Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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8,200 | <ASSISTANT_TASK:>
Python Code:
from mlens.utils.dummy import OLS
from mlens.parallel import Learner, Job
from mlens.index import FoldIndex
indexer = FoldIndex(folds=2)
learner = Learner(estimator=OLS(),
indexer=indexer,
name='ols')
import os, tempfile
import numpy as np
X = np.arang... | <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 learner doesn't do any heavy lifting itself, it manages the creation a sub-graph
Step2: Fitting the learner puts three copies of the OLS es... |
8,201 | <ASSISTANT_TASK:>
Python Code:
number={}
number[0]="zero"
number[1]="one"
number[2]="two"
number[3]="three"
number[4]='four'
number[5]='five'
number[6]='six'
number[7]='seven'
number[8]='eight'
number[9]='nine'
number[10]='ten'
number[11]='eleven'
number[12]='twelve'
number[13]='thirteen'
number[14]='fourteen'
number[1... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: First write a number_to_words(n) function that takes an integer n between 1 and 1000 inclusive and returns a list of words for the number as des... |
8,202 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import holoviews as hv
hv.extension('bokeh')
# Declare some points
points = hv.Points(np.random.randn(1000,2 ))
# Declare points as source of selection stream
selection = hv.streams.Selection1D(source=points)
# Write function that uses the selection indices to slice po... | <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: Purpose
Step2: <img src='https
Step3: python
Step4: Renderers can also have different modes. In this case we will instantiate the renderer in... |
8,203 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
wasteland =
APRIL is the cruellest month, breeding
Lilacs out of the dead land, mixing
Memory and desire, stirring
Dull roots with spring rain.
def tokenize(s, stop_words=None, punctuation='`~!@#$%^&*()_-+={[}]|\... | <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: Word counting
Step7: Write a function count_words that takes a list of words and returns a dictionary where the keys in the dictionary are the ... |
8,204 | <ASSISTANT_TASK:>
Python Code:
def test(element):
element = element * 2
return element
test(5)
lst = [3,7,14,222,6]
lst.reverse()
print(lst)
def maxi(element):
element.sort()
element.reverse()
return element[0]# ich könnte auch element.reverse weglassen und einfach return element[-1] - gibt mir da... | <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: Multipliziert Integers oder Floats mit 2
Step2: 1.Schreibe eine Funktion, die aus einer Liste, die grösste Zahl herauszieht. Es ist verboten mi... |
8,205 | <ASSISTANT_TASK:>
Python Code:
# Import our usual libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
import os
# OS-independent way to navigate the file system
# Data directory is one directory up in relation to directory of this notebook
data_dir_root = os.path.normpath... | <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: Step 1
Step2:
Step3:
Step4:
Step5:
Step6: Exercise 2
Step7: Step 2b
Step8: Step 3
Step9: Notice that the sigmoid is never less than ... |
8,206 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'thu', 'ciesm', 'atmos')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "email") ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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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... |
8,207 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
sys.path.append(os.getcwd().replace("notebooks", "cfncluster"))
## S3 input and output address.
s3_input_files_address = "s3://path/to/input folder"
s3_output_files_address = "s3://path/to/output folder"
## CFNCluster name
your_cluster_name = "cluster_name"
## The pri... | <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: <font color='blue'> Notice
Step2: After you verified the project information, you can execute the pipeline. When the job is done, you will see ... |
8,208 | <ASSISTANT_TASK:>
Python Code:
def solve(N):
return bin(sum(int(i) for i in str(N)))[2:]
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
8,209 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
import matplotlib.pyplot as plt
%matplotlib inline
import scipy.stats
import numpy as np
from scipy.ndimage import imread
import sys
# import image
img_orig = imread('testimg.jpg').flatten()
print("$img_orig")
print("shape: \t\t", img_orig.... | <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 1
Step2: Create a figure showing the 3 histograms (original & 2 sets of noise corrupted data – use
Step4: Take a subset of P = 100 ob... |
8,210 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
from IPython.core.pylabtools import figsize
import matplotlib.pyplot as plt
figsize(12.5, 5)
import pymc as pm
sample_size = 100000
expected_value = lambda_ = 4.5
poi = pm.rpoisson
N_samples = range(1, sample_size, 100)
for k in range(3):
samples ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Looking at the above plot, it is clear that when the sample size is small, there is greater variation in the average (compare how jagged and jum... |
8,211 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'giss-e2-1h', 'atmoschem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("na... | <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... |
8,212 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
def np_fact(n):
Compute n! = n*(n-1)*...*1 using Numpy.
if n == 0:
return 1
else:
a = np.arange(1,n+1,1)
b = a.cumprod(0)
return b[n-1]
assert np_fact(0... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step2: Factorial
Step4: Write a function that computes the factorial of small numbers using a Python loop.
Step5: Use the %timeit magic to time both ... |
8,213 | <ASSISTANT_TASK:>
Python Code:
from ltlcross_runner import LtlcrossRunner
from IPython.display import display
import pandas as pd
import spot
import sys
spot.setup(show_default='.a')
pd.options.display.float_format = '{: .0f}'.format
pd.options.display.latex.multicolumn_format = 'c'
import os
os.environ['SPOT_HOA_TOLE... | <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: Hack that alows to parse ltl3ba automata without universal branching.
