repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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abulbasar/machine-learning | SparkML - 06 Movie Recommendation.ipynb | apache-2.0 | import sys, glob, os
SPARK_HOME = "/Users/abulbasar/Downloads/spark-2.3.1-bin-hadoop2.7"
sys.path.append(SPARK_HOME + "/python")
sys.path.append(glob.glob(SPARK_HOME + "/python/lib/py4j*.zip")[0])
from pyspark.sql import SparkSession, functions as F
spark = (SparkSession
.builder
.config("spark.master... |
johnhw/summerschool2015 | CrashCourse_Lecture1.ipynb | mit | # standard imports
import numpy as np
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as axes3d
import seaborn
import sklearn.preprocessing, sklearn.cluster, sklearn.tree, sklearn.neighbors, sklearn.ensemble, sklearn.multiclass, sklearn.feature_selection
import ipy_table
import sklearn.svm, sklearn.c... |
skamkar/harvard-yard | boston_housing.ipynb | mit | # Import libraries necessary for this project
import numpy as np
import pandas as pd
from sklearn.cross_validation import ShuffleSplit
# Import supplementary visualizations code visuals.py
import visuals as vs
# Pretty display for notebooks
%matplotlib inline
# Load the Boston housing dataset
data = pd.read_csv('hou... |
GoogleCloudPlatform/mlops-on-gcp | workshops/guided-projects/guided_project_2.ipynb | apache-2.0 | import os
"""
Explanation: Guided Project 2
Learning Objective:
Learn how to adapt the tfx template to an existing model.
In this guided project, we will use the tfx template tool to create a TFX pipeline around the covertype dataset.
The goal is to adapt the template pipeline to make use of the model code for the c... |
dlsun/symbulate | tutorial/gs_joint.ipynb | mit | from symbulate import *
%matplotlib inline
"""
Explanation: Getting Started with Symbulate
Section 3. Multiple Random Variables and Joint Distributions
<Random variables | Contents | Conditioning>
Every time you start Symbulate, you must first run (SHIFT-ENTER) the following commands.
End of explanation
"""
def numb... |
Hasil-Sharma/Neural-Networks-CS231n | assignment1/knn.ipynb | gpl-3.0 | # Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.... |
betatim/BlackBox | examples/ask-and-tell.ipynb | bsd-3-clause | import numpy as np
np.random.seed(1234)
%matplotlib inline
import matplotlib.pyplot as plt
plt.set_cmap("viridis")
"""
Explanation: Async optimization Loop
Tim Head, February 2017.
End of explanation
"""
from skopt.learning import ExtraTreesRegressor
from skopt import Optimizer
noise_level = 0.1
# Our 1D toy prob... |
mattmcd/PyBayes | scripts/spectral_methods_20210306.ipynb | apache-2.0 | import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
"""
Explanation: Spectral Methods in TensorFlow
Converting some of the examples from Spectral Methods in MATLAB by Nick Trefethen into TensorFlow.
End of explanation
"""
def calc_diff_err_p1(n):
h... |
Flaviolib/dx | 00_dx_quickstart.ipynb | agpl-3.0 | import dx
import datetime as dt
import pandas as pd
import seaborn as sns; sns.set()
"""
Explanation: <img src="http://hilpisch.com/tpq_logo.png" alt="The Python Quants" width="45%" align="right" border="4">
Quickstart
This brief first part illustrates---without much explanation---the usage of the DX Analytics library... |
dsavransky/MAE4060 | Notebooks/Hemispherical Shell.ipynb | mit | from miscpy.utils.sympyhelpers import *
init_printing()
m,r,R,R1,R2,th,ph = symbols('m,r,R,R_1,R_2,theta,phi')
"""
Explanation: Geometric and Inertial Properties of a Hemispherical Shell
End of explanation
"""
rdmO = r*Matrix([cos(th)*sin(ph),sin(th)*sin(ph), cos(ph)]);rdmO
"""
Explanation: The shell has interior r... |
google-research/google-research | linear_vae/DontBlameTheELBO.ipynb | apache-2.0 | %tensorflow_version 1.x
import tensorflow as tf
from tensorflow.data import Dataset
import tensorflow_datasets as tfds
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
_LOG_2PI = np.log(2 * np.pi)
"""
Explanation: Copyright 2019 Google LLC.
