Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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12,700 | <ASSISTANT_TASK:>
Python Code:
import os
with open(os.path.join("datasets", "smsspam", "SMSSpamCollection")) as f:
lines = [line.strip().split("\t") for line in f.readlines()]
text = [x[1] for x in lines]
y = [x[0] == "ham" for x in lines]
from sklearn.model_selection import train_test_split
text_train, text_test, ... | <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: Previously, we applied the feature extraction manually, like so
Step2: The situation where we learn a transformation and then apply it to the t... |
12,701 | <ASSISTANT_TASK:>
Python Code:
%gui qt
import vtk
from vtkviewer import SimpleVtkViewer
#help(vtk.vtkRectilinearGridReader())
# do not forget to call "Update()" at the end of the reader
rectGridReader = vtk.vtkRectilinearGridReader()
rectGridReader.SetFileName("data/jet4_0.500.vtk")
rectGridReader.Update()
rectGridOu... | <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. Data input (source)
Step2: 2. Filters
Step3: 3. Mappers
Step4: 4. Actors
Step5: 5. Renderers and Windows
|
12,702 | <ASSISTANT_TASK:>
Python Code:
import logging
from conf import LisaLogging
LisaLogging.setup()
# Generate plots inline
%pylab inline
import json
import os
# Support to initialise and configure your test environment
import devlib
from env import TestEnv
# Support to configure and run RTApp based workloads
from wlgen imp... | <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: Test environment setup
Step2: Workload configuration
Step3: The output of the previous cell reports the main properties of the generated
Step4... |
12,703 | <ASSISTANT_TASK:>
Python Code:
from bs4 import BeautifulSoup
import requests
import pandas as pd
from pandas import Series,DataFrame
url = 'http://www.ucop.edu/operating-budget/budgets-and-reports/legislative-reports/2013-14-legislative-session.html'
# Request content from web page
result = requests.get(url)
c = resu... | <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 our quick web scraping tutorial, we'll look at some legislative reports from the University of California Web Page. Feel free to experiment ... |
12,704 | <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.add_dataset('lc', times=np.linspace(0,1,6))
b.add_dataset('mesh')
print b['times@mes... | <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 always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.
Step2: The 'Mesh' Dataset
Step3:... |
12,705 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import numpy as np
import math
import timeit
import matplotlib.pyplot as plt
%matplotlib inline
from cs231n.data_utils import load_CIFAR10
def get_CIFAR10_data(num_training=49000, num_validation=1000, num_test=10000):
Load the CIFAR-10 dataset from disk an... | <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's this TensorFlow business?
Step2: Example Model
Step3: TensorFlow supports many other layer types, loss functions, and optimizers - you ... |
12,706 | <ASSISTANT_TASK:>
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 consequence_prediction.vep_mapping_pipeline.consequence_mapping import *
from eva_cttv_pipeline.clinvar_xml_io.cl... | <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: Phenotypes
Step2: Summary for phenotypes
Step3: Precise
Step7: Uncertainty from spans
Step9: Uncertainty from genes
|
12,707 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
data = pd.read_csv("../data/iris.data")
# convert to NumPy arrays because they are the easiest to handle in sklearn
variables = data.drop(["class"], axis=1).as_matrix()
classes = data[["class"]].as_matrix().reshape(-1)
# import cross-validation scorer and KNeighborsCla... | <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: Exercise
|
12,708 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-1', 'sandbox-3', 'aerosol')
# 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: 1... |
12,709 | <ASSISTANT_TASK:>
Python Code:
import geosoft.gxpy.gx as gx
import geosoft.gxpy.utility as gxu
gxc = gx.GXpy()
url = 'https://github.com/GeosoftInc/gxpy/raw/9.3/examples/tutorial/Geosoft%20modules%20-%20gxapi%20and%20gxpy/'
gxu.url_retrieve(url + 'test.grd')
gxu.url_retrieve(url + 'test.grd.gi')
gxc = None
import geos... | <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: GX API (geosoft.gxapi)
Step2: GXPY (geosoft.gxpy)
Step3: You will find that many gxpy classes map closely to underlying gxapi classes, but wit... |
12,710 | <ASSISTANT_TASK:>
Python Code:
# Imports and directives
%matplotlib inline
import numpy as np
from math import log
import matplotlib.pyplot as plt
from matplotlib.mlab import PCA as mlabPCA
import javalang
import os, re, requests, zipfile, json, operator
from collections import Counter
import colorsys
import random
fro... | <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: Analyzing one project
Step2: 1. Commit frequency
Step3: 2. Distinct committers
Step4: 3. Class reference count
Step5: 4. Inheritance count
S... |
12,711 | <ASSISTANT_TASK:>
Python Code:
workDir = '../../t/SIPSim_example/'
nprocs = 3
import os
# Note: you will need to install `rpy2.ipython` and the necessary R packages (see next cell)
%load_ext rpy2.ipython
%%R
library(ggplot2)
library(dplyr)
library(tidyr)
workDir = os.path.abspath(workDir)
if not os.path.isdir(workDir)... | <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: Init
Step2: Experimental design
Step3: Pre-fractionation communities
Step4: Note
Step6: Simulating fragments
Step7: Simulation
Step8: Plot... |
12,712 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
from pandas import *
import matplotlib.pyplot as plt
%matplotlib inline
from ggplot import *
from numpy import random
plt.style.use('ggplot')
data = pd.read_csv("../Data/Histogram/pared_down.csv")
data
data.columns
table = pivot_table(data, index=['Tree'], columns=[... | <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: Read data using pandas.
Step2: Pivot the table to group the data by tree.
