repo_name stringlengths 6 77 | path stringlengths 8 215 | license stringclasses 15
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|---|---|---|---|
InsightLab/data-science-cookbook | 2020/04-unsupervised-learning-clustering/Notebook_WhyNumpy.ipynb | mit | # import the libraries
import time
import random
import numpy as np
# Inicializar vetor com uma grande quantidade de dados aleátorios
x = [random.randint(1,10) for i in range(50000)]
np_x = np.array(x)
# Selecionar valores k para a equação
k = [4, 8, 30]
"""
Explanation: (Extra) Porque usar numpy?
https://towardsda... |
Chipe1/aima-python | mdp_apps.ipynb | mit | from mdp import *
from notebook import psource, pseudocode
"""
Explanation: APPLICATIONS OF MARKOV DECISION PROCESSES
In this notebook we will take a look at some indicative applications of markov decision processes.
We will cover content from mdp.py, for Chapter 17 Making Complex Decisions of Stuart Russel's and Pe... |
ES-DOC/esdoc-jupyterhub | notebooks/cams/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', 'cams', 'sandbox-3', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: CAMS
Source ID: SANDBOX-3
Topic: Atmoschem
Sub-Topics: Transport, Emissions ... |
icrtiou/coursera-ML | ex1-linear regression/4- tensoflow batch gradient decent.ipynb | mit | %reload_ext autoreload
%autoreload 2
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append('..')
from helper import linear_regression as lr # my own module
from helper import general as general
import tensorflow as tf
"""
Explanation: notes:
tensorf... |
datascience-practice/data-quest | python_introduction/intermediate/Classes.ipynb | mit | class Car():
def __init__(self):
self.color = "black"
self.make = "honda"
self.model = "accord"
black_honda_accord = Car()
print(black_honda_accord.color)
"""
Explanation: 3: Class syntax
Instructions
Create a class called Team.
Inside the class, create a name property. Assign the value "... |
tensorflow/docs | site/en/guide/migrate/checkpoint_saver.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... |
sanabasangare/data-visualization | Line_chart.ipynb | mit | import matplotlib.pyplot as plt
from collections import Counter
"""
Explanation: Line Chart - Total Number of websites online
End of explanation
"""
def line_graph(plt):
years = [2000, 2002, 2005, 2007, 2010, 2012, 2014, 2015]
websites = [17, 38, 64, 121, 206, 697, 968, 863]
"""
Explanation: create a line c... |
dssg/diogenes | doc/notebooks/modify.ipynb | mit | import diogenes
data = diogenes.read.open_csv_url('https://data.cityofchicago.org/api/views/mab8-y9h3/rows.csv?accessType=DOWNLOAD',
parse_datetimes=['Creation Date', 'Completion Date'])
"""
Explanation: The Modify Module
:mod:diogenes.modify provides tools for manipulating arrays a... |
ondrejiayc/StatisticalMethods | examples/SDSScatalog/FirstLook.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
objects = "SELECT top 10000 \
ra, \
dec, \
type, \
dered_u as u, \
dered_g as g, \
dered_r as r, \
dered_i as i, \
petroR50_i AS size \
FROM PhotoObjAll \
WH... |
daleloogn/rp_extract | RP_extract_Tutorial.ipynb | gpl-3.0 | # to install iPython notebook on your computer, use this in Terminal
sudo pip install "ipython[notebook]"
"""
Explanation: <center><h1>Rhythm and Timbre Analysis from Music</h1></center>
<center><h2>Rhythm Pattern Music Features</h2></center>
<center><h2>Extraction and Application Tutorial</h2></center>
<br>
<center><... |
julienchastang/unidata-python-workshop | notebooks/AWIPS/Map_Resources_and_Topography.ipynb | mit | from __future__ import print_function
from awips.dataaccess import DataAccessLayer
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
import numpy as np
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
from cartopy.feature import ShapelyFeature,NaturalEarthFeature
from shapely.geometry ... |
kubeflow/pipelines | components/gcp/ml_engine/train/sample.ipynb | apache-2.0 | %%capture --no-stderr
!pip3 install kfp --upgrade
"""
Explanation: Name
Submitting a Cloud Machine Learning Engine training job as a pipeline step
Label
GCP, Cloud ML Engine, Machine Learning, pipeline, component, Kubeflow, Kubeflow Pipeline
Summary
A Kubeflow Pipeline component to submit a Cloud ML Engine training j... |
sarvex/tensorflow | tensorflow/lite/g3doc/performance/post_training_float16_quant.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... |
cjcardinale/climlab | docs/source/courseware/Spectral_OLR_with_RRTMG.ipynb | mit | %matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import climlab
import xarray as xr
