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Description:
Histograma da imagem
Uma imagem normalmente é composta de um grande número de pixels; hoje em dia, celulares tiram fotografias com a resolução espacial... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.image as mpimg
f = mpimg.imread('../data/cameraman.tif')
print(f.min(), f.max())
%matplotlib inline
import matplotlib.pyplot as plt
plt.imshow(f, cmap = 'gray')
plt.colorbar()
nbins = 20
h, bin_edges = np.histogram(f, nbins,(0,255))
print('h=\n',h)... |
401 | <SYSTEM_TASK:>
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Description:
透過二元分類訓練 IMDB 評論資料
二元分類或稱兩類分類可能是在機器學習中應用最廣泛問題。只要處理的問題只有兩個結果，就可以適用。在這個例子中，我們將根據 IMDB 評論的文本內容將電影評論分為「正面」評論和「負面」評論。
關於 IMDB Dataset 資料集
IMDB Dataset 是來自 I... | <ASSISTANT_TASK:>
Python Code:
from keras.datasets import imdb
(train_data, train_labels), (test_data, test_labels) = imdb.load_data(num_words=10000)
Explanation: 透過二元分類訓練 IMDB 評論資料
二元分類或稱兩類分類可能是在機器學習中應用最廣泛問題。只要處理的問題只有兩個結果，就可以適用。在這個例子中，我們將根據 IMDB 評論的文本內容將電影評論分為「正面」評論和「負面」評論。
關於 IMDB Dataset 資料集
IMDB Dataset 是來自 Interne... |
402 | <SYSTEM_TASK:>
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Description:
Limb Darkening
Setup
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 ... | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.2,<2.3"
Explanation: Limb Darkening
Setup
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
%matp... |
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Description:
LAB 1a
Step2: The source dataset
Our dataset is hosted in BigQuery. The CDC's Natality data has details on US births from 1969 to 2008 and is a public... | <ASSISTANT_TASK:>
Python Code:
from google.cloud import bigquery
Explanation: LAB 1a: Exploring natality dataset.
Learning Objectives
Use BigQuery to explore natality dataset
Use Vertex AI Notebooks to plot data explorations
Introduction
In this notebook, we will explore the natality dataset before we begin model deve... |
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Description:
Threading
2018-01-07
<br>
Basics
A thread is the smallest unit that computer can schedule to run.
One process can contain multiple threads.
These threa... | <ASSISTANT_TASK:>
Python Code:
import threading
from _thread import start_new_thread, allocate_lock
import logging
import time
import numpy as np
Explanation: Threading
2018-01-07
<br>
Basics
A thread is the smallest unit that computer can schedule to run.
One process can contain multiple threads.
These threads share t... |
405 | <SYSTEM_TASK:>
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Description:
Convolutional Cubic Splines
$C^2$-continuous cubic splines through evenly spaced data points can be created by convolving the data points with a $C^2$-... | <ASSISTANT_TASK:>
Python Code:
import math
#given an array of Y values at consecutive integral x abscissas,
#return array of corresponding derivatives to make a natural cubic spline
def naturalSpline(ys):
vs = [0.0] * len(ys)
if (len(ys) < 2):
return vs
DECAY = math.sqrt(3)-2;
endi = len(ys... |
406 | <SYSTEM_TASK:>
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Description:
Processes
Introduction
In simulation and modelling we encounter a wide range of stochastic processes. But most fall into a few common categories
Step1:... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
Explanation: Processes
Introduction
In simulation and modelling we encounter a wide range of stochastic processes. But most fall into a few common categories: Ito processes, martingales, Markov processes, Gaussian processes, etc. We attempt to take this into account in ... |
407 | <SYSTEM_TASK:>
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Description:
Predicting Average Marks Based on Facebook Likes
Introduction
It is common for students to have a Facebook group on which they post course relevant dis... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['figure.figsize'] = 12, 10
plt.rcParams.update({'font.size': 15})
data = pd.read_csv('../data/train.csv')
data.describe()
Explanation: Predicting Average Marks Based on Facebook Likes
In... |
408 | <SYSTEM_TASK:>
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Description:
Given a 2D set of points spanned by axes $x$ and $y$ axes, we will try to fit a line that best approximates the data. The equation of the line, in slop... | <ASSISTANT_TASK:>
Python Code:
def generate_random_points_along_a_line (slope, intercept, num_points, abs_value, abs_noise):
# randomly select x
x = np.random.uniform(-abs_value, abs_value, num_points)
# y = mx + b + noise
y = slope*x + intercept + np.random.uniform(-abs_noise, abs_noise, num_points)
... |
409 | <SYSTEM_TASK:>
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Description:
Copyright 2019 Google LLC.
Licensed under the Apache License, Version 2.0 (the "License")
Step1: On Variational Bounds of Mutual Information
Ben Poole... | <ASSISTANT_TASK:>
Python Code:
# 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, sof... |
410 | <SYSTEM_TASK:>
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Description:
Variables
Step1: That seems to indicate this identity
Step2: Hmm... now that is a very interesting structure. I'm even more convinced that there's ... | <ASSISTANT_TASK:>
Python Code:
from scipy.special import legendre
q = 20
n_steps = 100000
t = np.linspace(0, 1, n_steps)
P = np.asarray([legendre(i)(2*t - 1) for i in range(q)]).T
total = np.zeros((q,q))
for Pt in P:
Ct = np.outer(Pt, Pt)
total += Ct / n_steps
plt.figure(figsize=(12,6))
plt.subplot(1, 2, 1... |
411 | <SYSTEM_TASK:>
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Description:
Copyright 2019 The TensorFlow Authors.
