http://cdn.tutorialzine.com/wp-content/uploads/2013/05/featured-100x100.jpg'
        }, {
            url: 'http://tutorialzine.com/2013/05/mini-ajax-file-upload-form/',
            title: 'Mini AJAX File Upload Form',
            image: 'http://cdn.tutorialzine.com/wp-content/uploads/2013/05/ajax-file-upload-form-100x100.jpg'
        }, {
            url: 'http://tutorialzine.com/2013/04/services-chooser-backbone-js/',
            title: 'Your First Backbone.js App – Service Chooser',
            image: 'http://cdn.tutorialzine.com/wp-content/uploads/2013/04/service_chooser_form-100x100.jpg'
        }
    ]

On to the last example.

Example 5 (Switchable Grid)

First the RapydML code, as usual:

def layout($type, $imgtype):
    ul(ng-show="layout == '$type'", class="$type"):
        li(ng-repeat='p in pics'):
            a(href='{{p.link}}', target='_blank'):
                img(ng-src="{{p.images.$imgtype.url}}")

div(ng-app='switchableGrid', ng-controller='SwitchableGridController'):
    div(class='bar'):
        for $icon in [list, grid]:
            a(class='$icon-icon', ng-class="{active: layout == '$icon'}", ng-click="layout = '$icon'")
    layout(list, low_resolution)
    layout(grid, thumbnail)
            p:
                '{{p.caption.text}}'

Notice that I chose to be a little more DRY and create an extra function, you don’t have to do that if you believe it hurts readability. Next, the RapydScript code:

app = angular.module('switchableGrid', ['ngResource'])
app.factory('instagram', def($resource):
    return {
        fetchPopular: def(callback):
            api = $resource('https://api.instagram.com/v1/media/popular?client_id=:client_id&callback=JSON_CALLBACK', {
                client_id: '642176ece1e7445e99244cec26f4de1f'
            }, {
                fetch: { method: 'JSONP' }
            })

            api.fetch(def(response):
                callback(response.data)
            )
    }
)

def SwitchableGridController($scope, instagram):
    $scope.layout = 'grid'
    $scope.pics = []
    instagram.fetchPopular(def(data):
        $scope.pics = data
    )

Conclusion

While the RapydScript code above might be a bit cleaner than the original JavaScript code, it’s hard to argue that significant value was gained from porting it. The main benefit of RapydScript comes into play when building larger-scale applications that leverage classes and use numerous variables. Such code increases the chances of shooting yourself in the foot or getting confused when dealing with native JavaScript. Indeed, if you look at some of the jQuery plugins, you’ll see that the code itself seems unmaintainable. After a few layers of anonymous functions it’s hard to figure out what’s being passed around where. AngularJS keeps the code short by handling a lot of the logic for you on the background, RapydScript allows you to keep your own logic readable as it grows. No matter how awesome a framework is, eventually you’ll be writing a lot of your own logic as well.

Python & C

Python CAs promised, I took a script written in Python and ported parts of it to C. The Python interface is the same, but behind the scenes is blazing fast C code. The example is a little different from what I usually do, but I discovered a few things I had never seen.

I am working off the same example as the previous post, How to Write Faster Python, which has the full original code. The example takes an input image and resizes it down by a factor of N. It takes NxN blocks and brings them down to 1 pixel which is the median RGB of the original block.

Small Port

This example is unusual for me because it’s doing 1 task, resizing, and the core of this 1 task is a very small bit of logic – the median function. I first ported the median code to C, and checked the performance. I know the C code is better because C’s sorting function lets you specify how many elements from the list you want to sort. In Python, you sort the whole list (as far as I know) and so I was able to avoid checks on the size of the list vs. the number of elements I want to sort.

Below is the code, but I have a few changes I want to mention as well.

myscript2.py:

import cv2
import numpy as np
import math
from datetime import datetime
import ctypes

fast_tools = ctypes.cdll.LoadLibrary('./myscript_tools.so')
fast_tools.median.argtypes = (ctypes.c_void_p, ctypes.c_int)


def _median(list, ne):
    """
    Return the median.
    """

    ret = fast_tools.median(list.ctypes.data, ne)
    return ret


def resize_median(img, block_size):
    """
    Take the original image, break it down into blocks of size block_size
    and get the median of each block.
    """

    #figure out how many blocks we'll have in the output
    height, width, depth = img.shape
    num_w = math.ceil(float(width)/block_size)
    num_h = math.ceil(float(height)/block_size)

    #create the output image
    out_img = np.zeros((num_h, num_w, 3), dtype=np.uint8)

