repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
dmlc/mxnet | example/multi-task/multi-task-learning.ipynb | apache-2.0 | import logging
import random
import time
import matplotlib.pyplot as plt
import mxnet as mx
from mxnet import gluon, nd, autograd
import numpy as np
"""
Explanation: Multi-Task Learning Example
This is a simple example to show how to use mxnet for multi-task learning.
The network is jointly going to learn whether a n... |
crystalzhaizhai/cs207_yi_zhai | lectures/L3/L3-Part2.ipynb | mit | %%bash
cd /tmp
rm -rf playground #remove if it exists
git clone https://github.com/dsondak/playground.git
%%bash
cd /tmp/playground
git branch -avv
"""
Explanation: Lecture 3: Branches with Git
In Lecture 2, you worked with the playground repository. You learned how to navigate the repository from the Git point of v... |
ForestClaw/forestclaw | applications/clawpack/advection/2d/periodic/periodic.ipynb | bsd-2-clause | %%bash
periodic
"""
Explanation: Periodic
<hr style="border-width:4px; border-color:coral">
</hr>
Scalar advection problem of a disk in a periodic domain
Serial mode
<hr style="border-width:2px; border-color:coral">
</hr>
Run code in serial mode (will work, even if code is compiled with MPI)
End of explanation
"""
... |
google/learned_optimization | docs/notebooks/Part4_GradientEstimators.ipynb | apache-2.0 | import numpy as np
import jax.numpy as jnp
import jax
import functools
from matplotlib import pylab as plt
from typing import Optional, Tuple, Mapping
from learned_optimization.outer_trainers import full_es
from learned_optimization.outer_trainers import truncated_pes
from learned_optimization.outer_trainers import tr... |
obust/Pandas-Tutorial | Pandas II - Working with DataFrames.ipynb | mit | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
pd.set_option('max_columns', 50)
"""
Explanation: Pandas II - Working with DataFrames
End of explanation
"""
# pass in column names for each CSV
u_cols = ['user_id', 'age', 'sex', 'occupation', 'zip_code']
df_users = pd.read_csv('data/MovieLens-... |
Davidwwww/intro-numerical-methods | 5_root_finding_optimization.ipynb | mit | def total_value(P, m, r, n):
"""Total value of portfolio given parameters
Based on following formula:
A = \frac{P}{(r / m)} \left[ \left(1 + \frac{r}{m} \right)^{m \cdot n}
- 1 \right ]
:Input:
- *P* (float) - Payment amount per compounding period
- *m* (int) - ... |
nilbody/h2o-3 | h2o-py/demos/turbofan_phm_gtkerror_NOPASS.ipynb | apache-2.0 | import sys
import h2o
from h2o.estimators.glm import H2OGeneralizedLinearEstimator
from h2o.estimators.gbm import H2OGradientBoostingEstimator
from h2o.utils.shared_utils import _locate
import numpy as np
import pandas as pd
import seaborn as sns
import pykalman as pyk
sns.set()
doGridSearch = True
doKalmanSmoothing... |
KrisCheng/ML-Learning | archive/MOOC/Deeplearning_AI/ImprovingDeepNeuralNetworks/OptimizationMethods/Optimization+methods.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
import scipy.io
import math
import sklearn
import sklearn.datasets
from opt_utils import load_params_and_grads, initialize_parameters, forward_propagation, backward_propagation
from opt_utils import compute_cost, predict, predict_dec, plot_decision_boundary, load_data... |
pycomlink/pycomlink | notebooks/outdated_notebooks/Baseline determination.ipynb | bsd-3-clause | cml = pycml.io.examples.read_one_cml()
# Remove artifacts and plot data
cml.process.quality_control.set_to_nan_if('tx', '>=', 100)
cml.process.quality_control.set_to_nan_if('rx', '==', -99.9)
cml.plot_data(['tx', 'rx', 'txrx']);
"""
Explanation: Read in example data from one CML
End of explanation
"""
cml.process.... |
Danghor/Formal-Languages | ANTLR4-Python/SLR-Parser-Generator/Shift-Reduce-Parser-Pure.ipynb | gpl-2.0 | import re
"""
Explanation: A Shift-Reduce Parser for Arithmetic Expressions
In this notebook we implement a simple recursive descend parser for arithmetic expressions.