Step3: Plot using native pandas plotting.
|
12,713 | <ASSISTANT_TASK:>
Python Code:
def quad_func (x):
return 5 * x ** 2 -23 * x + 47
# Training Set + Eval Set: 200 samples (70%, 30% split)
# Test Set: 60 samples
# Total: 260 samples
np.random.seed(5)
samples = 260
x_vals = pd.Series(np.random.rand(samples) * 20)
x2_vals = x_vals ** 2
y_vals = x_vals.map(quad_func)
y... | <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: <h4>Training and Evaluation Set</h4>
Step2: Test 1
Step3: Test 1
|
12,714 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import scipy.spatial
import scipy.optimize
points1 = np.array([(x, y) for x in np.linspace(-1,1,7) for y in np.linspace(-1,1,7)])
N = points1.shape[0]
points2 = 2*np.random.rand(N,2)-1
C = scipy.spatial.distance.cdist(points1, points2, metric='minkowski', p=1)
_, result... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
12,715 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image, display
Image('images/08_transfer_learning_flowchart.png')
%matplotlib inline
import matplotlib.pyplot as plt
import tensorflow as tf
import numpy as np
import time
from datetime import timedelta
import os
# Functions and classes for loading and using 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: Imports
Step2: This was developed using Python 3.5.2 (Anaconda) and TensorFlow version
Step3: Load Data for CIFAR-10
Step4: The data dimensio... |
12,716 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import eland as el
import numpy as np
ES_URL = 'http://localhost:9200/'
df = el.read_es(ES_URL, 'ecs-search-metrics')
df.dtypes
print(df.info_es())
df.head()
df['SearchMetrics.click.result.rank'].describe()
df['SearchMetrics.click.result.rank'].hist()
df['source.user... | <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: Data Loading and Preparation
Step2: What is the distribution of ranks of results clicked on?
Step3: How many users are in the dataset?
Step4: ... |
12,717 | <ASSISTANT_TASK:>
Python Code:
%run 'ipython_startup.py'
import seaborn as sns
dspr = pd.read_csv(os.path.join(PROJ, 'analysis_output/mmc/dsrp_sex_det_genes_for_mmc.csv'), index_col='_NAME_')
cegs = pd.read_csv(os.path.join(PROJ, 'analysis_output/mmc/cegsV_sex_det_gene_for_mmc.csv'), index_col='_NAME_')
dspr.drop('Rm6... | <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 Data
Step2: Variation among genes in sex hierarchy
Step3: Correlation
|
12,718 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'mri-agcm3-2', 'aerosol')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
12,719 | <ASSISTANT_TASK:>
Python Code:
birthdays = dict()
print( birthdays )
birthdays['0704'] = 'Steve'
birthdays['0529'] = 'Tony'
print( birthdays )
print( birthdays['0529'] )
# Get the number of key-value pairs
print( len( birthdays ) )
# Get the values in the dictionary
print( birthdays.values() )
# Get the keys in the ... | <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 add an item to the dictionary, use square brackets like a list
Step2: Note that order isn't preserved in a dictionary (unlike a list)
Step3:... |
12,720 | <ASSISTANT_TASK:>
Python Code:
%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
#colorbrewer2 Dark2 qualitative color table
dark... | <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: HW4
Step2: Description of the data set
Step3: The data frame is a frame of reviews. We have joined in information about users and businesses i... |
12,721 | <ASSISTANT_TASK:>
Python Code:
# Data path/filename
t_ind = 38
data_path = '../data/'
file_name = data_path + 'data_sim_low.hdf5'
data_options = {'flag_cell': True, 'flag_electode': False}
data = data_in(file_name, **data_options)
localization_options = {'p_vres':20, 'p_jlen':0, 'p_erad': 5, 't_ind': 38, 'flag_depthwe... | <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: And chose the localization parameters. You can check the parameters necessary in the documentation.
Step2: You can see the different functions ... |
12,722 | <ASSISTANT_TASK:>
Python Code:
#!pip install --user miepython
import numpy as np
import matplotlib.pyplot as plt
try:
import miepython
except ModuleNotFoundError:
print('miepython not installed. To install, uncomment and run the cell above.')
print('Once installation is successful, rerun this cell again.')
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step4: Goals for this notebook
Step5: Mie scattering describes the special case of the interaction of light passing through a non-absorbing medium wit... |
12,723 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
from keras.applications import vgg16
from keras.layers import Input
from dream import *
from scipy.misc import imread
img_dir = '../images/dream/sky1024px.jpg'
I = imread(img_dir)
plt.imshow(I)
plt.axis('off')
plt.show()
settings = {'... | <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 will use the same image for the example.
Step2: Here are the settings we will use, including the layers of the network we want to "dream" an... |
12,724 | <ASSISTANT_TASK:>
Python Code:
% matplotlib inline
from __future__ import division
import os
import nibabel as nib
import numpy as np
from neuropower import peakdistribution
import scipy.integrate as integrate
import pandas as pd
import matplotlib.pyplot as plt
import palettable.colorbrewer as cb
if not 'FSLDIR' in os.... | <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: 1. What is the voxelwise threshold?
