# Installation
```
conda install -c conda-forge py2cytoscape
pip install -U handout
```
## Prerequisites
In addition to the RCy3 package, you will need the latest version of Cytoscape, which can be downloaded from http://www.cytoscape.org/download.php. Simply follow the installation instructions on screen.
## Getting started
First, launch Cytoscape and keep it running whenever using py2cytoscape. Confirm that you have everything installed and running:
"""
import handout
doc = handout.Handout('html_documents/py/differentially-expressed-genes')
import io
from contextlib import redirect_stdout
from py2cytoscape import cyrest
HOST = "localhost"
cytoscape = cyrest.cyclient(host=HOST)
f = io.StringIO()
with redirect_stdout(f):
cytoscape.status()
cytoscape.version()
s = f.getvalue()
doc.add_text(s)
doc.show()
"""
"""
# IN-STK5000 Reproducibility assignment
"""
import handout # Tool for generating report-type documents
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
doc = handout.Handout('output') # handout: exclude
"""
Load the dataset.
We used the Car Evaluation dataset from https://archive.ics.uci.edu/
To use the dataset for ML algorithms all variables are changed to numeric variables by label-encoder preprocessing.
"""
features = ["Buying", "Maintenaince", "Doors", "Persons", "Luggage", "Safety"]
target = 'Class'
df = pd.read_csv(
'https://archive.ics.uci.edu/ml/machine-learning-databases/car/car.data',
names=features + [target])
doc.add_text(df.head()) # handout: exclude
doc.show() # handout: exclude
from sklearn import preprocessing # handout: exclude
le = preprocessing.LabelEncoder() # handout: exclude
df = df.apply(le.fit_transform) # handout: exclude
"""
Some setup code for the KNN classifier; data splitting and scaling:
"""
# Python Handout
Turn Python scripts into handouts with Markdown comments and inline figures. An
alternative to Jupyter notebooks without hidden state that supports any text
editor.
"""
import handout
import matplotlib.pyplot as plt
import numpy as np
"""Start your handout with an output directory."""
doc = handout.Handout('output')
"""
## Markdown comments
Comments with triple quotes are converted to text blocks.
Text blocks support [Markdown formatting][1], for example:
- Headlines
- Hyperlinks
- Inline `code()` snippets
- **Bold** and *italic*
[1]: https://commonmark.org/help/
"""
"""
## Add text and variables
#### Underflow
Multiplying lots of probabilities can result in floating-point underflow. For this,
we can take advantage of a property of the $\log$ function: $\log(xy) = \log(x) + \log(y) $.
So, we take the $\log$ of each probability and *sum* them together instead of multiply.
# Part 4
"""
from operator import add
from random import random
import handout
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
doc = handout.Handout("handout")
"""## Word Count"""
lines = spark.read.text("./input/words.txt").rdd.map(lambda r: r[0])
counts = lines.flatMap(lambda x: x.split(" ")).map(lambda x: (x, 1)).reduceByKey(add)
output = counts.collect()
for word, count in output:
doc.add_text("%s: %i" % (word, count))
doc.show()
"""## Pi Estimation"""
partitions = 2
import builtins # handout: exclude
import io # handout: exclude
import handout # handout: exclude
import os # handout: exclude
import sys # handout: exclude
sys.stdout = io.StringIO() # handout: exclude
stdoutput = '' # handout: exclude
fname = os.path.basename(__file__).replace('.py', '') # handout: exclude
doc = handout.Handout(fname) # handout: exclude
"""
# Introduction to Python
## Using the tdt Package
This primer walks through installing the tdt Python package, shows how to convert existing Matlab code to Python, and highlights some of the differences when working in Python.
## Installation
1. Make sure that you have [**Miniconda**](https://docs.conda.io/en/latest/miniconda.html) or [**Anaconda**](https://www.anaconda.com/distribution/) installed. You can choose the Python 3.7 64-bit version for your operating system (Linux, Windows, or OS X).
2. **Open a terminal** (on Windows, `cmd`, not Powershell) and type:
```
pip install tdt
```
4. **Done**!
## Converting Existing Matlab Code to Python
The tdt Python library for reading TDT data is one-to-one compatible with the Matlab library, however the function names and parameter names are different.
