# coding: utf-8

# # Table of Contents

# <p><div class="lev1 toc-item"><a href="#&quot;Living-in-a-noisy-world...&quot;,-using-James-Powell's-(dutc)-rwatch-module" data-toc-modified-id="&quot;Living-in-a-noisy-world...&quot;,-using-James-Powell's-(dutc)-rwatch-module-1"><span class="toc-item-num">1&nbsp;&nbsp;</span><em>"Living in a noisy world..."</em>, using James Powell's (<code>dutc</code>) <code>rwatch</code> module</a></div><div class="lev2 toc-item"><a href="#Requirements-and-links" data-toc-modified-id="Requirements-and-links-11"><span class="toc-item-num">1.1&nbsp;&nbsp;</span>Requirements and links</a></div><div class="lev2 toc-item"><a href="#Defining-a-debugging-context-manager,-just-to-try" data-toc-modified-id="Defining-a-debugging-context-manager,-just-to-try-12"><span class="toc-item-num">1.2&nbsp;&nbsp;</span>Defining a debugging context manager, just to try</a></div><div class="lev3 toc-item"><a href="#Watching-just-one-object" data-toc-modified-id="Watching-just-one-object-121"><span class="toc-item-num">1.2.1&nbsp;&nbsp;</span>Watching just one object</a></div><div class="lev3 toc-item"><a href="#Can-we-delete-the-rwatch-?" data-toc-modified-id="Can-we-delete-the-rwatch-?-122"><span class="toc-item-num">1.2.2&nbsp;&nbsp;</span>Can we delete the <code>rwatch</code> ?</a></div><div class="lev3 toc-item"><a href="#More-useful-debuggin-information" data-toc-modified-id="More-useful-debuggin-information-123"><span class="toc-item-num">1.2.3&nbsp;&nbsp;</span>More useful debuggin information</a></div><div class="lev3 toc-item"><a href="#Watching-any-object" data-toc-modified-id="Watching-any-object-124"><span class="toc-item-num">1.2.4&nbsp;&nbsp;</span>Watching <em>any</em> object</a></div><div class="lev3 toc-item"><a href="#A-first-context-manager-to-have-debugging-for-one-object" data-toc-modified-id="A-first-context-manager-to-have-debugging-for-one-object-125"><span class="toc-item-num">1.2.5&nbsp;&nbsp;</span>A first context manager to have debugging for <em>one</em> object</a></div><div class="lev3 toc-item"><a href="#A-second-context-manager-to-debug-any-object" data-toc-modified-id="A-second-context-manager-to-debug-any-object-126"><span class="toc-item-num">1.2.6&nbsp;&nbsp;</span>A second context manager to debug <em>any</em> object</a></div><div class="lev2 toc-item"><a href="#Defining-a-context-manager-to-add-white-noise" data-toc-modified-id="Defining-a-context-manager-to-add-white-noise-13"><span class="toc-item-num">1.3&nbsp;&nbsp;</span>Defining a context manager to add white noise</a></div><div class="lev3 toc-item"><a href="#Capturing-any-numerical-value" data-toc-modified-id="Capturing-any-numerical-value-131"><span class="toc-item-num">1.3.1&nbsp;&nbsp;</span>Capturing any numerical value</a></div><div class="lev3 toc-item"><a href="#Adding-a-white-noise-for-numbers" data-toc-modified-id="Adding-a-white-noise-for-numbers-132"><span class="toc-item-num">1.3.2&nbsp;&nbsp;</span>Adding a white noise for numbers</a></div><div class="lev3 toc-item"><a href="#WhiteNoiseComplex-context-manager" data-toc-modified-id="WhiteNoiseComplex-context-manager-133"><span class="toc-item-num">1.3.3&nbsp;&nbsp;</span><code>WhiteNoiseComplex</code> context manager</a></div><div class="lev2 toc-item"><a href="#Defining-a-generic-noisy-context-manager" data-toc-modified-id="Defining-a-generic-noisy-context-manager-14"><span class="toc-item-num">1.4&nbsp;&nbsp;</span>Defining a generic <code>noisy</code> context manager</a></div><div class="lev2 toc-item"><a href="#Conclusion" data-toc-modified-id="Conclusion-15"><span class="toc-item-num">1.5&nbsp;&nbsp;</span>Conclusion</a></div>

# # *"Living in a noisy world..."*, using James Powell's (`dutc`) `rwatch` module

#

# Goal : I want to write a [context manager](https://docs.python.org/3.5/library/stdtypes.html#typecontextmanager) for [Python 3.5+](https://www.Python.org/), so that inside the context manager, every number is seen noisy (with a white Gaussian noise, for instance).