Step2: $\newcommand{\F}{\mathsf{F}}$
Step3: Literature
Step4: Mergeable... |
8,214 | <ASSISTANT_TASK:>
Python Code:
# calculate pi
import numpy as np
# N : number of iterations
def calc_pi(N):
x = np.random.ranf(N);
y = np.random.ranf(N);
r = np.sqrt(x*x + y*y);
c=r[ r <= 1.0 ]
return 4*float((c.size))/float(N)
# time the results
pts = 6; N = np.logspace(1,8,num=pts);
result = np.ze... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: <hr style="border
Step2:
Step3:
Step4:
Step5:
Step6:
Step7: <hr style="border
Step8:
Step9: &n... |
8,215 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
my_vector = np.array([1, 2, 3, 4])
my_vector
my_vector.shape
my_vector.dtype
my_matrix = np.array([[1, 2], [3, 4]])
my_matrix
my_matrix.shape
# Find the length of each element in bytes
my_matrix.itemsize
my_matrix2 = np.array([[1, 2], [3, 4]], dtype=np.int8)
my_matrix2... | <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 ndarray structure
Step2: Array creation methods
Step3: Aggregate methods (min and max)
Step4: Summations
Step5: Transform a 1D array int... |
8,216 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import html5lib
import matplotlib.pyplot as plt
%matplotlib inline
csv_path='exportPivot_POP105A.csv' #SAJAT HELY CSV FILE
df=pd.read_csv(csv_path)
df.head()
wiki_path="http://hu.wikipedia.org/wiki/Csíkszereda"
df2=pd.read_html(wiki_path)
df2[4]
gf=df2[4]
gf
ef=g... | <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: Romániai lakosság letöltése INSSE-ról
Step2: Wikipédia táblázatok letöltése
Step3: Ha html5llib not found hibaüzenetet kapunk, akkor egy konzo... |
8,217 | <ASSISTANT_TASK:>
Python Code:
# isntantiate a graph object
G = nx.Graph()
# add a single node
G.add_node(1)
# add multiple nodes from a list
G.add_nodes_from([2,3,5])
# return lists of nodes and edges in the graph
G.nodes(), G.edges()
# add a single edge between 3 and 5
G.add_edge(3,5)
# add multiple edges using list... | <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: Notice that the edge list is empty, since we haven't added any edges yet. Also, because the number of edges in a graph can become very large, th... |
8,218 | <ASSISTANT_TASK:>
Python Code:
# Rather than importing everything manually, we'll make things easy
# and load them all in utils.py, and just import them from there.
%matplotlib inline
import utils; reload(utils)
from utils import *
%matplotlib inline
from __future__ import division,print_function
import os, json
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: Introduction
Step2: Linear models in keras
Step3: We can use keras to create a simple linear model (Dense() - with no activation - in Keras) a... |
8,219 | <ASSISTANT_TASK:>
Python Code:
import molpx
%matplotlib ipympl
top = molpx._molpxdir(join='notebooks/data/ala2.pdb')
MD_trajfiles = [molpx._molpxdir(join='notebooks/data/ala2.mini.xtc')] #short trajectory
rama_files = [molpx._molpxdir(join='notebooks/data/ala2.mini.phi.psi.dat')]
mpx_wdg_box = molpx.visualize.FES(MD_... | <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: Start from files on disk
Step2: Visualize a FES and the molecular structures behind it
Step3: Visualize trajectories and molecular structures ... |
8,220 | <ASSISTANT_TASK:>
Python Code:
import MessageFormatting
import importlib
importlib.reload(MessageFormatting)
from MessageFormatting import *
from timeseries.ArrayTimeSeries import ArrayTimeSeries as ts
import numpy as np
from scipy.stats import norm
t = np.arange(0.0, 1.0, 0.01)
v = norm.pdf(t, 100, 100) + 1000*np.rand... | <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: Above is the output I'm getting- still need to discuss interpolation and also adding in the parameter for number of timeseries to find
|
8,221 | <ASSISTANT_TASK:>
Python Code:
import random
import gym
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam
import os # for creating directories
output_dir = 'model_output/cartpole/'
n_episodes = 1001 # n games we want ag... | <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 hyperparameters
Step2: Define class for Deep-Q-Learning agent
Step3: Set other parameters (some of these should be moved to top of file)
|
8,222 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
import pandas as pd
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import RandomForestRegressor
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
plt.style.use('ggplot')
plt.rc('figure',figsize=(13,13))
# Make things lo... | <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 want to count the number of rentals per vehicle ID in reservations.csv, appending these values as a column in vehicles.csv, in order to compar... |
8,223 | <ASSISTANT_TASK:>
Python Code:
import logging
logging.root.setLevel(logging.INFO)
import xcs
xcs.test()
from xcs import XCSAlgorithm
from xcs.scenarios import MUXProblem, ScenarioObserver
scenario = ScenarioObserver(MUXProblem(50000))
algorithm = XCSAlgorithm()
algorithm.exploration_probability = .1
algorithm.disc... | <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: Then we import the xcs module and run the built-in test() function. By default, the test() function runs the canonical XCS algorithm on the 11-b... |
8,224 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
import numpy as np
from scipy import stats
import statsmodels.api as sm
import matplotlib.pyplot as plt
from statsmodels.sandbox.regression.predstd import wls_prediction_std
from statsmodels.iolib.table import (SimpleTable, default_... | <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: WLS Estimation
Step2: WLS knowing the true variance ratio of heteroscedasticity
Step3: OLS vs. WLS
Step4: Compare the WLS standard errors to ... |
8,225 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nasa-giss', 'sandbox-3', '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... |
8,226 | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
tf.TF_CPP_MIN_LOG_LEVEL = 3
# Create a constant operation. This operation is added as a node to the default graph.
hello = tf.constant("hello world")
# Start a TensorFlow session.
sess = tf.Session()
