Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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Python Code:
!pip install nnabla-ext-cuda100
!git clone https://github.com/sony/nnabla-examples.git
%cd nnabla-examples
import numpy as np
import nnabla as nn
import nnabla.logger as logger
import nnabla.functions as F
import nnabla.parametric_functions as PF
import nnabla.solvers as S
import nnabla.... | <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: Let's start by importing dependencies.
Step2: Now let's define a function to download and load MNIST. This function will pass image-label pairs... |
7,601 | <ASSISTANT_TASK:>
Python Code:
# populate namespace with pylab functions and stuff
%pylab inline
# import NEST & NEST rasterplot
import nest
import nest.raster_plot
# simulation parameters
simtime = 1000. # simulation time (ms)
dt = 0.1 # simulation resolution (ms)
# network parameters
gam... | <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: Set parameters
Step2: Configure NEST
Step3: Create neurons and devices
Step4: Create connections
Step5: Simulate and analyse
|
7,602 | <ASSISTANT_TASK:>
Python Code:
data = dict(
labels=['Bananas','Apples','Oranges','Watermelons','Grapes','Kiwis'],
values=[4000,8000,3000,1600,1000,2500]
)
out = StdCharts.HBar(data)
HTML(out)
StdCharts.save(out,'report_chart.png')
data = dict(
labels=['Bananas','Apples','Oranges','Watermelons','Grapes','K... | <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: You can also save your chart with the save method
Step2: Example 2
Step3: Vertical Bar Charts
Step4: Example 4
|
7,603 | <ASSISTANT_TASK:>
Python Code:
def sumar(x, y): # Defino la función sumar
return x + y
x = 4
z = 5
print sumar(x, z) # Invoco a la función sumar con los parámetros x y z
print sumar(1, 2) # Invoco a la función sumar con los parámetros 1 y 2
print sumar('hola ', 'mundo')
def sumar(x, y):
Suma dos elementos ... | <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: Aunque en ningún momento indicamos que lo que tiene que sumar son números, por lo que también puede sumar strings
Step3: Además, a esta función... |
7,604 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'hadgem3-gc31-ll', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("nam... | <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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
7,605 | <ASSISTANT_TASK:>
Python Code:
# Python 2 and 3 compatibility
# pip install future
from __future__ import (absolute_import, division,
print_function, unicode_literals)
# отключим предупреждения Anaconda
import warnings
warnings.simplefilter('ignore')
import pandas as pd
import numpy as np
%matpl... | <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: Основными структурами данных в Pandas являются классы Series и DataFrame. Первый из них представляет собой одномерный индексированный массив дан... |
7,606 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.decomposition import PCA
import pandas as pd
df = pd.read_csv('Manhattan.txt', sep='\s+')
df.drop('id', axis=1, inplace=True)
df.tail()
tdf = df.iloc[:, 0:-3]
tdf.tail()
pca = PCA(n_components=8)
pca.fit(tdf)
np.set_printoptions(precision=6, suppress=True... | <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: how to index a given part of a DataFrame have been a problem for me.
Step2: 取一个主成分， 解释方差0.917864
|
7,607 | <ASSISTANT_TASK:>
Python Code:
# TensorBoard Helper Functions and Constants
# Directory to export TensorBoard summary statistics, graph data, etc.
TB_DIR = '/tmp/tensorboard/tf_basics'
def _start_tb(d):
Private function that calls `tensorboard` shell command
args:
d: The desired directory to lau... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: TensorBoard Jupyter Notebook Helpers
Step4: TensorFlow Fundamentals
Step5: My First TensorFlow Graph
Step 1
Step6: Step 2
Step7: Step 3ish
S... |
7,608 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bcc', 'sandbox-1', 'landice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "ema... | <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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
7,609 | <ASSISTANT_TASK:>
Python Code:
!pip install --upgrade watson_developer_cloud
import requests
import json
import os
from os.path import join, dirname
from watson_developer_cloud import SpeechToTextV1
# @hidden_cell
url = "https://stream.watsonplatform.net/speech-to-text/api/v1/recognize"
username= "$USERNAME"
password... | <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: Authentication Handling and File Details
Step2: Basic transcription with CURL
Step3: Output Handling with Requests
Step4: Pandas from Results... |
7,610 | <ASSISTANT_TASK:>
Python Code:
fname = io.download_occultation_times(outdir='../data/')
print(fname)
tlefile = io.download_tle(outdir='../data')
print(tlefile)
times, line1, line2 = io.read_tle_file(tlefile)
tstart = '2018-09-27T12:00:00'
tend = '2018-09-29T12:10:00'
orbits = planning.sunlight_periods(fname, tstart, ... | <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: Download the NuSTAR TLE archive.
Step2: Here is where we define the observing window that we want to use.
