How to use test_addmarker_order method in Pytest

It's just a list, so you can loop over it too:

for entry in dir(obj):
    print repr(entry)

or you could use pprint.pprint() to have the list formatted 'prettily' for you.

Demo on the pprint module itself:

>>> import pprint
>>> pprint.pprint(dir(pprint))
['PrettyPrinter',
 '_StringIO',
 '__all__',
 '__builtins__',
 '__doc__',
 '__file__',
 '__name__',
 '__package__',
 '_commajoin',
 '_id',
 '_len',
 '_perfcheck',
 '_recursion',
 '_safe_repr',
 '_sorted',
 '_sys',
 '_type',
 'isreadable',
 'isrecursive',
 'pformat',
 'pprint',
 'saferepr',
 'warnings']

Though classmethod and staticmethod are quite similar, there's a slight difference in usage for both entities: classmethod must have a reference to a class object as the first parameter, whereas staticmethod can have no parameters at all.

Example

class Date(object):
    
    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        date1 = cls(day, month, year)
        return date1

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        return day <= 31 and month <= 12 and year <= 3999

date2 = Date.from_string('11-09-2012')
is_date = Date.is_date_valid('11-09-2012')

Explanation

Let's assume an example of a class, dealing with date information (this will be our boilerplate):

class Date(object):
    
    def __init__(self, day=0, month=0, year=0):
        self.day = day
        self.month = month
        self.year = year

This class obviously could be used to store information about certain dates (without timezone information; let's assume all dates are presented in UTC).

Here we have __init__, a typical initializer of Python class instances, which receives arguments as a typical instance method, having the first non-optional argument (self) that holds a reference to a newly created instance.

Class Method

We have some tasks that can be nicely done using classmethods.

Let's assume that we want to create a lot of Date class instances having date information coming from an outer source encoded as a string with format 'dd-mm-yyyy'. Suppose we have to do this in different places in the source code of our project.

So what we must do here is:

1. Parse a string to receive day, month and year as three integer variables or a 3-item tuple consisting of that variable.
2. Instantiate Date by passing those values to the initialization call.

This will look like:

day, month, year = map(int, string_date.split('-'))
date1 = Date(day, month, year)

For this purpose, C++ can implement such a feature with overloading, but Python lacks this overloading. Instead, we can use classmethod. Let's create another constructor.

    @classmethod
    def from_string(cls, date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        date1 = cls(day, month, year)
        return date1

date2 = Date.from_string('11-09-2012')

Let's look more carefully at the above implementation, and review what advantages we have here:

1. We've implemented date string parsing in one place and it's reusable now.
2. Encapsulation works fine here (if you think that you could implement string parsing as a single function elsewhere, this solution fits the OOP paradigm far better).
3. cls is the class itself, not an instance of the class. It's pretty cool because if we inherit our Date class, all children will have from_string defined also.

Static method

What about staticmethod? It's pretty similar to classmethod but doesn't take any obligatory parameters (like a class method or instance method does).

Let's look at the next use case.

We have a date string that we want to validate somehow. This task is also logically bound to the Date class we've used so far, but doesn't require instantiation of it.

Here is where staticmethod can be useful. Let's look at the next piece of code:

    @staticmethod
    def is_date_valid(date_as_string):
        day, month, year = map(int, date_as_string.split('-'))
        return day <= 31 and month <= 12 and year <= 3999

# usage:
is_date = Date.is_date_valid('11-09-2012')

So, as we can see from usage of staticmethod, we don't have any access to what the class is---it's basically just a function, called syntactically like a method, but without access to the object and its internals (fields and other methods), which classmethod does have.