# Run the operation and get the result.
print(sess.run(hello))
n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: tensors, ranks, shapes and types
Step2: session
Step3: variables
Step4: single variable linear regression
Step5: placeholders and variables
... |
8,227 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'test-institute-2', 'sandbox-2', 'ocnbgchem')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contribut... | <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... |
8,228 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'miroc', 'miroc-es2l', 'toplevel')
# 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... |
8,229 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
n = 20 # number of datapoints in each line
v1 = np.array([-13, 0.9]) # first line
v2 = np.array([7, -1]) # second line
sig = 1.0
seq = np.array(range(n))+1
x = np.transpose(np.array([np.ones(n), seq])) # Half of Design... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Generate data
Step2: EM Algorithm
Step3: Division by zero should be avoided
Step4: Local Minima problem
Step5: What if we minimize the perpe... |
8,230 | <ASSISTANT_TASK:>
Python Code:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) / 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
print quick... | <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: Python versions
Step2: Booleans
Step3: more string methods can be found here
Step4: List comprehensions
Step5: You can make this code simple... |
8,231 | <ASSISTANT_TASK:>
Python Code:
fullbase = requests.compat.urljoin(baseurl, endpoint_datatypes)
r = requests.get(
fullbase,
headers=custom_headers,
# params={'limit':1000},
params={'limit':1000, 'datasetid':"NORMAL_DLY"},
)
r.headers
r.text
json.loads(r.text)
fullbase = requests.compat.urljoin(baseurl, ... | <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: Request 2
Step2: Request 3
Step3: Request 4
Step4: On a side note
|
8,232 | <ASSISTANT_TASK:>
Python Code:
p = (4, 5, 6, 7)
x, y, z, w = p # x -> 4
data = ['ACME', 50, 91.1, (2012, 12, 21)]
name, _, price, date = data # name -> 'ACME', data -> (2012, 12, 21)
s = 'Hello'
a, b, c, d, e = s # a -> H
p = (4, 5)
x, y, z = p # "ValueError"
def drop_first_last(grades):
Drop first and last exam... | <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: 1.2 Unpacking Elements from Iterables of Arbitrary Length
Step6: Discussion
Step8: 1.3 Keeping the Last N Items (in list queue with deque)
Ste... |
8,233 | <ASSISTANT_TASK:>
Python Code:
import git
GIT_LOG_FILE = r'${REPO}/spring-petclinic'
repo = git.Repo(GIT_LOG_FILE)
git_bin = repo.git
git_bin
git_log = git_bin.execute('git log --numstat --pretty=format:"\t\t\t%h\t%at\t%aN"')
git_log[:100]
import pandas as pd
from io import StringIO
commits_raw = pd.read_csv(StringI... | <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: With the <tt>git_bin</tt>, we can execute almost any Git command we like directly. In our hypothetical use case, we want to retrieve some inform... |
8,234 | <ASSISTANT_TASK:>
Python Code:
import os.path as op
import numpy as np
from mayavi import mlab
import mne
from mne.datasets import sample
from mne.minimum_norm import read_inverse_operator, apply_inverse
from mne.simulation import simulate_stc, simulate_evoked
seed = 42
# parameters for inverse method
method = 'sLORET... | <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: First, we set some parameters.
Step2: Load the MEG data
Step3: Estimate the background noise covariance from the baseline period
Step4: Gener... |
8,235 | <ASSISTANT_TASK:>
Python Code:
def generate_random_points_along_a_line (slope, intercept, num_points, abs_value, abs_noise):
# randomly select x
x = np.random.uniform(-abs_value, abs_value, num_points)
# y = mx + b + noise
y = slope*x + intercept + np.random.uniform(-abs_noise, abs_noise, num_points)
... | <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: If $N$ = num_points, then the error in fitting a line to the points (also defined as Cost, $C$) can be defined as
|
8,236 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import numpy as np
np.random.seed(42)
x = np.random.random(20)
y = np.sin(2 * x)
p = np.polyfit(x, y, 1) # fit a 1st-degree polynomial (i.e. a line) to the data
print p # slope and intercept
x_new = np.random.random(3)
y_new = np.polyval(p, x_new) # evaluate the polynomi... | <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: Polynomial regression can be done with the functions polyfit
Step2: Using a 1st-degree polynomial fit (that is, fitting a straight line to x an... |
8,237 | <ASSISTANT_TASK:>
Python Code:
import ee
ee.Initialize()
from geetools import batch
p1 = ee.Geometry.Point([-71,-42])
p2 = ee.Geometry.Point([-71,-43])
p3 = ee.Geometry.Point([-71,-44])
feat1 = ee.Feature(p1.buffer(1000), {'site': 1})
feat2 = ee.Feature(p2.buffer(1000), {'site': 2})
feat3 = ee.Feature(p3.buffer(1000),... | <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: FeatureCollection
Step2: Image
Step3: Execute
|
8,238 | <ASSISTANT_TASK:>
Python Code:
# import logging
# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s')
# logging.root.level = logging.INFO
from os import path
from random import shuffle
from corputil import FileCorpus, ListCorpus
from corputil.utils import load_stopwords
from gensim.models.word2vec 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: Training the Base Model
Step2: Save model to disk. Don't finalize the model because we need to train it with new data later!