Licensed under the Apache License, Version 2.0 (t... |
bayesimpact/bob-emploi | data_analysis/notebooks/datasets/rome/update_from_v342_to_v343.ipynb | gpl-3.0 | import collections
import glob
import os
from os import path
import matplotlib_venn
import pandas as pd
rome_path = path.join(os.getenv('DATA_FOLDER'), 'rome/csv')
OLD_VERSION = '342'
NEW_VERSION = '343'
old_version_files = frozenset(glob.glob(rome_path + '/*{}*'.format(OLD_VERSION)))
new_version_files = frozenset(... |
thisray/MS_Course | other/ray_workshop_0723.ipynb | mit | df = make_toy_df('ABC', range(5))
print('type of {}: {}'.format('df', type(df)))
df
print('type of {}: {}'.format("df['A']", type(df['A'])))
df['A']
## dictionary-style & attribute-style to access a column
print(df['A'])
print(df.A)
print(df['A'] is df.A)
## add a new column
df['shape'] = list(range(df.shape[0]))
d... |
opalytics/opalytics-ticdat | examples/expert_section/notebooks/toehold_problem.ipynb | bsd-2-clause | def exception_thrown(f):
try:
f()
except Exception as e:
return str(e)
"""
Explanation: The toehold problem
The "toehold problem" is named after a tech support response. The nature of the problem is that in order to take advantage of the algebraic constraint modeling provided by docplex, then t... |
intel-analytics/analytics-zoo | apps/variational-autoencoder/using_variational_autoencoder_to_generate_digital_numbers.ipynb | apache-2.0 | # a bit of setup
import numpy as np
from bigdl.nn.criterion import *
from bigdl.dataset import mnist
from zoo.pipeline.api.keras.layers import *
from zoo.pipeline.api.keras.models import Model
from zoo.pipeline.api.keras.utils import *
import datetime as dt
IMAGE_SIZE = 784
IMAGE_ROWS = 28
IMAGE_COLS = 28
IMAGE_CHANNE... |
phoebe-project/phoebe2-docs | 2.1/examples/diher_misaligned.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.1,<2.2"
"""
Explanation: DI Her: Misaligned Binary
In this example, we'll reproduce Figure 8 in the misalignment release paper (Horvat et al. 2018).
<img src="horvat+18_fig8.png" alt="Figure 8" width="400px"/>
Setup
Let's first make sure we have the latest version of PHOEBE 2.1 installed. (Y... |
gururajl/deep-learning | intro-to-tflearn/TFLearn_Sentiment_Analysis.ipynb | mit | import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
"""
Explanation: Sentiment analysis with TFLearn
In this notebook, we'll continue Andrew Trask's work by building a network for sentiment analysis on the movie review data. Instead of a network w... |
donK23/pyData-Projects | KC_RecSys/project/notebook/Recommenders_5Star.ipynb | apache-2.0 | # Import data
path = "../data/petdata_1000_100.csv"
raw_data = pd.read_csv(path, index_col="doc_uri")
assert raw_data.shape == (1000,100), "Import error, df has false shape"
"""
Explanation: Preprocess data
End of explanation
"""
# Convert df
data = raw_data.unstack().to_frame().reset_index()
data.columns = ["user",... |
newworldnewlife/TensorFlow-Tutorials | 04_Save_Restore.ipynb | mit | from IPython.display import Image
Image('images/02_network_flowchart.png')
"""
Explanation: TensorFlow Tutorial #04
Save & Restore
by Magnus Erik Hvass Pedersen
/ GitHub / Videos on YouTube
Introduction
This tutorial demonstrates how to save and restore the variables of a Neural Network. During optimization we save th... |
satishkt/lending_club_classification | LoanDefaultPrediction.ipynb | apache-2.0 | df3a.dropna(axis=1, how='all',inplace=True)
df3a.columns.tolist()
"""
Explanation: Since there are 111 columns , lets first find columns with null/empty through out so that we can quickly clean them up. We go for 111 columns to 57 columns which have some data.
End of explanation
"""
df3a.application_type.unique() #... |
diegocavalca/Studies | phd-thesis/benchmarkings/am207-NILM-project-master/CO.ipynb | cc0-1.0 | from __future__ import print_function, division
import numpy as np
import pandas as pd
from os.path import join
import pickle
import copy
from pylab import rcParams
import matplotlib.pyplot as plt
%matplotlib inline
rcParams['figure.figsize'] = (13, 6)
import nilmtk
from nilmtk import DataSet, TimeFrame, MeterGroup, ... |
Shirling-VT/davitpy_sam | docs/notebook/radarStruct.ipynb | gpl-3.0 | # Import radar module
%pylab inline
from davitpy.pydarn.radar import *
"""
Explanation: network(), radar() and site() objects
This notebook introduces the high-level python interface with the radar.dat and hdw.dat content.
For more in-depth access (i.e., your own hdw.dat), look at the radInfoIO module:
radInfoIo?
... |
tpin3694/tpin3694.github.io | machine-learning/loading_scikit-learns_boston_housing_dataset.ipynb | mit | # Load libraries
from sklearn import datasets
import matplotlib.pyplot as plt
"""
Explanation: Title: Loading scikit-learn's Boston Housing Dataset
Slug: loading_scikit-learns_boston_housing-dataset
Summary: Loading the built-in Boston housing datasets of scikit-learn.
