Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from IPython.display import Image
from IPython.html.widgets import interact, interactive, fixed
Image('fermidist.png')
def fermidist(energy, mu, kT):
Compute the Fermi distribution at energy, mu and kT.
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: Exploring the Fermi distribution
Step3: In this equation
Step4: Write a function plot_fermidist(mu, kT) that plots the Fermi distribution $F(\... |
8,101 | <ASSISTANT_TASK:>
Python Code:
class ListNode:
def __init__(self, data):
self.data = data
a = ListNode(11)
b = ListNode(52)
c = ListNode(18)
print(a)
class ListNode:
def __init__(self, data):
self.data = data
self.next = None
# Initial creation of nodes
a = ListNode(11)
b = ListNode(52... | <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 will give us just the containers that we can store data into.
Step2: Since we did not define a method to show the value stored, we cann... |
8,102 | <ASSISTANT_TASK:>
Python Code:
from parcels import FieldSet, ParticleSet, JITParticle, AdvectionRK4, Variable, Field
from glob import glob
import numpy as np
import xarray as xr
from datetime import timedelta as delta
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
from matplotlib.colors import L... | <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. A grids
Step2: Figure 1 shows how land grid points and boundaries can be interpreted, depending on whether you assume a grid node is at the ... |
8,103 | <ASSISTANT_TASK:>
Python Code:
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from matplotlib.colors import BoundaryNorm
import matplotlib.pyplot as plt
import numpy as np
from metpy.cbook import get_test_data
from metpy.interpolate import (interpolate_to_grid, remove_nan_observations,
... | <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: Point Interpolation
Step2: Scipy.interpolate linear
Step3: Natural neighbor interpolation (MetPy implementation)
Step4: Cressman interpolatio... |
8,104 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
%matplotlib inline
import time
import numpy as np
from landlab import RasterModelGrid as rmg
from landlab import load_params
from Ecohyd_functions_flat import (
Initialize_,
Empty_arrays,
Create_PET_lookup,
Save_,
Plot_,
)
grid1 =... | <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: Note
Step2: Include the input file that contains all input parameters needed for all components. This file can either be a python dictionary or... |
8,105 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib
import os
import scipy as sp
import matplotlib.pyplot as plt
from GeoData.GeoData import GeoData
from GeoData.utilityfuncs import readMad_hdf5
from GeoData.plotting import rangevsparam, rangevstime
madfile = os.path.join( 'pfa140105.004.hdf5')
data1 =... | <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 in Data
Step2: Range Time Plots
Step3: Range vs Parameter Plots
|
8,106 | <ASSISTANT_TASK:>
Python Code:
baseURL = "http://localhost:8000"
client = ga4gh.client.HttpClient(baseURL)
referenceSets = list(client.searchReferenceSets())
print("ReferenceSets")
for referenceSet in referenceSets:
print("NCBI Taxon Id: {}".format(referenceSet.ncbiTaxonId))
referenceSet = client.getReferenceSet(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: Great! Now we can run calls to query ReferenceSets, References, Datasets, VariantSets, CallSets, Variants, ReadGroups, ReadGroupSets, & Reads.
... |
8,107 | <ASSISTANT_TASK:>
Python Code:
import retina.core.axes
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
fig = plt.figure()
ax1 = plt.subplot('111', projection='Fovea2D')
plt.xlabel('x')
plt.ylabel('y')
plt.title('An Introduction to Retina')
x = np.linspace(-2 * np.pi, 2 * np.pi)
sin_y = np.sin(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: Let's set up our plot so as to make it look more professional.
Step2: Not too shabby. I find it annoying that the label for the y-axis is tilte... |
8,108 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/google/starthinker
from starthinker.util.configuration import Configuration
CONFIG = Configuration(
project="",
client={},
service={},
user="/content/user.json",
verbose=True
)
FIELDS = {
'auth_write':'service', # Credentials used for wri... | <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. Set Configuration
Step2: 3. Enter DV360 SDF To BigQuery Recipe Parameters
Step3: 4. Execute DV360 SDF To BigQuery
|
8,109 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
import os
plt.style.use('ggplot')
get_ipython().magic('pylab inline')
import nltk
from nltk.tokenize import word_tokenize
import re
from collections import Counter
from nltk.corpus 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: 1. Numero Dataset PA
Step2: 2. Numero Dataset PA senza Metadati
Step3: 3. Group by Catalogo Padre
Step4: 4. Group by Gruppo
Step5: Group by ... |
8,110 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
import sympy
from sympy import *
from sympy import Rational as frac
import simpletensors
from simpletensors import Vector, TensorProduct, SymmetricTensorProduct, Tensor
init_printing()
var('vartheta, varphi')
var('nu, m, delta, c, t')
# These are related sc... | <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: Examples and tests
Step2: Sympy can be a little tricky because it caches things, which means that the first implementation of this code silentl... |
8,111 | <ASSISTANT_TASK:>
Python Code:
# Author: Eric Larson <larson.eric.d@gmail.com>
#
# License: BSD (3-clause)
import os.path as op
import mne
data_path = mne.datasets.sample.data_path()
raw_erm = mne.io.read_raw_fif(op.join(data_path, 'MEG', 'sample',
'ernoise_raw.fif'), preload=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: We can plot the absolute noise levels
|
8,112 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
Sbase = 5000e3 # [VA]
Vp0 = 230e3 # [V]
Vs0 = 13.8e3 # [V]
Req_pu = 0.01
Xeq_pu = 0.05
Voc = 13.8e3 # [V]
Ioc = 21.1 # [A]
Poc = 90.8e3 # [W]
yex = Ioc / Voc
print('yex = {:.6f}'.format(yex))
theta = arccos(Poc/(Voc*Ioc))
print('theta = {:.2f}°'.format(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: Description
Step2: The open-circuit test performed on the low-voltage side of the transformer yielded the following data
Step3: (a)
Step4: $\... |
8,113 | <ASSISTANT_TASK:>
Python Code:
6 * 2
a = 6
b = 2
a * b
print a
print b
print a * b
print a / b
print 'the value of a is', a
a**b
print '1/3 gives', 1 / 3
print '1.0 / 3 gives', 1.0 / 3
print '1 / 3.0 gives', 1 / 3.0
print '1.0 / 3.0 gives', 1.0 / 3.0
print 'a:', a
import matplotlib.pyplot as plt
%matplotlib inl... | <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 of the extra space to make the code more (visually) readable.
