Examples of Jupyter features

Questions

  • Mixed examples/exercises to practice various aspects of using Jupyter

Objectives

  • Learn more advanced usage of widgets.

  • Learn how to profile code and install a new line-profiler tool.

  • Practice some data analysis using pandas dataframes.

  • Learn how to define your own magic command.

  • Learn how to parallelize Python code using ipyparallel.

  • Learn how to mix Python with R in the same noteobook.

Widgets for interactive data fitting

Widgets for interactive data fitting

Widgets are fun, but they can also be useful. Here’s an example showing how you can fit noisy data interactively.

  1. Execute the cell below. It fits a 5th order polynomial to a gaussian function with some random noise

  2. Use the @interact decorator around the last two code lines such that you can visualize fits with polynomial orders n ranging from, say, 3 to 30:

import numpy as np

import matplotlib.pyplot as plt
%matplotlib inline

def gaussian(x, a, b, c):
    return a * np.exp(-b * (x-c)**2)

def noisy_gaussian():
    # gaussian array y in interval -5 <= x <= 5
    nx = 100
    x = np.linspace(-5.0, 5.0, nx)
    y = gaussian(x, a=2.0, b=0.5, c=1.5)
    noise = np.random.normal(0.0, 0.2, nx)
    y += noise
    return x, y

def fit(x, y, n):
    pfit = np.polyfit(x, y, n)
    yfit = np.polyval(pfit, x)
    return yfit

def plot(x, y, yfit):
    plt.plot(x, y, "r", label="Data")
    plt.plot(x, yfit, "b", label="Fit")
    plt.legend()
    plt.ylim(-0.5, 2.5)
    plt.show()

x, y = noisy_gaussian()
yfit = fit(x, y, n=5)  # fit a 5th order polynomial to it
plot(x, y, yfit)

Cell profiling

Cell profiling

This exercise is about cell profiling, but you will get practice in working with magics and cells.

  1. Copy-paste the following code into a cell:

    import numpy as np
import matplotlib.pyplot as plt

def step():
    import random
    return 1. if random.random() > .5 else -1.

def walk(n):
    x = np.zeros(n)
    dx = 1. / n
    for i in range(n - 1):
        x_new = x[i] + dx * step()
        if x_new > 5e-3:
            x[i + 1] = 0.
        else:
            x[i + 1] = x_new
    return x

n = 100000
x = walk(n)

  2. Split up the functions over 4 cells (either via Edit menu or keyboard shortcut Ctrl-Shift-minus).

  3. Plot the random walk trajectory using plt.plot(x).

  4. Time the execution of walk() with a line magic.

  5. Run the prun cell profiler.

  6. Can you spot a little mistake which is slowing down the code?

  7. In the next exercise you will install a line profiler which will more easily expose the performance mistake.

Installing a magic command for line profiling

Installing a magic command for line profiling

Magics can be installed using pip and loaded like plugins using the %load_ext magic. You will now install a line-profiler to get more detailed profile, and hopefully find insight to speed up the code from the previous exercise.

  1. If you haven’t solved the previous exercise, copy paste the following code into a cell and run it:

    import numpy as np
import matplotlib.pyplot as plt

def step():
    import random
    return 1. if random.random() > .5 else -1.

def walk(n):
    x = np.zeros(n)
    dx = 1. / n
    for i in range(n - 1):
        x_new = x[i] + dx * step()
        if x_new > 5e-3:
            x[i + 1] = 0.
        else:
            x[i + 1] = x_new
    return x

n = 100000
x = walk(n)

  2. Then install the line profiler using !pip install line_profiler.

  3. Next load it using %load_ext line_profiler.

  4. Have a look at the new magic command that has been enabled with %lprun?

  5. In a new cell, run the line profiler on the walk and step functions in the way described on the help page.

  6. Inspect the output. Can you more easily see the mistake now?

Data analysis with pandas dataframes

Data analysis with pandas dataframes

Data science and data analysis are key use cases of Jupyter. In this exercise you will familiarize yourself with dataframes and various inbuilt analysis methods in the high-level pandas data exploration library. A dataset containing information on Nobel prizes will be viewed with the file browser.

  1. Start by navigating in the File Browser to the data/ subfolder, and double-click on the nobels.csv dataset. This will open JupyterLab’s inbuilt data browser.

  2. Have a look at the data, column names, etc.

  3. In a your own notebook, import the pandas module and load the dataset into a dataframe:

import pandas as pd
nobel = pd.read_csv("data/nobels.csv")

  1. The “share” column of the dataframe contains the number of Nobel recipients that shared the prize. Have a look at the statistics of this column using

nobel["share"].describe()

  1. The describe() method is smart about data types. Try this:

nobel["bornCountryCode"].describe()

- What country has received the largest number of Nobel prizes, and how many?
- How many countries are represented in the dataset?

