diff --git a/talks/packages/packages.ipynb b/talks/packages/packages.ipynb
index 8578a8044fef7192d3e11f32f3ad040aa89de1af..7d179c45853dd11b13d627e75635edd65bdb0bb3 100644
--- a/talks/packages/packages.ipynb
+++ b/talks/packages/packages.ipynb
@@ -68,8 +68,8 @@
"source": [
"from scipy import optimize\n",
"def costfunc(params):\n",
- " return params[0] ** 2 * (params[1] - 3) ** 2 + (params[0] - 2) ** 2\n",
- "optimize.minimize(costfunc, x0=[0, 0], method='l-bfgs-b')"
+ " return (params[0] - 3) ** 2\n",
+ "optimize.minimize(costfunc, x0=[0], method='l-bfgs-b')"
]
},
{
@@ -130,6 +130,91 @@
"- [Bokeh](https://bokeh.pydata.org/en/latest/) among many others: interactive plots in the browser (i.e., in javascript)\n",
"\n",
"## [Ipython](http://ipython.org/)/[Jupyter](https://jupyter.org/) notebook: interactive python environments\n",
+ "Supports:\n",
+ "- run code in multiple languages"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%bash\n",
+ "for name in python ruby ; do\n",
+ " echo $name\n",
+ "done"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- debugging"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from scipy import optimize\n",
+ "def costfunc(params):\n",
+ " return 1 / params[0] ** 2\n",
+ "optimize.minimize(costfunc, x0=[0], method='l-bfgs-b')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%debug"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- timing/profiling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%prun\n",
+ "plt.plot([0, 3])"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- getting help"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.plot?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- [and much more...](https://ipython.readthedocs.io/en/stable/interactive/magics.html)\n",
+ "\n",
+ "The next generation is already out: [jupyterlab](https://jupyterlab.readthedocs.io/en/latest/)\n",
+ "\n",
"There are many [useful extensions available](https://github.com/ipython-contrib/jupyter_contrib_nbextensions).\n",
"\n",
"## [Pandas](https://pandas.pydata.org/): Analyzing \"clean\" data\n",
@@ -139,11 +224,11 @@
"- fast IO to many tabular formats\n",
"- accurate handling of missing data\n",
"- Many, many routines to handle data\n",
- " - group by categorical data (i.e., male/female, or age groups)\n",
- " - joining/merging data\n",
+ " - group by categorical data (e.g., male/female)\n",
+ " - joining/merging data (all SQL-like operations and much more)\n",
" - time series support\n",
"- statistical models through [statsmodels](http://www.statsmodels.org/stable/index.html)\n",
- "- plotting though seaborn [seaborn](https://seaborn.pydata.org/)\n",
+ "- plotting though [seaborn](https://seaborn.pydata.org/)\n",
"- Use [dask](https://dask.pydata.org/en/latest/) if your data is too big for memory (or if you want to run in parallel)\n",
"\n",
"You should also install `numexpr` and `bottleneck` for optimal performance.\n",
@@ -356,7 +441,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%run test_argparse.py 3 8.5 -q"
+ "%run test_argparse.py 3 8.5"
]
},
{
@@ -430,7 +515,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%run test_gooey.py"
+ "!python.app test_gooey.py"
]
},
{
@@ -495,6 +580,10 @@
"metadata": {},
"outputs": [],
"source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "plt.ioff()\n",
+ "\n",
"def plot_sine(amplitude, frequency):\n",
" x = np.linspace(0, 2 * np.pi, 100)\n",
" y = amplitude * np.sin(frequency * x)\n",
@@ -509,7 +598,8 @@
"\n",
"!mkdir plots\n",
"amplitudes = [plot_sine(A, 1.) for A in [0.1, 0.3, 0.7, 1.0]]\n",
- "frequencies = [plot_sine(1., F) for F in [1, 2, 3, 4, 5, 6]]"
+ "frequencies = [plot_sine(1., F) for F in [1, 2, 3, 4, 5, 6]]\n",
+ "plt.ion()"
]
},
{
@@ -564,7 +654,6 @@
