Reliable Way To Test External APIs Without Mocking


Let us write a function which retrieves user information from GitHub API.

import requests


def get_github_user_info(username):
    url = f'https://api.github.com/users/{username}'
    response = requests.get(url)
    if response.ok:
        return response.json()
    else:
        return None

To test this function, we can write a test case to call the external API and check if it is returning valid data.

def test_get_github_user_info():
    username = 'ChillarAnand'
    info = get_github_user_info(username)
    assert info is not None
    assert username == info['login']

Even though this test case is reliable, this won't be efficient when we have many APIs to test as it sends unwanted requests to external API and makes tests slower due to I/O.

A widely used solution to avoid external API calls is mocking. Instead of getting the response from external API, use a mock object which returns similar data.

from unittest import mock


def test_get_github_user_info_with_mock():
    with mock.patch('requests.get') as mock_get:
        username = 'ChillarAnand'

        mock_get.return_value.ok = True
        json_response = {"login": username}
        mock_get.return_value.json.return_value = json_response

        info = get_github_user_info(username)

        assert info is not None
        assert username == info['login']

This solves above problems but creates additional problems.

  • Unreliable. Even though test cases pass, we are not sure if API is up and is returning a valid response.
  • Maintenance. We need to ensure mock responses are up to date with API.

To avoid this, we can cache the responses using requests-cache.

import requests_cache

requests_cache.install_cache('github_cache')


def test_get_github_user_info_without_mock():
    username = 'ChillarAnand'
    info = get_github_user_info(username)
    assert info is not None
    assert username == info['login']

When running tests from developer machine, it will call the API for the first time and uses the cached response for subsequent API calls. On CI pipeline, it will hit the external API as there won't be any cache.

When the response from external API changes, we need to invalidate the cache. Even if we miss cache invalidation, test cases will fail in CI pipeline before going into production.

Convert Browser Requests To Python Requests For Scraping


Scraping content behind a login page is bit difficult as there are wide variety of authentication mechanisms and web server needs correct headers, session, cookies to authenticate the request.

If we need a crawler which runs everyday to scrape content, then we have to implement authentication mechanism. If we need to quickly scrape content just for once, implementing authentication is an overhead.

Instead, we can manually login to the website, capture an authenticated request and use it for scraping other pages by changing url/form parameters.

From browser developer options, we can capture curl equivalent command for any request from Network tab with copy as cURL option.

Here is one such request.

curl 'http://avilpage.com/dummy' -H 'Cookie: ASPSESSIONIDSABAAQDA=FKOHHAGAFODIIGNNNDFKNGLM' -H 'Origin: http://avilpage.com' -H 'Accept-Encoding: gzip, deflate' -H 'Accept-Language: en-US,en;q=0.9,ms;q=0.8,te;q=0.7' -H 'Upgrade-Insecure-Requests: 1' -H 'User-Agent: Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36' -H 'Content-Type: application/x-www-form-urlencoded' -H 'Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8' -H 'Cache-Control: max-age=0' -H 'Referer: http://avilpage.com/' -H 'Connection: keep-alive' -H 'DNT: 1' --data 'page=2&category=python' --compressed

Once we get curl command, we can directly convert it to python requests using uncurl.

$ pip install uncurl

Since the copied curl request is in clipboard, we can pipe it to uncurl.

$ clipit -c | uncurl

requests.post("http://avilpage.com/dummy",
    data='page=2&category=python',
    headers={
        "Accept": "text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8",
        "Accept-Encoding": "gzip, deflate",
        "Accept-Language": "en-US,en;q=0.9,ms;q=0.8,te;q=0.7",
        "Cache-Control": "max-age=0",
        "Content-Type": "application/x-www-form-urlencoded",
        "Origin": "http://avilpage.com",
        "Referer": "http://avilpage.com/",
        "Upgrade-Insecure-Requests": "1",
        "User-Agent": "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/65.0.3325.181 Safari/537.36"
    },
    cookies={
        "ASPSESSIONIDSABAAQDA": "FKOHHAGAFODIIGNNNDFKNGLM"
    },
)

If we have to use some other programming language, we can use curlconverter to convert curl command to Go or Node.js equivalent code.

Now, we can use this code to get contents of current page and then continue scraping from the urls in it.

Running Django Web Apps On Android Devices


When deploying a django webapp to Linux servers, Nginx/Apache as server, PostgreSQL/MySQL as database are preferred. For this tutorial, we will be using django development server with SQLite database.

