Advanced features

Here are yet more tools that the average user won’t need, but might come in handy one day.

Nested dictionaries

Nested dictionaries are a useful way of storing complex data (and in fact are more or less the basis of JSON), but can be a pain to interact with if you don’t know the structure in advance. Sciris has several functions for working with nested dictionaries. For example:

import sciris as sc

# Create the structure
nest = {}
sc.makenested(nest, ['key1','key1.1'])
sc.makenested(nest, ['key1','key1.2'])
sc.makenested(nest, ['key1','key1.3'])
sc.makenested(nest, ['key2','key2.1','key2.1.1'])
sc.makenested(nest, ['key2','key2.2','key2.2.1'])

# Set the value for each "twig"
count = 0
for twig in sc.iternested(nest):
    count += 1
    sc.setnested(nest, twig, count)

# Convert to a JSON to view the structure more clearly
sc.printjson(nest)

# Get all the values from the dict
values = []
for twig in sc.iternested(nest):
    values.append(sc.getnested(nest, twig))
print(f'{values = }')
{
  "key1": {
    "key1.1": 1,
    "key1.2": 2,
    "key1.3": 3
  },
  "key2": {
    "key2.1": {
      "key2.1.1": 4
    },
    "key2.2": {
      "key2.2.1": 5
    }
  }
}
values = [1, 2, 3, 4, 5]

Sciris also has a sc.search() function, which can find either keys or values that match a certain pattern:

print(sc.search(nest, 'key2.1.1'))
print(sc.search(nest, value=5))
#0. ('key2', 'key2.1', 'key2.1.1'): 4
#0. ('key2', 'key2.2', 'key2.2.1'): 5

There’s even an sc.iterobj() function that can make arbitrary changes to an object:

def increment(obj):
    return obj + 1000 if isinstance(obj, int) and obj !=3 else obj

sc.iterobj(nest, increment, inplace=True)
sc.printjson(nest)
{
  "key1": {
    "key1.1": 1001,
    "key1.2": 1002,
    "key1.3": 3
  },
  "key2": {
    "key2.1": {
      "key2.1.1": 1004
    },
    "key2.2": {
      "key2.2.1": 1005
    }
  }
}

Context blocks

Sciris contains two context block (i.e. “with ... as”) classes for catching what happens inside them.

sc.capture() captures all text output to a variable:

import sciris as sc
import numpy as np

def verbose_func(n=200):
    for i in range(n):
        print(f'Here are 5 random numbers: {np.random.rand(5)}')

with sc.capture() as text:
    verbose_func()

lines = text.splitlines()
target = '777'
for l,line in enumerate(lines):
    if target in line:
        print(f'Found target {target} on line {l}: {line}')
Found target 777 on line 17: Here are 5 random numbers: [0.86434769 0.76208423 0.62616648 0.58109143 0.97077783]
Found target 777 on line 37: Here are 5 random numbers: [0.75759783 0.1830925  0.47770493 0.7601851  0.14085978]
Found target 777 on line 119: Here are 5 random numbers: [0.13971278 0.84046886 0.11777797 0.18922929 0.26975524]
Found target 777 on line 121: Here are 5 random numbers: [0.03777884 0.99598323 0.50191615 0.33829857 0.45410567]
Found target 777 on line 159: Here are 5 random numbers: [0.2195502  0.51647627 0.2311934  0.62816938 0.97772686]

The other function, sc.tryexcept(), is a more compact way of writing try ... except blocks, and gives detailed control of error handling:

def fickle_func(n=1):
    for i in range(n):
        rnd = np.random.rand()
        if rnd < 0.005:
            raise ValueError(f'Value {rnd:n} too small')
        elif rnd > 0.99:
            raise RuntimeError(f'Value {rnd:n} too big')

sc.heading('Simple usage, exit gracefully at first exception')
with sc.tryexcept():
    fickle_func(n=1000)

sc.heading('Store all history')
tryexc = None
for i in range(1000):
    with sc.tryexcept(history=tryexc, verbose=False) as tryexc:
        fickle_func()
tryexc.disp()




