Unnamed: 0 int64 0 15.9k | cleaned_code stringlengths 67 124k ⌀ | cleaned_prompt stringlengths 168 30.3k ⌀ |
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9,200 | <ASSISTANT_TASK:>
Python Code:
from bokeh.io import output_notebook, show
output_notebook()
import pandas as pd
diamonds = pd.read_csv('./data/diamonds.csv')
diamonds = diamonds.sample(n=1000)
diamonds.head()
from bokeh.charts import Scatter, Histogram, Bar
p = Scatter(diamonds, color='cut', x='carat', y='price', tit... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The data
Step2: 1) Colors autoselection
Step3: Now... you don't have to get stuck with the default palette. Bokeh comes with a pre-built list... |
9,201 | <ASSISTANT_TASK:>
Python Code:
grammar =
S -> NP VP
NP -> DET[GEN=?x] NOM[GEN=?x]
NOM[GEN=?x] -> ADJ NOM[GEN=?x] | N[GEN=?x]
ADJ -> "schöne" | "kluge" | "dicke"
DET[GEN=mask,KAS=nom] -> "der"
DET[GEN=fem,KAS=dat] -> "der"
DET[GEN=fem,KAS=nom] -> "die"
DET[GEN=fem,KAS=akk] -> "die"
DET[GEN=neut,KAS=nom] -> "das"
DET[GE... | <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: Übungsblatt 7
Step3: Aufgabe 2 CFG
Step5: Hausaufgaben
Step6: Aufgabe 8 Adverben und Verbzweitstellung
|
9,202 | <ASSISTANT_TASK:>
Python Code:
import hgvs.location
import hgvs.posedit
start = hgvs.location.BaseOffsetPosition(base=200,offset=-6,datum=hgvs.location.Datum.CDS_START)
start, str(start)
end = hgvs.location.BaseOffsetPosition(base=22,datum=hgvs.location.Datum.CDS_END)
end, str(end)
iv = hgvs.location.Interval(start=sta... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2. Make an edit object
Step2: 3. Make the variant
Step3: Important
|
9,203 | <ASSISTANT_TASK:>
Python Code:
import numpy; print('numpy:\t', numpy.__version__, sep='\t')
import scipy; print('scipy:\t', scipy.__version__, sep='\t')
import matplotlib; print('matplotlib:', matplotlib.__version__, sep='\t')
import sklearn; print('scikit-learn:', sklearn.__version__, sep='\t')
from skle... | <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 load some data.
Step2: Benchmark classificator by ml-benchmarks
|
9,204 | <ASSISTANT_TASK:>
Python Code:
# Authors: Denis Engemann <denis.engemann@gmail.com>
# Alexandre Gramfort <alexandre.gramfort@telecom-paristech.fr>
# License: BSD (3-clause)
import matplotlib.pyplot as plt
import mne
from mne.viz import plot_evoked_topo
from mne.datasets import sample
print(__doc__)
data_path =... | <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: Show topography for two different conditions
|
9,205 | <ASSISTANT_TASK:>
Python Code:
from __future__ import division
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
def ndft(x, f, N):
non-equispaced discrete Fourier transform
k = -(N // 2) + np.arange(N)
return np.dot(f, np.exp(2j * np.pi * k * x[:, np.newaxis]))
x = -0.5 + np.random.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:
Step2: We want to solve the following
Step3: Let's try evaluating this on some sinusoidal data, with a frequency of 10 cycles per unit time
Step4: As... |
9,206 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot
pyplot.rcParams['image.cmap'] = 'jet'
import numpy as np
x0 = -1.4
y0 = 0.5
x = [x0] # The algorithm starts at x0, y0
y = [y0]
eta = 0.1 # step size multiplier
precision = 0.00001
def f(x,y):
f1 = x**2/2-y**2/4+3
f2 = 2*x+1-np.exp(... | <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: Stochastic gradient descent (SGD)
Step2: We now write an SGD code for this problem. The training_data is a list of tuples (x, y) representing t... |
9,207 | <ASSISTANT_TASK:>
Python Code:
from sympy import *
from geom_util import *
from sympy.vector import CoordSys3D
N = CoordSys3D('N')
alpha1, alpha2, alpha3 = symbols("alpha_1 alpha_2 alpha_3", real = True, positive=True)
