Micah P. Dombrowski / Jun 10 2018
Python Template
Python Template
This article can act as a starting point for any basic Python article. It has cells to accomplish some basic setup and programming tasks, as well as a few more complex ones. Just Remix and go!
For explanations of what's going on, see this article on Bash setup and this one on Python setup and coding.
1. System Setup
1.
System Setup
apt-get update >> /dev/null apt-get install -y fortune cowsay make g++ /usr/games/fortune | /usr/games/cowsay
pwd ls -al mkdir -p ~/.keras echo '{ "epsilon": 1e-07, "image_data_format": "channels_last", "backend": "cntk", "floatx": "float32" }' > ~/.keras/keras.json
2. Installing Python Packages
2.
Installing Python Packages
conda install -y seaborn sympy
pip install xgboost
Remember to Review & Save the Setup environment before continuing!
3. Data intake
3.
Data intake
import pandas as pd data = pd.read_csv(cubic.csv↩)
with open(keras.jsonfile download & manipulation↩, "r") as f: str = f.read() str
print(str)
4. Calculations
4.
Calculations
from sympy import primerange [i for i in primerange(1,1000)]
split_data = [float(x[0])*(-1)**(i+1) for (i,x) in enumerate(data.values)] # Return data for use by Javascript cell split_data
4.1. Plotting
4.1.
Plotting
import matplotlib.pyplot as plt plt.figure() plt.plot(data) plt.title("Cubic Function") plt.ylabel("f(x)") plt.xlabel("x") plt.savefig("/results/blah.png")
import plotly.plotly as plt from plotly.graph_objs import Scatter,Layout,Figure Figure( data = [ Scatter(y=data.values[:,0]) ], layout = Layout( title = "Cubic Function", yaxis = dict(title="f(x)"), xaxis = dict(title="x") ) )
var data = nilmodify file data↩ plottypes = ['lines','markers'] var traces = plottypes.map(function(ptype) { return { type: 'scatter', mode: ptype, name: ptype, x: [Array(401).keys()], y: data } }) var layout = { title: 'Split Cubic Function', xaxis: { title: 'Year' }, yaxis: { title: 'Temperature [C]' } } Nextjournal.plot(traces, layout)