————————————————————————————————————————————————

Simple usage, exit gracefully at first exception

————————————————————————————————————————————————



<class 'ValueError'> Value 0.00307965 too small





—————————————————

Store all history

—————————————————



<sciris.sc_utils.tryexcept at 0x7f8f72e31f50>

[<class 'sciris.sc_utils.tryexcept'>, <class 'contextlib.suppress'>, <class 'contextlib.AbstractContextManager'>, <class 'abc.ABC'>]

————————————————————————————————————————————————————————————————————————

Methods:

  disp()                  to_df()                 traceback()             

————————————————————————————————————————————————————————————————————————

Properties:

  died                    exception               exceptions              

————————————————————————————————————————————————————————————————————————

catchtypes: ()

      data: [[<class 'ValueError'>, ValueError('Value 0.00182519 too

            small'), <tra [...]

defaultdie: False

  dietypes: ()

   message: ''

 outputstr: ''

   verbose: 0

 _abc_impl: <_abc._abc_data object at 0x7f8fb8a64300>

————————————————————————————————————————————————————————————————————————

Interpolation and optimization

Sciris includes two algorithms that complement their SciPy relatives: interpolation and optimization.

Interpolation

The function sc.smoothinterp() smoothly interpolates between points but does not use spline interpolation; this makes it somewhat of a balance between numpy.interp() (which only interpolates linearly) and scipy.interpolate.interp1d(..., method='cubic'), which takes considerable liberties between data points:

import sciris as sc
import numpy as np
import matplotlib.pyplot as plt
from scipy import interpolate

# Create the data
origy = np.array([0, 0.2, 0.1, 0.9, 0.7, 0.8, 0.95, 1])
origx = np.linspace(0, 1, len(origy))
newx  = np.linspace(0, 1)

# Create the interpolations
sc_y = sc.smoothinterp(newx, origx, origy, smoothness=5)
np_y = np.interp(newx, origx, origy)
si_y = interpolate.interp1d(origx, origy, 'cubic')(newx)

# Plot
kw = dict(lw=2, alpha=0.7)
plt.plot(newx, np_y, '--', label='NumPy', **kw)
plt.plot(newx, si_y, ':',  label='SciPy', **kw)
plt.plot(newx, sc_y, '-',  label='Sciris', **kw)
plt.scatter(origx, origy, s=50, c='k', label='Data')
plt.legend();

As you can see, sc.smoothinterp() gives a more “reasonable” approximation to the data, at the expense of not exactly passing through all the data points.

Optimization

Sciris includes a gradient descent optimization method, adaptive stochastic descent (ASD), that can outperform SciPy’s built-in optimization methods (such as simplex) for certain types of optimization problem. For example:

# Basic usage
import numpy as np
import sciris as sc
from scipy import optimize

# Very simple optimization problem -- set all numbers to 0
func = np.linalg.norm
x = [1, 2, 3]

with sc.timer('scipy.optimize()'):
    opt_scipy = optimize.minimize(func, x)

with sc.timer('sciris.asd()'):
    opt_sciris = sc.asd(func, x, verbose=False)

print(f'Scipy result:  {func(opt_scipy.x)}')
print(f'Sciris result: {func(opt_sciris.x)}')
scipy.optimize(): 8.85 ms
sciris.asd(): 2.98 ms
Scipy result:  4.829290232718364e-08
Sciris result: 2.915793584925527e-16

Compared to SciPy’s simplex algorithm, Sciris’ ASD algorithm was ≈3 times faster and found a result ≈8 orders of magnitude smaller.

Animation

And finally, let’s end on something fun. Sciris has an sc.animation() class with lots of options, but you can also just make a quick movie from a series of plots. For example, let’s make some lines dance:

plt.figure()
frames = [plt.plot(np.cumsum(np.random.randn(100))) for i in range(20)] # Create frames
sc.savemovie(frames, 'dancing_lines.gif'); # Save movie as a gif
MovieWriter imagemagick unavailable; using Pillow instead.
Saving 20 frames at 10.0 fps and 150 dpi to "dancing_lines.gif" using imagemagick...
Done; movie saved to "dancing_lines.gif"
File size: 272 KB
Elapsed time: 1.85 s

This creates the following movie, which is a rather delightful way to end:

We hope you enjoyed this series of tutorials! Remember, write to us if you want to get in touch.