init_printing()
%matplotlib inline
%reload_ext autoreload
%autoreload 2
%aimport geom_util
H1=symbol... | <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: Lame params
Step2: Metric tensor
Step3: ${\displaystyle \hat{G}=\sum_{i,j} g_{ij}\vec{R}^i\vec{R}^j}$
Step4: Christoffel symbols
Step5: Grad... |
9,208 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from sklearn.ensemble import GradientBoostingClassifier
from sklearn import cross_validation, metrics
from sklearn.grid_search import GridSearchCV
import matplotlib.pylab as plt
%matplotlib inline
from matplotlib.pylab import rcParams
rcParams['figur... | <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
Step2: Define a function for modeling and cross-validation
Step3: Baseline Model
Step4: GBM Models
Step5: So we got 60 as the opti... |
9,209 | <ASSISTANT_TASK:>
Python Code:
import jax
import jax.numpy as jnp
import numpy as np
from matplotlib import pyplot as plt
# Check connected accelerators. Depending on what runtime you're connected to,
# this will show a single CPU/GPU, or 8 TPU cores (jf_2x2 aka JellyDonut).
# You can start a TPU runtime via : "Connect... | <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: Randomness
Step2: jnp vs. np
Step3: grad()
Step4: vmap()
Step5: jit()
Step6: pmap()
Step7: pytrees
|
9,210 | <ASSISTANT_TASK:>
Python Code:
!pip install -I "phoebe>=2.0,<2.1"
%matplotlib inline
import phoebe
from phoebe import u # units
import numpy as np
import matplotlib.pyplot as plt
logger = phoebe.logger()
b = phoebe.default_binary()
times1 = np.linspace(0,1,201)
times2 = np.linspace(90,91,201)
b.add_dataset('lc', time... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: As always, let's do imports and initialize a logger and a new Bundle. See Building a System for more details.
Step2: Now we'll create empty lc... |
9,211 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from caffe2.proto import caffe2_pb2
import numpy as np
import skimage.io
import skimage.transform
from matplotlib import pyplot
import os
from caffe2.python import core, workspace, models
import urllib2
print("Required modules imported.")
# Configuration --- Change to 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: In the block below we're loading the mean file (if it exists) and the image and then pre-processing the image for ingestion into a Caffe2 convol... |
9,212 | <ASSISTANT_TASK:>
Python Code:
from sklearn.metrics import accuracy_score, precision_score,\
recall_score, f1_score
ground_truth = [1,0,1,0,0,1,1,1,1,0]
chunker1 = [1,1,1,0,1,0,1,1,1,1]
chunker2 = [1,0,1,0,0,0,0,0,1,0]
chunker3 = [0,0,0,0,0,1,1,1,1,0]
def evaluate(chunker):
print(
"Accurac... | <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: Betrachten Sie folgende Daten. Es handelt sich um ein vereinfachtes Tagging-Schema fürs Chunking, bei dem nur zwischen „Teil einer NP“ (1) und „... |
9,213 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
unbiasedCVs = np.genfromtxt('NVT_monitor/COLVAR',comments='#');
biasedCVs = np.genfromtxt('MetaD/COLVAR',comments='#');
unbiasedCVsHOT = np.genfromtxt('NVT_monitor/hot/COLVAR',comments='#');
%matplotlib inline
fig = plt.figure(figsize=(6... | <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: Plotting biased and unbiased CVS
Step2: Plotting contour plot of biased FES
|
9,214 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function
import astropy.io.fits as pyfits
import numpy as np
import astropy.visualization as viz
import matplotlib.pyplot as plt
%matplotlib inline
plt.rcParams['figure.figsize'] = (10.0, 10.0)
targdir = 'a1835_xmm/'
imagefile = targdir+'P0098010101M2U009IMAG... | <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 Many Photons Came From the Cluster?
