repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
values | content stringlengths 335 154k |
|---|---|---|---|
molgor/spystats | notebooks/Sandboxes/MCMC/.ipynb_checkpoints/SpatialGaussianProcess inference in PyMC3-Copy1-checkpoint.ipynb | bsd-2-clause | # Load Biospytial modules and etc.
%matplotlib inline
import sys
sys.path.append('/apps/external_plugins/spystats/')
#import django
#django.setup()
import pandas as pd
import matplotlib.pyplot as plt
## Use the ggplot style
plt.style.use('ggplot')
import numpy as np
## Model Specification
import pymc3 as pm
from spyst... |
mne-tools/mne-tools.github.io | 0.15/_downloads/plot_decoding_spatio_temporal_source.ipynb | bsd-3-clause | # Author: Denis A. Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import os
import numpy as np
import matplotlib.pyplot as plt
from sklearn.pipeline import make_pipeline
from sklear... |
UltronAI/Deep-Learning | CS231n/assignment3/LSTM_Captioning.ipynb | mit | # As usual, a bit of setup
from __future__ import print_function
import time, os, json
import numpy as np
import matplotlib.pyplot as plt
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.rnn_layers import *
from cs231n.captioning_solver import CaptioningSolver
from c... |
mne-tools/mne-tools.github.io | 0.13/_downloads/plot_source_power_spectrum.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
#
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, compute_source_psd
print(__doc__)
"""
Explanation: Compute power spect... |
Qumulo/python-notebooks | notebooks/Quickstart guide for Qumulo python API.ipynb | gpl-3.0 | import os
from qumulo.rest_client import RestClient
# set your environment variables or fill in the variables below
API_HOSTNAME = os.environ['API_HOSTNAME'] if 'API_HOSTNAME' in os.environ else '{your-cluster-hostname}'
API_USER = os.environ['API_USER'] if 'API_USER' in os.environ else '{api-cluster-user}... |
liufuyang/ManagingBigData_MySQL_DukeUniv | notebooks/MySQL_Exercise_02_Selecting_Data_Subsets_using_WHERE.ipynb | mit | %load_ext sql
%sql mysql://studentuser:studentpw@mysqlserver/dognitiondb
%sql USE dognitiondb
%config SqlMagic.displaylimit=25
"""
Explanation: Copyright Jana Schaich Borg/Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
MySQL Exercise 2: Using WHERE to select specific data
When you are querying a business-... |
mne-tools/mne-tools.github.io | 0.18/_downloads/846c9103e2de535e71d8b76a75654d36/plot_background_statistics.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
from functools import partial
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa, analysis:ignore
import mne
from mne.stats import (ttest_1samp_no_p, bonferroni_correctio... |
usantamaria/iwi131 | ipynb/02-DiagramaDeFlujo/DiagramaDeFlujo.ipynb | cc0-1.0 | def es_magico(N):
suma_divisores = 1
for m in range(2,N):
if N%m==0:
suma_divisores = suma_divisores + m
if suma_divisores==N:
print N, "es mágico"
else:
print N, "no es mágico"
mi_numero = int(raw_input("Ingrese un número: "))
es_magico(mi_numero)
"""
Explanation: ... |
VVard0g/ThreatHunter-Playbook | docs/notebooks/windows/07_discovery/WIN-190725024610.ipynb | mit | from openhunt.mordorutils import *
spark = get_spark()
"""
Explanation: SAM Registry Hive Handle Request
Metadata
| Metadata | Value |
|:------------------|:---|
| collaborators | ['@Cyb3rWard0g', '@Cyb3rPandaH'] |
| creation date | 2019/07/25 |
| modification date | 2020/09/20 |
| playbook related ... |
karlstroetmann/Formal-Languages | Python/FixedPoint.ipynb | gpl-2.0 | def fixpoint(S0, f):
Result = S0.copy() # don't change S0
while True:
NewElements = { x for o in Result
for x in f(o)
}
if NewElements.issubset(Result):
return Result
Result |= NewElements
"""
Explanation: Fixed-Point Iterati... |
stevenbergner/stevenbergner.github.io | Teaching/cmpt767/graph-tool-tutorial-todo.ipynb | mit | import graph_tool.all as gt
import pandas as pd
import numpy as np
from IPython.display import display
%matplotlib inline
print("graph-tool version: {}".format(gt.__version__.split(' ')[0]))
"""
Explanation: Graph-tool tutorial
The following tutorial demonstrates working with graphs using the graph-tool python module.... |
McIntyre-Lab/papers | fear_ase_2016/scripts/cis_summary/maren_equations_summary.ipynb | lgpl-3.0 | # Set-up default environment
%run '../ipython_startup.py'
# Import additional libraries
import sas7bdat as sas
import cPickle as pickle
import statsmodels.formula.api as smf
from ase_cisEq import marenEq
from ase_cisEq import marenPrintTable
from ase_normalization import meanCenter
from ase_normalization import q3No... |
haltaro/predicting-comic-end | 1_analyze_comic_data_j.ipynb | mit | %matplotlib inline
import json
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
sns.set(style='ticks')
import matplotlib
from matplotlib.font_manager import FontProperties
font_path = '/usr/share/fonts/truetype/takao-gothic/TakaoPGothic.ttf'
font_prop = FontProperties(fnam... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-1/cmip6/models/sandbox-2/landice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-2', 'landice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Landice
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-2
Topic: Landice
Sub-Topics: Gla... |
adityaka/misc_scripts | python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/04_04/Begin/Universal.ipynb | bsd-3-clause | import pandas as pd
