repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
dataDogma/Computer-Science | Courses/DAT-208x/.ipynb_checkpoints/DAT208X - Week 5 - Section 1 - Plotting_with_MatplotLib-checkpoint.ipynb | gpl-3.0 | # Print the last item from year and pop
# print(year[-1])
# print(pop[-1])
# Import matplotlib.pyplot as plt
# import matplotlib.pyplot as plt
# Make a line plot: year on the x-axis, pop on the y-axis
# plt.plot( year, pop)
# plt.show()
"""
Explanation: Table of Content
Why Visualization is important
Exercise 1
... |
Hyperparticle/deep-learning-foundation | language-translation/dlnd_language_translation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
"""
Explanation: Language Translation
In this project, you’re going... |
vravishankar/Jupyter-Books | pandas/09.Pandas - Applications to Finance.ipynb | mit | # import pandas, numpy and other libraries
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import datetime
import matplotlib.pyplot as plt
# set some pandas options
pd.set_option('display.notebook_repr_html',False)
pd.set_option('display.max_columns', 6)
pd.set_option('display.max_rows',10)... |
zipeiyang/liupengyuan.github.io | chapter4/pandas_Tutorial_4(visualization).ipynb | mit | %matplotlib inline
from pandas import Series, DataFrame
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
#import seaborn as sns #即使不适用seaborn内的功能，也可以使绘图自动带有seaborn风格
# df = pd.read_excel('https://github.com/liupengyuan/python_tutorial/blob/master/chapter4/countrys_freq.xlsx?r... |
silburt/rebound2 | ipython_examples/Churyumov-Gerasimenko.ipynb | gpl-3.0 | import rebound
sim = rebound.Simulation()
sim.add("Sun")
sim.add("Jupiter")
sim.add("Saturn")
"""
Explanation: 67P/Churyumov–Gerasimenko
This tutorial teaches you how to use the IAS15 integator (Rein and Spiegel, 2015) to simulate the orbit of 67P/Churyumov–Gerasimenko. We will download the data from NASA Horizons and... |
ClementPhil/deep-learning | sentiment-network/Sentiment_Classification_Projects.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
tpin3694/tpin3694.github.io | python/ifelse_on_any_or_all_elements.ipynb | mit | # import pandas as pd
import pandas as pd
"""
Explanation: Title: If Else On Any Or All Elements
Slug: ifelse_on_any_or_all_elements
Summary: If Else On Any Or All Elements
Date: 2016-05-01 12:00
Category: Python
Tags: Basics
Authors: Chris Albon
Preliminaries
End of explanation
"""
# Create an example dataframe
d... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_dipole_fit.ipynb | bsd-3-clause | from os import path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.forward import make_forward_dipole
from mne.evoked import combine_evoked
from mne.simulation import simulate_evoked
data_path = mne.datasets.sample.data_path()
subjects_dir = op.join(data_path, 'subjects')
fname_ave = op.... |
taliamo/Final_Project | organ_pitch/Scripts/upload_env_data.ipynb | mit | # I import useful libraries (with functions) so I can visualize my data
# I use Pandas because this dataset has word/string column titles and I like the readability features of commands and finish visual products that Pandas offers
import pandas as pd
import matplotlib.pyplot as plt
import re
import numpy as np
%matp... |
keras-team/autokeras | docs/ipynb/structured_data_classification.ipynb | apache-2.0 |
TRAIN_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/train.csv"
TEST_DATA_URL = "https://storage.googleapis.com/tf-datasets/titanic/eval.csv"
train_file_path = tf.keras.utils.get_file("train.csv", TRAIN_DATA_URL)
test_file_path = tf.keras.utils.get_file("eval.csv", TEST_DATA_URL)
"""
Explanation: A ... |
armandosrz/UdacityNanoMachine | customer_segments/customer_segments.ipynb | apache-2.0 | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from IPython.display import display # Allows the use of display() for DataFrames
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the wholesale custo... |
aakashsinha19/Aspectus | Image Classification/models/slim/slim_walkthough.ipynb | apache-2.0 | import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
import math
import numpy as np
import tensorflow as tf
import time
from datasets import dataset_utils
# Main slim library
slim = tf.contrib.slim
"""
Explanation: TF-Slim Walkthrough
This notebook will walk you through the basics of using TF-Slim to... |
ecalio07/enron-paper | deliver/.ipynb_checkpoints/010617-WJGH-art_struc-checkpoint.ipynb | gpl-3.0 | ## Functions
import sys
sys.path.append("../dev")
import bib_mri as FW
import numpy as np
import scipy as scipy
import scipy.misc as misc
import matplotlib as mpl
import matplotlib.pyplot as plt
from numpy import genfromtxt
import platform
%matplotlib inline
def sign_extract(seg, resols): #Function for shape signa... |
ericmjl/tiki-tracker-analysis-2016 | Data Analysis.ipynb | mit | tracker_ids_time = {'F4:B8:5E:C4:54:BE':datetime(2016, 1, 28, 12, 1, 0),
'F4:B8:5E:DD:42:D2':datetime(2016, 1, 28, 9, 52, 0),
'F4:B8:5E:C4:5F:8C':datetime(2016, 1, 28, 10, 12, 0),
'F4:B8:5E:C4:8F:EE':datetime(2016, 1, 26, 9, 59, 0),
'68:9E:... |
JanetMatsen/Neo4j_meta4 | jupyter/prepare_whole_network.ipynb | gpl-3.0 | ! ls -lh ../waffle_network_dir/*.tsv
! wc -l ../waffle_network_dir/network.py.tsv
! head -n 5 ../waffle_network_dir/network.py.tsv | csvlook -t
! ls -lh ../waffle_network_dir/network.py.tsv
"""
Explanation: The network directory in this share (which is still uploading, btw) contains a pickle (data.pkl) and the cod... |
crystalzhaizhai/cs207_yi_zhai | lectures/L17/L17.ipynb | mit | import sqlite3
"""
Explanation: Lecture 17: Databases
Monday, November 6th 2017
Introduction
Why Learn Databases
You will see many databases in your career.
