fidzu : Python

Continuous Integration and Continuous Delivery. As an extensible automation server, *Jenkins* can be used as a simple CI server or turned into the continuous delivery hub for any project.

$ pytest --variables env-ALPHA.yml --splinter-webdriver firefox --splinter-screenshot-dir /tmp -x

pytest-play:
  base_url: https://plone-demo.info

{
    "steps": [
        {
            "comment": "visit base url",
            "type": "get",
            "url": "$base_url"
        },
        {
            "comment": "click on login link",
            "locator": {
                "type": "id",
                "value": "personaltools-login"
            },
            "type": "clickElement"
        },
        {
            "comment": "provide a username",
            "locator": {
                "type": "id",
                "value": "__ac_name"
            },
            "text": "$username",
            "type": "setElementText"
        },
        {
            "comment": "provide a password",
            "locator": {
                "type": "id",
                "value": "__ac_password"
            },
            "text": "$password",
            "type": "setElementText"
        },
        {
            "comment": "click on login submit button",
            "locator": {
                "type": "css",
                "value": ".pattern-modal-buttons > input[name=submit]"
            },
            "type": "clickElement"
        },
        {
            "comment": "wait for page loaded",
            "locator": {
                "type": "css",
                "value": ".icon-user"
            },
            "type": "waitForElementVisible"
        }
    ]
} 

[pytest]
markers =
    login
test_data =
    {"username": "siteadmin", "password": "siteadmin"}
    {"username": "editor", "password": "editor"}
    {"username": "reader", "password": "reader"}

$ pytest --variables env-ALPHA.yml --splinter-webdriver firefox --splinter-screenshot-dir /tmp -x

pytest-play:
  base_url: https://plone-demo.info

{
    "steps": [
        {
            "comment": "visit base url",
            "type": "get",
            "url": "$base_url"
        },
        {
            "comment": "click on login link",
            "locator": {
                "type": "id",
                "value": "personaltools-login"
            },
            "type": "clickElement"
        },
        {
            "comment": "provide a username",
            "locator": {
                "type": "id",
                "value": "__ac_name"
            },
            "text": "$username",
            "type": "setElementText"
        },
        {
            "comment": "provide a password",
            "locator": {
                "type": "id",
                "value": "__ac_password"
            },
            "text": "$password",
            "type": "setElementText"
        },
        {
            "comment": "click on login submit button",
            "locator": {
                "type": "css",
                "value": ".pattern-modal-buttons > input[name=submit]"
            },
            "type": "clickElement"
        },
        {
            "comment": "wait for page loaded",
            "locator": {
                "type": "css",
                "value": ".icon-user"
            },
            "type": "waitForElementVisible"
        }
    ]
} 

[pytest]
markers =
    login
test_data =
    {"username": "siteadmin", "password": "siteadmin"}
    {"username": "editor", "password": "editor"}
    {"username": "reader", "password": "reader"}

pip install Nikola
ARCHIVE_PATH
ARCHIVE_FILENAME
GLOBAL_CONTEXT['isso_config']
default_metadata
nikola new_post
nikola serve
?
published
pip install Nikola
ARCHIVE_PATH
ARCHIVE_FILENAME
GLOBAL_CONTEXT['isso_config']
default_metadata
nikola new_post
nikola serve
?
published
artist = Artist.objects.get(name="Bad Religion")
names = []
for song in Song.objects.filter(artist=artist):
    names.append(song.name)
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

SELECT "main_song"."id", "main_song"."artist_id", "main_song"."name", 
"main_song"."text", "main_song"."language", "main_song"."key_phrases", 
"main_song"."popularity", "main_song"."text_length", "main_song"."metadata", 
"main_song"."created", "main_song"."modified", 
"main_song"."has_lastfm_listeners", "main_song"."has_spotify_popularity" 
FROM "main_song" WHERE "main_song"."artist_id" = 22729;

artist = Artist.objects.get(name="Bad Religion")
names = []
for song in Song.objects.filter(artist=artist).only("name"):
    names.append(song.name)
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

SELECT "main_song"."id", "main_song"."name" 
FROM "main_song" WHERE "main_song"."artist_id" = 22729;

names = []
for song in Song.objects.filter(artist=a).values("name"):
    names.append(song["name"])
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