Step2: Both a and b are now VARIABLES. Each variable has a type. In thi... |
8,114 | <ASSISTANT_TASK:>
Python Code:
import random
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
class Net(nn.Module):... | <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: Recall that a basic neural network in PyTorch can be set up like this
Step2: We load CIFAR-10 dataset and reshape them as individual vectors. T... |
8,115 | <ASSISTANT_TASK:>
Python Code:
#A variable stores a piece of data and gives it a name
#syntax of the form:
#variable_name = variable_value
#What are some types of variables you will need to use?
answer = 42
print(answer)
is_it_tuesday = True
is_it_wednesday = False
print(is_it_tuesday)
pi_approx = 3.1415
print(pi_appro... | <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: More Complicated Data Types
Step2: Basic Things to do with Variables. Especially Floats.
Step3: Conditionals in Python
Step4: Functions in Py... |
8,116 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import display
from sympy import init_printing, latex
init_printing()
from sympy.printing import StrPrinter
StrPrinter._print_Quantity = lambda self, expr: str(expr.abbrev) # displays short units (m instead of meter)
%run -i 'test_equation_definitions.py'
for eq ... | <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: Numerical evaluations
Step3: Substitution of equations and values into equations
Step4: We can substitute a range of equations into each other... |
8,117 | <ASSISTANT_TASK:>
Python Code:
# Authors: Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
import matplotlib.pyplot as plt
import mne
from mne import io
from mne.stats import permutation_cluster_test
from mne.datasets import sample
print(__doc__)
data_path = sample.data_path()
meg_path = 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: Set parameters
Step2: Read epochs for the channel of interest
Step3: Compute statistic
Step4: Plot
|
8,118 | <ASSISTANT_TASK:>
Python Code:
import functools
from datetime import datetime
def timer(func):
@functools.wraps(func)
def timed_func(*args, **kwargs):
start = datetime.now()
ret = func(*args, **kwargs)
tm = datetime.now() - start
print("Running time: {}".format(tm.total_seconds()... | <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: HTML tag adder.
Step2: Arbitrary tag adder.
Step3: Check if all parameters (keyword too) are of a given type.
Step4: Write a decorator that m... |
8,119 | <ASSISTANT_TASK:>
Python Code:
filename_brahms = 'brahms_hungarian_dance_5.mp3'
url = "http://audio.musicinformationretrieval.com/" + filename_brahms
if not os.path.exists(filename_brahms):
urllib.urlretrieve(url, filename=filename_brahms)
librosa.load?
x_brahms, fs_brahms = librosa.load(filename_brahms, duration=... | <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 120 seconds of an audio file
Step2: Plot the time-domain waveform of the audio signal
Step3: Play the audio file
Step4: Step 2
Step5: W... |
8,120 | <ASSISTANT_TASK:>
Python Code:
!wget http://blpd0.ssl.berkeley.edu/Voyager_data/Voyager1.single_coarse.fine_res.h5
%matplotlib inline
import pylab as plt
from blimpy import Waterfall
file_path = 'Voyager1.single_coarse.fine_res.h5'
obs = Waterfall(file_path)
obs.info()
print(obs.header)
print(obs.data.shape)
obs.p... | <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: Firstly, let's setup the notebook and import the Waterfall() class to read the data.