"outputs": [],
"source": [
"%%writefile wx_hello_world.py\n",
- "#!/usr/bin/env python\n",
"\"\"\"\n",
"Hello World, but with more meat.\n",
"\"\"\"\n",
@@ -671,7 +760,7 @@
"metadata": {},
"outputs": [],
"source": [
- "%run wx_hello_world.py"
+ "!python.app wx_hello_world.py"
]
},
{
@@ -683,6 +772,102 @@
"- theano/tensorflow/pytorch\n",
" - keras\n",
"\n",
+ "## [pymc3](http://docs.pymc.io/): Pobabilstic programming"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "# Initialize random number generator\n",
+ "np.random.seed(123)\n",
+ "\n",
+ "# True parameter values\n",
+ "alpha, sigma = 1, 1\n",
+ "beta = [1, 2.5]\n",
+ "\n",
+ "# Size of dataset\n",
+ "size = 100\n",
+ "\n",
+ "# Predictor variable\n",
+ "X1 = np.random.randn(size)\n",
+ "X2 = np.random.randn(size) * 0.2\n",
+ "\n",
+ "# Simulate outcome variable\n",
+ "Y = alpha + beta[0]*X1 + beta[1]*X2 + np.random.randn(size)*sigma"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pymc3 as pm\n",
+ "basic_model = pm.Model()\n",
+ "\n",
+ "with basic_model:\n",
+ "\n",
+ " # Priors for unknown model parameters\n",
+ " alpha = pm.Normal('alpha', mu=0, sd=10)\n",
+ " beta = pm.Normal('beta', mu=0, sd=10, shape=2)\n",
+ " sigma = pm.HalfNormal('sigma', sd=1)\n",
+ "\n",
+ " # Expected value of outcome\n",
+ " mu = alpha + beta[0]*X1 + beta[1]*X2\n",
+ "\n",
+ " # Likelihood (sampling distribution) of observations\n",
+ " Y_obs = pm.Normal('Y_obs', mu=mu, sd=sigma, observed=Y)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "with basic_model:\n",
+ "\n",
+ " # obtain starting values via MAP\n",
+ " start = pm.find_MAP(fmin=optimize.fmin_powell)\n",
+ "\n",
+ " # instantiate sampler\n",
+ " step = pm.Slice()\n",
+ "\n",
+ " # draw 5000 posterior samples\n",
+ " trace = pm.sample(5000, step=step, start=start)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "_ = pm.traceplot(trace)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "pm.summary(trace)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Alternative: [pystan](https://pystan.readthedocs.io/en/latest/): wrapper around the [Stan](http://mc-stan.org/users/) probabilistic programming language.\n",
+ "\n",
+ "\n",
"## [Pycuda](https://documen.tician.de/pycuda/): Programming the GPU\n",
"Wrapper around [Cuda](https://developer.nvidia.com/cuda-zone).\n",
"The alternative [Pyopencl](https://documen.tician.de/pyopencl/) provides a very similar wrapper around [OpenCL](https://www.khronos.org/opencl/)."
@@ -725,89 +910,124 @@
"source": [
"Also see [pyopenGL](http://pyopengl.sourceforge.net/): graphics programming in python (used in FSLeyes)\n",
"## Testing\n",
- "- [unittest](https://docs.python.org/3.6/library/unittest.html): python built-in testing\n",
- "> ```\n",
- "> import unittest\n",
- ">\n",
- "> class TestStringMethods(unittest.TestCase):\n",
- ">\n",
- "> def test_upper(self):\n",
- "> self.assertEqual('foo'.upper(), 'FOO')\n",
- ">\n",
- "> def test_isupper(self):\n",
- "> self.assertTrue('FOO'.isupper())\n",
- "> self.assertFalse('Foo'.isupper())\n",
- ">\n",
- "> def test_split(self):\n",
- "> s = 'hello world'\n",
- "> self.assertEqual(s.split(), ['hello', 'world'])\n",
- "> # check that s.split fails when the separator is not a string\n",
- "> with self.assertRaises(TypeError):\n",
- "> s.split(2)\n",
- ">\n",
- "> if __name__ == '__main__':\n",
- "> unittest.main()\n",
- "> ```\n",
- "- [doctest](https://docs.python.org/3.6/library/doctest.html): checks the example usage in the documentation\n",
- "> ```\n",
- "> def factorial(n):\n",
- "> \"\"\"Return the factorial of n, an exact integer >= 0.\n",
- ">\n",
- "> >>> [factorial(n) for n in range(6)]\n",
- "> [1, 1, 2, 6, 24, 120]\n",
- "> >>> factorial(30)\n",