Date: 2016-08-31 12:00
Category: Machine Learni... |
AdityaSoni19031997/Machine-Learning | AV/AV_Enigma_ML_initial_eda.ipynb | mit | %load_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.imports import *
from fastai.structured import *
from pandas_summary import DataFrameSummary
PATH = os.getcwd()
train_df = pd.read_csv(f'{PATH}\\train.csv', low_memory=False)
test_df = pd.read_csv(f'{PATH}\\test.csv', low_memory=False)
def display_a... |
jaidevd/inmantec_fdp | notebooks/day3/03_feature_extraction.ipynb | mit | X, y = make_classification(n_informative=2, random_state=11)
print(X.shape)
"""
Explanation: Example: Arbitrary classification
End of explanation
"""
xtrain, xtest, ytrain, ytest = train_test_split(X, y, random_state=2)
lr = LogisticRegression()
lr.fit(xtrain, ytrain)
prediction = lr.predict(xtest)
print("F1 score: ... |
weikang9009/pysal | notebooks/explore/esda/Spatial Autocorrelation for Areal Unit Data.ipynb | bsd-3-clause | import sys
import os
sys.path.append(os.path.abspath('..'))
from pysal.explore import esda
import pandas as pd
import geopandas as gpd
import pysal.lib as lps
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Exploratory Analysis of Spatial Data: Spatial Autocorrelation
In this n... |
mohsinhaider/pythonbootcampacm | Objects and Data Structures/Tuples.ipynb | mit | # Initializing a Tuple
day_tuple = ("MONDAY", "TUESDAY", "WEDNESDAY", "THURSDAY", "FRIDAY")
"""
Explanation: Tuples
A tuple is an immutable, indexable, and heterogenous sequence of objects. Unlike a list, the immutability of tuple bars it from being able to change its objects (in place). The semantics of the tuple all... |
mkuron/espresso | doc/tutorials/11-ferrofluid/11-ferrofluid_part3.ipynb | gpl-3.0 | import espressomd
espressomd.assert_features('DIPOLES', 'LENNARD_JONES')
from espressomd.magnetostatics import DipolarP3M
import numpy as np
"""
Explanation: Ferrofluid - Part III
Table of Contents
Susceptibility with fluctuation formulas
Derivation of the fluctuation formula
Simulation
Magnetization curve of a 3D... |
miaecle/deepchem | examples/tutorials/01_The_Basic_Tools_of_the_Deep_Life_Sciences.ipynb | mit | %tensorflow_version 1.x
!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import deepchem_installer
%time deepchem_installer.install(version='2.3.0')
"""
Explanation: Tutorial 1: The Basic Tools of the Deep Life Sciences
Welcome to DeepChem's introducto... |
google/flax | examples/mnist/mnist.ipynb | apache-2.0 | # Install ml-collections & latest Flax version from Github.
!pip install -q ml-collections git+https://github.com/google/flax
example_directory = 'examples/mnist'
editor_relpaths = ('configs/default.py', 'train.py')
repo, branch = 'https://github.com/google/flax', 'main'
# (If you run this code in Jupyter[lab], then... |
jameszw/udacity-deep-learning | project3-tv-script-generation/dlnd_tv_script_generation.ipynb | mit | """
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
"""
Explanation: TV Script Generation
In this project, you'll generate your own Simpsons TV scrip... |
banyh/ShareIPythonNotebook | NLP_With_Python/Ch10.ipynb | gpl-3.0 | import nltk
grammar = nltk.grammar.FeatureGrammar.fromstring("""
% start S
S[SEM=(?np + WHERE + ?vp)] -> NP[SEM=?np] VP[SEM=?vp]
VP[SEM=(?v + ?pp)] -> IV[SEM=?v] PP[SEM=?pp]
VP[SEM=(?v + ?ap)] -> IV[SEM=?v] AP[SEM=?ap]
NP[SEM=(?det + ?n)] -> Det[SEM=?det] N[SEM=?n]
PP[SEM=(?p + ?np)] -> P[SEM=?... |
stanleykylee/worldbank-world-development-indicators-distributed-tf | src/explore_indicators.ipynb | gpl-3.0 | import csv
count_dict = {}
with open('WDI_Data.csv', 'rb') as csv_file:
csv_reader = csv.reader(csv_file, delimiter=',')
idx = 0
for row in csv_reader:
# skip first row
if (idx == 0):
idx += 1
continue
# indicator name for this row
ind_name = row[3]
... |
supergis/git_notebook | geospatial/gdal/gdal-geometry.ipynb | gpl-3.0 | from osgeo import gdal, gdalconst
from osgeo.gdalconst import *
import gdal, gdalconst
from osgeo import ogr
"""
Explanation: OGR-Geometry and Projection
几何对象与地图投影的使用。
by openthings@163.com, 2016-04-25.
