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Exercise 3:
The function parse given below is the from the notebook Shift-Reduce-Parser.ipynb. Adapt this function so that it does not just return Trueor False
but rather returns a parse tree as a nested list. The key idea is that the list Symbols
should now be a list of parse trees and tokens instead of just syntacti... | def parse(self, TL):
"""
Edit this code so that it returns a parse tree.
Make use of the auxiliary function combine_trees that you have to
implement in Exercise 4.
"""
index = 0 # points to next token
Symbols = [] # stack of symbols
States = ['s0'] # stack of states, s0 is st... | ANTLR4-Python/SLR-Parser-Generator/Shift-Reduce-Parser-AST.ipynb | Danghor/Formal-Languages | gpl-2.0 |
Exercise 4:
Given a list of tokens and parse trees TL the function combine_trees combines these trees into a new parse tree. The parse trees are represented as nested tuples. The data type of a nested tuple is defined recursively:
- A nested tuple is a tuple of the form (Head,) + Body where
* Head is a string and
... | def combine_trees(TL):
if len(TL) == 0:
return ()
if isinstance(TL, str):
return (str(TL),)
Literals = [t for t in TL if isinstance(t, str)]
Trees = [t for t in TL if not isinstance(t, str)]
if len(Literals) > 0:
label = Literals[0]
else:
label = ''
res... | ANTLR4-Python/SLR-Parser-Generator/Shift-Reduce-Parser-AST.ipynb | Danghor/Formal-Languages | gpl-2.0 |
Exercise 5:
The function simplfy_tree(tree) transforms the parse tree tree into an abstract syntax tree. The parse tree tree is represented as a nested tuple of the form
tree = (head,) + body
The function should simplify the tree as follows:
- If head == '' and body is a tuple of length 2 that starts with an empty st... | def simplify_tree(tree):
if isinstance(tree, int) or isinstance(tree, str):
return tree
head, *body = tree
if body == []:
return tree
if head == '' and len(body) == 2 and body[0] == ('',):
return simplify_tree(body[1])
if head in VoidKeys and len(body) == 1:
return si... | ANTLR4-Python/SLR-Parser-Generator/Shift-Reduce-Parser-AST.ipynb | Danghor/Formal-Languages | gpl-2.0 |
Testing
The notebook ../AST-2-Dot.ipynb implements the function tuple2dot(nt) that displays the nested tuple nt as a tree via graphvis. | %run ../AST-2-Dot.ipynb
cat -n Examples/sum-for.sl
def test(file):
with open(file, 'r', encoding='utf-8') as file:
program = file.read()
parser = ShiftReduceParser(actionTable, gotoTable)
TL = tokenize(program)
st = parser.parse(TL)
ast = simplify_tree(st)
return st, ast | ANTLR4-Python/SLR-Parser-Generator/Shift-Reduce-Parser-AST.ipynb | Danghor/Formal-Languages | gpl-2.0 |
Calling the function test below should produce the following nested tuple as parse tree:
('', ('', ('', ('function', ('ID', 'sum'), ('', ('ID', 'n')), ('', ('', ('', ('',), (';', (':=', ('ID', 's'), ('', ('', ('', ('NUMBER', 0))))))), ('for', (':=', ('ID', 'i'), ('', ('', ('', ('NUMBER', 1))))), ('', ('', ('', ('≤', ('... | st, ast = test('Examples/sum-for.sl')
print(st)
print(ast)
display(tuple2dot(st))
display(tuple2dot(ast)) | ANTLR4-Python/SLR-Parser-Generator/Shift-Reduce-Parser-AST.ipynb | Danghor/Formal-Languages | gpl-2.0 |
Checkerboard
Write a Python function that creates a square (size,size) 2d Numpy array with the values 0.0 and 1.0:
Your function should work for both odd and even size.
The 0,0 element should be 1.0.
The dtype should be float. | # there's got to be a more efficient way using some sort
# of list comprehension
def checkerboard(size):
cb = np.ones((size,size), dtype = float)
for i in range(size):
for j in range(size):
if(i+j) % 2 == 1:
cb[i,j] = 0.0
return cb
checkerboard(4)
a = checkerboard... | assignments/assignment03/NumpyEx01.ipynb | aschaffn/phys202-2015-work | mit |
Use vizarray to visualize a checkerboard of size=20 with a block size of 10px. | va.set_block_size(10)
va.enable()
checkerboard(20)
assert True | assignments/assignment03/NumpyEx01.ipynb | aschaffn/phys202-2015-work | mit |
Use vizarray to visualize a checkerboard of size=27 with a block size of 5px. | va.set_block_size(5)
va.enable()
checkerboard(27)
assert True | assignments/assignment03/NumpyEx01.ipynb | aschaffn/phys202-2015-work | mit |
載入原始 RAW Data | import json
import pprint
pp = pprint.PrettyPrinter(indent=2)
path = "./pixnet.txt"
all_content = sc.textFile(path).map(json.loads).map(parseRaw) | 3.AnalysisArticle_HTML.ipynb | texib/spark_tutorial | gpl-2.0 |
利用 LXML Parser 來分析文章結構
lxml.html urlparse 需在涵式內被import，以供RDD運算時使用
其他import python package的方法 Submitting Applications
Use spark-submit --py-files to add .py, .zip or .egg files to be distributed with your application.
