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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1-D series
Step2: DataFrame
Step3: Data types
Step4: converting types
Step5: Filtering with Pandas
Step6: Creating a new index not on the v... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
s = pd.Series([3, 5, 67, 2, 4])
s
s.name = "OneDArray"
s
s.index
s.values
s.sum()
s.min()
s.count()
s * 3
s.sort_values()
s.value_counts()
s.abs?
eu = pd.read_csv('data/eu_revolving_loans.csv',
header=[1,2,3], index_col=0, skiprows=1)
eu.tail(4)
eu.in... |
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Description:
Step1: Integrating MinDiff with MinDiffModel
Step2: First, download the data. For succinctness, the input preparation logic has been factored out into... | <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... |
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Description:
Step1: We will output to a static html file.
Step2: See many examples of configuring plot tools at http
Step3: Here we'll interact with Glue from th... | <ASSISTANT_TASK:>
Python Code:
import bokeh
import numpy as np
from astropy.table import Table
sdss = Table.read('data/sdss_galaxies_qsos_50k.fits')
sdss
from bokeh.models import ColumnDataSource
from bokeh.plotting import figure, gridplot, output_notebook, output_file, show
umg = sdss['u'] - sdss['g']
gmr = sdss['g'] ... |
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Description:
Step1: Load data
Step2: Create the figure
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Python Code:
import shapefile
import numpy as np
from matplotlib import cm, rcParams
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
rcParams.update({'font.size': 16}) # Increase font-size
# Load the topo file to get header information
etopo1name = 'data/basemap/etopo1_bedroc... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Online Prediction with scikit-learn on AI Platform
Step2: Download the data
Step3: Part 1
Step4: Part 2
Step5: Note
Step6: Part 3
Step7: P... | <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... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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Python Code:
import numpy as np
value = 1.0
result = np.degrees(np.arcsin(value))
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Description:
Step1: The following example loads a cube from iris-sample-data and displays it as follows
Step2: With HoloViews, you can quickly view the data in the... | <ASSISTANT_TASK:>
Python Code:
import holoviews as hv
import holocube as hc
from cartopy import crs
from cartopy import feature as cf
hv.notebook_extension()
%%opts GeoFeature [projection=crs.Geostationary()]
coasts = hc.GeoFeature(cf.COASTLINE)
borders = hc.GeoFeature(cf.BORDERS)
ocean = hc.GeoFeature(cf.OCEAN)
oce... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ec-earth-consortium', 'sandbox-3', 'ocean')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributo... |
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Description:
Step1: Creating
Step2: You can also supply more extensive metadata
Step3: <div class="alert alert-info"><h4>Note</h4><p>When assigning new values to... | <ASSISTANT_TASK:>
Python Code:
import mne
import numpy as np
# Create some dummy metadata
n_channels = 32
sampling_rate = 200
info = mne.create_info(n_channels, sampling_rate)
print(info)
# Names for each channel
channel_names = ['MEG1', 'MEG2', 'Cz', 'Pz', 'EOG']
# The type (mag, grad, eeg, eog, misc, ...) of each c... |
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Description:
Step1: Datos LHC
Step2: Utilidades
Step3: Columnas
Step4: Y si quiero imprimir columnas, una por una?
Step5: Recuerda
Step6: Dividir datos
Step7: ... | <ASSISTANT_TASK:>
Python Code:
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
df = pd.read_csv('files/mini-LHC.csv')
df.head()
print(df.shape)
pri... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Creating an ICsound object automatically starts the engine
Step2: You can set the properties of the Csound engine with parameters to the startE... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%load_ext csoundmagics
cs = ICsound(port=12894)
help(cs.startEngine)
cs.startEngine()
%%csound 1
gkinstr init 1
%%csound
print i(gkinstr)
cs.printLog()
cs.fillTable(1, np.array([8, 7, 9, 1, 1, 1]))
cs.fillTable(2, [4, 5, 7, 0, 8, 7,... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Problem 1
Step2: Generate a set of $50$ one-dimensional inputs regularly spaced between -5 and 5 and store them in a variable called x, then co... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
from numpy.random import multivariate_normal
from numpy.linalg import inv
from numpy.linalg import slogdet
from scipy.optimize import fmin
def SEKernel(par, x1, x2):
A, Gamm... |
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-1', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... |
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Description:
Step1: Set parameters
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Python Code:
# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
#
# License: BSD (3-clause)
import mne
from mne import io
from mne.connectivity import spectral_connectivity
from mne.datasets import sample
from mne.viz import plot_sensors_connectivity
print(__doc__)
data_path = sample.data_path()
r... |
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Description:
Step1: Constants
Step2: You probably noticed that DROPOUT_RATE has been set 0.0. Dropout has been used
Step3: Implementing the DeiT variants of ViT
S... | <ASSISTANT_TASK:>
Python Code:
from typing import List
import tensorflow as tf
import tensorflow_addons as tfa
import tensorflow_datasets as tfds
import tensorflow_hub as hub
from tensorflow import keras
from tensorflow.keras import layers
tfds.disable_progress_bar()
tf.keras.utils.set_random_seed(42)
# Model
MODEL_TY... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step5: plotting a neuron
Step6: Testing function
Step7: Testing function
Step8: Testing seq2seq
Step9: We may have to write our own dense --> seq w... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import McNeuron
from keras.models import Sequential
from keras.layers.core import Dense, Reshape
from keras.layers.recurrent import LSTM
import matplotlib.pyplot as plt
from copy import deepcopy
import os
%matplotlib inline
neuron_list = McNeuron.visualize.get_all_path(... |
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Description:
Step1: Model parameters (parameters of the Generalized Maxwell model)
Step2: Cantilever and general simulation paramters
Step3: Main portion of the s... | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('d:\github\pycroscopy')
from pycroscopy.simulation.afm_lib import sfs_genmaxwell_lr, compliance_maxwell
from pycroscopy.simulation.nls_fit import nls_fit, linear_fit_nob
from pycroscopy.simulation.rheology import chi_th, j_t, theta_v, theta_g
from pycroscopy.sim... |
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Description:
Step2: Alternative model
Step3: Study corpus
Step4: Not really zipfian so far. Maybe read that if we really care about that.
