CRÜCIAL PŸTHON: IPython Notebook Demo

In [1]:
from IPython.core.display import Image 
Image(filename='logo.png', width=600) 


IPython Notebook is a cell-based development environment. Each block of code (or text) can be run independently, and all cells share the same kernel. Each evaluated cell is stored in the In array, and the corresponding output is stored in Out.

Given $a \in \mathbb{R}^D$, compute $\sum_{i = 1}^D a[i]^2$.

In [2]:
def sum_of_squares(a):
    return np.sum(a**2)
In [3]:
foo = np.array([1, 3, 5])
In [4]:
print Out[3] + 4
In [6]:
# With the --pylab inline option, matplotlib plots are inline as cell output
w = np.linspace(0, 4*pi, 1000)
plt.plot(w, np.sin(w))
[<matplotlib.lines.Line2D at 0x103d7ec90>]

Tab Completion and documentation

IPython will keep track of the members of all of the modules you import and give you tab-completion. It will also tab-complete function arguments and provide help() documentation inline.

In [7]:
import scipy.signal
In [ ]:
In [8]:
Help on function firwin in module scipy.signal.fir_filter_design:

firwin(numtaps, cutoff, width=None, window='hamming', pass_zero=True, scale=True, nyq=1.0)
    FIR filter design using the window method.
    This function computes the coefficients of a finite impulse response
    filter.  The filter will have linear phase; it will be Type I if
    `numtaps` is odd and Type II if `numtaps` is even.
    Type II filters always have zero response at the Nyquist rate, so a
    ValueError exception is raised if firwin is called with `numtaps` even and
    having a passband whose right end is at the Nyquist rate.
    numtaps : int
        Length of the filter (number of coefficients, i.e. the filter
        order + 1).  `numtaps` must be even if a passband includes the
        Nyquist frequency.
    cutoff : float or 1D array_like
        Cutoff frequency of filter (expressed in the same units as `nyq`)
        OR an array of cutoff frequencies (that is, band edges). In the
        latter case, the frequencies in `cutoff` should be positive and
        monotonically increasing between 0 and `nyq`.  The values 0 and
        `nyq` must not be included in `cutoff`.
    width : float or None
        If `width` is not None, then assume it is the approximate width
        of the transition region (expressed in the same units as `nyq`)
        for use in Kaiser FIR filter design.  In this case, the `window`
        argument is ignored.
    window : string or tuple of string and parameter values
        Desired window to use. See `scipy.signal.get_window` for a list
        of windows and required parameters.
    pass_zero : bool
        If True, the gain at the frequency 0 (i.e. the "DC gain") is 1.
        Otherwise the DC gain is 0.
    scale : bool
        Set to True to scale the coefficients so that the frequency
        response is exactly unity at a certain frequency.
        That frequency is either:
            0 (DC) if the first passband starts at 0 (i.e. pass_zero
                is True);
            `nyq` (the Nyquist rate) if the first passband ends at
                `nyq` (i.e the filter is a single band highpass filter);
            center of first passband otherwise.
    nyq : float
        Nyquist frequency.  Each frequency in `cutoff` must be between 0
        and `nyq`.
    h : 1D ndarray
        Coefficients of length `numtaps` FIR filter.
        If any value in `cutoff` is less than or equal to 0 or greater
        than or equal to `nyq`, if the values in `cutoff` are not strictly
        monotonically increasing, or if `numtaps` is even but a passband
        includes the Nyquist frequency.
    Low-pass from 0 to f::
    >>> firwin(numtaps, f)
    Use a specific window function::
    >>> firwin(numtaps, f, window='nuttall')
    High-pass ('stop' from 0 to f)::
    >>> firwin(numtaps, f, pass_zero=False)
    >>> firwin(numtaps, [f1, f2], pass_zero=False)
    >>> firwin(numtaps, [f1, f2])
    Multi-band (passbands are [0, f1], [f2, f3] and [f4, 1])::
    >>>firwin(numtaps, [f1, f2, f3, f4])
    Multi-band (passbands are [f1, f2] and [f3,f4])::
    >>> firwin(numtaps, [f1, f2, f3, f4], pass_zero=False)
    See also


IPython includes "magics", which are provide bonus useful functionality in IPython only.

In [9]:
# %timeit runs a single line a bunch of times and reports the average run time
%timeit np.arange(1000)
%timeit xrange(1000)
100000 loops, best of 3: 2.42 ┬Ás per loop
1000000 loops, best of 3: 241 ns per loop
In [10]:
# prun runs Python's profiler on the current cell
N = 1e5
scipy.signal.fftconvolve(np.random.rand(N), np.random.rand(N))

In [11]:
# Not a magic, but IPython also allows for running shell commands
!ls -lh
total 240
-rw-r--r--  1 dawenl  staff    76K Feb  7 12:23 demo.ipynb
-rw-r--r--  1 dawenl  staff   2.3K Feb  7 12:23
[email protected] 1 dawenl  staff    36K Feb  6 23:41 logo.png
In [12]:
# The Python-Matlab bridge can be used as a magic
import pymatbridge as pymat
ip = get_ipython()
Starting MATLAB on http://localhost:64732
 visit http://localhost:64732/exit.m to shut down same
.....MATLAB started and connected!
In [13]:
# Python variable - a filter cutoff
Wn = .2
In [14]:
%%matlab -i Wn -o b
b = fir1(30, Wn);
In [15]:
import scipy.signal
w, h = scipy.signal.freqz(b);
plt.plot(w, np.abs(h))
[<matplotlib.lines.Line2D at 0x107ba15d0>]