### Extracting Block Data
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import handout
doc = handout.Handout('docs/ntk')
"""
## Neural tangent kernel
definition
$$
k(x, x') = \langle\frac{\partial f_{\theta}(x)}{\partial \theta}, \frac{\partial f_{\theta}(x')}{\partial \theta} \rangle
$$
NN dynamics
$$
\frac{d f_{\theta(t)}(x)}{dt} = - K(t) \cdot (f_{\theta(t)}(x)) - y)
$$
Infinite limit of NTK
$$
\text{future talk...}
$$
"""
n = 500
# using inputs with the same norm! seems important?
# (should try with shattered grads)
t = np.linspace(-np.pi, np.pi, n).reshape((-1, 1))
x = np.concatenate([np.cos(t), np.sin(t)], axis=1)
"""
# 마크다운이 포함 된, Python 스크립트를 HTML 형태의 핸드아웃 페이지로 변환해 주는
# 패키지를 소개해 드립니다.
# https://github.com/danijar/handout
"""
print(__doc__)
import handout
import matplotlib.pyplot as plt
import numpy as np
# DESTIN_DIR = './handout_doc/' # Root Sub_folder
DESTIN_DIR = 'handout_doc' # Sub_folder
""" Start your handout with an output directory """
# doc = handout.Handout('/tmp/handout')
doc = handout.Handout(DESTIN_DIR)
""" Print text and Variables. """
for index in range(3):
doc.add_text(f'Interation ... {index}')
doc.show()
""" Insert matplotlib figure """
fig, ax = plt.subplots(1,1, figsize=(4,3))
fig.tight_layout()
doc.add_figure(fig)
doc.show()
# original code from http://katbailey.github.io/post/gaussian-processes-for-dummies/
# but somewhat modified
import numpy as np
import matplotlib.pyplot as pl
import handout
doc = handout.Handout('docs/gp')
"""
## GPs
definition
$$
f \sim \mathcal N(\mu, \Sigma),\quad\Sigma(x, x') = e^{-\frac{1}{2}(x-x')^2}
$$
mean update
$$
m_D(x) = m(x) + \Sigma(X, x)^T\Sigma^{-1}(f-m)
$$
var update
$$
k_D(x, x') =k(x, x') - \Sigma(X, x)^T \Sigma^{-1} \Sigma(X, x')
$$
posterior
$$
f^{* }|f \sim \mathcal N(m_D, k_D)
$$
# Installation
```
conda install -c conda-forge py2cytoscape
pip install -U handout
```
## Prerequisites
In addition to the RCy3 package, you will need the latest version of Cytoscape, which can be downloaded from http://www.cytoscape.org/download.php. Simply follow the installation instructions on screen.
## Getting started
First, launch Cytoscape and keep it running whenever using py2cytoscape. Confirm that you have everything installed and running:
"""
import handout
doc = handout.Handout('html_documents/py/stringApp')
import io
from contextlib import redirect_stdout
from py2cytoscape import cyrest
HOST = "localhost"
cytoscape = cyrest.cyclient(host=HOST)
f = io.StringIO()
with redirect_stdout(f):
cytoscape.status()
cytoscape.version()
s = f.getvalue()
doc.add_text(s)
doc.show()
"""
"""
# Python Handout
Turn Python scripts into handouts with Markdown comments and inline figures. An
alternative to Jupyter notebooks without hidden state that supports any text
editor.
"""
import handout
import matplotlib.pyplot as plt
import numpy as np
"""Start your handout with an output directory."""
doc = handout.Handout('./output')
"""
## Markdown comments
Comments with triple quotes are converted to text blocks.
Text blocks support [Markdown formatting][1], for example:
- Headlines
- Hyperlinks
- Inline `code()` snippets
- **Bold** and *italic*
- LaTeX math $f(x)=x^2$
[1]: https://commonmark.org/help/
"""
"""
## Add text and variables
Write to our handout using the same syntax as Python's `print()`:
"""
for index in range(3):
doc.add_text('Iteration', index)
doc.show()
"""
This demonstrates some simple ways to encode time so that models can
make sense of it.
The problem at hand is prediction of web traffic on various wikipedia pages.