This parser will implement the following grammar:
$$
\begin{eqnarray}
\mathrm{expr} & \rightarrow & \mathrm{expr}\;\;\texttt{'+'}\;\;\mathrm... |
DylanM-Marshall/FIDDLE | fiddle/predictions_visualization.ipynb | gpl-3.0 | %matplotlib inline
from matplotlib import pylab as pl
from scipy import stats
import numpy as np
import pandas as pd
import h5py
from matplotlib.backends.backend_pdf import PdfPages
"""
Explanation: FIDDLE Predictions Visualization Tutorial:
This notebook outlines how to create graphs from the data in the output files... |
poldrack/fmri-analysis-vm | analysis/RTmodeling/RTmodeling.ipynb | mit | import numpy as np
import pandas as pd
import nibabel
from nipy.modalities.fmri.hemodynamic_models import spm_hrf
import matplotlib.pyplot as plt
import nipype.algorithms.modelgen as model # model generation
from nipype.interfaces.base import Bunch
from nipype.interfaces import fsl
from statsmodels.tsa.arima_process ... |
hungiyang/StatisticalMethods | examples/SDSScatalog/GalaxySizes.ipynb | gpl-2.0 | %load_ext autoreload
%autoreload 2
from __future__ import print_function
import numpy as np
import SDSS
import pandas as pd
import matplotlib
%matplotlib inline
galaxies = "SELECT top 1000 \
petroR50_i AS size, \
petroR50Err_i AS err \
FROM PhotoObjAll \
WHERE \
(type = '3' AND petroR50Err_i > 0)"
print (galaxies)
#... |
muxuezi/jupyterworkflow | 101basic/001_Pandas_vs_SQL.ipynb | mit | import pandas as pd
import numpy as np
url = 'https://raw.github.com/pandas-dev/pandas/master/pandas/tests/data/tips.csv'
tips = pd.read_csv(url)
tips.head()
"""
Explanation: Why is Python Growing So Quickly?
<center><img width=512 src=https://zgab33vy595fw5zq-zippykid.netdna-ssl.com/wp-content/uploads/2017/09/relate... |
liganega/Gongsu-DataSci | previous/notes2017/W03/GongSu06_Errors_and_Exception_Handling.ipynb | gpl-3.0 | from __future__ import print_function
input_number = raw_input("A number please: ")
number = int(input_number)
print("제곱의 결과는", number**2, "입니다.")
"""
Explanation: 오류 및 예외 처리
개요
코딩할 때 발생할 수 있는 다양한 오류 살펴 보기
오류 메시지 정보 확인 방법
예외 처리, 즉 오류가 발생할 수 있는 예외적인 상황을 미리 고려하는 방법 소개
오늘의 주요 예제
아래 코드는 raw_input() 함수를 이용하여 사용자로... |
ireapps/cfj-2017 | completed/02. Working with data files.ipynb | mit | import csv
"""
Explanation: Working with data files
Reading and writing data files is a common task, and Python offers native support for working with many kinds of data files. Today, we're going to be working mainly with CSVs.
Import the csv module
We're going to be working with delimited text files, so the first thi... |
jamiebull1/eppy | docs/runningeplus.ipynb | mit | # you would normaly install eppy by doing
# python setup.py install
# or
# pip install eppy
# or
# easy_install eppy
# if you have not done so, uncomment the following three lines
import sys
# pathnameto_eppy = 'c:/eppy'
pathnameto_eppy = '../'
sys.path.append(pathnameto_eppy)
from eppy.modeleditor import IDF
iddfil... |
mne-tools/mne-tools.github.io | stable/_downloads/88563c785f9a977b7ce2000e660aeacf/30_annotate_raw.ipynb | bsd-3-clause | import os
from datetime import timedelta
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False)
raw.crop(tmax=60)... |
kubeflow/katib | examples/v1beta1/sdk/nas-with-darts.ipynb | apache-2.0 | # Install required package (Katib SDK).
!pip install kubeflow-katib==0.13.0
"""
Explanation: Neural Architecture Search with DARTS
In this example you will deploy Katib Experiment with Differentiable Architecture Search (DARTS) algorithm using Jupyter Notebook and Katib SDK. Your Kubernetes cluster must have at least ... |
zakandrewking/cobrapy | documentation_builder/io.ipynb | lgpl-2.1 | import cobra.test
import os
from os.path import join
data_dir = cobra.test.data_dir
print("mini test files: ")
print(", ".join(i for i in os.listdir(data_dir) if i.startswith("mini")))
textbook_model = cobra.test.create_test_model("textbook")
ecoli_model = cobra.test.create_test_model("ecoli")
salmonella_model = cob... |
XInterns/IPL-Sparkers | src/Match Outcome Prediction with IPL Data (Gursahej).ipynb | mit | # The %... is an iPython thing, and is not part of the Python language.