Step3: We want to know how to orient... |
7,611 | <ASSISTANT_TASK:>
Python Code:
import mne
from mne.preprocessing import maxwell_filter
data_path = mne.datasets.sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
ctc_fname = data_path + '/SSS/ct_sparse_mgh.fif'
fine_cal_fname = data_path + '/SSS/sss_cal_mgh.dat'
raw = mne.io.read_raw_fif(... | <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: Set parameters
Step2: Preprocess with Maxwell filtering
Step3: Select events to extract epochs from, pick M/EEG channels, and plot evoked
|
7,612 | <ASSISTANT_TASK:>
Python Code:
# THINGS TO IMPORT
# This is a baseline set of libraries I import by default if I'm rushed for time.
%matplotlib inline
import codecs # load UTF-8 Content
import json # load JSON files
import pandas as pd # Pandas handles dataframes
... | <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: Exploring Within-Group Variation and Between-Group Variation
Step2: Predicting Math Achievement from SES with Linear Models
Step3: Fixed Effec... |
7,613 | <ASSISTANT_TASK:>
Python Code:
_MIN = - 2147483648
_MAX = 2147483648
class getnode :
def __init__(self , data ) :
self . data = data
self . left = None
self . right = None
def getlevel(root , data ) :
q =[]
level = 1
q . append(root )
q . append(None )
while(len(q ) ) :
temp = q[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:
|
7,614 | <ASSISTANT_TASK:>
Python Code:
import dataset as ds
import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
# Download database
ds.download('UCI HAR')
# Paths and filenames
DATASET_PATH = "../dataset/UCI HAR/UCI HAR Dataset"
TEST_RELPATH = "/test"
TRAIN_RELPATH = "/train"
VARS_FILENAMES = [
'bod... | <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: Reading Dataset
Step2: Filtered plots
Step3: RNN
|
7,615 | <ASSISTANT_TASK:>
Python Code:
import pickle
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from BranchedGP import VBHelperFunctions as bplot
plt.style.use("ggplot")
%matplotlib inline
datafile = "syntheticdata/synthetic20.csv"
data = pd.read_csv(datafile, index_col=[0])
G = data.shape[1] ... | <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: Load the data
Step2: Plot the data
Step3: Run the BGP model
Step4: We can also plot with the predictive uncertainty of the GP.
Step5: Plot p... |
7,616 | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scipy,scikit-learn
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
from IPython.display import Image
fr... | <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: <br>
Step2: Grouping objects by similarity using k-means
Step3: <br>
Step4: <br>
Step5: Comparison to "bad" clustering
Step6: <br>
Step7: ... |
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Python Code:
%run dataFormating.ipynb
import sklearn
print (sklearn.__version__)
from sklearn import preprocessing
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import ExtraTreesClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import c... | <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: Questionnaire only
Step2: Conclusion
Step3: Conclusion
Step4: Can the score of a player be predicted with their RedMetrics data?
Step5: Pred... |
7,618 | <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'
numbers = [int(i) for i in numbers_str.split(",")]
max(numbers)
sorted(numbers)[-10:]
sorted([number for number in numbers if number%3 == 0])
from math import sqrt
[sqrt(number) for number in n... | <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: 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... |
7,619 | <ASSISTANT_TASK:>
Python Code:
import datetime
import numpy as np
import scipy as sp
from scipy import interpolate
import matplotlib.pyplot as plt
%matplotlib inline
import cmocean
import seawater as sw
from netCDF4 import Dataset
from llctools import llc_model
from pyspec import spectrum as spec
c1 = 'slateblue'
c2 = ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: This notebook showcases the analysis applied to LLC outputs. Here the calculations are performed for a single snapshot. The full LLC model outpu... |
7,620 | <ASSISTANT_TASK:>
Python Code:
# Some examples (you do not have to remember this now):
a_list = [1,2,3, "let's", "use", "containers"]
a_tuple = (1, 2, 3, "let's", "use", "containers")
a_set = {1, 2, 3, "let's", "use", "containers"}
a_dict = {1:"let's", 2:"use", 3: "containers"}
#print(a_list)
#print(a_tuple)
#print(a_... | <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: 2. Understanding class methods
Step2: In all of the examples above, a string method is called, e.g., upper or count.
Step3: The method documen... |
7,621 | <ASSISTANT_TASK:>
Python Code:
import openpnm as op
wrk = op.Workspace()
wrk.logelevel=50
pn = op.network.Cubic(shape=[10, 10, 10], spacing=1)
pn = op.network.Cubic(shape=[10, 10, 10], spacing=1, connectivity=26)
import scipy as sp
pn = op.network.Cubic(shape=[10, 10, 10], spacing=[1, 1, 1], connectivity=26)
print(pn... | <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: In this case pn will be a 10 x 10 x 10 cube with each pore spaced 1 unit away from it's neighbors in all directions. Each pore is connected to ... |
7,622 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import seaborn as sns; sns.set_context('notebook')
from pymc3 import exp, Normal, Binomial, sample, Model
# Samples for each dose level
n = 5 * np.ones(4, dtype=int)
# Log-dose
dose = np.array([-.86, -.3, -.05, .73])
deaths = np.array([0, 1, 3, 5])
de... | <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: Informal Methods
Step2: A similar approach involves
Step3: An extension of this approach can be taken
Step4: A principal reason that evidence... |
7,623 | <ASSISTANT_TASK:>
Python Code:
import io
from omegaconf import OmegaConf
import pp
from pp.config import connections
c0 = pp.c.mzi()
pp.qp(c0)
netlist = c0.get_netlist()
import pp
c1 = pp.component_from_yaml('mzi.yml')
pp.show(c1)
pp.plotgds(c1)
len(c1.get_netlist().connections)
connections.clear() # reset the conne... | <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: This is what we did to store the netlist
Step2: You can also reconstruct the circuit from the instances and placements but the you lose the mos... |
7,624 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
def square(n):
squares = []
for i in range(n):
squares.append(i**2)
return squares
print(square(10))
sq = lambda n: [i**2 for i in range(n)]
print(sq(10)) #actually this isn't really functional! pr... | <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: Programming "Paradigms"
Step2: Functional
Step3: Objects
Step4: Instances
Step5: Inheritance
Step6: Objects in practice
Step7: Example
|
7,625 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
# define colours used in plots
dark_purple = '#8464c5'
light_purple = '#ededfb'
dark_green = '#336433'
light_green = '#a0d9a0'
white = '#FFFFFF'
olive = '#aaa460'
def get_data(t):
Loads the hysteresis data for... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step3: We start by defining a few helper variables and functions which be used for creating the plots below.