import scipy.integrate as sp #Gives access to the ODE integration package
"""
Explanation: Spectrally-resolved Outgoing Longwave Radiation (OLR) with RRTMG_LW
In this notebook we will demonstrate how to use climla... |
tOverney/ADA-Project | preprocessing/process_csv.ipynb | apache-2.0 | def strip_id(s):
try:
index = s.index(':')
except ValueError:
index = len(s)
return s[:index]
columns = [
'agency_id',
'service_date_id', 'service_date_date',
'route_id', 'route_short_name', 'route_long_name',
'trip_id', 'trip_headsign', 'trip_short_name',
'stop_time_id... |
mommermi/Introduction-to-Python-for-Scientists | notebooks/Lists_and_Control_Flow_20160916.ipynb | mit | l1 = [1, 2, 3, 4, 5, 6] # list of the same data type
l2 = [1, 2.3, 'a'] # list of different data types
l3 = [[1, 2, 3], [4, 5, 6]] # a nested (multidimensional) list
l4 = range(3,10) # a neat way to generate a list of integers
"""
Explanation: Python Basics
Content
Lists
Dictionaries
Sets
Cont... |
AusCover/ml-biomass | ml-biomass.ipynb | apache-2.0 | # Imports for this Python3 notebook
import numpy
import matplotlib.pyplot as plt
from osgeo import gdal
from osgeo import ogr
from osgeo import osr
from rios import rat
from rios import ratapplier
from tpot import TPOTRegressor
"""
Explanation: Biomass Estimation - Putting the RAT into TPOT
<img src='http://www.au... |
anonyXmous/CapstoneProject | Mini_Project_Logistic_Regression.ipynb | unlicense | %matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
from matplotlib.colors import ListedColormap
import matplotlib.pyplot as plt
import pandas as pd
pd.set_option('display.width', 500)
pd.set_option('display.max_columns', 100)
pd.set_option('display.notebook_repr... |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/ml_ops/stage6/get_started_with_fastapi.ipynb | apache-2.0 | import os
# The Vertex AI Workbench Notebook product has specific requirements
IS_WORKBENCH_NOTEBOOK = os.getenv("DL_ANACONDA_HOME")
IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(
"/opt/deeplearning/metadata/env_version"
)
# Vertex AI Notebook requires dependencies to be installed with '--user'
USER_FLAG = ... |
jeroarenas/MLBigData | 2_Classification/MLLib_classification-students.ipynb | mit | #################################################
# TODO: Replace <FILL IN> with appropriate code
#################################################
# You need to include mnist file in your working directory
lines = sc.textFile("mnist")
# Examine dataset format
# 1. Number of lines
n_lines = #FILL
print 'Number of lin... |
NEONScience/NEON-Data-Skills | tutorials/Python/Hyperspectral/indices/Plot_Spectral_Signature_Tiles_py/Plot_Spectral_Signature_Tiles_py.ipynb | agpl-3.0 | import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore') #don't display warnings
"""
Explanation: syncID: c91d556c8fad4570a33a1aaa550a561d
title: "Plot a Spectral Signature in Python - Tiled Data"
description: "Learn how to extract and plot a spectral pro... |
liufuyang/deep_learning_tutorial | jizhi-pytorch-2/03_text_generation/Homework_3/Homeword_LSTM_Name_Generator-Copy1.ipynb | mit | # 第一步当然是引入PyTorch及相关包
import torch
import torch.nn as nn
import torch.optim
from torch.autograd import Variable
import numpy as np
"""
Explanation: 火炬上的深度学习（下）第三节：神经网络莫扎特
课后作业：使用 LSTM 编写一个国际姓氏生成模型
在火炬课程中，我们学习了使用 LSTM 来生成 MIDI 音乐。这节课我们使用类似的方法，再创建一个 LSTM 国际起名大师！
完成后的模型能够像下面这样使用，指定一个国家名，模型即生成几个属于这个国家的姓氏。
```
python gene... |
mne-tools/mne-tools.github.io | 0.17/_downloads/ad203e57e0d21d6623eb90e7bd84fa3c/plot_morph_surface_stc.ipynb | bsd-3-clause | # Author: Tommy Clausner <tommy.clausner@gmail.com>
#
# License: BSD (3-clause)
import os
import mne
from mne.datasets import sample
print(__doc__)
"""
Explanation: Morph surface source estimate
This example demonstrates how to morph an individual subject's
:class:mne.SourceEstimate to a common reference space. We a... |
sdpython/pyquickhelper | _unittests/ut_helpgen/notebooks2/custom_widget.ipynb | mit | from jyquickhelper import add_notebook_menu
add_notebook_menu()
"""
Explanation: Custom widgets in a notebook
The notebook explore a couple of ways to interact with the user and modifies the output based on these interactions. This is inspired from the examples from ipwidgets.
End of explanation
"""
import ipywidget... |
ireapps/cfj-2017 | completed/20. Exercise - Web scraping.ipynb | mit | import csv
import time
import requests
from bs4 import BeautifulSoup
"""
Explanation: Let's scrape some death row data
Texas executes a lot of criminals, and it has a web page that keeps track of people on its death row.