SQL (Structured Query Language) is still very popular and will remain so for a long time. Hence, you will need to code SQL.
SQL is the language used to qu... |
dtamayo/MachineLearning | Day2/Transit Classification - Before we start .ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.cross_validation import train_test_split
from sklearn.metrics import confusion_matrix
import matplotlib
from matplotlib import pyplot as plt
%matplotlib inline
#load the dataset, introduce the structur... |
xtr33me/deep-learning | intro-to-tensorflow/intro_to_tensorflow.ipynb | mit | import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfile import ZipFile
print('All m... |
sz2472/foundations-homework | homework12/311_time_series_homework.ipynb | mit | df=pd.read_csv("311-2014.csv",nrows=20000)
df.head()
df.columns
df.info()
dateutil.parser.parse('07/16/1990').month
def parse_date (str_date):
return dateutil.parser.parse(str_date)#dateutil is a module, import parser class, then transform a string into a python time object
df['Created Date']= df['Created Date... |
dchad/malware-detection | mmcc/feature-reduction.ipynb | gpl-3.0 | train_data = pd.read_csv('data/train-malware-features-asm.csv')
labels = pd.read_csv('data/trainLabels.csv')
sorted_train_data = train_data.sort(columns='filename', axis=0, ascending=True, inplace=False)
sorted_train_labels = labels.sort(columns='Id', axis=0, ascending=True, inplace=False)
X = sorted_train_data.iloc[:,... |
kaleoyster/nbi-data-science | Finding+Time+Interval+Before+Intervention+of+Bridge+Components.ipynb | gpl-2.0 | from pymongo import MongoClient
import time
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib.pyplot import *
import datetime as dt
import random as rnd
import warnings
import datetime as dt
import csv
%matplotlib inline
warnings.filterwarnings(action="ignore"... |
mbeyeler/opencv-machine-learning | notebooks/08.01-Understanding-k-Means-Clustering.ipynb | mit | import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('ggplot')
"""
Explanation: <!--BOOK_INFORMATION-->
<a href="https://www.packtpub.com/big-data-and-business-intelligence/machine-learning-opencv" target="_blank"><img align="left" src="data/cover.jpg" style="width: 76px; height: 100px; background: white; ... |
brclark-usgs/flopy | examples/Notebooks/flopy3_swi2package_ex4.ipynb | bsd-3-clause | %matplotlib inline
import os
import sys
import platform
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import flopy
print(sys.version)
print('numpy version: {}'.format(np.__version__))
print('matplotlib version: {}'.format(mpl.__version__))
print('flopy version: {}'.format(flopy.__version_... |
m2dsupsdlclass/lectures-labs | labs/10_unsupervised_generative_models/Variational_AutoEncoders.ipynb | mit | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
from tensorflow.keras.layers import Input, Dense, Lambda, Flatten, Reshape, Conv2D, Conv2DTranspose
from tensorflow.keras.models import Model
from tensorflow.keras import metrics
from tensorflow.keras.datasets impo... |
mne-tools/mne-tools.github.io | dev/_downloads/a786781c3c54739f0be7add5b76a068f/50_ssvep.ipynb | bsd-3-clause | # Authors: Dominik Welke <dominik.welke@web.de>
# Evgenii Kalenkovich <e.kalenkovich@gmail.com>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
import numpy as np
from scipy.stats import ttest_rel
"""
Explanation: Frequency-tagging: Basic analysis of an SSVEP/vSSR dataset
In this tutoria... |
norsween/data-science | springboard-answers-to-exercises/Mini_Project_Naive_Bayes-Answers.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from six.moves import range
# Setup Pandas
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('... |
maxis42/ML-DA-Coursera-Yandex-MIPT | 4 Stats for data analysis/Homework/10 test multiple hypothesis testing/Test Multiple hypothesis testing.ipynb | mit | from __future__ import division
import numpy as np
import pandas as pd
from scipy import stats
from statsmodels.sandbox.stats.multicomp import multipletests