  1. Now analyze the age of prize recipients. You first need to convert the “born” column to datetime format:

nobel["born"] = pd.to_datetime(nobel["born"],
                               errors ='coerce')

  1. Next subtract the birth date from the year of receiving the prize and insert it into a new column “age”:

nobel["age"] = nobel["year"] - nobel["born"].dt.year

  • Now print the “surname” and “age” of first 10 entries using the head() method.

  1. Now plot results in two different ways:

nobel["age"].plot.hist(bins=[20,30,40,50,60,70,80,90,100], alpha=0.6);
nobel.boxplot(column="age", by="category")

  1. Which Nobel laureates have been Swedish? See if you can use the nobel.loc[CONDITION] statement to extract the relevant rows from the nobel dataframe using the appropriate condition.

  2. Finally, try the powerful groupby() method to analyze the number of Nobel prizes per country, and visualize it with the high-level seaborn plotting library.

  • First add a column “number” to the nobel dataframe containing 1’s (to enable the counting below).

  • Then extract any 4 countries (replace below) and create a subset of the dataframe:

countries = np.array([COUNTRY1, COUNTRY2, COUNTRY3, COUNTRY4])
nobel2 = nobel.loc[nobel['bornCountry'].isin(countries)]

  • Next use groupby() and sum(), and inspect the resulting dataframe:

nobels_by_country = nobel2.groupby(['bornCountry',"category"], sort=True).sum()

  • Next use the pivot_table method to reshape the dataframe to a spreadsheet-like structure, and display the result:

table = nobel2.pivot_table(values="number", index="bornCountry", columns="category", aggfunc=np.sum)

  • Finally visualize using a heatmap:

import seaborn as sns
sns.heatmap(table,linewidths=.5);

  • Have a look at the help page for sns.heatmap and see if you can find an input parameter which annotates each cell in the plot with the count number.

Defining your own custom magic command

Defining your own custom magic command

It is possible to create new magic commands using the @register_cell_magic decorator from the IPython.core library. Here you will create a cell magic command that compiles C++ code and executes it. This exercise requires that you have the GNU g++ compiler installed on your computer.

This example has been adapted from the IPython Minibook, by Cyrille Rossant, Packt Publishing, 2015.

  1. First import register_cell_magic

from IPython.core.magic import register_cell_magic

  1. Next copy-paste the following code into a cell, and execute it to register the new cell magic command:

@register_cell_magic
def cpp(line, cell):
    """Compile, execute C++ code, and return the standard output."""

    # We first retrieve the current IPython interpreter instance.
    ip = get_ipython()
    # We define the source and executable filenames.
    source_filename = '_temp.cpp'
    program_filename = '_temp'
    # We write the code to the C++ file.
    with open(source_filename, 'w') as f:
        f.write(cell)
    # We compile the C++ code into an executable.
    compile = ip.getoutput("g++ {0:s} -o {1:s}".format(
        source_filename, program_filename))
    # We execute the executable and return the output.
    output = ip.getoutput('./{0:s}'.format(program_filename))
    print('\n'.join(output))

  • You can now start using the magic using %%cpp.

  1. Write some C++ code into a cell and try executing it.

  2. To be able to use the magic in another notebook, you need to add the following function at the end and then write the cell to a file in your PYTHONPATH. If the file is called cpp_ext.py, you can then load it by %load_ext cpp_ext.

def load_ipython_extension(ipython):
    ipython.register_magic_function(cpp,'cell')

Parallel Python with ipyparallel

Parallel Python with ipyparallel

Traditionally, Python is considered to not support parallel programming very well (see “GIL”), and “proper” parallel programming should be left to “heavy-duty” languages like Fortran or C/C++ where OpenMP and MPI can be utilised.

However, IPython now supports many different styles of parallelism which can be useful to researchers. In particular, ipyparallel enables all types of parallel applications to be developed, executed, debugged, and monitored interactively. Possible use cases of ipyparallel include:

  • Quickly parallelize algorithms that are embarrassingly parallel using a number of simple approaches.

  • Run a set of tasks on a set of CPUs using dynamic load balancing.

  • Develop, test and debug new parallel algorithms (that may use MPI) interactively.

  • Analyze and visualize large datasets (that could be remote and/or distributed) interactively using IPython

This exercise is just to get started, for a thorough treatment see the official documentation and this detailed tutorial.

  1. First install ipyparallel using conda or pip. Open a terminal window inside JupyterLab and do the installation.

  2. After installing ipyparallel, you need to start an “IPython cluster”. Do this in the terminal with ipcluster start.

  3. Then import ipyparallel in your notebook, initialize a Client instance, and create DirectView object for direct execution on the engines:

import ipyparallel as ipp
client = ipp.Client()
print("Number of ipyparallel engines:", len(client.ids))
dview = client[:]

  1. You have now started the parallel engines. To run something simple on each one of them, try the apply_sync() method:

dview.apply_sync(lambda : "Hello, World")

  1. A serial evaluation of squares of integers can be seen in the code snippet below.

serial_result = list(map(lambda x:x**2, range(30)))

  • Convert this to a parallel calculation on the engines using the map_sync() method of the DirectView instance. Time both serial and parallel versions using %%timeit -n 1.