lxml.html.fromstring 的input為HTML string，回傳為可供 xpath 處理的物件
XPath syntax Ref_1, Ref_2
/ Selects fr... | def parseImgSrc(x):
try:
urls = list()
import lxml.html
from urlparse import urlparse
node = lxml.html.fromstring(x)
root = node.getroottree()
for src in root.xpath('//img/@src'):
try :
host = urlparse(src).netloc
if '.' no... | 3.AnalysisArticle_HTML.ipynb | texib/spark_tutorial | gpl-2.0 |
取出 Image Src 的列表 | image_list = all_content.map(lambda x :parseImgSrc(x[1]))
pp.pprint(image_list.first()[:10]) | 3.AnalysisArticle_HTML.ipynb | texib/spark_tutorial | gpl-2.0 |
統計 Image Src 的列表 | img_src_count = all_content.map(
lambda x :parseImgSrc(x[1])).flatMap(
lambda x: x).countByValue()
for i in img_src_count:
print i , ':' , img_src_count[i] | 3.AnalysisArticle_HTML.ipynb | texib/spark_tutorial | gpl-2.0 |
<span style="color: blue">請使用 reduceByKey , sortBy 來計算出 img src 排行榜</span>
請參照以下文件
[http://spark.apache.org/docs/latest/api/python/pyspark.html#pyspark.RDD]
幾種RDD sorting的方式
針對key值排序
使用 sortByKey
sc.parallelize(tmp).sortByKey(True, 1).collect()
使用 sortBy
sc.parallelize(tmp).sortBy(lambda x: x[0]).collect()
使用 take... | from operator import add
all_content.map(
lambda x :parseImgSrc(x[1])).flatMap(lambda x: x).map(lambda x: (x,1)).reduceByKey(add).sortBy(
lambda x: x[1], ascending=False).collect() | 3.AnalysisArticle_HTML.ipynb | texib/spark_tutorial | gpl-2.0 |
Packaging the Model
In order to train on a TPU, we'll need to set up a python module for training. The skeleton for this has already been built out in tpu_models with the data processing functions from the pevious lab copied into <a href="tpu_models/trainer/util.py">util.py</a>.
Similarly, the model building and traini... | %%writefile tpu_models/trainer/task.py
import argparse
import json
import os
import sys
import tensorflow as tf
from . import model
from . import util
def _parse_arguments(argv):
"""Parses command-line arguments."""
parser = argparse.ArgumentParser()
parser.add_argument(
'--epochs',
help... | courses/machine_learning/deepdive2/image_classification/labs/4_tpu_training.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
The TPU server
Before we can start training with this code, we need a way to pull in MobileNet. When working with TPUs in the cloud, the TPU will not have access to the VM's local file directory since the TPU worker acts as a server. Because of this all data used by our model must be hosted on an outside storage system... | !wget https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/feature_vector/4?tf-hub-format=compressed | courses/machine_learning/deepdive2/image_classification/labs/4_tpu_training.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
This model is still compressed, so lets uncompress it with the tar command below and place it in our tpu_models directory. | %%bash
rm -r tpu_models/hub
mkdir tpu_models/hub
tar xvzf 4?tf-hub-format=compressed -C tpu_models/hub/ | courses/machine_learning/deepdive2/image_classification/labs/4_tpu_training.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Finally, we need to transfer our materials to the TPU. We'll use GCS as a go-between, using gsutil cp to copy everything. | !gsutil rm -r gs://$BUCKET/tpu_models
!gsutil cp -r tpu_models gs://$BUCKET/tpu_models | courses/machine_learning/deepdive2/image_classification/labs/4_tpu_training.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Spinning up a TPU
Time to wake up a TPU! Open the Google Cloud Shell and copy the gcloud compute command below. Say 'Yes' to the prompts to spin up the TPU.
gcloud compute tpus execution-groups create \
--name=my-tpu \
--zone=us-central1-b \
--tf-version=2.3.2 \
--machine-type=n1-standard-1 \
--accelerator-type=v3... | !echo "gsutil cp -r gs://$BUCKET/tpu_models ." | courses/machine_learning/deepdive2/image_classification/labs/4_tpu_training.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
Time to shine, TPU! Run the below cell and copy the output into your TPU terminal. Training will be slow at first, but it will pick up speed after a few minutes once the Tensorflow graph has been built out.