SELECT "main_song"."name" FROM "main_song" WHERE "main_song"."artist_id" = 22729;

names = []
for name in Song.objects.filter(artist=a).values_list("name", flat=True):
    names.append(name)
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

from songsearch.main.models import *

import hashlib

def f1(a):
    names = []
    for song in Song.objects.filter(artist=a):
        names.append(song.name)
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

def f2(a):
    names = []
    for song in Song.objects.filter(artist=a).only("name"):
        names.append(song.name)
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

def f3(a):
    names = []
    for song in Song.objects.filter(artist=a).values("name"):
        names.append(song["name"])
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

def f4(a):
    names = []
    for name in Song.objects.filter(artist=a).values_list("name", flat=True):
        names.append(name)
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

artist = Artist.objects.get(name="Bad Religion")
print(Song.objects.filter(artist=artist).count())

print(f1(artist) == f2(artist))
print(f2(artist) == f3(artist))
print(f3(artist) == f4(artist))

# Reporting
import time
import random
import statistics

functions = f1, f2, f3, f4
times = {f.__name__: [] for f in functions}

for i in range(500):
    func = random.choice(functions)
    t0 = time.time()
    func(artist)
    t1 = time.time()
    times[func.__name__].append((t1 - t0) * 1000)

for name in sorted(times):
    numbers = times[name]
    print("FUNCTION:", name, "Used", len(numbers), "times")
    print("\tBEST", min(numbers))
    print("\tMEDIAN", statistics.median(numbers))
    print("\tMEAN  ", statistics.mean(numbers))
    print("\tSTDEV ", statistics.stdev(numbers))

276
True
True
True
FUNCTION: f1 Used 135 times
    BEST 6.309986114501953
    MEDIAN 7.531881332397461
    MEAN   7.834429211086697
    STDEV  2.03779968066591
FUNCTION: f2 Used 135 times
    BEST 3.039121627807617
    MEDIAN 3.7298202514648438
    MEAN   4.012803678159361
    STDEV  1.8498943539073027
FUNCTION: f3 Used 110 times
    BEST 0.9920597076416016
    MEDIAN 1.4405250549316406
    MEAN   1.5053835782137783
    STDEV  0.3523240470133114
FUNCTION: f4 Used 120 times
    BEST 0.9369850158691406
    MEDIAN 1.3251304626464844
    MEAN   1.4017681280771892
    STDEV  0.3391019435930447

artist = Artist.objects.get(name="Bad Religion")
names = []
for song in Song.objects.filter(artist=artist):
    names.append(song.name)
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

SELECT "main_song"."id", "main_song"."artist_id", "main_song"."name", 
"main_song"."text", "main_song"."language", "main_song"."key_phrases", 
"main_song"."popularity", "main_song"."text_length", "main_song"."metadata", 
"main_song"."created", "main_song"."modified", 
"main_song"."has_lastfm_listeners", "main_song"."has_spotify_popularity" 
FROM "main_song" WHERE "main_song"."artist_id" = 22729;

artist = Artist.objects.get(name="Bad Religion")
names = []
for song in Song.objects.filter(artist=artist).only("name"):
    names.append(song.name)
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

SELECT "main_song"."id", "main_song"."name" 
FROM "main_song" WHERE "main_song"."artist_id" = 22729;

names = []
for song in Song.objects.filter(artist=a).values("name"):
    names.append(song["name"])
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

SELECT "main_song"."name" FROM "main_song" WHERE "main_song"."artist_id" = 22729;

names = []
for name in Song.objects.filter(artist=a).values_list("name", flat=True):
    names.append(name)
return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

from songsearch.main.models import *

import hashlib

def f1(a):
    names = []
    for song in Song.objects.filter(artist=a):
        names.append(song.name)
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

def f2(a):
    names = []
    for song in Song.objects.filter(artist=a).only("name"):
        names.append(song.name)
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

def f3(a):
    names = []
    for song in Song.objects.filter(artist=a).values("name"):
        names.append(song["name"])
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

def f4(a):
    names = []
    for name in Song.objects.filter(artist=a).values_list("name", flat=True):
        names.append(name)
    return hashlib.md5("".join(names).encode("utf-8")).hexdigest()

artist = Artist.objects.get(name="Bad Religion")
print(Song.objects.filter(artist=artist).count())

print(f1(artist) == f2(artist))
print(f2(artist) == f3(artist))
print(f3(artist) == f4(artist))

# Reporting
import time
import random
import statistics

functions = f1, f2, f3, f4
times = {f.__name__: [] for f in functions}

for i in range(500):
    func = random.choice(functions)
    t0 = time.time()
    func(artist)
    t1 = time.time()
    times[func.__name__].append((t1 - t0) * 1000)

for name in sorted(times):
    numbers = times[name]
    print("FUNCTION:", name, "Used", len(numbers), "times")
    print("\tBEST", min(numbers))
    print("\tMEDIAN", statistics.median(numbers))
    print("\tMEAN  ", statistics.mean(numbers))
    print("\tSTDEV ", statistics.stdev(numbers))