# In this case we're just telling the plotting library to draw things on
# the notebook, instead of on a separate window.
%matplotlib inline
#this line above prepares IPython notebook for working with matplotlib
# See all the "as ..." contructs? ... |
ini-python-course/ss15 | notebooks/PCA and Eigenfaces.ipynb | mit | # prepare some imports
import numpy as np
from sklearn.datasets import fetch_mldata
import matplotlib.pyplot as plt
%matplotlib inline
"""
Explanation: Principal Component Analysis & Eigenfaces
Here we are going to introduce and implement Principal Component Analysis (PCA) which is a very common and important algorith... |
mimoralea/applied-reinforcement-learning | notebooks/05-state-discretization.ipynb | mit | import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import tempfile
import base64
import pprint
import json
import sys
import gym
import io
from gym import wrappers
from subprocess import check_output
from IPython.display import HTML
"""
Explanation: State Space Discretization
From the previous no... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-3/cmip6/models/sandbox-1/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-3', 'sandbox-1', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: TEST-INSTITUTE-3
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical... |
tensorflow/examples | courses/udacity_intro_to_tensorflow_for_deep_learning/l08c07_forecasting_with_stateful_rnn.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... |
tensorflow/docs-l10n | site/en-snapshot/hub/tutorials/tf2_semantic_approximate_nearest_neighbors.ipynb | apache-2.0 | # Copyright 2018 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... |
DistrictDataLabs/yellowbrick | examples/jkeung/testing.ipynb | apache-2.0 | import numpy as np
import matplotlib.pyplot as plt
from sklearn import svm, datasets
from sklearn.metrics import roc_curve, auc
from sklearn.model_selection import train_test_split
"""
Explanation: ROC Curve Example
Inspired by: http://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html
This is an exa... |
Merinorus/adaisawesome | Homework/04 - Applied ML/Homework_04_Referees_teamawesome-Q1 + Bonus.ipynb | gpl-3.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Import the random forest package
from sklearn.ensemble import RandomForestClassifier
filename ="CrowdstormingDataJuly1st.csv"
Data = pd.read_csv(filename)
"""
Explanation: I. Setting up the Problem
End of explanation
"""
Data.ix[:10,:13]
D... |
antongrin/EasyMig | EasyMig_v4-interact3.ipynb | apache-2.0 | # -*- coding: utf-8 -*-
"""
Created on Fri Feb 12 13:21:45 2016
@author: GrinevskiyAS
"""
from __future__ import division
import numpy as np
from numpy import sin,cos,tan,pi,sqrt
import matplotlib as mpl
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from ipywidgets import interact, interactive, fixed
im... |
WNoxchi/Kaukasos | FAI_old/lesson1/dogs_cats_redux.ipynb | mit | #Verify we are in the lesson1 directory
%pwd
#Create references to important directories we will use over and over
import os, sys
current_dir = os.getcwd()
LESSON_HOME_DIR = current_dir
# DATA_HOME_DIR = current_dir+'/data/redux'
DATA_HOME_DIR = current_dir+'/data'
#Allow relative imports to directories above lesson1... |
ghvn7777/ghvn7777.github.io | content/fluent_python/21_metaclass.ipynb | apache-2.0 | class Dog:
def __init__(self, name, weight, owner):
self.name = name
self.weight = weight
self.owner = owner
rex = Dog('Rex', 30, 'Bob')
rex
"""
Explanation: 类元编程是指在运行时创建或定制类的技艺，在 Python 中，类是一等对象，因此任何时候都可以使用函数新建类，无需使用 class 关键字。类装饰器也是函数，不公审查，修改甚至可以把被装饰类替换成其它类。最后，元类是类元编程最高级的工具，使用元类... |
gtrichards/QuasarSelection | SpIESHighzQuasars2.ipynb | mit | %matplotlib inline
from astropy.table import Table
import numpy as np
import matplotlib.pyplot as plt
data = Table.read('GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean.fits')
# X is in the format need for all of the sklearn tools, it just has the colors
# X = np.vstack([ data['ug'], data['gr'], data['ri'], data['i... |
analysiscenter/dataset | examples/experiments/weights_distributions/weights_distributions.ipynb | apache-2.0 | import sys
sys.path.append('../../utils')
import pickle
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from utils import plot_weights
"""
Explanation: Distributions of weights in ResNet34 and ResNet50
In this notebook we will compare the distribution of weights from two almost identical arc... |
sbu-python-summer/python-tutorial | day-5/scipy-exercises.ipynb | bsd-3-clause | def hilbert(n):
""" return a Hilbert matrix, H_ij = (i + j - 1)^{-1} """
H = np.zeros((n,n), dtype=np.float64)
for i in range(1, n+1):
for j in range(1, n+1):
H[i-1,j-1] = 1.0/(i + j - 1.0)
return H
"""
Explanation: Q1: integrating a sampled vs. analytic function
Numerical integra... |
erikdrysdale/erikdrysdale.github.io | _rmd/extra_power/winners_curse.ipynb | mit | # modules used in the rest of the post
from scipy.stats import norm, truncnorm
import numpy as np
from numpy.random import randn
from scipy.optimize import minimize_scalar
import plotnine
from plotnine import *
import pandas as pd
"""
Explanation: A winner's curse adjustment for a single test statistic
Background
The ... |
machinelearningnanodegree/stanford-cs231 | solutions/levin/assignment1/two_layer_net.ipynb | mit | # A bit of setup
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.neural_net import TwoLayerNet
