repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
OceanPARCELS/parcels | parcels/examples/tutorial_SummedFields.ipynb | mit | %matplotlib inline
from parcels import Field, FieldSet, ParticleSet, JITParticle, plotTrajectoriesFile, AdvectionRK4
import numpy as np
"""
Explanation: Tutorial on how to combine different Fields for advection into a SummedField object
In some oceanographic applications, you may want to advect particles using a combi... |
Danghor/Algorithms | Python/Chapter-05/Calculator-Frame.ipynb | gpl-2.0 | import re
"""
Explanation: The Shunting Yard Algorithm (Operator Precedence Parsing)
End of explanation
"""
def isWhiteSpace(s):
whitespace = re.compile(r'[ \t]+')
return whitespace.fullmatch(s)
"""
Explanation: The function $\texttt{isWhiteSpace}(s)$ checks whether $s$ contains only blanks and tabulators.
... |
ES-DOC/esdoc-jupyterhub | notebooks/bnu/cmip6/models/sandbox-3/atmoschem.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: BNU
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
OceanPARCELS/parcels | parcels/examples/tutorial_parcels_structure.ipynb | mit | from IPython.display import SVG
SVG(filename='parcels_user_diagram.svg')
"""
Explanation: Getting started with Parcels: general structure
There are many different ways in which to use Parcels for research. The flexibility of the parcels code enables this wide range of applicability and allows you to build complex sim... |
exe0cdc/PyscesToolbox | example_notebooks/Thermokin.ipynb | bsd-3-clause | mod = pysces.model('lin4_fb')
mod.doLoad() # this method call is necessary to ensure that future `doLoad` method calls are executed correctly
tk = psctb.ThermoKin(mod)
"""
Explanation: Thermokin
Thermokin is used to assess the kinetic and thermodynamic aspects of enzyme catalysed reactions in metabolic pathways [5]. I... |
rreimche/infdiffusion | Diffusion of REAL news.ipynb | mit | client = pymongo.MongoClient("46.101.236.181")
db = client.allfake
# get collection names
collections = sorted([collection for collection in db.collection_names()])
"""
Explanation: Init config
Select appropriate:
- database server (line 1): give pymongo.MongoClient() an appropriate parameter, else it is localhost
- ... |
rigetticomputing/pyquil | docs/source/quilt_getting_started.ipynb | apache-2.0 | from pyquil import Program, get_qc
qc = get_qc("Aspen-8")
"""
Explanation: Getting Up and Running with Quil-T
Language Documentation
See https://github.com/rigetti/quil for documentation on the Quil-T language.
Construct a QuantumComputer object linked to the Quil-T compiler
End of explanation
"""
qc.compiler.get_ve... |
Erotemic/ubelt | docs/notebooks/demo_CacheStamp.ipynb | apache-2.0 | import ubelt as ub
dpath = ub.Path.appdir('stamp-demo').delete().ensuredir()
fpath1 = dpath / 'large-file1.txt'
fpath2 = dpath / 'large-file2.txt'
stamp = ub.CacheStamp('stamp-name', dpath=dpath, product=[fpath1, fpath2])
# If the stamp is expired, we need to recompute the process
if stamp.expired():
fpath1.writ... |
teuben/astr288p | notebooks/orbits-01.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import math
"""
Explanation: Two Dimensional Galactic Orbits
set initial conditions (x0,y0) and (vx0,vy0) in the plane z=0
set integration time step
set number of integrations or a final integration stop time
define the potential and the forces as... |
MingChen0919/learning-apache-spark | notebooks/06-machine-learning/classification/naive-bayes-classification.ipynb | mit | from pyspark import SparkContext
sc = SparkContext(master = 'local')
from pyspark.sql import SparkSession
spark = SparkSession.builder \
.appName("Python Spark SQL basic example") \
.config("spark.some.config.option", "some-value") \
.getOrCreate()
"""
Explanation: Create entry points to... |
square/pysurvival | notebooks/Churn Prediction - Predicting when your customers will churn.ipynb | apache-2.0 | # Importing modules
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
from pysurvival.datasets import Dataset
%pylab inline
# Reading the dataset
raw_dataset = Dataset('churn').load()
print("The raw_dataset has the following shape: {}.".format(raw_dataset.shape))
raw_dataset.head(2)
"""
Expl... |
kikocorreoso/brythonmagic | notebooks/Brython usage in the IPython notebook.ipynb | mit | import IPython
IPython.version_info
"""
Explanation: The brythonmagic extension has been tested on:
End of explanation
"""
%install_ext https://raw.github.com/kikocorreoso/brythonmagic/master/brythonmagic.py
%load_ext brythonmagic
"""
Explanation: brythonmagic installation
Just type the following:
End of explanati... |
jhprinz/openpathsampling | examples/misc/tutorial_storage.ipynb | lgpl-2.1 | import openpathsampling as paths
"""
Explanation: An introduction to Storage
Introduction
All we need is contained in the openpathsampling package
End of explanation
"""
storage = paths.Storage('mstis.nc')
storage
"""
Explanation: The storage itself is mainly a netCDF file and can also be used as such. Technically ... |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/sandbox-1/ocean.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'sandbox-1', 'ocean')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocean
MIP Era: CMIP6
Institute: MIROC
Source ID: SANDBOX-1
Topic: Ocean
Sub-Topics: Timestepping Framework, Advecti... |
dietmarw/EK5312_ElectricalMachines | Chapman/Ch6-Problem_6-11.ipynb | unlicense | %pylab notebook
"""
Explanation: Excercises Electric Machinery Fundamentals
Chapter 6
Problem 6-11
End of explanation
"""
fse = 60 # [Hz]
n_nl = 1100 # [r/min]
p = 6
"""
Explanation: Description
The input power to the rotor circuit of a six-pole, 60 Hz, induction motor running at 1100 r/min is 5 kW.