TODO: Complete the code below by adding flags for tpu_address and the hub_path. Have another look at task.py to s... | %%bash
export TPU_NAME=my-tpu
echo "export TPU_NAME="$TPU_NAME
echo "python3 -m tpu_models.trainer.task \
# TODO: Your code goes here \
# TODO: Your code goes here \
--job-dir=gs://$BUCKET/flowers_tpu_$(date -u +%y%m%d_%H%M%S)" | courses/machine_learning/deepdive2/image_classification/labs/4_tpu_training.ipynb | GoogleCloudPlatform/training-data-analyst | apache-2.0 |
3. Undirected graph
The following undirected graph:
|---0---|
| |
| |
1-------2
| |
| |
3-------4
|
|
5
can be defined as: | adj_undirected = np.array([[0, 1, 1, 0, 0, 0],
[1, 0, 1, 1, 0, 0],
[1, 1, 0, 0, 1, 0],
[0, 1, 0, 0, 1, 1],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 1, 0, 0]])
undirected_graph = UndirectedGraph(a... | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
or | adj_undirected = csr_matrix(([1] * 14, ([0, 1, 0, 2, 1, 2, 1, 3, 2, 4, 3, 4, 3, 5],
[1, 0, 2, 0, 2, 1, 3, 1, 4, 2, 4, 3, 5, 3])),
shape=(6, 6))
undirected_graph = UndirectedGraph(adj_undirected)
print(undirected_graph) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
4. Isolated vertices
Note that any directed or undirected graph (not a tree) can have isolated vertices, i.e. vertices with no edge connections. For example the following undirected graph:
```
0---|
|
|
1 2
|
|
3-------4
... | adj_isolated = np.array([[0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[1, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 0],
[0, 0, 1, 1, 0, 0],
[0, 0, 0, 0, 0, 0]])
isolated_graph = UndirectedGraph(adj_isolated)
pr... | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
or | adj_isolated = csr_matrix(([1] * 6, ([0, 2, 2, 4, 3, 4], [2, 0, 4, 2, 4, 3])), shape=(6, 6))
isolated_graph = UndirectedGraph(adj_isolated)
print(isolated_graph) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
5. Directed graph
The following directed graph:
|-->0<--|
| |
| |
1<----->2
| |
v v
3------>4
|
v
5
can be defined as: | adj_directed = np.array([[0, 0, 0, 0, 0, 0],
[1, 0, 1, 1, 0, 0],
[1, 1, 0, 0, 1, 0],
[0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
directed_graph = DirectedGraph(adj_directed)
prin... | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
or | adj_directed = csr_matrix(([1] * 8, ([1, 2, 1, 2, 1, 2, 3, 3], [0, 0, 2, 1, 3, 4, 4, 5])), shape=(6, 6))
directed_graph = DirectedGraph(adj_directed)
print(directed_graph) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
6. Tree
A Tree in Menpo is defined as a directed graph, thus Tree is a subclass of DirectedGraph. The following tree:
0
|
___|___
1 2
| |
_|_ |
3 4 5
| | |
| | |
6 7 8
can be defined a... | adj_tree = np.array([[0, 1, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 1, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 0, 0, 0, 1],
... | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
or | adj_tree = csr_matrix(([1] * 8, ([0, 0, 1, 1, 2, 3, 4, 5], [1, 2, 3, 4, 5, 6, 7, 8])), shape=(9, 9))
tree = Tree(adj_tree, root_vertex=0)
print(tree) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
7. Basic graph properties
Below we show how to retrieve basic properties from all the previously defined graphs, i.e. undirected_graph, isolated_graph, directed_graph and tree of Sections 3, 4, 5 and 6 repsectively.
Number of vertices and edges
For all the above defined graphs, we can get the number of vertices $|V|$ a... | print("The undirected_graph has {} vertices and {} edges.".format(undirected_graph.n_vertices, undirected_graph.n_edges))
print("The isolated_graph has {} vertices and {} edges.".format(isolated_graph.n_vertices, isolated_graph.n_edges))
print("The directed_graph has {} vertices and {} edges.".format(directed_graph.n_v... | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
Sets of vertices and edges
We can also get the sets of vertices and edges, i.e. $V$ and $E$ respectively, as: | print("undirected_graph: The set of vertices $V$ is")
print(undirected_graph.vertices)
print("and the set of edges $E$ is")
print(undirected_graph.edges) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
Adjacency list
We can also retrieve the adjacency list, i.e. a list that for each vertex stores the list of its neighbours (or children in the case of directed graphs). For example: | print("The adjacency list of the undirected_graph is {}.".format(undirected_graph.get_adjacency_list()))
print("The adjacency list of the directed_graph is {}.".format(directed_graph.get_adjacency_list())) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
Isolated vertices
There are methods to check and retrieve isolated vertices, for example: | print("Has the undirected_graph any isolated vertices? {}.".format(undirected_graph.has_isolated_vertices()))
print("Has the isolated_graph any isolated vertices? {}, it has {}.".format(isolated_graph.has_isolated_vertices(),
isolated_graph.is... | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
Neighbours and is_edge
We can check if a pair of vertices are connected with an edge: | i = 4
j = 7
print("Are vertices {} and {} of the tree connected? {}.".format(i, j, tree.is_edge(i, j)))
i = 5
j = 1
print("Are vertices {} and {} of the directed_graph connected? {}.".format(i, j, directed_graph.is_edge(i, j))) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
We can also retrieve whether a vertex has neighbours (or children) and who are they, as: | v = 1
print("How many neighbours does vertex {} of the isolated_graph have? {}.".format(v, isolated_graph.n_neighbours(v)))
print("How many children does vertex {} of the directed_graph have? {}, they are {}.".format(v, directed_graph.n_children(v),
... | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
Cycles and trees
We can check whether a graph has cycles | print("Does the undirected_graph have cycles? {}.".format(undirected_graph.has_cycles()))
print("Does the isolated_graph have cycles? {}.".format(isolated_graph.has_cycles()))
print("Does the directed_graph have cycles? {}.".format(directed_graph.has_cycles()))
print("Does the tree have cycles? {}.".format(tree.has_cyc... | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
and, of course whether a graph is a tree | print("Is the undirected_graph a tree? {}.".format(undirected_graph.is_tree()))
print("Is the directed_graph a tree? {}.".format(directed_graph.is_tree()))
print("Is the tree a tree? {}.".format(tree.is_tree())) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
8. Basic tree properties
Menpo's Tree instance has additional basic properties.