The current recommendation is to use python -m pip, where python is the version of Python you would like to use. This is the recommendation because it works across all versions of Python, and in all forms of virtualenv. For example:

# The system default python:
$ python -m pip install fish

# A virtualenv's python:
$ .env/bin/python -m pip install fish

# A specific version of python:
$ python-3.6 -m pip install fish

Previous answer, left for posterity:

Since version 0.8, Pip supports pip-{version}. You can use it the same as easy_install-{version}:

$ pip-2.5 install myfoopackage
$ pip-2.6 install otherpackage
$ pip-2.7 install mybarpackage

EDIT: pip changed its schema to use pipVERSION instead of pip-VERSION in version 1.5. You should use the following if you have pip >= 1.5:

$ pip2.6 install otherpackage
$ pip2.7 install mybarpackage

Check https://github.com/pypa/pip/pull/1053 for more details

References:

• https://github.com/pypa/pip/issues/200
• http://www.pip-installer.org/docs/pip/en/0.8.3/news.html#id4
  https://pip.pypa.io/en/stable/news/#v0-8 or
  https://web.archive.org/web/20140310013920/http://www.pip-installer.org:80/docs/pip/en/0.8.3/news.html#id4

As already discussed, tensorflow doesn't provide its own way to cross-validate the model. The recommended way is to use KFold. It's a bit tedious, but doable. Here's a complete example of cross-validating MNIST model with tensorflow and KFold:

from sklearn.model_selection import KFold
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# Parameters
learning_rate = 0.01
batch_size = 500

# TF graph
x = tf.placeholder(tf.float32, [None, 784])
y = tf.placeholder(tf.float32, [None, 10])
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
pred = tf.nn.softmax(tf.matmul(x, W) + b)
cost = tf.reduce_mean(-tf.reduce_sum(y*tf.log(pred), reduction_indices=1))
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
init = tf.global_variables_initializer()

mnist = input_data.read_data_sets("data/mnist-tf", one_hot=True)
train_x_all = mnist.train.images
train_y_all = mnist.train.labels
test_x = mnist.test.images
test_y = mnist.test.labels

def run_train(session, train_x, train_y):
  print "\nStart training"
  session.run(init)
  for epoch in range(10):
    total_batch = int(train_x.shape[0] / batch_size)
    for i in range(total_batch):
      batch_x = train_x[i*batch_size:(i+1)*batch_size]
      batch_y = train_y[i*batch_size:(i+1)*batch_size]
      _, c = session.run([optimizer, cost], feed_dict={x: batch_x, y: batch_y})
      if i % 50 == 0:
        print "Epoch #%d step=%d cost=%f" % (epoch, i, c)

def cross_validate(session, split_size=5):
  results = []
  kf = KFold(n_splits=split_size)
  for train_idx, val_idx in kf.split(train_x_all, train_y_all):
    train_x = train_x_all[train_idx]
    train_y = train_y_all[train_idx]
    val_x = train_x_all[val_idx]
    val_y = train_y_all[val_idx]
    run_train(session, train_x, train_y)
    results.append(session.run(accuracy, feed_dict={x: val_x, y: val_y}))
  return results

with tf.Session() as session:
  result = cross_validate(session)
  print "Cross-validation result: %s" % result
  print "Test accuracy: %f" % session.run(accuracy, feed_dict={x: test_x, y: test_y})

Don't show long options twice in print_help() from argparse

asks essentially the same thing. If you are not up to writing your own HelpFormatter subclass (it probably needs to change one method), you need to play with the existing formatting tools - help, metavar, and description.

Here also argparse help without duplicate ALLCAPS

and How do I avoid the capital placeholders in python's argparse module?

For that 88275023 question I worked out (but didn't post) this Formatter class. The change is near the end

class CustomFormatter(argparse.HelpFormatter):
    def _format_action_invocation(self, action):
        if not action.option_strings:
            metavar, = self._metavar_formatter(action, action.dest)(1)
            return metavar
        else:
            parts = []
            # if the Optional doesn't take a value, format is:
            #    -s, --long
            if action.nargs == 0:
                parts.extend(action.option_strings)

            # if the Optional takes a value, format is:
            #    -s ARGS, --long ARGS
            # change to 
            #    -s, --long ARGS
            else:
                default = action.dest.upper()
                args_string = self._format_args(action, default)
                for option_string in action.option_strings:
                    #parts.append('%s %s' % (option_string, args_string))
                    parts.append('%s' % option_string)
                parts[-1] += ' %s'%args_string
            return ', '.join(parts)

Your highlighter() function calls area.setText(), which will emit the textChanged signal which is connected to highlighter, which calls area.setText() which... and so on.