E... |
highb/deep-learning | 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... |
ktmud/deep-learning | 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... |
schaber/deep-learning | dcgan-svhn/DCGAN_Exercises.ipynb | mit | %matplotlib inline
import pickle as pkl
import matplotlib.pyplot as plt
import numpy as np
from scipy.io import loadmat
import tensorflow as tf
!mkdir data
"""
Explanation: Deep Convolutional GANs
In this notebook, you'll build a GAN using convolutional layers in the generator and discriminator. This is called a De... |
diegocavalca/Studies | deep-learnining-specialization/2. improving deep neural networks/week3/programming-assignment/Tensorflow+Tutorial.ipynb | cc0-1.0 | import math
import numpy as np
import h5py
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.python.framework import ops
from tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict
%matplotlib inline
np.random.seed(1)
"""
Explanation: TensorFlow Tutorial
Welcome to this w... |
albahnsen/ML_SecurityInformatics | notebooks/10_EnsembleMethods_cont.ipynb | mit | # read in and prepare the chrun data
# Download the dataset
import pandas as pd
import numpy as np
data = pd.read_csv('../datasets/churn.csv')
# Create X and y
# Select only the numeric features
X = data.iloc[:, [1,2,6,7,8,9,10]].astype(np.float)
# Convert bools to floats
X = X.join((data.iloc[:, [4,5]] == 'no').ast... |
Ironlors/SmartIntersection-Ger | Journal/data1.txt.ipynb | apache-2.0 | #Create Lists
time = [233.32,198.92,184.7,168.18,148.22,138.88,151.76,127.48,119.12,115.24,110.7,104.28,105.52,109.2,120.7401,147.027]
motorTorque = [100,110,121,133.1,146.41,161.051,161.051,177.1561,194.8717,214.3589,235.7948,259.3743,285.3117,313.8429,345.2272,379.74992]
print(time)
print('elements in time: '+str(len... |
crystalzhaizhai/cs207_yi_zhai | lectures/L13/L13.ipynb | mit | import reprlib
class Sentence:
def __init__(self, text):
self.text = text
self.words = text.split()
def __getitem__(self, index):
return self.words[index]
def __len__(self):
#completes sequence protocol, but not needed for iterable
return len(self.word... |
sdss/marvin | docs/sphinx/jupyter/Shanghai_Demo_Queries.ipynb | bsd-3-clause | # Python 2/3 compatibility
from __future__ import print_function, division, absolute_import
# import matplolib just in case
import matplotlib.pyplot as plt
# this line tells the notebook to plot matplotlib static plots in the notebook itself
%matplotlib inline
# this line does the same thing but makes the plots inter... |
david4096/bioapi-examples | python_notebooks/1kg_metadata_service.ipynb | apache-2.0 | from ga4gh.client import client
c = client.HttpClient("http://1kgenomes.ga4gh.org")
"""
Explanation: GA4GH 1000 Genomes Metadata Service
This example illustrates how to access the available datasets in a GA4GH server.
Initialize client
In this step we create a client object which will be used to communicate with the ... |
hunterowens/data-pipelines | chicago/chicago_permits.ipynb | mit | %matplotlib inline
import datetime
from datetime import date
import pickle
import StringIO
import zipfile
import luigi
import requests
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.colors import Normalize, rgb2hex
from matplotlib.collections import P... |
root-mirror/training | OldSummerStudentsCourse/2016/notebooks/FillHistogram_Example_py.ipynb | gpl-2.0 | import ROOT
"""
Explanation: Access TTree in Python using PyROOT and fill a histogram
<hr style="border-top-width: 4px; border-top-color: #34609b;">
First import the ROOT Python module.
End of explanation
"""
%jsroot on
"""
Explanation: Optional: activate the JavaScript visualisation to produce interactive plots.