Step4: The plots are produced below.
|
7,626 | <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... | <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: Decoding API
Step2: Initialize Sampling Module in TF-NLP.
Step3: In auto-regressive architectures like Transformer based Encoder-Decoder model... |
7,627 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import HTML
HTML('''<script>
code_show=true;
function code_toggle() {
if (code_show){
$('div.input').hide();
} else {
$('div.input').show();
}
code_show = !code_show
}
$( document ).ready(code_toggle);
</script>
<form action="javascript:code_toggle()"><input t... | <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: Run the Demo
Step2: Video
Step3: Audio
Step4: Advanced options
|
7,628 | <ASSISTANT_TASK:>
Python Code:
from problem_formulation import joint
joint
import chaospy
polynomial_expansion = chaospy.generate_expansion(3, joint)
polynomial_expansion[:4].round(10)
alpha, beta = chaospy.variable(2)
phi_phi = chaospy.outer(
polynomial_expansion, polynomial_expansion)
[polynomial_expansion.sha... | <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: Here the parameters are positional defined as $\alpha$ and $\beta$
Step2: Note again, that the variables are here defined positional. $\alpha$ ... |
7,629 | <ASSISTANT_TASK:>
Python Code:
import gzip
import pickle
import numpy as np
import matplotlib.pyplot as plt
import random
def vectorized_result(d):
e = np.zeros((10, 1), dtype=np.float32)
e[d] = 1.0
return e
vectorized_result(2)
def load_data():
with gzip.open('../mnist.pkl.gz', 'rb') as f:
... | <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: As our data is stored as a tuple of numpy arrays, we have to import numpy.
Step2: In order to be able to show the images of the handwritten dig... |
7,630 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%matplotlib inline
import os
SHOGUN_DATA_DIR=os.getenv('SHOGUN_DATA_DIR', '../../../data')
import shogun as sg
import numpy as np
# use scipy for generating samples
from scipy.stats import laplace, norm
def sample_gaussian_vs_laplace(n=220, mu=0.0, sigma2=1, b=np.sqrt(0.5))... | <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: Some Formal Basics (skip if you just want code examples)
Step2: Now how to compare these two sets of samples? Clearly, a t-test would be a bad ... |
7,631 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.signal import medfilt
import matplotlib.pyplot as plt
import kplr
%matplotlib inline
client = kplr.API()
koi = client.koi(1274.01)
lcs = koi.get_light_curves(short_cadence=True)
p = 704.2
time, flux, ferr, med = [], [], [], []
for lc in lcs:
with lc.open(... | <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: <hr>
Step2: <hr>
Step3: Se ejecuta batman como se explica en la documentación, entregando como parámetros los valores obtenidos a lo largo de ... |
7,632 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
from astropy.modeling import models, fitting
from astroquery.vizier import Vizier
import scipy.optimize
# Make plots display in notebooks
%matplotlib inline
catalog = Vizier.get_catalogs('J/A+A/605/A100')
period = np.array(catalog[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: 1) Fit a Linear model
Step2: This catalog has a lot of information, but for this tutorial we are going to work only with periods and magnitudes... |
7,633 | <ASSISTANT_TASK:>
Python Code:
df['Count'].plot.box()
df.groupby('Year')['Count'].sum().sort_values(ascending=False)
df.groupby('Year')['Count'].sum().mean()
fig, ax = plt.subplots(figsize=(9, 6))
df.groupby('Year')['Count'].sum().plot.barh()
mean = df.groupby('Year')['Count'].sum().mean()
ax.plot([mean, mean], [0, 12... | <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: 1. In what year new yorkers died the most?
Step2: 2. Who is more likely to die, a male newyorker or a female new yorker?
Step3: 3. Is Sex (in)... |
7,634 | <ASSISTANT_TASK:>
Python Code:
from floweaver import *
dataset = Dataset.from_csv('us-energy-consumption.csv',
dim_process_filename='us-energy-consumption-processes.csv')
sources = ['Solar', 'Nuclear', 'Hydro', 'Wind', 'Geothermal',
'Natural_Gas', 'Coal', 'Biomass', 'Petroleum']
... | <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: Load the dataset
Step2: This defines the order the nodes appear in
Step3: Now define the Sankey diagram definition.