Step2: 2. Definition of alternative
Step3: 3. How large statistic in a field be to exceed the threshold wi... |
12,725 | <ASSISTANT_TASK:>
Python Code:
# Import modules needed to reproduce results
import os
import plotnine
from plotnine import *
import pandas as pd
from scipy import stats
import numpy as np
from statsmodels.stats.proportion import proportion_confint as prop_CI
def tdist_2dist(mu1, mu2, se1, se2, n1, n2, var_eq=False):
... | <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 a rule, I always conduct statistical simulations to make sure the functions I have written actually perform the way I expect them to when the... |
12,726 | <ASSISTANT_TASK:>
Python Code:
%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,pandas,matplotlib,scikit-learn,nltk
# to install watermark just uncomment the following line:
#%install_ext https://raw.githubusercontent.com/rasbt/watermark/master/watermark.py
import pyprind
import pandas as pd
imp... | <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: <br>
Step2: Shuffling the DataFrame
Step3: Optional
Step4: <br>
Step5: Assessing word relevancy via term frequency-inverse document frequenc... |
12,727 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
#imshow(C.get_optical_path_map())
#colorbar()
#poly,error=C.get_optical_path_map_lsq(order=2)
#print(error)
#print(poly)
def opsystem(lp):
L=library.Edmund.get("32494")
C=CCD()
S=System(complist=[(L,(0,0,lp),(0,-pi,0)),(C,(0,0,570),(0,0,0))],n=1.)
R=point_so... | <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: Ejercicio
Step2: Utilizando otras librerias de python para optimizar el sistema
|
12,728 | <ASSISTANT_TASK:>
Python Code:
# Download example dataset
from msmbuilder.example_datasets import FsPeptide
fs_peptide = FsPeptide()
fs_peptide.cache()
# Work in a temporary directory
import tempfile
import os
os.chdir(tempfile.mkdtemp())
from msmbuilder.dataset import dataset
xyz = dataset(fs_peptide.data_dir + "/*.x... | <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 dataset object
Step2: Featurization
Step3: Intermediate kinetic model
Step4: tICA Heatmap
Step5: Clustering
Step6: MSM
Step7: Macrosta... |
12,729 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from pandas import Series, DataFrame
weather = pd.read_table('daily_weather.tsv')
weather.groupby('season_desc').agg({'temp': np.mean})
fix = weather.replace("Fall", "Summer_").replace("Summer", "Spring_").replace("Winter", "Fall_").replace("Spring",... | <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: Various of the columns represent dates or datetimes, but out of the box pd.read_table won't treat them correctly. This makes it hard to (for exa... |
12,730 | <ASSISTANT_TASK:>
Python Code::
model = Sequential()
model.add(Embedding(vocab_size, 10, input_length=1))
model.add(LSTM(1000, return_sequences=True))
model.add(LSTM(1000))
model.add(Dense(1000, activation="relu"))
model.add(Dense(vocab_size, activation="softmax"))
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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:
|
12,731 | <ASSISTANT_TASK:>
Python Code:
# Für die Standardausgabe benutzen wir die print() Funktion
print("Hallo Welt!")
# Wir können mit Kommata getrennt auch mehrere Werte ausgeben:
print("foo", "bar")
# Mit der help() Funktionen zeigen wir uns
# die Hilfe der print() Funktion an:
help(print)
# Ausgabe mit Seperatoren:
print(... | <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: Einfache Operationen
Step2: Genauer betrachtet besteht die Zeile 4 + 34 aus zwei Literalen (4 und 34) und einem Operator (+), die kombiniert de... |
12,732 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
from matplotlib.pylab import *
from pymc3 import *
import numpy as np
d = np.random.normal(size=(3, 30))
d1 = d[0] + 4
d2 = d[1] + 4
yd = .2*d1 +.3*d2 + d[2]
lam = 3
with Model() as model:
s = Exponential('s', 1)
tau = Uniform('tau', 0, 1000)
b = lam * tau
... | <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: Then define the random variables.
Step2: For most samplers, including Metropolis and HamiltonianMC, simply pass a list of variables to sample a... |
12,733 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
from scipy.cluster.hierarchy import dendrogram, linkage
import ggplot as gg
import networkx as nx
%matplotlib inline
data_dir = os.path.join(os.getenv('MDA_DATA_DIR', '/home/m... | <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:
Step3: Data import
Step4: The cell below reproduces the logic in the first cell of the original article. It doesn't feel quite as nice to me as the d... |
12,734 | <ASSISTANT_TASK:>
Python Code:
gap_fill_by_month = candles.groupby(["month", "gap_filled"]).size()
gap_fill_by_month.groupby("month").apply(lambda g: g / g.sum() * 100)
gap_fill_by_day_of_week = candles.groupby(["day_of_week", "gap_filled"]).size()
gap_fill_by_day_of_week.groupby("day_of_week").apply(lambda g: g / g.s... | <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: Month has no discernible effect on gap fill rate.
Step2: Monday has a slightly lower gap fill rate.
|
12,735 | <ASSISTANT_TASK:>
Python Code:
import requests
url = 'http://www.tripadvisor.com/'
response = requests.get(url)
print(response.status_code)
#print(response.headers)
import requests
url = 'http://www.tripadvisor.com/robots.txt'
response = requests.get(url)
if response.status_code == 200:
print(response.status_cod... | <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: Get the '/robots.txt' file contents
Step2: Get the HTML content from the website
Step3: Scraping websites
Step4: Step 1
Step5: Step 2
Step7:... |
12,736 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'snu', 'sandbox-2', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
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Python Code:
train = pd.read_csv("train.csv")
train.describe()
# Cleanup Gender and Embarked
train['Sex'] = np.where(train['Sex'] == 'male', 0, 1)
train['Embarked'] = train['Embarked'].fillna('Z').map(dict(C=0, S=1, Q=2, Z=3))
# AGE -- quickly look at data
train['hasage'] = np.isnan(train['Age'])
tra... | <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: Clean Data
Step2: There is a clear difference in the distributions in ages between thoes who survived and not. Also from the table you can see... |
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Python Code:
! gsutil ls gs://pyspark-workshop/so-posts
lines = sc.textFile("gs://pyspark-workshop/so-posts/*")
# or a smaller piece of them
lines = sc.textFile("gs://pyspark-workshop/so-posts/Posts.xml-*a")
lines.take(5)
rows = lines.filter(lambda x: x.lstrip().startswith('<row'))
import xml.etree... | <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 check what's inside these files...
