DEV Community: Maria Boldyreva

Accessibility For Beginners with HTML and CSS

Maria Boldyreva — Wed, 01 Aug 2018 12:17:12 +0000

This is a post for my #100DaysOfCode Day 2 based on freeCodeCamp material I learned.

Websites should be open and accessible to everyone, regardless of a user's abilities, but often the opposite happens. Here's a quick cheat sheet on improving a web site's accessibility using HTML and CSS.

1. Images should have alternative text when their contents are not obvious from reading the text

<img src="logo.jpeg" alt="Company logo">

2. h1 - h6 text are important for screen readers, keep their order

<!-- Don't do this -->
<h1>A header</h1>
<h4>A smaller header</h4>
<!-- Do this -->
<h1>A header</h1>
<h2>A smaller header</h2>

3. Give structure to your page by using main, header, footer, nav, article, section, and audio

<header>
  <h1>The header!</h1>
</header>
<main>The document body</main>
<footer></footer>

4. Use article element for blog entries, forum posts, or news articles

<div> - groups content
<section> - groups related content
<article> - groups independent, self-contained content

5. Use both visual and auditory content
So users with visual and/or auditory impairments could access it.

6. Use the figure element for charts

<figure>
  <img src="your_chart.jpeg" alt="Short description of the chart">
  <br>
  <figcaption>
    Description of the chart.
  </figcaption>
</figure>

7. Use label elements with inputs

<label for="name">Name:</label>
<input type="text" id="name" name="name">

8. Group radio buttons in fieldsets

<fieldset>
  <legend>Choose one of these three items:</legend>
  <input id="one" type="radio" name="items" value="one">
  <label for="one">Choice One</label><br>
  <input id="two" type="radio" name="items" value="two">
  <label for="two">Choice Two</label><br>
  <input id="three" type="radio" name="items" value="three">
  <label for="three">Choice Three</label>
</fieldset>

9. Use date fields with a picker

<label for="input1">Enter a date:</label>
<input type="date" id="input1" name="input1">

10. Standartize time with time element
This element's datetime attribute is the value accessed by assistive devices. It helps avoid confusion by stating a standardized version of a time, even if it's written in an informal or colloquial manner in the text.

<time datetime="2013-02-13">last Wednesday</time>

11. Make some content visible only for screen readers
Like this:

.sr-only {
  position: absolute;
  left: -10000px;
  width: 1px;
  height: 1px;
  top: auto;
  overflow: hidden;
}

Don't use:

display: none; or visibility: hidden; they hide content for everyone, including screen reader users
zero values for pixel sizes, such as width: 0px; height: 0px; it removes that element from the flow of your document, screen readers will ignore it

12. Use high contrast text
The Web Content Accessibility Guidelines (WCAG) recommend at least a 4.5 to 1 contrast ratio for normal text. The ratio is calculated by comparing the relative luminance values of two colors. This ranges from 1:1 for the same color, or no contrast, to 21:1 for white against black. Foreground and background colors need sufficient contrast so colorblind users can distinguish them.

13. Use descriptive link text

This:
<a href="">information about computers</a>
is better than this:
<a href="">Click here</a>

14. Use access keys for important links

<button accesskey="b">Important Button</button>

15. Use tabindex to add keyboard focus
Links and form controls automatically get keyboard focus when a user tabs through a page. This functionality can be given to any other element by using a tabindex="n" on them, where n is not negative.

<a href="" tabindex="1">First accessed link</a>
<a href="" tabindex="2">Second accessed link</a>

Photo by Rodion Kutsaev on Unsplash

Else clause for Python's for loop

Maria Boldyreva — Fri, 13 Jul 2018 01:57:33 +0000

Photo by Kelly Sikkema on Unsplash

Not everyone knows, but there is an "else" clause for For loops.
I often see this:

did_something = False
for element in elements:
    if element.something:
        do_something()
        did_something = True
        break

if not did_something:
    do_something_else()

But it should be like this:

for element in elements:
    if element.something:
        do_something()
        break
else:
    do_something_else()

Else clause executes only if the for loop exits naturally, without any break statements. That simple.

Python: unicode characters by name

Maria Boldyreva — Thu, 12 Jul 2018 00:57:18 +0000

Hey all 🤘!
Did you know you can display unicode characters in python by their name? Like this:

>>> '\N{SIGN OF THE HORNS}'
'🤘'
>>> '\N{KANGXI RADICAL HAND}'
'⼿'
>>> '\N{VICTORY HAND}'
'✌'

I didn't.
Docs

If you want to change the world, start off by making your bed

Maria Boldyreva — Wed, 11 Jul 2018 10:34:10 +0000

I'm reposting for you a great video Alex Gwartney posted here. This retiring Admiral and Navy Seal trainer delivers one of the most inspiring and uplifting speeches you will ever hear.