Step4: Define the colours... |
7,635 | <ASSISTANT_TASK:>
Python Code:
seconds_in_a_day = 24 * 60 * 60
seconds_in_a_day
seconds_in_a_week = 7 * seconds_in_a_day
seconds_in_a_week
import numpy as np
from matplotlib import pyplot as plt
ys = 200 + np.random.randn(100)
x = [x for x in range(len(ys))]
plt.plot(x, ys, '-')
plt.fill_between(x, ys, 195, where=(ys... | <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: To execute the code in the above cell, select it with a click and then either press the play button to the left of the code, or use the keyboard... |
7,636 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from sklearn import tree
X = [[0, 0], [1, 2]]
y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, y)
clf.predict([[2., 2.]])
clf.predict_proba([[2. , 2.]])
clf.predict([[0.4, 1.2]])
clf.predict_proba([[0.4, 1.2]])
clf.predict_proba([[0, 0.2]])
from sklearn... | <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: DecisionTreeClassifier is capable of both binary (where the labels are [-1, 1]) classification and multiclass (where the labels are [0, …, K-1])... |
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Python Code:
def check_python_version():
print 'Python version:\n', sys.version
assert sys.version_info < (3,0)
check_python_version()
def improved_check_python_version():
print 'Python version:\n', sys.version
try:
assert sys.version_info < (3,0)
except:
ra... | <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: Raising errors
Step2: Raising warnings
|
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Python Code:
# Generate data
import numpy as np
import pandas as pd
raw_data = np.array([0] * 30 + [1] * 70) # Representing heads by 0 and tails by 1
data = pd.DataFrame(raw_data, columns=['coin'])
print(data)
# Defining the Bayesian Model
from pgmpy.models import BayesianModel
from pgmpy.estimators i... | <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: We can see that we get the results as expected. In the maximum likelihood case we got the probability just based on the data where as in the bay... |
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Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
from keras.utils import np_utils
from keras.datasets import mnist
# for Multi-layer Perceptron (MLP) model
from keras.models import Sequential
from keras.layers import Dense
# for Convolutional Neura... | <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: Loading Data
Step2: Plotting images and their class values
Step3: Viewing shape and content of data
Step4: Flattening images
Step5: Normaliz... |
7,640 | <ASSISTANT_TASK:>
Python Code:
def f(t, x):
# Se importan funciones matematicas necesarias
from numpy import matrix, sin, cos
# Se desenvuelven las variables que componen al estado
q1, q2, q̇1, q̇2 = x
# Se definen constantes del sistema
g = 9.81
m1, m2, J1, J2 = 0.3, 0.2, 0.0005, 0.0002
... | <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: Mandamos llamar al simulador
|
7,641 | <ASSISTANT_TASK:>
Python Code:
import numpy
import chaospy
distribution = chaospy.Uniform(0, 15)
samples = distribution.sample(10, rule="sobol")
evaluations = samples*numpy.sin(samples)
evaluations.round(4)
from matplotlib import pyplot
expansion = chaospy.generate_expansion(9, distribution, normed=True)
t = numpy.lin... | <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: The goal is to create a so called "polynomial chaos kriging" model as defined in the paper with the same name. We are going to do this using the... |
7,642 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'hammoz-consortium', 'sandbox-3', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor... | <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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
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Python Code:
#Version 1
def main():
name, age = input("Enter name") , int( input("Enter age"))
for i in range( int(input("Enter a number"))):
print("You will turn 100 in {}".format(2017-age+100))
main()
from datetime import date
today = str(date.today()).split("-")
print(type(today)... | <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: Using datetime module to find current date and extracting year from the current date
Step2: Use the above and modify our program
|
7,644 | <ASSISTANT_TASK:>
Python Code:
def parse_fastq(fh):
Parse reads from a FASTQ filehandle. For each read, we
return a name, nucleotide-string, quality-string triple.
reads = []
while True:
first_line = fh.readline()
if len(first_line) == 0:
break # end of file
n... | <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: FASTQ
Step6: The nucleotide string can sometimes contain the character "N". N essentially means "no confidence." The sequencer knows there's a... |
7,645 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import pandas as pd
import metpy.calc as mpcalc
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_logo, SkewT
from metpy.units import units
col_names = ['pressure', 'height', 'temperature', 'dewpoint', 'direction', 'speed']
df = pd.re... | <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: Upper air data can be obtained using the siphon package, but for this example we will use
Step2: We will pull the data out of the example datas... |
7,646 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import tensorflow as tf
import utils
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import zipfile
dataset_folder_path = 'data'
dataset_filename = 'text8.zip'
dataset_name = 'Text8 Dataset'
class DLProgress(tq... | <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: Load the text8 dataset, a file of cleaned up Wikipedia articles from Matt Mahoney. The next cell will download the data set to the data folder. ... |
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Python Code:
import os
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.preprocessing import compute_proj_ecg
from mne_connectivity import envelope_correlation
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sam... | <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: For this tutorial we'll crop and resample the raw data to a manageable size
Step2: To create fixed length epochs, we simply call the function a... |
7,648 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import math
import matplotlib.pyplot as plt
%matplotlib inline
from vega_datasets import data
data.list_datasets()
from vega_datasets import local_data
local_data.list_datasets()
local_data.anscombe.description
df = local_data.anscombe()
df.head()
# TODO: put your... | <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: Vega datasets
Step2: or you can work with only smaller, local datasets.