import numpy as np
df = pd.DataFrame(np.random.randn(10, 4), columns=['A', 'B', 'C', 'D'])
df2 = pd.DataFrame(np.random.randn(7, 3), columns=['A', 'B', 'C'])
sum_df = df + df2
sum_df
"""
Explanation: NumPy Universal Functions
If the data within a DataFrame are numeric, NumPy's universal functions ... |
ES-DOC/esdoc-jupyterhub | notebooks/mri/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', 'mri', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: MRI
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics: Transport, Emissions Co... |
kadamkaustubh/project-Goldilocks | pymc proof of concept-final.ipynb | mit | import pymc as pm
import numpy as np
from scipy.integrate import odeint
import matplotlib.pyplot as plt
%matplotlib inline
#importing experimental data
timedata=np.genfromtxt("data/timedata.txt", delimiter=',')
glc_concentration=np.genfromtxt("data/GLCexconc.txt", delimiter=',')
"""
Explanation: Using PyMC for parame... |
nishadsingh1/clipper | examples/tutorial/tutorial_part_two.ipynb | apache-2.0 | import sys
import os
from clipper_admin import Clipper
# Change the username if necessary
user = ""
# Set the path to the SSH key
key = ""
# Set the SSH host
host = ""
clipper = Clipper(host, user, key)
"""
Explanation: Clipper Tutorial: Part 2
In this part of the tutorial, you will put on your data scientist hat and ... |
mjlong/openmc | docs/source/pythonapi/examples/mgxs-part-iii.ipynb | mit | import math
import pickle
from IPython.display import Image
import matplotlib.pylab as pylab
import numpy as np
import openmc
import openmc.mgxs
from openmc.statepoint import StatePoint
from openmc.summary import Summary
import openmoc
import openmoc.process
from openmoc.compatible import get_openmoc_geometry
from op... |
egentry/lamat-2016-solutions | day2/equilibrium_temperatures.ipynb | mit | def equilibrium_temperature(luminosity, distance, albedo=0):
"""
Calculates the equilibrium temperature of a planet, assuming blackbody radiation
and thermodynamic equilibrium.
Parameters
----------
luminosity : float
luminosity of the host star [ergs s**-1]
distance : ... |
atulsingh0/MachineLearning | python_DC/LP_Hierarchical_Indexing_#4.ipynb | gpl-3.0 | data = pd.Series(np.random.randn(10),
index=[['a', 'a', 'a', 'b', 'b', 'b', 'c', 'c', 'd', 'd'],
[1, 2, 3, 1, 2, 3, 1, 2, 2, 3]])
data
# getting the index
data.index
data['b']
data['b'][1]
data['b', 1]
data['b':'c']
data.ix['b':'d']
data[:, 2]
# Hierarchical indexing plays a c... |
mne-tools/mne-tools.github.io | dev/_downloads/8fdd7a9ad9a4bab4331f7c5da5e3bb1a/define_target_events.ipynb | bsd-3-clause | # Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
import mne
from mne import io
from mne.event import define_target_events
from mne.datasets import sample
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Define target events based on time la... |
jserenson/Python_Bootcamp | While loops.ipynb | gpl-3.0 | x = 0
while x < 10:
print 'x is currently: ',x
print ' x is still less than 10, adding 1 to x'
x+=1
"""
Explanation: while loops
The while statement in Python is one of most general ways to perform iteration. A while statement will repeatedly execute a single statement or group of statements as long as th... |
ernestyalumni/Propulsion | Physique.ipynb | gpl-2.0 | import os
print(os.getcwd())
os.chdir(os.getcwd() + "/Physique/") # change current working directory
print(os.getcwd())
%run -i ./Scripts/Refresh.py # this is the main, important, command to run
import Physique
import sys
sys.executable # Check which Python you are running in case you have ImportError's
print(di... |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_stats_cluster_methods.ipynb | bsd-3-clause | # Authors: Eric Larson <larson.eric.d@gmail.com>
# License: BSD (3-clause)
import numpy as np
from scipy import stats
from functools import partial
import matplotlib.pyplot as plt
# this changes hidden MPL vars:
from mpl_toolkits.mplot3d import Axes3D # noqa
from mne.stats import (spatio_temporal_cluster_1samp_test,... |
carltoews/tennis | .ipynb_checkpoints/tennis_predictions-checkpoint.ipynb | gpl-3.0 | import setup_mysql_database
"""
Explanation: <p style="text-align: center"> Predicting Professional Tennis Match Outcomes</p>
Author: Carl Toews
Date: August 8, 2017
Project Description: This project explores various machine learning techniques on professional tennis data. The data set was compiled by Jeff Sackm... |
regardscitoyens/consultation_an | exploitation/analyse_quanti_theme4.ipynb | agpl-3.0 | def loadContributions(file, withsexe=False):
contributions = pd.read_json(path_or_buf=file, orient="columns")
rows = [];
rindex = [];
for i in range(0, contributions.shape[0]):
row = {};
row['id'] = contributions['id'][i]
rindex.append(contributions['id'][i])
if (withsexe... |
robertoalotufo/ia898 | 2S2018/07 Filtragem no dominio espacial.ipynb | mit | from IPython.display import Image
from IPython.display import display
display(Image(url='../data/convrect.gif'))
"""
Explanation: Filtragem no domínio espacial
A convolução discreta é uma ferramenta para construir qualquer filtro linear ou de deslocamento. A equação da convolução, g(x), da sequência f(x) com o nucleo ... |
computational-class/cjc2016 | code/10.word2vec.ipynb | mit | # Personality Embeddings: What are you like?