    #iterate over the img and get medians
    row_min = 0
    num_elems = block_size * block_size
    block_b = np.zeros(num_elems, dtype=np.uint8)
    block_g = np.zeros(num_elems, dtype=np.uint8)
    block_r = np.zeros(num_elems, dtype=np.uint8)
    while row_min < height:
        r_max = row_min + block_size
        if r_max > height:
            r_max = height

        col_min = 0
        new_row = []
        while col_min < width:
            c_max = col_min + block_size
            if c_max > width:
                c_max = width

            #block info:
            num_elems = (r_max-row_min) * (c_max-col_min)
            block_i = 0
            for r_i in xrange(row_min, r_max):
                for c_i in xrange(col_min, c_max):
                    block_b[block_i] = img[r_i, c_i, 0]
                    block_g[block_i] = img[r_i, c_i, 1]
                    block_r[block_i] = img[r_i, c_i, 2]
                    block_i += 1

            #have the block info, sort by L
            median_colors = [
                int(_median(block_b, num_elems)),
                int(_median(block_g, num_elems)),
                int(_median(block_r, num_elems))
            ]

            out_img[int(row_min/block_size), int(col_min/block_size)] = median_colors
            col_min += block_size
        row_min += block_size
    return out_img

def run():
    img = cv2.imread('Brandberg_Massif_Landsat.jpg')
    block_size = 16
    start_time = datetime.now()
    resized = resize_median(img, block_size)
    print "Time: {0}".format(datetime.now() - start_time)
    cv2.imwrite('proc.png', resized)

if __name__ == '__main__':
    run()

myscript_tools.c

#include <stdint.h>
#include <stdio.h>
#include <stdlib.h> // for malloc
#include <math.h>

int cmpfunc (const void * a, const void * b)
{
   return ( *(uint8_t*)a - *(uint8_t*)b );
}

int _median(uint8_t * list, int ne){
    //sort inplace
    qsort(list, ne, sizeof(uint8_t), cmpfunc);
    int i;
    if (ne % 2 == 0){
        i = (int)(ne / 2);
        return ((int)(list[i-1]) + (int)(list[i])) / 2;
    } else {
        i = (int)(ne / 2);
        return list[i];
    }
}

int median(const void * outdatav, int ne){
    int med;
    uint8_t * outdata = (uint8_t *) outdatav;
    med = _median(outdata, ne);
    return med;
}

and to compile:

gcc -fPIC -shared -o myscript_tools.so myscript_tools.c

Note: I discovered the .so filename should not match any Python script you plan to import. Otherwise you will get a “ImportError: dynamic module does not define init function” because Python is trying to import the C code instead of your Python module.

The main change I made was to port the median function to C. I also switched from using a list of colors, to using a numpy array of colors. I did this because it’s easier to pass numpy arrays, and because that is more useful in general. Also, so I wouldn’t have to convert the list to a numpy array on every median call, I crated the numpy array in the resize_median code itself. Before this last change, my benchmark showed I was actually running slower. The faster C code was not making up for all the list –> numpy array conversions!

After the 2nd fix I timed it

python -m timeit "import myscript2; myscript2.run()"

and got

500 loops, best of 3: 2.04 sec per loop

Nice! That’s compared to 2.51 from my last post, so it’s around 18% faster.

All C (sort of)

I wasn’t happy with the 18% boost – I thought it would be faster! But, it was only a very small portion ported. I decided to port more, but there wasn’t a good natural place to split the resize function up any further, so I ported the whole thing. This way I could also show how to send and receive a whole image back from C – which I do by reference/pointer.

myscript2.py

import cv2
import numpy as np
import math
from datetime import datetime
import ctypes

fast_tools = ctypes.cdll.LoadLibrary('./myscript_tools.so')

def c_resize_median(img, block_size):
    height, width, depth = img.shape

    num_w = math.ceil(float(width)/block_size)
    num_h = math.ceil(float(height)/block_size)