Step3: Training t... |
8,239 | <ASSISTANT_TASK:>
Python Code:
!g.gisenv
!g.mapset location=nc_basic_spm_grass7 mapset=user1
!g.proj -p
!g.list rast
rasterlist = getLayerList(type='rast')
vectorlist = getLayerList(type='vect')
rasterlist
vectorlist
!r.info elevation@PERMANENT
rasterlayerinfo = rlayerInfo(map='elevation')
vectorlayerinfo = vlaye... | <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: Use of the g.mapset
Step2: print projection info with g.proj
Step3: list vector and raster layers with g.list
Step4: use the getLayerList fun... |
8,240 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
# plotting
%matplotlib inline
from matplotlib import pyplot as plt;
import matplotlib as mpl;
from mpl_toolkits.mplot3d import Axes3D
# scientific
import numpy as np;
import sklearn as skl;
import sklearn.datasets;
import sklearn.cluster;
import sklearn.mix... | <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: EECS 445
Step3: Dimensionality Reduction
Step4: Example
Step5: Break time!
|
8,241 | <ASSISTANT_TASK:>
Python Code:
# Make sure division of integers does not round to the nearest integer
from __future__ import division
# Make everything in python's symbolic math package available
from sympy import * # Make sure sympy functions are used in preference to numpy
import sympy # Make sympy. constructions ava... | <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: Fundamental variables
Step2: Derived variables
Step3: The system's vector basis is given by $(\hat{\ell}, \hat{n}, \hat{\lambda})$, and will b... |
8,242 | <ASSISTANT_TASK:>
Python Code:
G = nx.read_gpickle('Synthetic Social Network.pkl') #If you are Python 2.7, read in Synthetic Social Network 27.pkl
nx.draw(G)
# Who are represented in the network?
G.nodes(data=True)
len(G.nodes())
# Who is connected to who in the network?
G.edges()
len(G.edges())
# Let's get a 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: Basic Network Statistics
Step2: Exercise
Step3: Exercise
Step4: Since this is a social network of people, there'll be attributes for each ind... |
8,243 | <ASSISTANT_TASK:>
Python Code:
Image('./res/fig8_1.png')
Image('./res/fig8_2.png')
Image('./res/fig8_5.png')
Image('./res/fig8_6.png')
Image('./res/prioritized_sweeping.png')
Image('./res/fig8_7.png')
<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:
Step1: Learning and planning are deeply integrated in the sense that they share almost all the same machinery, differing only in the source of their ex... |
8,244 | <ASSISTANT_TASK:>
Python Code:
#Configuracion para recargar módulos y librerías cada vez
%reload_ext autoreload
%autoreload 2
... | <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: Contenido
Step2: Importante
Step3: 2. Librería Numpy
Step4: 2.1 Array vs Matrix
Step5: Desafío 1
Step6: 2.2 Indexación y Slicing
Step7: Ob... |
8,245 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import dateutil.parser
import datetime
from urllib.request import urlopen, Request
import simplejson as json
import pandas as pd
def extract_reference_time(API_data_loc):
Find reference time that corresponds to most... | <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: Planet OS API demo for GEFS
Step2: GEFS is a model with lots of output variables, which may also change depending of which particular output fi... |
8,246 | <ASSISTANT_TASK:>
Python Code:
def findstring(s ) :
n = len(s )
s = list(s )
i = 1
while i < n - 1 :
if(s[i - 1 ] == '0' and s[i + 1 ] == '0' ) :
s . pop(i )
i -= 1
if i > 0 and s[i - 1 ] == '0' :
i -= 1
n = len(s )
i += 1
return ' ' . join(s )
if __name__== ' __main __' :
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:
|
8,247 | <ASSISTANT_TASK:>
Python Code:
# Lo primero que ejecutarás será 'Hola Jupyter'
print('Hola Jupyter')
variable = 50
saludo = 'Hola'
# Importa matplotlib (paquete para graficar) y numpy (paquete para arreglos).
# Fíjate en el la función mágica para que aparezca nuestra gráfica en la celda.
%matplotlib inline
import mat... | <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: Cada celda la puedes usar para escribir el código que tu quieras y si de repente se te olvida alguna función o tienes duda de si el nombre es co... |
8,248 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as pl
import numpy as np
import shap
import xgboost as xgb
N = 2000
X = np.zeros((N,2))
X[:1000,0] = 1
X[:500,1] = 1
X[1000:1500,1] = 1
yA = 80 * (X[:,0] * X[:,1]) + 1e-4 * ((X[:,0] == 0) * (X[:,1] == 0)) # last term forces the creation of left split
Xd = xgb.DMa... | <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 Model A
Step2: Create Model B
Step3: SHAP Values
Step4: Saabas Values
Step5: mean(abs(SHAP Values))
Step6: mean(abs(Saabas Values))
... |
8,249 | <ASSISTANT_TASK:>
Python Code:
# Packages
import numpy as np
from testCases import *
from gc_utils import sigmoid, relu, dictionary_to_vector, vector_to_dictionary, gradients_to_vector
# GRADED FUNCTION: forward_propagation
def forward_propagation(x, theta):
Implement the linear forward propagation (compute J... | <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) How does gradient checking work?
Step4: Expected Output
Step6: Expected Output
Step8: Expected Output
Step10: Now, run backward propagati... |
8,250 | <ASSISTANT_TASK:>
Python Code:
# loading libraries and reading the data
import numpy as np
import pandas as pd
market_df = pd.read_csv("./global_sales_data/market_fact.csv")
customer_df = pd.read_csv("./global_sales_data/cust_dimen.csv")
product_df = pd.read_csv("./global_sales_data/prod_dimen.csv")
shipping_df = pd.re... | <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: Merging Dataframes Using pd.merge()
Step2: Merging Dataframes
Step3: Similary, you can merge the other dimension tables - shipping_df and orde... |
8,251 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import os
import numpy as np
import fitsio
from desitarget import desi_mask, brightmask
os.environ["CSCRATCH"] = '/global/cscratch1/sd/adamyers'
sourcemask = fitsio.read("$CSCRATCH/sourcemask150.fits")
brightmask.plot_mask(sourcemask,lim... | <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: You may have to set up your $CSCRATCH environment variable so that Python can find it, e.g.
