Starting a presentation on plotting with matplotlib

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+slides.pdf

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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
+----------------------
+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)
+
+.. code-block:: python
+
+   >>> import matplotlib.pyplot as plt
+   >>>
+   >>> t = range(50)
+   >>> x = (ran.rand(50)*50) + 2000 # I don't have real data
+   >>> plt.plot(t, x)
+   >>> plt.title('Some time series with left title', loc='left')
+   >>> plt.ylabel('Mass of test mass over time')
+   >>> plt.show()