jay = [-0.4, 0.8, 0.5, -0.2, 0.3]
john = [-0.3, 0.2, 0.3, -0.4, 0.9]
mike = [-0.5, -0.4, -0.2, 0.7, -0.1]
"""
Explanation: Introduction to Word Embeddings
Analyzing Meaning through Word Embeddings
Using vectors to represent things
- one of the most fascinating ideas in ma... |
DBWangGroupUNSW/COMP9318 | L1 - Pandas-2.ipynb | mit | import pandas as pd
import numpy as np
import plotly.plotly as py
import plotly.graph_objs as go
"""
Explanation: Pivot Table on Pandas
In this notebook, we focus on the pivot_table feature of pandas.
Import Modules
End of explanation
"""
df = pd.read_csv('./asset/sydney_housing_market.txt', sep='\t')
df.head()
"""... |
fastai/fastai | dev_nbs/course/lesson3-imdb.ipynb | apache-2.0 | %matplotlib inline
from fastai.text.all import *
from nbdev.showdoc import *
"""
Explanation: IMDB
End of explanation
"""
path = untar_data(URLs.IMDB_SAMPLE)
path.ls()
"""
Explanation: Preparing the data
First let's download the dataset we are going to study. The dataset has been curated by Andrew Maas et al. and ... |
malogrisard/NTDScourse | toolkit/01_ex_acquisition_exploration.ipynb | mit | # Number of posts / tweets to retrieve.
# Small value for development, then increase to collect final data.
n = 20 # 4000
"""
Explanation: A Python Tour of Data Science: Data Acquisition & Exploration
Michaël Defferrard, PhD student, EPFL LTS2
1 Exercise: problem definition
Theme of the exercise: understand the impac... |
CamDavidsonPilon/lifelines | examples/SaaS churn and piecewise regression models.ipynb | mit | kmf = KaplanMeierFitter().fit(df['T'], df['E'])
kmf.plot(figsize=(11,6));
"""
Explanation: Empircally observed SaaS churn
A subscribtion-as-a-service company has a typical customer churn pattern. During periods of no billing, the churn is relatively low compared to periods of billing (typically every 30 or 365 days). ... |
gutouyu/cs231n | cs231n/assignment/assignment2/Dropout.ipynb | mit | # As usual, a bit of setup
from __future__ import print_function
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_gradient, eval_numerical_gradient_array
from cs231n.solv... |
beangoben/HistoriaDatos_Higgs | Dia4/.ipynb_checkpoints/6_Arboles_de_decisión-Lleno-checkpoint.ipynb | gpl-2.0 | from IPython.core.display import HTML
import os
def css_styling():
"""Load default custom.css file from ipython profile"""
base = os.getcwd()
styles = "<style>\n%s\n</style>" % (open(os.path.join(base,'files/custom.css'),'r').read())
return HTML(styles)
css_styling()
"""
Explanation: <i class="fa fa-di... |
qutip/qutip-notebooks | docs/guide/TensorPtrace.ipynb | lgpl-3.0 | import numpy as np
from qutip import *
"""
Explanation: Tensor Products & Partial Traces
Contents
Tensor Products
Partial Trace
Super Operators & Tensor Manipulations
End of explanation
"""
tensor(basis(2, 0), basis(2, 0))
"""
Explanation: <a id='tensor'></a>
Tensor Products
To describe the states of multipartite ... |
nickmo464/ml-dress-patterns | ComputerVisionPatterns-Copy1.ipynb | mit | # re-load the saved data if needed
A = np.load('/home/nick/Documents/LewisUniversity/MachineLearning/Project/visionmatrix.npy')
#Let's start with the model parameters defined in the Week6 notebook for this data, changing the input shape as appropriate.