Step2: These are some circular regions that could b... |
8,252 | <ASSISTANT_TASK:>
Python Code:
data.pressure[-1*24*24:].plot()
# See how this compares to "normal" pressure
# Plot the last 10 days
data.pressure[-10*24*24:].plot()
data.tail()
!pwd
from IPython import display
display.Image('../galleries/Joaquin/joaquin.png')
<END_TASK> | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Bermuda Weather Radar
|
8,253 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
%matplotlib inline
# https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/
from pandas import read_csv
url = "https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd... | <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: Exposé
Step2: Données départements
Step3: Il faudrait aussi fusionner avec la population de chaque département. Ce sera pour une autre fois.
... |
8,254 | <ASSISTANT_TASK:>
Python Code:
fname = io.download_occultation_times(outdir='../data/')
print(fname)
tlefile = io.download_tle(outdir='../data')
print(tlefile)
times, line1, line2 = io.read_tle_file(tlefile)
tstart = '2017-09-11T00:00:00'
tend = '2017-09-15T00:00:00'
orbits = planning.sunlight_periods(fname, tstart, ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Download the NuSTAR TLE archive.
Step2: Here is where we define the observing window that we want to use.
Step3: We want to know how to orient... |
8,255 | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.insert(0, '../')
from paleopy import proxy
from paleopy import analogs
from paleopy.plotting import scalar_plot
djsons = '../jsons/'
pjsons = '../jsons/proxies'
proxies = pd.read_excel('../data/ProxiesLIANZSWP.xlsx')
proxies.head()
for irow in proxies.index:
p ... | <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: defines the folder where the JSON files are (for the datasets) and where to save the proxy JSON files
Step2: instantiates a proxy instance
|
8,256 | <ASSISTANT_TASK:>
Python Code:
import networkx
import obonet
%%time
url = 'http://purl.obolibrary.org/obo/go/go-basic.obo'
graph = obonet.read_obo(url)
# Number of nodes
len(graph)
# Number of edges
graph.number_of_edges()
# Check if the ontology is a DAG
networkx.is_directed_acyclic_graph(graph)
# Retreive propertie... | <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: Read the Gene Ontology
Step2: Lookup node properties
Step3: Create name mappings
Step4: Find parent or child relationships
Step5: Find all s... |
8,257 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
import mne
sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
'sample_audvis_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(0, 60... | <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: What are artifacts?
Step2: Low-frequency drifts
Step3: Low-frequency drifts are readily removed by high-pass filtering at a fairly
Step4: Her... |
8,258 | <ASSISTANT_TASK:>
Python Code:
%matplotlib notebook
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
import numpy as np
import matplotlib.pyplot as plt
learning_rate = 0.01
training_epochs = 1000
display_step = 50
train_X = np.asarray([3.3,4.4,5.5,6.71,6.93,4.168,9.779,6.182,7.5... | <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: Macierz $A$ dla regresji liniowej wynosi
Step2: Współczynniki dokładnie będą wynosiły
Step3: Optymalizacja metodą iteracyjną,
Step4: Tensor f... |
8,259 | <ASSISTANT_TASK:>
Python Code:
# To begin, define the prior as the probability of the car being behind door i (i=1,2,3), call this "pi".
# Note that pi is uniformly distributed.
p1 = ?
p2 = ?
p3 = ?
# Next, to define the class conditional, we need three pieces of information. Supposing Monty reveals door 3,
# we must ... | <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 2
Step2: C
Step3: D
Step5: <a id='prob1ans'></a>
|
8,260 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from pandas import Series, DataFrame
import pandas as pd
from itertools import *
import numpy as np
import csv
import math
import matplotlib.pyplot as plt
from matplotlib import pylab
from scipy.signal import hilbert, chirp
import scipy
import networkx as nx
c_dataset ... | <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 0750-0805
Step2: What is the number of different vehicles for the 15 min
Step3: 15min = 900 s = 9000 ms //
Step4: For eve... |
8,261 | <ASSISTANT_TASK:>
Python Code:
%%javascript
IPython.keyboard_manager.command_shortcuts.add_shortcut('r', {
help : 'run cell',
help_index : 'zz',
handler : function (event) {
IPython.notebook.execute_cell();
return false;
}}
);
%%javascript
IPython.keyboard_manager.command_shortcuts.add_... | <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: There are a couple of points to mention about this API
Step2: Likewise, to remove a shortcut, use remove_shortcut
|
8,262 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function, absolute_import
import tensorflow as tf
import numpy as np
import smrt
# this is our seed
seed = 42
# show versions for continuity
print("TensorFlow version: %s" % tf.__version__)
print("NumPy version: %s" % np.__version__)
print("SMRT vers... | <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 MNIST
Step2: Demonstrate the AutoEncoder
Step3: Fit and reconstruct using AutoEncoder
Step4: Show reconstruction examples
Step5: This l... |
8,263 | <ASSISTANT_TASK:>
Python Code:
urlre = re.compile( '(?P<url>https?://[^\s]+)' )
for page in doc :
print urlre.findall( page )
urlre = re.compile( '(?P<url>https?://[^\s]+)' )
for page in doc :
print urlre.findall( page.replace('\n','') )
from sgmllib import SGMLParser
class URLLister(SGMLParser):
def rese... | <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: PDF is garbage, continued
Step2: Nope.
Step3: Here are all the URLs in the document...
