Small visualization example¶

Import required packages¶

In [3]:

from unsat.io import tif_to_numpy
from unsat.plot import plot_slice, slicer
from unsat.Sampler import RectangularSampler
import matplotlib.pyplot as plt
import os

from unsat.io import tif_to_numpy from unsat.plot import plot_slice, slicer from unsat.Sampler import RectangularSampler import matplotlib.pyplot as plt import os

Set-up variables¶

In [4]:

PATH = "../data/"
EXAMPLE = os.path.join(PATH, "exp", "CoarseSand_Day2Growth.tif")

PATH = "../data/" EXAMPLE = os.path.join(PATH, "exp", "CoarseSand_Day2Growth.tif")

Store as numpy array¶

And explore some slices.

In [5]:

example_np = tif_to_numpy(EXAMPLE)

example_np = tif_to_numpy(EXAMPLE)

In [6]:

plot_slice(example_np, 300, 0)

plot_slice(example_np, 300, 0)

Out[6]:

<matplotlib.image.AxesImage at 0x7f7df86d09a0>

Use slicer and samplers¶

• slicer is just a wrapper for easy slicing
• Sampler class and subclasses contain functions for easy sampling

In [7]:

slice2d = slicer(example_np, 300, 1)  # Create a 2D array by sampling the 3D one
sam = RectangularSampler(slice2d, loc=[100, 200], size=[300, 400])
sam.plot()

slice2d = slicer(example_np, 300, 1) # Create a 2D array by sampling the 3D one sam = RectangularSampler(slice2d, loc=[100, 200], size=[300, 400]) sam.plot()

Extract the sampling area:

In [8]:

snap = sam.sample()
plt.imshow(snap)

snap = sam.sample() plt.imshow(snap)

Out[8]:

<matplotlib.image.AxesImage at 0x7f7df4a311b0>

Check if the sampling area is out of bounds:

In [9]:

sam.is_out()

sam.is_out()

Out[9]:

False