Predecessors list
Apart from the adjacency list mentioned above, a tree can also be represented by a predecessors list, i.e. a list that stores the parent for each vertex. None denotes the root vertex. For example | print(tree.predecessors_list) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
Depth
We can find the maximum depth of a tree | print(tree.maximum_depth) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
as well as the depth of a specific vertex | print("The depth of vertex 4 is {}.".format(tree.depth_of_vertex(4)))
print("The depth of vertex 0 is {}.".format(tree.depth_of_vertex(0))) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
Leaves
Finally, we can get the number of leaves as well as whether a specific vertex is a leaf (has no children): | print("The tree has {} leaves.".format(tree.n_leaves))
print("Is vertex 7 a leaf? {}.".format(tree.is_leaf(7))) | menpo/Shape/Graph.ipynb | grigorisg9gr/menpo-notebooks | bsd-3-clause |
Text Searcher with TensorFlow Lite Model Maker
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="https://www.tensorflow.org/lite/models/modify/model_maker/text_searcher"><img src="https://www.tensorflow.org/images/tf_logo_32px.png" />View on TensorFlow.org</a>
</td>
<td>
<a t... | !sudo apt -y install libportaudio2
!pip install -q tflite-model-maker
!pip install gdown | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.ipynb | tensorflow/tensorflow | apache-2.0 |
Import the required packages. | from tflite_model_maker import searcher | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.ipynb | tensorflow/tensorflow | apache-2.0 |
Prepare the dataset
This tutorial uses the dataset CNN / Daily Mail summarization dataset from the GitHub repo.
First, download the text and urls of cnn and dailymail and unzip them. If it
failed to download from google drive, please wait a few minutes to try it again or download it manually and then upload it to the c... | !gdown https://drive.google.com/uc?id=0BwmD_VLjROrfTHk4NFg2SndKcjQ
!gdown https://drive.google.com/uc?id=0BwmD_VLjROrfM1BxdkxVaTY2bWs
!wget -O all_train.txt https://raw.githubusercontent.com/abisee/cnn-dailymail/master/url_lists/all_train.txt
!tar xzf cnn_stories.tgz
!tar xzf dailymail_stories.tgz | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.ipynb | tensorflow/tensorflow | apache-2.0 |
Then, save the data into the CSV file that can be loaded into tflite_model_maker library. The code is based on the logic used to load this data in tensorflow_datasets. We can't use tensorflow_dataset directly since it doesn't contain urls which are used in this colab.
Since it takes a long time to process the data into... | #@title Save the highlights and urls to the CSV file
#@markdown Load the highlights from the stories of CNN / Daily Mail, map urls with highlights, and save them to the CSV file.
CNN_FRACTION = 0.05 #@param {type:"number"}
DAILYMAIL_FRACTION = 0.05 #@param {type:"number"}
import csv
import hashlib
import os
import te... | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.ipynb | tensorflow/tensorflow | apache-2.0 |
Build the text Searcher model
Create a text Searcher model by loading a dataset, creating a model with the data and exporting the TFLite model.
Step 1. Load the dataset
Model Maker takes the text dataset and the corresponding metadata of each text string (such as urls in this example) in the CSV format. It embeds the t... | !wget -O universal_sentence_encoder.tflite https://storage.googleapis.com/download.tensorflow.org/models/tflite_support/searcher/text_to_image_blogpost/text_embedder.tflite | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.ipynb | tensorflow/tensorflow | apache-2.0 |
Create a searcher.TextDataLoader instance and use data_loader.load_from_csv method to load the dataset. It takes ~10 minutes for this
step since it generates the embedding feature vector for each text one by one. You can try to upload your own CSV file and load it to build the customized model as well.
Specify the name... | data_loader = searcher.TextDataLoader.create("universal_sentence_encoder.tflite", l2_normalize=True)
data_loader.load_from_csv("cnn_dailymail.csv", text_column="highlights", metadata_column="urls") | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.ipynb | tensorflow/tensorflow | apache-2.0 |
For image use cases, you can create a searcher.ImageDataLoader instance and then use data_loader.load_from_folder to load images from the folder. The searcher.ImageDataLoader instance needs to be created by a TFLite embedder model because it will be leveraged to encode queries to feature vectors and be exported with th... | scann_options = searcher.ScaNNOptions(
distance_measure="dot_product",
tree=searcher.Tree(num_leaves=140, num_leaves_to_search=4),
score_ah=searcher.ScoreAH(dimensions_per_block=1, anisotropic_quantization_threshold=0.2))
model = searcher.Searcher.create_from_data(data_loader, scann_options) | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.ipynb | tensorflow/tensorflow | apache-2.0 |
In the above example, we define the following options:
* distance_measure: we use "dot_product" to measure the distance between two embedding vectors. Note that we actually compute the negative dot product value to preserve the notion that "smaller is closer".
tree: the dataset is divided the dataset into 140 partiti... | model.export(
export_filename="searcher.tflite",
userinfo="",
export_format=searcher.ExportFormat.TFLITE) | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.ipynb | tensorflow/tensorflow | apache-2.0 |
Test the TFLite model on your query
You can test the exported TFLite model using custom query text. To query text using the Searcher model, initialize the model and run a search with text phrase, as follows: | from tflite_support.task import text
# Initializes a TextSearcher object.
searcher = text.TextSearcher.create_from_file("searcher.tflite")
# Searches the input query.
results = searcher.search("The Airline Quality Rankings Report looks at the 14 largest U.S. airlines.")