Dijkstra's algorithm in python: algorithms for beginners

Maria Boldyreva — Tue, 10 Jul 2018 06:31:13 +0000

Photo by Ishan @seefromthesky on Unsplash

Dijkstra's algorithm can find for you the shortest path between two nodes on a graph. It's a must-know for any programmer. There are nice gifs and history in its Wikipedia page.

In this post I'll use the time-tested implementation from Rosetta Code changed just a bit for being able to process weighted and unweighted graph data, also, we'll be able to edit the graph on the fly. I'll explain the code block by block.

The algorithm

The algorithm is pretty simple. Dijkstra created it in 20 minutes, now you can learn to code it in the same time.

Mark all nodes unvisited and store them.
Set the distance to zero for our initial node and to infinity for other nodes.
Select the unvisited node with the smallest distance, it's current node now.
Find unvisited neighbors for the current node and calculate their distances through the current node. Compare the newly calculated distance to the assigned and save the smaller one. For example, if the node A has a distance of 6, and the A-B edge has length 2, then the distance to B through A will be 6 + 2 = 8. If B was previously marked with a distance greater than 8 then change it to 8.
Mark the current node as visited and remove it from the unvisited set.
Stop, if the destination node has been visited (when planning a route between two specific nodes) or if the smallest distance among the unvisited nodes is infinity. If not, repeat steps 3-6.

Python implementation

First, imports and data formats. The original implementations suggests using namedtuple for storing edge data. We'll do exactly that, but we'll add a default value to the cost argument. There are many ways to do that, find what suits you best.

from collections import deque, namedtuple


# we'll use infinity as a default distance to nodes.
inf = float('inf')
Edge = namedtuple('Edge', 'start, end, cost')


def make_edge(start, end, cost=1):
    return Edge(start, end, cost)

Let's initialize our data:

class Graph:
    def __init__(self, edges):
        # let's check that the data is right
        wrong_edges = [i for i in edges if len(i) not in [2, 3]]
        if wrong_edges:
            raise ValueError('Wrong edges data: {}'.format(wrong_edges))

        self.edges = [make_edge(*edge) for edge in edges]

Let's find the vertices. In the original implementation the vertices are defined in the _ _ init _ _, but we'll need them to update when edges change, so we'll make them a property, they'll be recounted each time we address the property. Probably not the best solution for big graphs, but for small ones it'll go.

    @property
    def vertices(self):
        return set(
            # this piece of magic turns ([1,2], [3,4]) into [1, 2, 3, 4]
            # the set above makes it's elements unique.
            sum(
                ([edge.start, edge.end] for edge in self.edges), []
            )
        )

Now, let's add adding and removing functionality.

    def get_node_pairs(self, n1, n2, both_ends=True):
        if both_ends:
            node_pairs = [[n1, n2], [n2, n1]]
        else:
            node_pairs = [[n1, n2]]
        return node_pairs

    def remove_edge(self, n1, n2, both_ends=True):
        node_pairs = self.get_node_pairs(n1, n2, both_ends)
        edges = self.edges[:]
        for edge in edges:
            if [edge.start, edge.end] in node_pairs:
                self.edges.remove(edge)

    def add_edge(self, n1, n2, cost=1, both_ends=True):
        node_pairs = self.get_node_pairs(n1, n2, both_ends)
        for edge in self.edges:
            if [edge.start, edge.end] in node_pairs:
                return ValueError('Edge {} {} already exists'.format(n1, n2))

        self.edges.append(Edge(start=n1, end=n2, cost=cost))
        if both_ends:
            self.edges.append(Edge(start=n2, end=n1, cost=cost))

Let's find neighbors for every node:

    @property
    def neighbours(self):
        neighbours = {vertex: set() for vertex in self.vertices}
        for edge in self.edges:
            neighbours[edge.start].add((edge.end, edge.cost))

        return neighbours

It's time for the algorithm! I renamed the variables so it would be easier to understand.

    def dijkstra(self, source, dest):
        assert source in self.vertices, 'Such source node doesn\'t exist'