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots
plt.rcParams['image.interpolation'] = 'nearest'
plt.rcParams['image.cmap'] = 'gray'
# for auto-reloadi... |
joshnsolomon/phys202-2015-work | assignments/assignment09/IntegrationEx02.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy import integrate
"""
Explanation: Integration Exercise 2
Imports
End of explanation
"""
def integrand(x, a):
return 1.0/(x**2 + a**2)
def integral_approx(a):
# Use the args keyword argument to feed extra a... |
mne-tools/mne-tools.github.io | 0.24/_downloads/0a1bad60270bfbdeeea274fcca0015d2/multidict_reweighted_tfmxne.ipynb | bsd-3-clause | # Author: Mathurin Massias <mathurin.massias@gmail.com>
# Yousra Bekhti <yousra.bekhti@gmail.com>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import os.path as op
import mne
from mne.datasets import somato
f... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/introduction_to_tensorflow/solutions/what_if_mortgage.ipynb | apache-2.0 | import sys
python_version = sys.version_info[0]
print("Python Version: ", python_version)
!pip3 install witwidget
import pandas as pd
import numpy as np
import witwidget
from witwidget.notebook.visualization import WitWidget, WitConfigBuilder
"""
Explanation: LABXX: What-if Tool: Model Interpretability Using Mortga... |
tpin3694/tpin3694.github.io | regex/match_any_character.ipynb | mit | # Load regex package
import re
"""
Explanation: Title: Match Any Character
Slug: match_any_character
Summary: Match Any Character
Date: 2016-05-01 12:00
Category: Regex
Tags: Basics
Authors: Chris Albon
Based on: Regular Expressions Cookbook
Preliminaries
End of explanation
"""
# Create a variable containing a tex... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/introduction_to_tensorflow/solutions/3_keras_sequential_api_vertex.ipynb | apache-2.0 | import datetime
import os
import shutil
import numpy as np
import pandas as pd
import tensorflow as tf
from google.cloud import aiplatform
from matplotlib import pyplot as plt
from tensorflow import keras
from tensorflow.keras.callbacks import TensorBoard
from tensorflow.keras.layers import Dense, DenseFeatures
from t... |
mbbrodie/fmri_reconstruction | tools/miyawaki_eigenbrain.ipynb | mit | # Some basic imports
import time
import sys
import eigenbrain
"""
Explanation: Reconstruction of visual stimuli from Miyawaki et al. 2008
This example reproduces the experiment presented in
Visual image reconstruction from human brain activity
using a combination of multiscale local image decoders
<http... |
datascience-practice/data-quest | python_introduction/beginner/.ipynb_checkpoints/Dictionaries-checkpoint.ipynb | mit | # Let's parse the data from the last mission as an example.
# First, we open the wait times file from the last mission.
f = open("crime_rates.csv", 'r')
data = f.read()
rows = data.split('\n')
full_data = []
for row in rows:
split_row = row.split(",")
full_data.append(split_row)
weather_data = []
f = open("la_... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb | apache-2.0 | import os
# Google Cloud Notebook
if os.path.exists("/opt/deeplearning/metadata/env_version"):
USER_FLAG = "--user"
else:
USER_FLAG = ""
! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG
"""
Explanation: Vertex SDK: AutoML training tabular binary classification model for batch explanation
<table al... |
tpin3694/tpin3694.github.io | machine-learning/random_forest_classifier_example.ipynb | mit | # Load the library with the iris dataset
from sklearn.datasets import load_iris
# Load scikit's random forest classifier library
from sklearn.ensemble import RandomForestClassifier
# Load pandas
import pandas as pd
# Load numpy
import numpy as np
# Set random seed
np.random.seed(0)
"""
Explanation: Title: Random F... |
xpharry/Udacity-DLFoudation | tutorials/sentiment_network/Sentiment Classification - Mini Project 4.ipynb | mit | def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].upper(),g.readlines()))
g.close()... |
zakandrewking/cobrapy | documentation_builder/building_model.ipynb | lgpl-2.1 | from __future__ import print_function
from cobra import Model, Reaction, Metabolite
# Best practise: SBML compliant IDs
model = Model('example_model')
reaction = Reaction('3OAS140')
reaction.name = '3 oxoacyl acyl carrier protein synthase n C140 '
reaction.subsystem = 'Cell Envelope Biosynthesis'
reaction.lower_bound... |
kowey/attelo | doc/tut_parser2.ipynb | gpl-3.0 | from __future__ import print_function
from os import path as fp
from attelo.io import (load_multipack)
CORPUS_DIR = 'example-corpus'
PREFIX = fp.join(CORPUS_DIR, 'tiny')
# load the data into a multipack
mpack = load_multipack(PREFIX + '.edus',
PREFIX + '.pairings',
PREFI... |
shumway/srt_bootcamp | SymPyExample.ipynb | mit | import sympy as sp
sp.init_printing()
"""
Explanation: SymPy
The SymPy package is useful for symbolic algebra, much like the commercial software Mathematica.
We won't make much use of SymPy during the boot camp, but it is definitely useful to know about
for mathematics courses.
End of explanation
"""
x, y = sp.symbo... |
tritemio/multispot_paper | usALEX - Corrections - Leakage fit.ipynb | mit | #bsearch_ph_sel = 'all-ph'
#bsearch_ph_sel = 'Dex'
bsearch_ph_sel = 'DexDem'
data_file = 'results/usALEX-5samples-PR-raw-%s.csv' % bsearch_ph_sel
"""
Explanation: Leakage coefficient fit
This notebook estracts the leakage coefficient from the set of 5 us-ALEX smFRET measurements.
What it does?
