# Code Images [](https://doi.org/10.5281/zenodo.1286417) Context This is a subset of the [Zenodo-ML Dinosaur Dataset](https://vsoch.github.io/datasets/2018/zenodo) [[Github](https://www.github.com/vsoch/zenodo-ml)] that has been converted to small png files and organized in folders by the language so you can jump right in to using machine learning methods that assume image input. Content Included are .tar.gz files, each named based on a file extension, and when extracted, will produce a folder of the same name. tree -L 1 . ├── c ├── cc ├── cpp ├── cs ├── css ├── csv ├── cxx ├── data ├── f90 ├── go ├── html ├── java ├── js ├── json ├── m ├── map ├── md ├── txt └── xml And we can peep inside a (somewhat smaller) of the set to see that the subfolders are zenodo identifiers. A zenodo identifier corresponds to a single Github repository, so it means that the png files produced are chunks of code of the extension type from a particular repository. $ tree map -L 1 map ├── 1001104 ├── 1001659 ├── 1001793 ├── 1008839 ├── 1009700 ├── 1033697 ├── 1034342 ... ├── 836482 ├── 838329 ├── 838961 ├── 840877 ├── 840881 ├── 844050 ├── 845960 ├── 848163 ├── 888395 ├── 891478 └── 893858 154 directories, 0 files Within each folder (zenodo id) the files are prefixed by the zenodo id, followed by the index into the original image set array that is provided with the full [dinosaur dataset archive](https://vsoch.github.io/datasets/2018/zenodo). $ tree m/891531/ -L 1 m/891531/ ├── 891531_0.png ├── 891531_10.png ├── 891531_11.png ├── 891531_12.png ├── 891531_13.png ├── 891531_14.png ├── 891531_15.png ├── 891531_16.png ├── 891531_17.png ├── 891531_18.png ├── 891531_19.png ├── 891531_1.png ├── 891531_20.png ├── 891531_21.png ├── 891531_22.png ├── 891531_23.png ├── 891531_24.png ├── 891531_25.png ├── 891531_26.png ├── 891531_27.png ├── 891531_28.png ├── 891531_29.png ├── 891531_2.png ├── 891531_30.png ├── 891531_3.png ├── 891531_4.png ├── 891531_5.png ├── 891531_6.png ├── 891531_7.png ├── 891531_8.png └── 891531_9.png 0 directories, 31 files > So what's the difference? The difference is that these files are organized by extension type, and provided as actual png images. The original data is provided as numpy data frames, and is organized by zenodo ID. Both are useful for different things - this particular version is cool because we can actually see what a code image looks like. > How many images total? We can count the number of total images: find "." -type f -name *.png | wc -l 3,026,993 # Dataset Curation The script to create the dataset is [provided here](https://github.com/vsoch/zenodo-ml/blob/master/preprocess/2.organize_by_language.py). Essentially, we start with the top extensions as identified by [this work](https://vsoch.github.io/2018/extension-counts/) (excluding actual images files) and then write each 80x80 image to an actual png image, organizing by extension then zenodo id (as shown above). # Saving the Image I tested a few methods to write the single channel 80x80 data frames as png images, and wound up liking cv2's imwrite function because it would save and then load the exact same content. import cv2 cv2.imwrite(image_path, image) # Loading the Image Given the above, it's pretty easy to load an image! Here is an example using scipy, and then for newer Python (if you get a deprecation message) using imageio. image_path = '/tmp/data1/data/csv/1009185/1009185_0.png' from imageio import imread image = imread(image_path) array([[116, 105, 109, ..., 32, 32, 32], [ 48, 44, 48, ..., 32, 32, 32], [ 48, 46, 49, ..., 32, 32, 32], ..., [ 32, 32, 32, ..., 32, 32, 32], [ 32, 32, 32, ..., 32, 32, 32], [ 32, 32, 32, ..., 32, 32, 32]], dtype=uint8) image.shape (80,80) # Deprecated from scipy import misc misc.imread(image_path) Image([[116, 105, 109, ..., 32, 32, 32], [ 48, 44, 48, ..., 32, 32, 32], [ 48, 46, 49, ..., 32, 32, 32], ..., [ 32, 32, 32, ..., 32, 32, 32], [ 32, 32, 32, ..., 32, 32, 32], [ 32, 32, 32, ..., 32, 32, 32]], dtype=uint8) Remember that the values in the data are characters that have been converted to ordinal. Can you guess what 32 is? ord(' ') 32 # And thus if you wanted to convert it back... chr(32) So how to reconstruct a line from the file? Let's try it! Here is the header for our file, followed by the first line. Based on it's location in the folder called csv, we know that this is a csv file. ''.join([chr(x) for x in image[0]]) # 'time,S1,S2 ' ''.join([chr(x) for x in image[1]]) # '0,0.00015,0 Inspiration These datasets can answer some of the same questions [discussed here](https://vsoch.github.io/datasets/2018/zenodo/#what-can-i-learn-from-this-dataset). Essentially, if we are able to classify languages based on patterns in the images, we can generate signatures for groupings of scripts (software packages). We can then detect these signatures in containers, associate signatures with domains of research, or with some quality or usage metric of the code.