check out the assert functions in [`numpy.testing`](https://numpy.org/doc/stable/reference/routines.testing.html), e.g.

`assert_array_equal`

for floating point arrays equality test might fail and `assert_almost_equal` is more reliable.

**update**

A few versions ago numpy obtained `assert_allclose` which is now my favorite since it allows us to specify both absolute and relative error and doesn&#39;t require decimal rounding as the closeness criterion.

I think `(arr1 == arr2).all()` looks pretty nice. But you could use:

    numpy.allclose(arr1, arr2)

but it&#39;s not quite the same. 

An alternative, almost the same as your example is:

    numpy.alltrue(arr1 == arr2)

Note that scipy.array is actually a reference numpy.array. That makes it easier to find the documentation. 



I find that using
`self.assertEqual(arr1.tolist(), arr2.tolist())`
is the easiest way of comparing arrays with unittest.

I agree it&#39;s not the prettiest solution and it&#39;s probably not the fastest but it&#39;s probably more uniform with the rest of your test cases, you get all the unittest error description and it&#39;s really simple to implement.

Since Python 3.2 you can use `assertSequenceEqual(array1.tolist(), array2.tolist())`.

This has the added value of showing you the exact items in which the arrays differ.

    self.assertTrue(np.array_equal(x, y, equal_nan=True))

`equal_nan = True` if you want to `np.nan == np.nan` returns `True`

or you can use [numpy.allclose](https://numpy.org/doc/stable/reference/generated/numpy.allclose.html) to compare with torelance.

In my tests I use this:
   
    numpy.testing.assert_array_equal(arr1, arr2)
    

How to find the maximum of 2 numbers?

    value = -9999
    run = problem.getscore()

I need to compare the 2 values i.e `value` and `run` and find the maximum of 2. I need some python function to operate it?


How do I find the maximum of 2 numbers?

In `bash`, what&#39;s the difference, if any, between the equal and double equal test operators?

    [[ &quot;a&quot; = &quot;a&quot; ]] &amp;&amp; echo equal || echo not-equal
    [[ &quot;a&quot; == &quot;a&quot; ]] &amp;&amp; echo equal || echo not-equal
    [[ &quot;a&quot; = &quot;b&quot; ]] &amp;&amp; echo equal || echo not-equal
    [[ &quot;a&quot; == &quot;b&quot; ]] &amp;&amp; echo equal || echo not-equal

results in:

    equal
    equal
    not-equal
    not-equal

bash string equality

I want to make some unit-tests for my app, and I need to compare two arrays. Since `array.__eq__` returns a new array (so `TestCase.assertEqual` fails), what is the best way to assert for equality?


Currently I&#39;m using

    self.assertTrue((arr1 == arr2).all())

but I don&#39;t really like it

Best way to assert for numpy.array equality?

I want to make some unit-tests for my app, and I need to compare two arrays. Since <code>array.__eq__</code> returns a new array (so <code>TestCase.assertEqual</code> fails), what is the best way to assert for equality?
Currently I'm using
<pre><code class="hljs language-ini">self.assertTrue((arr1 == arr2).all())
</code></pre>
but I don't really like it

I am using Python 2.5, I want an enumeration like so (starting at 1 instead of 0):

    [(1, 2000), (2, 2001), (3, 2002), (4, 2003), (5, 2004)]

I know in Python 2.6 you can do: h = enumerate(range(2000, 2005), 1) to give the above result but in python2.5 you cannot...

Using Python 2.5:

    &gt;&gt;&gt; h = enumerate(range(2000, 2005))
    &gt;&gt;&gt; [x for x in h]
    [(0, 2000), (1, 2001), (2, 2002), (3, 2003), (4, 2004)]


Does anyone know a way to get that desired result in Python 2.5?

How to enumerate a range of numbers starting at 1

When would someone use httplib and when urllib?

What are the differences?

I think I ready urllib uses httplib, I am planning to make an app that will need to make http request and so far I only used httplib.HTTPConnection in python for requests, and reading about urllib I see I can use that for request too, so whats the benefit of one or the other?

Python urllib vs httplib?

I have a CSV dumpfile from a Blackberry IPD backup, created using IPDDump.
The date/time strings in here look something like this
(where `EST` is an Australian time-zone):

```none
Tue Jun 22 07:46:22 EST 2010
```

I need to be able to parse this date in Python. At first, I tried to use the `strptime()` function from datettime.

    &gt;&gt;&gt; datetime.datetime.strptime(&#39;Tue Jun 22 12:10:20 2010 EST&#39;, &#39;%a %b %d %H:%M:%S %Y %Z&#39;)

However, for some reason, the `datetime` object that comes back doesn&#39;t seem to have any `tzinfo` associated with it.

I did read on [this page](http://www.enricozini.org/2009/debian/using-python-datetime/) that apparently `datetime.strptime` silently discards `tzinfo`, however, I checked the documentation, and I can&#39;t find anything to that effect documented [here](http://docs.python.org/library/datetime.html).