    #create a numpy array where the C will write the output
    out_img = np.zeros((num_h, num_w, 3), dtype=np.uint8)

    fast_tools.resize_median(ctypes.c_void_p(img.ctypes.data),
                             ctypes.c_int(height), ctypes.c_int(width),
                             ctypes.c_int(block_size),
                             ctypes.c_void_p(out_img.ctypes.data))
    return out_img


def run():
    img = cv2.imread('Brandberg_Massif_Landsat.jpg')
    block_size = 16
    start_time = datetime.now()
    resized = c_resize_median(img, block_size)
    print "Time: {0}".format(datetime.now() - start_time)
    cv2.imwrite('proc.png', resized)

if __name__ == '__main__':
    run()

myscript_tools.c

#include <stdint.h>
#include <stdio.h>
#include <stdlib.h> // for malloc
#include <math.h>

int cmpfunc (const void * a, const void * b)
{
   return ( *(uint8_t*)a - *(uint8_t*)b );
}

int _median(uint8_t * list, int ne){
    //sort inplace
    qsort(list, ne, sizeof(uint8_t), cmpfunc);
    int i;
    if (ne % 2 == 0){
        i = (int)(ne / 2);
        return ((int)(list[i-1]) + (int)(list[i])) / 2;
    } else {
        i = (int)(ne / 2);
        return list[i];
    }
}

void resize_median(const void * bgr_imgv, const int height, const int width,
                   const int block_size, void * outdatav) {
    const uint8_t * bgr_img = (uint8_t  *) bgr_imgv;
    uint8_t * outdata = (uint8_t *) outdatav;

    int col, row, c_max, r_max, c_si, r_si;
    int max_val;
    const int num_elems = block_size * block_size;
    int j, offset;

    uint8_t *b_values;
    uint8_t *g_values;
    uint8_t *r_values;
    b_values = (uint8_t *) malloc(sizeof(uint8_t) * num_elems);
    g_values = (uint8_t *) malloc(sizeof(uint8_t) * num_elems);
    r_values = (uint8_t *) malloc(sizeof(uint8_t) * num_elems);

    int out_i = 0;

    row = 0;
    while (row < height) {
        col = 0;
        r_max = row + block_size;
        if (r_max > height) {
            r_max = height;
        }
        while (col < width) {
            c_max = col + block_size;
            if (c_max > width) {
                c_max = width;
            }

            // block info:
            j = 0;
            for (r_si = row; r_si < r_max; ++r_si) {
                for (c_si = col; c_si < c_max; ++c_si) {
                    offset = ((r_si*width)+c_si)*3;
                    b_values[j] = bgr_img[offset];
                    g_values[j] = bgr_img[offset+1];
                    r_values[j] = bgr_img[offset+2];
                    j += 1;
                }
            }

            // have the block info, get medians
            max_val = j;
            outdata[out_i]   = _median(b_values, max_val);
            outdata[out_i+1] = _median(g_values, max_val);
            outdata[out_i+2] = _median(r_values, max_val);

            // update indexes
            out_i += 3;
            col += block_size;
        }
        row += block_size;
    }

    free(b_values);
    free(g_values);
    free(r_values);
}

This one is a little more complicated. With multi-dimensional arrays, numpy flattens the data. The new array is [pixel00_blue, pixel00_green, pixel00_red, pixel01_blue, ...] – see the offset variable above for the row/column equation. I also pass pointers with no type, so before using the arrays, I have to typecast them. I realize this example is unusual because the whole script is basically ported, but it illustrates many things that are non-trivial and required some time to figure out.

And for the moment of truth…

500 loops, best of 3: 440 msec per loop

82% faster!

Although it’s a mostly C at this point.

Final Thoughts

So in this case, the Python took about 5 times as long to run compared to the C version. When something takes a few milliseconds, 5-10x isn’t that important, but when it gets more complex, it starts to matter (assuming you’re not I/O bound). Here I ported pretty much the whole module. I think this only makes sense with a library, and not code that you will refer to often – Python is just so easy to read.

An idea popped in my head as I was writing this as well. This should be easy to port to RapydScript. JavaScript has the Image object I could use in place of opencv. I wonder where JavaScript would fall on the spectrum.

Displaced Aggression

Displaced Aggression I’m going through a very tough situation in my life – hence the slower development of Grafpad and RapydScript. I won’t get into the details, but basically I’m being sued over a house purchase that fell through. It’s a very stressful situation to be in, and I don’t believe the other party is enjoying it any more than I do. The question then is, why don’t we just discuss it like human beings?

I tried to reason with the other party repeatedly to minimize damages to both. She is not interested in negotiating, her attorney says “she is angry”. She insists that she wants to recover her damages. I still fail to see what those damages are. The mortgage is paid off, she may have incurred a few hundred dollars of extra utility bills she wouldn’t have paid if she moved earlier. I already offered several thousand to make up for it and avoid headaches, we also suggested that she offer her own “just” amount. She refused, her response was that she is angry and intends to take it to court – a court that will take over a year to get to and is just as likely to rule in my favor as in hers.