Step2: Only proper rows with posts
Step3: Let's parse this mess...
Step4: Better
Step5: Let's compu... |
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Python Code:
%matplotlib inline
# Common imports
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import sklearn.linear_model as skl
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import Mi... | <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: Simple preprocessing examples, breast cancer data and classification
Step2: More on Cancer Data
Step3: Principal Component Analysis
Step4: PC... |
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Python Code:
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import mne
from mne.datasets import sample
import matplotlib.pyplot as plt
print(__doc__)
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
fwd_fname = data_path + '/MEG/sa... | <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: Compute sensitivity maps
Step2: Show gain matrix a.k.a. leadfield matrix with sensitivity map
|
12,741 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
from __future__ import division, print_function
from collections import Counter, defaultdict
import re
import itertools
import random
Set = frozenset # Data will be frozensets, so they can't be mutate... | <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 are some "arbitrary lists" (see panel two of the comic) which we will be using to test out the code.
Step2: And here we show how it works
... |
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Python Code:
a = [4,5,6,8,10]
for i in a:
print(i)
# A fragment of `One Hundred Years of Solitude`
GGM = 'Many years later, as he faced the firing squad, \
Colonel Aureliano Buendía was to remember that dist \
ant afternoon when his father took him to discover ice. \
At that time Macondo was a vil... | <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: Iterating over dictionaries
Step2: Iterating over a sequence
|
12,743 | <ASSISTANT_TASK:>
Python Code:
def regexp_sum(S):
n = len(S)
if n == 0:
return 0
elif n == 1:
r, = S
return r
else:
r, *Rs = S
return ('+', r, regexp_sum(Rs))
def rpq(p1, p2, Σ, 𝛿, Allowed):
if len(Allowed) == 0:
AllChars = { c for c in Σ
... | <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 function rpq assumes there is some <span style="font-variant
Step2: The function dfa_2_regexp takes a deterministic <span style="font-varia... |
12,744 | <ASSISTANT_TASK:>
Python Code:
cat = True
dog = False
print(type(cat))
from cities import cities
print(cities)
first_alb = cities[0] == 'Albuquerque'
second_alb = cities[1] == 'Albuquerque'
first_last = cities[0] == cities[-1]
print(first_alb, second_alb, first_last)
crime_rates = [749, 371, 828, 503, 1379, 425, 408,... | <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
Step2: 3
Step3: 4
Step4: 5
Step5: 6
Step6: 7
Step7: 8
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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 _, r... | <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: 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... |
12,746 | <ASSISTANT_TASK:>
Python Code:
def odd_number(num):
L=[]
for i in range(num):
if i%2 == 1:
L.append(i)
return L
%time odd_sample1 = odd_number(100000000)
odd_sample1[:20]
odd_number1 = [x for x in range(100000000) if x % 2 == 1]
odd_number1 = []
for x in range(100000000):
... | <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: 1억 까지의 홀수들의 리스트를 생성하는 걸리는 시간을 확인해보자.
Step2: 지금 사용하는 컴퓨터에서는 9초 정도 걸린다.
Step3: 이제 질문을 좀 달리하자.
Step4: 위 코드에서 아래 부분이 핵심이다.
Step5: 이제 조건제시법 2를 구... |
12,747 | <ASSISTANT_TASK:>
Python Code:
from pyspark import SparkContext
sc = SparkContext('local[*]')
from pyspark.sql import SQLContext
sqlc = SQLContext(sc)
from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import StandardScaler
from pyspark.ml.feature import StringIndexer
from pyspark.ml.feature import... | <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: Spark MLLib imports
Step2: Unsupervised Learning
Step3: Pre-process the data
Step4: Transform 60 features into MMlib vectors
Step5: Scale fe... |
12,748 | <ASSISTANT_TASK:>
Python Code:
age = 33
print(age)
nouvelAge = age + 1
print(nouvelAge)
input(a)
a = input()
print(a)
print(a*3)
b = int(input())
print(b*5)
r_cercle = int(input ("Rayon du cercle ?"))
pi = 3.14
d_cercle = r_cercle *2
p_cercle =pi*d_cercle
a_cercle = pi*r_cercle*r_cercle
print("Diametre du cercle ... | <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: Ci-dessous, on affiche la valeur de la variable age
Step2: Ci-dessous, on crée une variable nouvelAge et on lui affecte la valeur de la variabl... |
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Python Code:
!pip install -I "phoebe>=2.2,<2.3"
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
b.add_dataset(phoebe.dataset.orb, compute_times=np.linspace(0,10,10), dataset='orb01', component=['primar... | <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 always, let's do imports and initialize a logger and a new Bundle. See the building a system tutorial for more details.