For each measurement,... |
atulsingh0/MachineLearning | MasteringML_wSkLearn/07_Dimensionality_Reduction_with_PCA.ipynb | gpl-3.0 | # import
import numpy as np
import matplotlib.pyplot as plt
from sklearn.decomposition import PCA
from sklearn.datasets import load_iris
%matplotlib inline
X = [[2, 0, -1.4],
[2.2, 0.2, -1.5],
[2.4, 0.1, -1],
[1.9, 0, -1.2]]
print(np.array(X))
print(np.array(X).T)
print(np.cov(np.array(X).T))
print(np.cov(np... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/how_google_does_ml/bigquery/solution/analyze_with_bigquery_solution.ipynb | apache-2.0 | import os
import pandas as pd
PROJECT = "<YOUR PROJECT>" #TODO Replace with your project id
os.environ["PROJECT"] = PROJECT
pd.options.display.max_columns = 50
"""
Explanation: Analyze a large dataset with Google BigQuery
Learning Objectives
Access an ecommerce dataset
Look at the dataset metadata
Remove duplicat... |
phoebe-project/phoebe2-docs | 2.2/tutorials/passband_updates.ipynb | gpl-3.0 | !pip install -I "phoebe>=2.2,<2.3"
"""
Explanation: Advanced: passband versioning & updates
Let's first make sure we have the latest version of PHOEBE 2.2 installed. (You can comment out this line if you don't use pip for your installation or don't want to update to the latest release).
End of explanation
"""
import... |
ES-DOC/esdoc-jupyterhub | notebooks/nasa-giss/cmip6/models/sandbox-1/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-1', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: NASA-GISS
Source ID: SANDBOX-1
Topic: Atmos
Sub-Topics: Dynamical Core, Radiati... |
ES-DOC/esdoc-jupyterhub | notebooks/bcc/cmip6/models/sandbox-2/toplevel.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bcc', 'sandbox-2', 'toplevel')
"""
Explanation: ES-DOC CMIP6 Model Properties - Toplevel
MIP Era: CMIP6
Institute: BCC
Source ID: SANDBOX-2
Sub-Topics: Radiative Forcings.
Properties: 85 (42 re... |
deflaux/linkage-disequilibrium | datalab/Exploring_Linkage_Disequilibrium_Data.ipynb | apache-2.0 | import gcp.bigquery as bq
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Get references to the BigQuery tables of linkage disequilibrium
# in the five superpopulations of the 1000 Genomes Project
# (http://www.1000genomes.org/faq/which-populations-are-part-your-study):
# AMR: Admixed Ameri... |
qingshuimonk/STA663 | docs/VAE_synthetic_Siyang.ipynb | mit | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
import time
from tensorflow.python.client import timeline
import matplotlib.pyplot as plt
%matplotlib inline
FLAGS = tf.app.flags.FLAGS
# number of device count
tf.app... |
Santana9937/Classification_ML_Specialization | Week_3_Decision_Trees/week_3_assign_1_safe_loans_decision_trees.ipynb | mit | import json
import numpy as np
import pandas as pd
import sklearn, sklearn.tree
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('darkgrid')
%matplotlib inline
"""
Explanation: Identifying safe loans with decision trees
The LendingClub is a peer-to-peer leading company that ... |
maxrose61/GA_DS | maxrose_hw/oct13/06_yelp_votes_homework.ipynb | gpl-3.0 | # access yelp.csv using a relative path
import pandas as pd
yelp = pd.read_csv('../data/yelp.csv')
yelp.head(1)
"""
Explanation: Linear regression homework with Yelp votes
Introduction
This assignment uses a small subset of the data from Kaggle's Yelp Business Rating Prediction competition.
Description of the data:
y... |
dkirkby/astroml-study | Chapter1/Chapter1.ipynb | mit | %pylab inline
import astroML
print astroML.__version__
"""
Explanation: Chapter 1
Prepared by David Kirkby dkirkby@uci.edu on 14-Jan-2016.
End of explanation
"""
"""
SDSS Spectrum Example
---------------------
Figure 1.2.
An example of an SDSS ... |
bmeaut/python_nlp_2017_fall | course_material/04_Object_oriented_programming/04_Object_oriented_programming_lecture.ipynb | mit | class ClassWithInit:
def __init__(self):
pass
class ClassWithoutInit:
pass
"""
Explanation: Introduction to Python and Natural Language Technologies
Lecture 03, Week 04
Object oriented programming
27 September 2017
Introduction
Python has been object oriented since its first version
basically eve... |
BelalC/keyword_i2x | notebooks/sketch.ipynb | mit | from sklearn.feature_extraction import stop_words
from nltk.corpus import stopwords
import math
from textblob import TextBlob as tb
with open("scripts/script.txt", "r") as f:
data = f.read()
#with open("scripts/script.txt", "r") as f:
# data2 = f.readlines()
#for line in data:
# words = data.split()
with... |
quasars100/Resonance_testing_scripts | python_tutorials/Megno.ipynb | gpl-3.0 | def simulation(par):
a, e = par # unpack parameters
rebound.reset()
rebound.integrator = "whfast-nocor"
rebound.dt = 5.