OGR矢量库：简单的矢量数据读写，是GDAL的一部分。
相关模块:
Numeric：高速的数组处理，对栅格数据尤其重要
NumPy：下一代的Numeric
更强大的gis库 http://www.gispython.org/
... |
jdhp-docs/python-notebooks | python_matplotlib_efficient_interactive_matplotlib_plots_in_jupyter_notebooks_en.ipynb | mit | %matplotlib notebook
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
import time
import ipywidgets
from ipywidgets import interact
"""
Explanation: Efficient interactive Matplotlib plots in Jupyter notebooks
End of explanation
"""
@interact(t=(0, 100, 1))
def plot(t):
fig = plt.figure... |
Edderic/BRML-non-OOP | solutions/ch-2.ipynb | gpl-3.0 | from graphviz import Digraph
# Sample graph
dag = Digraph()
dir(dag)
dag.edge('A', 'B')
dag.edge('B', 'C')
dag.edge('C', 'D')
dag.edge('A', 'E')
dag.edge('E', 'D')
dag
import pandas as pd
import numpy as np
adjacency_matrix = pd.DataFrame(
[
[0, 1, 0, 0, 1],
[1, 0, 1, 0, 0],
[0, 1, 0, 1, ... |
miaecle/deepchem | examples/tutorials/13_Modeling_Protein_Ligand_Interactions.ipynb | mit | %tensorflow_version 1.x
!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py
import deepchem_installer
%time deepchem_installer.install(additional_packages=['mdtraj'], version='2.3.0')
import deepchem as dc
from deepchem.utils import download_url
import o... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Python-for-Data-Visualization/Geographical Plotting/Choropleth Maps Exercise - Solutions.ipynb | mit | import plotly.graph_objs as go
from plotly.offline import init_notebook_mode,iplot,plot
init_notebook_mode(connected=True)
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
Choropleth Maps Exercise - Solutions
Welcome to the Choropleth Maps Exercise! In this exercise ... |
mne-tools/mne-tools.github.io | 0.19/_downloads/7e2bcdd12e5e1163023780ef6bd1e852/plot_evoked_arrowmap.ipynb | bsd-3-clause | # Authors: Sheraz Khan <sheraz@khansheraz.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.datasets.brainstorm import bst_raw
from mne import read_evokeds
from mne.viz import plot_arrowmap
print(__doc__)
path = sample.data_path()
fname = path + '/MEG/sample/samp... |
a301-teaching/a301_code | notebooks/satellite_radiance_solution.ipynb | mit | from IPython.display import Image
Image('figures/full_triangle.png')
"""
Explanation: MODIS satellite radiance solution -- discussed on day 10:
a) Show formally that:
$$\Delta \omega \approx \frac{area}{R^2}$$
by integrating a cone like this:
End of explanation
"""
import numpy as np
theta = 5*np.pi/180.
r_over_l... |
julienchastang/unidata-python-workshop | notebooks/Satellite_Data/PlottingSatelliteData.ipynb | mit | from datetime import datetime
# Create variables for URL generation
# YOUR CODE GOES HERE
# Construct the data_url string
# YOUR CODE GOES HERE
# Print out your URL and verify it works!
# YOUR CODE GOES HERE
"""
Explanation: <a name="pagetop"></a>
<div style="width:1000 px">
<div style="float:right; width:98 px; h... |
jan-rybizki/Chempy | tutorials/5-Chempy_function_and_stellar_tracer_sampling.ipynb | mit | %pylab inline
# loading the default parameters
from Chempy.parameter import ModelParameters
a = ModelParameters()
"""
Explanation: Chempy
we will now introduce the Chempy function which will calculate the chemical evolution of a one-zone open box model
End of explanation
"""
# Initialising sfr, infall, elements to... |
ohlemacher/behaviorDrivenDev | BehaviorDrivenDevWithLettuceAndFlask.ipynb | gpl-2.0 | %%bash
cat test/features/user.features
"""
Explanation: Experiments with Behavior Driven Development
This is my introduction to behavior driven development (BDD) in Python. I have implemented a simple RESTful application using the Flask web framework. Lettuce is used enforce the mapping of user stories to tests via ve... |
jo-tez/aima-python | agents.ipynb | mit | from agents import *
class BlindDog(Agent):
def eat(self, thing):
print("Dog: Ate food at {}.".format(self.location))
def drink(self, thing):
print("Dog: Drank water at {}.".format( self.location))
dog = BlindDog()
"""
Explanation: AGENT
An agent, as defined in 2.1 is anything th... |
tensorflow/docs-l10n | site/ko/tutorials/estimator/keras_model_to_estimator.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... |
ML4DS/ML4all | C4.Classification_SVM/.ipynb_checkpoints/SVM-checkpoint.ipynb | mit | # To visualize plots in the notebook
%matplotlib inline
# Imported libraries
#import csv
#import random
#import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits import mplot3d
#import pylab
import numpy as np
#from sklearn.preprocessing import PolynomialFeatures
from sklearn import svm
from sklearn.datase... |
JWarmenhoven/DBDA-python | Notebooks/Chapter 12.ipynb | mit | import pandas as pd
import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
import theano.tensor as tt
from matplotlib import gridspec
%matplotlib inline
plt.style.use('seaborn-white')
color = '#87ceeb'
%lo... |
cgpotts/cs224u | nli_02_models.ipynb | apache-2.0 | __author__ = "Christopher Potts"
__version__ = "CS224u, Stanford, Spring 2022"
"""
Explanation: Natural language inference: models
End of explanation
"""
from collections import Counter
from itertools import product
from nltk.tokenize.treebank import TreebankWordTokenizer
import numpy as np
import os
import pandas a... |
SylvainCorlay/bqplot | examples/Marks/Object Model/Graph.ipynb | apache-2.0 | fig_layout = Layout(width='960px', height='500px')
"""
Explanation: Nodes and Links should be supplied to the Graph mark.