Step3: Ah, we have the anscombe data here! Let's see the description o... |
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Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'thu', 'sandbox-1', 'toplevel')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "em... | <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:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 2... |
7,650 | <ASSISTANT_TASK:>
Python Code:
import chaospy
uniform = chaospy.Uniform(0, 4)
chaospy.E(uniform)
samples = uniform.sample(1e7)
numpy.mean(samples)
q0 = chaospy.variable()
chaospy.E(q0**3-1, uniform)
q0, q1, q2 = chaospy.variable(3)
joint3 = chaospy.J(chaospy.Normal(0, 1), chaospy.Uniform(0, 2), chaospy.Normal(2, 2))... | <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: Its empirical counterpart is the mean function
Step2: The operator can also be used on any polynomial, but would then require the distribution ... |
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Python Code:
from __future__ import division, print_function, unicode_literals
[10.5, 5.2, 3.25, 7.0]
import numpy as np
video = np.array([10.5, 5.2, 3.25, 7.0])
video
video.size
video[2] # 3rd element
%matplotlib inline
import matplotlib.pyplot as plt
u = np.array([2, 5])
v = np.array([3, 1])
... | <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: Vectors
Step2: Since we plan to do quite a lot of scientific calculations, it is much better to use NumPy's ndarray, which provides a lot of co... |
7,652 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
np.random.seed(42)
import keras
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.callbacks import TensorBoard # for part 3.5 on TensorBoard
i... | <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: Load dependencies
Step2: Load and preprocess data
Step3: Design neural network architecture
Step4: Configure model
Step5: Configure TensorBo... |
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Python Code:
# Author: Eric Larson <larson.eric.d@gmail.com>
# Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD-3-Clause
import mne
Brain = mne.viz.get_brain_class()
subjects_dir = mne.datasets.sample.data_path() / 'subjects'
mne.datasets.fetch_hcp_mmp_parcellation(subjects_dir=subje... | <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: We can also plot a combined set of labels (23 per hemisphere).
Step2: We can add another custom parcellation
|
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Python Code:
import pymatgen as pmg
si = pmg.Element('Si')
print('Si:')
print(si.atomic_mass)
print(si.common_oxidation_states)
print(si.number)
lattice = pmg.Lattice.cubic(4.2)
cscl = pmg.Structure(lattice, ["Cs", "Cl"], [[0,0,0],[0.5,0.5,0.5]])
print('\nCsCl structure')
print(cscl)
print('\n')
print... | <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: 3. Create the following Structures from Lattices. Instead of using the convience class methods e.g. Lattice.cubic, create them from basis vector... |
7,655 | <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... | <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: int16 アクティベーションによるトレーニング後の整数量子化
Step2: 16x8 量子化モードが使用可能であることを確認します
Step3: モデルをトレーニングしてエクスポートする
Step4: この例では、モデルを 1 エポックでトレーニングしたので、トレーニングの精度は... |
7,656 | <ASSISTANT_TASK:>
Python Code:
str1 = '"Hola" is how we say "hello" in Spanish.'
str2 = "Strings can also be defined with quotes; try to be sistematic."
print str1
print type(str1)
print type(3)
print type(3.)
print str1[0:5]
print str1+str2
print str1.lower()
print str1.upper()
print len(str1)
print str1.replace('h'... | <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: It is easy to check the type of a variable with the type() command
Step2: The following commands implement some common operations with strings ... |
7,657 | <ASSISTANT_TASK:>
Python Code:
from nilearn import plotting
%matplotlib inline
from os.path import join as opj
import json
from nipype.interfaces.spm import Level1Design, EstimateModel, EstimateContrast
from nipype.algorithms.modelgen import SpecifySPMModel
from nipype.interfaces.utility import Function, IdentityInterf... | <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: Experiment parameters
Step2: Specify Nodes
Step3: Specify GLM contrasts
Step4: Specify GLM Model
Step5: We can also create a data frame usin... |
7,658 | <ASSISTANT_TASK:>
Python Code:
writefile("funwave-input.tpl",
!INPUT FILE FOR FUNWAVE_TVD
! NOTE: all input parameter are capital sensitive
! --------------------TITLE-------------------------------------
! title only for log file
TITLE = VESSEL
! -------------------HOT START---------------------------------
HO... | <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: Adding dynamic inputs
Step3: Smarter wrapper templates
Step4: Using Agave commands, we make a directory on the storage server an deploy our wr... |
7,659 | <ASSISTANT_TASK:>
Python Code:
!pip install oauth2
!pip install unidecode
%matplotlib inline
from collections import defaultdict
import json
import numpy as np
import scipy as sp
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib import rcParams
import matplotlib.cm as cm
import matplotlib as mpl
#colo... | <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: Lab 9 - Graphs & Networks
Step2: 1. Get API key
Step3: 2. Get Access Token
Step4: 3. Get data, clean it and store to disk
Step5: When you ha... |
7,660 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cas', 'sandbox-3', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "email"... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
7,661 | <ASSISTANT_TASK:>
Python Code:
from pycobra.cobra import Cobra
from pycobra.diagnostics import Diagnostics
import numpy as np
%matplotlib inline
# setting up our random data-set
rng = np.random.RandomState(1)
# D1 = train machines; D2 = create COBRA; D3 = calibrate epsilon, alpha; D4 = testing
n_features = 20
D1, D2, ... | <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: Setting up data set
Step2: Setting up COBRA
Step3: When we are fitting, we initialise COBRA with an epsilon value of $0.5$ - this is because w... |
7,662 | <ASSISTANT_TASK:>
Python Code:
pulses = otx.getall()
len(pulses)
json_normalize(pulses)[0:5]
json_normalize(pulses[1]["indicators"])
indicator_types = [
{
"name": "IPv4",
"description": "An IPv4 address indicating the online location of a server or other computer."