Step4: Bleh. That is mostly links in the references, a... |
8,264 | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
#3.1
p = [0.2, 0.5, 0.8]
n = np.arange(1, 8)
for i, pi in enumerate(p):
plt.plot(n, pi * (1 - pi)**(n - 1), 'o-', label='$p={}$'.format(pi), color='C{}'.format(i))
plt.axvline(x = 1/ pi, color='C{}'.format(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: 4. Prediction Intervals and Loops (19 Points + 12 EC)
Step2: 5. Normal Distribution (8 Points)
Step3: 5.3
|
8,265 | <ASSISTANT_TASK:>
Python Code:
columns = pd.MultiIndex.from_tuples([
('A', 'cat', 'long'), ('B', 'cat', 'long'),
('A', 'dog', 'short'), ('B', 'dog', 'short')
],
names=['exp', 'animal', 'hair_length']
)
df = pd.DataFrame(np.random.randn(4, 4), columns=columns)
df
df.columns
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: missing data
Step2: groupby + reshaping
|
8,266 | <ASSISTANT_TASK:>
Python Code:
### Link to requirements.txt on github
business.head(2)
review.head(2)
review.text.head(2)
review_all = pd.read_csv('../../data/interim/original_csv/review.csv')
# Number of reviews by date
# The sharp seasonal falls are Chrismas Day and New Year's Day
# The sharp seasonal spikes are in... | <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: 3.1 Data Dictionary
Step2: 3.2.4 Cleaning 'review' table
Step3: 3.2.5 Cleaning 'checkin' table
Step4: 3.2.6 Cleaning 'user' table
Step5: 3.2... |
8,267 | <ASSISTANT_TASK:>
Python Code:
# Load relevant libraries.
from os import path
import pandas as pd
import numpy as np
import folium
import glob
from tqdm import tqdm
import random
%matplotlib inline
# Load custom modules.
import sys
sys.path.append('..')
from utils import getmedian, haversine
from utils import llaToECEF... | <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. Single user review
Step2: 1.2 Remove the columns that you do not require
Step3: 1.3 Remove location records with poor accuracy
Step4: It l... |
8,268 | <ASSISTANT_TASK:>
Python Code:
import re
import requests
import zipfile
import numpy as np
import pandas as pd
import matplotlib.pylab as plt
import seaborn as sns
import statsmodels.formula.api as sm
sns.set_context('talk')
pd.set_option('float_format', '{:6.2f}'.format)
%matplotlib inline
url = 'http://databank.worl... | <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 the zip file from the web and save it to your hard drive.
Step2: Show contents of the zip file.
Step3: Read csv-formatted data directly f... |
8,269 | <ASSISTANT_TASK:>
Python Code:
import rebound
import numpy as np
sim = rebound.Simulation()
np.random.seed(42)
#integrator options
sim.integrator = "mercurius"
sim.dt = 1
sim.testparticle_type = 1
#collision and boundary options
sim.collision = "direct"
sim.collision_resolve = "merge"
sim.collision_resolve_keep_sorted... | <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 let's choose the basic properties required for the MERCURIUS integrator to run correctly. In particular, we are
Step2: Now that the preli... |
8,270 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Plot of many regions!
l1, l2 = -3.5, 3.5
resolution = 0.01
[X, Y] = np.meshgrid(np.arange(-l2,l2,resolution), np.arange(-l2,l2,resolution))
Z = X + 1j*Y
def plot_complex_region(R, ax, title, cmap=plt.cm.gray, levels=... | <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: Regiones de estabilidad más comunes para estudiar
Step2: Quiz 3
Step3: 2) Aplique el método de Forward Euler para resolver el IVP hasta el tie... |
8,271 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from statsmodels.compat import lmap
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm
norms = sm.robust.norms
def plot_weights(support, weights_func, xlabels, xticks):
fig = plt.figure(figsize=(12,8))
ax = f... | <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: An M-estimator minimizes the function
Step2: Andrew's Wave
Step3: Hampel's 17A
Step4: Huber's t
Step5: Least Squares
Step6: Ramsay's Ea
St... |
8,272 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from time import time
from joblib import Parallel, delayed
import multiprocessing
import time
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')
from sklearn.cluster im... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: loading different datasets
Step2: I - Clustering Nodes
Step3: 1 - Parameters Optimization
Step4: Difficult to find an elbow criteria
Step5: ... |
8,273 | <ASSISTANT_TASK:>
Python Code:
import time
import numpy as np
import copy
from sklearn.linear_model import ElasticNetCV as ElasticNetCV_sk
from prox_elasticnet import ElasticNetCV as ElasticNetCV_px
np.random.seed(319159)
from sklearn import __version__ as sklearn_version
print("Using sklearn version {}.".format(sklear... | <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 initialise the ElasticNetCV object for each implementation using the default parameters. This means l1_ratio = 0.5 and alpha takes 100 values... |
8,274 | <ASSISTANT_TASK:>
Python Code:
import time, array, random, copy, math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
from deap import algorithms, base, benchmarks, tools, creator
random.seed(a=42)
creator.create("FitnessMin", 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: How we handle multiple -and conflictive- objectives?