276
True
True
True
FUNCTION: f1 Used 135 times
    BEST 6.309986114501953
    MEDIAN 7.531881332397461
    MEAN   7.834429211086697
    STDEV  2.03779968066591
FUNCTION: f2 Used 135 times
    BEST 3.039121627807617
    MEDIAN 3.7298202514648438
    MEAN   4.012803678159361
    STDEV  1.8498943539073027
FUNCTION: f3 Used 110 times
    BEST 0.9920597076416016
    MEDIAN 1.4405250549316406
    MEAN   1.5053835782137783
    STDEV  0.3523240470133114
FUNCTION: f4 Used 120 times
    BEST 0.9369850158691406
    MEDIAN 1.3251304626464844
    MEAN   1.4017681280771892
    STDEV  0.3391019435930447

8,7,1,6,9  

7,8,1,6,9  

7,1,8,6,9  

7,1,6,8,9  

    def bub_sort_datachange(self):
        end = None
        while end != self.start_node:
            p = self.start_node
            while p.ref != end:
                q = p.ref
                if p.item > q.item:
                    p.item, q.item = q.item, p.item
                p = p.ref
            end = p

10,45,65,35,1  

    def bub_sort_linkchange(self):
        end = None
        while end != self.start_node:
            r = p = self.start_node
            while p.ref != end:
                q = p.ref
                if p.item > q.item:
                    p.ref = q.ref
                    q.ref = p
                    if p != self.start_node:
                        r.ref = q
                    else:
                        self.start_node = q
                    p,q = q,p
                r = p
                p = p.ref
            end = p

10,45,65,  

5,15,35,68  

p = 10  
q = 5  
em = none  
newlist = none  

p = 10  
q = 15  
em = 5  
newlist = 5  

p = 45  
q = 15  
em = 10  
newlist = 5, 10  

p = 45  
q = 35  
em = 15  
newlist = 5, 10, 15  

p = 45  
q = 68  
em = 35  
newlist = 5, 10, 15, 35  

p = 65  
q = 68  
em = 45  
newlist = 5, 10, 15, 35, 45  

p = None  
q = 68  
em = 65  
newlist = 5, 10, 15, 35, 45, 65  

p = None  
q = None  
em = 68  
newlist = 5, 10, 15, 35, 45, 65, 68  

    def merge_helper(self, list2):
        merged_list = LinkedList()
        merged_list.start_node = self.merge_by_newlist(self.start_node, list2.start_node)
        return merged_list

    def merge_by_newlist(self, p, q):
        if p.item <= q.item:
            startNode = Node(p.item)
            p = p.ref
        else:
            startNode = Node(q.item)
            q = q.ref

        em = startNode

        while p is not None and q is not None:
            if p.item <= q.item:
                em.ref = Node(p.item)
                p = p.ref
            else:
                em.ref = Node(q.item)
                q = q.ref
            em = em.ref

        while p is not None:
            em.ref = Node(p.item)
            p = p.ref
            em = em.ref

        while q is not None:
            em.ref = Node(q.item)
            q = q.ref
            em = em.ref

        return startNode

10,45,65,  

5,15,35,68  

p = 10  
q = 5  
em = none  
newlist = none  

p = 10  
q = 15  
start = 5  
em = start  

p = 45  
q = 15  
em = 10  

p = 45  
q = 35  
em = 15  

p = 45  
q = 68  
em = 35  

p = 65  
q = 68  
em = 45  
newlist = 5, 10, 15, 35, 45  

p = None  
q = 68  
em = 65  
newlist = 5, 10, 15, 35, 45, 65  

p = None  
q = None  
em = 68  
newlist = 5, 10, 15, 35, 45, 65, 68  

    def merge_helper2(self, list2):
        merged_list = LinkedList()
        merged_list.start_node = self.merge_by_linkChange(self.start_node, list2.start_node)
        return merged_list

    def merge_by_linkChange(self, p, q):
        if p.item <= q.item:
            startNode = Node(p.item)
            p = p.ref
        else:
            startNode = Node(q.item)
            q = q.ref

        em = startNode

        while p is not None and q is not None:
            if p.item <= q.item:
                em.ref = Node(p.item)
                em = em.ref
                p = p.ref
            else:
                em.ref = Node(q.item)
                em = em.ref
                q = q.ref

        if p is None:
            em.ref = q
        else:
            em.ref = p

        return startNode

new_linked_list1 = LinkedList()  
new_linked_list1.make_new_list()  