- "> 265252859812191058636308480000000\n",
- "> >>> factorial(-1)\n",
- "> Traceback (most recent call last):\n",
- "> ...\n",
- "> ValueError: n must be >= 0\n",
- ">\n",
- "> Factorials of floats are OK, but the float must be an exact integer:\n",
- "> >>> factorial(30.1)\n",
- "> Traceback (most recent call last):\n",
- "> ...\n",
- "> ValueError: n must be exact integer\n",
- "> >>> factorial(30.0)\n",
- "> 265252859812191058636308480000000\n",
- ">\n",
- "> It must also not be ridiculously large:\n",
- "> >>> factorial(1e100)\n",
- "> Traceback (most recent call last):\n",
- "> ...\n",
- "> OverflowError: n too large\n",
- "> \"\"\"\n",
- ">\n",
- "> import math\n",
- "> if not n >= 0:\n",
- "> raise ValueError(\"n must be >= 0\")\n",
- "> if math.floor(n) != n:\n",
- "> raise ValueError(\"n must be exact integer\")\n",
- "> if n+1 == n: # catch a value like 1e300\n",
- "> raise OverflowError(\"n too large\")\n",
- "> result = 1\n",
- "> factor = 2\n",
- "> while factor <= n:\n",
- "> result *= factor\n",
- "> factor += 1\n",
- "> return result\n",
- ">\n",
- ">\n",
- "> if __name__ == \"__main__\":\n",
- "> import doctest\n",
- "> doctest.testmod()\n",
- "> ```\n",
+ "- [unittest](https://docs.python.org/3.6/library/unittest.html): python built-in testing"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import unittest\n",
+ "\n",
+ "class TestStringMethods(unittest.TestCase):\n",
+ "\n",
+ " def test_upper(self):\n",
+ " self.assertEqual('foo'.upper(), 'FOO')\n",
+ "\n",
+ " def test_isupper(self):\n",
+ " self.assertTrue('FOO'.isupper())\n",
+ " self.assertFalse('Foo'.isupper())\n",
+ "\n",
+ " def test_split(self):\n",
+ " s = 'hello world'\n",
+ " self.assertEqual(s.split(), ['hello', 'world'])\n",
+ " # check that s.split fails when the separator is not a string\n",
+ " with self.assertRaises(TypeError):\n",
+ " s.split(2)\n",
+ "\n",
+ "if __name__ == '__main__':\n",
+ " unittest.main()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "- [doctest](https://docs.python.org/3.6/library/doctest.html): checks the example usage in the documentation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def factorial(n):\n",
+ " \"\"\"Return the factorial of n, an exact integer >= 0.\n",
+ "\n",
+ " >>> [factorial(n) for n in range(6)]\n",
+ " [1, 1, 2, 6, 24, 120]\n",
+ " >>> factorial(30)\n",
+ " 265252859812191058636308480000000\n",
+ " >>> factorial(-1)\n",
+ " Traceback (most recent call last):\n",
+ " ...\n",
+ " ValueError: n must be >= 0\n",
+ "\n",
+ " Factorials of floats are OK, but the float must be an exact integer:\n",
+ " >>> factorial(30.1)\n",
+ " Traceback (most recent call last):\n",
+ " ...\n",
+ " ValueError: n must be exact integer\n",
+ " >>> factorial(30.0)\n",
+ " 265252859812191058636308480000000\n",
+ "\n",
+ " It must also not be ridiculously large:\n",
+ " >>> factorial(1e100)\n",
+ " Traceback (most recent call last):\n",
+ " ...\n",
+ " OverflowError: n too large\n",
+ " \"\"\"\n",
+ "\n",
+ " import math\n",
+ " if not n >= 0:\n",
+ " raise ValueError(\"n must be >= 0\")\n",
+ " if math.floor(n) != n:\n",
+ " raise ValueError(\"n must be exact integer\")\n",
+ " if n+1 == n: # catch a value like 1e300\n",
+ " raise OverflowError(\"n too large\")\n",
+ " result = 1\n",
+ " factor = 2\n",
+ " while factor <= n:\n",
+ " result *= factor\n",
+ " factor += 1\n",
+ " return result\n",
+ "\n",
+ "\n",
+ "if __name__ == \"__main__\":\n",
+ " import doctest\n",
+ " doctest.testmod()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
"Two external packages provide more convenient unit tests:\n",
"- [py.test](https://docs.pytest.org/en/latest/)\n",
- "- [nose2](http://nose2.readthedocs.io/en/latest/usage.html)\n",
- "> ```\n",