from keras.models import Sequential
from keras.layers import Dense... |
dfm/emcee3 | docs/user/parallel.ipynb | mit | import emcee3
import numpy as np
def log_prob(x):
return -0.5 * np.sum(x ** 2)
ndim, nwalkers = 10, 100
with emcee3.pools.InterruptiblePool() as pool:
ensemble = emcee3.Ensemble(log_prob, np.random.randn(nwalkers, ndim), pool=pool)
sampler = emcee3.Sampler()
sampler.run(ensemble, 1000)
"""
Explanatio... |
jnarhan/Breast_Cancer | src/models/JN_BC_Threshold_Diagnosis.ipynb | mit | import os
import sys
import time
import numpy as np
from tqdm import tqdm
import sklearn.metrics as skm
from sklearn import metrics
from sklearn.svm import SVC
from sklearn.model_selection import train_test_split
from skimage import color
import keras.callbacks as cb
import keras.utils.np_utils as np_utils
from ker... |
mlund/openmm-examples | qpotential/qpotential.ipynb | mit | %matplotlib inline
from simtk.openmm.app import *
from simtk.openmm import *
from simtk.unit import *
from sys import stdout
import matplotlib, matplotlib.pyplot as plt
import pandas as pd
from io import StringIO
import numpy as np, os
matplotlib.rcParams.update({'font.size': 12})
# simulation parameters
nsteps =... |
facemelters/data-science | Atlas/Publishing Volume.ipynb | gpl-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
df = pd.read_csv('All content.csv', index_col='Published',parse_dates=True)
df['count']=1
df = df[(df['Page Views'] > 200)]
df_resampled = df.resample('D',how ='sum')
"""
Explanation: Here we examine whether publishing volume h... |
geoscixyz/computation | notebooks/fundamentals/pixels_and_neighbors/weakformulation.ipynb | mit | %matplotlib inline
from SimPEG import Mesh
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots(1)
mesh = Mesh.TensorMesh([1,1])
mesh.plotGrid(ax=ax,centers=True,faces=True)
ax.set_xlim(-0.5,1.5)
ax.set_ylim(-0.5,1.5);
"""
Explanation: Weak Formulation
Equation (b) is a vector equation, so really... |
amitkaps/hackermath | Module_1c_linear_regression_ridge.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
plt.style.use('fivethirtyeight')
plt.rcParams['figure.figsize'] = (10, 6)
pop = pd.read_csv('data/cars_small.csv')
pop.shape
pop.head()
sample_rows = [35,17,11,25,12,22,13]
sample = pop.loc[sample_rows,:]
sample
"""
Explan... |
liangjg/openmc | examples/jupyter/search.ipynb | mit | # Initialize third-party libraries and the OpenMC Python API
import matplotlib.pyplot as plt
import numpy as np
import openmc
import openmc.model
%matplotlib inline
"""
Explanation: This Notebook illustrates the usage of the OpenMC Python API's generic eigenvalue search capability. In this Notebook, we will do a cr... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/feateng/labs/asl_2.0_bqml_feateng.ipynb | apache-2.0 | %%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$PROJECT
import os
PROJECT = "cloud-training-demos" # REPLACE WITH YOUR PROJECT NAME
REGION = "us-west1-b" # REPLACE WITH YOUR BUCKET REGION e.g. us-central1
# Do not change these
os.environ["P... |
samirma/deep-learning | 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... |
krischer/pyadjoint | doc/index.ipynb | bsd-3-clause | import obspy
import pyadjoint
"""
Explanation: Pyadjoint
Pyadjoint is a Python package to measure misfits and calculate adjoint sources. It aims to provide a comprehensive package and incorporate various types of measurements and associated adjoint sources.
Installation
Pyadjoint utilizes ObsPy (and some of its depend... |
211217613/python_meetup | Intro to Jupyter Notebooks.ipynb | unlicense | %%%timeit
maths = list()
for x in range(10):
maths.append(x**x)
%%%timeit
maths = [x**x for x in range(10)]
# maths
"""
Explanation: Jupyter Notebooks
Share the compute process with everyone
How to Install
pip3 install jupyter
How to Run
jupyter notebook
Uses
PDF books
Blog posts
Inline graphics
Multiple langua... |
thiagoqd/queirozdias-deep-learning | 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... |
tcmoore3/mbuild | docs/tutorials/tutorial_simple_LJ.ipynb | mit | import mbuild as mb
class MonoLJ(mb.Compound):
def __init__(self):
super(MonoLJ, self).__init__()
lj_particle1 = mb.Particle(name='LJ', pos=[0, 0, 0])
self.add(lj_particle1)
lj_particle2 = mb.Particle(name='LJ', pos=[1, 0, 0])
self.add(lj_particle2)
lj_particle3 = ... |
BioDocker/containers | pymol-jupiter/1.0.0/ipymol/iPyMol_example.ipynb | apache-2.0 | %pylab inline
from ipymol import viewer as pymol
"""
Explanation: Interacting with PyMol via iPyMol
(example from http://pymolwiki.org/index.php/Gallery )
Initialize
Jupyter needs the %pylab inline directive for showing plots within a notebook
End of explanation
"""
pymol.start()
"""
Explanation: Start the PyMol se... |
ALEXKIRNAS/DataScience | CS231n/assignment1/features.ipynb | mit | import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from __future__ import print_function
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gr... |
bgruening/EDeN | examples/RNA_example.ipynb | gpl-3.0 | %matplotlib inline
"""
Explanation: RNA example
We take a fasta file in input and write utility functions to build a graph representation based on the structure computed by RNAfold. Finally we use the EDeN vectorizer to perform clustering or build a predictive model.
