This module is an easy-to-use wrapper for word_cloud module.
The original module doesn't support Farsi Texts. But by using WordCloudFa you can generate word clouds from texts those are including Persian and English words.
This module is not only a wrapper, but it adds some features to the original module.
- How to Install
- How to Use
- Examples
- Font
- Persian Tutorial
- Contribution
- Common Problems
- There is any problem?
- Citations
For installing this module on other operating systems, you can simply run
pip install wordcloud-fa.
This module tested on python 3
WordCloudFa depends on numpy and pillow.
Also you should have Hazm module. Normally, all of them will install automatically when you install this module using
pip as described at the beginning of this section.
To save the wordcloud into a file, matplotlib can also be installed.
Attention
You need to have python-dev for python3 on your system. If you don't have it, you can install it on operating systems
those using apt as the package manager (Like Ubuntu) by this command:
sudo apt-get install python3-dev
And you can install it on operating systems those using yum as the package manager (like RedHat, Fedora and ...) you can
use the following command:
sudo yum install python3-devel
For creating a word cloud from a text, first you should import the class into your code:
from wordcloud_fa import WordCloudFa
you can create an instance of this class like:
wordcloud = WordCloudFa()
You can pass different parameters to the constructor. For see full documents of them, you can see WordCloud Documentations
There are three parameters that are not in the original class.
First one is persian_normalize. If you pass this parameter with True value, your data will normalize by using
Hazm normalizer. It's recommended to always pass this parameter. That will replace
arabic letters with persian ones and do some other stuff.
The default value of this parameter is False.
wordcloud = WordCloudFa(persian_normalize=True)
the second parameter is include_numbers that is not in the published original module. If you set this parameter to False,
all Persian, Arabic and English numbers will remove from your data.
The default value of this parameter is True
wordcloud = WordCloudFa(include_numbers=False)
Common problem Hint:
The last and very important parameter is: no_reshape. The default value of the parameter is False. But if you see
that the letters of the words in Farsi texts are separated in your local system, you should pass True value to this parameter.
wordcloud = WordCloudFa(no_reshape=True)for generating word cloud from a string, you can simply call generate method of you instance:
wordcloud = WordCloudFa(persian_normalize=True)
wc = wordcloud.generate(text)
image = wc.to_image()
image.show()
image.save('wordcloud.png')You can generate a word cloud from frequencies. You can use the output of process_text method as frequencies.
Also you can use any dictionary like this.
wordcloud = WordCloudFa()
frequencies = wordcloud.process_text(text)
wc = wordcloud.generate_from_frequencies(frequencies)generate_from_frequencies method in this module will exclude stopwords. But the original module will not exclude them
when you are using this method. Also you can use Persian words as keys in frequencies dict without any problem.
Stopwords are the words that we don't want to consider. If you dan't pass any stopword, the default words in the stopwords file will consider as stopwords.
You don't want to use them at all and you want to choose your stopwords? you can simply set stopwords parameter when
you are creating an instance from WordCloudFa and pass a set of words into it.
stop_words = set(['کلمهی اول', 'کلمهی دوم'])
wc = WordCloudFa(stopwords=stop_words)If you want to add additional words to the default stopwords, you can simply call add_stop_words method on your
instance of WordCloudFa and pass an iterable type (list, set, ...) into it.
wc = WordCloudFa()
wc.add_stop_words(['کلمهی اول', 'کلمهی دوم'])Also you can add stopwords from a file. That file should include stopwords and each word should be in a separate line.
For that, you should use add_stop_words_from_file method. The only parameter of this
method is relative or absolute path to the stop words file.
wc = WordCloudFa()
wc.add_stop_words_from_file("stopwords.txt")You can mask the final word cloud by an image. For example, the first image of this document is a wordcloud masked by an image
of the map of Iran country. For setting a mask, you should pass the mask parameter.
But before, you first should be sure you have a black and white image. Because other images will not create a good result.
Then, you should convert that image to a numpy array. For that, you should do something like this:
import numpy as np
from PIL import Image
mask_array = np.array(Image.open("mask.png"))You just should add those two imports, but you don't need to be worried about installing them, because those have been installed as dependencies of this module.
Then, you can pass that array to the constructor of the WordCloudFa class for masking the result.
wordcloud = WordCloudFa(mask=mask_array)Now you can use your worldcloud instance as before.
When you pass your texts into an instance of this class, all words will reshape for turning to a proper way for showing And avoiding the invalid shape of Persian or Arabic words (splitted and inverse letters).
If you want to do the same thing outside of this module, you can call reshape_words static method.
reshaped_words = WordCloudFa.reshape_words(['کلمهی اول', 'کلمهی دوم'])this method gets an Iterable as input and returns a list of reshaped words.
DONT FORGET THAT YOU SHOULD NOT PASS RESHAPED WORDS TO THE METHODS OF THIS CLASS AND THIS STATIC METHOD IS ONLY FOR USAGES OUT OF THIS MODULE
Some non-ASCII characters like emojies causing errors. By Default, those characters will remove from the input text (not when you are using the generate_from_frequencies method).
For disabling this feature, you can set the value of the remove_unhandled_utf_characters parameter to False when you are creating a new instance of the WordCloudFa.
Also you can access the compiled regex patten of those characters using the unhandled_characters_regex class attribute.
You can see Example codes in the Examples directory.
The default font is an unknown! font that supports both Persian and English letters. So you don't need to pass a font for
getting results. But if you want to change the font you can pass font_path parameter.
If you want to read a brief tutorial about how to use this package in Farsi (Persian), you can click on this link.
We want to keep this library fresh and useful for all Iranian developers. So we need your help for adding new features, fixing bugs and adding more documents.
You are wondering how you can contribute to this project? Here is a list of what you can do:
- Documents are not enough? You can help us by adding more documents.
- The current code could be better? You can make this cleaner or faster.
- Do you think one useful feature missed? You can open an issue and tell us about it.
- Did you find a good open and free font that supports Farsi and English? You can notify us by a pull request or if opening an issue
If you see separated Farsi letters in your output, you should pass no_reshape=True parameter to your WordCoudFa constructor:
wordcloud = WordCloudFa(no_reshape=True)In some cases you may see repeated Farsi words in the output. For solving that problem, you should pass collocations=False Parameter to your WordCloudFa constructor:
wordcloud = WordCloudFa(collocations=False)In some operating systems like Windows, you should specify the encoding of the example text files. If you can not open example files, add encoding="utf-8" to your open statements:
with open('persian-example.txt', 'r', encoding="utf-8") as file:If you have questions, find some bugs or need some features, you can open an issue and tell us. For some strange reasons this is not possible? so contact me by this email: [email protected].
Texts in the Example directory are from this and this Wikipedia pages.
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