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Python Code:
import numpy as np
from dictlearn.generate_synthetic_data import FakeTextGenerator
V = 100
embedding_size = 50
markov_order = 6
temperature=1.0
sentence_size = 20
model = FakeTextGenerator(V, embedding_size, markov_order, temperature)
n_sentences=1000
sentences = model.create_corpus(n_sen... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: From a sample of the RMS Titanic data, we can see the various features present for each passenger on the ship
Step3: The very same sample of th... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
# RMS Titanic data visualization code
from titanic_visualizations import survival_stats
from IPython.display import display
%matplotlib inline
# Load the dataset
in_file = 'titanic_data.csv'
full_data = pd.read_csv(in_file)
# Print the first few ent... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: Even though the top code cell contains no instructions to draw, Vpython's way of integrating into Jupyter Notebook seems to be by adding a scene... | <ASSISTANT_TASK:>
Python Code:
from vpython import *
class Vector:
def __init__(self, x, y, z):
self.v = vector(x, y, z)
def __add__(self, other):
v_sum = self.v + other.v
return Vector(*v_sum.value)
def __neg__(self):
return Vector(*((-self.v).value))
... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Next, let's define our potential by a $1\,\rm M_\odot$ point mass, and put our tracer particle initially at $1\,\rm AU$.
Step2: Let's also plac... | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import astropy.units as u
from astropy.constants import G
import streakline
%matplotlib inline
mpl.rcParams['figure.figsize'] = (8,8)
mpl.rcParams['font.size'] = 18
... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: This allows inline graphics in IPython (Jupyter) notebooks and imports functions nessesary for ploting as plt. In addition we import numpy as np... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pylab as plt
import numpy as np
x = np.linspace(0,10,20)
y = x ** 2
plt.plot(x,y);
plt.plot(x, y, 'r--o')
plt.xlabel('x')
plt.ylabel('y')
plt.title('title');
plt.plot(x, y, 'r--o', x, y ** 1.1, 'bs', x, y ** 1.2, 'g^-' );
mu, sigma = 100, 15
x = m... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Para inserir website, o display do IPython tem suporte para mostrar um IFrame, onde é possível colocar o endereço de um website
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Python Code:
from IPython.display import YouTubeVideo
# a talk about IPython at Sage Days at U. Washington, Seattle.
# Video credit: William Stein.
YouTubeVideo('1j_HxD4iLn8')
from IPython.display import IFrame
IFrame('http://adessowiki.fee.unicamp.br/rnpi', width=700, height=350)
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Description:
Step1: <a id='pyenv'></a>
Step2: Creating a VE
Step3: Notice that the * is missing.
Step4: Automatically activating and deactivating VEs
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Python Code:
%%bash
echo -n "System Python version: "
python --version
%%bash
which python
%%bash
echo -n "Trying to import pip-install-test ... "
python -c "
try:
import pip_install_test
except ModuleNotFoundError:
print('pip-install-test is not installed')
else:
print('pip-install-test i... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Análise manual
Step2: O deputado que mais usou a cota parlamentar totalizou R\$ 516.027,24 em 2015, uma média de um pouco mais que R$ 43.000,00... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
ceap = pd.read_csv('dados/ceap2015.csv.zip')
linhas, colunas = ceap.shape
print('Temos {} entradas com {} colunas cada.'.format(linhas, colunas))
print('Primeira entrada:')
ceap.iloc[0]
colunas = ['txNomeParlamentar', 'sgPartido', 'sgUF', 'vlrLiquido']
grupo = ['txNom... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'ipsl', 'sandbox-3', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "emai... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load Annotated Data
Step2: Params
Step3: Prep Data
Step4: Sklearn Experiments
Step5: No tfidf
Step6: With tfidf
Step7: TFIDF improves the ... | <ASSISTANT_TASK:>
Python Code:
def get_best_estimator(cv):
params = cv.best_params_
model = cv.estimator
model = model.set_params(**params)
return model
def save_best_estimator(cv, directory, name):
model = get_best_estimator(cv)
save_pipeline(model, directory, name)
task = 'attack'
data =... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Lower Dropout
Step2: It's definitely overfitting.