First install SSHDroid app on Android. It will start ssh server on port 2222. If android phone is rooted, we can run ssh on port 22.

Now install QPython. This comes bundled with pip, which will install required python packages.

Instead of installing these two apps, we can use Termux, GNURoot Debian or some other app which provides Linux environment in Android. These apps will provide apt package manager, which can install python and openssh-server packages.

I have used django-bookmarks, a simple CRUD app to test this setup. We can use rsync or adb shell to copy django project to android.

rsync -razP django-bookmarks :$USER@$HOST:/data/local/

Now ssh into android, install django and start django server.

$ ssh -v $USER@$HOST
$ python -m pip install django
$ cd /data/local/django-bookmarks
$ python manage.py runvserver

This will start development server on port 8000. To share this webapp with others, we will expose it with serveo.

$ ssh -R 80:localhost:8000 serveo.net

Forwarding HTTP traffic from https://incepro.serveo.net
Press g to start a GUI session and ctrl-c to quit.

Now we can share our django app with anyone.

I have used Moto G4 Plus phone to run this app. I have done a quick load test with Apache Bench.

ab -k -c 50 -n 1000  \
-H "Accept-Encoding: gzip, deflate" \
http://incepro.serveo/list/

It is able to server 15+ requests concurrently with an average response time of 800ms.

We can write a simple shell script or ansible playbook to automate this deployment process and we can host a low traffic website on an android phone if required.

Load Testing Celery With Different Brokers


Celery is mainly used to offload work from request/response cycle in web applications and to build pipelines in data processing applications. Lets run a load test on celery to see how well it queues the tasks with various brokers.

Let us take a simple add task and measure queueing time.

import timeit

from celery import Celery

broker = 'memory://'


app = Celery(broker=broker)


@app.task
def add(x, y):
    return x + y


tasks = 1000
start_time = timeit.default_timer()
results = [add.delay(1, 2) for i in range(tasks)]
duration = timeit.default_timer() - start_time
rate = tasks//duration
print("{} tasks/sec".format(str(rate))

On development machine, with AMD A4-5000 CPU, queueing time is as follows

  • memory ---> 400 tasks/sec
  • rabbitmq ---> 300 tasks/sec
  • redis ---> 250 tasks/sec
  • postgres ---> 30 tasks/sec

On production machine, with Intel(R) Xeon(R) CPU E5-2676, queueing time is as follows

  • memory ---> 2000 tasks/sec
  • rabbitmq ---> 1400 tasks/sec
  • redis ---> 1200 tasks/sec
  • postgres ---> 200 tasks/sec

For low/medium traffic webistes and applications, 1000 tasks/second should be fine. For high traffic webistes, there will be multiple servers queueing up the tasks.

Incase if we need to queue the tasks at a higher rate and if we have task arguments before hand, we can chunk the tasks.

tasks = add.chunks(zip(range(1000), range(1000)), 10)

This will divide 1000 tasks into 10 groups of 100 tasks each. As there is no messaging overhead, it can queue any number of tasks in less than a second.

How To Plot Renko Charts With Python?


Renko charts are time independent and are efficient to trade as they eliminate noise. In this article we see how to plot renko charts of any instrument with OHLC data using Python.

To plot renko charts, we can choose a fixed price as brick value or calculate it based on ATR(Average True Range) of the instrument.

There are two types of Renko charts based on which bricks are calculated.

Renko chart - Price movement

First one is based on price movement. In this, we will divide the price movement of current duration by brick size to get the bricks.

Once bricks are obtained, we need to assign the brick colors based on the direction of price movement and then plot rectangles for each available brick.

import pandas as pd
from matplotlib.patches import Rectangle
import matplotlib.pyplot as plt


brick_size = 2


def plot_renko(data, brick_size):
    fig = plt.figure(1)
    fig.clf()
    axes = fig.gca()
    y_max = max(data)

    prev_num = 0

    bricks = []

    for delta in data:
        if delta > 0:
            bricks.extend([1]*delta)
        else:
            bricks.extend([-1]*abs(delta))

    for index, number in enumerate(bricks):
        if number == 1:
            facecolor='green'
        else:
            facecolor='red'

        prev_num += number

        renko = Rectangle(
            (index, prev_num * brick_size), 1, brick_size,
            facecolor=facecolor, alpha=0.5
        )
        axes.add_patch(renko)

    plt.show()


df = pd.read_csv(file)

df['cdiff'] = df['close'] - df['close'].shift(1)
df.dropna(inplace=True)
df['bricks'] = df.loc[:, ('cdiff', )] / brick_size

bricks = df[df['bricks'] != 0]['bricks'].values
plot_renko(bricks, brick_size)

Here is a sample renko chart plotted using the above code.