Using what you've learned so far, let's scrape this table into a CSV. Then we're going write a fu... |
diegocavalca/Studies | programming/Python/tensorflow/exercises/Math_Part2.ipynb | cc0-1.0 | from __future__ import print_function
import tensorflow as tf
import numpy as np
from datetime import date
date.today()
author = "kyubyong. https://github.com/Kyubyong/tensorflow-exercises"
tf.__version__
np.__version__
sess = tf.InteractiveSession()
"""
Explanation: Math Part 2
End of explanation
"""
_x = np.a... |
rbiswas4/simlib | example/Demo_HealpixTree.ipynb | mit | from mpl_toolkits.basemap import Basemap
import opsimsummary as oss
oss.__VERSION__
from opsimsummary import HealpixTree, pixelsForAng, HealpixTiles
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import healpy as hp
"""
Explanation: Contents
This notebook shows how to use the functionality... |
anisfeld/MachineLearning | Building the Pipeline part 2 Write Up.ipynb | mit | first_grid = r.read_csv("small_loop_result.csv")
fg = first_grid.sort_values(by="auc-roc")
fg.head(10)
#top 10
fg.tail(10).sort_values(by="auc-roc", ascending=False)
"""
Explanation: While I was working on improving my previous homework and determining a method for feature selection, I ran the small grid search of Ma... |
rsnemmen/nmmn | docs/SEDs.ipynb | mit | %pylab inline
import nmmn.sed as sed
"""
Explanation: Handling spectral energy distributions
This notebook illustrates how to use the sed module of nmmn. This module is very convenient for dealing with spectral energy distributions (SEDs)—the distributions of luminosity $\nu L_\nu$ as a function of $\nu$.
Often, we ... |
ueapy/ueapy.github.io | content/notebooks/2020-09-10-github-scrape.ipynb | mit | import json
import requests
from collections import Counter
import pandas as pd
import numpy as np
credentials = json.loads(open('credentials-secret.json').read()) #don't forget to add your creds here!
username = credentials['username']
token = credentials['token']
"""
Explanation: A meta-hackweek hack
I put this no... |
pinga-lab/magnetic-ellipsoid | code/demagnetizing_factors_Stoner1945.ipynb | bsd-3-clause | from __future__ import division
%matplotlib inline
import numpy as np
import os
from matplotlib import pyplot as plt
from fatiando import utils
import mesher
import prolate_ellipsoid, oblate_ellipsoid, triaxial_ellipsoid
# Set some plot parameters
from matplotlib import rcParams
rcParams['figure.dpi'] = 300.
rcParams[... |
analysiscenter/dataset | examples/tutorials/02_pipeline_operations.ipynb | apache-2.0 | import sys
import warnings
warnings.filterwarnings("ignore")
import PIL
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
# the following line is not required if BatchFlow is installed as a python package.
sys.path.append("../..")
from batchflow import Dataset, DatasetIndex, R, P, V, C
from b... |
rflamary/POT | notebooks/plot_otda_classes.ipynb | mit | # Authors: Remi Flamary <remi.flamary@unice.fr>
# Stanislas Chambon <stan.chambon@gmail.com>
#
# License: MIT License
import matplotlib.pylab as pl
import ot
"""
Explanation: OT for domain adaptation
This example introduces a domain adaptation in a 2D setting and the 4 OTDA
approaches currently supported in ... |
fastai/fastai | nbs/31_text.data.ipynb | apache-2.0 | #|export
def reverse_text(x): return x.flip(0)
t = tensor([0,1,2])
r = reverse_text(t)
test_eq(r, tensor([2,1,0]))
"""
Explanation: Text data
Functions and transforms to help gather text data in a Datasets
Backwards
Reversing the text can provide higher accuracy with an ensemble with a forward model. All that is ne... |
neurohackweek/kids_rsfMRI_motion | kw_playingaround/Age_vs_Motion.ipynb | mit | import matplotlib.pylab as plt
%matplotlib inline
import numpy as np
import os
import pandas as pd
import seaborn as sns
sns.set_style('white')
sns.set_context('notebook')
from scipy.stats import kurtosis
import sys
%load_ext autoreload
%autoreload 2
sys.path.append('../SCRIPTS/')
import kidsmotion_stats as kms
impo... |
mattgiguere/doglodge | code/bf_qt_scraping.ipynb | mit | import sys
from PyQt4.QtGui import *
from PyQt4.QtCore import *
from PyQt4.QtWebKit import *
from lxml import html
class Render(QWebPage):
def __init__(self, url):
self.app = QApplication(sys.argv)
QWebPage.__init__(self)
self.loadFinished.connect(self._loadFinished)
... |
jalabort/templatetracker | notebooks/scrap/Correlation Filters.ipynb | bsd-3-clause | images = []
for i in mio.import_images('/Users/joan/PhD/DataBases/faces/lfpw/trainset/*', verbose=True,
max_images=300):
i.crop_to_landmarks_proportion_inplace(0.5)
i = i.rescale_landmarks_to_diagonal_range(100)
images.append(i)
visualize_images(images)
"""
Explanation: Kerneli... |