Step1: Load NumPy data
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_blank" href="ht... | <ASSISTANT_TASK:>
Python Code:
#@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 writin... |
412 | <SYSTEM_TASK:>
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Description:
Clase 10
Step1: 2. Uso de Pandas para descargar datos de precios de cierre
Bajar datos en forma de función
Step2: Una vez cargados los paquetes, es n... | <ASSISTANT_TASK:>
Python Code:
#importar los paquetes que se van a usar
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import datetime
from datetime import datetime
import scipy.stats as stats
import scipy as sp
import scipy.optimize as scopt
import matplotlib.pyplot as plt
import seaborn a... |
413 | <SYSTEM_TASK:>
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Description:
ANLP 2015 Text Classification Assignment
Emily Scharff and Juan Shishido
Write Up
Introduction
This notebook contains the code and documentation that w... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import re
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import nltk
from nltk.corpus import stopwords
from nltk.tokenize import regexp_tokenize
from nltk.stem.porter import PorterStemmer
from sklearn import cross_validation
from sklearn.feature_... |
414 | <SYSTEM_TASK:>
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Description:
Create a Dask cluster using Coiled
First, we'll create a Dask cluster with Coiled
Step1: Let's point the distributed client to the Dask cluster on Coi... | <ASSISTANT_TASK:>
Python Code:
import coiled
cluster = coiled.Cluster(n_workers=10)
Explanation: Create a Dask cluster using Coiled
First, we'll create a Dask cluster with Coiled:
End of explanation
from dask.distributed import Client
client = Client(cluster)
print('Dashboard:', client.dashboard_link)
Explanation: Let'... |
415 | <SYSTEM_TASK:>
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Description:
See http
Step1: not tested...
writer = pd.ExcelWriter('nd_out.xlsx')
nd.to_excel(writer,'Sheet1')
writer.save() | <ASSISTANT_TASK:>
Python Code:
from pandas.tools.plotting import scatter_matrix
p = scatter_matrix(nd.loc[:, use_col[:3]], alpha=0.2, figsize=(18, 12), diagonal='kde')
%%script bash --bg --out script_out
bokeh-server
nd.dtypes[:5]
use_col = [True if dtype in ['int64', 'float64'] else False for dtype in nd.dtypes]
from ... |
416 | <SYSTEM_TASK:>
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Description:
Vector space tutorial
The goal of this tutorial is to show how word co-occurrence statistics can be used to build their vectors, such that words that a... | <ASSISTANT_TASK:>
Python Code:
# This is a code cell. It can be executed by pressing CTRL+Enter
print('Hello')
Explanation: Vector space tutorial
The goal of this tutorial is to show how word co-occurrence statistics can be used to build their vectors, such that words that are similar in meaning are also close in a vec... |
417 | <SYSTEM_TASK:>
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Description:
Compare the read depth and number of strains
This data is the average read depth of each metagenome. The table in read_depth.strains.tsv has the SRA ID... | <ASSISTANT_TASK:>
Python Code:
#instantiate our environment
import os
import sys
%matplotlib inline
import pandas as pd
import statsmodels.api as sm
# read the data into a pandas dataframe
df = pd.read_csv("read_depth.strains.tsv", header=0, delimiter="\t")
print("Shape: {}".format(df.shape))
df.head()
Explanation: Com... |
418 | <SYSTEM_TASK:>
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Description:
Step1: Chicago taxi fare training experience
This experiment using Scikit-learn Random Forest to train a ML model on Chicago taxi dataset to estimate t... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from pandas_profiling import ProfileReport
from scipy import stats
from sklearn.ensemble import RandomForestRegressor
from sklearn.compose import ColumnTransformer
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
fr... |
419 | <SYSTEM_TASK:>
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Description:
ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era
Step1: Document Authors
Set document authors
Step2: Document Contributors
Specify document contribu... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'noaa-gfdl', 'sandbox-2', 'atmoschem')
Explanation: ES-DOC CMIP6 Model Properties - Atmoschem
MIP Era: CMIP6
Institute: NOAA-GFDL
Source ID: SANDBOX-2
Topic: Atmosche... |
420 | <SYSTEM_TASK:>
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Description:
Resolving Specifiers
Specifier grammars allow you to specify complex inputs for Batfish questions.
This category of questions reveals how specifier inp... | <ASSISTANT_TASK:>
Python Code:
bf.set_network('generate_questions')
bf.set_snapshot('generate_questions')
Explanation: Resolving Specifiers
Specifier grammars allow you to specify complex inputs for Batfish questions.
This category of questions reveals how specifier inputs are resolved
by Batfish.
Resolve Location Spec... |
421 | <SYSTEM_TASK:>
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Description:
内容概要
如何使用K折交叉验证来搜索最优调节参数
如何让搜索参数的流程更加高效
如何一次性的搜索多个调节参数
在进行真正的预测之前，如何对调节参数进行处理
如何削减该过程的计算代价
1. K折交叉验证回顾
交叉验证的过程
- 选择K的值（一般是10），将数据集分成K等份
- 使用其中的K-1份数据作为... | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn.grid_search import GridSearchCV
# read in the iris data
iris = load_iris()
# create X (features) and y (response)
X = iris.data
y = iri... |
422 | <SYSTEM_TASK:>
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Description:
Source of the materials
Step1: The SeqRecord Object
The SeqRecord (Sequence Record) class is defined in the Bio.SeqRecord module. This class allows hi... | <ASSISTANT_TASK:>
Python Code:
from Bio.SeqRecord import SeqRecord
Explanation: Source of the materials: Biopython Tutorial and Cookbook (adapted)
Sequence annotation objects
The previous notebook introduced the sequence classes. Immediately ``above'' the Seq class is the Sequence Record or SeqRecord class, defined in... |
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Description: | <ASSISTANT_TASK:>
Python Code:
def closest_integer(value):
'''
Create a function that takes a value (string) representing a number
and returns the closest integer to it. If the number is equidistant
from two integers, round it away from zero.