Step3: It takes longer to reach a slightly lower validation score, but does not overfit.
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Python Code:
import pylearn2.utils
import pylearn2.config
import theano
import neukrill_net.dense_dataset
import neukrill_net.utils
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import holoviews as hl
%load_ext holoviews.ipython
import sklearn.metrics
m = pylearn2.utils.serial... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: This code sets up everything we need
Step2: Put your code below this!
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Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
from IPython.display import display, clear_output
def calc_total_distance(table_of_distances, city_order):
'''
Calculates distances between a sequence of cities.
Inputs: N x N table containing distances... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: We would now like to classify the test data with the kNN classifier. Recall that we can break down this process into two steps
Step2: Inline Qu... | <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
# This is a bit of magic to make matplotlib figures appear inline in the notebook
# rather than in a new window.
%matplotlib inline
plt.rcPa... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Note
Step2: Now, let's start with the ANTs normalization workflow!
Step3: Experiment parameters (ANTs)
Step4: Note if you're not using the co... | <ASSISTANT_TASK:>
Python Code:
%%bash
datalad get -J 4 -d /data/ds000114 /data/ds000114/derivatives/fmriprep/sub-0[2345789]/anat/*h5
!ls /data/ds000114/derivatives/fmriprep/sub-*/anat/*h5
from os.path import join as opj
from nipype import Workflow, Node, MapNode
from nipype.interfaces.ants import ApplyTransforms
from... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <h2> Input </h2>
Step2: <h2> Create features out of input data </h2>
Step3: <h2> train_and_evaluate </h2>
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Python Code:
# Ensure the right version of Tensorflow is installed.
!pip freeze | grep tensorflow==2.6
import tensorflow as tf
import numpy as np
import shutil
print(tf.__version__)
CSV_COLUMNS = ['fare_amount', 'pickuplon','pickuplat','dropofflon','dropofflat','passengers', 'key']
LABEL_COLUMN = 'fa... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Fix the Contents sheet to correctly reflect the Worksheet names
Step2: Tidy up Data
Step3: One down, 31 to go...
Step4: Those '\n (Quarter 4 ... | <ASSISTANT_TASK:>
Python Code:
from bs4 import BeautifulSoup
import pandas as pd
import requests
# Pull the latest pages of https://www.finance-ni.gov.uk/publications/ni-house-price-index-statistical-reports and extract links
base_url= 'https://www.finance-ni.gov.uk/publications/ni-house-price-index-statistical-reports... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The test is condected at a fully confined two-aquifer system. Both the pumping well and the observation piezometer are screened at the second aq... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from ttim import *
Q = 82.08 #constant discharge in m^3/d
zt0 = -46 #top boundary of upper aquifer in m
zb0 = -49 #bottom boundary of upper aquifer in m
zt1 = -52 #top boundary of lower aquifer in m... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Set Caffe to CPU mode, load the net in the test phase for inference, and configure input preprocessing.
Step2: Let's start with a simple classi... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Make sure that caffe is on the python path:
caffe_root = '/home/ubuntu/digits/caffe/' # this file is expected to be in {caffe_root}/examples
import sys
sys.path.insert(0, caffe_root + 'python')
import caffe
plt.rcPar... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Preparing the data
Step2: Counting word frequency
Step3: Let's keep the first 10000 most frequent words. As Andrew noted, most of the words in... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import tensorflow as tf
import tflearn
from tflearn.data_utils import to_categorical
reviews = pd.read_csv('reviews.txt', header=None)
labels = pd.read_csv('labels.txt', header=None)
from collections import Counter
total_counts = Counter()
for i, r... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Compile and build.
Step2: We can wire up the GPIO pins to a logic analyzer to verify that our circuit produces the correct sine waveform.
Step3... | <ASSISTANT_TASK:>
Python Code:
import math
import numpy as np
def sine(x):
return np.sin(2 * math.pi * x)
x = np.linspace(0., 1., num=256, endpoint=False)
import magma as m
m.set_mantle_target('ice40')
import mantle
def DefineDDS(n, has_ce=False):
class _DDS(m.Circuit):
name = f'DDS{n}'
IO = ['I... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: With NumPy arrays, all the same functionality you know and love from lists is still there.