For a QTextEdit the textChaged signal is always emitted when the setText() method is called, even if the visible text content didn't actually change, because its internal representation does change.

A simple workaround in your case would be to block the delivery of signals when you call setText():

def highlighter():
        text = area.toPlainText()
        result = highlight(text, lexer, formatter)
        area.blockSignals(True)
        pos = area.textCursor().position()
        area.setText(result)
        cursor = area.textCursor()
        cursor.setPosition(min(pos, len(area.toPlainText())))
        area.setTextCursor(cursor)
        area.blockSignals(False)

Harvey's answer won't work with command groups. Effectively this would replace the 'hello' command with 'wrapper' which is not what we want. Instead try something like:

from functools import wraps

def test_decorator(f):
    @wraps(f)
    def wrapper(*args, **kwargs):
        kwargs['name'] = kwargs['name'].upper()
        return f(*args, **kwargs)
    return wrapper

Your example is coming out as gibberish, it's much worse than just some missing stop words.

If you re-read the docs for the start_char, oov_char, and index_from parameters of the [keras.datasets.imdb.load_data](https://keras.io/datasets/#imdb-movie-reviews-sentiment-classification ) method they explain what is happening:

start_char: int. The start of a sequence will be marked with this character. Set to 1 because 0 is usually the padding character.

oov_char: int. words that were cut out because of the num_words or skip_top limit will be replaced with this character.

index_from: int. Index actual words with this index and higher.

That dictionary you inverted assumes the word indices start from 1.

But the indices returned my keras have <START> and <UNKNOWN> as indexes 1 and 2. (And it assumes you will use 0 for <PADDING>).

This works for me:

import keras
NUM_WORDS=1000 # only use top 1000 words
INDEX_FROM=3   # word index offset

train,test = keras.datasets.imdb.load_data(num_words=NUM_WORDS, index_from=INDEX_FROM)
train_x,train_y = train
test_x,test_y = test

word_to_id = keras.datasets.imdb.get_word_index()
word_to_id = {k:(v+INDEX_FROM) for k,v in word_to_id.items()}
word_to_id["<PAD>"] = 0
word_to_id["<START>"] = 1
word_to_id["<UNK>"] = 2
word_to_id["<UNUSED>"] = 3

id_to_word = {value:key for key,value in word_to_id.items()}
print(' '.join(id_to_word[id] for id in train_x[0] ))

The punctuation is missing, but that's all:

"<START> this film was just brilliant casting <UNK> <UNK> story
 direction <UNK> really <UNK> the part they played and you could just
 imagine being there robert <UNK> is an amazing actor ..."

There are two ways you can do this; with patch and with patch.object

Patch assumes that you are not directly importing the object but that it is being used by the object you are testing as in the following

#foo.py
def some_fn():
    return 'some_fn'

class Foo(object):
    def method_1(self):
        return some_fn()

#bar.py
import foo
class Bar(object):
    def method_2(self):
        tmp = foo.Foo()
        return tmp.method_1()

#test_case_1.py
import bar
from mock import patch

@patch('foo.some_fn')
def test_bar(mock_some_fn):
    mock_some_fn.return_value = 'test-val-1'
    tmp = bar.Bar()
    assert tmp.method_2() == 'test-val-1'
    mock_some_fn.return_value = 'test-val-2'
    assert tmp.method_2() == 'test-val-2'

If you are directly importing the module to be tested, you can use patch.object as follows:

#test_case_2.py
import foo
from mock import patch

@patch.object(foo, 'some_fn')
def test_foo(test_some_fn):
    test_some_fn.return_value = 'test-val-1'
    tmp = foo.Foo()
    assert tmp.method_1() == 'test-val-1'
    test_some_fn.return_value = 'test-val-2'
    assert tmp.method_1() == 'test-val-2'

In both cases some_fn will be 'un-mocked' after the test function is complete.

Edit: In order to mock multiple functions, just add more decorators to the function and add arguments to take in the extra parameters

@patch.object(foo, 'some_fn')
@patch.object(foo, 'other_fn')
def test_foo(test_other_fn, test_some_fn):
    ...

Note that the closer the decorator is to the function definition, the earlier it is in the parameter list.