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
from itertools import combinations
from IPython.core.interactiveshell import Interac... |
vzg100/Post-Translational-Modification-Prediction | old/Lysine Acetylation -MLP -dbptm.ipynb | mit | from pred import Predictor
from pred import sequence_vector
from pred import chemical_vector
"""
Explanation: Template for test
End of explanation
"""
par = ["pass", "ADASYN", "SMOTEENN", "random_under_sample", "ncl", "near_miss"]
for i in par:
print("y", i)
y = Predictor()
y.load_data(file="Data/Trainin... |
torgebo/deep_learning_workshop | 3-convnet/convolutional-network.ipynb | mit | # Plots will be displaying plots within the notebook
%matplotlib notebook
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
# NumPy is a package for manipulating N-dimensional array objects
import numpy as np
# Pandas is a data analysis package
import pandas as pd
#Library To test/verify som... |
brettavedisian/phys202-2015-work | assignments/assignment09/IntegrationEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy import integrate
def X(x):
return x**2
I,e=integrate.quad(X,0,3)
I
"""
Explanation: Integration Exercise 2
Imports
End of explanation
"""
def integrand(x, a):
return 1.0/(x**2 + a**2)
def integral_approx... |
LogicWang/ml | deep/tf/deepdream/deepdream.ipynb | apache-2.0 | # boilerplate code
from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import PIL.Image
from IPython.display import clear_output, Image, display, HTML
import tensorflow as tf
"""
Explanation: DeepDreaming with TensorFlow
Loading and displaying the m... |
tensorflow/docs-l10n | site/zh-cn/tutorials/text/transformer.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
rishuatgithub/MLPy | nlp/UPDATED_NLP_COURSE/04-Semantics-and-Sentiment-Analysis/00-Semantics-and-Word-Vectors.ipynb | apache-2.0 | # Import spaCy and load the language library
import spacy
nlp = spacy.load('en_core_web_lg') # make sure to use a larger model!
nlp(u'lion').vector
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Semantics and Word Vectors
Sometimes called "opinion mining", Wikipedi... |
osemer01/regression-w-unknown-feature-names | regression_wo_knowing_feature_names.ipynb | cc0-1.0 | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
"""
Explanation: Regression with Unknown Feature Names
Author Information:
Oguz Semerci<br>
oguz.semerci@gmail.com<br>
Summary of the investigation
We have in hand a regression problem with 5000 observations and 254 features, whose names are not kno... |
avicennax/jedi | .ipynb_checkpoints/Noise axons and the binary DFORCE-checkpoint.ipynb | mit | import pylab as plt
import numpy as np
%matplotlib inline
from __future__ import division
from scipy.integrate import odeint,ode
from numpy import zeros,ones,eye,tanh,dot,outer,sqrt,linspace,cos,pi,hstack,zeros_like,abs,repeat
from numpy.random import uniform,normal,choice
%config InlineBackend.figure_format = 'retina'... |
quantopian/research_public | notebooks/data/eventvestor.earnings_calendar/notebook.ipynb | apache-2.0 | # import the dataset
from quantopian.interactive.data.eventvestor import earnings_calendar as dataset
# or if you want to import the free dataset, use:
# from quantopian.data.eventvestor import earnings_calendar_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd... |
nguy/AWOT | examples/awot_radar_cross_section.ipynb | gpl-2.0 | # Load the needed packages
from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import awot
from awot.graph.common import create_basemap
from awot.graph import RadarHorizontalPlot, RadarVerticalPlot, FlightLevel
%matplotlib inline
import warnings
warnings.filterwarnings("ignore", category=Depreca... |
rflamary/POT | notebooks/plot_WDA.ipynb | mit | # Author: Remi Flamary <remi.flamary@unice.fr>
#
# License: MIT License
import numpy as np
import matplotlib.pylab as pl
from ot.dr import wda, fda
"""
Explanation: Wasserstein Discriminant Analysis
This example illustrate the use of WDA as proposed in [11].