Step2: These operations all work whether you're usin... | <ASSISTANT_TASK:>
Python Code:
li = ["this", "is", "a", "list"]
print(li)
print(li[1:3]) # Print element 1 (inclusive) to 3 (exclusive)
print(li[2:]) # Print element 2 and everything after that
print(li[:-1]) # Print everything BEFORE element -1 (the last one)
import numpy as np
x = np.array([1, 2, 3, 4, 5])
print... |
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Description:
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Python Code:
import pandas as pd
df = pd.DataFrame({'Sp': ['MM1', 'MM1', 'MM1', 'MM2', 'MM2', 'MM2', 'MM4', 'MM4', 'MM4'],
'Mt': ['S1', 'S1', 'S3', 'S3', 'S4', 'S4', 'S2', 'S2', 'S2'],
'Value': ['a', 'n', 'cb', 'mk', 'bg', 'dgd', 'rd', 'cb', 'uyi'],
... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load the Captured Streaming Data in Python
Step3: Load Data in SparkSQL
| <ASSISTANT_TASK:>
Python Code:
sc
sqlContext
import time
import simplejson as json
filename = '/home/anaconda/md0/data/2016_potus/stream/tweets.json'
langs = {}
start_time = time.time()
f_p = open(filename,'r')
for line in f_p:
tweet = json.loads(line)
if 'lang' in tweet:
if tweet['lang'] in langs:
... |
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Description:
Step1: 6.2 Updating dictionaries
Step2: An implication of this is that we can start with an empty dictionary and add keys as we go along.
Step3: 6.2... | <ASSISTANT_TASK:>
Python Code:
# creating a dictionary and assigning it to a variable
staff = {'name': 'Andy', 'age': 28, 'email': 'andy@company.com' }
staff['name']
staff['age']
print(staff['email'])
# A dictionary is of class dict
print(type(staff))
# list of all keys, note the brackets at the end.
# .keys is a me... |
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Description:
Step1: Set the structure
Step2: Result from VASP DFPT calculations using the supercell structure
Step3: Initialize phonopy and set the force constant... | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import pymatgen as pmg
from pymatgen.io.vasp.outputs import Vasprun
from phonopy import Phonopy
from phonopy.structure.atoms import Atoms as PhonopyAtoms
%matplotlib inline
Si_primitive = PhonopyAtoms(symbols=['Si'] * 2,
scaled_pos... |
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Description:
Step1: Second, let's create the motion correction and smoothing node. For an explanation about this step, see the Nodes and Interfaces tutorial.
Step2:... | <ASSISTANT_TASK:>
Python Code:
from nipype import SelectFiles, Node
# Create SelectFiles node
templates={'func': '{subject_id}/func/{subject_id}_task-flanker_run-1_bold.nii.gz'}
sf = Node(SelectFiles(templates),
name='selectfiles')
sf.inputs.base_directory = '/data/ds102'
sf.inputs.subject_id = 'sub-01'
from... |
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Description:
Step1: In this example, we will download some text from wikipedia, split it up into chunks and then plot it. We will use the wikipedia package to retri... | <ASSISTANT_TASK:>
Python Code:
import hypertools as hyp
import wikipedia as wiki
%matplotlib inline
def chunk(s, count):
return [''.join(x) for x in zip(*[list(s[z::count]) for z in range(count)])]
chunk_size = 5
dog_text = wiki.page('Dog').content
cat_text = wiki.page('Cat').content
dog = chunk(dog_text, int(len(... |
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Description:
Step2: Task
Step3: A toy example
Step4: See some of the co-occrrence statistics
Step5: this says us that idea was seen with time 258 times in the co... | <ASSISTANT_TASK:>
Python Code:
# This is a code cell. It can be executed by pressing CTRL+Enter
print('Hello')
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import pandas
pandas.options.display.max_columns = 11
pandas.options.display.max_rows = 5
import matplotlib
matplotlib.rcParams['font.size'... |
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Description:
Step1: Given an ordered binary tree $t$, the expression $t.\texttt{isEmpty}()$ checks whether $t$ is the empty tree.
Step2: Given an ordered binary tr... | <ASSISTANT_TASK:>
Python Code:
class Set:
def __init__(self):
self.mKey = None
self.mLeft = None
self.mRight = None
self.mHeight = 0
def isEmpty(self):
return self.mKey == None
Set.isEmpty = isEmpty
def member(self, key):
if self.isEmpty():
return
elif sel... |
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Description:
Step1: Indefinite integrals
Step2: Integral 1
Step3: Integral 2
Step4: Integral 3
Step5: Integral 4
Step6: Integral 5
| <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy import integrate
def integrand(x, a):
return 1.0/(x**2 + a**2)
def integral_approx(a):
# Use the args keyword argument to feed extra arguments to your integrand
I, e = integ... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Euler's method
Step4: The midpoint method is another numerical method for solving the above differential equation. In general it is more accura... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
def solve_euler(derivs, y0, x):
Solve a 1d ODE using Euler's method.