walkon302/CDIPS_Recommender | notebook_versions/Recommendor_Method_Nathans_v2.ipynb | apache-2.0 | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('white')
%matplotlib inline
"""
Explanation: Recommendation Method 1: Most similar items to user's previous views
Algorithm
Offline:
1. For each item, calculate features on trained neural network $ f_j $
2. For ... |
UWashington-Astro300/Astro300-A16 | 08_Python_LaTeX.ipynb | mit | %matplotlib inline
import sympy as sp
import numpy as np
import matplotlib.pyplot as plt
"""
Explanation: Python and $\LaTeX$
End of explanation
"""
plt.style.use('ggplot')
x = np.linspace(0,2*np.pi,100)
y = np.sin(5*x) * np.exp(-x)
plt.plot(x,y)
plt.title("The function $y\ =\ \sin(5x)\ e^{-x}$")
plt.xlabel("This... |
kit-cel/wt | ccgbc/ch2_Codes_Basic_Concepts/BiAWGN_Capacity_Finitelength.ipynb | gpl-2.0 | import numpy as np
import scipy.integrate as integrate
from scipy.stats import norm
import matplotlib
import matplotlib.pyplot as plt
# plotting options
font = {'size' : 20}
plt.rc('font', **font)
plt.rc('text', usetex=matplotlib.checkdep_usetex(True))
matplotlib.rc('figure', figsize=(18, 6) )
"""
Explanation: Fi... |
muatik/my-coding-challenges | python/10daysOfStatistics/Day_5_Normal_Distribution_I.ipynb | mit | import math
from matplotlib import pylab as plt
%matplotlib inline
def pdf(x, m, variance):
sigma = math.sqrt(variance)
"""probability density function"""
return 1 / (sigma * math.sqrt(2 * math.pi)) * math.e ** (-1 * ((x - m)**2 / (2 * variance ** 2)))
pdf(20, 20, 4)
"""
Explanation: Day 5: Normal Distri... |
poldrack/fmri-analysis-vm | analysis/efficiency/DesignEfficiency.ipynb | mit | import os
import numpy
%matplotlib inline
import sys
sys.path.insert(0,'../utils')
from mkdesign import create_design_singlecondition
import matplotlib.pyplot as plt
#from spm_hrf import spm_hrf
from nipy.modalities.fmri.hemodynamic_models import spm_hrf,compute_regressor
tr=1.0
# the "blockiness" argument controls h... |
jcmgray/quijy | docs/examples/ex_tn_train_circuit.ipynb | mit | V = circ.uni
"""
Explanation: We can extract just the unitary part of the circuit as a tensor network like so:
End of explanation
"""
V.graph(color=['U3', gate2], show_inds=True)
V.graph(color=[f'ROUND_{i}' for i in range(depth)], show_inds=True)
V.graph(color=[f'I{i}' for i in range(n)], show_inds=True)
# the ha... |
Jackie789/JupyterNotebooks | 3.1.3+KNN+RegressionWithJackiesModel.ipynb | gpl-3.0 | from sklearn import neighbors
# Build our model.
knn = neighbors.KNeighborsRegressor(n_neighbors=10)
X = pd.DataFrame(music.loudness)
Y = music.bpm
knn.fit(X, Y)
# Set up our prediction line.
T = np.arange(0, 50, 0.1)[:, np.newaxis]
# Trailing underscores are a common convention for a prediction.
Y_ = knn.predict(T)... |
mne-tools/mne-tools.github.io | stable/_downloads/b2637a9801fb152d611a08a816cc5583/sensor_regression.ipynb | bsd-3-clause | # Authors: Tal Linzen <linzen@nyu.edu>
# Denis A. Engemann <denis.engemann@gmail.com>
# Jona Sassenhagen <jona.sassenhagen@gmail.com>
#
# License: BSD-3-Clause
import pandas as pd
import mne
from mne.stats import linear_regression, fdr_correction
from mne.viz import plot_compare_evokeds
from mne.data... |
tombstone/models | official/colab/nlp/nlp_modeling_library_intro.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... |
gouthambs/karuth-source | content/extra/notebooks/american-option-models.ipynb | artistic-2.0 | import QuantLib as ql
import matplotlib.pyplot as plt
%matplotlib inline
ql.__version__
"""
Explanation: American Option Pricing with QuantLib and Python
Gouthaman Balaraman
I wrote about pricing European options using QuantLib in an earlier post. Since then, I have received many questions from readers on how to exte... |
ajdawson/python_for_climate_scientists | course_content/solutions/iris_exercise_2.ipynb | gpl-3.0 | import iris
soi = iris.load_cube(iris.sample_data_path('SOI_Darwin.nc'))
print(soi)
"""
Explanation: A final exercise
This exercise puts together many of the topics covered in this session.
1. Load the single cube from the file iris.sample_data_path('SOI_Darwin.nc'). This contains monthly values of the Southern Oscil... |
DJCordhose/ai | notebooks/nlp/1b-glove-embedding.ipynb | mit | # Based on
# https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/6.1-using-word-embeddings.ipynb
# https://machinelearningmastery.com/develop-word-embeddings-python-gensim/
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import tensorflow as tf
tf.logging.se... |
saashimi/code_guild | wk0/notebooks/challenges/compress/.ipynb_checkpoints/compress_challenge-checkpoint.ipynb | mit | def compress_string(string):
# TODO: Implement me
string "!"
