`numpy.log10(prob)` calculates the base 10 logarithm for all elements of `prob`, even the ones that aren&#39;t selected by the `where`. If you want, you can fill the zeros of `prob` with `10**-10` or some dummy value before taking the logarithm to get rid of the problem. (Make sure you don&#39;t compute `prob &gt; 0.0000000001` with dummy values, though.)

You can turn it off with [seterr][1]

    numpy.seterr(divide = &#39;ignore&#39;) 

and back on with

    numpy.seterr(divide = &#39;warn&#39;) 

  [1]: https://docs.scipy.org/doc/numpy/reference/generated/numpy.seterr.html

Just use the `where` argument in `np.log10`

    import numpy as np
    np.random.seed(0)
    
    prob = np.random.randint(5, size=4) /4
    print(prob)
    
    result = np.where(prob &gt; 0.0000000001, prob, -10)
    # print(result)
    np.log10(result, out=result, where=result &gt; 0)
    print(result)
    
**Output**
    
    [1.   0.   0.75 0.75]
    [  0.         -10.          -0.12493874  -0.12493874]

I solved this by finding the lowest non-zero number in the array and replacing all zeroes by a number lower than the lowest :p

Resulting in a code that would look like:

&lt;!-- language: python --&gt;

    def replaceZeroes(data):
      min_nonzero = np.min(data[np.nonzero(data)])
      data[data == 0] = min_nonzero
      return data

     ...

    prob = replaceZeroes(prob)
    result = numpy.where(prob &gt; 0.0000000001, numpy.log10(prob), -10)

Note that all numbers get a tiny fraction added to them.

This solution worked for me, use `numpy.sterr` to turn `warnings` off followed by `where`
```
numpy.seterr(divide = &#39;ignore&#39;)
df_train[&#39;feature_log&#39;] = np.where(df_train[&#39;feature&#39;]&gt;0, np.log(df_train[&#39;feature&#39;]), 0)
```

I want to show all the markers that are on my map, after doing some searches I found that it should be done with **GMSCoordinateBounds** (Google Maps SDK) I&#39;ve read the official documentation about it, but I have not understand how to use it and implement it in my code.

https://developers.google.com/maps/documentation/ios/reference/interface_g_m_s_camera_update#aa5b05606808272f9054c54af6830df3e


Here is my code

  
    GMSCoordinateBounds *bounds = [[GMSCoordinateBounds alloc] init];   
    CLLocationCoordinate2D location;

    for (NSDictionary *dictionary in array) {
        location.latitude = [dictionary[@&quot;latitude&quot;] floatValue];
        location.longitude = [dictionary[@&quot;longitude&quot;] floatValue];

        // Creates a marker in the center of the map.
        GMSMarker *marker = [[GMSMarker alloc] init];
        marker.icon = [UIImage imageNamed:(@&quot;marker.png&quot;)];
        marker.position = CLLocationCoordinate2DMake(location.latitude, location.longitude);
        bounds = [bounds includingCoordinate:marker.position];
        marker.title = dictionary[@&quot;type&quot;];
        marker.map = mapView_;
    }   
     
    [mapView_ animateWithCameraUpdate:[GMSCameraUpdate fitBounds:bounds withPadding:30.0f]];


**Any help ?**

iOS, How to use GMSCoordinateBounds to show all the markers of the map?

Assuming I have a test.sh script that runs a server and Git Bash installed, how do I create a Windows shortcut that I can double click to run tesh.sh in Git Bash in the foreground and allows me to see the output of the server?



Windows shortcut to run a Git Bash script

I am using numpy.log10 to calculate the log of an array of probability values. There are some zeros in the array, and I am trying to get around it using
    
    result = numpy.where(prob &gt; 0.0000000001, numpy.log10(prob), -10)



However, `RuntimeWarning: divide by zero encountered in log10` still appeared and I am sure it is this line caused the warning. 

Although my problem is solved, I am confused why this warning appeared again and again?

RuntimeWarning: divide by zero encountered in log

I am using numpy.log10 to calculate the log of an array of probability values. There are some zeros in the array, and I am trying to get around it using
<pre><code class="hljs language-ini">result = numpy.where(prob > 0.0000000001, numpy.log10(prob), -10)
</code></pre>
However, <code>RuntimeWarning: divide by zero encountered in log10</code> still appeared and I am sure it is this line caused the warning.
Although my problem is solved, I am confused why this warning appeared again and again?

I&#39;m mocking out a call to `requests.post` using the `Mock` library: 

    requests.post = Mock()

The the call involves multiple arguments: the URL, a payload, some auth stuff, etc. I want to assert that `requests.post` is called with a particular URL, but I don&#39;t care about the other arguments. When I try this: 

    requests.post.assert_called_with(requests_arguments)

the test fails, as it expects it to be called with only that argument. 

Is there any way to check if a single argument is used somewhere in the function call without having to pass in the other arguments? 

Or, even better, is there a way to assert a specific URL and then abstract data types for the other arguments (i.e., data should be a dictionary, auth should be an instance of HTTPBasicAuth, etc.)? 

Assert that a method was called with one argument out of several

I am trying to use python to change the hostname in a url, and have been playing around with the urlparse module for a while now without finding a satisfactory solution. As an example, consider the url:

https://www.google.dk:80/barbaz

I would like to replace &quot;www.google.dk&quot; with e.g. &quot;www.foo.dk&quot;, so I get the following url:

https://www.foo.dk:80/barbaz. 

So the part I want to replace is what urlparse.urlsplit refers to as hostname. I had hoped that the result of urlsplit would let me make changes, but the resulting type ParseResult doesn&#39;t allow me to. If nothing else I can of course reconstruct the new url by appending all the parts together with +, but this would leave me with some quite ugly code with a lot of conditionals to get &quot;://&quot; and &quot;:&quot; in the correct places.

