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dark.style

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slides.rst

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+Plotting with Matplotlib
+------------------------
+
+Also creating a presentation with rst2pdf
+=========================================
+
+Data Structures
+---------------
+Favour simpler data structures if they do what you need.  In order:
+
+#. 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
+
+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.
+
+
+
+Generating Data for Testing
+---------------------------
+
+Generating the data on the fly with numpy is convenient.
+
+.. code-block:: python
+
+   >>> 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)
+
+
+Subplots
+--------
+
+
+Saving Plots
+------------
+
+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
+
+NumPy Reference: https://docs.scipy.org/doc/numpy/reference/index.html#reference
+
+Matplotlib example gallery: https://matplotlib.org/gallery/index.html
+
+Pandas: It probably exists.  Good luck.
+
+This presentation: https://git.friedersdorff.com/max/plotting_with_matplotlib.git