Step2: Now, let's read the observation data using Waterfal... |
8,121 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from qutip import *
def integrate(N, h, Jx, Jy, Jz, psi0, tlist, gamma, solver):
si = qeye(2)
sx = sigmax()
sy = sigmay()
sz = sigmaz()
sx_list = []
sy_list = []
sz_list = []
for n in 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: Hamiltonian
Step2: Software version
|
8,122 | <ASSISTANT_TASK:>
Python Code:
import argparse
import datetime
from dateutil import parser as date_parser
import json
import logging
import numpy as np
import os
import pandas as pd
import pprint
import requests
from pandas.io.json import json_normalize
query_template={{
search(query: "org:kubeflow is:pr is:open cre... | <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: Background
Step2: Distribution of PRs' open time (age)
Step3: PRs which need attention
Step4: PRs which need authors' attentions
Step5: PRs ... |
8,123 | <ASSISTANT_TASK:>
Python Code:
# Import modules
import sys
import numpy as np
import matplotlib.pyplot as plt
# Import PySwarms
import pyswarms as ps
print('Running on Python version: {}'.format(sys.version))
def cost_function(I):
#Fixed parameters
U = 10
R = 100
I_s = 9.4e-12
v_t = 25.85e-3
... | <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: Defining the cost fuction
Step2: Setting the optimizer
Step3: Checking the solution
Step4: Another way of solving non-linear equations is by ... |
8,124 | <ASSISTANT_TASK:>
Python Code:
### FIRST SOME CODE ####
from __future__ import division, print_function, absolute_import
from IPython.display import SVG, display, Image, HTML
import numpy as np, scipy as sp, pylab as pl, matplotlib.pyplot as plt, scipy.stats as stats, sklearn, sklearn.datasets
from scipy.spatial.distan... | <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: Gaussian Processes
Step2: Support Vector Machines
Step3: Feature engineering and two classification algorithms
Step4: Working in Feature Spac... |
8,125 | <ASSISTANT_TASK:>
Python Code:
class Edge :
def __init__(self ) :
self . src = 0
self . dest = 0
self . weight = 0
class Graph :
def __init__(self ) :
self . V = 0
self . E = 0
self . edge =[]
def createGraph(V , E ) :
graph = Graph() ;
graph . V = V ;
graph . E = E ;
graph . ed... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
8,126 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
x = np.array([-2, -1.4, -1.1, 0, 1.2, 2.2, 3.1, 4.4, 8.3, 9.9, 10, 14, 16.2])
result = x[x >=0]
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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:
|
8,127 | <ASSISTANT_TASK:>
Python Code:
cylinder_app()
plot_layer_potentials_app()
MidpointPseudoSectionWidget()
DC2DPseudoWidget()
DC2DfwdWidget()
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2. Potential differences and Apparent Resistivities
Step2: 3. Building Pseudosections
Step3: DC pseudo-section app
Step4: 4. Parametric Inver... |
8,128 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pymc as mc
H = mc.Normal('H', 2.00, (0.03)**-2)
h = mc.Normal('h', 0.88, (0.04)**-2)
@mc.deterministic()
def Q(H=H, h=h):
return H-h
model = mc.MCMC((H,h,Q))
model.sample(1e4, burn=100, burn_till_tuned=True)
# mc.Matplot.plot(model)
# mc.Matplot.plot(Q)
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: Example 2
Step2: Example 3
Step3: Example 4
Step4: Example 5
Step5: Example 6
Step6: Example 7
|
8,129 | <ASSISTANT_TASK:>
Python Code:
class Person:
##SCOPE OF CLASS DOWN BELOW
institute="IIT"
def __init__(self,name,department):
self.name=name
self.department=department
def getName(self):
return self.name
p=Person("mangesh","physics")
p.getName()
help(map)
store1=[10,12,8,3,5... | <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: MAP FUCNTION
Step2: We get lazy evaluation here and so map is not computed here so we get just the map object back
Step3: Lambda
Step4: Exam... |
8,130 | <ASSISTANT_TASK:>
Python Code:
ls
# Load Biospytial modules and etc.
%matplotlib inline
import sys
sys.path.append('/apps/external_plugins/spystats/spystats/')
sys.path.append('..')
import django
django.setup()
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
## Use the ggplot style
plt.style.use(... | <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: Use this to automate the process. Be carefull it can overwrite current results
Step2: Now we will obtain the data from the calculated empirical... |
8,131 | <ASSISTANT_TASK:>
Python Code:
import requests
from lxml import html
%%HTML
<html>
<body>
<h1>Favorite Python Librarires</h1>
<ul>
<li>Numpy</li>
<li>Pandas</li>
<li>requests</li>
</ul>
</body>
</html>
html_code = In[2]
html_code = html_code[42:-2].replace("\\n","\n")
print(html_code)... | <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 HTML Code
Step2: Using xpath to find nodes in a document
Step3: To read the text inside that tag you can use the text variable.
Step4: A... |
8,132 | <ASSISTANT_TASK:>
Python Code:
from eden.util import load_target
y = load_target( 'http://www.bioinf.uni-freiburg.de/~costa/bursi.target' )
from eden.converter.graph.gspan import gspan_to_eden
graphs = gspan_to_eden( 'http://www.bioinf.uni-freiburg.de/~costa/bursi.gspan' )
from eden.graph import Vectorizer
vectorizer... | <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 data and convert it to graphs
Step2: 2 Vectorization
Step3: extract features and build data matrix
Step4: 3 Modelling
|
8,133 | <ASSISTANT_TASK:>
Python Code:
net = clstm.make_net_init("lstm1","ninput=1:nhidden=4:noutput=2")
print net
net.setLearningRate(1e-4,0.9)
print clstm.network_info(net)
print net.sub.size()
print net.sub[0]
print net.sub[0].name
N = 20
xs = array(randn(N,1,1)<0.2, 'f')
net.inputs.aset(xs)
net.forward()
N = 20
test = 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: You can navigate the network structure as you would in C++. You can use similar methods to create more complex network architectures than possib... |
8,134 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'csir-csiro', 'vresm-1-0', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name"... | <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... |
8,135 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import display
from sympy import init_printing
from sympy import symbols, as_finite_diff, solve, latex
from sympy import Function, Eq
fg, f0, f1, f2 = symbols('f_g, f_0, f_1, f_2')
z, h = symbols('z, h')
a, b = symbols('a, b')
f = Function('f')
init_printing()
extraP... | <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: Extrapolation of $f(0) = a$ to the ghost point yields (see ghost4thOrder for calculation) yields
Step2: Which can be rewritten to
Step3: Furth... |
8,136 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from mpl_toolkits.axes_grid1 import make_axes_locatable
from pandas import set_option
set_option("display.max_rows", 10)
pd.options.mode.ch... | <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 data is from the Council Grove gas reservoir in Southwest Kansas. The Panoma Council Grove Field is predominantly a carbonate gas reservoi... |