Is there any way to get `strptime()` to play nicely with timezones?

How to preserve timezone when parsing date/time strings with strptime()?

In [matplotlib][1], I can set the axis scaling using either [`pyplot.xscale()`][2] or [`Axes.set_xscale()`][3]. Both functions accept three different scales: `&#39;linear&#39;` | `&#39;log&#39;` | `&#39;symlog&#39;`.

What is the difference between `&#39;log&#39;` and `&#39;symlog&#39;`? In a simple test I did, they both looked exactly the same.

I know the documentation says they accept different parameters, but I still don&#39;t understand the difference between them. Can someone please explain it? The answer will be the best if it has some sample code and graphics! (also: where does the name &#39;symlog&#39; come from?)

[1]: http://matplotlib.sourceforge.net/
[2]: http://matplotlib.sourceforge.net/api/pyplot_api.html#matplotlib.pyplot.xscale
[3]: http://matplotlib.sourceforge.net/api/axes_api.html#matplotlib.axes.Axes.set_xscale

What is the difference between &#39;log&#39; and &#39;symlog&#39;?

Anyone know of a simple library or function to parse a csv encoded string and turn it into an array or dictionary?

I don&#39;t think I want the built in [csv module][1] because in all the examples I&#39;ve seen that takes filepaths, not strings.



  [1]: http://docs.python.org/library/csv.html

Python csv string to array

It seems that if a testcase fails, nose will attempt to execute the next testcases. How can I  make nose  to abort all execution upon the first error in any testcase? I tried sys.exit() but it gave me some ugly and lengthy messages about it

Python nose framework: How to stop execution upon first failure

Let&#39;s say you have some code like this (in a made-up langague, since it does not matter for this question):

    constant float PI = 3.14;
    float getPi() 
    { 
       return PI;
    }

Would you test it like this:

    testPiIs3point14()
    {
       // Test using literal in test case
       AssertEquals( getPi(), 3.14 );
    }

Or like this:

    testPiIs3Point14()
    {
       // Test using constant from implementation in test case
       AssertEquals( getPi(), PI );
    }

In other words, do you use constants from your system under test in your test cases?  Or is this considered an implementation detail?

Do you use constants from the implementation in your test cases?

I completed my first proper project in Python and now my task is to write tests for it.

Since this is the first time I did a project, this is the first time I would be writing tests for it.

The question is, *how* do I start? I have absolutely no idea. Can anyone point me to some documentation/ tutorial/ link/ book that I can use to start with writing tests (and I guess unit testing in particular)

Any advice will be welcomed on this topic. 

Writing unit tests in Python: How do I start?

I&#39;m strongly considering adding unit testing to an existing project that is in production. It was started 18 months ago before I could really see any benefit of TDD ***(face palm)***, so now it&#39;s a rather large solution with a number of projects and I haven&#39;t the foggiest idea where to start in adding unit tests. What&#39;s making me consider this is that occasionally an old bug seems to resurface, or a bug is checked in as fixed without *really* being fixed. Unit testing would reduce or prevents these issues occuring.

By reading *[similar][1]* questions on SO, I&#39;ve seen recommendations such as starting at the bug tracker and writing a test case for each bug to prevent regression. However, I&#39;m concerned that I&#39;ll end up missing the big picture and end up missing fundamental tests that would have been included if I&#39;d used TDD from the get go.

Are there any process/steps that should be adhered to in order to ensure that an existing solutions is **properly** unit tested and not just bodged in? How can I ensure that the tests are of a good quality and aren&#39;t just a case *of any test is better than no tests*.

So I guess what I&#39;m also asking is;

 - Is it worth the effort for an
   existing solution that&#39;s in production?
 - Would it better to ignore the testing
   for this project and add it in a
   possible future re-write?
 - What will be more benefical; spending
   a few weeks adding tests or a few
   weeks adding functionality?

(Obviously the answer to the third point is entirely dependant on whether you&#39;re speaking to management or a developer)


----------


**Reason for Bounty**
-----------------

Adding a bounty to try and attract a broader range of answers that not only confirm my existing suspicion that it is a good thing to do, but also some good reasons against. 

I&#39;m aiming to write this question up later with pros and cons to try and show management that it&#39;s worth spending the man hours on moving the future development of the product to TDD. I want to approach this challenge and develop my reasoning without my own biased point of view.

  [1]: https://stackoverflow.com/questions/1046816/adding-unit-testing-to-an-existing-asp-net-web-forms-application

Can unit testing be successfully added into an existing production project? If so, how and is it worth it?

what I want to do is construct a moq for I1 - which is fine ... however in the course of the method that I am testing that uses this mock I need to cast it to I2 in order to access some properties that are not on I1

    Interface I1 
    { int AProperty{get;set;}}
    
    Interface I2
    {int AnotherProperty{get;set;}}

I then have some objects

    Class O1 : I1 {}

and 

    Class O2 : O1 , I2 {}

the problem is that when i have an instance of a I2 implementing object I can cast it to I1 in order to access the methods that are implmented through that interface. In code this is not a problem and everythign works as expected.