pass
"""
Explanation: <small><i>This notebook was prepared by Donne Martin. Source and license info is on GitHub.</i></small>
Challenge Notebook
Problem: Compress a string such that 'AAABCCDDDD' becomes 'A3B1C2D4'. Only compress the string if it... |
landlab/landlab | notebooks/tutorials/flow_direction_and_accumulation/compare_FlowDirectors.ipynb | mit | %matplotlib inline
# import plotting tools
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator, FormatStrFormatter
import matplotlib as mpl
# import numpy
import numpy as np
# import necessary landlab components
from landlab im... |
matt-graham/auxiliary-pm-mcmc | experiment_notebooks/Auxiliary Pseudo-Marginal MCMC - E-SS u updates and RD-SS theta updates.ipynb | mit | data_dir = os.path.join(os.environ['DATA_DIR'], 'uci')
exp_dir = os.path.join(os.environ['EXP_DIR'], 'apm_mcmc')
"""
Explanation: Construct data and experiments directorys from environment variables
End of explanation
"""
data_set = 'pima'
method = 'apm(ess+rdss)'
n_chain = 10
chain_offset = 0
seeds = np.random.rand... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/Functions and Methods Homework - Solutions.ipynb | apache-2.0 | def vol(rad):
return (4.0/3)*(3.14)*(rad**3)
"""
Explanation: Functions and Methods Homework Solutions
Write a function that computes the volume of a sphere given its radius.
End of explanation
"""
def ran_check(num,low,high):
#Check if num is between low and high (including low and high)
if num in rang... |
ThierryMondeel/FBA_python_tutorial | FBA_tutorials/5_biomarker_prediction_PKU.ipynb | mit | import cobra
from utils import findBiomarkers
import pandas as pd
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
M = cobra.io.load_json_model('models/recon_2_2_simple_medium.json')
model = M.copy() # this way we can edit model but leave M unaltered
"""
Expl... |
NYUDataBootcamp/Materials | Code/notebooks/bootcamp_indicators.ipynb | mit | # import packages
import pandas as pd # data management
import matplotlib.pyplot as plt # graphics
import numpy as np # numerical calculations
# IPython command, puts plots in notebook
%matplotlib inline
# check Python version
import datetime as dt
import sys
print('To... |
ES-DOC/esdoc-jupyterhub | notebooks/ec-earth-consortium/cmip6/models/ec-earth3-lr/atmos.ipynb | gpl-3.0 | # DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'ec-earth3-lr', 'atmos')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmos
MIP Era: CMIP6
Institute: EC-EARTH-CONSORTIUM
Source ID: EC-EARTH3-LR
Topic: Atmos
Sub-Topic... |
GoogleCloudPlatform/asl-ml-immersion | notebooks/bigquery/labs/b_bqml.ipynb | apache-2.0 | from google import api_core
from google.cloud import bigquery
PROJECT = !gcloud config get-value project
PROJECT = PROJECT[0]
%env PROJECT=$PROJECT
"""
Explanation: Big Query Machine Learning (BQML)
Learning Objectives
- Understand that it is possible to build ML models in Big Query
- Understand when this is appropr... |
adamsteer/nci-notebooks | pgpointcloud/PGpointlcloud tests.ipynb | apache-2.0 | import os
import psycopg2 as ppg
import numpy as np
import ast
from osgeo import ogr
import shapely as sp
from shapely.geometry import Point,Polygon,asShape
from shapely.wkt import loads as wkt_loads
from shapely import speedups
import cartopy as cp
import cartopy.crs as ccrs
import pandas as pd
import pandas.io.s... |
JuBra/cobrapy | documentation_builder/milp.ipynb | lgpl-2.1 | cone_selling_price = 7.
cone_production_cost = 3.
popsicle_selling_price = 2.
popsicle_production_cost = 1.
starting_budget = 100.
"""
Explanation: Mixed-Integer Linear Programming
Ice Cream
This example was originally contributed by Joshua Lerman.
An ice cream stand sells cones and popsicles. It wants to maximize its... |
theandygross/TCGA_differential_expression | Notebooks/Imports.ipynb | mit | %matplotlib inline
import matplotlib.pyplot as plt
from matplotlib.pyplot import subplots
"""
Explanation: Global Imports
End of explanation
"""
import os as os
import pickle as pickle
import pandas as pd
"""
Explanation: External Package Imports
End of explanation
"""
from Stats.Scipy import *
from Stats.Surviv... |
yevheniyc/Python | 1m_ML_Security/notebooks/answers/Worksheet 6 - DGA Detection ML Classification - Answers.ipynb | mit | df_final = pd.read_csv('../../data/dga_features_final_df.csv')
print(df_final.isDGA.value_counts())
df_final.head()
# Load dictionary of common english words from part 1
from six.moves import cPickle as pickle
with open('../../data/d_common_en_words' + '.pickle', 'rb') as f:
d = pickle.load(f)
"""
Explanation... |
opensanca/trilha-python | 04-python-prat/data_science/Python and Data Science _sem_spoilers.ipynb | mit | import pandas as pd
import matplotlib
%matplotlib inline
"""
Explanation: 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
Modo de edição: enter para ativar, modo de inserção... |
befelix/Safe-RL-Benchmark | examples/SafeOpt.ipynb | mit | import GPy, safeopt
from SafeRLBench.algo import SafeOptSwarm
from SafeRLBench.envs import Quadrocopter, LinearCar
from SafeRLBench.policy import NonLinearQuadrocopterController, LinearPolicy
from SafeRLBench.measure import BestPerformance, SafetyMeasure
from SafeRLBench import Bench
# set up logging
from SafeRLBen... |
napsternxg/ControversialTweetAnalysis | Merge URLs and tweets.ipynb | apache-2.0 | len(data)
data[0].keys()
data[0][u'source']
data[0][u'is_quote_status']
data[0][u'quoted_status']['text']
data[0]['text']
count_quoted = 0
has_coordinates = 0
count_replies = 0
language_ids = defaultdict(int)
count_user_locs = 0
user_locs = Counter()
count_verified = 0
for d in data:
count_quoted += d.get('is... |
mohsinhaider/pythonbootcampacm | Objects and Data Structures/Print Formatting.ipynb | mit | print("We are printing")
"""
Explanation: Print Formatting
There are various ways to write print statements. In Python 3, to print to console you use print functions.