As a result of the lawsuit, both parties have already incurred several thousand dollars more in lawsuit fees. What did I do that she is so angry about, you may ask? I simply wasn’t able to complete the transaction due to a perfect storm of unfortunate events (recent change in local laws, problems with the bank due to these laws, as well as misinformation from a third party). Another buyer walked away right before me as well, and the seller was angry with them too. I should have seen that as a red flag.

Why am I sharing this? Because I don’t believe that the seller is trying to make a quick buck off of me. If the goal was to maximize her own good, she would have taken the settlement or offered a counter, minimizing harm to both parties, while still maximizing her gain. That is how rational people think. Unfortunately, it seems like it’s not what the seller is after. She is angry. It’s not her own good or damages she cares about, it’s maximizing harm to me – even if it means incurring the same harm herself in the process. Why? Because she is angry, because she wants to “punish” someone for the pain she feels.

When you think like this, you will eventually find someone to punish. Problem is, you’re punishing another victim, you’re not addressing the real cause of the problem. She was angry at the previous buyer, that same anger was later projected at me, amplified by additional headaches. Why was she angry at me? Because this house was a pain to sell (it was on the market for over a year) and because I happened to be the easiest target in this interaction. When you’re angry, you don’t think rationally. When you’re angry, you’re simply searching for a scapegoat, you’re not searching for a solution. If we asked ourselves “how to solve this problem?” more often and “who caused the problem?” less often, we’d create less stress for everyone and we’d need less lawsuits to settle things.

How to Write Faster Python

Peugeot F1 This is a high level guide on how to approach speeding up slow apps. I have been writing a computer vision app on the backend of a server and came to the realization that it is easy to write slow Python, and Python itself is not fast. If you’re iterating over a 1000 x 1000 pixels (1 megapixel) image, whatever you’re doing inside will run 1 million times. My code ran an iterative algorithm, and it took over 2 minutes to run the first version of the code. I was able to get that time down to under 10 seconds – and it only took a few hours time with minimal code changes. The hardest part was converting some snippets of code into C, which I plan to detail in the future. The basic approach I followed was:

  1. Set a baseline with working code & test
  2. Find the bottlenecks
  3. Find a way to optimize bottlenecks
  4. Repeat

And fair warning, these sorts of optimizations only work if you have selected a good algorithm. As we used to say at work “You can’t polish a turd”.

Slow, but working code

I am going to start with an example program. This resizes an image using the medians of each region. If you resize to 1/4th of the original size the new image will be the medians of 4×4 blocks. It is not too important you understand the details, but I still included code for reference:

import cv2
import numpy as np
import math
from datetime import datetime

def _median(list):
    """
    Return the median
    """

    sorted_list = sorted(list)
    if len(sorted_list) % 2 == 0:
        #have to take avg of middle two
        i = len(sorted_list) / 2
        #int() to convert from uint8 to avoid overflow
        return (int(sorted_list[i-1]) + int(sorted_list[i])) / 2.0
    else:
        #find the middle (remembering that lists start at 0)
        i = len(sorted_list) / 2
        return sorted_list[i]

def resize_median(img, block_size):
    """
    Take the original image, break it down into blocks of size block_size
    and get the median of each block.
    """

    #figure out how many blocks we'll have in the output
    height, width, depth = img.shape
    num_w = math.ceil(float(width)/block_size)
    num_h = math.ceil(float(height)/block_size)

    #create the output image
    out_img = np.zeros((num_h, num_w, 3), dtype=np.uint8)

    #iterate over the img and get medians
    row_min = 0
    #TODO: optimize this, maybe precalculate height & widths
    while row_min < height:
        r_max = row_min + block_size
        if r_max > height:
            r_max = height

        col_min = 0
        new_row = []
        while col_min < width:
            c_max = col_min + block_size
            if c_max > width:
                c_max = width

            #block info:
            block_b = []
            block_g = []
            block_r = []
            for r_i in xrange(row_min, r_max):
                for c_i in xrange(col_min, c_max):
                    block_b.append(img[r_i, c_i, 0])
                    block_g.append(img[r_i, c_i, 1])
                    block_r.append(img[r_i, c_i, 2])

            #have the block info, sort by L
            median_colors = [
                int(_median(block_b)),
                int(_median(block_g)),
                int(_median(block_r))
            ]

            out_img[int(row_min/block_size), int(col_min/block_size)] = median_colors
            col_min += block_size
        row_min += block_size
    return out_img

def run():
    img = cv2.imread('Brandberg_Massif_Landsat.jpg')
    block_size = 16
    start_time = datetime.now()
    resized = resize_median(img, block_size)
    print "Time: {0}".format(datetime.now() - start_time)
    cv2.imwrite('proc.png', resized)

if __name__ == '__main__':
    run()

This takes around 3 seconds to run against a random 1000×1000 image from Wikipedia (link):

500 loops, best of 3: 3.09 sec per loop

So this is a good starting point – it works! I am also saving the output image, so that when I make changes, I can spotcheck that I didn’t break something.