End of explanation
"""
def rfam_uri(family_id):
... |
JarnoRFB/qtpyvis | notebooks/keras/introspection.ipynb | mit | model.get_config()
"""
Explanation: Keras model are serialzed in a JSON format.
End of explanation
"""
# Weights and biases of the entire model.
model.get_weights()
# Weights and bias for a single layer.
conv_layer = model.get_layer('conv2d_1')
conv_layer.get_weights()
"""
Explanation: Getting the weights
Weights ... |
AdriaanRol/AutoDepGraph | examples/Example notebook.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import networkx as nx
from importlib import reload
import os
import autodepgraph as adg
from autodepgraph import AutoDepGraph_DAG
"""
Explanation: Example notebook
This example will contain the following examples
- Creating and saving a graph
- Plotting the graph
-... |
rnder/data-science-from-scratch | notebook/ch20_natural_language_processing.ipynb | unlicense | import math, random, re
from collections import defaultdict, Counter
from bs4 import BeautifulSoup
import requests
import matplotlib.pyplot as plt
#데이터 과학 관련 키워드목록, 빈도 0~100
data = [ ("big data", 100, 15), ("Hadoop", 95, 25), ("Python", 75, 50),
("R", 50, 40), ("machine learning", 80, 20), ("statistics", 20, ... |
mne-tools/mne-tools.github.io | 0.22/_downloads/3ca4dabff6bcea5b8da3afc9052669d2/plot_45_projectors_background.ipynb | bsd-3-clause | import os
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D # noqa
from scipy.linalg import svd
import mne
def setup_3d_axes():
ax = plt.axes(projection='3d')
ax.view_init(azim=-105, elev=20)
ax.set_xlabel('x')
ax.set_ylabel('y')
ax.set_zlabel('z')
ax.... |
OSGeoLabBp/tutorials | english/data_processing/lessons/ransac_sphere.ipynb | cc0-1.0 | import numpy as np
from math import sin, cos, pi, sqrt
from random import random, seed
import plotly.graph_objects as go
"""
Explanation: <a href="https://colab.research.google.com/github/OSGeoLabBp/tutorials/blob/master/english/data_processing/lessons/ransac_sphere.ipynb" target="_parent"><img src="https://colab.rese... |
probml/pyprobml | notebooks/book2/17/gp_kernel_opt.ipynb | mit | try:
import tinygp
except ImportError:
!pip install -q tinygp
try:
import numpyro
except ImportError:
!pip uninstall -y jax jaxlib
!pip install -q numpyro jax jaxlib
try:
import arviz
except ImportError:
!pip install arviz
try:
import flax
except ImportError:
!pip install -q flax
... |
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies | ex19-EOF analysis in central pacific ocean.ipynb | mit | % matplotlib inline
import numpy as np
from scipy import signal
import numpy.polynomial.polynomial as poly
from netCDF4 import Dataset
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
from eofs.standard import Eof
"""
Explanation: EOF analysis in central pacific ocean
In statistics and signal... |
openai/openai-python | examples/finetuning/olympics-1-collect-data.ipynb | mit | import pandas as pd
import wikipedia
def filter_olympic_2020_titles(titles):
"""
Get the titles which are related to Olympic games hosted in 2020, given a list of titles
"""
titles = [title for title in titles if '2020' in title and 'olympi' in title.lower()]
return titles
def get_wiki_page(... |
ssunkara1/bqplot | examples/Interactions/Interaction Layer.ipynb | apache-2.0 | ## First we define a Figure
dt_x_fast = DateScale()
lin_y = LinearScale()
x_ax = Axis(label='Index', scale=dt_x_fast)
x_ay = Axis(label=(symbol + ' Price'), scale=lin_y, orientation='vertical')
lc = Lines(x=dates_actual, y=prices, scales={'x': dt_x_fast, 'y': lin_y}, colors=['orange'])
lc_2 = Lines(x=dates_actual[50:]... |
jmschrei/pomegranate | tutorials/old/Tutorial_7_Parallelization.ipynb | mit | %pylab inline
from sklearn.mixture import GaussianMixture
from pomegranate import *
import seaborn, time
seaborn.set_style('whitegrid')
def create_dataset(n_samples, n_dim, n_classes, alpha=1):
"""Create a random dataset with n_samples in each class."""