[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy,... |
queirozfcom/python-sandbox | python3/notebooks/files-directories-2021/Untitled.ipynb | mit | new_directory_path = "/path/to/new/directory"
if not os.path.exists(new_directory_path):
os.mkdir(new_directory_path)
"""
Explanation: Create directory if not exists
End of explanation
"""
new_directory_path = "/path/to/new/directory"
if not os.path.exists(new_directory_path):
os.makedirs(new_directory_pat... |
kecnry/autofig | docs/tutorials/limits.ipynb | gpl-3.0 | import autofig
import numpy as np
#autofig.inline()
t = np.linspace(0, 2*np.pi, 101)
x = np.sin(t)
y1 = np.cos(t)
y2 = -0.5*y1
y3 = 1.5*y1
"""
Explanation: Autofig Limits
End of explanation
"""
fig1 = autofig.Figure()
fig1.plot(x=t, y=y1, i='x', marker='None', color='b', linestyle='solid', uncover=True)
fig1.plot(... |
vvscloud/python-notes | python-data-structures.ipynb | mit | a = [1,2,3,4]
b = a
a[2] = 44 # b list also changes here
b
a is b # This shows a and b references are same
"""
Explanation: Python datastructures notes
This contains operations on lists, dictionaries and tuples
Also covers
- Augumented assignment trick
- Map, filter and lambda
- Iterators
- Generators
List operati... |
pyannote/pyannote-audio | tutorials/applying_a_model.ipynb | mit | # clone pyannote-audio Github repository and update ROOT_DIR accordingly
ROOT_DIR = "/Users/bredin/Development/pyannote/pyannote-audio"
AUDIO_FILE = f"{ROOT_DIR}/tutorials/assets/sample.wav"
from pyannote.database.util import load_rttm
REFERENCE = f"{ROOT_DIR}/tutorials/assets/sample.rttm"
reference = load_rttm(REFERE... |
citxx/sis-python | crash-course/lists.ipynb | mit | a = [1, 2, "Hi"] # Создать список и присвоить переменной `а` этот список
print(a[0], a[1], a[2]) # Обращение к элементам списка, индексация с нуля
b = list() # Создать пустой список
c = [] # Другой способ создать пустой список
"""
Explanation: <h1>Содержание<span class="tocSki... |
SubhankarGhosh/NetworkX | 7. Bipartite Graphs (Student).ipynb | mit | G = cf.load_crime_network()
G.edges(data=True)[0:5]
G.nodes(data=True)[0:10]
"""
Explanation: Introduction
Bipartite graphs are graphs that have two (bi-) partitions (-partite) of nodes. Nodes within each partition are not allowed to be connected to one another; rather, they can only be connected to nodes in the othe... |
oxpeter/library_bioinformatics_service | Pandas/Pandas plotting.ipynb | apache-2.0 | import pandas as pd
df_asthma = pd.read_csv("data/asthma.csv")
df_asthma.head()
"""
Explanation: Intermediate Pandas : Plotting with Pandas, Matplotlib and Seaborn
A short workshop run by the Library Bioinformatics Service
tinyurl.com/wcmpandas05
Based on the Data Carpentry curriculum for Data Visualization in Pytho... |
elinahkk/Masters | GHCN_Data_Analysis.ipynb | mit | def get_date(date_number):
"""
Turn the int64 value from the DATE of GHCN into a pd.datetime
"""
dstring = str(date_number)
return pd.datetime(int(dstring[0:4]),int(dstring[4:6]),int(dstring[6:8]))
def get_df(fnm, var, no_missing = True):
"""
Create a dataframe for a single station, with a ... |
SunnyBUPT/Awesome_Machine_Learning | Feature Engineering/KMeans Impute Missing Data.ipynb | mit | import numpy as np
from sklearn.cluster import KMeans
def kmeans_missing(X, n_clusters, max_iter=10):
"""Perform K-Means clustering on data with missing values.
Args:
X: An [n_samples, n_features] array of data to cluster.
n_clusters: Number of clusters to form.
max_iter: Maximum number of E... |
h0s/c3py | docs/c3py_examples.ipynb | mit | import c3py
from IPython.display import HTML
"""
Explanation: <h1>Contents</h1>
<div id="table_of_contents"></div>
<h1>c3py</h1>
c3py is a Python wrapper around c3js (http://c3js.org/).
<h2>Introduction</h2>
c3js has a function named <a href="http://c3js.org/gettingstarted.html#generate">generate</a>, which takes ... |
Yichuans/wilderness-wh | old_with_antarctica/wilderness_analysis_with_antarctica.ipynb | gpl-3.0 | # load default libraries
import os, sys
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
# make sure gdal is correctly installed
from osgeo import gdal
import gc
%matplotlib inline
"""
Explanation: Wilderness World Heritage analysis for the marine environment
Based on the discussion with Basti... |
prasants/pyds | 02.Python_Basics.ipynb | mit | print ("Hello World!")