Profiling

Python’s standard library has a profiler that works very well (see The Python Profilers). Although I’m not a fan of the text output – I feel it does not intuitively roll up results – you can dump stats to a file which you can view in a UI.

So I can profile the script:

python -m cProfile -o stats.profile myscript.py

Note that it can sometimes add a lot of overhead.

And after installing runsnakerun I can view the stats:

runsnake stats.profile

which shows me: Runsnakerun GUI

So there are a few things that jump out as easy fixes. First, that app spends around 1/3rd of the time appending colors. Second, finding the median takes a significant amount of time as well – most of it from the sorted call – this is not visible in the image above. The other lines of code are not significant enough to display.

Optimizing

I have a few quick rules of thumb for optimizing:

  • creating/appending to lists is slow due to memory allocation – try to use list comprehensions
  • try not to run operations that create copies of objects unless it’s required – this is also slow due to memory allocation
  • dereferencing is slow: if you’re doing mylist[i] several times, just do myvar = mylist[i] up front
  • use libraries as much as possible – many are written in C/C++ and fast

Other than that, make use of search like Google or DuckDuckGo. You can tweak your Python code (you might discover you’re doing something wrong!), use Cython, write C libraries, or find another solution.

So profiling tells me that appending is slowing my code. I can get around this problem by declaring the list once and keeping track of how many elements I “add”. I also know that sorted() is not preferred because it creates a copy of the original list. I can instead use list.sort() and sort the list in place. I make these changes and run the code, and see the output is still good so I probably did not break it. Let’s time it.

500 loops, best of 3: 2.51 sec per loop

That’s almost 20% faster! Not bad for a few minutes of effort.

For completeness, here is the modified code:

import cv2
import numpy as np
import math
from datetime import datetime

def _median(list, ne):
    """
    Return the median.
    """

    #sort inplace
    if ne != len(list):
        list = list[0:ne]
    list.sort()
    if len(list) % 2 == 0:
        #have to take avg of middle two
        i = len(list) / 2
        #int() to convert from uint8 to avoid overflow
        return (int(list[i-1]) + int(list[i])) / 2.0
    else:
        #find the middle (remembering that lists start at 0)
        i = len(list) / 2
        return list[i]

def resize_median(img, block_size):
    """
    Take the original image, break it down into blocks of size block_size
    and get the median of each block.
    """

    #figure out how many blocks we'll have in the output
    height, width, depth = img.shape
    num_w = math.ceil(float(width)/block_size)
    num_h = math.ceil(float(height)/block_size)

    #create the output image
    out_img = np.zeros((num_h, num_w, 3), dtype=np.uint8)

    #iterate over the img and get medians
    row_min = 0
    #TODO: optimize this, maybe precalculate height & widths
    num_elems = block_size * block_size
    block_b = [0] * num_elems
    block_g = [0] * num_elems
    block_r = [0] * num_elems
    while row_min < height:
        r_max = row_min + block_size
        if r_max > height:
            r_max = height

        col_min = 0
        new_row = []
        while col_min < width:
            c_max = col_min + block_size
            if c_max > width:
                c_max = width

            #block info:
            num_elems = (r_max-row_min) * (c_max-col_min)
            block_i = 0
            for r_i in xrange(row_min, r_max):
                for c_i in xrange(col_min, c_max):
                    block_b[block_i] = img[r_i, c_i, 0]
                    block_g[block_i] = img[r_i, c_i, 1]
                    block_r[block_i] = img[r_i, c_i, 2]
                    block_i += 1

            #have the block info, sort by L
            median_colors = [
                int(_median(block_b, num_elems)),
                int(_median(block_g, num_elems)),
                int(_median(block_r, num_elems))
            ]

            out_img[int(row_min/block_size), int(col_min/block_size)] = median_colors
            col_min += block_size
        row_min += block_size
    return out_img

def run():
    img = cv2.imread('Brandberg_Massif_Landsat.jpg')
    block_size = 16
    start_time = datetime.now()
    resized = resize_median(img, block_size)
    print "Time: {0}".format(datetime.now() - start_time)
    cv2.imwrite('proc.png', resized)

if __name__ == '__main__':
    run()

Repeat

Now that I optimized the code a little, I repeat the process to make it even better. I try to decide what should be slow, and what should not. So, for example, sorting is not very fast here, but that makes sense. Sorting is the most complex part of this code – the rest is iterating and keeping track of observed colors.

