Base code to make context menu work with wxPython CustomTreeCtrl

After it took me an hour or two to figure that out, here is some base Python code snippet showing how to make context menues work with wxPython’s CustomTreeCtrl:

class CustomTreeCtrl(CT.CustomTreeCtrl):

    def __init__(self, parent, id=wx.ID_ANY, pos=wx.DefaultPosition,
                 size=wx.DefaultSize,
                 style=wx.SUNKEN_BORDER|wx.WANTS_CHARS,
                 agwStyle=CT.TR_HAS_BUTTONS|CT.TR_HAS_VARIABLE_ROW_HEIGHT,
                 log=None):

        CT.CustomTreeCtrl.__init__(self, parent, id, pos, size, style, agwStyle)
        
        # ...        
        self.item = None
        self.Bind(wx.EVT_CONTEXT_MENU, self.OnContextMenu)
        self.Bind(wx.EVT_RIGHT_DOWN, self.OnRightDown)
        
    def OnRightDown(self, event):

        pt = event.GetPosition()
        item, flags = self.HitTest(pt)

        if item:
            self.item = item    
        
    def OnContextMenu(self, event):
        # Setup right-click menu for tree items
        menu = wx.Menu()
        treeMenuItem1 = menu.Append(wx.ID_ANY, "Menu Entry 1")
        treeMenuItem2 = menu.Append(wx.ID_ANY, "Menu Entry 2")
        self.Bind(wx.EVT_MENU, self.OnMenuEntry1, treeMenuItem1)
        self.Bind(wx.EVT_MENU, self.OnMenuEntry2, treeMenuItem2)
        
        self.PopupMenu(menu)
        menu.Destroy()
        
    def OnMenuEntry1(self, event):
        print "Method OnMenuEntry1 not implemented yet."
        
    def OnMenuEntry2(self, event):
        print "Method OnMenuEntry2 not implemented yet."

In essence:

  • we need a method to capture what item has been selected in a tree. This is done by OnRightDown() and the HitTest() method. We store the selected item in an attribute of our CustomTreeCtrl object: self.item
  • we need a method to display the context menu: OnContextMenu()
  • we need one method per context menu entry, yet to be implemented: OnMenuEntry1(), onMenuEntry2(), …
  • we need to ensure the proper bindings so that all these methods working, thus:
    • binding wx.EVT_CONTEXT_MENU to OnContextMenu() in the constructor of our CustomTreeCtrl object
    • binding wx.EVT_RIGHT_DOWN to OnRightDown() in the constructor of our CustomTreeCtrl object
    • binding wx.EVT_MENU to every method used to handle clicking a menu entry, like OnMenuEntry1(), onMenuEntry2(), in OnContextMenu()
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wxGlade and Accelerator Keys

Currently I am using wxGlade 0.8.3 and wxPython 4.0.3 and I observe a strange behavior when using accelerator keys for menus.

In wxGlade I have to define them using a double back-slash, otherwise it won’t work:

wxGlade Accelerator Keys

The weird outcome is, my GUI afterwards looks like this:

wxPython menu with Accelerator Keys
Thus I need this Python code to fix my menu entries:

for mi in self.GetMenuBar().GetMenu(0).GetMenuItems():
  txt = mi.GetItemLabel()
  if "\\" in txt:
    mi.SetItemLabel(txt.replace("\\",""))

Hmmm

Jupyter: Plotting pivots & changing legend entries

A while ago I blogged about project Jupyter and in the last days I have been working a lot with it and I am still fascinated by its power.

Today I faced and solved two challenges I like to share here:
. plotting a pivot table
. changing legend entries

Assume we have the following dataframe:

Creating a pivot is a piece of cake by using the pandas pivot_table method on that dataframe:

Code:
pivot = pd.pivot_table(df,index=["Org"],values=["Male employees","Female employees"], 
    aggfunc=[len,np.mean,np.min,np.max,np.sum])

 

This gets us
. the number of departments per org ( = len Female employees or len Male employees )
. the sum of male and female employees per org ( = sum Female employees and sum Male employees )
. as well as mean, min and max

How to plot ?
We can simply save the pivot tables as a new dataframe ‘pivot’ and call its plot method. Let’s say we want to plot sum of male and female employees per org. First we need to drop the other statistics from the pivot table we don’t need for the plot. Then we plot:

 

Code:
pivot.drop(['len','mean','amin','amax'],axis=1).plot(kind="barh") 
plt.show()

 

Only problem here is that the legend entries of this plot look a bit cryptic. Here is some code to fix this:

 

Code:
ax = plt.gca() 
handles,labels = ax.get_legend_handles_labels() 
new_labels = [] 
for l in labels: 
    new_labels.append(l.split(",")[-1][:-1]) 
ax.legend(handles, new_labels)  
plt.show()

 

I have shared the entire notebook here.

How to print ipython notebooks without the source code

This is something I really need to create sort of standard reports based on ipython notebooks which should not contain the source code and input prompts of ipython cells: the capability to print ipython notebooks without the source code.

There are ways to do that as discussed here on stackoverflow but all these methods involve adding some ugly code to your ipython cells or tweaking the way the ipython server is started ( or running nbconvert ) which might be out of your control if you use some cloud offering like Data Science Experience on IBM Cloud and not your own ipython installation.

Here is how I achieve this:

I simply download my notebook as html.