8,137 | <ASSISTANT_TASK:>
Python Code:
# Author: Martin Luessi <mluessi@nmr.mgh.harvard.edu>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#
# License: BSD (3-clause)
import numpy as np
import mne
from mne.datasets import sample
from mne.inverse_sparse import gamma_map, make_stc_from_dipoles
from mne.viz import... | <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: Plot dipole activations
Step2: Show the evoked response and the residual for gradiometers
Step3: Generate stc from dipoles
Step4: View in 2D ... |
8,138 | <ASSISTANT_TASK:>
Python Code:
# Location of digested data
input_directory = '/digested/'
# Location of saved trained model
model_directory = '/model_directory/'
# Desired location for outputs
output_directory = '/output_directory/'
%matplotlib inline
import keras
import pickle
from keras.layers import *
from keras.mo... | <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: ------------- (semi)-Automatic -------------
Step2: Configure GPU/CPU devices
Step3: Data queueing
Step4: Load data
Step5: Load trained mode... |
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Python Code:
%run ../graph/graph.py
def bfs(root, visit_func):
# TODO: Implement me
pass
%run ../utils/results.py
# %load test_bfs.py
from nose.tools import assert_equal
class TestBfs(object):
def __init__(self):
self.results = Results()
def test_bfs(self):
nodes = []
... | <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: Unit Test
|
8,140 | <ASSISTANT_TASK:>
Python Code:
# a szokásos rutinok betöltése
%pylab inline
from scipy.integrate import * # az integráló rutinok betöltése
def f(x):
return (x**2+3*x+2)
quad(f,-1,1)
# az integrandus definiálása
def gauss(x):
return exp(-x**2)
quad(gauss,-inf,inf)
sqrt(pi)
def h(x):
return ((x-1.0)**(-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: Vizsgáljuk meg az alábbi egyszerű integrált $$ \int_{-1}^1 (x^2+3x +2)\mathrm{d}x .$$
Step2: Most már meghívhatjuk a quad-ot. Az első változó ... |
8,141 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import skrf as rf
rf.stylely()
Pin = 400 # W
z0 = 50 # Ohm
freq = rf.Frequency(13.56, npoints=1, unit='MHz')
VF = 0.84
RL = 50 # Ohm
L = 20 # m
alpha = rf.db_2_np(1.483/100) # Np/m
beta = freq.w/rf.c/VF
gamma = al... | <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 define the problem constants
Step2: The propagation constant of the transmission line $\gamma=\alpha+j\beta$ is
Step3: The matched line ... |
8,142 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib
import matplotlib.cm as cm
import numpy as np
import matplotlib.pyplot as plt
import jinja2, json, sys
from math import log, fabs, pi, cos, sin
from scipy.stats import ks_2samp
try:
try:
import ruamel.yaml as yaml
except ImportError:
... | <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 simulation
Step2: Plot sampled histogram against expected result
Step3: Statistical test of output against expected result
|
8,143 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import time
import pandas
import random
import numpy
import matplotlib.pyplot as plt
import seaborn; seaborn.set_style('whitegrid')
import itertools
from pomegranate import *
random.seed(0)
numpy.random.seed(0)
numpy.set_printoptions(suppress=True)
%load_ext watermark
%... | <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: Building a new normal distribution
Step2: The custom objects have a few requirements.
Step3: Looks good so far. Looks like there might be a sm... |
8,144 | <ASSISTANT_TASK:>
Python Code:
import socket
print(socket.gethostname())
import socket
HOSTS = [
'apu',
'pymotw.com',
'www.python.org',
'nosuchname',
]
for host in HOSTS:
try:
print('{} : {}'.format(host, socket.gethostbyname(host)))
except socket.error as msg:
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: Use gethostbyname() to consult the operating system hostname resolution API and convert the name of a server to its numerical address.
Step2: F... |
8,145 | <ASSISTANT_TASK:>
Python Code:
git clone https://github.com/sungeunbae/python-files.git
cd python-files
!ls
import numpy
numpy.loadtxt(fname='inflammation-01.csv', delimiter=',')
weight_kg = 55
print weight_kg
print 'weight in pounds:', 2.2 * weight_kg
weight_kg = 57
weight_lb = 2.2*weight_kg
print 'weight in kilog... | <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: Enter "ipython notebook" to get started
Step2: We import other modules to utilize other people's work. numpy enables to do fancy things with nu... |
8,146 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import statsmodels.api as sm
from statsmodels.tsa.stattools import coint, adfuller
import matplotlib.pyplot as plt
from quantopian.research.experimental import continuous_future, history
soy_meal_mult = symbols('SMF17').multiplier
soy_oil_mult = sym... | <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: Pairs of Futures (or Spreads)
Step2: However, from looking at the p-value for our test, we conclude that soybean meal and soybean meal and soyb... |
8,147 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL
import helper
import problem_unittests as tests
source_path = 'data/small_vocab_en'
target_path = 'data/small_vocab_fr'
source_text = helper.load_data(source_path)
target_text = helper.load_data(target_path)
view_sentence_range = (0, 10)
DON'T MODIFY AN... | <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: Language Translation
Step3: Explore the Data
Step7: Implement Preprocessing Function
Step9: Preprocess all the data and save it
Step11: Chec... |
8,148 | <ASSISTANT_TASK:>
Python Code:
import random, math
import pandas as pd
import numpy as np
import scipy.io
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
matplotlib.style.use('ggplot') # Look Pretty
# Leave this alone until indicated:
Test_PCA = False
def plotDecisionBoundary(model, X, 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: A Convenience Function
Step2: The Assignment
Step3: Copy out the status column into a slice, then drop it from the main dataframe. Always veri... |
8,149 | <ASSISTANT_TASK:>
Python Code:
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
import tensorflow as tf
x = tf.placeholder(tf.float32, [None, 784])
x
W = tf.Variable(tf.zeros([784, 10]))
b = tf.Variable(tf.zeros([10]))
y = tf.nn.softmax(tf.matm... | <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: Softmax Regressions
Step2: We describe these interacting operations by manipulating symbolic variables. Let's create one
Step3: $\mathrm{x}$ i... |
8,150 | <ASSISTANT_TASK:>
Python Code:
# http://www.glozman.com/textpages.html
# Harry Potter 1 - Sorcerer's Stone.txt
# Harry Potter 2 - Chamber of Secrets.txt
# Harry Potter 3 - The Prisoner Of Azkaban.txt
# Harry Potter 4 - The Goblet Of Fire.txt
# Harry Potter 5 - Order of the Phoenix.txt
# Harry Potter 6 - The Half B... | <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 the Training set
Step2: One-hot encode
Step3: Create the Model
Step4: Train the Model
Step5: Generate new sequence
|
8,151 | <ASSISTANT_TASK:>
Python Code:
cd ..