Step2: Planting a constant seed to always have the same results (and avoid surprises in cl... |
8,275 | <ASSISTANT_TASK:>
Python Code:
%load_ext autoreload
%autoreload 2
import prepare_EMG, prepare_outputs, prepare_data, pandas
EMG_Prep = prepare_EMG.EMG_preparer()
Output_Prep = prepare_outputs.output_preparer()
Data_Prep = prepare_data.data_preparer()
singles_1 = Data_Prep.load_singletons(1)
singles_2 = Data_Prep.load_... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Loading the Data
Step2: Labeling the Data
Step3: Preparing Input, Output 'Master' DataFrames
Step4: Preprocessing, continued
Step5: Explorat... |
8,276 | <ASSISTANT_TASK:>
Python Code:
import mmap
with open('lorem.txt', 'r') as f:
with mmap.mmap(f.fileno(), 0,
access=mmap.ACCESS_READ) as m:
print('First 10 bytes via read :', m.read(10))
print('First 10 bytes via slice:', m[:10])
print('2nd 10 bytes via read :', m.read(10)... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Writing
Step2: Copying Mode
Step3: Regular Expressions
|
8,277 | <ASSISTANT_TASK:>
Python Code:
%pylab notebook
%precision %.4g
V = 120 # [V]
p = 4
R1 = 2.0 # [Ohm]
R2 = 2.8 # [Ohm]
X1 = 2.56 # [Ohm]
X2 = 2.56 # [Ohm]
Xm = 60.5 # [Ohm]
s = 0.025
Prot = 51 # [W]
Zf = ((R2/s + X2*1j)*(Xm*1j)) / (R2/s + X2*1j + Xm*1j)
Zf
Zb = ((R2/(2-s) + X2*1j)*(Xm*1j)) / (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: Description
Step2: SOLUTION
Step3: $$Z_B = \frac{(R_2/(2-s) + jX_2)(jX_M)}{R_2/(2-s) + jX_2 + jX_M}$$
Step4: (a)
Step5: (b)
Step6: (c)
Step... |
8,278 | <ASSISTANT_TASK:>
Python Code:
from __future__ import absolute_import, division, print_function
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_context('poster')
# sns.set_style('whitegrid')
sns.set_style('darkgrid')
plt.rcParams['figure.figsize'] = 12, 8 # plotsize
import numpy as ... | <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: Notebook Extensions -- qgrid
|
8,279 | <ASSISTANT_TASK:>
Python Code:
# Import biochemical model module
import steps.model as smod
# Create model container
mdl = smod.Model()
# Create chemical species
A = smod.Spec('A', mdl)
B = smod.Spec('B', mdl)
C = smod.Spec('C', mdl)
# Create reaction set container
vsys = smod.Volsys('vsys', mdl)
# Create reaction
# 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: For complex model, we can break it down into elementary reactions, for example, the following model
Step2: Setup geometry
Step3: Create a rand... |
8,280 | <ASSISTANT_TASK:>
Python Code:
import os
import pandas as pd
import math
import numpy as np
from sklearn.tree import DecisionTreeClassifier
headers = ["buying", "maint", "doors", "persons","lug_boot", "safety", "class"]
data = pd.read_csv("car_data.csv", header=None, names=headers)
data = data.sample(frac=1).reset_inde... | <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: No código acima, fizemos a leitura do arquivo informando que não há cabeçalho (obrigatório) e embaralhamos os dados.
Step2: Um problema é que n... |
8,281 | <ASSISTANT_TASK:>
Python Code:
cos_credentials = {
"apikey": "-------",
"cos_hmac_keys": {
"access_key_id": "------",
"secret_access_key": "------"
},
"endpoints": "https://cos-service.bluemix.net/endpoints",
"iam_apikey_description": "------",
"iam_apikey_name": "------",
"iam_role_crn": "------"... | <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: Define the endpoint.
Step2: Prepare model
Step3: Configure docker credentials
Step4: Create a config-map in the namespace you're using with t... |
8,282 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
%matplotlib inline
from cops_and_robots.robo_tools.fusion.softmax import SoftMax, make_regular_2D_poly
poly = make_regular_2D_poly(5, max_r=2, theta=np.pi/3.1)
labels = ['Interior',
'Mall Terrace Entrance',
'Heliport Facade',
'South Parking... | <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: NOTE
Step2: As expected, our boundaries stayed the same but our probabilities are less spread out. Looking good!
Step3: Great! We've successf... |
8,283 | <ASSISTANT_TASK:>
Python Code:
class IndicatorCommand:
Indicator command.
def __init__(self, indicator, selector):
self.indicator = indicator
self.selector = selector
def __call__(self, music):
for selection in self.selector(music):
indicator = copy.copy(self.in... | <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: Class collaboration
Step7: 2. Extend our music-maker
Step8: 3. Initializing commands
Step9: 4. Making the score
|
8,284 | <ASSISTANT_TASK:>
Python Code:
# THINGS TO IMPORT
# This is a baseline set of libraries I import by default if I'm rushed for time.
import codecs # load UTF-8 Content
import json # load JSON files
import pandas as pd # Pandas handles dataframes
import numpy as np ... | <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: Acquire Dee Dataset from Methods Matter
Step2: Summary Statistics
Step3: Cross-Tabulation
Step4: Correlation Matrix
Step5: Linear Regression... |
8,285 | <ASSISTANT_TASK:>
Python Code:
# Import numpy and alias to "np"
import numpy as np
# Import and alias to "plt"
import matplotlib.pyplot as plt
def planck(wavelength, temp):
Return the emitted radiation from a blackbody of a given temp and wavelength
Args:
wavelength (float): wavelength (m)
... | <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: Advanced
Step3: Let's also combine our plotting code into a cohesive function
Step4: Now we can tie our plot function, plot_planck, to the int... |
8,286 | <ASSISTANT_TASK:>
Python Code:
import os
import sys
import tensorflow as tf
from tensorflow.keras import layers
import pandas as pd
import numpy as np
import cv2
import matplotlib.pyplot as plt
tf.random.set_seed(123)
annotation_folder = "/dataset/"
if not os.path.exists(os.path.abspath(".") + annotation_folder):
... | <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: Downloading the dataset
Step2: Preparing the dataset
Step3: Preparing hyperparameters
Step7: Building a data pipeline
Step8: Visualizing sam... |
8,287 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
d = ({'Date': ['1/1/18','1/1/18','2/1/18','3/1/18','1/2/18','1/3/18','2/1/19','3/1/19'],
'Val': ['A','B','C','D','A','B','C','D']})
df = pd.DataFrame(data=d)
def g(df):
df['Date'] = pd.to_datetime(df['Date'], format='%d/%m/%y')
y = df['Date'].dt.year
... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
8,288 | <ASSISTANT_TASK:>
Python Code:
import paramz, numpy as np
from scipy.optimize import rosen_der, rosen
x = np.array([-1,1])
class Rosen(paramz.Model): # Inherit from paramz.Model to ensure all model functionality.
