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slides.rst

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-Surviving phishing
-------------------
-Password reuse, password managers and strong passwords
-======================================================
-.. contents:: :depth: 1
+Plotting with Matplotlib
+------------------------
 
-Why is Password Reuse a Problem?
---------------------------------
-.. image:: password_reuse_1.png
-   :height: 6.5cm
+Also creating a presentation with rst2pdf
+=========================================
 
-Consider the following hypothetical users that reuse a strong password in
-most places and the following common scenario:
+Data Structures
+---------------
+Favour simpler data structures if they do what you need.  In order:
 
-+------------------+--------------------------+
-| User             | Password                 |
-+==================+==========================+
-| mark1@gmail.com  | QUo5Qt+1Wa/Q1smDJRDbFg== |
-+------------------+--------------------------+
-| mark2@gmail.com  | +9Hz+/20rVkSkbcsmgdVFw== |
-+------------------+--------------------------+
-| mark3@gmail.com  | wnYkRcbi7Kkh7Fx2uR8EeA== |
-+------------------+--------------------------+
+#. Built-in Lists
+    - 2xN data or simpler
+    - Can't install system dependencies
+#. Numpy arrays
+    - 2 (or higher) dimensional data
+    - Lots of numerical calculations
+#. Pandas series/dataframes
+    - 'Data Wrangling', reshaping, merging, sorting, querying
+    - Importing from complex formats
 
-#. User registers an account with a careless service, eg Facebook, Yahoo,
-   Google, Equifax etc. etc.
-#. The service is hacked and the password and email is leaked
-#. The hacker logs in to the email account
-#. The hacker resets passwords on all important accounts tied to that email 
-   address
+Shamelessly stolen from https://stackoverflow.com/a/45288000
+
+Loading Data from Disk
+----------------------
+Natively
+========
+
+.. code-block:: python
+
+   >>> import csv
+   >>> with open('eggs.csv', newline='') as csvfile:
+   ...     spam = csv.reader(csvfile, 
+   ...                       delimiter=' ', 
+   ...                       quotechar='|')
+   ...     for row in spam:
+   ...         # Do things
+   ...         pass
+
+Loading Data from Disk
+----------------------
+Numpy
+=====
+
+.. code-block:: python
+
+   >>> import numpy
+   >>> spam = numpy.genfromtxt('eggs.csv', 
+   ...                         delimiter=' ', 
+   ...                         dtype=None) # No error handling!
+   >>> for row in spam:
+   ...     # Do things
+   ...     pass
+
+``numpy.genfromtxt`` will try to infer the datatype of each column if 
+``dtype=None`` is set.
+
+``numpy.loadtxt`` is generally faster at runtime if your data is well formated 
+(no missing values, only numerical data or constant length strings)
+
+Loading Data from Disk
+----------------------
+Numpy NB.
+=========
+**Remind me to look at some actual numpy usage at the end**
+
+- I think numpy does some type coercion when creating arrays.
+- Arrays created by ``numpy.genfromtxt`` can not in general be indexed like
+  ``data[xstart:xend, ystart:yend]``.
+- Data of unequal types are problematic!  Pandas *may* be a better choice in
+  that case.
+- Specifying some value for ``dtype`` is probably necessary in most cases in
+  practice: https://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html
+
+Loading Data from Disk
+----------------------
+Pandas
+======
+
+.. code-block:: python
+
+   >>> import pandas
+   >>> # dtype=None is def
+   >>> spam = pandas.read_csv('eggs.csv',
+   ...                        delimiter=' ',
+   ...                        header=None) 
+   >>> for row in spam:
+   ...     # Do things
+   ...     pass
+
+``header=None`` is required if the flie does not have a header.
 
 
-About password strength
------------------------
 
-How is strength measured?
-=========================
-'Entropy' `s` depends on the size of the alphabet `a` and the length `n` of the 
-password:
+Generating Data for Testing
+---------------------------
 
-.. math:: 
-        s = log_2(a^n)
+Generating the data on the fly with numpy is convenient.
 
-* 0889234877724602 -> 53 bits
-* ZeZJieatdH -> 60 bits
+.. code-block:: python
 
-Why are weak passwords problematic?
-===================================
-Weak passwords are trivial to crack in many situations. A password with 53 bits
-may be cracked by a criminal organisation in less than an hour.
+   >>> import numpy.random as ran
+   >>> # For repeatability 
+   >>> ran.seed(7890234) 
+   >>> # Uniform [0, 1) floats
+   >>> data = ran.rand(100, 2)
+   >>> # Uniform [0, 1) floats
+   >>> data = ran.rand(100, 100, 100)
+   >>> # Std. normal floats
+   >>> data = ran.randn(100)
+   >>> # 3x14x15 array of binomial ints with n = 100, p = 0.1
+   >>> data = ran.binomial(100, 0.1, (3, 14, 15))
+
+Plotting Time Series
+--------------------
+
+Plot data of the form:
+
+.. math:: y=f(t)
 