In the following lecture we will cover:
1. "%" notation for Strings, Floats, and Integers
2. Use the .format() method
3. Other Print function uses
Here... |
mayanks43/auto-tag | k-means.ipynb | mit | def assign_points_to_clusters(centroids, points, k):
# 1 list for each centroid (will contain indices of points)
clusters = [[] for i in range(k)]
for i in range(points.shape[0]):
# find nearest centroid to this point
best_centroid = 0
best_distance = euclidean(centroids[best_centroi... |
HazyResearch/snorkel | tutorials/advanced/Hyperparameter_Search.ipynb | apache-2.0 | from snorkel.learning import GenerativeModelWeights
from snorkel.learning.structure import generate_label_matrix
weights = GenerativeModelWeights(10)
for i in range(10):
weights.lf_accuracy[i] = 2.5
weights.dep_similar[0, 1] = 0.25
weights.dep_similar[2, 3] = 0.25
L_gold_train, L_train = generate_label_matrix(wei... |
beangoben/HistoriaDatos_Higgs | Dia2/4_Datos_LHC.ipynb | gpl-2.0 | import pandas as pd
import numpy as np # modulo de computo numerico
import matplotlib.pyplot as plt # modulo de graficas
# esta linea hace que las graficas salgan en el notebook
import seaborn as sns
%matplotlib inline
"""
Explanation: A explorar los datos del LHC
Hoy vamos a combinar dos conceptos que vimos ayer:
A... |
mne-tools/mne-tools.github.io | 0.22/_downloads/0f6b60b574bc5e5c341b148b90d0f456/plot_evoked_whitening.ipynb | bsd-3-clause | # Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
# Denis A. Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.datasets import sample
from mne.cov import compute_covariance
print(__doc__)
"""
Explanation: Whitening evoked data with a noise cova... |
thunder-project/thunder-docs | tutorials/basics.ipynb | mit | import thunder as td
series = td.series.fromexample('fish')
"""
Explanation: Basics
Thunder provides data structures, read/write patterns, and simple processing of spatial and temporal data. All operations in Thunder are designed to scale to very large data sets through the distributed comptuing engine Spark, but als... |
yashdeeph709/Algorithms | PythonBootCamp/Complete-Python-Bootcamp-master/Functions.ipynb | apache-2.0 | def name_of_function(arg1,arg2):
'''
This is where the function's Document String (doc-string) goes
'''
# Do stuff here
#return desired result
"""
Explanation: Functions
Introduction to Functions
This lecture will consist of explaining what a function is in Python and how to create one. Functions w... |
suchit-upx/suchit-upx.github.io | Titanic_Decision_trees_and_Random_Forest.ipynb | mit | import pandas as pd
from sklearn.preprocessing import Imputer
from sklearn import tree
from sklearn import metrics
import numpy as np
import matplotlib.pyplot as plt
% matplotlib inline
#train_df = pd.read_csv("titanic.csv")
#test_df = pd.read_csv("titanic_test.csv")
from google.colab import files
import io
uploaded ... |
pbeens/ICS-Computer-Studies | Python/Class Demos/Introduction to Data Science using Python.ipynb | mit | import numpy as np
"""
Explanation: From http://www.codemag.com/article/1611081
<h1>NumPy Array Basics</h1>
In NumPy, an array is of type ndarray (n-dimensional array). A NumPy array is an array of homogeneous values (all of the same type), and all items occupy a contiguous block of memory.