Final Thoughts

After having optimized a few projects I have a few final thoughts and lessons learned:

  • Optimizing a slow/bad algorithm is a waste of time, you’ll need to redesign it anyways
  • The granularity of the profiler is sometimes a little funny. You can get around this by making sections of code into functions – metrics on functions are usually captured.
  • The profile will group the calls to the library functions together even if called from different places. So be careful with metrics for libraries/utils – i.e. numpy.
  • Use search to find ways to optimize your code.

If all else fails, you can implement slow parts in C relatively painlessly. This is the option I usually go with, and I will detail it out with some code in the next few weeks.

Have fun speeding up your code!

Business Ping Pong

Ping Pong I used to be notorious at procrastination. I still do it, but I got much better at it. The problem was my thought process. We all think in terms of pain. We avoid new pain or seek to minimize existing pain. Procrastination is simply a mechanism to avoid new pain. What we fail to realize, however, is that same pain dwells in our mind. That mental to do list is eating away our brain capacity, causing distractions from other tasks. It’s a numbing pain that keeps coming back. It’s like storing clutter in your house, where the house is your brain. After thinking about my own procrastination, I realized that the pain of dwelling on a task is bigger than the pain of actually doing the task.

As I started tackling these tasks, it became easier to do them. The hardest part is starting. Indeed I got better at this once I started forcing myself to start. The other trick is that you don’t have to do it all in one go, once you start tackling the problem, it gets smaller. The other side of the coin is addressing tasks that involve other people. We tend to procrastinate our responses until the nagging from the other side becomes severe enough. But what if you were to handle the responses as soon as you can? I recently heard a term for this in a conversation: business ping pong. When a ball comes your way, hit it back as soon as you can. Don’t procrastinate, be the one waiting on others, not the other way around. The sooner you reply, the less time you spend thinking about it, the less clutter you keep in your house.

Bringing a Project to Life

I have always found that building something out of nothing is hard – whether it be a new feature, or a new product. It’s much easier to fix a bug or make an improvement to working code. Yet, I’ve been on some ambitious projects that got done (building a completely new API for a large ad serving company & a radar program that was designed & built from scratch in 3 years, to name the most difficult). Even though many people thought it was a miracle we got these done, I noticed some techniques that were useful in bringing these projects to fruition which apply to open source projects as well.

Before getting started, you must first give up the idea of building a finished product. You need to make something just usable to start. If you have something useable (and useful), people will start using it – and for open source projects, some of those uses will help make it better. After a project is brought to life, it can evolve into something more – a finished product. The difficultly then lies in making that first usable product.

Fail Fast

Nothing has been accomplished yet. You have to start planning and try to eliminate bad ideas before they take up too much time. The most efficient way of doing this is by getting feedback from others. If you find experts, or even just people with different perspectives, they can draw upon their experiences to quickly point out problems, and save you spending days or weeks working on a solution that will never work.

In addition to getting feedback, prototype things and make proof of concept models. Most importantly, don’t invest a lot of time here. You don’t need pretty code – but it should be accurate. The goal is to wrap your head around the problem and discover potential solutions and failures early – not to write something maintainable. The models themselves aren’t important, the data/information you learn from these experiments is what matters.

Build a pre-alpha version

You need to get something working that you can build off of. Most projects have more than 1 component. Even small web apps have a view & controller. Connecting the different parts together is difficult because it is easy to make assumptions about how & what data passed between interfaces.

On the radar system I worked on, many properties of the raw input signal were not planned to be passed along to processes further down the line. But after building the first version of the code, that format of that data quickly changed to include additional information. And after the first version of every component was done, we could look at a display, albeit a very crude one, and see a real object – that is very motivating.

Focus, Iterate & Test

The radar system I worked on was also hard because we were short on time due to an accelerated schedule – time was our most valuable resource. The main reasons we were still able to finish was because the motto of the project was an idea from Voltaire: “perfect is the enemy of good enough”. We were told not to spend any time working on something that already met the specs – we could not spare it. Instead we had to find something that was not good enough, and focus there.

This list of areas we needed to focus became our list of improvements/bugs. We made it past the difficult part of building something from nothing with our pre-alpha version, and now we were working on fixes and improvements – the easy part. After repeating the focus & fix process many times, we got to the point where we had a useable product.