Examples
>>> closest_integer("10")
10
>>> clo... |
424 | <SYSTEM_TASK:>
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Description:
Step2: <center><h1>Probabilistic Programming in Quantitative Finance</h1><br>
<h3>Thomas Wiecki</h3>
<br>
<h3>@twiecki</h3>
<br>
<img width=40% src="ht... | <ASSISTANT_TASK:>
Python Code:
%pyplot inline
figsize(12, 12)
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import itertools
import scipy as sp
import pymc3 as pm
import theano.tensor as T
from scipy import stats
import scipy
data_0 = pd.read_csv('data0.csv', index_col=0, ... |
425 | <SYSTEM_TASK:>
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Description:
Python graphics
Step1: Comment. When you run the code cell above, its output appears below it.
Exercise. Enter pd.read_csv? in the empty cell below... | <ASSISTANT_TASK:>
Python Code:
# make plots show up in notebook
%matplotlib inline
import pandas as pd # data package
import matplotlib.pyplot as plt # pyplot module
Explanation: Python graphics: Matplotlib fundamentals
We illustrate three approaches to graphing data with... |
426 | <SYSTEM_TASK:>
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Description:
Sensors
Hi ha quatre sensors diferents montats i connectats al robot
Step1: Sensor de tacte
És un polsador, que segons estiga polsat o no, donarà un v... | <ASSISTANT_TASK:>
Python Code:
from functions import connect, touch, light, sound, ultrasonic, disconnect, next_notebook
connect()
Explanation: Sensors
Hi ha quatre sensors diferents montats i connectats al robot:
<img src="img/sensors.jpg" width=400>
Els de la figura corresponen al model NXT, però els de l'EV3 són equ... |
427 | <SYSTEM_TASK:>
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Description:
Adding Custom Operator Steps in Integration Schemes
In addition to forces that modify particle accelerations every timestep, we can use REBOUNDx to add... | <ASSISTANT_TASK:>
Python Code:
import rebound
import reboundx
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
def makesim():
sim = rebound.Simulation()
sim.G = 4*np.pi**2
sim.add(m=1.)
sim.add(m=1.e-4, a=1.)
sim.add(m=1.e-4, a=1.5)
sim.move_to_com()
return sim
Explanati... |
428 | <SYSTEM_TASK:>
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Description:
k-Nearest Neighbor (kNN) exercise
Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet... | <ASSISTANT_TASK:>
Python Code:
# Run some setup code for this notebook.
import random
import numpy as np
from cs231n.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
from __future__ import print_function
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a n... |
429 | <SYSTEM_TASK:>
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Description:
Self-Driving Car Engineer Nanodegree
Project
Step1: Read in an Image
Step9: Ideas for Lane Detection Pipeline
Some OpenCV functions (beyond those int... | <ASSISTANT_TASK:>
Python Code:
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
Explanation: Self-Driving Car Engineer Nanodegree
Project: Finding Lane Lines on the Road
In this project, you will use the tools you learned a... |
430 | <SYSTEM_TASK:>
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Description:
Basic Metrics
When we think about summarizing data, what are the metrics that we look at?
In this notebook, we will look at the car dataset
To read how... | <ASSISTANT_TASK:>
Python Code:
#Import the required libraries
import numpy as np
import pandas as pd
from datetime import datetime as dt
from scipy import stats
Explanation: Basic Metrics
When we think about summarizing data, what are the metrics that we look at?
In this notebook, we will look at the car dataset
To rea... |
431 | <SYSTEM_TASK:>
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Description:
In this notebook the datsets for the predictor will be generated.
Step1: Let's first define the list of parameters to use in each dataset.
Step2: Now... | <ASSISTANT_TASK:>
Python Code:
# 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
%matplotlib inline
%pylab inline
pylab.rcParams[... |
432 | <SYSTEM_TASK:>
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Description:
Chapter 7 - Sets
This chapter will introduce a different kind of container
Step1: Curly brackets surround sets, and commas separate the elements in th... | <ASSISTANT_TASK:>
Python Code:
a_set = {1, 2, 3}
a_set
empty_set = set() # you have to use set() to create an empty set! (we will see why later)
print(empty_set)
Explanation: Chapter 7 - Sets
This chapter will introduce a different kind of container: sets. Sets are unordered lists with no duplicate entries. You might w... |
433 | <SYSTEM_TASK:>
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Description:
Image features exercise
Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with you... | <ASSISTANT_TASK:>
Python Code:
import random
import numpy as np
from skynet.utils.data_utils import load_CIFAR10
import matplotlib.pyplot as plt
%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'... |
434 | <SYSTEM_TASK:>
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Description:
Files
File is a named location on disk to store related information. Python uses the file objects to interact with external files on the computer. Thes... | <ASSISTANT_TASK:>
Python Code:
%%writefile test.txt
This is a test file
Explanation: Files
File is a named location on disk to store related information. Python uses the file objects to interact with external files on the computer. These files could be of any format like text, binary, excel, audio, video files. Please ... |
435 | <SYSTEM_TASK:>
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Description:
Análise de um oscilador com N graus de liberdade sujeito a uma excitação dinâmica aplicada nalguns graus de liberdade
Formulação do problema
Equação de... | <ASSISTANT_TASK:>
Python Code:
import sys
import math
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
%matplotlib inline
print('System: {}'.format(sys.version))
for package in (np, mpl):
print('Package: {} {}'.format(package.__name__, package.__version__))
Explanation: Análise de um osci... |
436 | <SYSTEM_TASK:>
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Description:
Creating Ukulele Chord Diagrams in SVG with Python
With the Python modul uchord you can create ukulele chord diagrams in SVG format.
Step1: <img src="... | <ASSISTANT_TASK:>
Python Code:
import uchord
uchord.write_chord('c.svg','C','0003')
Explanation: Creating Ukulele Chord Diagrams in SVG with Python
With the Python modul uchord you can create ukulele chord diagrams in SVG format.