X = numpy.concatenate([numpy.random.normal(i*alpha, ... |
ubcgif/gpgTutorials | notebooks/em/FDEM_ThreeLoopModel.ipynb | mit | %matplotlib inline
from geoscilabs.em.FDEM3loop import interactfem3loop
from geoscilabs.em.FDEMpipe import interact_femPipe
from matplotlib import rcParams
rcParams['font.size'] = 14
"""
Explanation: Electromagnetics: 3-loop model
In the first part of this notebook, we consider a 3 loop system, consisting of a transm... |
dchandan/rebound | ipython_examples/PoincareMap.ipynb | gpl-3.0 | import rebound
import numpy as np
"""
Explanation: Poincare Map
This example shows how to calculate a simple Poincare Map with REBOUND. A Poincare Map (or sometimes calles Poincare Section) can be helpful to understand dynamical systems.
End of explanation
"""
sim = rebound.Simulation()
sim.add(m=1.)
sim.add(m=1e-3,... |
paolorivas/homeworkfoundations | 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 ... |
rbiswas4/simlib | example/UsingOpSimSummary.ipynb | mit | import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
# Required packages sqlachemy, pandas (both are part of anaconda distribution, or can be installed with a python installer)
# One step requires the LSST stack, can be skipped for a particular OPSIM database in question
import OpSimSummary.summarize... |
mbonix/finance-notebooks | Volatility.ipynb | mit | import pandas as pd
import pandas_datareader.data as web
import matplotlib.pyplot as plt
# Defines the chart color scheme using Tableu's Tableau10
plt.style.use('https://gist.githubusercontent.com/mbonix/8478091db6a2e6836341c2bb3f55b9fc/raw/7155235ed03e235c38b66c160d402192ad4d94d9/tableau10.mplstyle')
%matplotlib in... |
qkitgroup/qkit | qkit/doc/notebooks/measurement_class_basics.ipynb | gpl-2.0 | ## start qkit and import the necessary classes; here we assume a already configured qkit environment
import qkit
qkit.start()
from qkit.measure.measurement_class import Measurement
"""
Explanation: Measurement class basics
Objects of the measurement class are used to save all the information for one single measureme... |
tylere/earthengine-api | python/examples/ipynb/EarthEngineColabInstall.ipynb | apache-2.0 | !pip install earthengine-api
"""
Explanation: Copyright 2018 Google LLC.
SPDX-License-Identifier: Apache-2.0
Earth Engine Colab installation
This notebook demonstrates a simple installation of Earth Engine to a Colab notebook.
Colab setup
This notebook section installs the Earth Engine Python API on your Colab virtual... |
ajrichards/phylogenetic-models | lda/simon-high-low-example.ipynb | bsd-3-clause | from IPython.display import Image
Image(filename='lda_plate.png')
"""
Explanation: LDA on simon's example
The LDA is a well-known probabilistic model to handle mixtures of topics in an unsupervised way. It has been applied to a large number of problems (Blei, 2012). The original paper has over 10000 citations (Blei... |
andreyf/machine-learning-examples | numpy_and_pandas/habr_pandas.ipynb | gpl-3.0 | import numpy as np
import pandas as pd
"""
Explanation: <center>
<img src="../img/ods_stickers.jpg">
Открытый курс по машинному обучению
Авторы материала: программист-исследователь Mail.ru Group, старший преподаватель Факультета Компьютерных Наук ВШЭ Юрий Кашницкий и Data Scientist в Segmento Екатерина Демидова. Матер... |
mediagit2016/workcamp-maschinelles-lernen-grundlagen | 20-10-14-ml-workcamp/wc-arbeiten-tf-10-aufgabe-loes.ipynb | gpl-3.0 | #importieren sie die Bibliothek pandas
import pandas as pd
#importieren sie matplotlib.pyplot as plt
import matplotlib.pyplot as plt
%matplotlib inline
#laden Sie die Datei "daten.csv" auf Ihren Hub
#laden Sie die Datei "daten.csv" in einen Datframe df
#Einlesen der Dateien
df=pd.read_csv("daten.csv")
#Betrachten Si... |
madmax983/h2o-3 | h2o-py/demos/H2O_tutorial_medium.ipynb | apache-2.0 | import pandas as pd
import numpy
from numpy.random import choice
from sklearn.datasets import load_boston
from h2o.estimators.random_forest import H2ORandomForestEstimator
import h2o
h2o.init()
# transfer the boston data from pandas to H2O
boston_data = load_boston()
X = pd.DataFrame(data=boston_data.data, columns=b... |
ES-DOC/esdoc-jupyterhub | notebooks/noaa-gfdl/cmip6/models/gfdl-esm4/seaice.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'gfdl-esm4', 'seaice')