print(results) | tensorflow/lite/g3doc/models/modify/model_maker/text_searcher.ipynb | tensorflow/tensorflow | apache-2.0 |
Write to a file
Create a new file overwriting any previous file with the same name, write text, then close the file: | new_file_path = 'hello_world.txt'
with open(new_file_path, 'w') as new_file:
new_file.write('hello world!') | jup_notebooks/data-science-ipython-notebooks-master/python-data/files.ipynb | steinam/teacher | mit |
Read and Write UTF-8 | import codecs
with codecs.open("hello_world_new.txt", "a", "utf-8") as new_file:
with codecs.open("hello_world.txt", "r", "utf-8") as old_file:
for line in old_file:
new_file.write(line + '\n') | jup_notebooks/data-science-ipython-notebooks-master/python-data/files.ipynb | steinam/teacher | mit |
With the above input problem dictionary for water we now create an AquaChemistry object and call run on it passing in the dictionary to get a result. We use ExactEigensolver first as a reference. | solver = AquaChemistry()
result = solver.run(aqua_chemistry_dict) | community/aqua/chemistry/h2o.ipynb | antoniomezzacapo/qiskit-tutorial | apache-2.0 |
The run method returns a result dictionary. Some notable fields include 'energy' which is the computed ground state energy. | print('Ground state energy: {}'.format(result['energy'])) | community/aqua/chemistry/h2o.ipynb | antoniomezzacapo/qiskit-tutorial | apache-2.0 |
There is also a 'printable' field containing a complete ready to print readable result | for line in result['printable']:
print(line) | community/aqua/chemistry/h2o.ipynb | antoniomezzacapo/qiskit-tutorial | apache-2.0 |
We update the dictionary, for VQE with UCCSD, and run the computation again. | aqua_chemistry_dict['algorithm']['name'] = 'VQE'
aqua_chemistry_dict['optimizer'] = {'name': 'COBYLA', 'maxiter': 25000}
aqua_chemistry_dict['variational_form'] = {'name': 'UCCSD'}
aqua_chemistry_dict['initial_state'] = {'name': 'HartreeFock'}
solver = AquaChemistry()
result = solver.run(aqua_chemistry_dict)
print('G... | community/aqua/chemistry/h2o.ipynb | antoniomezzacapo/qiskit-tutorial | apache-2.0 |
Here are some example values for x and y. I assume that there are no repeated values in x. | xs = np.arange(10, 14)
ys = np.arange(20, 25)
print(xs, ys) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
indices is the list of indices I'll choose from at random: | n = len(xs)
m = len(ys)
indices = np.arange(n) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
Now I'll make an array to hold the values of y: | array = np.tile(ys, (n, 1))
print(array) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
And shuffle the rows independently | [np.random.shuffle(array[i]) for i in range(n)]
print(array) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
I'll keep track of how many unused ys there are in each row | counts = np.full_like(xs, m)
print(counts) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
Now I'll choose a row, using the counts as weights | weights = np.array(counts, dtype=float)
weights /= np.sum(weights)
print(weights) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
i is the row I chose, which corresponds to a value of x. | i = np.random.choice(indices, p=weights)
print(i) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
Now I decrement the counter associated with i, assemble a pair by choosing a value of x and a value of y.
I also clobber the array value I used, which is not necessary, but helps with visualization. | counts[i] -= 1
pair = xs[i], array[i, counts[i]]
array[i, counts[i]] = -1
print(pair) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
We can check that the counts got decremented | print(counts) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
And one of the values in array got used | print(array) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
The next time through is almost the same, except that when we assemble the weights, we give zero weight to the index we just used. | weights = np.array(counts, dtype=float)
weights[i] = 0
weights /= np.sum(weights)
print(weights) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
Everything else is the same | i = np.random.choice(indices, p=weights)
counts[i] -= 1
pair = xs[i], array[i, counts[i]]
array[i, counts[i]] = -1
print(pair)
print(counts)
print(array) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
Now we can wrap all that up in a function, using a special value for i during the first iteration. | def generate_pairs(xs, ys):
n = len(xs)
m = len(ys)
indices = np.arange(n)
array = np.tile(ys, (n, 1))
[np.random.shuffle(array[i]) for i in range(n)]
counts = np.full_like(xs, m)
i = -1
for _ in range(n * m):
weights = np.array(counts, dtype=float)
if i != -1:... | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
And here's how it works: | for pairs in generate_pairs(xs, ys):
print(pairs) | shuffle_pairs.ipynb | AllenDowney/ProbablyOverthinkingIt | mit |
Propagating a single particle
The simulation can now be used to propagate a cosmic ray, which is called candidate. We create a 100 EeV proton and propagate it using the simulation. The propagation stops when the energy drops below the minimum energy requirement that was specified. The possible propagation distances are... | cosmicray = Candidate(nucleusId(1, 1), 200 * EeV, Vector3d(100 * Mpc, 0, 0))
sim.run(cosmicray)
print(cosmicray)
print('Propagated distance', cosmicray.getTrajectoryLength() / Mpc, 'Mpc') | doc/pages/example_notebooks/basics/basics.v4.ipynb | lukasmerten/CRPropa3 | gpl-3.0 |
Defining an observer
To define an observer within the simulation we create a Observer object.
The convention of 1D simulations is that cosmic rays, starting from positive coordinates, propagate in the negative direction until the reach the observer at 0. Only the x-coordinate is used in the three-vectors that represent... | # add an observer
obs = Observer()
obs.add(ObserverPoint()) # observer at x = 0
sim.add(obs)
print(obs) | doc/pages/example_notebooks/basics/basics.v4.ipynb | lukasmerten/CRPropa3 | gpl-3.0 |
Defining the output file
We want to save the propagated cosmic rays to an output file.