  1. You will now parallelize the evaluation of pi using a Monte Carlo method. First load modules, and export the random module to the engines:

from random import random
from math import pi
dview['random'] = random

Then execute the following code in a cell. The function mcpi is a Monte Carlo method to calculate $\pi$. Time the execution of this function using %timeit -n 1 and a sample size of 10 million (int(1e7)).

def mcpi(nsamples):
    s = 0
    for i in range(nsamples):
        x = random()
        y = random()
        if x*x + y*y <= 1:
            s+=1
    return 4.*s/nsamples

Now take the incomplete function below which takes a DirectView object and a number of samples, divides the number of samples between the engines, and calls mcpi() with a subset of the samples on each engine. Complete the function (by replacing the ____ fields), call it with $10^7$ samples, time it and compare with the serial call to mcpi().

def multi_mcpi(dview, nsamples):
    # get total number target engines
    p = len(____.targets)
    if nsamples % p:
        # ensure even divisibility
        nsamples += p - (nsamples%p)

    subsamples = ____//p

    ar = view.apply(mcpi, ____)
    return sum(ar)/____

Final note: While parallelizing Python code is often worth it, there are other ways to get higher performance out of Python code. In particular, fast numerical packages like Numpy should be used, and significant speedup can be obtained with just-in-time compilation with Numba and/or C-extensions from Cython.

Mixing Python and R

Mixing Python and R

Your goal now is to define a pandas dataframe, and pass it into an R cell and plot it with an R plotting library.

  1. First you need to install the necessary packages:

!conda install -c r r-essentials
!conda install -y rpy2

  1. To run R from the Python kernel we need to load the rpy2 extension:

%load_ext rpy2.ipython

  1. Run the following code in a code cell and plot it with the basic plot method of pandas dataframes:

import pandas as pd
df = pd.DataFrame({
    'cups_of_coffee': [0, 1, 2, 3, 4, 5, 6, 7, 8, 9],
    'productivity': [2, 5, 6, 8, 9, 8, 0, 1, 0, -1]
})

  1. Now take the following R code, and use the %%R magic command to pass in and plot the pandas dataframe defined above (to find out how, use %%R?):

library(ggplot2)
ggplot(df, aes(x=cups_of_coffee, y=productivity)) + geom_line()

  1. Play around with the flags for height, width, units and resolution to get a good looking graph.

Word-count analysis with widgets

Word-count analysis with widgets

This exercise uses the word-count project from earlier lessons.

  1. Have a look under the data/ directory. You will see four .dat files containing word-count statistics from books. You can try opening one.

  2. Open the Launcher, and open a new Text File.

  3. Copy-paste the code below to the text file, and save it to a file zipf.py (note how syntax highlighting gets activated).

    def load_word_counts(filename):
    """
    Load a list of (word, count, percentage) tuples from a file where each
    line is of the form "word count percentage". Lines starting with # are
    ignored.
    """
    counts = []
    with open(filename, "r") as input_fd:
    	     for line in input_fd:
          if not line.startswith("#"):
          	   fields = line.split()
                counts.append((fields[0], int(fields[1]), float(fields[2])))
    return counts

def top_n_word(counts, n):
    """
    Given a list of (word, count, percentage) tuples,
    return the top n word counts.
    """
    limited_counts = counts[0:n]
    count_data = [count for (_, count, _) in limited_counts]
    return count_data

def zipf_analysis(input_file, n=10):
    counts = load_word_counts(input_file)
    top_n = top_n_word(counts, n)
    return top_n

  4. Import the new zipf module, and have a look at the docstring for one of the functions:

    import zipf
zipf.top_n_word?

  5. Run the zipf_analysis() function for a processed datafile. Plot the output, and compare with a 1/N function, using the following code:

    import matplotlib.pyplot as plt
%matplotlib inline

nmax = 10
z = zipf.zipf_analysis("data/isles.dat", nmax)
n = range(1,nmax+1)
z_norm = [i/z[0] for i in z]
plt.plot(n,z_norm)
inv_n = [1.0/i for i in n]
plt.plot(n, inv_n)

  6. Add an interactive widget to analyze Zipf’s law, using for example this code:

    from ipywidgets import interact
import matplotlib.pyplot as plt
%matplotlib inline

nmax = 10
@interact(p=-1.0)
def zipf_plot(p):
    plt.clf()
    n = range(1,nmax+1)
    for f in ["data/isles.dat", "data/last.dat", "data/abyss.dat", "data/sierra.dat"]:
        z = zipf.zipf_analysis(f, nmax)
        z_norm = [i/z[0] for i in z]
        plt.plot(n,z_norm)
    inv_n = [i**p for i in n]
    plt.plot(n, inv_n)

  7. Add another widget parameter nmax to the above code to control the number of words displayed on the x-axis, e.g. nmax=(6,14), and play around with both sliders.