        # 1. Mark all nodes unvisited and store them.
        # 2. Set the distance to zero for our initial node 
        # and to infinity for other nodes.
        distances = {vertex: inf for vertex in self.vertices}
        previous_vertices = {
            vertex: None for vertex in self.vertices
        }
        distances[source] = 0
        vertices = self.vertices.copy()

        while vertices:
            # 3. Select the unvisited node with the smallest distance, 
            # it's current node now.
            current_vertex = min(
                vertices, key=lambda vertex: distances[vertex])

            # 6. Stop, if the smallest distance 
            # among the unvisited nodes is infinity.
            if distances[current_vertex] == inf:
                break

            # 4. Find unvisited neighbors for the current node 
            # and calculate their distances through the current node.
            for neighbour, cost in self.neighbours[current_vertex]:
                alternative_route = distances[current_vertex] + cost

                # Compare the newly calculated distance to the assigned 
                # and save the smaller one.
                if alternative_route < distances[neighbour]:
                    distances[neighbour] = alternative_route
                    previous_vertices[neighbour] = current_vertex

            # 5. Mark the current node as visited 
            # and remove it from the unvisited set.
            vertices.remove(current_vertex)


        path, current_vertex = deque(), dest
        while previous_vertices[current_vertex] is not None:
            path.appendleft(current_vertex)
            current_vertex = previous_vertices[current_vertex]
        if path:
            path.appendleft(current_vertex)
        return path

Let's use it.

graph = Graph([
    ("a", "b", 7),  ("a", "c", 9),  ("a", "f", 14), ("b", "c", 10),
    ("b", "d", 15), ("c", "d", 11), ("c", "f", 2),  ("d", "e", 6),
    ("e", "f", 9)])

print(graph.dijkstra("a", "e"))
>>> deque(['a', 'c', 'd', 'e'])

The whole code from above:

from collections import deque, namedtuple


# we'll use infinity as a default distance to nodes.
inf = float('inf')
Edge = namedtuple('Edge', 'start, end, cost')


def make_edge(start, end, cost=1):
  return Edge(start, end, cost)


class Graph:
    def __init__(self, edges):
        # let's check that the data is right
        wrong_edges = [i for i in edges if len(i) not in [2, 3]]
        if wrong_edges:
            raise ValueError('Wrong edges data: {}'.format(wrong_edges))

        self.edges = [make_edge(*edge) for edge in edges]

    @property
    def vertices(self):
        return set(
            sum(
                ([edge.start, edge.end] for edge in self.edges), []
            )
        )

    def get_node_pairs(self, n1, n2, both_ends=True):
        if both_ends:
            node_pairs = [[n1, n2], [n2, n1]]
        else:
            node_pairs = [[n1, n2]]
        return node_pairs

    def remove_edge(self, n1, n2, both_ends=True):
        node_pairs = self.get_node_pairs(n1, n2, both_ends)
        edges = self.edges[:]
        for edge in edges:
            if [edge.start, edge.end] in node_pairs:
                self.edges.remove(edge)

    def add_edge(self, n1, n2, cost=1, both_ends=True):
        node_pairs = self.get_node_pairs(n1, n2, both_ends)
        for edge in self.edges:
            if [edge.start, edge.end] in node_pairs:
                return ValueError('Edge {} {} already exists'.format(n1, n2))

        self.edges.append(Edge(start=n1, end=n2, cost=cost))
        if both_ends:
            self.edges.append(Edge(start=n2, end=n1, cost=cost))

    @property
    def neighbours(self):
        neighbours = {vertex: set() for vertex in self.vertices}
        for edge in self.edges:
            neighbours[edge.start].add((edge.end, edge.cost))

        return neighbours

    def dijkstra(self, source, dest):
        assert source in self.vertices, 'Such source node doesn\'t exist'
        distances = {vertex: inf for vertex in self.vertices}
        previous_vertices = {
            vertex: None for vertex in self.vertices
        }
        distances[source] = 0
        vertices = self.vertices.copy()

        while vertices:
            current_vertex = min(
                vertices, key=lambda vertex: distances[vertex])
            vertices.remove(current_vertex)
            if distances[current_vertex] == inf:
                break
            for neighbour, cost in self.neighbours[current_vertex]:
                alternative_route = distances[current_vertex] + cost
                if alternative_route < distances[neighbour]:
                    distances[neighbour] = alternative_route
                    previous_vertices[neighbour] = current_vertex

        path, current_vertex = deque(), dest
        while previous_vertices[current_vertex] is not None:
            path.appendleft(current_vertex)
            current_vertex = previous_vertices[current_vertex]
        if path:
            path.appendleft(current_vertex)
        return path


graph = Graph([
    ("a", "b", 7),  ("a", "c", 9),  ("a", "f", 14), ("b", "c", 10),
    ("b", "d", 15), ("c", "d", 11), ("c", "f", 2),  ("d", "e", 6),
    ("e", "f", 9)])

print(graph.dijkstra("a", "e"))

P.S. For those of us who, like me, read more books about the Witcher than about algorithms, it's Edsger Dijkstra, not Sigismund.