How many nodes do you want to create: 4  
Enter the value for the node:12  
Enter the value for the node:45  
Enter the value for the node:32  
Enter the value for the node:61  

new_linked_list2 = LinkedList()  
new_linked_list2.make_new_list()  

How many nodes do you want to create: 4  
Enter the value for the node:36  
Enter the value for the node:41  
Enter the value for the node:25  
Enter the value for the node:9  

new_linked_list1. bub_sort_datachange()  
new_linked_list2. bub_sort_datachange()  

list3 = new_linked_list1.merge_helper2(new_linked_list2)  

list3.traverse_list()  

9  
12  
25  
32  
36  
41  
45  
61  

8,7,1,6,9  

7,8,1,6,9  

7,1,8,6,9  

7,1,6,8,9  

    def bub_sort_datachange(self):
        end = None
        while end != self.start_node:
            p = self.start_node
            while p.ref != end:
                q = p.ref
                if p.item > q.item:
                    p.item, q.item = q.item, p.item
                p = p.ref
            end = p

10,45,65,35,1  

    def bub_sort_linkchange(self):
        end = None
        while end != self.start_node:
            r = p = self.start_node
            while p.ref != end:
                q = p.ref
                if p.item > q.item:
                    p.ref = q.ref
                    q.ref = p
                    if p != self.start_node:
                        r.ref = q
                    else:
                        self.start_node = q
                    p,q = q,p
                r = p
                p = p.ref
            end = p

10,45,65,  

5,15,35,68  

p = 10  
q = 5  
em = none  
newlist = none  

p = 10  
q = 15  
em = 5  
newlist = 5  

p = 45  
q = 15  
em = 10  
newlist = 5, 10  

p = 45  
q = 35  
em = 15  
newlist = 5, 10, 15  

p = 45  
q = 68  
em = 35  
newlist = 5, 10, 15, 35  

p = 65  
q = 68  
em = 45  
newlist = 5, 10, 15, 35, 45  

p = None  
q = 68  
em = 65  
newlist = 5, 10, 15, 35, 45, 65  

p = None  
q = None  
em = 68  
newlist = 5, 10, 15, 35, 45, 65, 68  

    def merge_helper(self, list2):
        merged_list = LinkedList()
        merged_list.start_node = self.merge_by_newlist(self.start_node, list2.start_node)
        return merged_list

    def merge_by_newlist(self, p, q):
        if p.item <= q.item:
            startNode = Node(p.item)
            p = p.ref
        else:
            startNode = Node(q.item)
            q = q.ref

        em = startNode

        while p is not None and q is not None:
            if p.item <= q.item:
                em.ref = Node(p.item)
                p = p.ref
            else:
                em.ref = Node(q.item)
                q = q.ref
            em = em.ref

        while p is not None:
            em.ref = Node(p.item)
            p = p.ref
            em = em.ref

        while q is not None:
            em.ref = Node(q.item)
            q = q.ref
            em = em.ref

        return startNode

10,45,65,  

5,15,35,68  

p = 10  
q = 5  
em = none  
newlist = none  

p = 10  
q = 15  
start = 5  
em = start  

p = 45  
q = 15  
em = 10  

p = 45  
q = 35  
em = 15  

p = 45  
q = 68  
em = 35  

p = 65  
q = 68  
em = 45  
newlist = 5, 10, 15, 35, 45  

p = None  
q = 68  
em = 65  
newlist = 5, 10, 15, 35, 45, 65  

p = None  
q = None  
em = 68  
newlist = 5, 10, 15, 35, 45, 65, 68  

    def merge_helper2(self, list2):
        merged_list = LinkedList()
        merged_list.start_node = self.merge_by_linkChange(self.start_node, list2.start_node)
        return merged_list

    def merge_by_linkChange(self, p, q):
        if p.item <= q.item:
            startNode = Node(p.item)
            p = p.ref
        else:
            startNode = Node(q.item)
            q = q.ref

        em = startNode

        while p is not None and q is not None:
            if p.item <= q.item:
                em.ref = Node(p.item)
                em = em.ref
                p = p.ref
            else:
                em.ref = Node(q.item)
                em = em.ref
                q = q.ref

        if p is None:
            em.ref = q
        else:
            em.ref = p

        return startNode

new_linked_list1 = LinkedList()  
new_linked_list1.make_new_list()  

How many nodes do you want to create: 4  
Enter the value for the node:12  
Enter the value for the node:45  
Enter the value for the node:32  
Enter the value for the node:61  

new_linked_list2 = LinkedList()  
new_linked_list2.make_new_list()  