Step2: And we'll at... |
12,750 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
%pylab inline
pylab.style.use('ggplot')
import seaborn as sns
pp_data = pd.read_csv('ccpp.csv')
pp_data.head()
for c in pp_data.columns:
_ = pylab.figure()
pp_data.loc[:, c].plot(kind='hist')
feature_data = pp_data.drop('AT', axis=1)
corrs ... | <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: Attribute Information
Step2: Correlation With the Target column
Step3: Feature Correlations
Step4: Bivariate Analysis
Step5: OLS Regression
... |
12,751 | <ASSISTANT_TASK:>
Python Code:
def split_data(data, prob):
split data into fractions [prob, 1 - prob]
results = [], []
for row in data:
results[0 if random.random() < prob else 1].append(row)
return results
def train_test_split(x, y, test_pct):
data = zip(x, y) # pair corresponding values
... | <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: Modeling
Step2: When splitting data, it's important to keep input data and target data in the same order
Step3: Correctness
Step4: Precision ... |
12,752 | <ASSISTANT_TASK:>
Python Code:
import math
import numpy as np
import pandas as pd
from scipy import stats
from scipy import optimize
import emcee
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
clr_plt = sns.color_palette()
import models
# the true parameters
eps_true = 5e-4
t_true = 3e5
rho... | <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 mathematical (deterministic, forward) model
Step2: The data
Step3: The gendata Python module is used to generate the dataset (see the note... |
12,753 | <ASSISTANT_TASK:>
Python Code:
# These are all the modules we'll be using later. Make sure you can import them
# before proceeding further.
from __future__ import print_function
import matplotlib.pyplot as plt
import numpy as np
import os
import sys
import tarfile
from IPython.display import display, Image
from scipy 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:
Step3: First, we'll download the dataset to our local machine. The data consists of characters rendered in a variety of fonts on a 28x28 image. The lab... |
12,754 | <ASSISTANT_TASK:>
Python Code:
all_df=[]
nfiles=15
for i in range(nfiles):
filename = 'msample%d.csv' % i
print i
all_df.append(pd.read_csv(filename, header=None))
all_df[0]
Y=[]
for i in range(nfiles):
Y.append(all_df[i][8]=='Success')
Y[1]
def map_user(x):
if x.startswith('C'):
return '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: I here repeat my procedure for generating labeled data and features for training/test data.
Step2: I just discovered that my sample sets do not... |
12,755 | <ASSISTANT_TASK:>
Python Code:
paired_bp_tn_split??
cc = codes.ix[matched_rna.columns.get_level_values(0)].dropna().unique()
r = pd.DataFrame({c: ttest_rel(matched_rna.ix['PLAU'].ix[ti(codes==c)])
for c in cc}).T
fig, ax = subplots(figsize=(7,3))
cc = ['HNSC','LUSC','LUAD','BLCA','THCA','BRCA','COAD','REA... | <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: TPA protease
Step2: Collagenase
Step3: elastases
Step4: Cathepsin
|
12,756 | <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
g = 9.81 # m/s^2
l = 0.5 # length of pendulum, in meters
tmax = 50. # seconds
t = np.linspace(0, tmax, int(100*tmax))
def derivs... | <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: Damped, driven nonlinear pendulum
Step4: Write a function derivs for usage with scipy.integrate.odeint that computes the derivatives for the da... |
12,757 | <ASSISTANT_TASK:>
Python Code:
apikey = '34b41fe7b9db6c1bd5f8ea3492bca332'
# TA-COMMENT: Nice!
coordinates = {'San Antonio': '29.4241,-98.4936', 'Miami': '25.7617,-80.1918', 'Central Park': '40.7829,-73.9654'}
import requests
url = 'https://api.forecast.io/forecast/' + apikey + '/' + coordinates['San Antonio']
respons... | <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) What's the current wind speed? How much warmer does it feel than it actually is?
Step2: 3) The first daily forecast is the forecast for toda... |
12,758 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import sklearn
import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import random
import copy
from sklearn.datasets import fetch_mldata
from sklearn import cross_validation
from sklearn import base
from sklearn.linear_model import Lasso
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: Data generation
Step2: L1
Step3: Selecting lambda
Step4: L2
Step5: Test with only 200 samples
Step6: Selecting lambda
Step7: Evaluation us... |
12,759 | <ASSISTANT_TASK:>
Python Code:
# Import Numpy, TensorFlow, TFLearn, and MNIST data
import numpy as np
import tensorflow as tf
import tflearn
import tflearn.datasets.mnist as mnist
# Retrieve the training and test data
trainX, trainY, testX, testY = mnist.load_data(one_hot=True)
# Visualizing the data
import matplotli... | <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: Retrieving training and test data
Step2: Visualize the training data
Step3: Building the network
Step4: Training the network
Step5: Testing
|
12,760 | <ASSISTANT_TASK:>
Python Code:
#@title Imports
!pip install jax_md
import jax.numpy as np
import numpy as onp
from jax import jit
from jax import random
from jax import lax
from jax.config import config
config.update('jax_enable_x64', True)
from jax_md import space
from jax_md import energy
from jax_md import simulate
... | <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: 64k Particle LJ System
Step2: Prepare the system
Step3: Benchmark using fixed size neighbor list.
Step4: On an A100 this comes out to 22.4 s ... |
12,761 | <ASSISTANT_TASK:>
Python Code:
import gensim, logging, os
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
class Corpus(object):
'''Clase Corpus que permite leer de manera secuencial un directorio de documentos de texto'''
def __init__(self, directorio):
... | <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: Entrenamiento de un modelo
Step2: CORPUSDIR contiene una colección de noticias en español (normalizada previamente a minúsculas y sin signos de... |
12,762 | <ASSISTANT_TASK:>
Python Code:
# %matplotlib inline
# %config InlineBackend.figure_format='retina' # mac
# %load_ext autoreload
# %autoreload 2
import pandas as pd
import gseapy as gp
import matplotlib.pyplot as plt
gp.__version__
# read in an example gene list
gene_list = pd.read_csv("./tests/data/gene_list.txt",hea... | <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: Check gseapy version
Step2: 1. (Optional) Convert IDs Using Biomart API
Step3: See all supported enrichr library names
Step4: 2.1 Assign ... |
12,763 | <ASSISTANT_TASK:>
Python Code:
# sphinx_gallery_thumbnail_number = 2
import os.path as op
import matplotlib.pyplot as plt
import mne
data_path = mne.datasets.sample.data_path()
fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif')
evokeds = mne.read_evokeds(fname, baseline=(None, 0), proj=True)
print(ev... | <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 for convenience we read the evoked dataset from a file.