The features we will use include:
* Lagged values for traffic
* Time of day expressed as continuous variables
* Day of week expressed as continuous variables
* Day of week expressed as one-hot variables
* Page URL
"""
import handout
import os
os.mkdir("handouts") # handout: exclude
doc = handout.Handout("handouts/time") # handout: exclude
doc.show() # handout: exclude
# Installation
```
conda install -c conda-forge py2cytoscape
pip install -U handout
```
## Prerequisites
In addition to the RCy3 package, you will need the latest version of Cytoscape, which can be downloaded from http://www.cytoscape.org/download.php. Simply follow the installation instructions on screen.
## Getting started
First, launch Cytoscape and keep it running whenever using py2cytoscape. Confirm that you have everything installed and running:
"""
import handout
doc = handout.Handout('html_documents/py/basic-data-visualization')
import io
from contextlib import redirect_stdout
from py2cytoscape import cyrest
cytoscape = cyrest.cyclient()
f = io.StringIO()
with redirect_stdout(f):
cytoscape.status()
cytoscape.version()
s = f.getvalue()
doc.add_text(s)
doc.show()
"""
# Basic Data Visualization
import handout
doc = handout.Handout('handout_output/ex00_original_after',
'ex00_original_after')
"""
# 封装集合-代码原型-重构后
---
"""
class Foo:
def __init__(self):
"""将列表属性私有，保证用户无法从外部进行访问"""
self.__items = []
def add(self, item):
"""
类内部的 add() 函数依然可以使用私有属性 __items
:param item: 要添加的元素
:return:
"""
self.__items.append(item)
@property
def items(self):
"""
只读属性，不允许用户从外部修改列表元素
:return: 列表属性
"""
return tuple(self.__items)
import handout
import numpy as np
doc = handout.Handout('output/full_width')
# Add some images.
for _ in range(16):
image = np.ones((64, 64, 3)) * np.random.uniform(0, 1, 3)
doc.add_image((255 * image).astype(np.uint8), width=0.24)
# Unset document width.
doc.add_html('<style>article { max-width: none; }</style>')
doc.show()
def test_handout_on_title_arg_inserts_title(tmp_path):
output = handout.Handout(directory=tmp_path, title='This string')._generate(source='')
assert '<title>This string</title>' in output
import handout
doc = handout.Handout('handout_output/ex00_original_before', 'ex00_original_before')
"""
# 封装集合-代码原型-重构前
---
"""
class Foo:
def __init__(self):
"""定义了一个列表属性"""
self.items = []
def add(self, item):
"""
定义了 add() 函数用来向列表中添加新的元素
:param item: 要添加的元素
:return:
"""
self.items.append(item)
# 客户端
foo = Foo()
foo.add('a') # 希望客户端的操作方式
print(foo.items) # 输出列表中所有的元素
foo.items.append('b') # 有安全隐患的操作方式，绕过自定义的 add() 函数
print(foo.items) # 输出列表中所有的元素
"""
---
domkn @ 2019-08-12
"""
Ch + I + Cg + NX S = I + Net lending
|
W + t' + P Always a mystery:
| S - I = NX = Net lending
X - AX (See Open Economy identitites below)
```
"""
"""
## Preparations
"""
import pandas as pd
import handout
doc = handout.Handout("handout") # handout: exclude
"""
`eq` function will check identities considering some rounding error.
"""
def eq(df1, df2, precision=0.5) -> bool:
"""Compare two dataframes by element with precision margin."""
return ((df1 - df2).abs() < precision).all()
"""
Read dataset from file.
"""
df = pd.read_csv("data/sna.csv", index_col=0)
"""
# Python Handout
Turn Python scripts into handouts with Markdown comments and inline figures. An
alternative to Jupyter notebooks without hidden state that supports any text
editor.
"""
import handout
import matplotlib.pyplot as plt
import numpy as np
"""Start your handout with an output directory."""
doc = handout.Handout('output/start')
"""
## Markdown comments
Comments with triple quotes are converted to text blocks.
Text blocks support [Markdown formatting][1], for example:
- Headlines
- Hyperlinks
- Inline `code()` snippets
- **Bold** and *italic*
- LaTeX math $f(x)=x^2$
[1]: https://commonmark.org/help/
"""
"""
## Add text and variables