To use NumPy, you first ne... |
stellaxux/machine-learning-in-python | ch4/data_preprocessing.ipynb | mit | import pandas as pd
from io import StringIO
csv_data = '''A,B,C,D
1.0,2.0,3.0,4.0
5.0,6.0,,8.0
10.0,11.0,,'''
data = pd.read_csv(StringIO(csv_data))
## checking for missing data
df.isnull().sum()
# Another example of a dataframe with missing data
# creating dataframe from dictionary; key is the colume name
import n... |
bmorris3/gsoc2015 | presentation.ipynb | mit | # Altitude-azimuth frame:
from astropy.coordinates import SkyCoord, EarthLocation, AltAz
import astropy.units as u
from astropy.time import Time
# Specify location of Apache Point Observatory with astropy.coordinates.EarthLocation
apache_point = EarthLocation.from_geodetic(-105.82*u.deg, 32.78*u.deg, 2798*u.m)
# Spe... |
xzturn/tensorflow | tensorflow/lite/g3doc/models/style_transfer/overview.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... |
grehujt/SmallPythonProjects | jupyterNotebooks/ml_advice.ipynb | mit | import time
import numpy as np
np.random.seed(0)
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# <!-- collapse=True -->
# Modified from http://scikit-learn.org/stable/auto_examples/plot_learning_curve.html
from sklearn.learning_curve import learning_curve
def plot_learning_curve(estimator,... |
jseabold/statsmodels | examples/notebooks/ols.ipynb | bsd-3-clause | %matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import statsmodels.api as sm
from statsmodels.sandbox.regression.predstd import wls_prediction_std
np.random.seed(9876789)
"""
Explanation: Ordinary Least Squares
End of explanation
"""
nsample = 100
x = np.linspace(0, 10, 1... |
eneskemalergin/Data_Mining_Spring2017 | Final_Project/theAwesome_EnsModel.ipynb | mit | import pandas as pd
import matplotlib.pyplot as plt
# Read CSV data into df
df = pd.read_csv('./theAwesome_EnsModel.csv')
# delete id column no need
df.drop('Id',axis=1,inplace=True)
df.head()
# Learn the unique values in diagnosis column
print("Classification labels: ", df.Species.unique() )
# Mapping labels to num... |
junhwanjang/DataSchool | Lecture/05. 기초 선형 대수 1 - 행렬의 정의와 연산/2) NumPy 배열 생성과 변형.ipynb | mit | x = np.array([1, 2, 3])
x.dtype
"""
Explanation: NumPy 배열 생성과 변형
NumPy의 자료형
NumPy의 ndarray클래스는 포함하는 모든 데이터가 같은 자료형(data type)이어야 한다. 또한 자료형 자체도 일반 파이썬에서 제공하는 것보다 훨씬 세분화되어 있다.
NumPy의 자료형은 dtype 이라는 인수로 지정한다. dtype 인수로 지정할 값은 다음 표에 보인것과 같은 dtype 접두사로 시작하는 문자열이고 비트/바이트 수를 의미하는 숫자가 붙을 수도 있다.
| dtype 접두사 | 설명 | 사용 예 |
|-|-... |
IEMLdev/ieml-api | notebooks/USL.ipynb | gpl-3.0 | from ieml.usl.usl import usl
u = usl("[E:.b.E:B:.- E:S:. (E:.-wa.-t.o.-' E:.-'wu.-S:.-'t.o.-',)(a.T:.-) > ! E:.l.- (E:.wo.- E:S:.-d.u.-')]")
u.check()
print(u)
u1 = usl("[E:.b.E:B:.- E:S:. (E:.-'wu.-S:.-'t.o.-', E:.-wa.-t.o.-' )(a.T:.-) > ! E:.l.- (E:.wo.- E:S:.-d.u.-')]")
u1.check()
print(u1)
assert u1 == u
"""
Expla... |
PhonologicalCorpusTools/PolyglotDB | examples/tutorial/tutorial_2_enrichment.ipynb | mit | import os
from polyglotdb import CorpusContext
corpus_root = '/mnt/e/Data/pg_tutorial'
"""
Explanation: Tutorial 2: Adding extra information
Note
In general, enrichment can be performed in any order (i.e., speaker enrichment is independent of syllable encoding),
so you can perform the major sections in any order and... |
wittawatj/fsic-test | ipynb/nfsic_optimization.ipynb | mit | %load_ext autoreload
%autoreload 2
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
#%config InlineBackend.figure_format = 'pdf'
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import fsic.util as util
import fsic.data as data
import fsic.kernel as kernel
import fsic.indtest as it
im... |
open-forcefield-group/openforcefield | utilities/deprecated/convert_frosst/check_different_smirnoffs.ipynb | mit | # Imports
from __future__ import print_function
from convert_frcmod import *
import openeye.oechem as oechem
import openeye.oeiupac as oeiupac
import openeye.oeomega as oeomega
import openeye.oedepict as oedepict
from IPython.display import display
from openff.toolkit.typing.engines.smirnoff.forcefield import *
from op... |
ilogue/pyrsa | demos/example_dissimilarities.ipynb | lgpl-3.0 | # relevant imports
import numpy as np
from scipy import io
import matplotlib.pyplot as plt
import pyrsa
import pyrsa.data as rsd # abbreviation to deal with dataset
import pyrsa.rdm as rsr
# create a dataset object
measurements = io.matlab.loadmat('92imageData/simTruePatterns.mat')
measurements = measurements['simTrue... |
obscode/bootcamp | MoreNotebooks/Skyfit.ipynb | mit | import pandas as pd
data = pd.read_csv('data/skyfit.dat')
"""
Explanation: Putting it All Together
This notebook is a case study in working with python and several 3rd-party modules. There are many ways to attack a problem such as this; this is simply one way. The point is to illustrate how you can get existing module... |
mtasende/Machine-Learning-Nanodegree-Capstone | notebooks/prod/n08_simple_q_learner_fast_learner_11_actions.ipynb | mit | # Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error