"""
Explanation: ES-DOC CMIP6 Model Properties - Seaice
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: GFDL-ESM4
Topic: Seaice
Sub-Topics: Dynamics, Thermodyna... |
Py101/py101-assignments-marcosco | 02/homework_day2.ipynb | mit | for x in range(13):
if x==0:
fact = 1
else:
fact = fact * x
fact
"""
Explanation: <center>
<hr>
<h1>Python Crash Course</h1>
<h2>Master in Data Science - Sapienza University</h2>
<h2>Homework 2: Python Challenges</h2>
<h3>A.A. 2017/18</h3>
<h3>Tutor: Francesco Fabbri</h3>
<hr>
</center>
Instru... |
fsilva/deputado-histogramado | notebooks/Deputado-Histogramado-1.ipynb | gpl-3.0 | from urllib import request
import zlib
import pandas
from bs4 import BeautifulSoup #para processar o HTML
import re #para processar o html
lista_datas = []
lista_sessoes = []
bytesTransferidos = 0
i = 0
for ano in range(1976,2016):
for mes in range(1,13):
print("Processando %04d/%02d - bytes transferid... |
jastarex/DeepLearningCourseCodes | 05_Image_recognition_and_classification/cnn.ipynb | apache-2.0 | import time
import math
import random
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
import dataset
import cv2
from sklearn.metrics import confusion_matrix
from datetime import timedelta
%matplotlib inline
"""
Explanation: 卷积神经网络 - 图像分类与识别 - TensorFlow实现
需要Anaconda3-1... |
oseledets/fastpde2017 | lectures/laplace_interior_dirichlet_original.ipynb | mit | import bempp.api
import numpy as np
"""
Explanation: Solving a Laplace problem with Dirichlet boundary conditions
Background
Laplace problem inside the unit sphere with Dirichlet boundary conditions. Let $\Omega$ be the unit sphere with boundary $\Gamma$. Let $\nu$ be the outward pointing normal on $\Gamma$. The PDE ... |
Jackporter415/phys202-2015-work | days/day11/Interpolation.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
"""
Explanation: Interpolation
Learning Objective: Learn to interpolate 1d and 2d datasets of structured and unstructured points using SciPy.
End of explanation
"""
x = np.linspace(0,4*np.pi,10)
x
"""
Explanation: Overview
W... |
40323150/2016springcd_aG9 | static/plugin/liquid_tags/test_data/content/notebooks/test_nbformat4.ipynb | agpl-3.0 | # <!-- collapse=True -->
a = 1
b = 1.0
print(a, type(a), b, type(b))
"""
Explanation: Il s’agit de repérer les données nécessaires voire indispensables à la
résolution. Ces données peuvent être:
numériques,
ou sous forme de textes (on dit souvent chaînes de caractères),
ou de type logique (à deux valeurs possibles, ... |
smorton2/think-stats | code/chap12soln.ipynb | gpl-3.0 | from __future__ import print_function, division
%matplotlib inline
import warnings
warnings.filterwarnings('ignore', category=FutureWarning)
import numpy as np
import pandas as pd
import random
import thinkstats2
import thinkplot
"""
Explanation: Examples and Exercises from Think Stats, 2nd Edition
http://thinkst... |
kinverarity1/lasio | notebooks/build LAS file from scratch.ipynb | mit | import lasio
import datetime
import numpy
import os
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Building a LAS file from scratch
End of explanation
"""
depths = numpy.arange(10, 50, 0.5)
fake_curve = numpy.random.random(len(depths))
fake_curve[-10:] = numpy.nan # Add some null values at th... |
sk-rai/Network-Analysis_made-Simple | 7. Bipartite Graphs (Student).ipynb | mit | import networkx as nx
from networkx.algorithms import bipartite
# Initialize the city/person bipartite graph.
B = nx.Graph()
cities = [] # populate a list of cities
people = [] # populate a list of people's names
B.add_nodes_from(cities, bipartite='cities')
B.add_nodes_from(people, bipartite='people')
edges = [] #... |
hail-is/hail | notebook/images/hail/resources/Hail-Workshop-Notebook.ipynb | mit | print('Hello, world')
"""
Explanation: Hail workshop
This notebook will introduce the following concepts:
Using Jupyter notebooks effectively
Loading genetic data into Hail
General-purpose data exploration functionality
Plotting functionality
Quality control of sequencing data
Running a Genome-Wide Association Study ... |
millernj/phys202-project | [1]Making Perceptrons.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
"""
Explanation: Perceptrons
Here we will create a functions the make Peceptrons, the processors that make up a Neural Network.