<p>Node attributes
| Attribute| Type | Description | Default |
|:----------:|:-------------:|:------:|
| label | str | node label | mandatory attribute |
| label_display | {center, ou... |
pligor/predicting-future-product-prices | 04_time_series_prediction/32_price_history_autoencoder.ipynb | agpl-3.0 | batch_size = 1
#full_train_size = 55820
#train_size = 55800
#small_train_size = 6000 #just because of performance reasons, no statistics behind this decision
#test_size = 6200
data_path = '../../../../Dropbox/data'
phae_path = data_path + '/price_hist_autoencoder'
csv_in = '../price_history_03_seq_start_suddens_tri... |
sahilm89/lhsmdu | lhsmdu/benchmark/.ipynb_checkpoints/Comparing LHSMDU and MC sampling - Large dimensions-checkpoint.ipynb | mit | import numpy as np
import lhsmdu
import matplotlib.pyplot as plt
def simpleaxis(axes, every=False):
if not isinstance(axes, (list, np.ndarray)):
axes = [axes]
for ax in axes:
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
if every:
ax.spine... |
dib-lab/SSUsearch | notebooks-pc-linux/data-preparation.ipynb | bsd-3-clause | cd ~/Desktop/SSUsearch/
mkdir -p ./data
cd ./data
"""
Explanation: Setup data directory
End of explanation
"""
!wget https://s3.amazonaws.com/ssusearchdb/SSUsearch_db.tgz
!tar -xzvf SSUsearch_db.tgz
"""
Explanation: Download database files
End of explanation
"""
!wget https://s3.amazonaws.com/ssusearchdb/test.... |
abhi1509/deep-learning | 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... |
mdeff/ntds_2017 | projects/reports/course_suggester/Data Cleaning.ipynb | mit | # Useful starting lines
%matplotlib inline
import numpy as np
import scipy
import scipy.sparse as sp
import matplotlib.pyplot as plt
import pandas as pd
import re
import nltk
import string
from time import time
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
from sklearn.decomposition impo... |
mjones01/NEON-Data-Skills | code/Python/remote-sensing/rgb-camera/plot-neon-rgb-camera-data.ipynb | agpl-3.0 | import sys
sys.version
"""
Explanation: syncID: 67a5e95e1b7445aca7d7750b75c0ee98
title: "Plotting a NEON RGB Camera Image (GeoTIFF) in Python"
description: "This lesson is a brief introduction to RGB camera images and the GeoTIFF raster format in Python."
dateCreated: 2018-06-30
authors: Bridget Hass,
contributors:
... |
mne-tools/mne-tools.github.io | 0.23/_downloads/58e35821e0f211b843d5ead3e33d8849/20_sensors_time_frequency.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Stefan Appelhoff <stefan.appelhoff@mailbox.org>
# Richard Höchenberger <richard.hoechenberger@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.time_frequenc... |
dsavransky/MAE4060 | Notebooks/Torque Free 3-1-3 Body Dynamics.ipynb | mit | aCi = rotMat(3,psi)
cCa = rotMat(1,th)
bCc = rotMat(3,ph)
aCi,cCa,bCc
bCi = bCc*cCa*aCi; bCi #3-1-3 rotation
bCi_dot = difftotalmat(bCi,t,{th:thd,psi:psid,ph:phd});
bCi_dot
"""
Explanation: Set up rotation matrices representing a 3-1-3 $(\psi,\theta,\phi)$ Euler angle set.
End of explanation
"""
omega_tilde = bCi*... |
aparafita/news-similarity | notebooks/sgd.ipynb | gpl-3.0 | folder = os.path.join('..', 'data')
newsbreaker.init(os.path.join(folder, 'topic_model'), 'topic_model.pkl', 'vocab.txt')
entries = load_entries(folder)
entries_dict = defaultdict(list)
for entry in entries:
entries_dict[entry.feed].append(entry)
client = MongoClient()
db = client.newstagger
"""
Explanation: ... |
daniestevez/jupyter_notebooks | dslwp/DSLWP GMSK SSDV pic11.ipynb | gpl-3.0 | %matplotlib inline
import numpy as np
import scipy.signal
import matplotlib.pyplot as plt
fs = 40000 # sample rate
"""
Explanation: Recovering SSDV image 0xAB, transmitted on 2019-02-27 08:20
End of explanation
"""
skip = 3200 * fs * 8 # first transmission seems to have a missing samples from the recorder
skip = 5... |
jobovy/misc-notebooks | apogee/AtmosphereSynthesisInconsistency.ipynb | bsd-3-clause | atm= atlas9.Atlas9Atmosphere(teff=3500.,logg=2.5,metals=0.,am=0.,cm=0.)