rebound.add(m=1.) # Star
rebound.add(m=0.000954, a=5.204, anom=0.600, omega=0.257, e=0.048)
rebound.add(m=0.000285, a=a, anom=0.871, omega=1.616, e=e)
rebound.move_to_... |
nathanshammah/pim | doc/notebooks/piqs_steadystate_superradiance.ipynb | mit | import matplotlib.pyplot as plt
from qutip import *
from piqs import *
"""
Explanation: Steady-state superradiance
We consider a system of $N$ two-level systems (TLSs) with identical frequency $\omega_{0}$, incoherently pumped at a rate $\gamma_\text{P}$ and de-excitating at a collective emission rate $\gamma_\text{CE... |
DaveBackus/Data_Bootcamp | Code/SQL/SQL_Bootcamp_Stern_2016.ipynb | mit | from IPython.display import display, HTML, clear_output
HTML('''<script> code_show=true; function code_toggle() {if (code_show){$('div.input').hide();}
else {$('div.input').show();}code_show = !code_show} $( document ).ready(code_toggle);
</script> <form action="javascript:code_toggle()"><input type="submit" value="Hi... |
tgsmith61591/pyramid | examples/quick_start_example.ipynb | mit | import numpy as np
import pmdarima as pm
print('numpy version: %r' % np.__version__)
print('pmdarima version: %r' % pm.__version__)
"""
Explanation: auto_arima
Pmdarima bring R's auto.arima functionality to Python by wrapping statsmodel ARIMA and SARIMAX models into a singular scikit-learn-esque estimator (pmdarima.a... |
aflaxman/siaman16-va-minitutorial | 2-tutorial-notebook-solutions/4-va_csmf.ipynb | gpl-3.0 | import numpy as np, pandas as pd
"""
Explanation: We won't work through this notebook
We won't have time. But I thought I'd include it, in case you want to see exactly how I implement my population-level quality metric.
End of explanation
"""
def measure_prediction_quality(csmf_pred, y_test):
"""Calculate popul... |
ES-DOC/esdoc-jupyterhub | notebooks/messy-consortium/cmip6/models/emac-2-53-aerchem/land.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'emac-2-53-aerchem', 'land')
"""
Explanation: ES-DOC CMIP6 Model Properties - Land
MIP Era: CMIP6
Institute: MESSY-CONSORTIUM
Source ID: EMAC-2-53-AERCHEM
Topic: Land
Sub-Topi... |
rjurney/Agile_Data_Code_2 | ch09/Improving_Predictions.ipynb | mit | import sys, os, re
import json
import datetime, iso8601
from tabulate import tabulate
# Initialize PySpark
APP_NAME = "Improving Predictions"
# If there is no SparkSession, create the environment
try:
sc and spark
except NameError as e:
import findspark
findspark.init()
import pyspark
import pyspa... |
google/learned_optimization | docs/notebooks/no_dependency_learned_optimizer.ipynb | apache-2.0 | import jax
import jax.numpy as jnp
import tensorflow_datasets as tfds
import matplotlib.pylab as plt
import numpy as onp
import functools
import os
"""
Explanation: No dependency introduction to learned optimizers in JAX
This notebook contains a self contained implementation of learned optimizers in JAX.
It is minimal... |
ernestyalumni/cuBlackDream | examples/RModule.ipynb | mit | import numpy
import numpy as np
m=6
n=4
k=5
"""
Explanation: $R$-Module
Doing $R$-module (equipped with Hadamard operators) algebraic operations; addition, multiplication, and element-wise Hadamard operations
Certainly, we'd want to ensure, or at least evince, through examples that we can do the same algebraic opera... |
mne-tools/mne-tools.github.io | 0.21/_downloads/bc5044f9d3ef1d29067dd6b7d83ceed2/plot_20_visualize_epochs.ipynb | bsd-3-clause | import os
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file, verbose=False).crop(tmax=120)
"""
Explanation: Visualizing epo... |
agile-geoscience/notebooks | To_build_a_better_wedge.ipynb | apache-2.0 | import numpy as np
% matplotlib inline
import matplotlib.pyplot as plt
"""
Explanation: To make a better wedge
This notebook is an update to the notebook entitled "To make a wedge" featured in the blog post, To make a wedge, on December 12, 2013.