End of explanation
pip install uchord
Explanation: <img src="pic/c.svg" align="left"><br><... |
437 | <SYSTEM_TASK:>
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Description:
Components
Step1: IPython Console
IPython started as a terminal based interactive console with tab completion, integrated help, plotting support, etc.... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import display, Image, HTML
from talktools import website, nbviewer
Explanation: Components
End of explanation
Image('images/ipython_console.png')
Explanation: IPython Console
IPython started as a terminal based interactive console with tab completion, integrated help... |
438 | <SYSTEM_TASK:>
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Description:
Community Node Layout / Bundled Edges
Step1: Alternatively, the best partition into communities can be inferred, for example
using the Louvain algorit... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import networkx as nx
from netgraph import Graph
# create a modular graph
partition_sizes = [10, 20, 30, 40]
g = nx.random_partition_graph(partition_sizes, 0.5, 0.1)
# create a dictionary that maps nodes to the community they belong to
node_to_community = d... |
439 | <SYSTEM_TASK:>
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Description:
Antenna Position Measurement
Author
Step1: Locating the third reference point
The distances from reference point 2 to the other two reference points (... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.optimize import minimize
x0 = [0,0]
x1 = [0, 2209]
Explanation: Antenna Position Measurement
Author: Tim Molteno. tim@elec.ac.nz.
The antennas are laid out on tiles, and these tiles are placed on site. Once this is done, a survey is needed to refine the posi... |
440 | <SYSTEM_TASK:>
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Description:
Copyright 2019 Google LLC
Step1: scikit-learn Training on AI Platform
This notebook uses the Census Income Data Set to demonstrate how to train a mode... | <ASSISTANT_TASK:>
Python Code:
# 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, sof... |
441 | <SYSTEM_TASK:>
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Problem:
I have a pandas DataFrame data | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from sklearn import preprocessing
data = load_data()
df_out = pd.DataFrame(preprocessing.scale(data), index=data.index, columns=data.columns)
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442 | <SYSTEM_TASK:>
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Description:
Plot train and valid set NLL
Step1: Plot ratio of update norms to parameter norms across epochs for different layers | <ASSISTANT_TASK:>
Python Code:
tr = np.array(model.monitor.channels['valid_y_y_1_nll'].time_record) / 3600.
fig = plt.figure(figsize=(12,8))
ax1 = fig.add_subplot(111)
ax1.plot(model.monitor.channels['valid_y_y_1_nll'].val_record)
ax1.plot(model.monitor.channels['train_y_y_1_nll'].val_record)
ax1.plot(model_no_mom.moni... |
443 | <SYSTEM_TASK:>
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Description:
Plot 1
Step1: Description
Step2: Load data and take a peak at it.
Step3: Separate data into training, validation, and test sets. (This division is n... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import display, HTML
display(HTML('''<img src="image1.png",width=800,height=500">'''))
Explanation: Plot 1: The predictive potential of rank difference
End of explanation
import numpy as np # numerical libraries
import pandas as pd # for data analysis
import matplotli... |
444 | <SYSTEM_TASK:>
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Description:
Загрузка данных
Step1: Выходная переменная
Step2: Посмотрим самые частые и самые редкие категории.
Step3: 7 = сигареты
6 = продукты питания
32 = поз... | <ASSISTANT_TASK:>
Python Code:
train_data = pd.read_csv('./data/evo_train.csv', sep=',', header=0)
test_data = pd.read_csv('./data/evo_test.csv', sep=',', header=0)
print train_data.shape
print test_data.shape
train_data.head()
Explanation: Загрузка данных
End of explanation
train_data.GROUP_ID.value_counts()
Explanati... |
445 | <SYSTEM_TASK:>
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Description:
DataLoaders
The DataLoader class
Step1: DataLoader helpers
fastai includes a replacement for Pytorch's DataLoader which is largely API-compatible, and... | <ASSISTANT_TASK:>
Python Code:
#|export
from __future__ import annotations
from fastai.torch_basics import *
from torch.utils.data.dataloader import _MultiProcessingDataLoaderIter,_SingleProcessDataLoaderIter,_DatasetKind
_loaders = (_MultiProcessingDataLoaderIter,_SingleProcessDataLoaderIter)
#|hide
from nbdev.showdoc... |
446 | <SYSTEM_TASK:>
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Description:
Introduzione a Pandas
Pandas è una libreria, costruita sulla base della libreria numpy, che ha lo scopo di manipolare data frames.
Oggetto di tipo Data... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
Explanation: Introduzione a Pandas
Pandas è una libreria, costruita sulla base della libreria numpy, che ha lo scopo di manipolare data frames.
Oggetto di tipo DataFrame = tabella organizzata in righe (records) e colonne intestate.
Pandas offre tre funzionalità princip... |
447 | <SYSTEM_TASK:>
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Description:
Test suite for Jupyter-notebook
Sample example of use of PyCOMPSs from Jupyter
First step
Import ipycompss library
Step1: Second step
Initialize COMPS... | <ASSISTANT_TASK:>
Python Code:
import pycompss.interactive as ipycompss
Explanation: Test suite for Jupyter-notebook
Sample example of use of PyCOMPSs from Jupyter
First step
Import ipycompss library
End of explanation
ipycompss.start(graph=True, trace=True, debug=True, project_xml='../project.xml', resources_xml='../r... |
448 | <SYSTEM_TASK:>
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Description:
Lecture 3
Step1: We'll train a logistic regression model of the form
$$
p(y = 1 ~|~ {\bf x}; {\bf w}) = \frac{1}{1 + \textrm{exp}[-(w_0 + w_1x_1 + w_... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
from sklearn import datasets
iris = datasets.load_iris()
X_train = iris.data[iris.target != 2, :2] # first two features and
y_train = iris.target[iris.target != 2] # first two labels only
fig = plt.figure(figsize=(8,8))
mycolors = {"b... |
449 | <SYSTEM_TASK:>
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Description:
Index
Weak-field approximation for Stokes V
Strong-field approximation for Stokes V
Longitudinal magnetograph
Center-of-gravity
Unresolved fields - inc... | <ASSISTANT_TASK:>
Python Code:
lambda0 = 6301.5
JUp = 1.0
JLow = 1.0
gUp = 2.5
gLow = 0.0
lambdaStart = 6300.8
lambdaStep = 0.01
nLambda = 150
wavelength = lambdaStart + np.arange(nLambda) * lambdaStep
lineInfo = np.asarray([lambda0, JUp, JLow, gUp, gLow, lambdaStart, lambdaStep])
s = pymilne.milne(nLambda, lineInfo)
B... |
450 | <SYSTEM_TASK:>
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Description:
How to set priors on stellar parameters.