"""
Explanation: Table of Contents
<p><div class="lev1 toc-item"><a href="#Welcome-to-Python" data-toc-modified-id="Welcome-to-Python-1"><span class="toc-item-num">1 </span>Welcome to Python</a></div><div class="lev2 toc-item"><a href="#Exercise" data-toc-modified-id="Exercise-11"><sp... |
leoferres/prograUDD | labs/ejercicios_for.ipynb | mit | passwd = input("Ingrese su contraseña: ")
if len(passwd) < 8:
print("Contraseña no valida, faltan caracteres")
else:
cantnum = 0
cantsimb = 0
for i in passwd:
if i.isdigit():
cantnum += 1
elif not i.isalpha():
cantsimb += 1
if cantsimb != 1:
print("Co... |
bauman/bsonsearch | bsonsearch_project_bson_vs_json.ipynb | mit | import bson
import re
from bsonsearch import bsoncompare
from datetime import datetime
bc = bsoncompare()
source_data = {"a":[bson.objectid.ObjectId(), datetime.now(), re.compile(r".*test string.*", re.IGNORECASE)]}
echo_projection = bc.generate_matcher({"$project":{"a":True}})
source_data_doc_id = bc.generate_doc(sour... |
TomTranter/OpenPNM | examples/extractions/Managing Geometrical Properties of Imported Networks.ipynb | mit | import numpy as np
import openpnm as op
ws = op.Workspace()
ws.settings['loglevel'] = 50 # Supress warnings, but see error messages
"""
Explanation: Managing Geometry Properties of Imported Networks
The Imported geometry class is used to store the geometrical properties of imported networks. When importing an extrac... |
abelatnvidia/tfodapi | lab/intro_obj_detection.ipynb | apache-2.0 | import os
import io
import sys
import PIL
import json
import copy
import random
import zipfile
import tarfile
import operator
import collections
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import six.moves.urllib as urllib
import PIL.ImageDraw as ImageDraw
import PIL.ImageFont as Image... |
gaufung/Data_Analytics_Learning_Note | DesignPattern/ProxyPattern.ipynb | mit | info_struct=dict()
info_struct['addr']=10000
info_struct['content']=''
class Server(object):
content=''
def recv(self, info):
pass
def send(self, info):
pass
def show(self):
pass
class infoServer(Server):
def recv(self,info):
self.content=info
return 'recv OK!... |
pysal/pysal | notebooks/explore/giddy/Mobility measures.ipynb | bsd-3-clause | from pysal.explore.giddy import markov,mobility
mobility.markov_mobility?
"""
Explanation: Measures of Income Mobility
Author: Wei Kang weikang9009@gmail.com, Serge Rey sjsrey@gm&... |
theandygross/CancerData | Notebooks/Download_From_Firehose.ipynb | mit | import NotebookImport
from Imports import *
"""
Explanation: <h1 class="alert alert-info">Initialization <small> <i class="icon-download"></i> Download and process all of the data fom Firehose</small></h1>
Notebook Summary
Here we are downloading and processing most of the necissary data to run this analysis pipeli... |
avirmaux/parrainage | affectation.ipynb | gpl-3.0 | # Nom de fichiers
fichierParrain = "parrains.csv"
fichierFilleul = "filleuls.csv"
fichierResultat = "parrainage.csv"
# Imports
import csv
import glob
import pulp # LP
# pulp.pulpTestAll() # Test
"""
Explanation: Parrainage
Usage:
Deux fichiers situé dans le même repertoire que cette feuille:
- parrains.csv
- fill... |
tanmay987/deepLearning | intro-to-tflearn/TFLearn_Digit_Recognition.ipynb | mit | # Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
"""
Explanation: Handwritten Number Recognition with TFLearn and MNIST
In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9.
This... |
tensorflow/tensorflow | tensorflow/lite/g3doc/models/modify/model_maker/text_classification.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
tukkyr/nlp100 | nlp100.ipynb | mit | import random
string = "I couldn't believe that I could actually understand what I was reading : the phenomenal power of the human mind ."
def typoglycemia(string):
ans = ""
for word in string.split(" "):
if len(word) > 4:
indexs = range(1,len(word)-1)
random.shuffle(indexs)
... |
mathnathan/notebooks | dissertation/tests_for_colloquium.ipynb | mit | p = GMM([1.0], np.array([[0.5,0.05]]))
num_samples = 1000
beg = 0.0
end = 1.0
t = np.linspace(beg,end,num_samples)
num_neurons = len(p.pis)
colors = [np.random.rand(num_neurons,) for i in range(num_neurons)]
p_y = p(t)
p_max = p_y.max()
np.random.seed(110)
num_neurons = 1
neuron = Neuron((1,1),[[0.6]], bias=0.0006, ... |
irockafe/revo_healthcare | notebooks/MTBLS315/exploratory/Old_notebooks/MTBLS315_uhplc_pos_classifer-25ppm.ipynb | mit | import time
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.cross_validation import cross_val_score
#from sklearn.... |
fantasycheng/udacity-deep-learning-project | tutorials/autoencoder/Convolutional_Autoencoder.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='Greys_r')
"""
Explanation: C... |
raschuetz/foundations-homework | 14/14 - TF-IDF Homework.ipynb | mit | # If you'd like to download it through the command line...