Start by importing Numpy and Matplotlib's pyplot module in the usual way... |
Caranarq/01_Dmine | Datasets/Pigoo/.ipynb_checkpoints/pigoo_desagregacion-checkpoint.ipynb | gpl-3.0 | # Librerias utilizadas
import pandas as pd
import sys
module_path = r'D:\PCCS\01_Dmine\Scripts'
if module_path not in sys.path:
sys.path.append(module_path)
from SUN.asignar_sun import asignar_sun
from SUN_integridad.SUN_integridad import SUN_integridad
from SUN.CargaSunPrincipal import getsun
# Configuracion del ... |
Astrohackers-TW/IANCUPythonAdventure | notebooks/notebooks4HowtoSeries/how_to_cross-match_two_tables.ipynb | mit | table_a = pd.read_csv('files/table_A.csv')
table_b = pd.read_csv('files/table_B.csv')
table_a.head()
table_b.head()
print('Table A 有', len(table_a), '個樣本')
print('Table B 有', len(table_b), '個樣本')
ra_a = table_a['ra']
dec_a = table_a['dec']
ra_b = table_b['wise_ra']
dec_b = table_b['wise_dec']
"""
Explanation: 載入 ... |
adolfoguimaraes/machinelearning | Tensorflow/Tutorial02_MLP.ipynb | mit | import numpy as np
import matplotlib.pyplot as plt
def soma(x, y):
return x + y
#Criando os dados de entrada (x = features e y = classes)
x_train = np.array([[2., 2.],[1., 3.],[2., 3.],[5., 3.],[7., 3.],[2., 4.],[3., 4.],[6., 4.],
[1., 5.],[2., .5],[5., 5.],[4., 6.],[6., 6.],[5., 7.]],dtype="... |
mne-tools/mne-tools.github.io | 0.23/_downloads/775a4c9edcb81275d5a07fdad54343dc/channel_epochs_image.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
"""
Explanation: Visualize channel over epochs as an image
This will... |
Upward-Spiral-Science/uhhh | code/Graph Analysis/Delaunay.ipynb | apache-2.0 | import csv
from scipy.stats import kurtosis
from scipy.stats import skew
from scipy.spatial import Delaunay
import numpy as np
import math
import skimage
import matplotlib.pyplot as plt
import seaborn as sns
from skimage import future
import networkx as nx
from ragGen import *
%matplotlib inline
sns.set_color_codes("pa... |
rdhyee/diversity-census-calc | 03_01_Geographical_Hierarchies.ipynb | apache-2.0 | # YouTube video I made on how to use the American Factfinder site to look up addresses
from IPython.display import YouTubeVideo
YouTubeVideo('HeXcliUx96Y')
# standard numpy, pandas, matplotlib imports
import numpy as np
import matplotlib.pyplot as plt
from pandas import DataFrame, Series, Index
import pandas as pd
... |
bollwyvl/dangerous-playgrounds | index.ipynb | bsd-3-clause | import traitlets
import ipywidgets as widgets
import types
"""
Explanation: Building Dangerous Live-Coding Playgrounds with Jupyter Widgets
Motivation
Playground applications, where each user keystroke in any number of source documents updates an output document, allow you to fail fast, and are a great way to learn n... |
xR86/ml-stuff | kaggle/zmisc/Book Recommendations from Charles Darwin/notebook_bad.ipynb | mit | # Import library
import glob
# The books files are contained in this folder
folder = "datasets/"
# List all the .txt files and sort them alphabetically
files = glob.glob(folder + '*.txt')
# ... YOUR CODE FOR TASK 1 ...
files.sort()
"""
Explanation: 1. Darwin's bibliography
<p><img src="https://assets.datacamp.com/pr... |
atulsingh0/MachineLearning | HandsOnML/code/16_reinforcement_learning.ipynb | gpl-3.0 | # To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
import sys
# to make this notebook's output stable across runs
def reset_graph(seed=42):
tf.reset_default_graph()
tf.set_random_seed(seed)
np.random.seed(... |
amueller/scipy-2017-sklearn | notebooks/19.Feature_Selection.ipynb | cc0-1.0 | from sklearn.datasets import load_breast_cancer, load_digits
from sklearn.model_selection import train_test_split
cancer = load_breast_cancer()
# get deterministic random numbers
rng = np.random.RandomState(42)
noise = rng.normal(size=(len(cancer.data), 50))
# add noise features to the data
# the first 30 features ar... |
david4096/bioapi-examples | python_notebooks/regionSearch.ipynb | apache-2.0 | from ga4gh.client import client
c = client.HttpClient("http://1kgenomes.ga4gh.org")
import sys
import collections
import math
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact, interactive, fixed
from IPython.display import display
import ipywidget... |
hmenke/pairinteraction | doc/sphinx/examples_python/comparison_to_saffman_fig13.ipynb | gpl-3.0 | %matplotlib inline
# Arrays
import numpy as np
# Plotting
import matplotlib.pyplot as plt
# Operating system interfaces
import os, sys
# Parallel computing
if sys.platform != "win32": from multiprocessing import Pool
from functools import partial
# pairinteraction :-)
from pairinteraction import pireal as pi
# Cr... |
MegaShow/college-programming | Homework/Principles of Artificial Neural Networks/Week 3 Backpropagation/week_3_numpy.ipynb | mit | # set some inputs
x1 = -2; x2 = 5;
# perform the forward pass
f = x1 * x2 # f becomes -10
# perform the backward pass (backpropagation) in reverse order:
# backprop through f = x * y
dfdx1 = x2 # df/dx = y, so gradient on x becomes 5
print("gradient on x is {:2}".format(dfdx1))
dfdx2 = x1 # df/dy = x, so gradient on ... |
skkandrach/foundations-homework | Homework 11 Soma.ipynb | mit | plate_info = {'Plate ID': 'str'}
df = pd.read_csv("small-violations.csv", dtype=plate_info)
df
df.head()
df.head(10)
df.tail()
"""
Explanation: 1. I want to make sure my Plate ID is a string. Can't lose the leading zeroes!