... |
opensanca/trilha-python | 04-python-prat/data_science/Python and Data Science.ipynb | mit | import pandas as pd
import matplotlib
%matplotlib inline
"""
Explanation: Python and Data Science
Mariana Lopes
28/07/2016
Trabalhando com o Jupyter
Ferramenta que permite criação de código, visualização de resultados e documentação no mesmo documento (.ipynb)
Modo de comando: esc para ativar, o cursor fica inativo
Mo... |
Upward-Spiral-Science/team1 | code/ScrapingImageData_Jay.ipynb | apache-2.0 | import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
import urllib2
from __future__ import division
plt.style.use('ggplot')
np.random.seed(1)
url = ('https://raw.githubusercontent.com/Upward-Spiral-Science'
'/data/master/syn-density/output.csv')
data = urllib2.urlopen(url)
csv = np.genfromtxt(d... |
nkoep/pymanopt | examples/MoG.ipynb | bsd-3-clause | import autograd.numpy as np
np.set_printoptions(precision=2)
import matplotlib.pyplot as plt
%matplotlib inline
# Number of data points
N = 1000
# Dimension of each data point
D = 2
# Number of clusters
K = 3
pi = [0.1, 0.6, 0.3]
mu = [np.array([-4, 1]), np.array([0, 0]), np.array([2, -1])]
Sigma = [np.array([[3, 0... |
arogozhnikov/einops | docs/3-einmix-layer.ipynb | mit | from einops.layers.torch import EinMix as Mix
"""
Explanation: EinMix: universal toolkit for advanced MLP architectures
Recent progress in MLP-based architectures demonstrated that very specific MLPs can compete with convnets and transformers (and even outperform them).
EinMix allows writing such architectures in a mo... |
peendebak/SPI-rack | examples/D4.ipynb | mit | # Import SPI Rack, D5a module and D4 module
from spirack import SPI_rack, D4_module, D5a_module
from time import sleep
import numpy as np
%matplotlib notebook
import matplotlib.pyplot as plt
"""
Explanation: D4 example notebook
Example notebook of the D4 2 channel, 24-bit ADC module. To use this notebook, we need a ... |
idies/pyJHTDB | examples/isotropic_spectra_1D.ipynb | apache-2.0 | import numpy as np
import pyJHTDB
from pyJHTDB.dbinfo import mhd1024, isotropic1024coarse
from pyJHTDB import libJHTDB
import time as tt
#import mkl_fft
"""
Explanation: import numpy and pyJHTDB stuff
End of explanation
"""
%matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: now import matplotlib an... |
mbohlool/client-python | examples/notebooks/intro_notebook.ipynb | apache-2.0 | from kubernetes import client, config
"""
Explanation: Managing kubernetes objects using common resource operations with the python client
Some of these operations include;
create_xxxx : create a resource object. Ex create_namespaced_pod and create_namespaced_deployment, for creation of pods and deployments respecti... |
fcollonval/coursera_data_visualization | BasicLinearRegression.ipynb | mit | # Magic command to insert the graph directly in the notebook
%matplotlib inline
# Load a useful Python libraries for handling data
import pandas as pd
import numpy as np
import statsmodels.formula.api as smf
import scipy.stats as stats
import seaborn as sns
import matplotlib.pyplot as plt
from IPython.display import Ma... |
pablormier/yabox | notebooks/yabox-de-animations.ipynb | apache-2.0 | %matplotlib inline
# Load local version of yabox
import sys
sys.path.insert(0, '../')
from yabox import DE, PDE
import numpy as np
# Imports required for 3d animations
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import cm
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animation,... |
jhconning/Dev-II | notebooks/Stata_in_jupyter.ipynb | bsd-3-clause | %matplotlib inline
import seaborn as sns
import pandas as pd
import statsmodels.formula.api as smf
import ipystata
"""
Explanation: Stata and R in a jupyter notebook
The jupyter notebook project is now designed to be a 'language agnostic' web-application front-end for any one of many possible software language kernels... |
carian2996/big_data | capstone_project/clustering/scripts/Week 3 pySpark MLlib Clustering.ipynb | gpl-2.0 | import pandas as pd
from pyspark.mllib.clustering import KMeans, KMeansModel
from numpy import array
"""
Explanation: <br><br><br><br><br><h1 style="font-size:4em;color:#2467C0">Welcome to Week 3</h1><br><br><br>
<div style="color:black;font-family: Arial; font-size:1.1em;line-height:65%">
<p style="line-height:31px;... |
statsmodels/statsmodels.github.io | v0.12.1/examples/notebooks/generated/robust_models_1.ipynb | bsd-3-clause | %matplotlib inline
from statsmodels.compat import lmap
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
"""
Explanation: M-Estimators for Robust Linear Modeling
End of explanation
"""
norms = sm.robust.norms
def plot_weights(support, weights_func, xlabels, xt... |
bjshaw/phys202-project | galaxy_project/Ib) Base Question Visualization.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import YouTubeVideo
from plotting_function import plotter,static_plot,com_plot,static_plot_com
"""
Explanation: Base Question Visu... |