The only problem comes when writing a unit test on that class.

The interfaces also expose a method called GetNewInstance which returns an initialised instance of the the implementing object cast into the IGetNewInstance&lt;T&gt; interface ... i can usually mock this fine and make it return itself (and so I keep working with the mock object).

however when you try to cast this returned object of type I2 into I1 a null reference results - this makes sense as the mock object that implements I2 does not inherit from anything that inherits I1.

the question is how can i force the mock object to inherit from both I1 ansd I2 at the same time?

MOQ - how to mock an interface that needs to be cast to another interface?

I am using numpy. I have a matrix with 1 column and N rows and I want to get an array from with N elements.

For example, if i have `M = matrix([[1], [2], [3], [4]])`, I want to get `A = array([1,2,3,4])`.

To achieve it, I use `A = np.array(M.T)[0]`. Does anyone know a more elegant way to get the same result?

Thanks!

Numpy matrix to array

`numpy.vectorize` takes a function f:a-&gt;b and turns it into g:a[]-&gt;b[].

This works fine when `a` and `b` are scalars, but I can&#39;t think of a reason why it wouldn&#39;t work with b as an `ndarray` or list, i.e. f:a-&gt;b[] and g:a[]-&gt;b[][]

For example:

    import numpy as np
    def f(x):
        return x * np.array([1,1,1,1,1], dtype=np.float32)
    g = np.vectorize(f, otypes=[np.ndarray])
    a = np.arange(4)
    print(g(a))
This yields:
    
    array([[ 0.  0.  0.  0.  0.],
           [ 1.  1.  1.  1.  1.],
           [ 2.  2.  2.  2.  2.],
           [ 3.  3.  3.  3.  3.]], dtype=object)

Ok, so that gives the right values, but the wrong dtype.  And even worse:

    g(a).shape

yields:

    (4,)

So this array is pretty much useless.  I know I can convert it doing:

    np.array(map(list, a), dtype=np.float32)


to give me what I want:

    array([[ 0.,  0.,  0.,  0.,  0.],
           [ 1.,  1.,  1.,  1.,  1.],
           [ 2.,  2.,  2.,  2.,  2.],
           [ 3.,  3.,  3.,  3.,  3.]], dtype=float32)

but that is neither efficient nor pythonic.  Can any of you guys find a cleaner way to do this?  

Thanks in advance!

Using Numpy Vectorize on Functions that Return Vectors

is there a builtin function of Python that does on `python.array` what [`argsort()`][1] does on a `numpy.array`?


  [1]: http://www.scipy.org/Numpy_Example_List_With_Doc#head-82d26af744a08992cf3422d6df9dc9e276e78263

Equivalent of Numpy.argsort() in basic python?

I would like to create a two dimensional numpy array of arrays that has a different number of elements on each row.  

Trying

    cells = numpy.array([[0,1,2,3], [2,3,4]])

gives an error

    ValueError: setting an array element with a sequence.



How to make a multidimension numpy array with a varying row size?

If I have an array like

    a = np.array([2, 3, -1, -4, 3])

I want to set all the negative elements to zero: `[2, 3, 0, 0, 3]`. How to do it with numpy without an explicit for? I need to use the modified `a` in a computation, for example

    c = a * b

where `b` is another array with the same length of the original `a`

Conclusion
==========

    import numpy as np
    from time import time

    a = np.random.uniform(-1, 1, 20000000)
    t = time(); b = np.where(a&gt;0, a, 0); print (&quot;1. &quot;, time() - t)
    a = np.random.uniform(-1, 1, 20000000)
    t = time(); b = a.clip(min=0); print (&quot;2. &quot;, time() - t)
    a = np.random.uniform(-1, 1, 20000000)
    t = time(); a[a &lt; 0] = 0; print (&quot;3. &quot;, time() - t)
    a = np.random.uniform(-1, 1, 20000000)
    t = time(); a[np.where(a&lt;0)] = 0; print (&quot;4. &quot;, time() - t)
    a = np.random.uniform(-1, 1, 20000000)
    t = time(); b = [max(x, 0) for x in a]; print (&quot;5. &quot;, time() - t)

1.  1.38629984856
2.  0.516846179962      &lt;- faster a.clip(min=0);
3.  0.615426063538
4.  0.944557905197
5.  51.7364809513





Content Type	Original Author	Original Content on Stackoverflow
Question	maker	View Question on Stackoverflow
Solution 1 - Python	Josef	View Answer on Stackoverflow
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Best way to assert for numpy.array equality?

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bash string equality

How do I find the maximum of 2 numbers?

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