synspec_correct= apogee.modelspec.turbospec.synth(modelatm=atm,
linelist='20150714',
lsf='all',cont='true',vmacro=0.,
... |
zhaojijet/UdacityDeepLearningProject | image-classification/dlnd_image_classification.ipynb | apache-2.0 | """
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_block = 0
def hoo... |
sujitpal/polydlot | src/tensorflow/05b-experiment-from-keras.ipynb | apache-2.0 | from __future__ import division, print_function
from tensorflow.contrib.learn.python.learn.estimators import model_fn as model_fn_lib
from tensorflow.contrib import keras
import matplotlib.pyplot as plt
import numpy as np
import os
import shutil
import tensorflow as tf
DATA_DIR = "../../data"
TRAIN_FILE = os.path.join... |
OpenWeavers/openanalysis | doc/Langauge/11- Inbuilt Data Structures 6 - Comprehensions and Lambdas.ipynb | gpl-3.0 | num = [10,8,3,5,2,7,0,1,4,9,6]
num
def square_classic_approach(x):
"""
Input: x - List of Integers
Return: A list containing squares of each element of list
"""
squared = [] # Empty list
for i in range(len(num)): # Equivalent to for(i=0;i<num;i++)
squared.append(num[i]**2) # Power ... |
tensorflow/docs-l10n | site/en-snapshot/hub/tutorials/cord_19_embeddings.ipynb | apache-2.0 | # Copyright 2019 The TensorFlow Hub Authors. All Rights Reserved.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by app... |
Naereen/notebooks | Prise de rdv automatique sur Doctolib.ipynb | mit | URL = "https://partners.doctolib.fr/hopital-public/perigueux/vaccination-covid?speciality_id=5494&enable_cookies_consent=1"
from selenium import webdriver
try:
print(f"Downloading '{URL}'...")
firefoxOptions = webdriver.FirefoxOptions()
firefoxOptions.headless = True
browser = webdriver.Firefox(option... |
saashimi/code_guild | interactive-coding-challenges/staging/sorting_searching/group_ordered/group_ordered_challenge.ipynb | mit | def group_ordered(list_in):
# TODO: Implement me
pass
"""
Explanation: <small><i>This notebook was prepared by wdonahoe. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem: Implement a function that groups identical items based on their order in the list.
Constraints
Test Cases
Algor... |
tpin3694/tpin3694.github.io | statistics/demonstrate_the_central_limit_theorem.ipynb | mit | # Import packages
import pandas as pd
import numpy as np
# Set matplotlib as inline
%matplotlib inline
"""
Explanation: Title: Demonstrate The Central Limit Theorem
Slug: demonstrate_the_central_limit_theorem
Summary: Python introduction to the central limit theorem
Date: 2016-05-01 12:00
Category: Statistics
Tags:... |
molgor/spystats | notebooks/Sandboxes/Sketches2_for_geopystats.ipynb | bsd-2-clause | plotdata_path = "/RawDataCSV/idiv_share/plotsClimateData_11092017.csv"
empirical_data_path = "/apps/external_plugins/spystats/HEC_runs/results/logbiomas_logsppn_res.csv"
"""
Explanation: Sketches for automating spatial models
This notebook is for designing the tool box and methods for fitting spatial data.
I´m using t... |
M-R-Houghton/euroscipy_2015 | scikit_image/lectures/solutions/012_solutions.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Color and exposure
End of explanation
"""
import skdemo
from skimage import data
color_image = data.chelsea()
red_image = np.zeros_like(color_image)
green_image = np.zeros_like(color_image)
blue_image = np.zeros_like(color_image)
red_image[:, :, 0... |
datactive/bigbang | examples/experimental_notebooks/EME Diversity Analysis.ipynb | mit | import bigbang.ingress.mailman as mailman
import bigbang.analysis.process as process
from bigbang.archive import Archive
import pandas as pd
import datetime
from commonregex import CommonRegex
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: This work was done by Harsh Gupta as part of his inter... |
amitkaps/machine-learning | time_series/2-Acquire.ipynb | mit | # Import the library we need, which is Pandas
import pandas as pd
# Read all the tables from the html document
AllTables = pd.read_html('MonthWiseMarketArrivalsJan2016.html')
# Let us find out how many tables has it found?
len(AllTables)
type(AllTables)
"""
Explanation: 2. Acquire the Data
Finding Data Sources
The... |
jseabold/statsmodels | examples/notebooks/statespace_concentrated_scale.ipynb | bsd-3-clause | import numpy as np
import pandas as pd
import statsmodels.api as sm
dta = sm.datasets.macrodata.load_pandas().data
dta.index = pd.date_range(start='1959Q1', end='2009Q4', freq='Q')
"""
Explanation: State space models - concentrating the scale out of the likelihood function
End of explanation
"""
class LocalLevel(sm... |
ecervera/Baxter-Vision | 02 Compute Items SIFT Features.ipynb | mit | import json
from utils import load_items
with open('parameters.json', 'r') as infile:
params = json.load(infile)
RESIZE_X = params['resize']['x']
RESIZE_Y = params['resize']['y']
ITEM_FOLDER = params['item_folder']