import sys
import os
sys.path.append(os.path.abspath('./faiss/'))
sys.path.append(os.path.abspath('./python/'))
from experiments.data import get_data
from misc.utils import to_ft, load_sift
X, Y, words_mask, labels_mask = get_data('./data/LSHTC', 'train', min_words=3, min_labels... | <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
Step2: Add these directories to PYTHONPATH so that we can import them
Step3: Import our functions to read Extreme-Repository data format ... |
8,152 | <ASSISTANT_TASK:>
Python Code:
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
from urllib.request import urlretrieve
from os.path import isfile, isdir
from tqdm import tqdm
import problem_unittests as tests
import tarfile
cifar10_dataset_folder_path = 'cifar-10-batches-py'
class DLProgress(tqdm):
last_b... | <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: Image Classification
Step2: Explore the Data
Step5: Implement Preprocess Functions
Step8: One-hot encode
Step10: Randomize Data
Step12: Che... |
8,153 | <ASSISTANT_TASK:>
Python Code:
# Render our plots inline
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import os
pd.set_option('display.mpl_style', 'default') # Make the graphs a bit prettier
plt.rcParams['figure.figsize'] = (16, 6)
# adjust to your local directories
embem_d... | <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: Compare the fraction of emotional sentences per text
Step2: Compare the number of lines per text
Step3: Compare the average number of labels p... |
8,154 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.keras import layers
from tensorflow.keras.datasets import mnist
from tensorflow.keras.models import Model
def preprocess(array):
Normalizes the supplied array and reshapes it into the appro... | <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: Convolutional autoencoder for image denoising
Step4: Prepare the data
Step5: Build the autoencoder
Step6: Now we can train our autoencoder us... |
8,155 | <ASSISTANT_TASK:>
Python Code:
MOD = 1000000007 ;
def rangeSum(l , r ) :
a = 1 ; b = 9 ; res = 0 ;
for i in range(1 , 11 ) :
L = max(l , a ) ;
R = min(r , b ) ;
if(L <= R ) :
sum =(L + R ) *(R - L + 1 ) // 2 ;
res +=(i * i ) *(sum % MOD ) ;
res %= MOD ;
a *= 10 ;
b = b * 10 + 9 ;
return 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:
|
8,156 | <ASSISTANT_TASK:>
Python Code:
#%matplotlib inline
## The usual packages (numpy, matplotlib, etc)
import matplotlib.pyplot as plt
import numpy as np
# nicer figures using ggg plot style.
plt.style.use('ggplot')
from IPython.core.display import HTML
from IPython.display import clear_output
from IPython.core.pylabtools i... | <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: Most important step, import all tools in the L2_tools.py file
Step2: Download data first
Step3: Now read in the data, two dictionaries for OCO... |
8,157 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import pandas as pd
import copy
from IPython.display import HTML
import sqlite3 as db
import networkx as nx
import collections
con = db.connect('C:/Users/cliff/workspace/rushHour/Data Model and SQL/Schema Definition and Bulk Load/rush_hour.db')
c = con.cursor(... | <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: Function to take connected component id as input and update all states in that component by setting the solution depth and the optimal neighbor ... |
8,158 | <ASSISTANT_TASK:>
Python Code:
import gzip
import cPickle as pickle
with gzip.open("../data/train.pklz", "rb") as train_file:
train_set = pickle.load(train_file)
with gzip.open("../data/test.pklz", "rb") as test_file:
test_set = pickle.load(test_file)
with gzip.open("../data/questions.pklz", "rb") as questions_... | <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: What they have?
Step5: Step2
Step6: Look at the feature vector.
Step8: Step3
Step9: Step4
Step10: Testing model(Prediction)
Step11: Step5
|
8,159 | <ASSISTANT_TASK:>
Python Code:
print (42)
x = 42
print (x)
x_float = float(42)
x_scientific = 42e0
x_str = '42'
print ('42 as a float', x_float)
print ('42 as a float in scientific notation', x_scientific)
print ('42 as a string', x_str)
x_list = [x, x_str, x_float, x_scientific]
print ("All 42 in a list:", x_list)
... | <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 can store it in a variable and print it out. It doesn't sound confusing at all!
Step2: Here, a variable x stores number 42 as an integer. Ho... |
8,160 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
raw_data = {'first_name': ['Jason', 'Molly', 'Tina', 'Jake', 'Amy'],
'pre_score': [4, 24, 31, 2, 3],
'mid_score': [25, 94, 57, 62, 70],
'post_score': [5, 43, 23, 23, 51]}
df ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Create dataframe
Step2: Make plot
|
8,161 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
%matplotlib inline
# TODO 1: Read in the advertising.csv file and set it to a data frame called ad_data.
ad_data = pd.... | <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: Load the Dataset
Step2: Check the head of ad_data
Step3: Use info and describe() on ad_data
Step4: Let's check for any null values.