End of explanation
"""
plate_info = {'Plate ID': 'str'}
df = pd.read_csv("small-violations... |
LSSTC-DSFP/LSSTC-DSFP-Sessions | Sessions/Session05/Day4/stackdiff_Narayan/02_Reprojection/Reproject_images_exercise.ipynb | mit | import numpy as np
import matplotlib
import astropy.io.fits as afits
from astropy.wcs import WCS
import reproject
from astropy.visualization import ZScaleInterval
import astropy.table as at
import astropy.coordinates as coords
import astropy.units as u
from astropy.visualization.wcsaxes import WCSAxes
import astropy.vi... |
asazo/ANN | tarea3/Pregunta 2.ipynb | mit | import numpy as np
from theano.tensor.shared_randomstreams import RandomStreams
from matplotlib import pyplot
from keras.preprocessing import sequence
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
from keras.layers.embeddings import Embedding
from keras.layers import D... |
Oli4/lsi-material | Algorithmic_Basics_of_Bioinformatics/Algorithmic Basics of Bioinformatics Tutorial Sheet 6.ipynb | mit | def DPChange(M,c,d):
import math
best_num_coins = [0]
for m in range(1,M+1):
best_num_coins.append(math.inf)
for i in range(0,d):
if m >= c[i]:
if best_num_coins[m-c[i]] +1 < best_num_coins[m]:
best_num_coins[m] = best_num_coins[m-c[i]] + 1
... |
nonotone79/investigativ | 02 Jupyter Notebook & Python Intro.ipynb | mit | #Mit einem Hashtag vor einer Zeile können wir Code kommentieren, auch das ist sehr wichtig.
#Immer, wirklich, immer den eigenen Code zu kommentieren. Vor allem am Anfang.
print('hello world')
#Der Printbefehl druckt einfach alles aus. Nicht wirklich wahnsinnig toll.
#Doch er ist später sehr nützlich. Vorallem wenn ... |
datascienceguide/datascienceguide.github.io | tutorials/Exploratory-Data-Analysis-Tutorial.ipynb | mit | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
anscombe_i = pd.read_csv('../datasets/anscombe_i.csv')
anscombe_ii = pd.read_csv('../datasets/anscombe_ii.csv')
anscombe_iii = pd.read_csv('../datasets/anscombe_iii.csv')
anscombe_iv = pd.read_csv('../datasets/anscombe_iv.csv')
... |
google/starthinker | colabs/anonymize_query.ipynb | apache-2.0 | !pip install git+https://github.com/google/starthinker
"""
Explanation: BigQuery Anonymize Query
Runs a query and anynonamizes all rows. Used to create sample table for dashboards.
License
Copyright 2020 Google LLC,
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in c... |
ericmjl/Network-Analysis-Made-Simple | archive/8-US-airports-case-study-student.ipynb | mit | %matplotlib inline
import networkx as nx
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import warnings
warnings.filterwarnings('ignore')
pass_air_data = pd.read_csv('datasets/passengers.csv')
"""
Explanation: Exploratory analysis of the US Airport Dataset
This dataset contains data for 25 yea... |
hektor-monteiro/curso-python | erros-velocidade.ipynb | gpl-2.0 | x = 1.e308
y = x * 10.
print x,y
"""
Explanation: Acurácia e velocidade
Agora já temos os componentes básicos da linguagem Python para poder atacar os problemas de física
no entanto, precisamos explorar ainda as limitações do computador visto que não pode guardar números com precisão infinita
existe um limite superior... |
cuemacro/chartpy | chartpy_examples/notebooks/web_page_examples.ipynb | apache-2.0 | import sys
try:
sys.path.append('E:/Remote/chartpy')
except:
pass
"""
Explanation: Creating charts (& webpages!) with chartpy
By Saeed Amen (@saeedamenfx) - saeed@cuemacro.com
A great way to present a group of charts is via a webpage. How can we do this in a quick and easy way in Python? Furthemore, how can w... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/While loops -checkpoint.ipynb | apache-2.0 | 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... |
AntArch/Presentations_Github | 20150916_OGC_Reuse_under_licence/.ipynb_checkpoints/20150916_OGC_Reuse_under_licence-checkpoint_conflict-20150910-195436.ipynb | cc0-1.0 | from IPython.display import YouTubeVideo
YouTubeVideo('F4rFuIb1Ie4')
## PDF output using pandoc
import os
### Export this notebook as markdown
commandLineSyntax = 'ipython nbconvert --to markdown 20150916_OGC_Reuse_under_licence.ipynb'
print (commandLineSyntax)
os.system(commandLineSyntax)
### Export this noteboo... |
harish-garg/Machine-Learning | udacity/enron/ud120-projects-master/final_project/Data Exploration and Cleanup.ipynb | mit | print "print out some values of the observation 'TOTAL'"
for name, person in data_dict.iteritems():
if name == 'TOTAL':
print person
salary = []
for name, person in data_dict.iteritems():
if float(person['salary']) > 0:
salary.append(float(person['salary']))
print "the sum of salary of all other person... |
BL-Labs/poetryhunt | WordFrequencyClassifier.ipynb | mit | from newspaperaccess import *
# Get the connection set up to get access to the newspaper text
n = NewspaperArchive()
# Load up the references to the pages that we know reference Abolitionists
import csv
# Month list to convert a name to a number:
MONTHS = {"january": "01", "february": "02", "march": "03", "april": "... |
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