items = load_items(ITEM_FOLDER)
"""
Explanation: <a id="top"></a>
Compute Items SIFT Features
Fir... |
jserenson/Python_Bootcamp | Range().ipynb | gpl-3.0 | range(0,10)
x =range(0,10)
type(x)
start = 0 #Default
stop = 20
x = range(start,stop)
x
"""
Explanation: range()
In this short lecture we will be discussing the range function. We haven't developed a very deep level of knowledge of functions yet, but we can understand the basics of this simple (but extremely usefu... |
timzhangau/ml_nano | practice_projects/imdb/IMDB_In_Keras.ipynb | mit | # Imports
import numpy as np
import keras
from keras.datasets import imdb
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation
from keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
%matplotlib inline
np.random.seed(42)
"""
Explanation: Analyzing IMDB Data in... |
ALEXKIRNAS/DataScience | CS231n/assignment2/TensorFlow.ipynb | mit | import tensorflow as tf
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
%matplotlib inline
from cs231n.data_utils import load_CIFAR10
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
"""
Load the CIFAR-10 dataset from disk and perform preprocessing to... |
agvergara/DatatonURJC | Day1/Lab_1_Pandas.ipynb | gpl-3.0 | #Representar gráficas inline
%matplotlib inline
import matplotlib.pyplot as plt
#Importe el módulo de pandas
import pandas as pd
diabetes = pd.read_csv("diabetes.csv", delimiter=';')
print(diabetes)
#Lea el documento diabetes.csv. Recuerde que el fichero debe estar en el mismo directorio en el que esté creando el
#not... |
jbogaardt/chainladder-python | docs/tutorials/deterministic-tutorial.ipynb | mit | # Black linter, optional
%load_ext lab_black
import pandas as pd
import numpy as np
import chainladder as cl
import matplotlib.pyplot as plt
import os
%matplotlib inline
print("pandas: " + pd.__version__)
print("numpy: " + np.__version__)
print("chainladder: " + cl.__version__)
"""
Explanation: Applying Determinist... |
huseinzol05/Deep-Learning-Tensorflow | Bayesian-Optimization/bayesian-feedforward-iris.ipynb | mit | df = pd.read_csv('Iris.csv')
df.head()
x_data = df.iloc[:, :-1].values.astype(np.float32)
y_datalabel = df.iloc[:, -1]
y_data = LabelEncoder().fit_transform(df.iloc[:, -1])
onehot = np.zeros((y_data.shape[0], np.unique(y_data).shape[0]))
for i in range(y_data.shape[0]):
onehot[i, y_data[i]] = 1.0
x_train, x_... |
google/applied-machine-learning-intensive | content/xx_misc/probability_and_statistics/colab.ipynb | apache-2.0 | # 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 the L... |
JannesKlaas/MLiFC | Week 2/Ch. 12 - Trees and Ensembles.ipynb | mit | import numpy as np
import pandas as pd
# Set seed for reproducability
np.random.seed(42)
import matplotlib.pyplot as plt
# Supress warnings for better readability
import warnings; warnings.simplefilter('ignore')
# Load data
df = pd.read_csv('processed_bank.csv',index_col=0)
# Check that data is okay
df.head()
# Pro... |
gvasold/gdp17 | objekte/objekte_1.ipynb | apache-2.0 | books = [
("Klein, Bernd", "Einführung in Python", "Hanser", "3", 2017),
("Sweigart, Al", "Automate the Boring Stuff with Python", "No Starch Press", "1", 2015),
("Weigend, Michael", "Python", "mitp", "6., erw. Aufl.", 2016),
("Downey, Allen B.", "Programmieren lernen mit Python", "O'Reilly", "1", 2014)... |
AllenDowney/ThinkBayes2 | examples/btp01.ipynb | mit | from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import numpy as np
from thinkbayes2 import Hist, Pmf, Cdf, Suite, Beta
import thinkplot
"""
Explanation: Bayes Theorem Problems
This notebook presents code and exercises from Think Bayes, second edi... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/production_ml/labs/samples/contrib/kubeflow-katib/early-stopping.ipynb | apache-2.0 | # Update the PIP version.
!python -m pip install --upgrade pip
!pip install kfp==1.1.1
!pip install kubeflow-katib==0.10.1
"""
Explanation: Kubeflow Pipelines with Katib component
In this notebook you will:
- Create Katib Experiment using random algorithm.
- Use median stopping rule as an early stopping algorithm.
- U... |
goodwordalchemy/thinkstats_notes_and_exercises | code/chap02_distributions_notes.ipynb | gpl-3.0 | import thinkstats2
hist = thinkstats2.Hist([1,2,2,3,5])
hist
print hist.Freq(2) == hist[2]
print hist[4]
print hist.Values()
for val in sorted(hist.Values()):
print (val, hist.Freq(val))
for val, freq in hist.Items():
print (val, freq)
import thinkplot
thinkplot.Hist(hist)
thinkplot.Show(xlabel='value', ylab... |
SBRG/ssbio | docs/notebooks/I-TASSER and TMHMM Install Guide.ipynb | mit | import os
import os.path as op
"""
Explanation: I-TASSER and TMHMM Install Guide
This notebook provides a guide to installing I-TASSER and TMHMM in Binder, since these require you to register with your email and cannot be installed beforehand.