Step5: E... |
8,162 | <ASSISTANT_TASK:>
Python Code:
import holoviews as hv
import numpy as np
hv.notebook_extension()
def sine(x, phase=0, freq=100):
return np.sin((freq * x + phase))
phases = np.linspace(0,2*np.pi,11) # Explored phases
freqs = np.linspace(50,150,5) # Explored frequencies
dist = np.linspace(-0.5,0.5,202) # Li... | <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: A simple function
Step2: We will examine the effect of varying phase and frequency
Step3: Over a specific spatial area, sampled on a grid
Step... |
8,163 | <ASSISTANT_TASK:>
Python Code:
import sys
print sys.version
x = [10, 20, 30, 40, 50]
for item in x:
print "Item is... you guessed it! ", item
!pip install BeautifulSoup seaborn pyquery
#IPython is what you are using now to run the notebook
import IPython
print "IPython version: %6.6s (need at least 3.0.0)" ... | <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: Problem
Step2: Python Libraries
Step3: If you've successfully completed the above install, all of the following statements should run.
Step4: ... |
8,164 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
%load_ext memory_profiler
from pomegranate import BayesianNetwork
import seaborn, time
seaborn.set_style('whitegrid')
X = numpy.random.randint(2, size=(2000, 7))
X[:,3] = X[:,1]
X[:,6] = X[:,1]
X[:,0] = X[:,2]
X[:,4] = X[:,5]
model = BayesianNetwork.from_samples(X, algorithm... | <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: The structure attribute returns a tuple of tuples, where each inner tuple corresponds to that node in the graph (and the column of data learned ... |
8,165 | <ASSISTANT_TASK:>
Python Code:
!pip install ott-jax
from IPython import display
import jax
from jax import numpy as jnp
from jax import random
import numpy as np
from matplotlib import animation
from matplotlib import cm
import matplotlib.pyplot as plt
import mpl_toolkits.mplot3d.axes3d as p3
import ott
from ott.core i... | <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: Matching between spaces with different dimensions
Step2: We then run OTT's Gromov-Wasserstein solver to find a matching between the points of e... |
8,166 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
def distance_table(x1, y1, x2, y2):
return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5
distance_table(0, 0, 2, 1)
def distance_bord(x1, y1, R):
dist = distance_table(x1, y1, 0, 0)
return R - dist
distance_bord(1, 1... | <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: On sait d'après les dernières questions qu'il faudra tout répéter plusieurs fois. On prend le soin d'écrire chaque question dans une fonction. C... |
8,167 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./mnist/", one_hot=True)
# Load data
x_train = mnist.train.images
y_train = mnist.train.labels
x_test = mnist.test.images
y_test = mnist.test.labels
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: 数据维度
Step2: 数据可视化
|
8,168 | <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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in wri... | <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: Environment Preparation
Step2: Distributed automl for time series forecasting using Chronos AutoTS
Step 0
Step3: Step 1
Step4: This is the on... |
8,169 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
df=pd.DataFrame({"Category":['Foo','Bar','Cho','Foo'],'Index':[1,2,3,4]})
filter_list=['Foo','Bar']
def g(df, filter_list):
return df.query("Category == @filter_list")
result = g(df.copy(), filter_list)
<END_TASK> | <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:
|
8,170 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
sys.path.append(os.getcwd().replace("notebooks", "cfncluster"))
## Input the AWS account access keys
aws_access_key_id = "/**aws_access_key_id**/"
aws_secret_access_key = "/**aws_secret_access_key**/"
## CFNCluster name
your_cluster_name = "cluster_name"
## The priva... | <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. Install CFNCluster
Step2: 2. Upgrade CFNCluster
Step3: 3. Configure CFNCluster
Step4: After you finish configuration, you can call the bel... |
8,171 | <ASSISTANT_TASK:>
Python Code:
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt #for plotting
from collections import Counter
from sklearn.metrics import confusion_matrix
import itertools
import seaborn as sns
from subprocess impo... | <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: Loading The Dataset
Step2: Normalising The Data
Step3: Printing the shape of the Datasets
Step4: ## Reshape To Match The Keras's Expectations... |
8,172 | <ASSISTANT_TASK:>
Python Code:
import torch
import numpy as np
from torchvision import datasets
import torchvision.transforms as transforms
# convert data to torch.FloatTensor
transform = transforms.ToTensor()
# load the training and test datasets
train_data = datasets.MNIST(root='data', train=True,
... | <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: Visualize the Data
Step2: Denoising
Step3: Training
Step4: Checking out the results
|
8,173 | <ASSISTANT_TASK:>
Python Code:
import csv
import time
import h5py
import numpy
CROWDASTRO_H5_PATH = '../crowdastro.h5'
CROWDASTRO_CSV_PATH = '../crowdastro.csv'
ARCMIN = 0.0166667
with h5py.File(CROWDASTRO_H5_PATH) as f_h5:
positions = f_h5['/swire/cdfs/catalogue'][:, :2]
times = []
for i in range(1000... | <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 let's try using a tree. I'll use a $k$-d tree to store SWIRE indices.