Parameters
----------
derivs : function
... |
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Description:
Step1: Survey
Step2: Trivariate
| <ASSISTANT_TASK:>
Python Code:
import dit
from dit.pid.helpers import compare_measures
from dit.pid.distributions import bivariates, trivariates
dit.ditParams['print.exact'] = dit.ditParams['repr.print'] = True
dit.ditParams['text.font'] = 'linechar'
for name, dist in bivariates.items():
compare_measures(dist, nam... |
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Description:
Step1: 1.3 Lecture des données métier
Step2: 2 Exploration par Analyse en composantes principales
Step3: La fonction définie ci-après affiche un nuag... | <ASSISTANT_TASK:>
Python Code:
# Importation des principals librairies et
# Affichage des graphiques dans le notebook
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import time
# Lecture des données d'apprentissage
# Attention, il peut y avoir plusieurs espaces comme sépara... |
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Description:
Step1: Language Translation
Step3: Explore the Data
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (0, 10)
DON'T MODIFY AN... |
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Description:
Step1: Problem 1
Step2: Generate a set of $50$ one-dimensional inputs regularly spaced between -5 and 5 and store them in a variable called x, then co... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy.spatial.distance import cdist
from numpy.random import multivariate_normal
from numpy.linalg import inv
from numpy.linalg import slogdet
from scipy.optimize import fmin
def SEKernel(par, x1, x2):
A, Gamm... |
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Description:
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Python Code:
def get_row(lst, x):
coords = [(i, j) for i in range(len(lst)) for j in range(len(lst[i])) if lst[i][j] == x]
return sorted(sorted(coords, key=lambda x: x[1], reverse=True), key=lambda x: x[0])
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Description:
Step1: Loss under a Mixture of Gaussians model
Step2: We use autograd for functions that deliver gradients of those losses
Step3: Just a pretty displ... | <ASSISTANT_TASK:>
Python Code:
def sigmoid(phi):
return 1.0/(1.0 + np.exp(-phi))
def calc_prob_class1(params):
# Sigmoid perceptron ('logistic regression')
tildex = X - params['mean']
W = params['wgts']
phi = np.dot(tildex, W)
return sigmoid(phi) # Sigmoid perceptron ('logistic regression')
def... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: What's the Fast Inverse Root Method you ask?
Step2: Close Enough!
Step3: Here, $$\begin{align}
Step4: A Tale of two Variables
Step5: Recall ... | <ASSISTANT_TASK:>
Python Code:
setup_html = r'''
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/github-fork-ribbon-css/0.2.0/gh-fork-ribbon.min.css" />
<!--[if lt IE 9]>
<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/github-fork-ribbon-css/0.2.0/gh-fork-ribbon.ie.min.css" /... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Part 1
Step2: The first two sentences have very similar content, and as such the SCM should be large. Before we compute the SCM, we want to rem... | <ASSISTANT_TASK:>
Python Code:
# Initialize logging.
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
sentence_obama = 'Obama speaks to the media in Illinois'.lower().split()
sentence_president = 'The president greets the press in Chicago'.lower().split()
sente... |
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Description:
Step1: 建立 ndarray
Step2: 看 ndarray 的第一件事情： shape , dtype
Step3: 有時候，可以看圖
Step4: 有很多其他建立的方式
Step5: 這是一堆資料
Step6: Q0
Step7: Q1
Step8: Indexing
St... | <ASSISTANT_TASK:>
Python Code:
# 起手式
import numpy as np
np.array([1,2,3,4])
x = _
y = np.array([[1.,2,3],[4,5,6]])
y
x.shape
y.shape
x.dtype
y.dtype
# import matplotlib
%matplotlib inline
import matplotlib.pyplot as plt
# 畫圖
plt.plot(x, 'x');
# 建立 0 array
np.zeros_like(y)
np.zeros((10,10))
# 跟 range 差不多
x = np.aran... |
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Description:
Step1: Part 1
Step2: Notes
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Python Code:
from collections import defaultdict, Counter
from itertools import zip_longest
import json
import os
import re
import sys
import urllib
import numpy as np
import requests
from eva_cttv_pipeline.clinvar_xml_io.clinvar_xml_io import *
%matplotlib inline
import matplotlib.pyplot as plt
from ... |
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Description:
Step1: Toy Network
Step2: Centrality
Step3: Eigenvector Centrality
Step4: Betweenness Centrality
Step5: Centrality Measures Are Different
Step6: T... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import networkx as nx
import numpy as np
import scipy as sp
import itertools
import matplotlib.pyplot as plt
import statsmodels.api as sm
%matplotlib inline
G = nx.Graph()
G.add_nodes_from(['A','B','C','D','E','F','G'])
G.add_edges_from([('A','B'),('A','C'),
... |
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Description:
Step1: Simulating A/B testing to build intuition
Step2: Each test is like flipping a fair coin N times
Step3: Run the cell above a few times.