- "> # content of test_sample.py\n",
- "> def inc(x):\n",
- "> return x + 1\n",
- ">\n",
- "> def test_answer():\n",
- "> assert inc(3) == 5\n",
- "> ```\n",
+ "- [nose2](http://nose2.readthedocs.io/en/latest/usage.html)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# content of test_sample.py\n",
+ "def inc(x):\n",
+ " return x + 1\n",
"\n",
+ "def test_answer():\n",
+ " assert inc(3) == 5"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
"- [coverage](https://coverage.readthedocs.io/en/coverage-4.5.1/): measures which part of the code is covered by the tests\n",
"\n",
"## Linters\n",
@@ -815,7 +1035,38 @@
"- [pylint](https://pypi.python.org/pypi/pylint): most extensive linter\n",
"- [pyflake](https://pypi.python.org/pypi/pyflakes): if you think pylint is too strict\n",
"- [pep8](https://pypi.python.org/pypi/pep8): just checks for style errors\n",
- "- [mypy](http://mypy-lang.org/): adding explicit typing to python\n",
+ "### Optional static typing\n",
+ "- Document how your method/function should be called\n",
+ " - Static checking of whether your type hints are still up to date\n",
+ " - Static checking of whether you call your own function correctly\n",
+ "- Even if you don't assign types yourself, static type checking can still check whether you call typed functions/methods from other packages correctly."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from typing import List\n",
+ "\n",
+ "def greet_all(names: List[str]) -> None:\n",
+ " for name in names:\n",
+ " print('Hello, {}'.format(name))\n",
+ "\n",
+ "greet_all(['python', 'java', 'C++']) # type checker will be fine with this\n",
+ "\n",
+ "greet_all('matlab') # this will actually run fine, but type checker will raise an error"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Packages:\n",
+ "- [typing](https://docs.python.org/3/library/typing.html): built-in library containing generics, unions, etc.\n",
+ "- [mypy](http://mypy-lang.org/): linter doing static type checking\n",
+ "- [pyAnnotate](https://github.com/dropbox/pyannotate): automatically assign types to most of your functions/methods based on runtime\n",
"\n",
"## Web frameworks\n",
"- [Django2](https://www.djangoproject.com/): includes the most features, but also forces you to do things their way\n",
@@ -824,15 +1075,13 @@
"\n",
"There are also many, many libraries to interact with databases, but you will have to google those yourself.\n",
"\n",
- "# Several honourable mentions\n",
+ "# Quick mentions\n",
"- [trimesh](https://github.com/mikedh/trimesh): Triangular mesh algorithms\n",
"- [Pillow](https://pillow.readthedocs.io/en/latest/): Read/write/manipulate a wide variety of images (png, jpg, tiff, etc.)\n",
"- [psychopy](http://www.psychopy.org/): equivalent of psychtoolbox (workshop coming up in April in Nottingham)\n",
"- [Buit-in libraries](https://docs.python.org/3/py-modindex.html)\n",
" - [collections](https://docs.python.org/3.6/library/collections.html): deque, OrderedDict, namedtuple, and more\n",
" - [datetime](https://docs.python.org/3/library/datetime.html): Basic date and time types\n",
- " - [enum](https://docs.python.org/3/library/enum.html): Enumerators\n",
- " - [fractions](https://docs.python.org/3/library/fractions.html): rational numbers\n",
" - [functools](https://docs.python.org/3/library/functools.html): caching, decorators, and support for functional programming\n",
" - [json](https://docs.python.org/3/library/json.html)/[ipaddress](https://docs.python.org/3/library/ipaddress.html)/[xml](https://docs.python.org/3/library/xml.html#module-xml): parsing/writing\n",
" - [itertools](https://docs.python.org/3/library/itertools.html): more tools to loop over sequences\n",
@@ -843,10 +1092,38 @@