Also crucial here is testing. You need to test so you can confidently make changes without causing regressions. The less effort required to run them, better (automated ;))

Share your project

Alex Martelli gave a talk at PyCon 2013, “Good enough is good enough”. He said, if you make something people need, they will use it, even if it’s not perfect. The catch here is it has to be something people find useful. If enough people use your open source project, some of them will contribute back, fix bugs, and maybe even clean up and optimize code. Even if people do not fix anything, they can file issue tickets. This last weekend we had someone file several issues with example code – perfect test cases.

Modifying Native JavaScript Objects

Random Number Generator There are still debates in JavaScript community about modifying native JavaScript objects (Array, Number, String, Object). Some developers believe it’s evil, while other encourage it. If you’ve looked at RapydScript’s stdlib, you probably already know my stance on it – I’m actually in favor of such modifications when they make sense. I might not have as much experience in JavaScript as the gurus, but I have enough experience in language design to form sane opinions about which practices are evil and which are not.

Most arguments against modifying the native objects hold no water. They give examples where the developer overrides a native method to work differently from original implementation. That is indeed evil, assuming that calling this method with the same arguments as original JavaScript implementation no longer produces the same result. For example, if I decided to rewrite String.prototype.replace such that it worked globally, like in most other languages, instead of on a first occurrence:

String.prototype.replace = function(orig, sub) {
    return this.split(orig).join(sub);
}

I could break (or make them behave differently than they should) other libraries/widgets on the same page that assume that replace() will only replace the first occurrence. I completely agree that one should never override existing methods to do something else. It’s important that the basic subset of the JavaScript language works exactly as others would expect it to. If you can’t guarantee that the foundation of your house stays level, you can’t guarantee that your house will not collapse.

There are some gray area cases, where the developer could extend the functionality of existing method, such that it still works as expected given original arguments, but does something else when more arguments are given:

Array.prototype.pop = function(index) {
    if (!arguments.length) {
        index = this.length - 1;
    }
    return this.splice(index, 1)[0];
}

In this case, myArray.pop() will still work exactly as the user would expect. However, if called with myArray.pop(0), the function will behave like myArray.shift(), or like splice() method if called with an index in the middle. The only real disadvantage here is that by overriding the native method, the developer has made the function slower than the original (native methods tend to work faster). Claiming that this is bad because a function could break another logic that mistakenly called it with an argument (which it ignored before) on the other hand is not a legitimate argument. The bug is in the function making the bad call, not in the overriding logic. This is why I like Python, it will complain about unexpected arguments right away instead of ignoring them so they could become a bigger problem later.

Appending your own methods, on the other hand, is the most benign way to modify a native object. This is when we implement something like this, for example:

Array.prototype.copy = function() {
    return this.slice();
}

When doing, so, however, it’s important to be aware if other libraries are appending a method with the same name but different functionality, then you might want to pick a different name (this is usually not a problem with libraries/APIs since you’re unlikely to need more than one library for native object manipulation). So far, the most popular argument I’ve seen against this type of native object modification is potential name collision if future version of JavaScript decides to add a method by the same name. This argument is moot. You’re not developing your app for a hypothetical language that will exist 10 years from now, and when the time comes you’ll easily be able to rename the offending function, since you know all references to it are your own. The entire EcmaScript specification is easily available, and unless you plan to drop support for all browsers older than 6 months, the JavaScript implementation you’ll be using as basis will probably lag behind that by a few months to a few years, giving you more than enough time to handle naming collisions.

You might notice, however, that if you start appending methods to the native Object object, any jQuery code running on the same page will break. The problem is not with overwriting native methods, but with poor assumptions made by jQuery developers who wrote buggy code. The problem is that jQuery developers are part of the group that believe that appending to native JavaScript objects is evil, and instead of properly testing that they’re iterating through object’s own properties, they make the assumption that no one else will append anything to Object when they scan through it using “for key in Object” type logic. To avoid the same mistake as jQuery made, make sure to iterate only through your own keys:

for (var key in obj) {
    if obj.hasOwnProperty(key) {
        ...
    }
}

Alternatively, if you only care about the latest browsers, you can use Object.getOwnPropertyNames() instead. It was a poor design on JavaScript’s part to default to iterating through every property of the object, but that can’t be changed now. jQuery developers have been confronted about their assumption before, they claim the main reason was performance. Independent tests showed about 5% performance hit from adding this check, so I don’t buy the performance claim, especially since jQuery already makes multiple performance sacrifices in the name of usability (show/hide logic having safety checks to figure out the object’s current state, for example). In my opinion, John Resig’s stance on this is no different than failing to do a “division by zero” check (in Python, because JavaScript will automatically return infinity) and then claiming that it’s for performance reasons and anyone who passes arguments that eventually result in a division by zero is the one at fault.

