Inserting spaces into CamelCase

I have some trauma related to regular expressions from my university days. Yes, they are super useful. Do I enjoy using them? Does anyone?

Image result for regular expressions meme

I routinely modify and create new instances of the AMIS GIS data model. Depending on the client and the type of assets they have, there can be 4 feature datasets, containing about 5 feature classes each, or 9 feature datasets with up to 50 feature classes spread across the database.

Inevitably at some point in the process of losing my mind, I will forget that the alias is lost when creating a new feature class, or randomly copying it over or whatever. I then end up with dozens of feature classes with really ugly looking abbreviated layer names in CamelCase. So for posterity, and so that I will never ever forget this ridiculously easy thing again:

One thing I’ve omitted from the script is that I usually store the feature class names with a prefix, e.g. fc_name = wps_WaterPumpStation. In this case, I would use split on the feature class name before passing it to the alterFCAlias function i.e. fc_name.split("_")[1].

Link for mobile users is here.

Aaaaaaand I’ve just realised that I’ve forgotten this basic task so many times over the last few years that I actually already have a blog post about it, except for some reason I was updating the layer names in ArcMap every time instead of resetting it once on the feature classes themselves.

Image result for what were you thinking meme


How to run VBA code from a Python script

I recently modified a script I wrote to extract data from a Word document to a csv file. The modified script had to iterate over multiple docs and extract data from certain tables based on certain keywords and fields.

I used the python-docx module to do this, but hit an obstacle when I realised that it could not (as yet) parse Word’s content controls. Since I only had 9 documents, I opened each, pasted some VBA code pilfered off StackOverflow to remove all content controls from the document.

While that worked temporarily, my next step is of course to schedule the script to automatically pull the data out once the folder is updated with the new batch of docs for the month. A solution suggested entails the code being saved inside the doc so it can be called via com.

I’m not happy with that solution because I would still need to open each document and insert the code. What I need to do now is fiddle around some more so that the code can be saved inside the script and then run on each document as needed.

Convert a list of field names and aliases from Excel to table using ArcPy

I went digging through my old workspace and started looking at some of my old scripts. My style of coding back then is almost embarrassing now 🙂 but that’s just the process of learning. I decided to post this script I wrote just before ArcGIS released their Excel toolset in 10.2.

From what I can recall, I needed to create a file geodatabase table to store records of microbial sample data. Many of the field names were the chemical compound themselves, such as phosporus or nitrogen, or bacterial names. For brevity’s sake, I had to use the shortest field names possible while still retaining the full meaning.

I set up a spreadsheet containing the full list of field names in column FIELD_NAMES and their aliases in ALIAS. I created an empty table in a file gdb, and used a SearchCursor on the spreadsheet to create the fields and fill in their aliases.

This solution worked for me at the time, but of course there are now better ways to do this.

Reverse geocode spreadsheet coordinates using geocoder and pandas

I had a spreadsheet of coordinates, along with their addresses. The addresses were either inaccurate or missing. Without access to an ArcGIS licence, and knowing the addresses were not available on our enterprise geocoding service, I sought to find a quicker (and open-source) way.

I used the geocoder library to do this. I used it previously when I still had an ArcGIS Online account and a Bing key to check geocoding accuracy amongst the three providers.

Since I don’t have those luxuries anymore, I used pandas to read in the spreadsheet and reverse geocode the coordinates found in the the third and fourth columns. I then added a new column to the data frame to contain the returned address, and copied the data frame to a new spreadsheet.

Filter a pandas data frame using a mask

After using pandas for quite some time now, I started to question if I was really using it effectively. After two MOOCs in R about 2 or 3 years ago, I realised that because my GIS work wasn’t in analysis, I would not be able to use it properly.

Similarly, because pandas is essentially the R of Python, I thought I wouldn’t be able to use all the features it had to offer. As it stands, I’m still hovering around in the data munging side of pandas.

I used a pandas mask to filter a spreadsheet (or csv) based on some value. I originally used this to filter out which feature classes need to be created from a list of dozens of templates, but I’ve also used it to filter transactions in the money tracking app I made for my household.

Add a new field to a feature class using NumPy

I needed to add a field to dozens of layers. Some of the layers contained the field already, some of them contained a similar field, and some of them did not have the field. I did not want to batch Add Field, because not only would it fail on the layers which already had the field, but it is super slow and I would then still have to transfer the existing values from the old field to the new field.

I wrote the tool in the ArcMap Python window, because I found it easier to load my layers into an mxd first as they were lying all over the place. The new field to be added is set up as a NumPy array, with the relevant dtype.

The script loops over all the layers in the document, adding the field via the Extend Table tool, and then transferring the values from the old field to the new field. Deleting the old field at the end would be an appropriate step to include, but I didn’t, purely because I’ve lost data that way before.