Then I run this python script to convert that html file so that prompts and code cells are gone:

FILE = "/somewhere/myHTMLFile.html"

with open(FILE, 'r') as html_file:
    content = html_file.read()

# Get rid off prompts and source code
content = content.replace("div.input_area {","div.input_area {\n\tdisplay: none;")    
content = content.replace(".prompt {",".prompt {\n\tdisplay: none;")

f = open(FILE, 'w')
f.write(content)
f.close()

That script bascially adds the CSS ‘display: none’ attribute for all divs of class ‘prompt’ or ‘input_area’.

That tweaked html page now easily can be printed into a pdf file for me to get my standard report without any code or input prompt cells.

If you know what you are doing you can add more CSS tweaking, like e.g. this one, to that Python code:

# For dataframe tables use Courier font family with smaller font size
content = content.replace(".dataframe thead","table.dataframe { font-size: 7px; font-family: Courier; }\n.dataframe thead")

To figure out things like that I used Firefox Inspector to determine class names of DOM elements ( like e.g. ‘div.data_frame’ is used to display dataframe tables in ipython ) and some CSS knowledge to achieve the manipulations I find useful, like reducing the font size of tables in order to make them fit on pages printed with portrait orientation.

Scrapy

Yesterday during another boring phone call I googled for “fun python packages” and bumped into this nice article: “20 Python libraries you can’t live without“. While I already knew many of the packages mentioned there one caught my interest: Scrapy. Scrapy seems to be an elegant way not only for parsing web pages but also for travelling web pages, mainly those which have some sort of ‘Next’ or ‘Older posts’ button you wanna click through to e.g. retrieve all pages from a blog.

I installed Scrapy and ran into one import error, thus as mentioned in the FAQ and elsewhere I had to manually install pypiwin32:

pip install pypiwin32

Based on the example on the home page I wrote a little script to retrieve titles and URLs from my German blog “Axel Unterwegs” and enhanced it to write those into a Table-Of-Contents type HTML file, after figuring out how to overwrite the Init and Close method of my spider class.

import scrapy
header = """
<html><head>
<meta content='text/html; charset=UTF-8' http-equiv='Content-Type'/>
</head><body>
"""
footer = """
</body></html> 
"""

class BlogSpider(scrapy.Spider):
 name = 'blogspider'
 start_urls = ['http://axelunterwegs.blogspot.co.uk/']
 
 def __init__(self, *a, **kw):
   super(BlogSpider, self).__init__(*a, **kw)
   self.file = open('blogspider.html','w')
   self.file.write(header)

 def parse(self, response):
   for title in response.css('h3.post-title'):
     t = title.css('a ::text').extract_first()
     url = title.css('a ::attr(href)').extract_first()
     self.file.write("<a target=\"_NEW_\" href=\"%s\">%s</a>\n<br/>" % (url.encode('utf8'),t.encode('utf8')))
     yield {'title': t, 'url': url}

   for next_page in response.css('a.blog-pager-older-link'):
     yield response.follow(next_page, self.parse)
 
 def spider_closed(self, spider):
   self.file.write(footer)
   self.file.close()

Thus, here is the TOC of my German blog.

I tried to get the same done with my English blog here on WordPress but have been struggling so far. One challenge is that the modern UI of WordPress does not have any ‘Older posts’ type of button anymore; new postings are retrieved as soon as you scroll down. Also the parsing doesn’t seem to work for now, but may be I figure it out some time later.

 

 

Project Jupyter

Project Jupyter is an open source project allowing to run Python code in a web browser, focusing to support interactive data science and scientific computing not only for Python but across all programming languages. It is a spin-off from IPython I blogged about here.
Typically you would have to install Jupyter and a full stack of Python packages on your computer and start the Jupyter server to get started.
But there is also an alternative available in the web where you can run IPython notebooks for free: https://try.jupyter.org/
This site does not allow you to save your projects permanently but you can export projects and download and also upload notebooks from your local computer.
IPython notebooks are a great way to get started with Python and learn the language. It makes it easy to run your script in small increments and preserves the state of those increments aka cells. It also nicely integrates output into your workflow including graphical plots created with packages like matplotlib.pyplot, and it comes with some primitive markup language to add documentation to your scripts.
The possibilities are endless with IPython or Jupyter – to learn Python as a language or data analysis techniques.
I was inspired by this video on IBM developerWorks to again get started with this: “Use data science to up your game performance“. And the book “Learning IPython for Interactive Computing and Data Visualization – Second Edition” by Cyrille Rossant is the source where I got this tip from about free Jupyter in the web.

Of course you can also sign up for a trial on IBMs Bluemix and start a IBM Data Science Experience project.

How to tag mp3 files

I have a collection of mp3 files which I have named in the form "ARTIST – TITLE.mp3" and wanted to get them tagged properly.
My first plan was to write a Python script to do so, I tried two Python libraries: pytaglib and eyeD3. pytaglib didn’t install, on Windows you need a Visual Studio C++ compiler installed to make it work, which I don’t have currently. pytaglib was the reason why I tried to deal with ubuntu which confronted me with lots of other problems and finally didn’t buy me anything since pytaglib also didn’t install properly on ubuntu and ran into some other compile issues.
eyeD3 installed but apparenty can not handle modern mp3 tag formats.
I also tried MusicBrainz recommend in this article "How to tag all your audio files in the fastest possible way", but its user interface is weird and didn’t get me my files tagged. And I tried the linux id3tag command mentioned in the same article, again no success, looks like it does not support latest tag formats neither.
Then I bumped into Mp3tag for Windows. Brilliant. It made it a piece of cake to tag my mp3 files through a function ‘filename to tag’ where you can specify some sort of pattern for the filenames you have been using, %Artist% – %Title%.mp3 in my case, and a few clicks later all my files have been tagged properly.
I right away donated 5 bucks to the author of this freeware tool.