" - [pickle](https://docs.python.org/3/library/pickle.html): Store/load any python object\n",
" - [shutil](https://docs.python.org/3/library/shutil.html): copy/move files\n",
" - [subprocess](https://docs.python.org/3/library/subprocess.html): call shell commands\n",
- " - [time](https://docs.python.org/3/library/time.html)/[timeit](https://docs.python.org/3/library/timeit.html): keeping track of it\n",
- " - [turtule](https://docs.python.org/3/library/turtle.html#module-turtle): teach python to your kids!\n",
+ " - [time](https://docs.python.org/3/library/time.html)/[timeit](https://docs.python.org/3/library/timeit.html): Timing your code\n",
+ " - [turtle](https://docs.python.org/3/library/turtle.html#module-turtle): teach python to your kids!\n",
" - [warnings](https://docs.python.org/3/library/warnings.html#module-warnings): tell people they are not using your code properly"
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from turtle import *\n",
+ "color('red', 'yellow')\n",
+ "begin_fill()\n",
+ "speed(10)\n",
+ "while True:\n",
+ " forward(200)\n",
+ " left(170)\n",
+ " if abs(pos()) < 1:\n",
+ " break\n",
+ "end_fill()\n",
+ "done()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import this"
+ ]
}
],
"metadata": {},
diff --git a/talks/packages/packages.md b/talks/packages/packages.md
index 6575e3274ab305b096ffc3a2b2807ef0af2de35c..e72e5bd61d1cf83e986677d09fbf08276c22d64e 100644
--- a/talks/packages/packages.md
+++ b/talks/packages/packages.md
@@ -33,8 +33,8 @@ The main strength in scipy lies in its sub-packages:
```
from scipy import optimize
def costfunc(params):
- return params[0] ** 2 * (params[1] - 3) ** 2 + (params[0] - 2) ** 2
-optimize.minimize(costfunc, x0=[0, 0], method='l-bfgs-b')
+ return (params[0] - 3) ** 2
+optimize.minimize(costfunc, x0=[0], method='l-bfgs-b')
```
Tutorials for all sub-packages can be found [here](https://docs.scipy.org/doc/scipy-1.0.0/reference/).
@@ -69,6 +69,41 @@ Alternatives:
- [Bokeh](https://bokeh.pydata.org/en/latest/) among many others: interactive plots in the browser (i.e., in javascript)
## [Ipython](http://ipython.org/)/[Jupyter](https://jupyter.org/) notebook: interactive python environments
+Supports:
+- run code in multiple languages
+```
+%%bash
+for name in python ruby ; do
+ echo $name
+done
+```
+
+- debugging
+```
+from scipy import optimize
+def costfunc(params):
+ return 1 / params[0] ** 2
+optimize.minimize(costfunc, x0=[0], method='l-bfgs-b')
+```
+```
+%debug
+```
+
+- timing/profiling
+```
+%%prun
+plt.plot([0, 3])
+```
+
+- getting help
+```
+plt.plot?
+```
+
+- [and much more...](https://ipython.readthedocs.io/en/stable/interactive/magics.html)
+
+The next generation is already out: [jupyterlab](https://jupyterlab.readthedocs.io/en/latest/)
+
There are many [useful extensions available](https://github.com/ipython-contrib/jupyter_contrib_nbextensions).
## [Pandas](https://pandas.pydata.org/): Analyzing "clean" data
@@ -78,11 +113,11 @@ Pandas has excellent support for:
- fast IO to many tabular formats
- accurate handling of missing data
- Many, many routines to handle data
- - group by categorical data (i.e., male/female, or age groups)
- - joining/merging data
+ - group by categorical data (e.g., male/female)
+ - joining/merging data (all SQL-like operations and much more)
- time series support
- statistical models through [statsmodels](http://www.statsmodels.org/stable/index.html)
-- plotting though seaborn [seaborn](https://seaborn.pydata.org/)
+- plotting though [seaborn](https://seaborn.pydata.org/)
- Use [dask](https://dask.pydata.org/en/latest/) if your data is too big for memory (or if you want to run in parallel)
You should also install `numexpr` and `bottleneck` for optimal performance.