#

# > It will be like being drunk, except that your it will be my Python interpretor and not me !

#

# For instance, I will like to have this feature:

# ```python

# >>> x = 120193

# >>> print(x)

# 120193

# >>> np.random.seed(1234)

# >>> with WhiteNoise():

# >>> print(x)

# 120193.47143516373249306

# ```

# ----

# ## Requirements and links

# First, we will need [numpy](https://docs.scipy.org/doc/numpy/user/whatisnumpy.html) to have some random number generator, as `numpy.random.normal` to have some 1D Gaussian noise.

# In[1]:

import numpy as np

# In[2]:

np.random.seed(1234)

np.random.normal()

# Then, the core part will be to install and import [James Powell (`dutc`)](https://github.com/dutc/) [`rwatch`](https://github.com/dutc/rwatch) module.

# If you don't have it installed :

#

# 1. Be sure to have CPython 3.5. `rwatch` patches the CPython eval loop, so it's fixed to specific versions of Python & the author lazy about keeping it updated.

# 2. Then `pip install dutc-rwatch`. It should work, but it fails for me.

# 3. (alternative) You can just `cd /tmp/ && git clone https://github.com/dutc/rwatch && cd rwatch/src/ && make` and copy the `rwatch.so` dynamic library wherever you need...

# In[5]:

get_ipython().run_cell_magic('bash', '', 'tmpdir=$(mktemp -d)\ncd $tmpdir\ngit clone https://github.com/dutc/rwatch\ncd rwatch/src/\nmake\nls -larth ./rwatch.so\nfile ./rwatch.so\n# cp ./rwatch.so /where/ver/you/need/ # ~/publis/notebook/ for me')

# Anyhow, if `rwatch` is installed, we can import it, and it enables two new functions in the `sys` module:

# In[6]:

import rwatch

from sys import setrwatch, getrwatch

setrwatch({}) # clean any previously installed rwatch

getrwatch()

# Finally, we need the [`collections`](https://docs.python.org/3/library/collections.html) module and its [`defaultdict`](https://docs.python.org/3/library/collections.html#collections.defaultdict) magical datastructure.

# In[7]:

from collections import defaultdict

# ----

# ## Defining a debugging context manager, just to try

#

# This is the first example given in [James presentation]() at PyCon Canada 2016.

#

# We will first define and add a `rwatch` for just one object, let say a variable `x`, and then for any object using `defaultdict`.

# From there, writing a context manager that enables this feature only locally is easy.

# ### Watching just one object

# 1. Write the function, that needs two argument `frame, obj`, and should return `obj`,

# 2. Install it...

# 3. Check it!

# In[15]:

def basic_view(frame, obj):

print("Python saw the object {} from frame {}".format(obj, frame))

return obj

# In[16]:

x = "I am alive!"

# In[17]:

setrwatch({

id(x): basic_view

})

# In[18]:

print(x)

# That's awesome, it works!

# ### Can we delete the `rwatch` ?

# Sure!

# In[19]:

def delrwatch(idobj):

getrwatch().pop(idobj, None)

# In[20]:

print(x)

delrwatch(id(x))

print(x) # no more rwatch on this!

print(x) # no more rwatch on this!

# We can also delete rwatches that are not defined, without a failure:

# In[21]:

y = "I am Zorro !"

print(y)

delrwatch(y) # No issue!

print(y)

# ### More useful debuggin information

# What is this `frame` thing?

# It is described in the documentation of the [`inspect`](https://docs.python.org/3/library/inspect.html) module.

#

# We can actually use it to display some useful information about the object and where was it called etc.

# In[22]:

from inspect import getframeinfo

def debug_view(frame, obj):

return obj

# In[23]:

setrwatch({})

setrwatch({

id(x): debug_view

})

getrwatch()

# In[24]:

print(x)

# That can be quite useful!

# ### Watching *any* object

# We can actually pass a `defaultdict` to the `setrwatch` function, so that *any* object will have a rwatch!

#

# > **Warning**: obviously, this will crazily slowdown your interpreter!

#

# So let be cautious, and only deal with strings here.

#

# But I want to be safe, so it will only works if the frame indicate that the variable does not come from a file.

# In[25]:

setrwatch({})

# In[26]:

def debug_view_for_str(frame, obj):

return obj

# In[27]:

setrwatch(defaultdict(lambda: debug_view_for_str))

# In[28]:

print(x)

# Clearly, there is a lot of strings involved, mainly because this is a notebook and not the simple Python interpreter, so filtering on the `info.filename` as I did was smart.