How many nodes do you want to create: 4  
Enter the value for the node:36  
Enter the value for the node:41  
Enter the value for the node:25  
Enter the value for the node:9  

new_linked_list1. bub_sort_datachange()  
new_linked_list2. bub_sort_datachange()  

list3 = new_linked_list1.merge_helper2(new_linked_list2)  

list3.traverse_list()  

9  
12  
25  
32  
36  
41  
45  
61  

import six
import unittest

class MyTestCase(unittest.TestCase):

    @unittest.skipIf(six.PY3, "not compatible with Python 3")
    def test_example(self):
        # This test won't run under Python 3
        pass

import six
import unittest

class MyTestCase(unittest.TestCase):

    @unittest.skipIf(six.PY3, "not compatible with Python 3")
    def test_example(self):
        # This test won't run under Python 3
        pass

$ curl --version

$ curl --version

$ pip --version

$ curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py

$ python get-pip.py

$ pip --version

$ pip install pyinstaller

$ pyinstaller --version

$ conda install -c conda-forge pyinstaller
$ conda install -c anaconda pywin32

$ python name_of_your_file.py

$ pyinstaller --onefile <file_name>.py

$ curl --version

$ curl --version

$ pip --version

$ curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py

$ python get-pip.py

$ pip --version

$ pip install pyinstaller

$ pyinstaller --version

$ conda install -c conda-forge pyinstaller
$ conda install -c anaconda pywin32

$ python name_of_your_file.py

$ pyinstaller --onefile <file_name>.py

pip install matplotlib==2.2.3
pip install scipy==1.1.0

pip install pillow==5.2.0

import matplotlib
matplotlib.__version__

'3.0.2'

import PIL
PIL.__version__

'5.3.0'

import scipy
scipy.__version__

'1.1.0'

from matplotlib import image as img

image = img.imread('./dataquest.jpg')
image.shape

(200, 200, 3)

%matplotlib inline

from matplotlib import pyplot as plt

plt.imshow(image)
plt.show()

r = []
g = []
b = []

for line in image:
    for pixel in line:
        temp_r, temp_g, temp_b = pixel
        r.append(temp_r)
        g.append(temp_g)
        b.append(temp_b)

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(r, g, b)
plt.show()

import pandas as pd

df = pd.DataFrame({'red': r,
             'blue': b,
             'green': g})

from scipy.cluster.vq import whiten

df['scaled_red'] = whiten(df['red'])
df['scaled_blue'] = whiten(df['blue'])
df['scaled_green'] = whiten(df['green'])
df.sample(n = 10)

from scipy.cluster.vq import kmeans

cluster_centers, distortion = kmeans(df[['scaled_red', 'scaled_green', 'scaled_blue']], 2)

print(cluster_centers)

[[2.94579782 3.1243935  3.52525635]
 [0.91860119 1.05099931 1.4465091 ]]

colors = []

r_std, g_std, b_std = df[['red', 'green', 'blue']].std()

for cluster_center in cluster_centers:
    scaled_r, scaled_g, scaled_b = cluster_center
    colors.append((
        scaled_r * r_std / 255,
        scaled_g * g_std / 255,
        scaled_b * b_std / 255
    ))
plt.imshow([colors])
plt.show()

pip install matplotlib==2.2.3
pip install scipy==1.1.0

pip install pillow==5.2.0

import matplotlib
matplotlib.__version__

'3.0.2'

import PIL
PIL.__version__

'5.3.0'

import scipy
scipy.__version__

'1.1.0'

from matplotlib import image as img

image = img.imread('./dataquest.jpg')
image.shape

(200, 200, 3)

%matplotlib inline

from matplotlib import pyplot as plt

plt.imshow(image)
plt.show()

r = []
g = []
b = []

for line in image:
    for pixel in line:
        temp_r, temp_g, temp_b = pixel
        r.append(temp_r)
        g.append(temp_g)
        b.append(temp_b)

from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
ax.scatter(r, g, b)
plt.show()

import pandas as pd

df = pd.DataFrame({'red': r,
             'blue': b,
             'green': g})

from scipy.cluster.vq import whiten

df['scaled_red'] = whiten(df['red'])
df['scaled_blue'] = whiten(df['blue'])
df['scaled_green'] = whiten(df['green'])
df.sample(n = 10)

from scipy.cluster.vq import kmeans

cluster_centers, distortion = kmeans(df[['scaled_red', 'scaled_green', 'scaled_blue']], 2)

print(cluster_centers)