Step2: Notice that the reader function returned a list of evoked instances. This i... |
12,764 | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
import sys
import os
sys.path.insert(0, os.path.abspath('..'))
import time
import numpy as np
import matplotlib.pyplot as plt
from cs231n.classifiers.fc_net import *
from cs231n.data_utils import get_CIFAR10_data
from cs231n.gradient_check import eval_numerical_... | <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: Batch Normalization
Step2: Batch normalization
Step3: Batch Normalization
Step4: Batch Normalization
Step5: Fully Connected Nets with Batch ... |
12,765 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import mmlspark
# load raw data from small-sized 30 MB CSV file (trimmed to contain just what we use)
dataFile = "On_Time_Performance_2012_9.csv"
import os, urllib
if not os.path.isfile(dataFile):
urllib.request.urlretrieve("https://mmlspark.azu... | <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: Next, import the CSV dataset.
Step2: Split the dataset into train and test sets.
Step3: Train a regressor on dataset with l-bfgs.
Step4: Scor... |
12,766 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
from sklearn import linear_model
x = np.array([[0, 0], [1, 1], [2, 2]])
y = np.array([0, 1, 2])
print(x,y)
clf = linear_model.LinearRegression()
clf.fit(x, y)
print(clf.coef_)
x_missing = np.array([... | <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: Tabular data
Step2: Normalization
Step3: Categorical data
Step4: Exercises
Step6: Image data
Step7: Text
Step8: Exercises
|
12,767 | <ASSISTANT_TASK:>
Python Code:
% matplotlib inline
%config InlineBackend.figure_format = 'retina'
%load_ext line_profiler
from __future__ import division
import numpy as np
import glob
import matplotlib.pyplot as plt
import scipy.linalg as sl
import enterprise
from enterprise.pulsar import Pulsar
import enterprise.sign... | <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: Get par and tim files
Step2: Load pulsars into Pulsar objects
Step3: Setup and run a simple noise model on a single pulsar
Step4: We can see ... |
12,768 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.model_selection import train_test_split
from tensorflow import keras
img_rows, img_cols = 28, 28
num_classes = 10
def prep_data(raw):
y = raw[:, 0]
out_y = keras.utils.to_categorical(y, num_classes)
x = raw[:,1:]
num_images = raw.shape[... | <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) Start the model
Step2: 2) Add the first layer
Step3: 3) Add the remaining layers
Step4: 4) Compile Your Model
Step5: 5) Fit The Model
Ste... |
12,769 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='... | <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: Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits.
Step2: We'll train an autoe... |
12,770 | <ASSISTANT_TASK:>
Python Code:
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
from gensim.summarization import summarize
text = "Thomas A. Anderson is a man living two lives. By day he is an " + \
"average computer programmer and by night a hacker known 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: We will try summarizing a small toy example; later we will use a larger piece of text. In reality, the text is too small, but it suffices as an ... |
12,771 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/",one_hot=True)
type(mnist)
mnist.train.images
mnist.train.num_examples
mnist.test.num_examples
mnist.validation.num_examples
import matplotlib.pyplot as plt
... | <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: Alternative sources of the data just in case
Step2: Visualizing the Data
Step3: Create the Model
Step4: Loss and Optimizer
Step5: Create Ses... |
12,772 | <ASSISTANT_TASK:>
Python Code:
from poliastro.atmosphere import COESA62, COESA76
from astropy import units as u
import numpy as np
import matplotlib.pyplot as plt
# We build the atmospheric instances
coesa62 = COESA62()
coesa76 = COESA76()
# Create the figure
fig, ax = plt.subplots(figsize=(10,10))
ax.set_title("U.S 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: Comparing coesa62 and coesa76
Step2: Temperature, pressure and density distrubutions
|
12,773 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import string
from sklearn.ensemble import GradientBoostingClassifier
def read_file(filename):
with open(filename) as f:
content = f.readlines()
y = [line[0] for line in content]
X = [line[2:].strip() for line in content]
return X,y
X_train,... | <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 file from data and convert to training set and test set (reading from two distinct files)
Step2: A simple class that converts the string i... |
12,774 | <ASSISTANT_TASK:>
Python Code:
import notebook
from __future__ import print_function
from jupyter_core.paths import jupyter_data_dir, jupyter_path
print(jupyter_data_dir())
print(jupyter_path())
! sudo jupyter nbextension install sas_kernel/sas_kernel/nbextensions/showSASLog
if notebook.nbextensions.check_nbextension... | <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: To Install Systemwide
Step2: This python code will check on the nbextension in systemwide folders (user=False is the flag for this)
Step3: To ... |
12,775 | <ASSISTANT_TASK:>
Python Code:
X = np.linspace(0, 20, 100)
def f(x):
if x < 7:
return 'a', 2. + np.random.random()
elif x < 14:
return 'b', 4 + np.random.random()
else:
return 'c', 6 + np.random.random()
K, Y = zip(*[f(x) for x in X])
colors = plt.get_cmap('Set1')
categories = ['a', ... | <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: LDA is like inverted ANOVA
Step2: LDA assumes that the variance in each group is the same, and that the predictor(s) are normally distributed f... |
12,776 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import, division, print_function, unicode_literals
try:
%tensorflow_version 2.x
except Exception:
pass
import tensorflow as tf
tf.__version__
# To generate GIFs
!python3 -m pip install -q imageio
import glob
import imageio
import matplotlib.pyplot as pl... | <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: Load and prepare the dataset
Step2: Next, we define our input pipeline using tf.data. The pipeline below reads in train_images as tensor slices... |
12,777 | <ASSISTANT_TASK:>
Python Code:
n_colors = 5 # number of possible colors
n_bags = 3 # number of bags
n_trials = 20 # number of draws from each bag
from bayespy import nodes
import numpy as np
p_colors = nodes.Dirichlet(n_colors * [0.5], plates=(n_bags,)).random()
import bayespy.plot as bpplt
bpplt.hinton(p_colors)... | <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: Generate randomly a color distribution for each bag
Step2: The concentration parameter $\begin{bmatrix}0.5 & \ldots & 0.5\end{bmatrix}$ makes t... |
12,778 | <ASSISTANT_TASK:>
Python Code:
# A dictionary of movie critics and their ratings of a small
# set of movies
critics={'Lisa Rose': {'Lady in the Water': 2.5, 'Snakes on a Plane': 3.5,
'Just My Luck': 3.0, 'Superman Returns': 3.5, 'You, Me and Dupree': 2.5,
'The Night Listener': 3.0},
'Gene Seymour': {'L... | <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: 1. User-based filtering
Step2: This formula calculates the distance, which will be smaller for people who are more similar.
