Images, hierarchical data, time

Image manipulation with scikit-image

Several image-processing libraries use numpy data structures underneath, e.g. Pillow and skimage.io. Let’s take a look at the latter.

from skimage import io   # scikit-image is a collection of algorithms for image processing
image = io.imread(fname="https://raw.githubusercontent.com/razoumov/publish/master/grids.png")
type(image)   # numpy array
image.shape   # 1024^2 image, with three colour (RGB) channels

Let’s plot this image using matplotlib:

io.imshow(image)
# io.show()   # only if working in a terminal
# io.imsave("tmp.png", image)

Using numpy, you can easily manipulate pixels, e.g.

image[:,:,2] = 255 - image[:,:,2]

and then plot it again.

Hierarchical data formats

We already saw Python dictionaries. You can save them in a file using a variety of techniques. One of the most popular techniques, especially among web developers, is JSON (JavaScript Object Notation), as its internal mapping is similar to that of a Python dictionary, with key-value pairs. In the file all data are stored as human-readable text, including any non-ASCII (Unicode) characters.

import json
x = {
  "name": "John",
  "age": 30,
  "married": True,
  "children": ("Ann","Billy"),
  "pets": None,
  "cars": [
    {"model": "BMW 230", "mpg": 27.5},
    {"model": "Ford Edge", "mpg": 24.1}
  ]
}
len(x)     # 6 key-value pairs
x.keys()   # here are the keys

filename = open("personal.json", "w")
json.dump(x, filename, indent = 2)   # serialize `x` as a JSON-formatted stream to `filename`
                  # `indent` sets field offsets in the file (for human readability)
filename.close()

...

import json
filename = open("personal.json", "r")
data = json.load(filename)   # read into a new dictionary
filename.close()
for k in data:
    print(k, data[k])

If you want to read larger and/or binary data, there is BSON format. Going step further, there are popular scientific data formats such as NetCDF and HDF5 for storing large multi-dimensional arrays and/or large hierarchical datasets, but we won’t study them here.

Working with time

In its standard library Python has high-level functions to work with time and dates:

from time import *
gmtime(0)   # show the starting epoch on my system (typically 1970-Jan-01 on Unix-like systems)
time()      # number of seconds since then = current time
ctime(time())   # convert that to human-readable time
ctime()         # same = current time

local = localtime()   # convert current date/time to a structure
local.tm_year, local.tm_mon, local.tm_mday
local.tm_hour, local.tm_min, local.tm_sec
local.tm_zone     # my time zone
local.tm_isdst    # Daylight Saving Time 1=on or 0=off

You can find many more examples here.