NREL/bifacial_radiance | docs/tutorials/8 - Advanced topics - Calculating Power Output and Electrical Mismatch.ipynb | bsd-3-clause | import bifacial_radiance
import os
from pathlib import Path
testfolder = str(Path().resolve().parent.parent / 'bifacial_radiance' / 'TEMP'/ 'Tutorial_08')
if not os.path.exists(testfolder):
os.makedirs(testfolder)
simulationName = 'tutorial_8'
moduletype = "test-module"
albedo = 0.25
lat = 37.5
lon = -77.6
... |
rvuduc/cse6040-ipynbs | 26--logreg-mle-numopt.ipynb | bsd-3-clause | import pandas as pd
import seaborn as sns
import numpy as np
from IPython.display import display
%matplotlib inline
import plotly.plotly as py
from plotly.graph_objs import *
# @YOUSE: Fill in your credentials (user ID, API key) for Plotly here
py.sign_in ('USERNAME', 'APIKEY')
%reload_ext autoreload
%autoreload 2
... |
bowenliu16/deepchem | examples/broken/protein_ligand_complex_notebook.ipynb | gpl-3.0 | %load_ext autoreload
%autoreload 2
%pdb off
# set DISPLAY = True when running tutorial
DISPLAY = False
# set PARALLELIZE to true if you want to use ipyparallel
PARALLELIZE = False
import warnings
warnings.filterwarnings('ignore')
dataset_file= "../datasets/pdbbind_core_df.pkl.gz"
from deepchem.utils.save import load_f... |
Applied-Groundwater-Modeling-2nd-Ed/Chapter_4_problems-1 | P4.4_Flopy_Hubbertville_areal_model_with_pumping.ipynb | gpl-2.0 | %matplotlib inline
import sys
import os
import shutil
import numpy as np
from subprocess import check_output
# Import flopy
import flopy
"""
Explanation: <img src="AW&H2015.tiff" style="float: left">
<img src="flopylogo.png" style="float: center">
Problem P4.4 Adding Pumping to Hubbertville Areal Model
In Problem P4.... |
charlesll/RamPy | examples/Resample_and_flip_spectra.ipynb | gpl-2.0 | %matplotlib inline
import sys
sys.path.append("../")
import numpy as np
import scipy
from matplotlib import pyplot as plt
import rampy as rp
from sklearn import preprocessing
"""
Explanation: Use of resample and flipsp functions
Spectral data are often delivered with decreasing and non-regularly sampled frequencies. ... |
samzhang111/frontpages | analysis/data_exploration.ipynb | gpl-3.0 | # <help>
# <api>
from collections import defaultdict
import datetime
import pandas as pd
import numpy as np
def load_data(clean=True, us=True):
df = pd.read_sql_table('frontpage_texts', 'postgres:///frontpages')
df_newspapers = pd.read_sql_table('newspapers', 'postgres:///frontpages')
if clean:... |
RedHatInsights/insights-core | docs/notebooks/Filters Tutorial.ipynb | apache-2.0 | """ Some imports used by all of the code in this tutorial """
import sys
sys.path.insert(0, "../..")
from __future__ import print_function
import os
from insights import run
from insights.specs import SpecSet
from insights.core import IniConfigFile
from insights.core.plugins import parser, rule, make_fail
from insights... |
ledeprogram/algorithms | class7/donow/Zhao_Shengying_DoNow_7.ipynb | gpl-3.0 | import pandas as pd
%matplotlib inline
import numpy as np
from sklearn.linear_model import LogisticRegression
"""
Explanation: Apply logistic regression to categorize whether a county had high mortality rate due to contamination
1. Import the necessary packages to read in the data, plot, and create a logistic regressi... |
gdementen/larray | doc/source/tutorial/tutorial_indexing.ipynb | gpl-3.0 | from larray import *
"""
Explanation: Indexing, Selecting and Assigning
Import the LArray library:
End of explanation
"""
# let's start with
population = load_example_data('demography_eurostat').population
population
"""
Explanation: Import the test array population:
End of explanation
"""
population['Belgium', '... |
google/starthinker | colabs/airflow.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: Airflow Composer Example
Demonstration that uses Airflow/Composer native, Airflow/Composer local, and StarThinker tasks in the same generated DAG.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
... |
albahnsen/ML_RiskManagement | notebooks/07_decision_trees.ipynb | mit | # vehicle data
import pandas as pd
import zipfile
with zipfile.ZipFile('../datasets/vehicles_train.csv.zip', 'r') as z:
f = z.open('vehicles_train.csv')
train = pd.io.parsers.read_table(f, index_col=False, sep=',')
# before splitting anything, just predict the mean of the entire dataset
train['prediction'] = t... |
jdhp-docs/python_notebooks | nb_sci_ai/ai_ml_id3_fr.ipynb | mit | import pandas as pd
"""
Explanation: L'apprentissage d'arbres de décision avec ID3
TODO:
- faire un document séparé pour ID3, CART, C4.5, etc. ou mettre tout dans ce notebook ???
End of explanation
"""
data_list = [['soleil', 'chaud', 'haute', 'faux', 'NePasJouer'],
['soleil', 'chaud', 'haute', 'vrai', ... |
ES-DOC/esdoc-jupyterhub | notebooks/cmcc/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', 'cmcc', 'sandbox-3', 'ocnbgchem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Ocnbgchem
MIP Era: CMIP6
Institute: CMCC
Source ID: SANDBOX-3
Topic: Ocnbgchem
Sub-Topics: Tracers.