I haven’t checked if this is fixed in jQuery 2.0. Without support for older browsers there is no reason not to use Object.getOwnPropertyNames(). In the meantime, try not to overwrite Object (other native objects are fine) if you’re using jQuery anywhere at all (if you’re not, it’s not a problem). I should also mention that if you don’t plan to support older browsers (such as IE8), a better practice would be to use defineProperty method rather than appending to the prototype, since it will omit the method from getting picked up when iterating through the object, making the jQuery bug a non-issue.

As far as my own stance, it’s fine to append to the functionality of a native object, but never to remove. Modifying existing property to work differently than before, given the same function signature, counts as removal, and is evil as well. I have removed the dependence on overwriting native methods in RapydScript’s stdlib2, but that was mainly to clean up the standard library and make it compatible with jQuery, not because I’m against modifying native objects.

Productivity vs Performance

Productivity vs Performance When I was writing software in college, there was more emphasis on program execution speed than on time spent implementing it. In startups and most work environments, the reverse tends to be true. It took me a while to figure this out, and for the first few years of programming, I would often introduce optimizations that were not necessary, or make code uglier than it needed to be for the sake of performance. I’m not talking about premature optimization, I’m talking about poor design decisions stemming from assumption that performance trumps legibility.

I’ve spent a lot of time refactoring poorly written code in Grafpad – code that wasn’t necessarily bad to begin with, but quickly outgrew its initial purpose as more special cases were introduced to it. What gave me even more grief, however, were the special cases I imposed on myself, in an attempt to preserve bandwidth, CPU and memory usage. For example, each shape point in Grafpad consists of 3 items: x-coordinate, y-coordinate, and curvature flag. In original version of Grafpad, shapes that I knew couldn’t have curvatures omitted that curvature flag. As a further optimization, I wrote faster versions of multiple algorithms (edge detection, intersection, bounding box computations) which didn’t have to deal with curved lines. I later ended up regretting that, having realized that I did twice as much work to handle a case that may have been fast enough anyway. I wasted time I could have invested elsewhere, I introduced special case logic that didn’t need to be there.

Another example is the logic I created for transmitting data to server and back. I didn’t like that JSON.stringify included a lot of irrelevant information, which I wouldn’t need since I knew exactly what kind of object I’m sending over. My packing method transmitted only the values themselves, and I was unpacking them correctly by following the same order of operations. Once again, I performed a bunch of work that JSON.stringify could have handled for me, and ended up with a more fragile solution that depended on pack/unpack logic on both front-end and back-end to be identical.

I’m not saying the work I did was pointless, it simply wasn’t the kind of work I needed to do at an early-stage startup. By the time these kinds of optimizations become relevant, the product should already have multiple users and a team of developers who have time to do these optimizations. An early-stage startup should concentrate on getting the product out the door and fixing bugs that affect users, performance issues rarely matter at that stage. And with proper use of polymorphism, those optimizations will be easy to add later on in cases where they do matter.

RapydScript is Self-Hosting

While the RapydScript community is small, our members are passionate about the project, and I’m grateful for that. Salvatore, for example, has put together a number of demos showcasing RapydScript integration with WebGL, NodeBox, GlowScript, and a number of other JavaScript projects. Charles made a Chip’s Challenge clone. There are also examples of RapydScript integration with D3, and a Paint app that uses HTML5 canvas and jQuery. When we started a little over a year ago, members would ask us “can RapydScript work with ___?”. Now instead of just saying “yes” we can often point them to a demo.

To take things one step further, I decided to port the compiler itself (originally in JavaScript) to RapydScript as well. This effort took a few weeks, and a lot of tweaking to a Decompiler project that was put together a while ago. The decompilation wasn’t without issues, and I had to manually tweak some code. Along the way, I also discovered a few minor bugs in RapydScript, which I have fixed in the process. Overall, however, I was pleasantly surprised by how easy it was to port the code. The ported version is not written in Pythonic style that RapydScript tries to encourage because the code-generation was mostly automated, yet it already looks more legible than the JavaScript version it was ported from. I’m very proud of this, since it’s an important step for a compiler to achieve the self-hosting status, and I’m not yet aware of any other Python-to-JS compiler that can say the same (they’re all written in Python, but the subset of Python that the languages use isn’t enough to support the compiler itself).

I hope that this will make future enhancements and tweaks to RapydScript easier, both for myself and other members of the community. As a bonus, I’ve also added kwargs implementation to RapydScript. It works similar to Python, but not completely like it (for performance reasons), I suggest you read the manual on it before use to avoid surprises.