Renko chart - Period close

In this bricks are calculated based on the close price of the instrument. Calculation of bricks is sligtly complex compared to price movement chart. I have created a seperate package called stocktrends which has this calculation.

from stocktrends import Renko

renko = Renko(df)
renko.brick_size = 2
data = renko.get_ohlc_data()
print(data.tail())

This will give OHLC data for the renko chart. Now we can use this values to plot the charts as mentioned above.

Automatic Magnetometer Calibration With Arduino


If we take readings from a 3-axis magnetometers like HMC5883L, AK8963C (used in MPU9250) or LSM303DLHC and plot them, its response should be a sphere with ceter at origin.

In practice, due to the presence of hard and soft iron distortions, the response will be an ellipsiod with its center shifted away from origin. We need to calibrate the magnetometer to nullify the distortions.

First we need to get sample readings of magnetometer in various positions. Depending on the magnetometer, we need to connect it to arduino and take readings by rotating it in 8 shape.

Calibration

Hard iron biases shifts center away from origin. We can eliminate this error by calculating the offsets and shifting the readings.

int mx, my, mz;

int mx_min, my_min, mz_min;
int mx_max, my_max, mz_max;
int mx_offset, my_offset, mz_offset;

int mx_calibrated, my_calibrated, mz_calibrated;

// get min/max values by taking readings
// from magnetometer of your choice

mx_offset = (mx_min + mx_max)/2;
my_offset = (my_min + my_max)/2;
mz_offset = (mz_min + mz_max)/2;

mx_calibrated = mx - mx_offset;
my_calibrated = my - my_offset;
mz_calibrated = mz - mz_offset;

Soft iron biases makes the axial responses uneven which results in ellipsiod shape. An easier way to correct this is to rescale the axial readings to an average value.

int mx_scale, my_scale, mz_scale;

mx_scale = (mx_max - mx_min)/2;
my_scale = (my_max - my_min)/2;
mz_scale = (mz_max - mz_min)/2;

float avg_scale = (mx_scale + my_scale + mz_scale)/3;

mx_calibrated = avg_scale/(mx - mx_offset);
my_calibrated = avg_scale/(my - my_offset);
mz_calibrated = avg_scale/(mz - mz_offset);

We can caclulate these biases once and store them in our code so that we don't need to calibrate it everytime. We can also write an auto update function which will recalibrate offsets & scale for every new reading.

Django Tips & Tricks #9 - Auto Register Models In Admin


Inbuilt admin interface is one the most powerful & popular feature of Django. Once we create the models, we need to register them with admin, so that it can read metadata and populate interface for it.

If the django project has too many models or if it has a legacy database, then adding all those models to admin becomes a tedious task. To automate this process, we can programatically fetch all the models in the project and register them with admin.

from django.apps import apps


models = apps.get_models()

for model in models:
    admin.site.register(model)

This works well if we are just auto registering all the models. However if we try some customisations and try to register them in admin.py files in our apps, there will be conflicts as Django doesn't allow registering the same model twice.

So, we need to make sure this piece of code runs after all admin.py files are loaded and it should ignore models which are already registered. We can safely hook this code in appconfig.

from django.apps import apps, AppConfig
from django.contrib import admin


class CustomApp(AppConfig):
    name = 'foo'

    def ready(self):
        models = apps.get_models()
        for model in models:
            try:
                admin.site.register(model)
            except admin.sites.AlreadyRegistered:
                pass

Now all models will get registed automatically. If we go to a model page in admin, it will just show 1 column like this.