Properties:... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/recommendation_systems/solutions/wals.ipynb | apache-2.0 | import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT ID
BUCKET = "cloud-training-demos-ml" # REPLACE WITH YOUR BUCKET NAME
REGION = "us-central1" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["PROJECT"] = PROJECT
os.environ["BUCKET"] = BUCKET
os.environ["REGION"]... |
ES-DOC/esdoc-jupyterhub | notebooks/inpe/cmip6/models/sandbox-2/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'inpe', 'sandbox-2', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: INPE
Source ID: SANDBOX-2
Topic: Seaice
Sub-Topics: Dynamics, Thermodynamics, Radi... |
robblack007/clase-cinematica-robot | Practicas/practica4/Problemas.ipynb | mit | def DH_simbolico(a, d, α, θ):
from sympy import Matrix, sin, cos
# YOUR CODE HERE
raise NotImplementedError()
from sympy import Matrix, sin, cos, pi
from nose.tools import assert_equal
assert_equal(DH_simbolico(0,0,0,pi/2), Matrix([[0,-1,0,0],[1,0,0,0], [0,0,1,0],[0,0,0,1]]))
assert_equal(DH_simbolico(0,0,... |
palrogg/foundations-homework | Data_and_databases/Homework_4_Paul_Ronga_graded.ipynb | mit | numbers_str = '496,258,332,550,506,699,7,985,171,581,436,804,736,528,65,855,68,279,721,120'
"""
Explanation: Grade: 12 / 11
Homework #4
These problem sets focus on list comprehensions, string operations and regular expressions.
Problem set #1: List slices and list comprehensions
Let's start with some data. The followi... |
brettavedisian/phys202-2015-work | assignments/assignment08/InterpolationEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
sns.set_style('white')
from scipy.interpolate import griddata
"""
Explanation: Interpolation Exercise 2
End of explanation
"""
# Collaborated with James A. on this part
x=np.empty(1,)
x[0]=0
x=np.hstack((x,[-5]*11))
for i i... |
jupyter/nbgrader | nbgrader/tests/apps/files/test-v2.ipynb | bsd-3-clause | def squares(n):
"""Compute the squares of numbers from 1 to n, such that the
ith element of the returned list equals i^2.
"""
### BEGIN SOLUTION
if n < 1:
raise ValueError("n must be greater than or equal to 1")
return [i ** 2 for i in range(1, n + 1)]
### END SOLUTION
"""
Exp... |
ramseylab/networkscompbio | class07_clustcoeff_python3_template.ipynb | apache-2.0 | from igraph import Graph
from igraph import summary
import pandas
import numpy
import timeit
from pympler import asizeof
import bintrees
"""
Explanation: CS446/519 - Class Session 7 - Transitivity (Clustering Coefficients)
In this class session we are going to compute the local clustering coefficient of all vertices i... |
junhwanjang/DataSchool | Lecture/12. Scikit-Learn & statsmodels 패키지 소개/4) Scikit-Learn 패키지의 샘플 데이터 - classification용.ipynb | mit | from sklearn.datasets import load_iris
iris = load_iris()
print(iris.DESCR)
df = pd.DataFrame(iris.data, columns=iris.feature_names)
sy = pd.Series(iris.target, dtype="category")
sy = sy.cat.rename_categories(iris.target_names)
df['species'] = sy
df.tail()
sns.pairplot(df, hue="species")
plt.show()
"""
Explanation: ... |
metpy/MetPy | v0.10/_downloads/4d64a32e8cfca4a5a78f2d1f68ae3c83/Gradient.ipynb | bsd-3-clause | import numpy as np
import metpy.calc as mpcalc
from metpy.units import units
"""
Explanation: Gradient
Use metpy.calc.gradient.
This example demonstrates the various ways that MetPy's gradient function
can be utilized.
End of explanation
"""
data = np.array([[23, 24, 23],
[25, 26, 25],
... |
1x0r/pspis | lectures/lecture_04/demos-1.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
n = 19
print("Каждая цифра представлена матрицей формы ", digits.data[n, :].shape)
"""
Explanation: Какую задачу можно поставить для этого набора данных?
End of explanation
"""
digit = 255 - digits.data[n, :].reshape(8, 8)
plt.imshow(digit, cmap='gray', interpolatio... |
yugangzhang/CHX_Pipelines | 2017_3/Mask_pipeline_2017_V6.ipynb | bsd-3-clause | from chxanalys.chx_libs import (np, roi, time, datetime, os, getpass, db, get_images,LogNorm, plt,ManualMask)
from chxanalys.chx_libs import cmap_albula, cmap_vge, random
from chxanalys.chx_generic_functions import (get_detector, get_meta_data,create_user_folder,
get_fields... |
anthonyng2/FX-Trading-with-Python-and-Oanda | Oanda v20 REST-oandapyV20/06.00 Position Management.ipynb | mit | import pandas as pd
import oandapyV20
import oandapyV20.endpoints.positions as positions
import configparser
config = configparser.ConfigParser()
config.read('../config/config_v20.ini')
accountID = config['oanda']['account_id']
access_token = config['oanda']['api_key']
client = oandapyV20.API(access_token=access_toke... |
AtmaMani/pyChakras | udemy_ml_bootcamp/Python-for-Data-Analysis/NumPy/Numpy Indexing and Selection.ipynb | mit | import numpy as np
#Creating sample array
arr = np.arange(0,11)
#Show
arr
"""
Explanation: <a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>
NumPy Indexing and Selection
In this lecture we will discuss how to select elements or groups of elements from an array.