gully
https
Step1: We want a a continuous prior
Step2: The normalization doesn't matter, but it's nice to kn... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
% config InlineBackend.figure_format = 'retina'
Explanation: How to set priors on stellar parameters.
gully
https://github.com/iancze/Starfish/issues/32
The strategy here is to define a lnprior and... |
451 | <SYSTEM_TASK:>
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Description:
network(), radar() and site() objects
This notebook introduces the high-level python interface with the radar.dat and hdw.dat content.
For more in-de... | <ASSISTANT_TASK:>
Python Code:
# Import radar module
%pylab inline
from davitpy.pydarn.radar import *
Explanation: network(), radar() and site() objects
This notebook introduces the high-level python interface with the radar.dat and hdw.dat content.
For more in-depth access (i.e., your own hdw.dat), look at the radIn... |
452 | <SYSTEM_TASK:>
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Description:
Statistics
The executed version of this tutorial is at https
Step1: The function requires four parameters
Step2: The nice thing about Quantities is t... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from quantities import ms, s, Hz
from elephant.spike_train_generation import homogeneous_poisson_process, homogeneous_gamma_process
help(homogeneous_poisson_process)
Explanation: Statistics
The executed version of this ... |
453 | <SYSTEM_TASK:>
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Description:
<a id="Top"></a>
___ ___ ___
_____ /\__\ ... | <ASSISTANT_TASK:>
Python Code:
# Standard library
import datetime
import time
# Third party libraries
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Digitre code
import digitre_preprocessing as prep
import digitre_model
import digitre_classifier
# Reload digitre code in the same session (during... |
454 | <SYSTEM_TASK:>
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Description:
12-752
Step1: Short Introduction to Python and Jupyter
Jupyter notebooks consist of cells. This cell is a Markdown cell. Try double-clicking this cell... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import sys
print('Python version:')
print(sys.version)
print('Numpy version:')
print(np.__version__)
import sklearn
print('Sklearn version:')
print(sklearn.__version__)
Explanation: 12-752: Data-Driven Building Energy ... |
455 | <SYSTEM_TASK:>
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Description:
(NVM)=
1.3 Normas vectoriales y matriciales
```{admonition} Notas para contenedor de docker
Step1: Norma $2$
Step2: Norma $1$
Step3: Norma $\infty$
... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
Explanation: (NVM)=
1.3 Normas vectoriales y matriciales
```{admonition} Notas para contenedor de docker:
Comando de docker para ejecución de la nota de forma local:
nota: cambiar <ruta a mi directorio> por la ruta de directorio que... |
456 | <SYSTEM_TASK:>
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Description:
syncID
Step1: Define functions
Next, we'll define a few functions that we will use throughout the code.
Step2: This next piece of code just helps ide... | <ASSISTANT_TASK:>
Python Code:
import sys
sys.version
import gdal
import h5py
import numpy as np
from math import floor
import os
import matplotlib.pyplot as plt
Explanation: syncID: a6db1047adb34f41b9d17d6ed41f5fd5
title: "Exploring Uncertainty in LiDAR Data using Python"
description: "Learn to analyze the difference ... |
457 | <SYSTEM_TASK:>
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Description:
Smooth Tree
Single decision trees generally overfit, leading to poor predictive performance. Tree ensembles (RF, GBM) perform well, but are black-box m... | <ASSISTANT_TASK:>
Python Code:
from arboretum.datasets import load_diabetes
xtr, ytr, xte, yte = load_diabetes()
xtr.shape, xte.shape
Explanation: Smooth Tree
Single decision trees generally overfit, leading to poor predictive performance. Tree ensembles (RF, GBM) perform well, but are black-box models. In this noteboo... |
458 | <SYSTEM_TASK:>
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Description:
<h1> Preprocessing using Cloud Dataflow </h1>
<h2>Learning Objectives</h2>
<ol>
<li>Create ML dataset using <a href="https
Step1: After installing... | <ASSISTANT_TASK:>
Python Code:
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.1
%pip install apache-beam[gcp]==2.13.0
Explanation: <h1> Preprocessing using Cloud Dataflow </h1>
<h2>Learning Objectives</h2>
<ol>
<li>Create ML dataset using <a href="https://cloud.google.com/da... |
459 | <SYSTEM_TASK:>
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Description:
TPOT tutorial on the Titanic dataset
The Titanic machine learning competition on Kaggle is one of the most popular beginner's competitions on the platf... | <ASSISTANT_TASK:>
Python Code:
# Import required libraries
from tpot import TPOTClassifier
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
# Load the data
titanic = pd.read_csv('data/titanic_train.csv')
titanic.head(5)
Explanation: TPOT tutorial on the Titanic dataset
The Ti... |
460 | <SYSTEM_TASK:>
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Description:
Session 2
Step1: import
The Python import statement makes other packages available to your current session.