Class notes: Getting started with Pandas DataFrames

Maria Boldyreva — Fri, 22 Jun 2018 04:46:54 +0000

Here I'll put down the basics of working with Pandas DataFrames.

DataFrame is the primary Pandas data structure, which allows us to easily work with data tables.

A data frame can be constructed from a dict:

import pandas as pd
frame = pd.DataFrame({'numbers': range(3), 'chars': ['a'] * 3})

it gives us the following output:

	chars	numbers
0	a	0
1	a	1
2	a	2

Also, DataFrame can be initialized from a .csv file:

frame = pd.read_csv('file.csv', headers=0, sep='\t')

The first argument is a file name, the second is indexes of rows containing headers (int or a list of ints), the third is a data separator that will be used, a tab here ('\s' for a single whitespace, '\s+' for multiple whitespaces).

Columns headers can be extracted using the following:

frame.columnsm
# Index([u'chars', u'numbers], dtype='object')

Another useful command returns the frame size:

frame.shape
# (3, 2)

Let's add a row:

new_line = {'chars': 'b', 'numbers': 8}
frame.append(new_line, ignore_index=True, inplace=True)
frame

it gives us the following output:

	chars	numbers
0	a	0
1	a	1
2	a	2
3	b	8

The inplace keyword shows that the result should be written into the original variable, not just return the resulting frame to the output.

Adding a columns is easier:

frame['bools'] = [False] * 3 + [True]
frame

	chars	numbers	bools
0	a	0	False
1	a	1	False
2	a	2	False
3	b	8	True

Rows and columns can be dropped:

# the first argument is a list if indexes, 
# the second is the axis (0 is for rows, 1 is for columns)
frame.drop([0,1], axis=0, inplace=True)

	chars	numbers	bools
0	a	2	False
1	b	8	True

The result can be saved into a .csv file:

frame.to_csv('updated.csv', set=',', header=True, index=None)

JavaScript: getting the first element of an array without indexes

Maria Boldyreva — Fri, 22 Jun 2018 04:45:33 +0000

const a = [1, 2, 3];
const [b] = a;
b
// 1

[] is the decomposing operator.

Also:

const [c, ...rest] = a;
c
// 1
rest
// [2, 3]

... is the rest operator.

Updating a dict in python

Maria Boldyreva — Fri, 22 Jun 2018 04:22:13 +0000

I've been a python dev for more than two years and somehow I didn't know that dicts can be updated by kwargs. Like this:

>>> a = {'a': 1}
>>> a.update(b=5)
>>> a
{'a': 1, 'b': 5}

So obvious, yet.

Python: update a dict by object

Maria Boldyreva — Fri, 11 May 2018 09:02:17 +0000

When you have a dict and a non-iterable object you'd like to update the dict from, you can do this:

class A:
    def __init__(self):
        self.fields_names_list = [..attribute names here..]

    def __iter__(self):
        for name in self.fields_names_list:
            yield name, getattr(self, name, None)

And voila, it works:

a = A()
a.foo = 'bar'
b = dict()
b.update(a)
a
# {'foo': 'bar'}

Update: my solution is for legacy code, in general, it's better to use @rhymes solution from the comments.

Introduction to Java: modifiers cheatsheet

Maria Boldyreva — Wed, 04 Apr 2018 01:59:39 +0000

Modifiers in Java still seem tricky to me, so I'll try to put it as short as possible.

Modifiers are words that alter the class / method / variable behavior. The two main types are access and non-access modifiers.

Access modifiers alter visibility:
- no modifier - the class is visible to its package
- private - only to the class
- public - to every class everywhere
- protected - to the package and its subclasses
Non-access modifiers alter other functionalities:
- static - makes the declaration class specific, not instance specific. It allows to use the method without an actual instance
- final - doesn't allow the reference to change
- abstract - for a method, declares that the method should be overriden in subclasses, for a class, that it cannot be instantiated, only subclassed