Changing hostname in a url

I&#39;d like to use numpy to calculate the inverse.  But I&#39;m getting an error: 

    &#39;numpy.ndarry&#39; object has no attribute I

To calculate inverse of a matrix in numpy, say matrix M, it should be simply:
`print M.I` 

Here&#39;s the code:


    x = numpy.empty((3,3), dtype=int)
    for comb in combinations_with_replacement(range(10), 9):
       x.flat[:] = comb
       print x.I

I&#39;m presuming, this error occurs because x is now flat, thus &#39;`I`&#39; command is not compatible. Is there a work around for this?

My goal is to print the INVERSE MATRIX of every possible numerical matrix combination.

Inverse of a matrix using numpy

I am writing some python code and I am receiving the error message as in the title, from searching this has to do with the character set. 

Here is the line that causes the error

    hc = HealthCheck(&quot;instance_health&quot;, interval=15, target808=&quot;HTTP:8080/index.html&quot;)

I cannot figure out what character is not in the ANSI ASCII set? Furthermore searching &quot;\xe2&quot; does not give anymore information as to what character that appears as. Which character in that line is causing the issue? 

I have also seen a few fixes for this issue but I am not sure which to use. Could someone clarify what the issue is (python doesn&#39;t interpret unicode unless told to do so?), and how I would clear it up properly?

EDIT:
Here are all the lines near the one that errors

    def createLoadBalancer():
        conn = ELBConnection(creds.awsAccessKey, creds.awsSecretKey)
        hc = HealthCheck(&quot;instance_health&quot;, interval=15, target808=&quot;HTTP:8080/index.html&quot;)
        lb = conn.create_load_balancer(&#39;my_lb&#39;, [&#39;us-east-1a&#39;, &#39;us-east-1b&#39;],[(80, 8080, &#39;http&#39;), (443, 8443, &#39;tcp&#39;)])
        lb.configure_health_check(hc)
        return lb
    

Python &quot;SyntaxError: Non-ASCII character &#39;\xe2&#39; in file&quot;

What is the method to convert a Python list of strings to a `pd.Series` object?

(pandas Series objects can be converted to list using `tolist()` method--but how to do the reverse conversion?)

Convert Python list to pandas Series

    from numpy import *

    m = array([[1,0],
               [2,3]])

I would like to compute the element-wise `log2(m)`, but only in the places where `m` is not 0. In those places, I would like to have 0 as a result.

I am now fighting against:

    RuntimeWarning: divide by zero encountered in log2

---

**Try 1: using `where`**

    res = where(m != 0, log2(m), 0)

which computes me the correct result, but I still get logged a `RuntimeWarning: divide by zero encountered in log2`. It looks like (and syntactically it is quite obvious) numpy still computes `log2(m)` on the full matrix and only afterwards `where` picks the values to keep.

I would like to avoid this warning.

---

**Try 2: using masks**

    from numpy import ma

    res = ma.filled(log2(ma.masked_equal(m, 0)), 0)

Sure masking away the zeros will prevent `log2` to get applied to them, won&#39;t it? Unfortunately not: We still get `RuntimeWarning: divide by zero encountered in log2`.

Even though the matrix is masked, `log2` still seems to be applied to every element.

---

**How can I efficiently compute the element-wise log of a numpy array without getting division-by-zero warnings?**

* Of course I could temporarily disable the logging of these warnings using `seterr`, but that doesn&#39;t look like a clean solution.
* And sure a double *for* loop would help with treating 0s specially, but defeats the efficiency of numpy.

Any ideas?

numpy: Efficiently avoid 0s when taking log(matrix)

How can I import factorial function from numpy and scipy separately in order to see which one is faster? 

I already imported factorial from python itself by import math. But, it does not work for numpy and scipy.

Factorial in numpy and scipy

I need to calculate the number of non-NaN elements in a numpy ndarray matrix. How would one efficiently do this in Python? Here is my simple code for achieving this: 

    import numpy as np

    def numberOfNonNans(data):
        count = 0
        for i in data:
            if not np.isnan(i):
                count += 1
        return count 

Is there a built-in function for this in numpy? Efficiency is important because I&#39;m doing Big Data analysis. 

Thnx for any help! 

Counting the number of non-NaN elements in a numpy ndarray in Python

I am a bit struggled with so many `int` data types in cython.

`np.int, np.int_, np.int_t, int`

I guess `int` in pure python is equivalent to `np.int_`, then where does `np.int` come from? I cannot find the document from numpy? Also, why does `np.int_` exist given we do already have `int`?

In cython, I guess `int` becomes a C type when used as `cdef int` or `ndarray[int]`, and when used as `int()` it stays as the python caster?

Is `np.int_` equivalent to `long` in C? so `cdef long` is the identical to `cdef np.int_`?

Under what circumstances should I use `np.int_t` instead of `np.int`? e.g. `cdef np.int_t`, `ndarray[np.int_t]` ...

Can someone briefly explain how the wrong use of those types would affect the performance of compiled cython code?


Content Type	Original Author	Original Content on Stackoverflow
Question	GeauxEric	View Question on Stackoverflow
Solution 1 - Python	user2357112	View Answer on Stackoverflow
Solution 2 - Python	john ktejik	View Answer on Stackoverflow
Solution 3 - Python	Markus Dutschke	View Answer on Stackoverflow
Solution 4 - Python	Ramon Balthazar	View Answer on Stackoverflow
Solution 5 - Python	E.Zolduoarrati	View Answer on Stackoverflow

RuntimeWarning: divide by zero encountered in log

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Windows shortcut to run a Git Bash script

iOS, How to use GMSCoordinateBounds to show all the markers of the map?

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