There are a few forms of import, two of wh... | <ASSISTANT_TASK:>
Python Code:
import veneer
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
Explanation: Session 2: Quick tour of Veneer
Main features of Veneer (and veneer-py)
Starting a new notebook
Querying models
Running models and Retrieving results
Manipulating model set... |
461 | <SYSTEM_TASK:>
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Description:
TF-IDF based Recommender System
Recommender System based on tf-idf as vector representation of documents
TF-IDF Based Recommender
Represent articles in... | <ASSISTANT_TASK:>
Python Code:
PATH_NEWS_ARTICLES="/home/phoenix/Documents/HandsOn/Final/news_articles.csv"
ARTICLES_READ=[2,7]
NUM_RECOMMENDED_ARTICLES=5
try:
import numpy
import pandas as pd
import pickle as pk
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwi... |
462 | <SYSTEM_TASK:>
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Description:
Step1: Python Basics (2016-09-09)
Content
Comments
Data Types
Simple Arithmetics
Strings
Comments
Comments provide important documentation for your cod... | <ASSISTANT_TASK:>
Python Code:
# this is a single line comment
this is a
multi line
comment
Explanation: Python Basics (2016-09-09)
Content
Comments
Data Types
Simple Arithmetics
Strings
Comments
Comments provide important documentation for your code.
End of explanation
a = 5.1
print 'a', type(a)
b = 3
print 'b', type... |
463 | <SYSTEM_TASK:>
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Description:
DMDU 2019 Training Day - Introduction to SALib
Will Usher
Assistant Professor, Division of Energy Systems Analysis, KTH Royal Institute of Technology
H... | <ASSISTANT_TASK:>
Python Code:
from ipywidgets import widgets, interact
from IPython.display import display
import seaborn as sbn
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
from IPython.core.pylabtools import figsize
sbn.set_context("talk", font_scale=.8)
figsize(10, 8)
# The model used for t... |
464 | <SYSTEM_TASK:>
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Description:
DoWhy example on Twins dataset
Here we study the twins dataset as studied by <a href="https
Step1: <font size="4">Load the Data</font>
The data loadin... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import dowhy
from dowhy import CausalModel
from dowhy import causal_estimators
# Config dict to set the logging level
import logging.config
DEFAULT_LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'loggers': {
'': {
... |
465 | <SYSTEM_TASK:>
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Description:
Continuing Graphs of Specimens Over Time - Continents and GBIF
This notebook continues the work done in 01_iDigBio_Specimens_Collected_Over_Time.ipynb,... | <ASSISTANT_TASK:>
Python Code:
# col() selects columns from a data frame, year() works on dates, and udf() creates user
# defined functions
from pyspark.sql.functions import col, year, udf
# Plotting library and configuration to show graphs in the notebook
import matplotlib.pyplot as plt
%matplotlib inline
Explanation:... |
466 | <SYSTEM_TASK:>
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Description:
<a href='http
Step1: <font color=green>In the above sentence, running, run and ran all point to the same lemma run (...11841) to avoid duplication.</f... | <ASSISTANT_TASK:>
Python Code:
# Perform standard imports:
import spacy
nlp = spacy.load('en_core_web_sm')
doc1 = nlp(u"I am a runner running in a race because I love to run since I ran today")
for token in doc1:
print(token.text, '\t', token.pos_, '\t', token.lemma, '\t', token.lemma_)
Explanation: <a href='http:/... |
467 | <SYSTEM_TASK:>
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Description:
Copyright 2020 DeepMind Technologies Limited
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compli... | <ASSISTANT_TASK:>
Python Code:
!pip install tensorflow==1.15 dm-sonnet==1.36 tensor2tensor==1.14
import time
import numpy as np
import tensorflow.compat.v1 as tf
tf.logging.set_verbosity(tf.logging.ERROR) # Hide TF deprecation messages
import matplotlib.pyplot as plt
%cd /tmp
%rm -rf /tmp/deepmind_research
!git clone ... |
468 | <SYSTEM_TASK:>
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Description:
Test of POPIII star input
Test of SSP with POPIII yields. Focus are basic GCE features.
You can find the documentation <a href="doc/sygma.html">here</a... | <ASSISTANT_TASK:>
Python Code:
%pylab nbagg
import sygma as s
reload(s)
s.__file__
#from imp import *
#s=load_source('sygma','/home/nugrid/nugrid/SYGMA/SYGMA_online/SYGMA_dev/sygma.py')
from scipy.integrate import quad
from scipy.interpolate import UnivariateSpline
import matplotlib.pyplot as plt
import numpy as np
Exp... |
469 | <SYSTEM_TASK:>
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Description:
Natural language image search with a Dual Encoder
Author
Step1: Prepare the data
We will use the MS-COCO dataset to train our
dual encoder model. MS-C... | <ASSISTANT_TASK:>
Python Code:
import os
import collections
import json
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
import tensorflow_hub as hub
import tensorflow_text as text
import tensorflow_addons as tfa
import matplotlib.pyplot as plt
import matplotli... |
470 | <SYSTEM_TASK:>
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Description:
1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
Step1: 2. Get Cloud P... | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/google/starthinker
Explanation: 1. Install Dependencies
First install the libraries needed to execute recipes, this only needs to be done once, then click play.
End of explanation
CLOUD_PROJECT = 'PASTE PROJECT ID HERE'
print("Cloud Project Set To: %s" ... |
471 | <SYSTEM_TASK:>
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Description:
Copyright 2021 The TensorFlow Authors.
Step1: TensorFlow Lite Model Analyzer
<table class="tfo-notebook-buttons" align="left">
<td>
<a target="_... | <ASSISTANT_TASK:>
Python Code:
#@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 writin... |
472 | <SYSTEM_TASK:>
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Description:
Executed
Step1: Notebook arguments
measurement_id (int)
Step2: Selecting a data file
Step3: Data load and Burst search
Load and process the data
Ste... | <ASSISTANT_TASK:>
Python Code:
measurement_id = 0
windows = (60, 180)
# Cell inserted during automated execution.
windows = (30, 180)
measurement_id = 1
Explanation: Executed: Tue Mar 28 00:43:40 2017
Duration: 41 seconds.
