diff --git a/talks/packages/packages.ipynb b/talks/packages/packages.ipynb
index f96cca7eba78337021c01e5b1a540448a0dc0c07..8578a8044fef7192d3e11f32f3ad040aa89de1af 100644
--- a/talks/packages/packages.ipynb
+++ b/talks/packages/packages.ipynb
@@ -7,7 +7,7 @@
"# Main scientific python libraries\n",
"See https://scipy.org/\n",
"\n",
- "Most of these packages have or are in thr progress of dropping support for python2.\n",
+ "Most of these packages have or are in the progress of dropping support for python2.\n",
"So use python3!\n",
"\n",
"## [Numpy](http://www.numpy.org/): arrays\n",
@@ -19,10 +19,8 @@
},
{
"cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "collapsed": true
- },
+ "execution_count": null,
+ "metadata": {},
"outputs": [],
"source": [
"import numpy as np"
@@ -49,9 +47,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import scipy as sp"
@@ -67,9 +63,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from scipy import optimize\n",
@@ -90,9 +84,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import matplotlib as mpl\n",
@@ -110,9 +102,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"x = np.linspace(0, 2, 100)\n",
@@ -166,9 +156,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import statsmodels.api as sm\n",
@@ -179,9 +167,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"df = sm.datasets.get_rdataset(\"Guerry\", \"HistData\").data\n",
@@ -191,9 +177,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.describe()"
@@ -202,9 +186,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"df.groupby('Region').mean()"
@@ -213,9 +195,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"results = smf.ols('Lottery ~ Literacy + np.log(Pop1831)', data=df).fit()\n",
@@ -225,9 +205,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"df['log_pop'] = np.log(df.Pop1831)\n",
@@ -237,9 +215,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"results = smf.ols('Lottery ~ Literacy + log_pop', data=df).fit()\n",
@@ -249,9 +225,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"results = smf.ols('Lottery ~ Literacy + np.log(Pop1831) + Region', data=df).fit()\n",
@@ -261,9 +235,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"results = smf.ols('Lottery ~ Literacy + np.log(Pop1831) + Region + Region * Literacy', data=df).fit()\n",
@@ -273,9 +245,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%matplotlib nbagg\n",
@@ -293,9 +263,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import sympy as sym # no standard nickname"
@@ -304,9 +272,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"x, a, b, c = sym.symbols('x, a, b, c')\n",
@@ -316,9 +282,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"sym.integrate(x/(x**2+a*x+2), x)"
@@ -327,9 +291,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"f = sym.utilities.lambdify((x, a), sym.integrate((x**2+a*x+2), x))\n",
@@ -347,9 +309,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%writefile test_argparse.py\n",
@@ -375,9 +335,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%run test_argparse.py 3 8 -v"
@@ -386,9 +344,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%run test_argparse.py -h"
@@ -397,9 +353,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%run test_argparse.py 3 8.5 -q"
@@ -445,9 +399,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%writefile test_gooey.py\n",
@@ -475,9 +427,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%run test_gooey.py"
@@ -486,9 +436,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"!gcoord_gui"
@@ -508,9 +456,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%writefile image_list.jinja2\n",
@@ -546,9 +492,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"def plot_sine(amplitude, frequency):\n",
@@ -571,9 +515,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"from jinja2 import Environment, FileSystemLoader\n",
@@ -595,9 +537,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"!open image_list.html"
@@ -620,9 +560,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%%writefile wx_hello_world.py\n",
@@ -730,9 +668,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"%run wx_hello_world.py"
@@ -755,9 +691,7 @@
{
"cell_type": "code",
"execution_count": null,
- "metadata": {
- "collapsed": true
- },
+ "metadata": {},
"outputs": [],
"source": [
"import pycuda.autoinit\n",
@@ -789,6 +723,7 @@
"cell_type": "markdown",
"metadata": {},
"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",
@@ -908,49 +843,13 @@
" - [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",
" - [warnings](https://docs.python.org/3/library/warnings.html#module-warnings): tell people they are not using your code properly"
]
}
],
- "metadata": {
- "kernelspec": {
- "display_name": "Python 3",
- "language": "python",
- "name": "python3"
- },
- "language_info": {
- "codemirror_mode": {
- "name": "ipython",
- "version": 3
- },
- "file_extension": ".py",
- "mimetype": "text/x-python",
- "name": "python",
- "nbconvert_exporter": "python",
- "pygments_lexer": "ipython3",
- "version": "3.6.2"
- },
- "toc": {
- "colors": {
- "hover_highlight": "#DAA520",
- "running_highlight": "#FF0000",
- "selected_highlight": "#FFD700"
- },
- "moveMenuLeft": true,
- "nav_menu": {
- "height": "417px",
- "width": "252px"
- },
- "navigate_menu": true,
- "number_sections": true,
- "sideBar": true,
- "threshold": 4,
- "toc_cell": false,
- "toc_section_display": "block",
- "toc_window_display": false
- }
- },
+ "metadata": {},
"nbformat": 4,
"nbformat_minor": 2
}
diff --git a/talks/packages/packages.md b/talks/packages/packages.md
index 49f3a6d046d46a5b59133b19fc897cca8200a7c3..6575e3274ab305b096ffc3a2b2807ef0af2de35c 100644
--- a/talks/packages/packages.md
+++ b/talks/packages/packages.md
@@ -1,7 +1,7 @@
# Main scientific python libraries
See https://scipy.org/
-Most of these packages have or are in thr progress of dropping support for python2.