End of explanation
"""
... |
geoneill12/phys202-2015-work | assignments/assignment07/AlgorithmsEx02.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
"""
Explanation: Algorithms Exercise 2
Imports
End of explanation
"""
def find_peaks(a):
"""Find the indices of the local maxima in a sequence."""
b = np.array(a)
c = b.max()
return b[c]
p1 = find_peaks(... |
JorisBolsens/PYNQ | Pynq-Z1/notebooks/examples/overlay_download.ipynb | bsd-3-clause | # Using base.bit located in pynq package
from pynq import Overlay
ol = Overlay("base.bit")
"""
Explanation: Downloading Overlays
This notebook demonstrates how to download an FPGA overlay and examine programmable logic state.
1. Instantiating an overlay
To instantiate an overlay, a bitstream file name is passed to t... |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/robust_models_1.ipynb | bsd-3-clause | %matplotlib inline
from statsmodels.compat import lmap
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
"""
Explanation: M-Estimators for Robust Linear Modeling
End of explanation
"""
norms = sm.robust.norms
def plot_weights(support, weights_func, xlabels, xt... |
metpy/MetPy | dev/_downloads/5f6dfc4b913dc349eba9f04f6161b5f1/GINI_Water_Vapor.ipynb | bsd-3-clause | import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import xarray as xr
from metpy.cbook import get_test_data
from metpy.io import GiniFile
from metpy.plots import add_metpy_logo, add_timestamp, colortables
# Open the GINI file from the test data
f = GiniFile(get_test_data('WEST-CONUS_4km_WV_20151208_2... |
statkclee/ThinkStats2 | code/chap02soln-kor.ipynb | gpl-3.0 | %matplotlib inline
import chap01soln
resp = chap01soln.ReadFemResp()
resp.columns
"""
Explanation: 통계적 사고 (2판) 연습문제 (thinkstats2.com, think-stat.xwmooc.org)<br>
Allen Downey / 이광춘(xwMOOC)
여성 응답자 파일을 읽어들여 변수명을 표시하시오.
End of explanation
"""
import thinkstats2
hist = thinkstats2.Hist(resp.totincr)
"""
Explanation: 응답... |
JeffreyWang98/JeffreyWang98.github.io | Projects/Grains/Grains.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
"""
Explanation: Tutorial content
This tutorial will show how to use the Feed Grains open data source, provided by the USDA Economic Research Service to learn about grain production and historical effects. This tutorial will also ... |
dolittle007/dolittle007.github.io | notebooks/GP-introduction.ipynb | gpl-3.0 | %matplotlib inline
import matplotlib.pyplot as plt
import matplotlib.cm as cmap
cm = cmap.inferno
import numpy as np
import scipy as sp
import theano
import theano.tensor as tt
import theano.tensor.nlinalg
import sys
sys.path.insert(0, "../../..")
import pymc3 as pm
"""
Explanation: Gaussian Process Regression
Gauss... |
zingale/pyreaclib | library-examples-filtering.ipynb | bsd-3-clause | %matplotlib inline
import pynucastro as pyna
library_file = '20180201ReaclibV2.22'
mylibrary = pyna.rates.Library(library_file)
"""
Explanation: Using RateFilter to Search Rates in a Library
The Library class in pynucastro provides a high level interface for reading files containing one or more Reaclib rates and the... |
laantoi/fun-with-python | coin_games.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Coin games: classical and quantum
In this notebook we play a set of interesting coin tossing games using coins obeying classical (games 1-2) and quantum (game 3) mechanics.
Game 1: Gambler's ruin
A gambler enters the casino with a bankroll of size $k$... |
thonstad/acoustical_monitoring | notebooks/Synchronization.ipynb | bsd-3-clause | # the cross-correlation function in statsmodels does not use FFT so it is really slow
# from statsmodels.tsa.stattools import ccf
# res = ccf(ts1[1][200000:400000,1],ts2[1][200000:400000,1])
"""
Explanation: Let's try to find the lag of asynchrony by looking at the cross-correlation.
End of explanation
"""
# Warning... |
tpin3694/tpin3694.github.io | machine-learning/adaboost_classifier.ipynb | mit | # Load libraries
from sklearn.ensemble import AdaBoostClassifier
from sklearn import datasets
"""
Explanation: Title: Adaboost Classifier
Slug: adaboost_classifier
Summary: How to conduct adaboost classifier and boosting in scikit-learn for machine learning in Python.