Step2: Note that this measurement actually depends a lot on leafsize.... |
8,174 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.style as style
style.use('ggplot')
#print(style.available)
import matplotlib.pyplot as plt
%matplotlib inline
import csv
import datetime
from IPython.core.display import display, HTML
data = pd.read_csv("train.csv", header = 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:
Step1: Import data
Step2: Cleaning
Step3: Observations
Step4: Prune outliers (similar approaches
Step5: EDA
Step6: Conclusions on cleaned data wit... |
8,175 | <ASSISTANT_TASK:>
Python Code:
# Use this when you want to nbconvert the notebook (used by nbviewer)
from krisk import init_notebook; init_notebook()
from krisk import Chart
chart = Chart()
chart
chart.option
chart.set_title('This is a blank visualization', x_pos='center')
chart.set_theme('vintage')
chart.option['se... | <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: Here you see that there is a blank figure for chart that you want to use. You can inspect the characteristic of the chart by using its option me... |
8,176 | <ASSISTANT_TASK:>
Python Code:
import sys
import os
from itertools import count
from pathlib import Path
# Include utils.py for asvt_utils
sys.path.insert(0, str(Path(os.environ['HOME'], 'git', 'skanb', 'pea-test-set')))
import utils as asvt_utils
import numpy as np
import matplotlib.pyplot as plt
from astropy.table im... | <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: Get acq stats data and clean
Step2: Get ASVT data and make it look more like acq stats data
Step3: Combine flight acqs and ASVT data
Step4: C... |
8,177 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import scipy as sp
import matplotlib
import pylab as pl
matplotlib.rcParams.update({'font.size': 15})
from sklearn.linear_model import Ridge
from sklearn.svm import SVC
from sklearn.model_selection import KFold, StratifiedKFold, GridSearchCV,StratifiedShuffleSplit
from ... | <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: 1.2 Train Ridge Regression on training data
Step2: 1.3 Train Ridge Regression with optimal $\alpha$ and evaluate model in test data
Step3: <di... |
8,178 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.stats import beta
import matplotlib.pyplot as plt
def quadratic(x):
return 4*x*(1-x)
N = 100000
x = np.empty(N)
# arbitary irrational starting point
x[0] = 1/np.sqrt(3)
for i in range(1, N):
x[i] = quadratic( x[i-1] )
plt.plot(x[0:100])
plt.xlabel("i... | <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: And here's my code, using CDFs.
|
8,179 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
%matplotlib inline
from fastai.imports import *
from fastai.structured import *
from pandas_summary import DataFrameSummary
from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier
from IPython.display import display
from sklearn import... | <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: 2. Data
Step2: Entering a DataFrame to display it will truncate it if it's too long.
Step3: df_raw.tail() will show the last few rows of the D... |
8,180 | <ASSISTANT_TASK:>
Python Code:
%%python
code='''
#include <iostream>
#include <typeinfo>
/// A trivial class
class A {
public:
A();
~A();
};
/// A trivial function
int CountCharacters(const std::string s);
/// A trivial template
template<class T>
class B {
public:
B()
{
std::cout << "The typeid name o... | <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: Implementation
Step2: Creation of the Library
Step3: So far, so good. Now we'll see how easy it is to use this library from within Python than... |
8,181 | <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
<END_TASK>
<USER_TASK:>
Description:
Step1: Next we define the inverse problem parameters as a dictionary. Parameter's not specified will be filled with default values.
Step2: Now we are ... |
8,182 | <ASSISTANT_TASK:>
Python Code:
# Import libraries
import py_stringsimjoin as ssj
import py_stringmatching as sm
import pandas as pd
import os
import sys
print('python version: ' + sys.version)
print('py_stringsimjoin version: ' + ssj.__version__)
print('py_stringmatching version: ' + sm.__version__)
print('pandas versi... | <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: Joining two tables using TD-IDF measure typically consists of six steps
Step2: 2. Profiling the tables
Step3: Based on the profile output, we ... |
8,183 | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.4,<2.5"
import phoebe
from phoebe import u,c
logger = phoebe.logger(clevel='WARNING')
b = phoebe.default_binary()
b.get_parameter(qualifier='sma', component='binary', context='component')
b.get_parameter(qualifier='sma', component='binary', context='component... | <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: Units
Step2: From the representation above, we can already see that the units are in solar radii. We can access the units directly via get_def... |
8,184 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'bnu', 'sandbox-3', '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
<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... |
8,185 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import warnings
warnings.simplefilter('ignore')
import numpy
from matplotlib import pyplot
from scipy import stats
import seaborn
clear_bkgd = {'axes.facecolor':'none', 'figure.facecolor':'none'}
seaborn.set(style='ticks', context='talk', color_codes=True, rc=clear_bkgd... | <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: Using different formulations of plotting positions
Step2: Normal vs Weibull scales and Cunnane vs Weibull plotting positions
Step3: Now let's ... |
8,186 | <ASSISTANT_TASK:>
Python Code:
ls ..\..\Scripts\hello-world*.py
%%sh
cat ../../Scripts/hello-world.py
!python ..\..\Scripts\hello-world.py
%%sh
cat ../../Scripts/hello-world-in-swedish.py
!python ../../Scripts/hello-world-in-swedish.py
import math
import math
x = math.cos(2 * math.pi)
print(x)
from math import *
... | <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: built in magic commands start with
Step2: Character encoding
Step3: Other than these two optional lines in the beginning of a Python code file... |
8,187 | <ASSISTANT_TASK:>
Python Code:
#!pip install --user --upgrade watson-developer-cloud
#Making a local folder to put my data.