+Most of these packages have or are in the progress of dropping support for python2.
So use python3!
## [Numpy](http://www.numpy.org/): arrays
diff --git a/talks/speed/speed.ipynb b/talks/speed/speed.ipynb
index c8c9cde26a3ab849a1850b45bbbe7fb47023daf0..e6eed3229f5179f5326c33c87cc89ca6d831894b 100644
--- a/talks/speed/speed.ipynb
+++ b/talks/speed/speed.ipynb
@@ -53,14 +53,14 @@
{
"ename": "ValueError",
"evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
- "output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-19-5d1fed3ed2df>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1e6\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mroot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
"\u001b[0;32m<ipython-input-18-54b500cd66b1>\u001b[0m in \u001b[0;36mroot\u001b[0;34m(a, b, c)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mroot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mb\u001b[0m \u001b[0;34m**\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m4\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mc\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mif\u001b[0m \u001b[0mD\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnan\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mx1\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mb\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mD\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
- ]
+ ],
+ "output_type": "error"
}
],
"source": [
@@ -253,7 +253,6 @@
{
"ename": "SystemError",
"evalue": "CPUDispatcher(<function root at 0x114035620>) returned a result with an error set",
- "output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
@@ -262,7 +261,8 @@
"\u001b[0;31mSystemError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<timed eval>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n",
"\u001b[0;31mSystemError\u001b[0m: CPUDispatcher(<function root at 0x114035620>) returned a result with an error set"
- ]
+ ],
+ "output_type": "error"
}
],
"source": [
@@ -350,7 +350,6 @@
{
"ename": "NotImplementedError",
"evalue": "(float64 x 2) cannot be represented as a Numpy dtype",
- "output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNotImplementedError\u001b[0m Traceback (most recent call last)",
@@ -360,7 +359,8 @@
"\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/npyufunc/ufuncbuilder.py\u001b[0m in \u001b[0;36m_build_element_wise_ufunc_wrapper\u001b[0;34m(cres, signature)\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;31m# Get dtypes\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 164\u001b[0m \u001b[0mdtypenums\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0ma\u001b[0m \u001b[0;32min\u001b[0m \u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 165\u001b[0;31m \u001b[0mdtypenums\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msignature\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreturn_type\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 166\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mdtypenums\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mptr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0menv\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 167\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m~/miniconda3/lib/python3.6/site-packages/numba/numpy_support.py\u001b[0m in \u001b[0;36mas_dtype\u001b[0;34m(nbtype)\u001b[0m\n\u001b[1;32m 134\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mas_dtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnbtype\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 135\u001b[0m raise NotImplementedError(\"%r cannot be represented as a Numpy dtype\"\n\u001b[0;32m--> 136\u001b[0;31m % (nbtype,))\n\u001b[0m\u001b[1;32m 137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 138\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNotImplementedError\u001b[0m: (float64 x 2) cannot be represented as a Numpy dtype"
- ]
+ ],
+ "output_type": "error"
}
],
"source": [
@@ -2049,7 +2049,7 @@
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
- "threshold": 4,
+ "threshold": 4.0,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": true