[[2.94579782 3.1243935  3.52525635]
 [0.91860119 1.05099931 1.4465091 ]]

colors = []

r_std, g_std, b_std = df[['red', 'green', 'blue']].std()

for cluster_center in cluster_centers:
    scaled_r, scaled_g, scaled_b = cluster_center
    colors.append((
        scaled_r * r_std / 255,
        scaled_g * g_std / 255,
        scaled_b * b_std / 255
    ))
plt.imshow([colors])
plt.show()

new_list = []  
for n in range(0, 10):  
    if n % 2 == 0:
        new_list.append(n**2)

new_list = [ n**2 for n in range(0,10) if n%2 == 0]  

import timeit

def for_square(n):  
    new_list = []
    for i in range(0, n):
        if i % 2 == 0:
            new_list.append(n**2)
    return new_list

def list_comp_square(n):  
    return [i**2 for i in range(0, n) if i % 2 == 0]

print("Time taken by For Loop: {}".format(timeit.timeit('for_square(10)', 'from __main__ import for_square')))

print("Time taken by List Comprehension: {}".format(timeit.timeit('list_comp_square(10)', 'from __main__ import list_comp_square')))  

$ python for-vs-lc.py 
Time taken by For Loop: 2.56907987595  
Time taken by List Comprehension: 2.01556396484  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 2.37083697319  
Time taken by List Comprehension: 1.94110512733  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 2.52163410187  
Time taken by List Comprehension: 1.96427607536  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 2.44279003143  
Time taken by List Comprehension: 2.16282701492  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 2.63641500473  
Time taken by List Comprehension: 1.90950393677  

$ python for-vs-lc.py 
Time taken by For Loop: 16.0991549492  
Time taken by List Comprehension: 13.9700510502  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 16.6425571442  
Time taken by List Comprehension: 13.4352738857  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 16.2476081848  
Time taken by List Comprehension: 13.2488780022  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 15.9152050018  
Time taken by List Comprehension: 13.3579590321  

$ python
Python 2.7.10 (default, Oct 23 2015, 19:19:21)  
[GCC 4.2.1 Compatible Apple LLVM 7.0.0 (clang-700.0.59.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.  
>>> import sys
>>> 
>>> r = range(1000000)
>>> x = xrange(1000000)
>>> 
>>> print(sys.getsizeof(r))
8000072  
>>> 
>>> print(sys.getsizeof(x))
40  
>>> 
>>> print(type(r))
<type 'list'>  
>>> print(type(x))
<type 'xrange'>  

import timeit

r = range(1000000)  
x = xrange(1000000)

def lookup_range():  
    return r[999999]

def lookup_xrange():  
    return x[999999]

print("Look up time in Range: {}".format(timeit.timeit('lookup_range()', 'from __main__ import lookup_range')))

print("Look up time in Xrange: {}".format(timeit.timeit('lookup_xrange()', 'from __main__ import lookup_xrange')))  

$ python range-vs-xrange.py 
Look up time in Range: 0.0959858894348  
Look up time in Xrange: 0.140854120255  
$ 
$ python range-vs-xrange.py 
Look up time in Range: 0.111716985703  
Look up time in Xrange: 0.130584001541  
$ 
$ python range-vs-xrange.py 
Look up time in Range: 0.110965013504  
Look up time in Xrange: 0.133008003235  
$ 
$ python range-vs-xrange.py 
Look up time in Range: 0.102388143539  
Look up time in Xrange: 0.133061170578  

import timeit

# here we create a new list and add the elements one by one
# while checking for duplicates
def manual_remove_duplicates(list_of_duplicates):  
    new_list = []
    [new_list.append(n) for n in list_of_duplicates if n not in new_list]
    return new_list

# using a set is as simple as
def set_remove_duplicates(list_of_duplicates):  
    return list(set(list_of_duplicates))

list_of_duplicates = [10, 54, 76, 10, 54, 100, 1991, 6782, 1991, 1991, 64, 10]

print("Manually removing duplicates takes {}s".format(timeit.timeit('manual_remove_duplicates(list_of_duplicates)', 'from __main__ import manual_remove_duplicates, list_of_duplicates')))

print("Using Set to remove duplicates takes {}s".format(timeit.timeit('set_remove_duplicates(list_of_duplicates)', 'from __main__ import set_remove_duplicates, list_of_duplicates')))  