@@ -191,7 +226,7 @@ if __name__ == '__main__':
```
```
-%run test_argparse.py 3 8.5 -q
+%run test_argparse.py 3 8.5
```
Alternatives:
@@ -249,7 +284,7 @@ if __name__ == '__main__':
```
```
-%run test_gooey.py
+!python.app test_gooey.py
```
```
@@ -293,6 +328,10 @@ This can for example be used to produce static HTML output in a highly flexible
```
```
+import numpy as np
+import matplotlib.pyplot as plt
+plt.ioff()
+
def plot_sine(amplitude, frequency):
x = np.linspace(0, 2 * np.pi, 100)
y = amplitude * np.sin(frequency * x)
@@ -308,6 +347,7 @@ def plot_sine(amplitude, frequency):
!mkdir plots
amplitudes = [plot_sine(A, 1.) for A in [0.1, 0.3, 0.7, 1.0]]
frequencies = [plot_sine(1., F) for F in [1, 2, 3, 4, 5, 6]]
+plt.ion()
```
```
@@ -341,7 +381,6 @@ The [nipy](http://nipy.org/) ecosystem covers most of these.
- [wxpython](https://www.wxpython.org/): Wrapper around the C++ wxWidgets library
```
%%writefile wx_hello_world.py
-#!/usr/bin/env python
"""
Hello World, but with more meat.
"""
@@ -443,7 +482,7 @@ if __name__ == '__main__':
```
```
-%run wx_hello_world.py
+!python.app wx_hello_world.py
```
## Machine learning
@@ -451,6 +490,71 @@ if __name__ == '__main__':
- theano/tensorflow/pytorch
- keras
+## [pymc3](http://docs.pymc.io/): Pobabilstic programming
+```
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Initialize random number generator
+np.random.seed(123)
+
+# True parameter values
+alpha, sigma = 1, 1
+beta = [1, 2.5]
+
+# Size of dataset
+size = 100
+
+# Predictor variable
+X1 = np.random.randn(size)
+X2 = np.random.randn(size) * 0.2
+
+# Simulate outcome variable
+Y = alpha + beta[0]*X1 + beta[1]*X2 + np.random.randn(size)*sigma
+```
+
+```
+import pymc3 as pm
+basic_model = pm.Model()
+
+with basic_model:
+
+ # Priors for unknown model parameters
+ alpha = pm.Normal('alpha', mu=0, sd=10)
+ beta = pm.Normal('beta', mu=0, sd=10, shape=2)
+ sigma = pm.HalfNormal('sigma', sd=1)
+
+ # Expected value of outcome
+ mu = alpha + beta[0]*X1 + beta[1]*X2
+
+ # Likelihood (sampling distribution) of observations
+ Y_obs = pm.Normal('Y_obs', mu=mu, sd=sigma, observed=Y)
+```
+
+```
+with basic_model:
+
+ # obtain starting values via MAP
+ start = pm.find_MAP(fmin=optimize.fmin_powell)
+
+ # instantiate sampler
+ step = pm.Slice()
+
+ # draw 5000 posterior samples
+ trace = pm.sample(5000, step=step, start=start)
+```
+
+```
+_ = pm.traceplot(trace)
+```
+
+```
+pm.summary(trace)
+```
+
+Alternative: [pystan](https://pystan.readthedocs.io/en/latest/): wrapper around the [Stan](http://mc-stan.org/users/) probabilistic programming language.
+
+
## [Pycuda](https://documen.tician.de/pycuda/): Programming the GPU
Wrapper around [Cuda](https://developer.nvidia.com/cuda-zone).
The alternative [Pyopencl](https://documen.tician.de/pyopencl/) provides a very similar wrapper around [OpenCL](https://www.khronos.org/opencl/).