End of explanation
"""
step_function = lambda x: 1 if not x < 0 else 0
# The step function will be used to output the n... |
WomensCodingCircle/CodingCirclePython | Lesson05_Strings/Strings.ipynb | mit | chinese_zodiac = "Rat Ox Tiger Rabbit Dragon Snake Horse Goat Monkey Rooster Dog Pig"
print(chinese_zodiac[0])
print(chinese_zodiac[1])
"""
Explanation: Strings
A sting is a sequence of characters. A string's characters can be treated similarly to a list. Each character has an index starting at 0 and can be accessed j... |
azhurb/deep-learning | first-neural-network/Your_first_neural_network.ipynb | mit | %matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
"""
Explanation: Your first neural network
In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code... |
rochefort-lab/fissa | examples/cNMF example.ipynb | gpl-3.0 | # FISSA package
import fissa
# For plotting our results, import numpy and matplotlib
import matplotlib.pyplot as plt
import numpy as np
# Need this utility from scipy to load data from matfiles
from scipy.io import loadmat
"""
Explanation: Using FISSA with CNMF from MATLAB
CNMF is blind source separation toolbox for... |
cdt15/lingam | examples/LongitudinalLiNGAM.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import graphviz
import lingam
from lingam.utils import print_causal_directions, print_dagc, make_dot
import warnings
warnings.filterwarnings('ignore')
print([np.__version__, pd.__version__, graphviz.__version__, lingam.__version__])
np.set_printo... |
ibm-cds-labs/simple-data-pipe-connector-flightstats | notebook/Flight Predict PyCon 2016.ipynb | apache-2.0 | sc.addPyFile("https://github.com/ibm-watson-data-lab/simple-data-pipe-connector-flightstats/raw/master/flightPredict/training.py")
sc.addPyFile("https://github.com/ibm-watson-data-lab/simple-data-pipe-connector-flightstats/raw/master/flightPredict/run.py")
import training
import run
%matplotlib inline
from pyspark.mll... |
ioos/notebooks_demos | notebooks/2016-11-15-glider_data_example.ipynb | mit | # See https://github.com/Unidata/netcdf-c/issues/1299 for the explanation of `#fillmismatch`.
url = (
"https://data.ioos.us/thredds/dodsC/deployments/rutgers/"
"ru29-20150623T1046/ru29-20150623T1046.nc3.nc#fillmismatch"
)
import iris
iris.FUTURE.netcdf_promote = True
glider = iris.load(url)
print(glider)
... |
GoogleCloudPlatform/ai-notebooks-extended | dataproc-hub-example/build/infrastructure-builder/mig/files/gcs_working_folder/examples/Python/bigquery/BigQuery query magic.ipynb | apache-2.0 | %%bigquery
SELECT name, SUM(number) as count
FROM `bigquery-public-data.usa_names.usa_1910_current`
GROUP BY name
ORDER BY count DESC
LIMIT 10
"""
Explanation: BigQuery query magic
Jupyter magics are notebook-specific shortcuts that allow you to run commands with minimal syntax. Jupyter notebooks come with many built-... |
ozorich/phys202-2015-work | assignments/midterm/AlgorithmsEx03.ipynb | mit | %matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact
"""
Explanation: Algorithms Exercise 3
Imports
End of explanation
"""
def char_probs(s):
"""Find the probabilities of the unique characters in the string s.
Parameters
----------
... |
astroumd/GradMap | notebooks/Lectures2017/Lecture3/Lecture 3 - Answer Key.ipynb | gpl-3.0 | np.random.rand(5)
"""
Explanation: Random Numbers
Documentation: https://docs.scipy.org/doc/numpy/reference/routines.random.html
Numpy gives us the ability to select random numbers from a variety of different distribution functions (see the np.random documentation for a full list). However, the two most frequently use... |
quantopian/research_public | notebooks/lectures/Violations_of_Regression_Models/notebook.ipynb | apache-2.0 | # Import all the libraries we'll be using
import numpy as np
import statsmodels.api as sm
from statsmodels import regression, stats
import statsmodels
import matplotlib.pyplot as plt
residuals = np.random.normal(0, 1, 100)
_, pvalue, _, _ = statsmodels.stats.stattools.jarque_bera(residuals)
print pvalue
residuals = ... |
AlphaGit/deep-learning | dcgan-svhn/DCGAN.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... |
mne-tools/mne-tools.github.io | 0.23/_downloads/7b89f7dac105a44e25d2fbdd898b911f/vector_mne_solution.ipynb | bsd-3-clause | # Author: Marijn van Vliet <w.m.vanvliet@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
print(__doc__)
data_path = sample.data_path()
subjects_dir = data_path + '/subjects'
# Read evoked data
fnam... |
jklynch/mr-fitty | notebooks/sample_data_clustering.ipynb | mit | import glob
import itertools
import logging
from operator import attrgetter
import os
import pprint
import sys
import time
import matplotlib.pyplot as plt
%matplotlib inline
import scipy.misc
import numpy as np
import pandas as pd
import scipy.cluster.hierarchy as hc
from scipy.spatial.distance import pdist
import s... |
gaufung/PythonStandardLibrary | cryptography/hmac.ipynb | mit | import hmac
digest_maker = hmac.new(b'secret-shared-key-goes-here')
with open('hmac.ipynb', 'rb') as f:
while True:
block = f.read(1024)
if not block:
break
digest_maker.update(block)
digest = digest_maker.hexdigest()
print(digest)
"""
Explanation: The HMAC algorithm can be u... |
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