},
{
"name": "IPv6"... | <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: Let's list a few pulses
Step2: author_name
Step3: _id
Step4: Besides receiving the pulse information, there is another function that can retr... |
7,663 | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv('atlas-taggings.csv')
df[2:5]
articles = df[df.tagged_type == 'Article']
articles.tag_url = articles.tag_url.apply(get_tag_name)
articles = get_dummies_and_join(articles,'tag_url')
articles = articles.drop(['tag_id','tag_url','tagged_type','tagged_id'],axis=1)
articles ... | <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 only care about the content type "Article"
Step2: But we need to get the tag name out of the url string for the tag
Step3: Import the table... |
7,664 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
sns.set_style('white')
fpvals = 'schubert-sb-table.txt'
fotu = 'data/cdi_schubert_results/RDP/cdi_schubert.otu_table.100.denovo.rdp_assigned'
fmeta = 'data/cdi_schubert_results/... | <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: It looks like OTUs with an uncorrected pvalue of 0.9 get smushed down to 0.08 with qvalue - this seems fishy!
Step2: So these are, for the most... |
7,665 | <ASSISTANT_TASK:>
Python Code:
# This is our distribution (we assume gaussian)
data = np.random.normal(240, 25, 1000)
# we want to calcluate the 95CI (alpha = 0.95)
alpha = 0.95
# out data
data = np.random.normal(240, 25, 1000)
print "The sample mean is: ", data.mean()
# now we get the least of mean values using boots... | <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: - using Bootstrap
Step2: - using t-distribution table
Step3: Use-Case
Step4: Use-Case
|
7,666 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
view_sentence_range = (0, 10)
DON'T MODIFY ANYTHING IN THIS CELL
import num... | <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: TV Script Generation
Step3: Explore the Data
Step6: Implement Preprocessing Functions
Step9: Tokenize Punctuation
Step11: Preprocess all the... |
7,667 | <ASSISTANT_TASK:>
Python Code:
df = pd.read_csv("311-2014.csv", nrows=200000, low_memory = False)
df.head(3)
df.columns
type(df['Created Date'][0])
print(df['Created Date'][0])
dateutil.parser.parse(df['Created Date'][0])
def str_to_time(str_date):
datetype_date = dateutil.parser.parse(str_date)
return datetype... | <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: What was the most popular type of complaint, and how many times was it filed?
Step2: Make a horizontal bar graph of the top 5 most frequent com... |
7,668 | <ASSISTANT_TASK:>
Python Code:
g_dataset_name = "Notebook4Test"
g_fastq_counts_run_prefix = "TestSet4"
g_fastq_counts_dir = '~/dual_crispr/test_data/test_set_4'
g_collapsed_counts_run_prefix = ""
g_collapsed_counts_dir = '~/dual_crispr/test_outputs/test_set_4'
g_combined_counts_dir = ""
g_combined_counts_run_prefix = "... | <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:
Step 4
Step1: Automated Set-Up
Step2: Count Combination Functions
Step3: Input Count Filenames
Step4: Count Combination Execution
|
7,669 | <ASSISTANT_TASK:>
Python Code:
import networkx as nx
import numpy as np
import matplotlib.pyplot as plt
import random
import copy
from Bio.PDB import *
from IPython.display import HTML, display
import tabulate
from __future__ import division
from IPython.display import Image
## Initialization
genes = 25
tfbs_pool = 10... | <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: Question 1
Step2: Final Coexpression network (images exported from Cytoscape)
Step3: Degree Distribution for cases <br>
Step6: Analysis and c... |
7,670 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
%matplotlib inline
import numpy as np
import nsfg
import first
import thinkstats2
import thinkplot
preg = nsfg.ReadFemPreg()
live = preg[preg.outcome == 1]
hist = thinkstats2.Hist(live.birthwgt_lb, label='birthwgt_lb')
thinkplot.Hist(hist)... | <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: Again, I'll load the NSFG pregnancy file and select live births
Step2: Here's the histogram of birth weights
Step3: To normalize the disrtibut... |
7,671 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from scipy import stats
# use seaborn plotting defaults
import seaborn as sns; sns.set()
from sklearn.datasets.samples_generator import make_blobs
X, y = make_blobs(n_samples=50, centers=2,
random_sta... | <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: Motivating Support Vector Machines
Step2: A linear discriminative classifier would attempt to draw a straight line separating the two sets of d... |
7,672 | <ASSISTANT_TASK:>
Python Code:
from sklearn import datasets
digits = datasets.load_digits()
X = digits.data
n_classes = len(digits.target_names)
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=512, max_depth=7, min_samples_leaf=30, random_state = 1960)
clf.fit(digits.data, ... | <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: Generate SQL Code from the Model
Step2: Execute the SQL Code
Step3: Scikit-learn Prediction
Step4: Comparing the SQL and Scikit-learn Predict... |
7,673 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import sys
from corner import corner
sys.path.append('.')
from src.mocklc import MockLC, SimulationSetup
from src.blendlpf import MockLPF
import src.plotting as pl
lc = MockLC(SimulationSetup('M', 0.1, 0.0, 0.15, 'short_transit', cteff=5500, know_orbit=False))
lc.create(wn... | <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: Create a mock light curve
Step2: Initialize the log posterior function
Step3: Optimize
Step4: Estimate the posterior
Step5: Analysis
Step6: ... |
7,674 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from opsimsummary import OpSimOutput
%matplotlib inline
import matplotlib.pyplot as plt
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
opsout = OpSimOutput.fromOpSimDB('/Users/rbiswas/data/LSST/OpSimData/minion_1016_sqlite.db',
... | <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: Declination Boundaries for the WFD proposal
Step2: Total area covered, should be ~ 0.5 sky
Step3: Equations for the boundary values
Step4: Pl... |
7,675 | <ASSISTANT_TASK:>
Python Code:
!pip install wikipedia
import wikipedia
bky = wikipedia.page("Berkeley, California")
bky
bk_split = bky.content.split()
bk_split[:10]
!pip install nltk
import nltk
fdist1 = nltk.FreqDist(bk_split)
fdist1.most_common(10)
print(bky.references[:10])
print(bky.links[:10])
user_config=
fami... | <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: In this example, we will get the page for Berkeley, California and count the most commonly used words in the article. I'm using nltk, which is a... |
7,676 | <ASSISTANT_TASK:>
Python Code:
!pip3 install 'tfx==0.15.0' --upgrade
!python3 -m pip install 'kfp>=0.1.35' --quiet
# Directory and data locations (uses Google Cloud Storage).
import os
_input_bucket = '<your gcs bucket>'
_output_bucket = '<your gcs bucket>'
_pipeline_root = os.path.join(_output_bucket, 'tfx')
# Google... | <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: Enable DataFlow API for your GKE cluster
Step2: Configure the TFX pipeline example
Step3: Submit pipeline for execution on the Kubeflow cluste... |
7,677 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import os
import urllib
import gzip
import struct
def download_data(url, force_download=True):
fname = url.split("/")[-1]
if force_download or not os.path.exists(fname):
urllib.urlretrieve(url, fname)
return fname
def read_data(label_url, image_url)... | <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: We plot the first 10 images and print their labels.
Step2: Next we create data iterators for MXNet. The data iterator, which is similar the ite... |
7,678 | <ASSISTANT_TASK:>
Python Code:
!date
import numpy as np
import torch
import math
import matplotlib.mlab as mlab
import torch.nn.functional as F
import matplotlib.pyplot as plt
from torch.autograd import Variable
import scipy.stats as stats
import scipy
import gc
from sklearn.utils import check_random_state
seed = 133... | <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: Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to expe... |
7,679 | <ASSISTANT_TASK:>
Python Code:
print hex(1024)
print round(5.23222,2)
s = 'hello how are you Mary, are you feeling okay?'
retVal = 1
for word in s.split():
print word
for item in word:
# print item
if not item.islower():
# print item
print 'The string has Uppercase cha... | <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: Problem 2
Step2: Advanced Strings
Step3: Problem 4
Step4: Advanced Sets
Step5: Problem 6
Step6: Advanced Dictionaries
Step7: Advanced List... |
7,680 | <ASSISTANT_TASK:>
Python Code:
import graphlab as gl
sf = gl.SFrame('data/people-example.csv')
sf # we can view first few lines of the table
sf.head()
sf.tail()
sf['Country']
sf['age'].mean()
sf
sf['Full Name'] = sf['First Name'] + ' ' + sf['Last Name']
sf
sf['Country']
def transform_country(country):
return '... | <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: Load a tabular dataset
Step2: SFrame basics
Step3: Inspect Dataset
Step4: Creating new columns
Step5: Apply Function for Data transformation... |
7,681 | <ASSISTANT_TASK:>
Python Code:
def power(x , y , p ) :
res = 1
x = x % p
if(x == 0 ) :
return 0
while(y > 0 ) :
if(( y & 1 ) == 1 ) :
res =(res * x ) % p
y = y >> 1
x =(x * x ) % p
return res
N = 3
if(( N == 1 ) or(N == 2 ) ) :
print(62 )
else :
m =(10 ** 9 ) + 7
if(N % 2... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
7,682 | <ASSISTANT_TASK:>
Python Code:
def pet_names(name1, name2):
print("Pet 1: ", name1)
print("Pet 2: ", name2)
pet1 = "King"
pet2 = "Reginald"
pet_names(pet1, pet2) # pet1 variable, then pet2 variable
pet_names(pet2, pet1) # notice we've switched the order in which they're passed to the function
pet1 = "Rocco"
... | <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: In this example, we switched the ordering of the arguments between the two function calls; consequently, the ordering of the arguments inside th... |
7,683 | <ASSISTANT_TASK:>
Python Code:
#We can import the obspy module, and give it a shorter name (PS as you can see, the # symbol starts a comment)
import obspy as op #note we can start a comment after a line that has executable code on it.
#matplotlib is a large plotting package, with a submodule pyplot that we can import b... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: One of the key cornerstones of the scientific Python platform is Numpy, which is a highly optimized library for multidimensional arrays. It allo... |
7,684 | <ASSISTANT_TASK:>
Python Code:
import pylab as plt
import numpy as np
%matplotlib inline
from __future__ import division
from scipy.integrate import odeint,ode
from numpy import zeros,ones,eye,tanh,dot,outer,sqrt,linspace,cos,pi,hstack,zeros_like,abs,repeat
from numpy.random import uniform,normal,choice
%config InlineB... | <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: FORCE
Step2: The loss function
Step3: FORCE does a pretty nice job learning how to be a sin wave. If you rerun this a few times, you'll see th... |
7,685 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import theano
# By convention, the tensor submodule is loaded as T
import theano.tensor as T
# The theano.tensor submodule has various primitive symbolic variable types.
# Here, we're defining a scalar (0-d) variable.
... | <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: Basics
Step2: Functions
Step3: theano.tensor
Step4: Shared variables
Step5: updates
Step6: Gradients
Step7: Debugging
Step8: The above er... |
7,686 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import numpy as np
tf.reset_default_graph()
# Define our Dataset
X = np.array([[0,0],[0,1],[1,0],[1,1]])
Y = np.array([0,0,0,1]).reshape(-1,1)
# Define the tensorflow tensors
x = tf.placeholder(tf.float32, [None, 2], name='X') # inputs
y = tf.placeholder(tf.float3... | <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: To visualize the graph you just created, launch tensorbord.