$ python sets-vs-lists.py 
Manually removing duplicates takes 2.64614701271s  
Using Set to remove duplicates takes 2.23225092888s  
$ 
$ python sets-vs-lists.py 
Manually removing duplicates takes 2.65356898308s  
Using Set to remove duplicates takes 1.1165189743s  
$ 
$ python sets-vs-lists.py 
Manually removing duplicates takes 2.53129696846s  
Using Set to remove duplicates takes 1.15646100044s  
$ 
$ python sets-vs-lists.py 
Manually removing duplicates takes 2.57102680206s  
Using Set to remove duplicates takes 1.13189387321s  
$ 
$ python sets-vs-lists.py 
Manually removing duplicates takes 2.48338890076s  
Using Set to remove duplicates takes 1.20611810684s  

import timeit

# create a list of 1000 words
list_of_words = ["foo "] * 1000

def using_join(list_of_words):  
    return "".join(list_of_words)

def using_concat_operator(list_of_words):  
    final_string = ""
    for i in list_of_words:
        final_string += i
    return final_string

print("Using join() takes {} s".format(timeit.timeit('using_join(list_of_words)', 'from __main__ import using_join, list_of_words')))

print("Using += takes {} s".format(timeit.timeit('using_concat_operator(list_of_words)', 'from __main__ import using_concat_operator, list_of_words')))  

$ python join-vs-concat.py 
Using join() takes 14.0949640274 s  
Using += takes 79.5631570816 s  
$ 
$ python join-vs-concat.py 
Using join() takes 13.3542580605 s  
Using += takes 76.3233859539 s  

new_list = []  
for n in range(0, 10):  
    if n % 2 == 0:
        new_list.append(n**2)

new_list = [ n**2 for n in range(0,10) if n%2 == 0]  

import timeit

def for_square(n):  
    new_list = []
    for i in range(0, n):
        if i % 2 == 0:
            new_list.append(n**2)
    return new_list

def list_comp_square(n):  
    return [i**2 for i in range(0, n) if i % 2 == 0]

print("Time taken by For Loop: {}".format(timeit.timeit('for_square(10)', 'from __main__ import for_square')))

print("Time taken by List Comprehension: {}".format(timeit.timeit('list_comp_square(10)', 'from __main__ import list_comp_square')))  

$ python for-vs-lc.py 
Time taken by For Loop: 2.56907987595  
Time taken by List Comprehension: 2.01556396484  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 2.37083697319  
Time taken by List Comprehension: 1.94110512733  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 2.52163410187  
Time taken by List Comprehension: 1.96427607536  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 2.44279003143  
Time taken by List Comprehension: 2.16282701492  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 2.63641500473  
Time taken by List Comprehension: 1.90950393677  

$ python for-vs-lc.py 
Time taken by For Loop: 16.0991549492  
Time taken by List Comprehension: 13.9700510502  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 16.6425571442  
Time taken by List Comprehension: 13.4352738857  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 16.2476081848  
Time taken by List Comprehension: 13.2488780022  
$ 
$ python for-vs-lc.py 
Time taken by For Loop: 15.9152050018  
Time taken by List Comprehension: 13.3579590321  

$ python
Python 2.7.10 (default, Oct 23 2015, 19:19:21)  
[GCC 4.2.1 Compatible Apple LLVM 7.0.0 (clang-700.0.59.5)] on darwin
Type "help", "copyright", "credits" or "license" for more information.  
>>> import sys
>>> 
>>> r = range(1000000)
>>> x = xrange(1000000)
>>> 
>>> print(sys.getsizeof(r))
8000072  
>>> 
>>> print(sys.getsizeof(x))
40  
>>> 
>>> print(type(r))
<type 'list'>  
>>> print(type(x))
<type 'xrange'>  

import timeit

r = range(1000000)  
x = xrange(1000000)

def lookup_range():  
    return r[999999]

def lookup_xrange():  
    return x[999999]

print("Look up time in Range: {}".format(timeit.timeit('lookup_range()', 'from __main__ import lookup_range')))

print("Look up time in Xrange: {}".format(timeit.timeit('lookup_xrange()', 'from __main__ import lookup_xrange')))  

$ python range-vs-xrange.py 
Look up time in Range: 0.0959858894348  
Look up time in Xrange: 0.140854120255  
$ 
$ python range-vs-xrange.py 
Look up time in Range: 0.111716985703  
Look up time in Xrange: 0.130584001541  
$ 
$ python range-vs-xrange.py 
Look up time in Range: 0.110965013504  
Look up time in Xrange: 0.133008003235  
$ 
$ python range-vs-xrange.py 
Look up time in Range: 0.102388143539  
Look up time in Xrange: 0.133061170578  

import timeit

# here we create a new list and add the elements one by one
# while checking for duplicates
def manual_remove_duplicates(list_of_duplicates):  
    new_list = []
    [new_list.append(n) for n in list_of_duplicates if n not in new_list]
    return new_list