This is not informative for the users who want to see the data. We can create a ListAdminMixin, which will populate list_display with all the fields in the model. We can create a new admin class which will subclass ListAdminMixin & ModelAdmin. We can use this admin class when we are registering the model so that all the fields in the model will show up in the admin.

from django.apps import apps, AppConfig
from django.contrib import admin


class ListAdminMixin(object):
    def __init__(self, model, admin_site):
        self.list_display = [field.name for field in model._meta.fields if field.name != "id"]
        super(ListAdminMixin, self).__init__(model, admin_site)


class CustomApp(AppConfig):
    name = 'foo'

    def ready(self):
        models = apps.get_models()
        for model in models:
            admin_class = type('AdminClass', (ListAdminMixin, admin.ModelAdmin), {})
            try:
                admin.site.register(model, admin_class)
            except admin.sites.AlreadyRegistered:
                pass

Now whenever we create a new model or add a new field to an existing model, it will get reflected in the admin automatically.

How To Install Custom ROMs In Xiamo MiPad?


Mi Pad and other Xiamo devices has Mi UI OS which is a dual boot system. A major problem with this system is it has only ~600 MB of space in 1st partition. Because of this, we cannot install some custom ROMs as they need more space. In this article we will see how to merge both partitions to get more free space and install any custom ROM.

Install TWRP

Dowload latest recovery from twrp.me. Copy it to android device or push it to android using adb.

adb push -p twrp-3.1.1-0-mocha.img /sdcard/

Now put the device into fastboot mode by pressing Volume down & Power button simultanesouly when you switch it on. When in fastboot mode, flash the downloaded recovery file

sudo fastboot flash recovery twrp-3.1.1-0-mocha.img

Now we can go to recovery mode using adb.

adb reboot recovery

Merge partitions

Before installing custom ROM, we need to merge partitions so that we will have enough space to install ROM. If you are familiar with parted command, you can directly merge the partitions from terminal in TWRP recovery.

There is also a script which you can flash to do the partition. You can read this guide on mi forum for more information.

After partition is completed, from TWRP go to Wipe -> Advanced wipe -> Select System -> Click on Repair or change file system. Here it should show that free space in system is more than 1GB.

Install ROM

After partitions are merged, it is straight forward to install any custom ROM. Download a custom ROM like lineage or RR, push it to device and then install it from TWRP. After reboot, you will see the custom ROM booting.

Django Tips & Tricks #8 - Hyperlink Foreignkey Fields In Admin


Consider Book model which has Author as foreignkey.

from django.db import models


class Author(models.Model):
    name = models.CharField(max_length=100)

class Book(models.Model):
    title = models.CharField(max_length=100)
    author = models.ForeignKey(Author)

We can register these models with admin interface as follows.

from django.contrib import admin

from .models import Author, Book

class BookAdmin(admin.ModelAdmin):
    list_display = ('name', 'author', )

admin.site.register(Author)
admin.site.register(Book, BookAdmin)

Once they are registed, admin page shows Book model like this.

While browing books, to go to a particular author, we have to previous page, go to Author model and then find relevant author. This becomes tedious if we spend lot of time in admin. Instead, if author field has a hyperlink, we can directly go to its page.

Django provides an option to access admin views by its URL reversing system. For example, we can get add view of author model in book app from reverse("admin:book_author_add").

To hyperlink author field in book admin, get url from reversing book_author_change with its id and return required html.

class BookAdmin(admin.ModelAdmin):
    list_display = ('name', 'author_link', )

    def author_link(self, book):
        link = reverse("admin:book_author_change", args=[book.author.id])
        return u'<a href="%s">%s</a>' % (link, book.author.name)
    author_link.allow_tags = True
    author_link.short_description = 'Author'

Now in the book admin view, author field will be hyperlinked and we can visit just by clicking it.

Update:

Django has inbuilt option for this. It provides list_display_links, to control which fields should be linked to change page. So, we can just add author field to it.

from django.contrib import admin

from .models import Author, Book

class BookAdmin(admin.ModelAdmin):
    list_display = ('name', 'author', )
    list_display_links = ('name', 'author',)

admin.site.register(Author)

Now, author field will be hyperlinked to its change page.

How To Remove Clock From LockScreen/StatusBar?


Last year, I wrote a blog post on how to remove clock from lock screen and status bar if xposed is installed on your android device. You can also do this without xposed if you are using RR(Resurrection Remix) as it comes with a lot of inbuilt customization.

Remove Clock From LockScreen

To remove clock from lockscreen, go to Settings -> Configurations -> Lock screen -> Show lock screen lock.

Remove Time From StatusBar

To remove clock from status bar, go to Settings -> Configurations -> Status bar -> System UI tuner -> Time -> Dont show this icon.

Once you do this, you will have a clean lockscreen and statusbar without any date or time them.