End of explanation
import time
from pathlib import Path
import pandas as pd
from scipy.stats impo... |
473 | <SYSTEM_TASK:>
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Description:
Tutorial "Algorithmic Methods for Network Analysis with NetworKit" (Part 2)
Step1: Eulerian Cycles
Before we look at different network types, let us r... | <ASSISTANT_TASK:>
Python Code:
from networkit import *
%matplotlib inline
cd ~/workspace/NetworKit
G = readGraph("input/PGPgiantcompo.graph", Format.METIS)
Explanation: Tutorial "Algorithmic Methods for Network Analysis with NetworKit" (Part 2)
End of explanation
# 2-2) and 2-3) Decide whether graph is Eulerian or not
... |
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Description:
Configuring MNE python
This tutorial gives a short introduction to MNE configurations.
Step1: MNE-python stores configurations to a folder called .mne... | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import mne
from mne.datasets.sample import data_path
fname = op.join(data_path(), 'MEG', 'sample', 'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(fname).crop(0, 10)
original_level = mne.get_config('MNE_LOGGING_LEVEL', 'INFO')
Explanation: Configuring MNE python
Th... |
475 | <SYSTEM_TASK:>
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Description:
Chapter 3
Examples and Exercises from Think Stats, 2nd Edition
http
Step1: Again, I'll load the NSFG pregnancy file and select live births
Step2: Her... | <ASSISTANT_TASK:>
Python Code:
from os.path import basename, exists
def download(url):
filename = basename(url)
if not exists(filename):
from urllib.request import urlretrieve
local, _ = urlretrieve(url, filename)
print("Downloaded " + local)
download("https://github.com/AllenDowney/Thin... |
476 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Ferrofluid - Part 2
Table of Contents
Applying an external magnetic field
Magnetization curve
Remark
Step1: and set up the simulation parameters where... | <ASSISTANT_TASK:>
Python Code:
import espressomd
import espressomd.magnetostatics
import espressomd.magnetostatic_extensions
espressomd.assert_features('DIPOLES', 'LENNARD_JONES')
import numpy as np
Explanation: Ferrofluid - Part 2
Table of Contents
Applying an external magnetic field
Magnetization curve
Remark: The eq... |
477 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
CSAL4243
Step1: Feature Scaling and Mean Normalization
Step2: Initialize Hyper Parameters
Step3: Model/Hypothesis Function
Step5: Cost Function
Ste... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
from sklearn import linear_model
import matplotlib.pyplot as plt
import matplotlib as mpl
# read data in pandas frame
dataframe = pd.read_csv('datasets/house_dataset2.csv', encoding='utf-8')
# check data by printing first few rows
... |
478 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Short introduction to working with DEMs in Python GDAL
Greg Tucker, CU Boulder, Feb 2016
Install GDAL library
You'll need to install the GDAL library. ... | <ASSISTANT_TASK:>
Python Code:
from osgeo import gdal
import numpy as np
Explanation: Short introduction to working with DEMs in Python GDAL
Greg Tucker, CU Boulder, Feb 2016
Install GDAL library
You'll need to install the GDAL library. If you have Anaconda installed, you can do this from the command line by:
conda ins... |
479 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Configuring the model, running g-tools and output files and infos
This tutorial shows how to configure the model and how to run Fermipy-LAT g-tools wit... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import numpy as np
from fermipy.gtanalysis import GTAnalysis
from fermipy.plotting import ROIPlotter, SEDPlotter
import matplotlib.pyplot as plt
import matplotlib
from IPython.display import Image
Explanation: Configuring the model, running g-tools and output ... |
480 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Tutorial "Algorithmic Methods for Network Analysis with NetworKit" (Part 1)
Welcome to the hands-on session of our tutorial! This tutorial is based on ... | <ASSISTANT_TASK:>
Python Code:
from networkit import *
%matplotlib inline
Explanation: Tutorial "Algorithmic Methods for Network Analysis with NetworKit" (Part 1)
Welcome to the hands-on session of our tutorial! This tutorial is based on the user guide of NetworKit, our network analysis software. You will learn in this... |
481 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
GLM
Step1: The Adult Data Set is commonly used to benchmark machine learning algorithms. The goal is to use demographic features, or variables, to pre... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import pymc3 as pm
import matplotlib.pyplot as plt
import seaborn
import warnings
warnings.filterwarnings('ignore')
from collections import OrderedDict
from time import time
import numpy as np
import pandas as pd
import matplotlib.... |
482 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
BOW model and Naive Bayes
Step1: Table of Contents
BOW model and Naive Bayes
Rotten Tomatoes data set
Explore
The Vector space model and a search engi... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import scipy as sp
import matplotlib as mpl
import matplotlib.cm as cm
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_html', True)
... |
483 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Logarithmic Parameters
This notebook explores Bayesian optimisation of a function who's parameter is best thought of logarithmically (the order of magn... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
from IPython.core.debugger import Tracer # debugging
from IPython.display import clear_output, display
import time
%matplotlib inline
#%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
import seaborn as sns; sns.set() # prettify... |
484 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Python for Webscraping
SOC 590
Step1: open US News Rankings for Sociology webpage
view page source to see html
Step3: create a function to extract pa... | <ASSISTANT_TASK:>
Python Code:
import os
import urllib
import webbrowser
import pandas as pd
from bs4 import BeautifulSoup
Explanation: Python for Webscraping
SOC 590: Big Data and Population Processes
17th October 2016
Tutorial 2: Webscraping with a function
Outline
Import modules
Examine html structure of a webpage
U... |
485 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
How to locally run parallel code with mpi4py in an IPython notebook
Step1: Now, to make the code run on all of our engines (and not just on one), the ... | <ASSISTANT_TASK:>
Python Code:
from ipyparallel import Client
import os
c = Client()
view = c[:]
print(c.ids)
%%px
def find(name, path):
for root, dirs, files in os.walk(path):
if name in files:
return root
path = find('02_LocalParallelization.ipynb', '/home/')
print(path)
os.chdir(path)
Explana... |
486 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Pyflex
Pyflex is a Python port of the FLEXWIN algorithm for automatically selecting windows for seismic tomography. For the most part it mimicks the ca... | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import obspy
import pyflex
Explanation: Pyflex
Pyflex is a Python port of the FLEXWIN algorithm for automatically selecting windows for seismic tomography. For the most part it mimicks the calculations of the original FLEXWIN package; minor differences and their reasoning ar... |
487 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Regression Week 2
Step1: Load in house sales data
Dataset is from house sales in King County, the region where the city of Seattle, WA is located.