def __init__(self, x, name='rosen'): # Initialize the Rosen model with a numpy array `x` and name `na... | <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 starting position of the rosen function is set to be
Step2: For paramz to understand your model there is three steps involved
Step3: The ... |
8,289 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
from matplotlib import rcParams
rcParams["savefig.dpi"] = 100
rcParams["figure.dpi"] = 100
rcParams["figure.figsize"] = 12, 4
rcParams["font.size"] = 16
rcParams["text.usetex"] = False
rcParams["font.family"] = ["sans-serif... | <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:
Step5: Benchmarking our implementation
Step6: <div style="background-color
Step7: <div style="background-color
Step8: <div style="background-color
S... |
8,290 | <ASSISTANT_TASK:>
Python Code:
import logging
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
import collections
SentimentDocument = collections.namedtuple('SentimentDocument', 'words tags split sentiment')
import io
import re
import tarfile
import os.path
import smart_open... | <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: Introduction
Step2: We can now proceed with loading the corpus.
Step3: Here's what a single document looks like.
Step4: Extract our documents... |
8,291 | <ASSISTANT_TASK:>
Python Code:
y, sr = librosa.load('audio/prelude_cmaj.wav')
ipd.Audio(y, rate=sr)
est_tempo, est_beats = librosa.beat.beat_track(y=y, sr=sr, bpm=120)
est_beats = librosa.frames_to_time(est_beats, sr=sr)
est_beats
ref_beats = numpy.array([0, 0.50, 1.02, 1.53, 1.99, 2.48, 2.97,
3.43, 3.90, 4.41... | <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: Detect Beats
Step2: Load a fictional reference annotation.
Step3: Plot the estimated and reference beats together.
Step4: Evaluate
Step5: Ex... |
8,292 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import scipy.stats
# Author: Jake VanderPlas
# License: BSD
# The figure produced by this code is published in the textbook
# "Statistics, Data Mining, and Machine Learning in Astronomy" (2013)
# For more information, see http://astroML.github.com
# To report a bug ... | <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: Bootstrap
Step2: Jackknife
Step3: Hypothesis Testing
Step4: Benjamini and Hochberg Method
Step5: 4.7 - Comparing Distributions
Step6: U tes... |
8,293 | <ASSISTANT_TASK:>
Python Code:
!sudo chown -R jupyter:jupyter /home/jupyter/training-data-analyst
!pip install --user google-cloud-bigquery==1.25.0
import os
from google.cloud import bigquery
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name is: "$P... | <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: Note
Step2: Lab Task #1
Step3: The source dataset
Step4: Create the training and evaluation data tables
Step5: Lab Task #3
Step6: Lab Task ... |
8,294 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
def make_squares(n):
squares = [i**2 for i in range(n)]
def make_squares(n):
squares = [i**2 for i in range(n)]
print ( make_squares(2) )
s = 1
a = 0
for i in range(4):
a += s
s += 2
a
s = 1
a = 0
for i in... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Enoncé 1
Step2: Comme il n'y a pas d'instruction return, la fonction retourne toujours None quelque chose le résultat de ce qu'elle calcule.
St... |
8,295 | <ASSISTANT_TASK:>
Python Code:
from bigbang.archive import Archive
urls = [#"analytics",
"conferences",
"design",
"education",
"gendergap",
"historic",
"hot",
"ietf-privacy",
"ipython-dev",
"ipython-user",
"languages",
"maps-l",
... | <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 above code reads in preprocessed email archive data. These mailing lists are from a variety of different sources
Step2: Now we have process... |
8,296 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import mapp4py
from mapp4py import md
from lib.elasticity import rot, cubic, resize, displace, crack
from mapp4py import mpi
if mpi().rank!=0:
with open(os.devnull, 'w') as f:
sys.stdout = f;
xprt = md.export_cfg("");
_ = n... | <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: Block the output of all cores except for one
Step2: Define an md.export_cfg object
Step3: Asymptotic Displacement Field of Crack from Linear E... |
8,297 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
np.any?
def makeMPGData(filename='auto-mpg.data'):
def missingIsNan(s):
return np.nan if s == b'?' else float(s)
data = np.loadtxt(filename, usecols=range(8), converters={3: missingIsNan})
print("Rea... | <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: So, our linear model seems to be quite a bit better than using just the mean mpg.
Step2: Which $\lambda$ value is best? Careful. What is the ... |
8,298 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import matplotlib.pylab as plt
import numpy as np
from distutils.version import StrictVersion
import sklearn
print(sklearn.__version__)
assert StrictVersion(sklearn.__version__ ) >= StrictVersion('0.18.1')
... | <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: Modell-Architektur
Step2: Sigmoid
Step3: Relu
Step4: The classic VGG16 Architecture
Step5: VGG starts with a number of convolutional blocks ... |
8,299 | <ASSISTANT_TASK:>
Python Code:
!pip install --user google-cloud-bigquery==1.25.0
# Installing the latest version of the package
import tensorflow as tf
print("TensorFlow version: ",tf.version.VERSION)
%%bash
export PROJECT=$(gcloud config list project --format "value(core.project)")
echo "Your current GCP Project Name... | <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: Note
Step2: The source dataset
Step3: Create the training data table
Step4: Verify table creation
Step5: Baseline Model
Step6: REMINDER
Ste... |
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