Plain text output is provided by the TextOutput module.
For the type of information being stored we can use one of five presets: Event1D, Event3D, Trajectory1D, Trajectory3D and Everything.
We can also fine tune with enable(XXXColumn... | # trajectory output
output1 = TextOutput('trajectories.txt', Output.Trajectory1D)
#sim.add(output1) # generates a lot of output
#output1.disable(Output.RedshiftColumn) # don't save the current redshift
#output1.disableAll() # disable everything to start from scratch
#output1.enable(Output.CurrentEnergyColumn) # cu... | doc/pages/example_notebooks/basics/basics.v4.ipynb | lukasmerten/CRPropa3 | gpl-3.0 |
If in the example above output1 is added to the module list, it is called on every propagation step to write out the cosmic ray information.
To save only cosmic rays that reach our observer, we add an output to the observer that we previously defined.
This time we are satisfied with the output type Event1D. | # event output
output2 = TextOutput('events.txt', Output.Event1D)
obs.onDetection(output2)
#sim.run(cosmicray)
#output2.close() | doc/pages/example_notebooks/basics/basics.v4.ipynb | lukasmerten/CRPropa3 | gpl-3.0 |
Similary, the output could be linked to the MinimumEnergy module to save those cosmic rays that fall below the minimum energy, and so on.
Note: If we want to use the CRPropa output file from within the same script that runs the simulation, the output module should be explicitly closed after the simulation run in order... | # cosmic ray source
source = Source()
source.add(SourcePosition(100 * Mpc))
source.add(SourceParticleType(nucleusId(1, 1)))
source.add(SourcePowerLawSpectrum(1 * EeV, 200 * EeV, -1))
print(source) | doc/pages/example_notebooks/basics/basics.v4.ipynb | lukasmerten/CRPropa3 | gpl-3.0 |
Running the simulation
Finally we run the simulation to inject and propagate 10000 cosmic rays. An optional progress bar can show the progress of the simulation. | sim.setShowProgress(True) # switch on the progress bar
sim.run(source, 10000) | doc/pages/example_notebooks/basics/basics.v4.ipynb | lukasmerten/CRPropa3 | gpl-3.0 |
(Optional) Plotting
This is not part of CRPropa, but since we're at it we can plot the energy spectrum of detected particles to observe the GZK suppression.
The plotting is done here using matplotlib, but of course you can use whatever plotting tool you prefer. | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
output2.close() # close output file before loading
data = np.genfromtxt('events.txt', names=True)
print('Number of events', len(data))
logE0 = np.log10(data['E0']) + 18
logE = np.log10(data['E']) + 18
plt.figure(figsize=(10, 7))
h1 = plt.hist(lo... | doc/pages/example_notebooks/basics/basics.v4.ipynb | lukasmerten/CRPropa3 | gpl-3.0 |
Field Description
C/A = Control Area (A002)
UNIT = Remote Unit for a station (R051)
SCP = Subunit Channel Position represents an specific address for a device (02-00-00)
STATION = Represents the station name the device is located at
LINENAME = Represents all train lines that can be boarded at this stati... | turnstile = {}
# looping through all files in data dir starting with MTA_Turnstile
for filename in os.listdir('data'):
if filename.startswith('MTA_Turnstile'):
# reading file and writing each row in a dict
with open(os.path.join('data', filename), newline='') as csvfile:
mtareader = cs... | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
Exercise 2
Let's turn this into a time series.
For each key (basically the control area, unit, device address and station of a specific turnstile), have a list again, but let the list be comprised of just the point in time and the cumulative count of entries.
This basically means keeping only the date, time, and entr... | import numpy as np
import datetime
from dateutil.parser import parse
# With respect to the solutions I converted the cumulative entries in the number of entries in the period
# That's ok I think since it is required below to do so...
turnstile_timeseries = {}
# looping through each key in dict, parsing the date and ... | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
Exercise 3
These counts are cumulative every n hours. We want total daily entries.
Now make it that we again have the same keys, but now we have a single value for a single day, which is not cumulative counts but the total number of passengers that entered through this turnstile on this day. | # In the solutions there's a check for abnormal values, I added it in the exercises below
# because I found out about the problem later in the analysis
turnstile_daily = {}
# looping through each key in the timeseries, tracking if the date change while cumulating partial counts
for key in turnstile_timeseries:
va... | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
Exercise 4
We will plot the daily time series for a turnstile.
In ipython notebook, add this to the beginning of your next cell:
%matplotlib inline
This will make your matplotlib graphs integrate nicely with the notebook.