@@ -483,87 +587,87 @@ print(dest-a*b)
Also see [pyopenGL](http://pyopengl.sourceforge.net/): graphics programming in python (used in FSLeyes)
## Testing
- [unittest](https://docs.python.org/3.6/library/unittest.html): python built-in testing
-> ```
-> import unittest
->
-> class TestStringMethods(unittest.TestCase):
->
-> def test_upper(self):
-> self.assertEqual('foo'.upper(), 'FOO')
->
-> def test_isupper(self):
-> self.assertTrue('FOO'.isupper())
-> self.assertFalse('Foo'.isupper())
->
-> def test_split(self):
-> s = 'hello world'
-> self.assertEqual(s.split(), ['hello', 'world'])
-> # check that s.split fails when the separator is not a string
-> with self.assertRaises(TypeError):
-> s.split(2)
->
-> if __name__ == '__main__':
-> unittest.main()
-> ```
+```
+import unittest
+
+class TestStringMethods(unittest.TestCase):
+
+ def test_upper(self):
+ self.assertEqual('foo'.upper(), 'FOO')
+
+ def test_isupper(self):
+ self.assertTrue('FOO'.isupper())
+ self.assertFalse('Foo'.isupper())
+
+ def test_split(self):
+ s = 'hello world'
+ self.assertEqual(s.split(), ['hello', 'world'])
+ # check that s.split fails when the separator is not a string
+ with self.assertRaises(TypeError):
+ s.split(2)
+
+if __name__ == '__main__':
+ unittest.main()
+```
- [doctest](https://docs.python.org/3.6/library/doctest.html): checks the example usage in the documentation
-> ```
-> def factorial(n):
-> """Return the factorial of n, an exact integer >= 0.
->
-> >>> [factorial(n) for n in range(6)]
-> [1, 1, 2, 6, 24, 120]
-> >>> factorial(30)
-> 265252859812191058636308480000000
-> >>> factorial(-1)
-> Traceback (most recent call last):
-> ...
-> ValueError: n must be >= 0
->
-> Factorials of floats are OK, but the float must be an exact integer:
-> >>> factorial(30.1)
-> Traceback (most recent call last):
-> ...
-> ValueError: n must be exact integer
-> >>> factorial(30.0)
-> 265252859812191058636308480000000
->
-> It must also not be ridiculously large:
-> >>> factorial(1e100)
-> Traceback (most recent call last):
-> ...
-> OverflowError: n too large
-> """
->
-> import math
-> if not n >= 0:
-> raise ValueError("n must be >= 0")
-> if math.floor(n) != n:
-> raise ValueError("n must be exact integer")
-> if n+1 == n: # catch a value like 1e300
-> raise OverflowError("n too large")
-> result = 1
-> factor = 2
-> while factor <= n:
-> result *= factor
-> factor += 1
-> return result
->
->
-> if __name__ == "__main__":
-> import doctest
-> doctest.testmod()
-> ```
+```
+def factorial(n):
+ """Return the factorial of n, an exact integer >= 0.
+
+ >>> [factorial(n) for n in range(6)]
+ [1, 1, 2, 6, 24, 120]
+ >>> factorial(30)
+ 265252859812191058636308480000000
+ >>> factorial(-1)
+ Traceback (most recent call last):
+ ...
+ ValueError: n must be >= 0
+
+ Factorials of floats are OK, but the float must be an exact integer:
+ >>> factorial(30.1)
+ Traceback (most recent call last):
+ ...
+ ValueError: n must be exact integer
+ >>> factorial(30.0)
+ 265252859812191058636308480000000
+
+ It must also not be ridiculously large:
+ >>> factorial(1e100)
+ Traceback (most recent call last):
+ ...
+ OverflowError: n too large
+ """
+
+ import math
+ if not n >= 0:
+ raise ValueError("n must be >= 0")
+ if math.floor(n) != n:
+ raise ValueError("n must be exact integer")
+ if n+1 == n: # catch a value like 1e300
+ raise OverflowError("n too large")
+ result = 1
+ factor = 2
+ while factor <= n:
+ result *= factor
+ factor += 1
+ return result
+
+
+if __name__ == "__main__":
+ import doctest
+ doctest.testmod()
+```
Two external packages provide more convenient unit tests:
- [py.test](https://docs.pytest.org/en/latest/)
- [nose2](http://nose2.readthedocs.io/en/latest/usage.html)
-> ```
-> # content of test_sample.py
-> def inc(x):
-> return x + 1
->
-> def test_answer():
-> assert inc(3) == 5
-> ```
+```
+# content of test_sample.py
+def inc(x):
+ return x + 1
+
+def test_answer():
+ assert inc(3) == 5
+```
- [coverage](https://coverage.readthedocs.io/en/coverage-4.5.1/): measures which part of the code is covered by the tests
@@ -572,7 +676,28 @@ Linters check the code for any syntax errors, [style errors](https://www.python.