!curl -O http://www.cs.cornell.edu/home/llee/data/convote/convote_v1.1.tar.gz
# And then extract it through the command line...
!tar -zxf convote_v1.1.tar.gz
"""
Explanation: Homework 14 (or so): TF-IDF text analysis and clustering
Hooray, we kind of figured ... |
Hyperparticle/deep-learning-foundation | lessons/tensorboard/Anna_KaRNNa_Hyperparameters.ipynb | mit | import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
"""
Explanation: Anna KaRNNa
In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on the text from the book.
This network is base... |
yongtang/tensorflow | tensorflow/compiler/xla/g3doc/tutorials/autoclustering_xla.ipynb | apache-2.0 | #@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under... |
Yu-Group/scikit-learn-sandbox | jupyter/backup_deprecated_nbs/10_RIT_initial_setup.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import pydotplus
import numpy as np
import pprint
from sklearn import metrics
from sklearn import tree
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import tree
from sklearn.tree import _tree
from... |
metpy/MetPy | v0.5/_downloads/Advanced_Sounding.ipynb | bsd-3-clause | from datetime import datetime
import matplotlib.pyplot as plt
import metpy.calc as mpcalc
from metpy.io import get_upper_air_data
from metpy.io.upperair import UseSampleData
from metpy.plots import SkewT
with UseSampleData(): # Only needed to use our local sample data
# Download and parse the data
dataset =... |
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn | doc/notebooks/Automata.ipynb | gpl-3.0 | import vcsn
vcsn.automaton('''
context = "lal_char(ab), z
$ -> p <2>
p -> q <3>a,<4>b
q -> q a
q -> $
''')
"""
Explanation: Automata
Editing Automata
Vcsn provides different means to enter automata. One, which also applies to plain Python, is using the automaton constructor:
End of explanation
"""
%%automaton a
co... |
darcamo/pyphysim | notebooks/Alamouti.ipynb | gpl-2.0 | %matplotlib inline
import numpy as np
from IPython.display import clear_output
from matplotlib import pyplot as plt
"""
Explanation: Alamouti Space-time block code
This notebook illustrates the simulation of an Alamouti MIMO scheme transmission through a flat fading Rayleight channel.
It also illustrates how to read c... |
vanheck/blog-notes | Analyzes/Volatile movements/01 Volatile movements in python and pandas 1.ipynb | mit | import pandas as pd
import pandas_datareader.data as web
import datetime
start = datetime.datetime(2015, 1, 1)
end = datetime.datetime(2018, 8, 31)
spy_data = web.DataReader('SPY', 'yahoo', start, end)
spy_data = spy_data.drop(['Volume', 'Adj Close'], axis=1) # sloupce 'Volume' a 'Adj Close' nebudu potřebovat
spy_dat... |
bourneli/deep-learning-notes | DAT236x Deep Learning Explained/.ipynb_checkpoints/Lab3_MultiLayerPerceptron-checkpoint.ipynb | mit | # Figure 1
Image(url= "http://3.bp.blogspot.com/_UpN7DfJA0j4/TJtUBWPk0SI/AAAAAAAAABY/oWPMtmqJn3k/s1600/mnist_originals.png", width=200, height=200)
"""
Explanation: Lab 3 - Multi Layer Perceptron with MNIST
This lab corresponds to Module 3 of the "Deep Learning Explained" course. We assume that you have successfully ... |
girving/tensorflow | tensorflow/contrib/lite/tutorials/post_training_quant.ipynb | apache-2.0 | ! pip uninstall -y tensorflow
! pip install -U tf-nightly
import tensorflow as tf
tf.enable_eager_execution()
! git clone --depth 1 https://github.com/tensorflow/models
import sys
import os
if sys.version_info.major >= 3:
import pathlib
else:
import pathlib2 as pathlib
# Add `models` to the python path.
mo... |
statsmodels/statsmodels.github.io | v0.12.2/examples/notebooks/generated/formulas.ipynb | bsd-3-clause | import numpy as np # noqa:F401 needed in namespace for patsy
import statsmodels.api as sm
"""
Explanation: Formulas: Fitting models using R-style formulas
Since version 0.5.0, statsmodels allows users to fit statistical models using R-style formulas. Internally, statsmodels uses the patsy package to convert formulas... |
abulbasar/machine-learning | Scikit - 31 Credit Card Fraud Classification Problem.ipynb | apache-2.0 | df.Amount.values[training_size:][(y_test == 0) & (y_test_predict == 0)].sum()
"""
Explanation: Sum of amounts in the TN bucket of the test dataset.