Step4: ... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image(filename='img/treat_aud_reward.jpg')
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import pandas as pd
from numpy.random import binomial
from ggplot import *
import random
import sys
plt.figure(figsize=(... |
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Description:
Step1: In the following cell, complete the code with an expression that evaluates to a list of integers derived from the raw numbers in numbers_str, as... | <ASSISTANT_TASK:>
Python Code:
numbers_str = '496,258,332,550,506,699,7,985,171,581,436,804,736,528,65,855,68,279,721,120'
number_list = numbers_str.split(",")
numbers = [int(item) for item in number_list]
max(numbers)
#len(numbers)
sorted(numbers)[10:]
from math import sqrt
squared = []
for item in numbers:
if... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Exercise A.1.1
Step2: Exercise A.1.2
Step3: Exercise A.1.3
Step4: Exercise A.1.4
Step5: Exercise A.1.5
Step6: Exercise A.1.6
Step7: <a id=... | <ASSISTANT_TASK:>
Python Code:
import random
x=random.random()
print ("The Value of x is", x)
# Skeleton
def GenerateData(N,min,max):
out = []
### BEGIN SOLUTION
# Fill in your solution here
### END SOLUTION
return out
Data=GenerateData(1000,-10,10)
print ("Data Type:", type(Data))
print ("Da... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Linear regression
Step2: Quick exploration
Step3: You can also do quick plotting of the data. The results are not aesthetically the best, but ... | <ASSISTANT_TASK:>
Python Code:
# Cumulative probability P(X<120) where X ~ N(100, 10^2)
print("P(X<120) where X ~ N(100, 10^2) = %.3f" % stats.norm.cdf(120, loc=100, scale=10))
# Calculate value
print("x for which P(X < x = 0.97) = %.1f" % stats.norm.ppf(0.97, loc=100, scale=10))
# Cumulative probability P(X<120) where... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-3', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: BERT Question Answer with TensorFlow Lite Model Maker
Step2: Import the required packages.
Step3: The "End-to-End Overview" demonstrates a sim... | <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... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Question 1
Step2: We can then print the Sum of the costs of all those rows. (The cost column is named total_cost.)
Step3: Question 2
Step4: W... | <ASSISTANT_TASK:>
Python Code:
import agate
table = agate.Table.from_csv('examples/realdata/ks_1033_data.csv')
print(table)
kansas_city = table.where(lambda r: r['county'] in ('JACKSON', 'CLAY', 'CASS', 'PLATTE'))
print(len(table.rows))
print(len(kansas_city.rows))
print('$%d' % kansas_city.aggregate(agate.Sum('total... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
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Python Code:
import numpy as np
import pandas as pd
import torch
softmax_output = load_data()
y = torch.argmax(softmax_output, dim=1).view(-1, 1)
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Description:
Step1: Reading data from BigQuery with TFX and Vertex Pipelines
Step2: Did you restart the runtime?
Step3: Login in to Google for this notebook
Step4... | <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... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: There is much, much more to know, but these few operations are fundamental to what we'll
Step2: The CSR representation can be very efficient fo... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# Generating a random array
X = np.random.random((3, 5)) # a 3 x 5 array
print(X)
# Accessing elements
# get a single element
print(X[0, 0])
# get a row
print(X[1])
# get a column
print(X[:, 1])
# Transposing an array
print(X.T)
# Turning a row vector into a column vec... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Part 2
Step2: Part 3
Step4: Assignment wrapup
| <ASSISTANT_TASK:>
Python Code:
# put your code here!
myfile = open('oshea.txt','w')
myfile.write('I love pizza.\n')
myfile.write('I have two children, both of whom are girls.\n')
myfile.write('I also have two dogs.\n')
myfile.write('Arguably, I spend far too much time on the computer.\n')
myfile.close()
myfilenew = ope... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Language Detection
Step2: Tokenization
Step3: Part of Speech Tagging
Step4: Named Entity Recognition
Step5: Polarity
Step6: Embeddings
Step... | <ASSISTANT_TASK:>
Python Code:
import polyglot
from polyglot.text import Text, Word
text = Text("Bonjour, Mesdames.")
print("Language Detected: Code={}, Name={}\n".format(text.language.code, text.language.name))
zen = Text("Beautiful is better than ugly. "
"Explicit is better than implicit. "
"S... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: 2. Build map
Step4: 2.2. HTML for popups
Step5: 2.3. Create map
| <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('toc_trends_long_format.csv')
df.dropna(subset=['latitude', 'longitude'], inplace=True)
df = df.query('(analysis_period == "1990-2016") and (non_missing > 0)')
base = "http://77.104.141.195/~icpwater/wp-content/core_plots/trends_plots_1990-2016/"
fname = df['station_id'].... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Simulation setup
Step2: Create the coordinate systems
Step3: Step 1
Step4: Step 2
Step5: Now compute the point-spread function via
Step6: S... | <ASSISTANT_TASK:>
Python Code:
import sys
%pylab inline
import scipy.special
from scipy.interpolate import interp1d
from scipy.interpolate import RectBivariateSpline
print('Python {}\n'.format(sys.version))
print('NumPy\t\t{}'.format(np.__version__))
print('matplotlib\t{}'.format(matplotlib.__version__))
print('SciPy\t... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: This was developed using Python 3.5.2 (Anaconda) and TensorFlow version
Step2: The VGG-16 model is downloaded from the internet. This is the de... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import PIL.Image
tf.__version__
import vgg16
# vgg16.data_dir = 'vgg16/'
vgg16.maybe_download()
def load_image(filename, max_size=None):
image = PIL.Image.open(filename)
if max_size is... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-3', 'ocean')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
| <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.preprocessing import MinMaxScaler
a = np.array([[-1, 2], [-0.5, 6]])
scaler = MinMaxScaler()
a_one_column = a.reshape(-1, 1)
result_one_column = scaler.fit_transform(a_one_column)
result = result_one_column.reshape(a.shape)
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The speech commands
Step2: The below code is generating a label ID for a command. The ID will be -1 for any command not in the to_keep list. Ot... | <ASSISTANT_TASK:>
Python Code:
import cmsisdsp as dsp
import cmsisdsp.fixedpoint as fix
import numpy as np
import os.path
import glob
import pathlib
import random
import soundfile as sf
import matplotlib.pyplot as plt
from IPython.display import display,Audio,HTML
import scipy.signal
from numpy.lib.stride_tricks import... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: As always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.