Date: 2017-09-18 12:00
Category: Machine Lea... |
sysid/nbs | lstm/LTSM_BasicStockMarket.ipynb | mit | dpath = 'data/basic/'
#path_to_dataset = dpath + 'household_power_consumption.txt'
%mkdir -p dpath
!wget -P $dpath https://raw.githubusercontent.com/jaungiers/LSTM-Neural-Network-for-Time-Series-Prediction/master/sinwave.csv
!wget -P $dpath https://raw.githubusercontent.com/jaungiers/LSTM-Neural-Network-for-Time-Serie... |
rsignell-usgs/notebook | ROMS/sandy_sgrid.ipynb | mit | from netCDF4 import Dataset
url = ('http://geoport.whoi.edu/thredds/dodsC/clay/usgs/users/'
'jcwarner/Projects/Sandy/triple_nest/00_dir_NYB05.ncml')
nc = Dataset(url)
"""
Explanation: pysgrid only works with raw netCDF4 (for now!)
End of explanation
"""
import pysgrid
# The object creation is a lit... |
philmui/datascience | lecture06.stats/lecture06.eu.data.ipynb | mit | import pandas as pd
df = pd.DataFrame()
df
"""
Explanation: Data Import, Merge, Wrangle
We will be using the real dataset for extra-EU trade percentages for a few different years to illutrate the real-world usage of data import, cleanse, merge and wrangle.
End of explanation
"""
for chunk in pd.read_csv('data/ext_... |
ubcgif/gpgLabs | notebooks/mag/Mag_Induced2D.ipynb | mit | import numpy as np
from geoscilabs.mag import Mag, Simulator
%matplotlib inline
"""
Explanation: This is the <a href="https://jupyter.org/">Jupyter Notebook</a>, an interactive coding and computation environment. For this lab, you do not have to write any code, you will only be running it.
To use the notebook:
- "Shi... |
massimo-nocentini/on-python | vigenere/vigenere-cryptoanalysis.ipynb | mit | import itertools
from itertools import *
from copy import copy, deepcopy
from heapq import *
from random import *
import matplotlib.pyplot as plt
from collections import Counter
from sympy import *
init_printing()
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 10.0)
"""
Explanation: Vigenere cipher crypt... |
umutkarahan/Project100 | sample/Tuples.ipynb | bsd-2-clause | mancoloji = "Barış Manço", "Mançoloji", 1999
print(mancoloji)
"""
Explanation: Tuples Kullanımı
Tuples'lar değişmeyen sıralardır. Bir kere tanımlandığı zaman değiştirmenin imkanı yoktur. Listelere göre tubles farklı tipteki elemanlara sahip olabilir.
Örneğin;
End of explanation
"""
benbilirim = ("Barış Manço", "Ben ... |
decisionstats/pythonfordatascience | Web+Scraping.ipynb | apache-2.0 | r = urllib.request.urlopen('https://www.rottentomatoes.com/franchise/batman_movies').read()
#Using Beautiful Soup Library to parse the data
soup = BeautifulSoup(r, "lxml")
type(soup)
len(str(soup.prettify()))
soup
soup.prettify()
#We convert the data to a string format using str.
#Note in R we use str for struct... |
catalyst-cooperative/pudl | test/validate/notebooks/validate_bf_eia923.ipynb | mit | %load_ext autoreload
%autoreload 2
import sys
import pandas as pd
import sqlalchemy as sa
import pudl
import warnings
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
handler = logging.StreamHandler(stream=sys.stdout)
formatter = logging.Formatter('%(message)s')
handler.setFormatter(formatter... |
tylere/docker-tmpnb-ee | notebooks/2 - Earth Engine API Examples/2 - EE 101.ipynb | apache-2.0 | from IPython.display import Image
"""
Explanation: Earth Engine 101
This workbook is an introdution to Earth Engine analysis in an IPython Notebook, using the Python API. The content is similar to what is covered in the Introduction to the Earth Engine API workshop using the Earth Engine Javascript "Playground".
Let'... |
sotirisnik/dqn | dqn.ipynb | mit | import gym
import time
import random
import numpy as np
from collections import deque
import os
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
"""
Explanation: Import essential libraries
End of explanation
"""
import tensorflow as tf
import keras.backend.tensorflow_backend as KTF
def get_session(gpu_fraction=0.3):
... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/machine_learning_in_the_enterprise/solutions/distributed-hyperparameter-tuning.ipynb | apache-2.0 | import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG = "--user"
# Install necess... |
owenjhwilliams/ASIIT | FindSwirlLocs-AstroScriptV1.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import h5py
from importlib import reload
import sep
f = h5py.File('/Users/Owen/Dropbox/Data/ABL/SBL PIV data/RNV45-RI2.mat')
#list(f.keys())
Swirl = np.asarray(f['Swirl'])
X = np.asarray(f['X'])
Y = np.asarray(f['Y'])
X = np.transpose(X,(1,0))
Y... |
antoniomezzacapo/qiskit-tutorial | community/algorithms/iterative_phase_estimation_algorithm.ipynb | apache-2.0 | from math import pi
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
%matplotlib inline
# importing Qiskit
from qiskit import Aer, IBMQ
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
from qiskit import execute
from qiskit.tools.visualization import plot_histogram
from qiskit... |
nceder/nceder.github.io | course_materials/data-cleaning/data_cleaning.ipynb | gpl-3.0 | #Using dir() and help()
import pandas
"""
Explanation: # Extracting & Cleaning Data with Python
$~$
“I Have a Data File, Now What?”