from multiprocessing import Pool
%matplotlib inline
%pylab inline
pylab.rcPar... |
NYUDataBootcamp/Projects | UG_S17/Sairam Sivaraj-Climate.ipynb | mit | # Packages needed including Advanced Plotly functions
import pandas as pd # data package
import datetime as dt # date and time module
import numpy as np # foundation for Pandas
#WB functions
import wbdata
from pandas_datareader import wb # worldbank data
... |
Saytiras/StalkerML | Crawling Political Party Sites.ipynb | gpl-2.0 | domain = 'http://www.die-linke.de'
keyword = 'artikel'
site = 'http://www.die-linke.de/nc/die-linke/nachrichten'
pages = ['{}/browse/{}'.format(site, i) for i in range(1, 99)]
pages.append(site)
def get_data():
for page in pages:
try:
req = requests.get(page, timeout=10)
soup = Bea... |
gfabieno/SeisCL | docs/notebooks/ForwardModeling/1_SourcesReceivers.ipynb | gpl-3.0 | import matplotlib.pyplot as plt
import numpy as np
from SeisCL import SeisCL
seis = SeisCL()
"""
Explanation: Sources and receivers
Defining the sources and receiver position is necessary for any seismic simulation or inversion problem. This notebook shows how to do so, and present the different functionalities allowe... |
mdda/fossasia-2016_deep-learning | notebooks/5-RNN/6-RNN-Tagger-theano.ipynb | mit | import numpy as np
import theano
import lasagne
import os
import pickle
import time
SENTENCE_LENGTH_MAX = 32
EMBEDDING_DIM=50
"""
Explanation: RNN Tagger
This example trains a RNN to tag words from a corpus -
The data used for training is from a Wikipedia download, which is the artificially annotated with parts of ... |
astroumd/GradMap | notebooks/Haiti2016/python-basic.ipynb | gpl-3.0 | # setting a variable
a = 1.23
# although just writing the variable will show it's value, but this is not the recommended
# way, because per cell only the last one will be printed and stored in the out[]
# list that the notebook maintains
a
a+1
"""
Explanation: Some very basic python
Showing some very basic pytho... |
FFIG/ffig | demos/LLVM-Cauldron.ipynb | mit | outputfile = "Shape.h"
%%file $outputfile
#include <stdexcept>
#include <string>
#ifdef __clang__
#define C_API __attribute__((annotate("GENERATE_C_API")))
#else
#define C_API
#endif
#include <ffig/attributes.h>
struct FFIG_EXPORT Shape
{
virtual ~Shape() = default;
virtual double area() const = 0;
virtua... |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/launching_into_ml/solutions/explore_data.ipynb | apache-2.0 | !sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
from google.cloud import bigquery
import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
"""
Explanation: Explore and create ML datasets
In this notebook, we will explore data corresponding to taxi rides in New Yo... |
AutuanLiu/Python | nbs/func.ipynb | mit | %matplotlib inline
# 多行结果输出支持
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
"""
Explanation: 函数
End of explanation
"""
# 可变参数 packing and unpacking
def avg(first, *rest):
return (first + sum(rest)) / (1 + len(rest))
# Sample use
avg(1, 2) # 1.5
avg(1,... |
decisionstats/pythonfordatascience | text+mining.ipynb | apache-2.0 | import textmining
tdm = textmining.TermDocumentMatrix()
tdm.add_doc(raw)
for row in tdm.rows(cutoff=1):
print(row)
"""
Explanation: !pip install stemmer
For Python 3 from https://stackoverflow.com/questions/15717752/python3-3-importerror-with-textmining-1-0
Converting the textmining code to python3 so... |
ES-DOC/esdoc-jupyterhub | notebooks/test-institute-1/cmip6/models/sandbox-2/atmoschem.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', 'atmoschem')
"""
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: TEST-INSTITUTE-1
Source ID: SANDBOX-2
Topic: Atmoschem
Sub-Topic... |
PMEAL/OpenPNM-Examples | Topology/various_cubic_networks.ipynb | mit | import openpnm as op
wrk = op.Workspace()
wrk.logelevel=50
pn = op.network.Cubic(shape=[10, 10, 10], spacing=1)
"""
Explanation: Generate Cubic Lattices of Various Shape, Sizes and Topologies
The Cubic lattice network is easily the most commonly used pore network topology. When people first learn about pore network mo... |
WNoxchi/Kaukasos | misc/KMeans_tutorial_1_sentdex.ipynb | mit | # the μ's
centroids = kmeans.cluster_centers_
# these are the labels the KMeans Algo actually suplpies us
labels = kmeans.labels_
print(centroids)
print(labels)
colors = ["g.","r."] # green/red dots
# visualize dat points according to cluster
for i in range(len(X)):
print("coordinate:", X[i], "label:", labels... |
iurilarosa/thesis | codici/Archiviati/numpy/Prove numpy.ipynb | gpl-3.0 | unimatr = numpy.ones((10,10))
#unimatr
duimatr = unimatr*2
#duimatr
uniarray = numpy.ones((10,1))
#uniarray
triarray = uniarray*3
scalarray = numpy.arange(10)
scalarray = scalarray.reshape(10,1)
#NB fare il reshape da orizzontale a verticale è come se aggiungesse
#una dimensione all'array facendolo diventare un nda... |
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