Step2: In order for apsidal moti... | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.1,<2.2"
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
b['ecc'] = 0.2
b['dperdt'] = 2.0 * u.deg/u.d
b.add_dataset('lc', times=np.linspace(0... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Error plots for MiniZephyr vs. the AnalyticalHelmholtz response
Step2: Relative error of the MiniZephyr solution (in %)
| <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.append('../')
import numpy as np
from zephyr.backend import MiniZephyr, SparseKaiserSource, AnalyticalHelmholtz
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
%matplotlib inline
from IPython.display import set_matplotlib_formats
set_matplo... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Create datasets
Step2: Feature Space
Step3: We'll be using the popular data manipulation framework pandas.
Step4: We can use head() to get a ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import math, keras, datetime, pandas as pd, numpy as np, keras.backend as K
import matplotlib.pyplot as plt, xgboost, operator, random, pickle
from utils2 import *
np.set_printoptions(threshold=50, edgeitems=20)
limit_mem()
from isoweek import Week
from pandas_summary i... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: EXPLORE THE DATA
Step2: SUBSET THE DATA
Step3: STANDARDIZE THE DATA
Step5: K-MEANS ANALYSIS - INITIAL CLUSTER SET
Step6: Interpret 2 cluster... | <ASSISTANT_TASK:>
Python Code:
# read training and test data from the url link and save the file to your working directory
url = "http://archive.ics.uci.edu/ml/machine-learning-databases/poker/poker-hand-training-true.data"
urllib.request.urlretrieve(url, "poker_train.csv")
url2 = "http://archive.ics.uci.edu/ml/machine... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The goal of this notebook is to illustrate how a model can wrapped and used in different tasks in Emukit.
Step2: Define the objective function
... | <ASSISTANT_TASK:>
Python Code:
# General imports and parameters of figures should be loaded at the beginning of the overview
import numpy as np
from emukit.test_functions import branin_function
from emukit.core import ParameterSpace, ContinuousParameter
from emukit.core.initial_designs import RandomDesign
from GPy.mod... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Interactive Line Plotting of Data Frames
Step2: Plotting independent series
Step3: This does not affect filtering or pivoting in any way
Step4... | <ASSISTANT_TASK:>
Python Code:
import sys,os
sys.path.append("..")
import numpy.random
import pandas as pd
import shutil
import tempfile
import trappy
trace_thermal = "./trace.txt"
trace_sched = "../tests/raw_trace.dat"
TEMP_BASE = "/tmp"
def setup_thermal():
tDir = tempfile.mkdtemp(dir="/tmp", prefix="trappy_doc",... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The driver is used to execute the harmonization. It will handle the data formatting needed to execute the harmonizaiton operation and stores the... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import pyomo.environ as pyomo
import aneris
from aneris.tutorial import load_data
%matplotlib inline
model, hist, driver = load_data()
driver.overrides = model[["Model", "Scenario", "Region", "Variable", "Unit"]].... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: OLS Analysis Using Full PSU dataset
Step3: Partitioning a dataset in training and test sets
Step4: Determine Feature Importances
Step5: Test ... | <ASSISTANT_TASK:>
Python Code:
#Import required packages
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
def format_date(df_date):
Splits Meeting Times and Dates into datetime objects where applicable using regex.
df_date['Days'] = df_date['Meeting_Times'].str.ex... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The command %matplotlib inline is not a Python command, but an IPython command. When using the console, or the notebook, it makes the plots appe... | <ASSISTANT_TASK:>
Python Code:
from matplotlib import pyplot
%matplotlib inline
from math import sin, pi
x = []
y = []
for i in range(201):
x.append(0.01*i)
y.append(sin(pi*x[-1])**2)
pyplot.plot(x, y)
pyplot.show()
from math import sin, pi
x = []
y = []
for i in range(201):
x.append(0.01*i)
y.append(... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 1. Load and prepare the data
Step3: Tokenize
Step5: Padding
Step7: Preprocess pipeline
Step8: Split the data into training and test sets
Ste... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import helper
import keras
helper.info_gpu()
np.random.seed(9)
%matplotlib inline
%load_ext autoreload
%autoreload 2
with open('data/small_vocab_en', "r") as f:
english_sentences = f.read().split('\n')
with open... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Login to and initialize wandb. You will need to use your wandb API key to run this demo.