To plot the time series, import matplotlib with
import matplotlib.pyplot as plt
Take the ... | import matplotlib.pyplot as plt
%matplotlib inline
# using list comprehension, there are other ways such as dict.keys() and dict.items()
dates = [el[0] for el in turnstile_daily[test]]
counts = [el[1] for el in turnstile_daily[test]]
fig = plt.figure(figsize=(14, 5))
ax = plt.axes()
ax.plot(dates, counts)
plt.grid('o... | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
Exercise 5
So far we've been operating on a single turnstile level, let's combine turnstiles in the same ControlArea/Unit/Station combo. There are some ControlArea/Unit/Station groups that have a single turnstile, but most have multiple turnstilea-- same value for the C/A, UNIT and STATION columns, different values fo... | temp = {}
# for each key I form the new key and check if it's already in the new dict
# I append the date in this temp dict to make it easier to sum the values
# then I create a new dict with the required keys
for key in turnstile_daily:
new_key = list(key[0:2]) + list(key[-1:])
for el in turnstile_daily[key]:... | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
Exercise 6
Similarly, combine everything in each station, and come up with a time series of [(date1, count1),(date2,count2),...] type of time series for each STATION, by adding up all the turnstiles in a station. | temp = {}
# for each key I form the new key and check if it's already in the new dict
# I append the date in this temp dict to make it easier to sum the values
# then I create a new dict with the required keys
for key in turnstile_daily:
new_key = key[-1]
for el in turnstile_daily[key]:
# setting singl... | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
Exercise 7
Plot the time series for a station | test_station = '59 ST'
dates = [el[0] for el in station[test_station]]
counts = [el[1] for el in station[test_station]]
fig = plt.figure(figsize=(14, 5))
ax = plt.axes()
ax.plot(dates, counts)
plt.grid('on'); | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
Exercise 8
Make one list of counts for one week for one station. Monday's count, Tuesday's count, etc. so it's a list of 7 counts.
Make the same list for another week, and another week, and another week.
plt.plot(week_count_list) for every week_count_list you created this way. You should get a rainbow plot of weekly c... | fig = plt.figure(figsize=(16, 6))
ax = plt.axes()
n = len(station[test_station])
# creating a list with all the counts for the station
all_counts = [el[1] for el in station[test_station]]
# splitting counts every 7 values to get weekly data
for i in range(int(np.floor(n/7))):
ax.plot(all_counts[i*7: 7 + i*7])
ax.se... | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
Exercise 9
Over multiple weeks, sum total ridership for each station and sort them, so you can find out the stations with the highest traffic during the time you investigate | total_ridership = {}
# just looping through keys and summing all elements inside the dict
for key in station:
for el in station[key]:
if key in total_ridership:
total_ridership[key] += el[1]
else:
total_ridership[key] = el[1]
import operator
sorted(total_ridership.items(), ... | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
Exercise 10
Make a single list of these total ridership values and plot it with plt.hist(total_ridership_counts) to get an idea about the distribution of total ridership among different stations.
This should show you that most stations have a small traffic, and the histogram bins for large traffic volumes have small ... | fig = plt.figure(figsize=(16, 10))
ax = plt.axes()
ax.hist(list(total_ridership.values()));
fig = plt.figure(figsize=(16, 10))
ax = plt.axes()
ax.bar(range(len(total_ridership)), sorted(list(total_ridership.values()))); | EDA/EDA_MTA_Exercises.ipynb | aleph314/K2 | gpl-3.0 |
DATA
After downloading both files, I created a consolidated sheet on excel. This was difficult because the UN and World Banks did not use the same names for the same country. For example, "PDR Korea" in the UN file was classified as "the people's republic of Korea" in the World Bank. There were around 25 instances of t... | #after combining both files, I uploaded the excel document to my dropbox at the link below
xls_file = pd.ExcelFile('https://dl.dropboxusercontent.com/u/16846867/UN-WB%202010%20PG%20vs%20YU.xlsx')
xls_file
#Here is the data I will analyze. Population growth is an average from 1985-2005 and youth unemployment data is f... | MBA_S16/Jonathan Broch - Demographics & War.ipynb | NYUDataBootcamp/Projects | mit |
The Grand Finale
And now back to what I thought was interesting. Here is the scatter plot with countries that have had thousands of death in the past years due to armed conflict as well as some of the largest economies in the world highlighted
in RED Afghanistan, Iraq, Yemen, Sudan, Syria, and Libya
in BLUE The United ... | fig, ax = plt.subplots()
Data.plot.scatter(ax=ax, x="Population Growth", y="Youth Unemployment", figsize=(20,10), alpha=.5, color = 'white')
Data.iloc[199:201].plot.scatter(ax=ax, x="Population Growth", y="Youth Unemployment", figsize=(20,10), alpha=.9, color = 'red', s=100)
Data.iloc[172:173].plot.scatter(ax=ax, x="Po... | MBA_S16/Jonathan Broch - Demographics & War.ipynb | NYUDataBootcamp/Projects | mit |
Fanfiction Story Analysis
Performance benchmarking and prediction
The success of a story is typically judged by the number of reviews, favorites, or followers it recieves. Here, we will try to predict how successful a story will be given select observable features, as well as develop a way to benchmark existing stories... | # examines distribution of number of words
df_online['reviews'].fillna(0).plot.hist(normed=True,
bins=np.arange(0, 50, 1), alpha=0.5, histtype='step', linewidth='2')
df_online['favs'].fillna(0).plot.hist(normed=True,
bins=np.arange(0, ... | jupyter_notebooks/story_performance.ipynb | lily-tian/fanfictionstatistics | mit |
As expected, reviews, favorites, and follows all have heavily right-skewing distributions. However, there are also differences. A story is mostly likely to have 1 or 2 reviews, not 0. A story is mostly likely to have 0 favorites, but otherwise the favorites distribution looks very similar to reviews. Follows is the one... | df_online.columns.values
# creates regressand variables
df_online['ratedM'] = [row == 'M' for row in df_online['rated']]
df_online['age'] = [cyear - int(row) for row in df_online['pub_year']]
df_online['fansize'] = [fandom[row] for row in df_online['fandom']]
df_online['complete'] = [row == 'Complete' for row in df_on... | jupyter_notebooks/story_performance.ipynb | lily-tian/fanfictionstatistics | mit |
Multicollinearity | # displays correlation matrix
df_active.corr()
# creates design_matrix
X = df_active
X['intercept'] = 1
# displays variance inflation factor
vif_results = pd.DataFrame()
vif_results['VIF Factor'] = [vif(X.values, i) for i in range(X.shape[1])]
vif_results['features'] = X.columns
vif_results | jupyter_notebooks/story_performance.ipynb | lily-tian/fanfictionstatistics | mit |
Results indicate there is some correlation between two of the independent variables: 'fa' and 'fs', implying one of them may not be necessary in the model.