Ste... | <ASSISTANT_TASK:>
Python Code:
import graphlab
Explanation: Regression Week 2: Multiple Regression (gradient descent)
In the first notebook we explored multiple regression using graphlab create. Now we will use graphlab along with numpy to solve for the regression weights with gradient descent.
In this notebook we will... |
488 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Protein - Structure Mapping, Alignments, and Visualization
This notebook gives an example of how to map a single protein sequence to its structure, alo... | <ASSISTANT_TASK:>
Python Code:
import sys
import logging
# Import the Protein class
from ssbio.core.protein import Protein
# Printing multiple outputs per cell
from IPython.core.interactiveshell import InteractiveShell
InteractiveShell.ast_node_interactivity = "all"
Explanation: Protein - Structure Mapping, Alignments,... |
489 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Using OPSPiggybacker to stub in test data for analysis
One of the main uses of OPSPiggybacker is to make data from one source readable for OPS analysis... | <ASSISTANT_TASK:>
Python Code:
from openpathsampling.tests.test_helpers import make_1d_traj
left_state_edge = 0.0
right_state_edge = 10.0
def make_traj(suffix, stride=1):
frame = left_state_edge -1.0 + suffix
coords = [frame]
while frame < right_state_edge:
frame += 1.0*stride
coords.append(... |
490 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
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Description:
Much of the world isn't mapped. This seems odd at first, but it basically comes down to a question of cash, and a large chunk of the world doesn't have... | <ASSISTANT_TASK:>
Python Code:
from mapswipe_analysis import *
all_projects_solution = Solution(
ground_truth_solutions_file_to_map('../experiment_1/all_projects_dataset/test/solutions.csv'),
predictions_file_to_map('../experiment_1/inception_v3_all_layers.results')
)
all_projects_solution.accuracy
Explanation:... |
491 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Introduction
Run this cell to set everything up!
Step1: Examine the following seasonal plot
Step2: And also the periodogram
Step3: 1) Determine seas... | <ASSISTANT_TASK:>
Python Code:
# Setup feedback system
from learntools.core import binder
binder.bind(globals())
from learntools.time_series.ex3 import *
# Setup notebook
from pathlib import Path
from learntools.time_series.style import * # plot style settings
from learntools.time_series.utils import plot_periodogram,... |
492 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
<h1> Create Keras Wide-and-Deep model </h1>
<h2>Learning Objectives</h2>
<ol>
<li>Use the tf.data API to create our ML datasets</li>
<li>Use the Keras ... | <ASSISTANT_TASK:>
Python Code:
# change these to try this notebook out
BUCKET = 'cloud-training-demos-ml'
PROJECT = 'cloud-training-demos'
REGION = 'us-central1'
import os
os.environ['BUCKET'] = BUCKET
os.environ['PROJECT'] = PROJECT
os.environ['REGION'] = REGION
%%bash
if ! gsutil ls | grep -q gs://${BUCKET}/; then
... |
493 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Copyright 2018 The TensorFlow Hub Authors.
Licensed under the Apache License, Version 2.0 (the "License");
Step1: How to build a simple text classifie... | <ASSISTANT_TASK:>
Python Code:
# 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... |
494 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
DSFP Object Oriented Programming Notebook
Incorporating classes, objects, and functions into your code will improve its efficiency, readability, and ma... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import random
import numpy as np
%matplotlib inline
Explanation: DSFP Object Oriented Programming Notebook
Incorporating classes, objects, and functions into your code will improve its efficiency, readability, and make it easier to extend to other programs ... |
495 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
How to import the data
1. Define search filters. This is needed if some data has to be filtered out.
2. Import data from ase databases.
3. Store refere... | <ASSISTANT_TASK:>
Python Code:
# Import and instantiate energy_landscape object.
from catmap.api.ase_data import EnergyLandscape
energy_landscape = EnergyLandscape()
# Import all gas phase species from db.
search_filter_gas = []
energy_landscape.get_molecules('molecules.db', selection=search_filter_gas)
# Import all ad... |
496 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Pandas
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
Explanation: Pandas
pandas is a Python package providing fast, flexible, and expressive data structures designed to make working with “relational” or “labeled” data both easy and intuitive. It aims t... |
497 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Agile and Test-Driven Development
TDD Worked Example
Robert Haines, University of Manchester, UK
Adapted from "Test-Driven Development By Example", Ken... | <ASSISTANT_TASK:>
Python Code:
import unittest
def run_tests():
suite = unittest.TestLoader().loadTestsFromTestCase(TestFibonacci)
unittest.TextTestRunner().run(suite)
Explanation: Agile and Test-Driven Development
TDD Worked Example
Robert Haines, University of Manchester, UK
Adapted from "Test-Driven Developm... |
498 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Character Sequence to Sequence
In this notebook, we'll build a model that takes in a sequence of letters, and outputs a sorted version of that sequence... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import time
import helper
source_path = 'data/letters_source.txt'
target_path = 'data/letters_target.txt'
source_sentences = helper.load_data(source_path)
target_sentences = helper.load_data(target_path)
Explanation: Character Sequence to Sequence
In this notebook, we'l... |
499 | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Compute a sparse inverse solution using the Gamma-Map empirical Bayesian method
See [1]_ for details.
References
.. [1] D. Wipf, S. Nagarajan
"A uni... | <ASSISTANT_TASK:>
Python Code:
# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.inverse_sparse import gamma_map, make_stc_from_dipoles
from mne.viz import... |
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