Step2: Dataset and Dataloader for Custom Object Detect... | <ASSISTANT_TASK:>
Python Code:
import torch
import numpy as np
import wandb
import label_utils
from torch.utils.data import DataLoader
from torchvision import transforms
from PIL import Image
wandb.login()
config = {
"num_workers": 4,
"pin_memory": True,
"batch_size": 32,
"dataset": "drinks",
"trai... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Load Digits Dataset
Step2: Create Pipeline
Step3: Create k-Fold Cross-Validation
Step4: Conduct k-Fold Cross-Validation
Step5: Calculate Mea... | <ASSISTANT_TASK:>
Python Code:
# Load libraries
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn.model_selection import KFold, cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import Standard... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: That's all we need to create and train a model
Step2: Movielens 100k
Step3: Here's some benchmarks on the same dataset for the popular Librec ... | <ASSISTANT_TASK:>
Python Code:
user,item,title = 'userId','movieId','title'
path = untar_data(URLs.ML_SAMPLE)
path
ratings = pd.read_csv(path/'ratings.csv')
ratings.head()
data = CollabDataBunch.from_df(ratings, seed=42)
y_range = [0,5.5]
learn = collab_learner(data, n_factors=50, y_range=y_range)
learn.fit_one_cycle(... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Exercise 1
Step2: Exercise 2
Step3: Exercise 3
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Python Code:
# Import all necessary libraries, this is a configuration step for the exercise.
# Please run it before the simulation code!
import numpy as np
import matplotlib.pyplot as plt
# Show the plots in the Notebook.
plt.switch_backend("nbagg")
def fourier_derivative(f, dx):
# Length of vec... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Read in train & val data
Step2: Extract X and Y matrices
Step4: Convert to SystemML Matrices
Step6: Trigger Caching (Optional)
Step8: Save M... | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
import os
import matplotlib.pyplot as plt
import numpy as np
from pyspark.sql.functions import col, max
import systemml # pip3 install systemml
from systemml import MLContext, dml
plt.rcParams['figure.figsize'] = (10, 6)
ml = MLContex... |
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Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Dados
Step2: Análise modal
Step3: Ordenação dos modos
Step4: Normalização dos modos de vibração relativamente à matriz de massa
Step5: Massa... | <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__))
MM = np.matrix(np.diag([1.,2.,... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
| <ASSISTANT_TASK:>
Python Code::
import tensorflow as tf
from tensorflow.keras.losses import MeanAbsoluteError
y_true = [1., 0.]
y_pred = [2., 3.]
mae_loss = MeanAbsoluteError()
loss = mae_loss(y_true, y_pred).numpy()
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Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Date string for filenames
Step2: Load data
Step3: Because the 2017 facility dataframe only includes annually reporting facilities I'm going to... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import os
from os.path import join
import pandas as pd
from sklearn import neighbors, metrics
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split, GridSearchCV
from collections import Counte... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Create Noisy Data
Step2: Model Fitting
Step3: Fit result from an lmfit Model can be inspected with
Step4: These methods a re convenient but e... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from numpy import sqrt, pi, exp, linspace
from lmfit import Model
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format='retina' # for hi-dpi displays
import lmfit
print('lmfit: %s' % lmfit.__version__)
import pybroom as br
x = np.lins... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: aws cli
Step2: IAM (identity and access management)
Step3: Find cheapest prices
Step4: Finding AMIs
| <ASSISTANT_TASK:>
Python Code:
import boto3
s3=boto3.client('s3')
list=s3.list_objects(Bucket='mert01')['Contents']
list[1:3]
# https://stackoverflow.com/questions/3337912/quick-way-to-list-all-files-in-amazon-s3-bucket
import boto
s3 = boto.connect_s3()
bucket = s3.get_bucket('mert01')
#bl = bucket.list()
#for key in ... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: And the distribution of time between goals is given by the exponential
Step2: I use the variable lam because lambda is a
Step3: Figure 7.1
Ste... | <ASSISTANT_TASK:>
Python Code:
def EvalPoissonPmf(k, lam):
return (lam)**k * math.exp(-lam) / math.factorial(k)
def EvalExponentialPdf(x, lam):
return lam * math.exp(-lam * x)
from hockey import *
import thinkplot
suite1 = Hockey('bruins')
suite1.UpdateSet([0, 2, 8, 4])
suite2 = Hockey('canucks')
suite2.Updat... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: We explore the Lorenz system of differential equations
Step2: For the default set of parameters, we see the trajectories swirling around two po... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from ipywidgets import interactive, fixed
from lorenz import solve_lorenz
w=interactive(solve_lorenz,sigma=(0.0,50.0),rho=(0.0,50.0))
w
t, x_t = w.result
w.kwargs
xyz_avg = x_t.mean(axis=1)
xyz_avg.shape
from matplotlib import pyplot as plt
plt.hist(xyz_avg[:,0])
pl... |
<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: La clase poly1D representa polinomios unidimensionales con base en sus coeficientes. Sea el polinomio
Step2: Se puede evaluar el valor de $p(x)... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
p = np.poly1d([6., 1., -2.])
p(0), p(1), p(10)
p.order
p.roots
# Comprobando las raíces
p(p.roots)
np.poly1d([2.,-3.,7.]).roots
import matplotlib.pyplot as plt
xvalues = np.linspace(-10.,10.,100)
plt.plot(xvalues, p(xvalues), '-')
plt.show()
y = p(xvalues) + np.r... |
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