Nonlinearity
We know from earlier distributions that some of the variables are heavily right-skewed. We created some scatter plots to confirm that the assumption of... | # runs OLS regression
formula = 'st ~ fa + fs + cc + age'
reg = smf.ols(data=df_active, formula=formula).fit()
print(reg.summary()) | jupyter_notebooks/story_performance.ipynb | lily-tian/fanfictionstatistics | mit |
The log transformations helped increase the fit from and R-squared of ~0.05 to ~0.20.
From these results, we can see that:
A 1% change in number of authors favorited is associated with a ~15% change in the number of stories written.
A 1% change in number of stories favorited is associated with a ~4% change in the numb... | # runs OLS regression
formula = 'st ~ fa + cc + age'
reg = smf.ols(data=df_active, formula=formula).fit()
print(reg.summary()) | jupyter_notebooks/story_performance.ipynb | lily-tian/fanfictionstatistics | mit |
Without 'fs', we lost some information but not much:
A 1% change in number of authors favorited is associated with a ~20% change in the number of stories written.
Being in a community is associated with a ~0.7 increase in the number of stories written.
One more year on the site is associated with a ~3% change in the n... | def graph(formula, x_range):
y = np.array(x_range)
x = formula(y)
plt.plot(y,x)
graph(lambda x : (np.exp(reg.params[0]+reg.params[1]*(np.log(x-1)))),
range(2,100,1))
graph(lambda x : (np.exp(reg.params[0]+reg.params[1]*(np.log(x-1))+reg.params[2])),
range(2,100,1))
plt.show()
ages = [0... | jupyter_notebooks/story_performance.ipynb | lily-tian/fanfictionstatistics | mit |
Essential Libraries and Tools
NumPy | import numpy as np
x = np.array([[1,2,3],[4,5,6]])
print("x:\n{}".format(x)) | introduction_to_ml_with_python/1_Introduction.ipynb | bgroveben/python3_machine_learning_projects | mit |
SciPy | from scipy import sparse
# Create a 2D NumPy array with a diagonal of ones, and zeros everywhere else (aka an identity matrix).
eye = np.eye(4)
print("NumPy array:\n{}".format(eye))
# Convert the NumPy array to a SciPy sparse matrix in CSR format.
# The CSR format stores a sparse m × n matrix M in row form using th... | introduction_to_ml_with_python/1_Introduction.ipynb | bgroveben/python3_machine_learning_projects | mit |
Usually it isn't possible to create dense representations of sparse data (they won't fit in memory), so we need to create sparse representations directly.
Here is a way to create the same sparse matrix as before using the COO format: | data = np.ones(4)
row_indices = np.arange(4)
col_indices = np.arange(4)
eye_coo = sparse.coo_matrix((data, (row_indices, col_indices)))
print("COO representation:\n{}".format(eye_coo)) | introduction_to_ml_with_python/1_Introduction.ipynb | bgroveben/python3_machine_learning_projects | mit |
More details on SciPy sparse matrices can be found in the SciPy Lecture Notes.
matplotlib | # %matplotlib inline -- the default, just displays the plot in the browser.
# %matplotlib notebook -- provides an interactive environment for the plot.
import matplotlib.pyplot as plt
# Generate a sequnce of numbers from -10 to 10 with 100 steps (points) in between.
x = np.linspace(-10, 10, 100)
# Create a second arra... | introduction_to_ml_with_python/1_Introduction.ipynb | bgroveben/python3_machine_learning_projects | mit |
pandas
Here is a small example of creating a pandas DataFrame using a Python dictionary. | import pandas as pd
from IPython.display import display
# Create a simple dataset of people
data = {'Name': ["John", "Anna", "Peter", "Linda"],
'Location' : ["New York", "Paris", "Berlin", "London"],
'Age' : [24, 13, 53, 33]
}
data_pandas = pd.DataFrame(data)
# IPython.display allows for "prett... | introduction_to_ml_with_python/1_Introduction.ipynb | bgroveben/python3_machine_learning_projects | mit |
There are several possible ways to query this table.
Here is one example: | # Select all rows that have an age column greater than 30:
display(data_pandas[data_pandas.Age > 30]) | introduction_to_ml_with_python/1_Introduction.ipynb | bgroveben/python3_machine_learning_projects | mit |
mglearn
The mglearn package is a library of utility functions written specifically for this book, so that the code listings don't become too cluttered with details of plotting and data loading.
The mglearn library can be found at the author's Github repository, and can be installed with the command pip install mglearn.... | # Make sure your dependencies are similar to the ones in the book.
import sys
print("Python version: {}".format(sys.version))
import pandas as pd
print("pandas version: {}".format(pd.__version__))
import matplotlib
print("matplotlib version: {}".format(matplotlib.__version__))
import numpy as np
print("NumPy versio... | introduction_to_ml_with_python/1_Introduction.ipynb | bgroveben/python3_machine_learning_projects | mit |
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