- [pylint](https://pypi.python.org/pypi/pylint): most extensive linter
- [pyflake](https://pypi.python.org/pypi/pyflakes): if you think pylint is too strict
- [pep8](https://pypi.python.org/pypi/pep8): just checks for style errors
-- [mypy](http://mypy-lang.org/): adding explicit typing to python
+### Optional static typing
+- Document how your method/function should be called
+ - Static checking of whether your type hints are still up to date
+ - Static checking of whether you call your own function correctly
+- Even if you don't assign types yourself, static type checking can still check whether you call typed functions/methods from other packages correctly.
+
+```
+from typing import List
+
+def greet_all(names: List[str]) -> None:
+ for name in names:
+ print('Hello, {}'.format(name))
+
+greet_all(['python', 'java', 'C++']) # type checker will be fine with this
+
+greet_all('matlab') # this will actually run fine, but type checker will raise an error
+```
+
+Packages:
+- [typing](https://docs.python.org/3/library/typing.html): built-in library containing generics, unions, etc.
+- [mypy](http://mypy-lang.org/): linter doing static type checking
+- [pyAnnotate](https://github.com/dropbox/pyannotate): automatically assign types to most of your functions/methods based on runtime
## Web frameworks
- [Django2](https://www.djangoproject.com/): includes the most features, but also forces you to do things their way
@@ -581,15 +706,13 @@ Linters check the code for any syntax errors, [style errors](https://www.python.
There are also many, many libraries to interact with databases, but you will have to google those yourself.
-# Several honourable mentions
+# Quick mentions
- [trimesh](https://github.com/mikedh/trimesh): Triangular mesh algorithms
- [Pillow](https://pillow.readthedocs.io/en/latest/): Read/write/manipulate a wide variety of images (png, jpg, tiff, etc.)
- [psychopy](http://www.psychopy.org/): equivalent of psychtoolbox (workshop coming up in April in Nottingham)
- [Buit-in libraries](https://docs.python.org/3/py-modindex.html)
- [collections](https://docs.python.org/3.6/library/collections.html): deque, OrderedDict, namedtuple, and more
- [datetime](https://docs.python.org/3/library/datetime.html): Basic date and time types
- - [enum](https://docs.python.org/3/library/enum.html): Enumerators
- - [fractions](https://docs.python.org/3/library/fractions.html): rational numbers
- [functools](https://docs.python.org/3/library/functools.html): caching, decorators, and support for functional programming
- [json](https://docs.python.org/3/library/json.html)/[ipaddress](https://docs.python.org/3/library/ipaddress.html)/[xml](https://docs.python.org/3/library/xml.html#module-xml): parsing/writing
- [itertools](https://docs.python.org/3/library/itertools.html): more tools to loop over sequences
@@ -600,6 +723,24 @@ There are also many, many libraries to interact with databases, but you will hav
- [pickle](https://docs.python.org/3/library/pickle.html): Store/load any python object
- [shutil](https://docs.python.org/3/library/shutil.html): copy/move files
- [subprocess](https://docs.python.org/3/library/subprocess.html): call shell commands
- - [time](https://docs.python.org/3/library/time.html)/[timeit](https://docs.python.org/3/library/timeit.html): keeping track of it
- - [turtule](https://docs.python.org/3/library/turtle.html#module-turtle): teach python to your kids!
+ - [time](https://docs.python.org/3/library/time.html)/[timeit](https://docs.python.org/3/library/timeit.html): Timing your code
+ - [turtle](https://docs.python.org/3/library/turtle.html#module-turtle): teach python to your kids!
- [warnings](https://docs.python.org/3/library/warnings.html#module-warnings): tell people they are not using your code properly
+
+```
+from turtle import *
+color('red', 'yellow')
+begin_fill()
+speed(10)
+while True:
+ forward(200)
+ left(170)
+ if abs(pos()) < 1:
+ break
+end_fill()
+done()
+```
+
+```
+import this
+```