 
-What about strong passwords?
-============================
-They are difficult to remember, a problem especially when you use a different 
-strong password for every service.  You are also tempted to write them down, or 
-reuse them.
-
-It's surprisingly difficult for humans to generate good passwords!
-
-A strong password, as of 2019, has at least 80 bits of entropy.
-
-Password Managers to the Rescue!
---------------------------------
-Password managers allow you to create a unique and strong password for every
-service.
-
-Additional benefits:
-
-* Remembers passwords for you
-* Generates passwords for you
-* Automagically fills in passwords on websites for you, this is important!
-* Makes passwords available on all your configured devices
-* Can store additional related data, usernames, answers to security questions,
-  pins for debit/credit cards
-
-Any of the mainstream password manager is equivalent in the above respects.
-
-Can you trust password managers?
---------------------------------
-Yes*
-
-How do they keep passwords secure?
-----------------------------------
-1. User supplies a password
-2. A slow function derives an encryption key
-3. The encryption key is used to encrypt/decrypt your passwords
-
-Security of the encryption depends on the strengh of your
-password:  
-
-+---------+------------------------+
-| Entropy | Time to crack,         |
-|         | assuming 1 second per  |
-|         | attempt per typical    |
-|         | CPU                    |
-+=========+========================+
-| 50b     | < 1 Month              |
-+---------+------------------------+
-| 60b     | ~ 50 Years             |
-+---------+------------------------+
-| 70b     | ~ 50,000 yers          |
-+---------+------------------------+
-
-Generating a Strong Password
-----------------------------
-Passphrases are better than passwords:
-
-* Tr0ub4dor&3 -> 28 bits of entropy, hard to remember
-* correct horse battery stable -> 44 bits of entropy, easy to remember
-
-If you have to remember it, use a passphrase.
-
-Generate passphrases with Diceware_
-===================================
-1. Roll 5, 6 sided, *physical* dice
-2. Read the numbers left to right
-3. Find the word with that number on a list 6^5 (7776) words
-4. Repeat until desired length is reached.  For a password manager, use at 
-   least 7.
-5. Write down your passphrase on paper and keep it somewhere secure
-6. If you are 100% confident that you will not forget the passphrase, destroy
-   the paper by burning
-
-What about phishing?
-====================
-* A password manager will refuse to fill out a password on a spoofed website, 
-  for instance faceb00k.com vs facebook.com
-* Using different passwords on every service protects all other services even 
-  if phishing is successful on one of them
-* Good password managers will navigate to the login page for you, reducing the
-  risk of spoofed websites
+Subplots
+--------
 
 
-Other advice
+Saving Plots
 ------------
-In no particular order:
 
-* Only log in on webpages that you navigated to by typing in the url yourself,
-  by searching on google, duckduckgo or some other reputable search engine or 
-  from a bookmark.  If after clicking a link in an email you are directed to a 
-  log in page, it's probably a phishing attempt
-* Only log in to webpages that are protected by SSL/TLS (HTTPS).  Look for a 
-  green address bar, or a green lock icon or similar in your browser
-* Use two factor or two step authentication everywhere if possible
-* Turn of automatic image rendering. Better still, disable HTML rendering and 
-  authoring entirely in your email client
-* Be suspicious of *all* emails. Risky things: HTML email, images, unknown 
-  sender, poor spelling/grammer, 'Your email client can't display this email,
-  click here to view in your browser' or similar attempts to coerce you to click
-  on things
+So far I've just displayed plots with ``plt.show()``.  You can actually save 
+the plots from that interface manually, but when scripting, it's convenient
+to do so automatically:
+
+.. code-block:: python
+   
+   >>> # Some plotting has previously occured
+   >>> plt.savefig('eggs.pdf', dpi=300, transparent=False)
+
+The output format is interpreted from the file extension.  
+The keyword arguments are optional here.  Other options exist.
+
+Error Bars
+----------
+
+
+Stacked Bar Graph
+-----------------
+
 
 Resources
 ---------
+NumPy User Guide: https://docs.scipy.org/doc/numpy/user/index.html
 
-`EFF notes on Diceware`_ They generally have good advice for these kinds of 
-topics.
+NumPy Reference: https://docs.scipy.org/doc/numpy/reference/index.html#reference
 
-`This Presentation`_
+Matplotlib example gallery: https://matplotlib.org/gallery/index.html
 
-`Keepass`_, an offline password manager
+Pandas: It probably exists.  Good luck.
 
-`1Password`_, a pay to use password manager with some nice features
-
-`LastPass`_, an online password manager with a gratis tier
-
-.. _Diceware: http://world.std.com/~reinhold/diceware.html
-.. _EFF notes on Diceware: https://www.eff.org/dice
-.. _This Presentation: https://git.friedersdorff.com/max/intro_dice_and_pmgmnt
-.. _Keepass: https://keepass.info/
-.. _1Password: https://1password.com/
-.. _LastPass: https://www.lastpass.com/
-
-
-.. target-notes::
+This presentation: https://git.friedersdorff.com/max/plotting_with_matplotlib.git