# using a set is as simple as
def set_remove_duplicates(list_of_duplicates):  
    return list(set(list_of_duplicates))

list_of_duplicates = [10, 54, 76, 10, 54, 100, 1991, 6782, 1991, 1991, 64, 10]

print("Manually removing duplicates takes {}s".format(timeit.timeit('manual_remove_duplicates(list_of_duplicates)', 'from __main__ import manual_remove_duplicates, list_of_duplicates')))

print("Using Set to remove duplicates takes {}s".format(timeit.timeit('set_remove_duplicates(list_of_duplicates)', 'from __main__ import set_remove_duplicates, list_of_duplicates')))  

$ python sets-vs-lists.py 
Manually removing duplicates takes 2.64614701271s  
Using Set to remove duplicates takes 2.23225092888s  
$ 
$ python sets-vs-lists.py 
Manually removing duplicates takes 2.65356898308s  
Using Set to remove duplicates takes 1.1165189743s  
$ 
$ python sets-vs-lists.py 
Manually removing duplicates takes 2.53129696846s  
Using Set to remove duplicates takes 1.15646100044s  
$ 
$ python sets-vs-lists.py 
Manually removing duplicates takes 2.57102680206s  
Using Set to remove duplicates takes 1.13189387321s  
$ 
$ python sets-vs-lists.py 
Manually removing duplicates takes 2.48338890076s  
Using Set to remove duplicates takes 1.20611810684s  

import timeit

# create a list of 1000 words
list_of_words = ["foo "] * 1000

def using_join(list_of_words):  
    return "".join(list_of_words)

def using_concat_operator(list_of_words):  
    final_string = ""
    for i in list_of_words:
        final_string += i
    return final_string

print("Using join() takes {} s".format(timeit.timeit('using_join(list_of_words)', 'from __main__ import using_join, list_of_words')))

print("Using += takes {} s".format(timeit.timeit('using_concat_operator(list_of_words)', 'from __main__ import using_concat_operator, list_of_words')))  

$ python join-vs-concat.py 
Using join() takes 14.0949640274 s  
Using += takes 79.5631570816 s  
$ 
$ python join-vs-concat.py 
Using join() takes 13.3542580605 s  
Using += takes 76.3233859539 s  

>>> from pybites import Bob, Julian

Keep Calm and Code in Python!

>>> from pybites import Bob, Julian

Keep Calm and Code in Python!

	red	blue	green	scaled_red	scaled_blue	scaled_green
24888	255	255	255	3.068012	3.590282	3.170015
38583	255	255	255	3.068012	3.590282	3.170015
2659	67	91	72	0.806105	1.281238	0.895063
36954	255	255	255	3.068012	3.590282	3.170015
39967	255	255	255	3.068012	3.590282	3.170015
13851	75	101	82	0.902357	1.422033	1.019377
14354	75	101	82	0.902357	1.422033	1.019377
18613	75	100	82	0.902357	1.407954	1.019377
6719	75	100	82	0.902357	1.407954	1.019377
14299	71	104	82	0.854231	1.464272	1.019377

	red	blue	green	scaled_red	scaled_blue	scaled_green
24888	255	255	255	3.068012	3.590282	3.170015
38583	255	255	255	3.068012	3.590282	3.170015
2659	67	91	72	0.806105	1.281238	0.895063
36954	255	255	255	3.068012	3.590282	3.170015
39967	255	255	255	3.068012	3.590282	3.170015
13851	75	101	82	0.902357	1.422033	1.019377
14354	75	101	82	0.902357	1.422033	1.019377
18613	75	100	82	0.902357	1.407954	1.019377
6719	75	100	82	0.902357	1.407954	1.019377
14299	71	104	82	0.854231	1.464272	1.019377


Aber wenigstens scheinen die ihren Spaß zu haben ;) - Wie auch wir denn gücklicher Weise mussten wir nie länger als 10 min. warten.

fidzu : Python

22 Feb 2019

Test automation goals: ROI

Test automation solution features and characteristics

Jenkins

Python for testing

Why Python for testing

Why pytest

Pytest plugins

Pytest + Jenkins together

Target environment (ENVIRONMENT)

Browser settings (BROWSER)

OS settings (OS)

Resolution settings (RESOLUTION)

Select tests by names expressions (KEYWORDS)

Select tests to be executed by tag expressions (MARKERS)

Test management tool integration (TESTRAIL_ENABLE)

Enable debug mode (DEBUG)

Block on first failure (BLOCK_FIRST_FAILURE)

Parallel test executions (PARALLEL_SESSIONS)

Switch from different selenium grid providers (SELENIUM_GRID_URL)

Repeat test execution for a given amount of times (COUNT)

Enable random test ordering execution (RANDOM_ENABLE)

Specify a random seed (RANDOM_SEED)

Play option (PLAY)

How to create a pytest project

Conclusions