Step2: Print the weights of your model
Step3: Build a CNN to predict the MNIST dig... |
7,687 | <ASSISTANT_TASK:>
Python Code:
# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD (3-clause)
from mne import read_evokeds
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
fname = data_path + '/MEG/sample/sample_audvis-ave.fif'
# Reading
condition = 'Left Auditory'
e... | <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: Show result as a butterfly plot
|
7,688 | <ASSISTANT_TASK:>
Python Code:
# Tensorflow
import tensorflow as tf
print('Tested with TensorFLow 1.2.0')
print('Your TensorFlow version:', tf.__version__)
# Feeding function for enqueue data
from tensorflow.python.estimator.inputs.queues import feeding_functions as ff
# Rnn common functions
from tensorflow.contrib.le... | <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: Loading Data
Step2: We can also search our word list for a word like "baseball", and then access its corresponding vector through the embedding... |
7,689 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib
#matplotlib.use('nbagg')
#%matplotlib inline
import matplotlib.pyplot as plt
fig, ax = plt.subplots(figsize=(10,3))
ax.set_ylim([-0.1,1.1])
ax.set_xticklabels([])
ax.set_yticklabels([])
ax.set_title('Universe')
ax.plot([-1,1],[0,0],c='k')
plt.show()
f... | <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: To begin testing our powers, let's make something happen. An event in the center of the universe may be exciting.
Step2: Actually, that wasn't ... |
7,690 | <ASSISTANT_TASK:>
Python Code:
# initialize environment
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import sympy as sy
x = np.linspace(0.0,10.0,1000)
dx = x[1]-x[0]
def numDeriv( x, f ):
return (x[1:]+x[:-1])*0.5, (f[1:]-f[:-1])/(x[1:]-x[:-1])
def func1(r):
r2 = r**2
E = 1-r2
... | <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: How to use $|\vec r|^2$ instead of $|\vec r|$ (get rid of sqrt())
Step2: Factorized Polynominals
Step3: Approx exponential
Step4: Approx Gaus... |
7,691 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'messy-consortium', 'emac-2-53-aerchem', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_c... | <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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
7,692 | <ASSISTANT_TASK:>
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
cd ..
m = pylearn2.utils.s... | <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: Now check the model specified in alexnet_extra_layer_dropouts2.json model, which has 0.9 dropout on all but last convolutional layers, and 0.5 d... |
7,693 | <ASSISTANT_TASK:>
Python Code:
!pip install --user --upgrade --no-deps pixiedust
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
from pixiedust.packageManager import PackageManager
pkg=PackageManager()
pkg.installPackage("graphframes:graphframes:0")
properties = {
'twitter': {
'res... | <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: Pixiedust provides a nice visualization plugin for d3 style plots. Have a look at https
Step2: When the library has been loaded successfully yo... |
7,694 | <ASSISTANT_TASK:>
Python Code:
import glob
import os
import time
import imageio
import matplotlib.pyplot as plt
import numpy as np
import PIL
import tensorflow as tf
from IPython import display
from tensorflow.keras import layers
np.random.seed(1)
tf.random.set_seed(1)
BATCH_SIZE = 128
BUFFER_SIZE = 60000
EPOCHS = 60
... | <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: Next, we'll define some of the environment variables we'll use in this notebook. Note that we are setting the EMBED_DIM to be 64. This is the di... |
7,695 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df = pd.read_csv("provstore/data.csv")
df.head()
df.describe()
# The number of each label in the dataset
df.label.value_counts()
from analytics import test_classification
results, importances = test_classification(df)
from analytics import balance_smote
df = balanc... | <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: Classification on unbalanced (original) data
Step2: Cross Validation tests
Step3: ## Classification on balanced data
Step4: Balancing the dat... |
7,696 | <ASSISTANT_TASK:>
Python Code:
import os, sys
import shutil, time, warnings
from contextlib import redirect_stdout
import numpy as np
import matplotlib.pyplot as plt
from astropy.table import Table, vstack
from PIL import Image, ImageDraw, ImageFont
import multiprocessing
nproc = multiprocessing.cpu_count() // 2
%matpl... | <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: Preliminaries
Step2: Build a sample with the objects of interest.
Step3: Some rejected objects.
Step8: Ensure all objects are in the DR6+DR7 ... |
7,697 | <ASSISTANT_TASK:>
Python Code:
from scipy import sparse
import numpy as np
sa = sparse.csr_matrix(np.array([[1,2,3],[4,5,6],[7,8,9]]))
sb = sparse.csr_matrix(np.array([0,1,2]))
result = sa.multiply(sb)
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
7,698 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import sys
sys.path.append("../../../bayesianpy")
import bayesianpy
from bayesianpy.network import Builder as builder
import logging
import os
import math
import numpy as np
import scipy.stats as ss
import matplotlib.pyplot as plt
import seaborn as s... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Create the network, specifying a latent variable.
Step2: And finally, query the model, specifying each variable in a separate query (otherwise ... |
7,699 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from topoflow.components.met_base import met_component
m = met_component()
cfg_file = './input/meteorology.cfg'
m.initialize(cfg_file)
precip = m.get_value('atmosphere_water__precipitation_leq-volume_flux') # `P` internally
print type(precip)
prin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Import the Meteorology component and create an instance
Step2: Locate the cfg file and initialize the component
Step3: Despite setting a value... |
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