# In[29]:

setrwatch({})

# But obviously, having this for all objects is incredibly verbose!

# In[30]:

def debug_view_for_any_object(frame, obj):

return obj

# Let check that one, on a very simple example (which runs in less than `20` micro seconds):

# In[52]:

print(x)

get_ipython().magic('time 123 + 134')

# In[53]:

setrwatch({})

setrwatch(defaultdict(lambda: debug_view_for_any_object))

print(x)

get_ipython().magic('time 123 + 134')

setrwatch({})

# It seems to work very well!

#

# But it slows down everything, obviously the filtering takes time (for *every* object!)

# Computing `123 + 134 = 257` took about `10` miliseconds! That's just CRAZY!

# ### A first context manager to have debugging for *one* object

#

# It would be nice to be able to turn on and off this debugging tool whenever you want.

#

# Well, it turns out that [context managers](https://docs.python.org/3.5/library/stdtypes.html#typecontextmanager) are exactly meant for that!

# They are simple classes with just a `__enter__()` and `__exit__()` special methods.

#

# First, let us write a context manager to debug ONE object.

# In[54]:

class InspectThisObject(object):

delrwatch(self.idobj)

# We can check it:

# In[55]:

z = "I am Batman!"

print(z)

with InspectThisObject(z):

print(z)

print(z)

# The first debug information shows line 5, which is the line where `print(z)` is.

# ### A second context manager to debug *any* object

# Easy:

# In[56]:

class InspectAllObjects(object):

setrwatch({})

# It will probably break everything in the notebook, but works in a basic Python interpreter.

# In[57]:

with InspectAllObjects():

print(0)

# The 5th debug information printed is `Access to 0 (@0xXXX) at <ipython-input-41-XXX>:2:<module>`, showing the access in line `#2` of the constant `0`.

# In[58]:

with InspectAllObjects():

print("Darth Vader -- No Luke, I am your Father!")

print("Luke -- I have a father? Yay! Let's eat cookies together!")

# We also see here the `None` and `{}` objects being given to the context manager (see the `__enter__` method at first, and `__exit__` at the end).

# ----

# ## Defining a context manager to add white noise

#

# Basically, we will do as above, but instead of debug information, a white noise sampled from a Normal distribution (i.e., $\sim \mathcal{N}(0, 1)$) will be *added* to any number.

# ### Capturing any numerical value

# To capture both integers and float numbers, the [`numbers.Number`](https://docs.python.org/2/library/numbers.html#numbers.Number) abstract class is useful.

# In[59]:

from numbers import Number

# ### Adding a white noise for numbers

# This is very simple.

#

# But I want to be safe, so it will only works if the frame indicate that the number does not come from a file, as previously.

# In[60]:

def add_white_noise_to_numbers(frame, obj):

return obj

# Let us try it out!

# In[63]:

np.random.seed(1234)

setrwatch({})

x = 1234

print(x)

getrwatch()[id(x)] = add_white_noise_to_numbers

print(x) # huhoww, that's noisy!

print(10 * x + x + x**2) # and noise propagate!

setrwatch({})

print(x)

print(10 * x + x + x**2)

# It seems to work!

# Let's do it for any number then...

#

# ... Sadly, it's actually breaking the interpreter, which obviously has to have access to non-noisy constants and numbers to work !

#

# We can lower the risk by only adding noise to *complex* numbers.

# I guess the interpreter doesn't need complex numbers, write?

# In[64]:

def add_white_noise_to_complex(frame, obj):

return obj

# In[65]:

np.random.seed(1234)

setrwatch({})

y = 1234j

print(y)

setrwatch(defaultdict(lambda: add_white_noise_to_complex))

print(y) # huhoww, that's noisy!

setrwatch({})

print(y)

# Awesome!

#

# > « Now, the real world is non noisy, but the complex one is! »

#

# That's one sentence I thought I would never say!

# ### `WhiteNoiseComplex` context manager

#

# To stay cautious, I only add noise to complex numbers.

# In[67]:

class WhiteNoiseComplex(object):

setrwatch({})

# And it works as expected:

# In[69]:

np.random.seed(120193)

print(120193, 120193j)

with WhiteNoiseComplex():

print(120193, 120193j) # Huhoo, noisy!

print(120193, 120193j)

print(0*1j)

with WhiteNoiseComplex():

print(0*1j) # Huhoo, noisy!

print(0*1j)

# ----

# ## Defining a generic `noisy` context manager

#

# This will be a very simple change from the previous one, by letting the `Noisy` class accept *any* noisy function, which takes `obj` and return a noisy version of `obj`, only for complex-valued objects.

# In[71]:

class Noisy(object):

setrwatch({})

# In[73]:

print(1j)

with Noisy(lambda obj: obj + np.random.normal()):

print(1j)

print(1j)

# In[74]:

print(1j)

with Noisy(lambda obj: obj * np.random.normal()):

print(1j)

print(1j)

# In[75]:

print(1j)

with Noisy(lambda obj: obj + np.random.normal(10, 0.1) + np.random.normal(10, 0.1) * 1j):

print(1j)

print(1j)

# ----

# ## Conclusion

# Clearly, that was a BAD idea, and not so useful.

#

# But it was interesting!

#

# I don't have any idea of a context where this could be useful, but still!