End of explanation
"""
# Step 2
itasser_download_link = 'my_download_link... |
liufuyang/deep_learning_tutorial | jizhi-pytorch-2/01_word_embedding/homework-Copy1.ipynb | mit | # 加载必要的程序包
# PyTorch的程序包
import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
# 数值运算和绘图的程序包
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
# 加载机器学习的软件包，主要为了词向量的二维可视化
from sklearn.decomposition import PCA
#加载Word2Vec的... |
pauliacomi/pyGAPS | docs/examples/alphas.ipynb | mit | # import isotherms
%run import.ipynb
# import the characterisation module
import pygaps.characterisation as pgc
"""
Explanation: The $\alpha_s$ method
Very similar to the t-plot method, the $\alpha_s$ method compares an isotherm on
a porous material with one that was taken on a reference non-porous surface. The
refer... |
Chipe1/aima-python | learning_apps.ipynb | mit | from learning import *
from probabilistic_learning import *
from notebook import *
"""
Explanation: LEARNING APPLICATIONS
In this notebook we will take a look at some indicative applications of machine learning techniques. We will cover content from learning.py, for chapter 18 from Stuart Russel's and Peter Norvig's b... |
ethen8181/machine-learning | python/iterator/iterator.ipynb | mit | # code for loading the format for the notebook
import os
# path : store the current path to convert back to it later
path = os.getcwd()
os.chdir(os.path.join('..', '..', 'notebook_format'))
from formats import load_style
load_style(plot_style=False)
os.chdir(path)
# magic to print version
%load_ext watermark
%water... |
arcyfelix/Courses | 18-10-30-Think-Stats-by-Allen-B-Downey/Notes.ipynb | apache-2.0 | from typing import List
import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import sklearn
% matplotlib inline
"""
Explanation: Notes for Think Stats by Allen B. Downey
End of explanation
"""
sample = [1, 3, 5, 6]
np.mean(sample)
pd.DataFrame(sample).mean()
"""
Explanat... |
ixkael/Delight | notebooks/Tutorial - getting started with Delight.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import scipy.stats
import sys
sys.path.append('../')
from delight.io import *
from delight.utils import *
from delight.photoz_gp import PhotozGP
%cd ..
"""
Explanation: Tutorial: getting started with Delight
We will use the parameter file "tests/pa... |
probml/pyprobml | notebooks/misc/jax_md_parallel_tpu_example.ipynb | mit | !pip install -q git+https://www.github.com/google/jax-md
import jax
try:
import jax.tools.colab_tpu
jax.tools.colab_tpu.setup_tpu()
except KeyError:
import os
jax.devices()
import os
import jax.numpy as np
from jax import jit
from jax import vmap, pmap
import jax.numpy as jnp
from jax import random
fro... |
w4zir/ml17s | lectures/lec09-logistic-regression-example.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from sklearn import neighbors
df = pd.read_csv('datasets/exam_dataset1.csv', encoding='utf-8')
n_neighbors = 5
X = np.array(df[['exam1','exam2']])
y = np.array(df[['admission']]).ravel()
h = .02 # ste... |
mfouesneau/pyphot | examples/astropy_QuickStart.ipynb | mit | %matplotlib inline
import pylab as plt
import numpy as np
import sys
sys.path.append('../')
from pyphot import astropy as pyphot
"""
Explanation: pyphot - A tool for computing photometry from spectra
Some examples are provided in this notebook
Full documentation available at http://mfouesneau.github.io/docs/pyphot/
... |
CommonClimate/teaching_notebooks | GEOL351/ZeroD.ipynb | mit | %matplotlib inline
# ensures that graphics display in the notebook
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("./CoursewareModules")
from ClimateUtilities import * # import Ray Pierrehumbert's climate utilities
import phys
import seaborn as sns
"""
Explanatio... |
yyl/btc-price-analysis | notes/news_analysis.ipynb | gpl-2.0 | score_data = pd.read_csv("../data/nyt_bitcoin_with_score.csv", index_col='time',
parse_dates=[0], date_parser=lambda x: datetime.datetime.strptime(x, time_format))
score_data.head()
"""
Explanation: Weekly sentiment score analysis
End of explanation
"""
score_data.sentiment.unique()
score_data.gr... |
mez/carnd | P3_behavioral_cloning/playground.ipynb | mit | zero_steering = drive_log_df[drive_log_df.steering == 0].sample(frac=0.9)
drive_log_df = drive_log_df.drop(zero_steering.index)
plt.figure(figsize=(10,4))
drive_log_df.steering.hist(bins=100, color='r')
plt.xlabel('steering angle bins')
plt.ylabel('counts')
plt.show()
print("Current Dataset Size: ", len(drive_log_df.s... |
mayankjohri/LetsExplorePython | Section 2 - Advance Python/Chapter S2.01 - Functional Programming/06_Excercise.ipynb | gpl-3.0 | names = ["Aalok", "Chandu", "Roshan", "Prashant", "Saurabh"]
for i in range(len(names)):
names[i] = hash(names[i])
print(names)
"""
Explanation: Excercise - Functional Programming
Q: Try rewriting the code below as a map. It takes a list of real names and replaces them with code names produced using a more robus... |
marcelomiky/PythonCodes | Intro ML Semcomp/semcomp17_ml/.ipynb_checkpoints/semcomp17_ml_tutorial-checkpoint.ipynb | mit | #essential imports
import sys
sys.path.insert(1,'utils')
import numpy as np
import matplotlib.pyplot as plt
# display plots in this notebook
%matplotlib nbagg
import pandas as pd
import ml_utils
print(np.__version__)
print(np.__file__)
"""
Explanation: This workshop describes an overview of machine learning. Basic con... |
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