End of explanation
"""
df.Amount.values[training_size:][(y_test == 1) & (y_test_predict == 0)].sum()
100 * 8336.05/7224977.58
"""
Explanation: Sum of amounts in the FN... |
lhcb/opendata-project | LHCb_Open_Data_Project.ipynb | gpl-2.0 | from __future__ import print_function
from __future__ import division
%pylab inline
execfile('Data/setup_main_analysis.py')
"""
Explanation: Measuring Matter Antimatter Asymmetries at the Large Hadron Collider
Introduction
Press the grey arrow to expand each section
<b> Welcome to the first guided LHCb Open Data Po... |
IST256/learn-python | content/lessons/07-Files/SmallGroup-Files.ipynb | mit | !curl https://httpbin.org/ -o httpbin-org.html
!curl https://ischool.syr.edu/directory/?cat=all -o ischool-directory.html
!curl https://ist256.com -o ist256-com.html
!curl https://en.wikipedia.org/wiki/President_of_the_United_States -o wikipedia-president-of-the-united-states.html
"""
Explanation: Now You Code In ... |
ubcgif/gpgLabs | notebooks/seismic/Seis_NMO.ipynb | mit | %pylab inline
from geoscilabs.seismic.NMOwidget import ViewWiggle, InteractClean, InteractNosiy, NMOstackthree
from SimPEG.utils import download
# Define path to required data files
synDataFilePath = 'http://github.com/geoscixyz/geosci-labs/raw/main/assets/seismic/syndata1.npy'
obsDataFilePath = 'https://github.com/ge... |
cdt15/lingam | examples/VARMALiNGAM.ipynb | mit | import numpy as np
import pandas as pd
import graphviz
import lingam
from lingam.utils import make_dot, print_causal_directions, print_dagc
import warnings
warnings.filterwarnings('ignore')
print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])
np.set_printoptions(precision=3, suppress=Tru... |
ES-DOC/esdoc-jupyterhub | notebooks/nims-kma/cmip6/models/sandbox-3/ocnbgchem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nims-kma', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: NIMS-KMA
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Pro... |
ssunkara1/bqplot | examples/Marks/Object Model/Bins.ipynb | apache-2.0 | # Create a sample of Gaussian draws
np.random.seed(0)
x_data = np.random.randn(1000)
"""
Explanation: Bins Mark
This Mark is essentially the same as the Hist Mark from a user point of view, but is actually a Bars instance that bins sample data.
The difference with Hist is that the binning is done in the backend, so it... |
ajgpitch/qutip-notebooks | development/development-smesolve-milstein-speed-test.ipynb | lgpl-3.0 | %pylab inline
from qutip import *
from numpy import log2, cos, sin
from scipy.integrate import odeint
from qutip.cy.spmatfuncs import cy_expect_rho_vec, spmv
"""
Explanation: Speed test of stochastic solvers
Based on development-smesolve-milstein notebook
Denis V. Vasilyev
30 september 2013
Modified by Eric Giguere, ... |
dolittle007/dolittle007.github.io | notebooks/lda-advi-aevb.ipynb | gpl-3.0 | %matplotlib inline
import sys, os
import theano
theano.config.floatX = 'float64'
from collections import OrderedDict
from copy import deepcopy
import numpy as np
from time import time
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.datasets import fetch_20newsgroups
import ma... |
kgrodzicki/machine-learning-specialization | course-1-machine-learning-foundations/notebooks/week3/Analyzing product sentiment.ipynb | mit | import graphlab
"""
Explanation: Predicting sentiment from product reviews
Fire up GraphLab Create
End of explanation
"""
products = graphlab.SFrame('amazon_baby.gl/')
"""
Explanation: Read some product review data
Loading reviews for a set of baby products.
End of explanation
"""
products.head()
"""
Explanation... |
WillenZh/deep-learning-project | tutorials/autoencoder/Simple_Autoencoder.ipynb | mit | %matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
"""
Explanation: A Simple Autoencoder
We'll start off by building a simple autoencoder to compres... |
muraliparimi/Python | The_Notebook.ipynb | mit | from google.colab import drive
drive.mount('/content/drive')
"""
Explanation: <a href="https://colab.research.google.com/github/muraliparimi/Python/blob/master/The_Notebook.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
Welcome to the Notebook
Let'... |
bobmyhill/burnman | tutorial/tutorial_01_material_classes.ipynb | gpl-2.0 | #!pip install -q -e ..
import burnman
"""
Explanation: <h1>The BurnMan Tutorial</h1>
Part 1: Material Classes
This file is part of BurnMan - a thermoelastic and thermodynamic toolkit
for the Earth and Planetary Sciences
Copyright (C) 2012 - 2021 by the BurnMan team,
released under the GNU GPL v2 or later.