$~$
Naomi Ceder
naomi@naomiceder.tech
@NaomiCeder
projects.naomiceder.tech
My main qualification? I'm quite old...
<img src="/notebooks/old.jpg">
Who am I?
Python since 2001
Author o... |
olinguyen/shogun | doc/ipython-notebooks/pca/pca_notebook.ipynb | gpl-3.0 | %pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
# import all shogun classes
from shogun import *
"""
Explanation: Principal Component Analysis in Shogun
By Abhijeet Kislay (GitHub ID: <a href='https://github.com/kislayabhi'>kislayabhi</a>)
This notebook is about... |
JakeColtman/BayesianSurvivalAnalysis | Full presentation.ipynb | mit | running_id = 0
output = [[0]]
with open("E:/output.txt") as file_open:
for row in file_open.read().split("\n"):
cols = row.split(",")
if cols[0] == output[-1][0]:
output[-1].append(cols[1])
output[-1].append(True)
else:
output.append(cols)
output = out... |
jpn--/larch | book/example/101_swissmetro_mnl.ipynb | gpl-3.0 | # TEST
import os
import pandas as pd
pd.set_option("display.max_columns", 999)
pd.set_option('expand_frame_repr', False)
pd.set_option('display.precision', 3)
import larch
larch._doctest_mode_ = True
from pytest import approx
import larch.numba as lx
import larch.numba as lx
"""
Explanation: 101: Swissmetro MNL Mode ... |
TurkuNLP/BINF_Programming | supplementary/Sets and exceptions.ipynb | gpl-2.0 | s=set() #this is how you create a set
s.add(5) #this is how you add items to sets
s.add("hi")
s.add(5)
s.add("ho")
s.add("hi")
s1=set([1,2,3,4,5]) #and you can also create sets from lists or any other iterables
s2=set([4,5,6,7])
#sets allow basic set operations
print("s1",s1)
print("s2",s2)
print("s1&s2",s1&s2) #int... |
metpy/MetPy | v0.9/_downloads/53923345d98c487825399f76f4de00e7/Station_Plot_with_Layout.ipynb | bsd-3-clause | import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import pandas as pd
from metpy.calc import wind_components
from metpy.cbook import get_test_data
from metpy.plots import (add_metpy_logo, simple_layout, StationPlot,
StationPlotLayout, wx_code_map)
fr... |
Scripta-Qumranica-Electronica/Data-Processing | Text_Extraction/Retrieving-text-with-the-SQE-API.ipynb | mit | import sys, json, copy
from pprint import pprint
try:
import requests
except ImportError:
!conda install --yes --prefix {sys.prefix} requests
import requests
try:
from genson import SchemaBuilder
except ImportError:
!conda install --yes --prefix {sys.prefix} genson
from genson import Schem... |
mattmcd/PyBayes | scripts/edward_simple.ipynb | apache-2.0 | # Generative model
mu_x = 10.0
sigma_x = 2.0
x_s = edm.Normal(mu_x, sigma_x)
# Sample data produced by model
n_samples = 100
samples = np.zeros(n_samples)
with tf.Session() as sess:
for i in range(n_samples):
samples[i] = sess.run(x_s)
# Descriptive statistics
print('Mean: {}'.format(np.mean(samples)))
pr... |
pysg/caiq | CAIQ.ipynb | mit | pyplot.scatter(VolumenLiqVAP,PresionVAP, color = 'red', label = 'Líquido')
pyplot.scatter(VolumenVapVAP,PresionVAP, color = 'blue', label = 'Vapor')
pyplot.title('Diagrama Densidad-Presión')
pyplot.legend(loc="upper right")
pyplot.xlabel('Densidad [=] -')
pyplot.ylabel('Presión [=] bar')
"""
Explanation: PyTher: UN... |
besser82/shogun | doc/ipython-notebooks/structure/multilabel_structured_prediction.ipynb | bsd-3-clause | import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
from __future__ import print_function
try:
from sklearn.datasets import make_classification
except ImportError:
import pip
pip.main(['install', '--user', 'scikit-learn'])
from sklearn.datasets import make_classification
import... |
BinRoot/TensorFlow-Book | ch02_basics/Concept01_defining_tensors.ipynb | mit | import tensorflow as tf
import numpy as np
"""
Explanation: Ch 02: Concept 01
Defining tensors
Import TensorFlow and Numpy:
End of explanation
"""
m1 = [[1.0, 2.0],
[3.0, 4.0]]
m2 = np.array([[1.0, 2.0],
[3.0, 4.0]], dtype=np.float32)
m3 = tf.constant([[1.0, 2.0],
[3.0, 4.... |
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