Step3: Pearson co... |
12,779 | <ASSISTANT_TASK:>
Python Code:
# constants
k_B = Boltzmann
eta_air = 18.27e-6 # Pa # (J.T.R.Watson (1995)).
d_gas = 0.372e-9 #m #(Sone (2007)), ρSiO2
rho_SiO2 = 1800 # #kg/m^3 - Number told to us by
T0 = 300
R = 50e-9 # m
def mfp(P_gas):
mfp_val = k_B*T0/(2**0.5*pi*d_gas**2*P_gas)
return mfp_val
m_gas = 4.81e... | <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: Alternativity one can use
Step2: Muddassar and Gieseler's simplified formula for the environmental damping is
Step3: Relation 2
Step4: Relati... |
12,780 | <ASSISTANT_TASK:>
Python Code:
class User:
def __init__(self, user_id):
self.user_id = user_id
def __repr__(self):
return "User({})".format(self.user_id)
def sort_notcompare():
users = [User(23), User(3), User(99)]
print(users)
print(sorted(users, key = lambda u: u... | <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: 另外一种方式是使用 operator.attrgetter() 来代替 lambda 函数：
Step2: 讨论
|
12,781 | <ASSISTANT_TASK:>
Python Code:
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision
import torchvision.transforms as T
import PIL
import numpy as np
from scipy.misc import imread
from collections import namedtuple
import matplotlib.pyplot as plt
from cs231n.image_utils import SQUEEZ... | <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: We provide you with some helper functions to deal with images, since for this part of the assignment we're dealing with real JPEGs, not CIFAR-10... |
12,782 | <ASSISTANT_TASK:>
Python Code:
!mkdir cifar10
!curl -o cifar-10-python.tar.gz https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
!tar -xvzf cifar-10-python.tar.gz -C cifar10
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from cifar import load_CIFAR10
plt.rcParams['figure.figsize'] = (10.0, ... | <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: <h1 align="center">First of all -- Checking Questions</h1>
Step2: Соберите нейронку
Step3: Вот и всё, пошли её учить
Step4: Процесс обучения
|
12,783 | <ASSISTANT_TASK:>
Python Code:
import cobra
from cobra.solvers import get_solver_name
from cobra import Model, Reaction, Metabolite
from cobra.flux_analysis import parsimonious
import pandas as pd
from utils import show_map, findBiomarkers
# set escher map
map_loc = './maps/escher_map_geenen_2012.json'
M = cobra.io.loa... | <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: Analyze basic flux distributions
|
12,784 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import numpy as np
import matplotlib.pyplot as plt
from dcprogs.likelihood import QMatrix
tau = 0.2
qmatrix = QMatrix([[-1, 1, 0], [19, -29, 10], [0, 0.026, -0.026]], 1)
from dcprogs.likelihood._methods import exponential_pdfs
def plot_exponentials(qmatrix, tau, x0=N... | <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: We then create a function to plot each exponential component in the asymptotic expression. An explanation on how to get to these plots can be fo... |
12,785 | <ASSISTANT_TASK:>
Python Code:
import zipfile
with zipfile.ZipFile(path + "glove.6B.zip","r") as zip_ref:
zip_ref.extractall(path)
%ls $path
import pickle
def get_glove(name):
with open(path+ 'glove.' + name + '.txt', 'r') as f: lines = [line.split() for line in f]
words = [d[0] for d in lines]
vecs = ... | <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: Process the data
Step2: Takes just under 2 min, no output.
Step3: Looking at the vectors
Step4: Here's the first 25 "words" in glove.
Step5: ... |
12,786 | <ASSISTANT_TASK:>
Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | <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: Semantic Search with Approximate Nearest Neighbors and Text Embeddings
Step2: Import the required libraries
Step3: 1. Download Sample Data
Ste... |
12,787 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
from spacy.symbols import pobj
site_scrape_dict = {
# the following represents html selector to retrieve the header + 2 first test paragraphs
'aol.com': '#article-wrapper h1, #article-wrapper > div.article-content > p:nth-child(2) , #article-... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: scraping video geo context
Step3: create a unique ['url', 'site']
Step4: create a new csv that will hold url to extracted locations (pipe del... |
12,788 | <ASSISTANT_TASK:>
Python Code:
# This imports the OpenContextAPI from the api.py file in the
# opencontext directory.
%run '../opencontext/api.py'
import matplotlib.pyplot as plt
import matplotlib.cm as cm
def make_group_markers_colors_for_df(df, group_col):
Makes group markers and colors for consistence in multip... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step3: Below I define two little utility functions to make scatter plots from the data contained in a dataframe that was populated by the OpenContextAP... |
12,789 | <ASSISTANT_TASK:>
Python Code:
# Original book version
def vector_sum(vectors):
return reduce(vector_add, vectors)
vectors = [v,w,v,w,v,w]
vector_sum(vectors)
# Modified version by sc82.choi at Gachon - *은 여러개의 argument를 list로 전환해줌
def vector_sum_modified(vectors):
return [sum(value) for value in zip(*vectors)... | <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: Scalar * Vector의 연산 ex) 2 * [1,2,3,4] = [2,4,6,8]
Step3: vector 의 평균 구하기
Step5: Vector dot product
Step7: 하나의 vector에서 값 element들을 제곱하여 더한 후 ... |
12,790 | <ASSISTANT_TASK:>
Python Code:
from time import clock
from scipy.io import mmwrite
import matplotlib.pyplot as plt
from qutip import *
from qutip.piqs import *
nnn = 10
N = nnn
jj_mat = nnn/2
[jx_mat, jy_mat, jz_mat] = jmat(jj_mat)
jp_mat = jx_mat + 1j * jy_mat
jm_mat = jx_mat - 1j * jy_mat
w0 = 1
kappa = 2 * w0
gg =... | <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: Spectrum of the Liouvillian - Strong dissipation limit $\omega_{0} = 0.5 \kappa $
Step2: The Figure above reproduces qualitatively the study p... |
12,791 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from sklearn.grid_search import GridSearchCV
from sklearn import datasets, svm
import matplotlib.pyplot as plt
# Load the digit data
digits = datasets.load_digits()
# View the features of the first observation
digits.data[0:1]
# View the target of the first observatio... | <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 Two Datasets
Step2: The target data is a vector containing the image's true digit. For example, the first observation is a handwritten d... |
12,792 | <ASSISTANT_TASK:>
Python Code:
import deepchem as dc
from deepchem.models.tensorgraph.models.graph_models import GraphConvModel
# Load Tox21 dataset
tox21_tasks, tox21_datasets, transformers = dc.molnet.load_tox21(featurizer='GraphConv')
train_dataset, valid_dataset, test_dataset = tox21_datasets
model = GraphConvMod... | <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: Now, let's use MoleculeNet to load the Tox21 dataset. We need to make sure to process the data in a way that graph convolutional networks can us... |
12,793 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License"); { display-mode: "form" }
# 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 l... | <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: Fitting Generalized Linear Mixed-effects Models Using Variational Inference
Step2: Abstract
Step3: We will also do a quick check for availabli... |
12,794 | <ASSISTANT_TASK:>
Python Code:
!cat -n Pure.g4
!cat sum.sl
!cat -n Simple.g4
!cat sum.ast
!antlr4 -Dlanguage=Python3 Simple.g4
from SimpleLexer import SimpleLexer
from SimpleParser import SimpleParser
import antlr4
%run ../AST-2-Dot.ipynb
def main(file):
with open(file, 'r') as handle:
program_text = h... | <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: The grammar shown above does only contain skip actions. The corrsponding grammar that is enriched with actions is stored in the file Simple.g4.... |
12,795 | <ASSISTANT_TASK:>
Python Code:
import markovify
# Get raw text as string
with open("brown.txt") as f:
text = f.read()
# Build the model.
text_model = markovify.Text(text)
# Print three randomly-generated sentences of no more than 140 characters
for i in range(3):
print(text_model.make_short_sentence(140))
<... | <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: Load Corpus
Step2: Build Markov Chain
Step3: Generate One Tweet
|
12,796 | <ASSISTANT_TASK:>
Python Code:
# import libraries
# linear algebra
import numpy as np
# data processing
import pandas as pd
# data visualization
from matplotlib import pyplot as plt
# load the data with pandas
dataset = pd.read_csv('dataset.csv', header=None)
dataset = np.array(dataset)
plt.scatter(dataset[:,0], dat... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step2: 1. Implementar o algoritmo K-means
Step3: Teste a função criada e visualize os centróides que foram calculados.
Step5: 1.2 Definir os clusters... |
12,797 | <ASSISTANT_TASK:>
Python Code:
# ←此為Python的註解符號，在這之後的文字不會被當作程式碼執行
# Python不用宣告變數型態，在指定變數的值時即會動態決定其型態
n_solar_mass = 10 # 整數
MASS_SUN = 1.99 * 10 ** 30 # 浮點數
z = complex(3., -1.) # 複數
unit = "kg" ... | <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: 基本輸出輸入
Step2: 強型別
Step3: 對變數的操作
Step4: 資料型態(List、Tuple、Dictionary)
Step5: tuple 是有順序但不可以變動
Step6: list 是有順序且可以變動
Step7: 雙重list
Step8: 須注意... |
12,798 | <ASSISTANT_TASK:>
Python Code:
%%capture
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Data.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/images.zip
!wget https://github.com/cltl/python-for-text-analysis/raw/master/zips/Extra_Material.zip
!unzip Data.zip -d ../
!unz... | <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: Chapter 17
Step2: We will show how JSON looks like and how to deal with JSON in Python with the example dictionary shown below.
Step3: You can... |
12,799 | <ASSISTANT_TASK:>
Python Code:
fruit_season = {
'raspberry': 'May',
'apple' : 'September',
'peach' : 'July',
'grape' : 'August'
}
print(type(fruit_season))
print(fruit_season)
raspberry_season = fruit_season['raspberry']
print(raspberry_season)
print(fruit_season['mangos'])
fruit_season['st... | <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: To access a value, you index into it similarly to a list using square brackets.
Step2: Trying to access a key not in the dictionary throws an e... |
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