#NOTE: YOU MUST do something like this on a Spark Enterprise cluster at the hackathon so that
#you can put your data into a separate local file space. Otherwise, you'll likely collide with
#your ... | <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: <br/>
Step2: <br/>
Step3: <br/>
Step4: <br/>
Step5: <br/>
Step6: Generate CSV file for Scoreboard
|
8,188 | <ASSISTANT_TASK:>
Python Code:
!pip3 install tensorflow
import numpy as np
import tensorflow as tf
print(tf.__version__)
users = ['Ryan', 'Danielle', 'Vijay', 'Chris']
movies = [
'Star Wars', 'The Dark Knight', 'Shrek',
'The Incredibles', 'Bleu', 'Memento'
]
features = ['Action', 'Sci-Fi', 'Comedy', 'Cartoon... | <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: Make sure to restart your kernel to ensure this change has taken place.
Step2: To start, we'll create our list of users, movies and features. W... |
8,189 | <ASSISTANT_TASK:>
Python Code:
# change working dir
path = "/Users/albertlee/claritycontrol/code/scripts" # use your own path
import os
os.chdir(path)
import numpy as np
import matplotlib.pyplot as plt
import os
import csv
import igraph as ig
%matplotlib inline
# Initializing dataset names
dnames = list(['../data/hist'... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step 1
Step1: Independent Graph Assumption
Step2: From the above, we conclude that the assumption that the graphs were independent is false. This is b... |
8,190 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import pandas as pd
import numpy as np
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
train.shape
train.head()
train.Age.hist()
train.Age.describe()
train[train['Age'] > 60][['Sex', 'Pclass', 'Age', 'Survived']]
females = train[train['Sex'] == 'female... | <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: Let's Explore the data
Step2: Now I'm starting to see a pattern here. Let's see how many female survived.
Step3: Looks like the majority of pe... |
8,191 | <ASSISTANT_TASK:>
Python Code:
import time
from collections import namedtuple
import numpy as np
import tensorflow as tf
with open('anna.txt', 'r') as f:
text=f.read()
vocab = set(text)
vocab_to_int = {c: i for i, c in enumerate(vocab)}
int_to_vocab = dict(enumerate(vocab))
encoded = np.array([vocab_to_int[c] for ... | <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: First we'll load the text file and convert it into integers for our network to use. Here I'm creating a couple dictionaries to convert the chara... |
8,192 | <ASSISTANT_TASK:>
Python Code:
import magma as m
m.set_mantle_target("ice40")
from loam.boards.icestick import IceStick
# Create an instance of an IceStick board
icestick = IceStick()
# Turn on the Clock
# The clock must turned on because we are using a synchronous counter
icestick.Clock.on()
# Turn on the LED D5
ice... | <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 next step is to setup the IceStick board. We import the class IceStick from Loam.
Step2: Now that the IceStick setup is done,
Step3: We ... |
8,193 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
%matplotlib inline
import matplotlib.pyplot as plt
import openpathsampling as paths
import numpy as np
%%time
storage = paths.AnalysisStorage("ala_mstis_production.nc")
print("PathMovers:", len(storage.pathmovers))
print("Engines:", len(storage.engin... | <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 optimum way to use storage depends on whether you're doing production or analysis. For analysis, you should open the file as an AnalysisStor... |
8,194 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
# Set up the data and network:
n_outputs = 5 # We're attempting to learn XOR in this example, so our inputs and outputs will be the same.
n_hidden_units = 10 # We'll use a single hidden layer with this number of hidden units in it.
n_obs = 500 # How many observations... | <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 some data to learn from
Step2: Select activation and loss functions
Step3: Initialize the weights
Step5: Forward propagation
Step7: B... |
8,195 | <ASSISTANT_TASK:>
Python Code:
from scipy.io import loadmat
dataset = loadmat('../datasets/mnist-data.mat') # comes as dictionary
dataset.keys()
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
# Example of a picture
indexImage = 4000 # try any index between 0 and 4999. They are sorted, from ... | <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: Visualise the data
Step2: 2 - Data preprocessing
Step3: The second part of the training set is a 5000-dimensional vector y that contains label... |
8,196 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
!wget http://files.grouplens.org/datasets/movielens/ml-100k.zip
!ls
!unzip ml-100k
folder = "ml-100k"
!wget http://files.grouplens.org/datasets/movielens/ml-1m.zip
!unzip ml-1m
!ls
folder = "ml-1m"
ratings_li... | <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: These look good, but I want the format of my ratings matrix to be one row per user and one column per movie. I'll pivot ratings_df to get that a... |
8,197 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
with open('itemsets.dat') as f:
transactions = []
for row in f:
transactions.append(row.strip().split(','))
transactions[0:5]
items_counts = {}
for tran in transactions:
for item... | <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: 2. For a bit of EDA
Step2: 3. Create a function to compute support for a given itemset from a transaction
Step3: 4. Write an apriori function ... |
8,198 | <ASSISTANT_TASK:>
Python Code:
import os
import matplotlib.pyplot as plt
import numpy as np
import openmc
%matplotlib inline
# Instantiate some elements
elements = {}
for elem in ['H', 'O', 'U', 'Zr', 'Gd', 'B', 'C', 'Fe']:
elements[elem] = openmc.Element(elem)
materials = {}
# Fuel
materials['Fuel'] = openmc.Mat... | <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 will be running a rodded 8x8 assembly with Gadolinia fuel pins. Let's create all the elemental data we would need for this case.
Step2: With... |
8,199 | <ASSISTANT_TASK:>
Python Code:
from pprint import pprint
from time import sleep
from pynq import PL
from pynq import Overlay
from pynq.drivers import Trace_Buffer
from pynq.iop import Pmod_OLED
from pynq.iop import PMODA
from pynq.iop import PMODB
from pynq.iop import ARDUINO
ol = Overlay("base.bit")
ol.download()
ppri... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step 1
Step1: Step 2
Step2: Step 3
Step3: Step 4
Step4: Step 5
|
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