Introductio... |
srcole/qwm | burrito/Burrito_correlations.ipynb | mit | %config InlineBackend.figure_format = 'retina'
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
sns.set_style("white")
"""
Explanation: San Diego Burrito Analytics: Correlations
Scott Cole
21 May 2016
This notebook investigates the cor... |
encima/Comp_Thinking_In_Python | Session_7a/7a_Math_Binary.ipynb | mit | 13 % 5 == 3
12 ** 2 == 144
146 % 67 == 12
19 % 8.5 == 2
4 ** (3 % 11)
"""
Explanation: Math, Representation and Binary Arithmetic
Dr. Chris Gwilliams
gwilliamsc@cardiff.ac.uk
Contents
Mathematic Operations
Binary Conversion
Boolean Logic
Binary Arithmetic
Operations
We have covered (+, -, / and *), but there are mo... |
CoderDojoTC/python-minecraft | classroom-code/exercises/Exercise 4 -- Change the Minecraft world using Python.ipynb | mit | import mcpi.minecraft as minecraft
import mcpi.block as block
from time import sleep
"""
Explanation: Change the Minecraft world using Python
The Pyramid
This program links Python to Minecraft and uses that link to change things in the Minecraft world. In the game, move your player to an open area, then work your way ... |
google/trax | trax/examples/illustrated_wideresnet.ipynb | apache-2.0 | %%capture
!pip install --upgrade trax
"""
Explanation: Author
SauravMaheshkar- @MaheshkarSaurav
End of explanation
"""
import trax
from trax import layers as tl
from trax.supervised import training
# Trax offers the WideResnet architecture in it's models module
from trax.models.resnet import WideResnet
trax.fastma... |
ES-DOC/esdoc-jupyterhub | notebooks/cnrm-cerfacs/cmip6/models/cnrm-cm6-1/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cnrm-cerfacs', 'cnrm-cm6-1', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CNRM-CERFACS
Source ID: CNRM-CM6-1
Topic: Atmoschem
Sub-Topics: Tra... |
bird-house/birdy | notebooks/examples/emu-example.ipynb | apache-2.0 | from birdy import WPSClient
"""
Explanation: Birdy WPSClient example with Emu WPS
End of explanation
"""
emu = WPSClient(url='http://localhost:5000/wps')
emu_i = WPSClient(url='http://localhost:5000/wps', progress=True)
"""
Explanation: Use Emu WPS
https://github.com/bird-house/emu
End of explanation
"""
emu.hell... |
saashimi/code_guild | wk9/notebooks/.ipynb_checkpoints/ch.1-getting-started-with-django-checkpoint.ipynb | mit | # Make a directory called examples
#!mkdir ../examples
%cd ../examples
!ls
# Write functional_tests.py
#%%writefile functional_tests.py
from selenium import webdriver
browser = webdriver.Firefox()
browser.get('http://localhost:8000')
assert 'Django' in browser.title
"""
Explanation: Wk9.0
Ch. 1 Getting Django Set... |
alanwilter/acpype | Acpype_API_Jupyter.ipynb | gpl-3.0 | import acpype
from acpype.topol import ACTopol, MolTopol
from glob import glob
"""
Explanation: For calculating topologies via antechamber
End of explanation
"""
print(acpype.__version__)
glob("tests/*.pdb")
molecule = ACTopol("tests/dmp.pdb", chargeType="gas")
molecule.createACTopol()
molecule.createMolTopol()
... |
opentraffic/reporter-quality-testing-rig | notebooks/map_matching_part_III.ipynb | lgpl-3.0 | import os
import sys; sys.path.insert(0, os.path.abspath('..'));
import validator.validator as val
import numpy as np
from random import choice
import pandas as pd
%matplotlib inline
"""
Explanation: Since we announced our collaboration with the World Bank and more partners to create the Open Traffic platform, we’ve b... |
PySCeS/PyscesToolbox | documentation/notebooks/Symca.ipynb | bsd-3-clause | mod = pysces.model('lin4_fb')
sc = psctb.Symca(mod)
"""
Explanation: Symca
Symca is used to perform symbolic metabolic control analysis [3,4] on metabolic pathway models in order to dissect the control properties of these pathways in terms of the different chains of local effects (or control patterns) that make up the... |
brian-rose/env-415-site | notes/EBM_albedo_feedback.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import climlab
# for convenience, set up a dictionary with our reference parameters
param = {'D':0.55, 'A':210, 'B':2, 'a0':0.3, 'a2':0.078, 'ai':0.62, 'Tf':-10.}
model1 = climlab.EBM_annual( num_lat=180, D=0.55, A=210., B=2., Tf=-10., a0=0.3, a2=0... |
matplotlib/mpl-probscale | docs/tutorial/getting_started.ipynb | bsd-3-clause | %matplotlib inline
import warnings
warnings.simplefilter('ignore')
import numpy
from matplotlib import pyplot
from scipy import stats
import seaborn
clear_bkgd = {'axes.facecolor':'none', 'figure.facecolor':'none'}
seaborn.set(style='ticks', context='talk', color_codes=True, rc=clear_bkgd)
"""
Explanation: Getting ... |
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