Step2: Estimating the background
Step3: First, let's visualize the background region by masking out ev... |
9,215 | <ASSISTANT_TASK:>
Python Code:
from jyquickhelper import add_notebook_menu
add_notebook_menu()
racine = NoeudTri("un") # noeud tri n'est pas encore défini
racine.insere ("unite")
racine.insere ("deux")
print(racine)
from pyensae.graphhelper import draw_diagram
img = draw_diagram(
blockdiag {
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: Q1
Step3: Q5
|
9,216 | <ASSISTANT_TASK:>
Python Code:
from poppy.creatures import PoppyHumanoid
poppy = PoppyHumanoid(simulator='vrep')
%pylab inline
# the class time is used to set the time object to be the simulated time in V-REP and not the default python time
import time as real_time
class time:
def __init__(self,robot):
sel... | <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 need a primitive to run the force apply to poppy's chest.
Step2: To start and stop the force primitive
Step3: Prepared poppy for experimen... |
9,217 | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_iris
import numpy as np
iris = load_iris()
X = iris.data.astype(np.float32)
y = iris.target
from sklearn.model_selection import train_test_split
X_fold1, X_fold2, y_fold1, y_fold2 = train_test_split(
X, y, random_state=37, train_size=0.5
)
import cv... | <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: Split the data into two equally sized parts
Step2: Instantiate the classifier
Step3: Train the classifier on the first fold, then predict the ... |
9,218 | <ASSISTANT_TASK:>
Python Code:
from PIL import Image
img = Image.open('eye.png')
img = img.convert("L") # grayscale
img # same as display(img)
# define function flip()
# open 'eye.png', convert to grayscale, flip, and display
# define getpixel, region3x3, avg, and blur functions
img = Image.open('pcb.png')
img = 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: Flip
Step2: Blur
Step3: Denoise
Step4: Generic filter
Step5: Blur refactored
Step6: Denoise refactored
Step7: Edges
Step8: Sharpen
|
9,219 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import scipy as sp
import scipy.fftpack as scfft
from SimISR.utilFunctions import makesumrule,MakePulseDataRepLPC,spect2acf,acf2spect,CenteredLagProduct
from SimISR.IonoContainer import IonoContainer,MakeTestIonoclass
from ISRSpectrum.ISR... | <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 up
Step2: IS Spectra
Step3: White Noise
Step4: Shaped Noise
Step5: Window Function
Step6: Full ISR Data Creation and Estimator
Step7: ... |
9,220 | <ASSISTANT_TASK:>
Python Code::
from sklearn.metrics import confusion_matrix
from sklearn.preprocessing import normalize
import seaborn as sns
cm = confusion_matrix(target, pred)
normed_confusion_matrix = normalize(cm, axis = 1, norm = 'l1')
cm_df = pd.DataFrame(normed_confusion_matrix,index, columns)
sns.heatmap(cm_df... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
|
9,221 | <ASSISTANT_TASK:>
Python Code:
from __future__ import print_function, division
% matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import math
import numpy as np
from thinkbayes2 import Pmf, Cdf, Suite, Joint
import thinkplot
class Euro(Suite):
Represents hypotheses about the probability of heads... | <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: The Euro problem
Step4: If we know the coin is fair, we can evaluate the likelihood of the data directly.
Step5: If we cheat an pretend that t... |
9,222 | <ASSISTANT_TASK:>
Python Code:
# Run this cell before trying examples
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
# Numpy arrays are classes
import numpy as np
a = np.array([0, 1, 6, 8, 12])
print(a.__class__)
print(type(a))
# We want to operate on the array: try numpy cumulative sum function... | <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: Intro to Python OOP
Step2: Example
Step3: Example
Step4: There's a few things here which I haven't introduced, but all will become clear in t... |
9,223 | <ASSISTANT_TASK:>
Python Code:
import graphviz
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import sklearn.datasets
import sklearn.tree
plt.rcParams["figure.figsize"] = [17, 10]
# features
X = [
[0, 0],
[1, 1]
]
# targets
Y = [
0,
1
]
classifier = sklea... | <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: Decision trees are directed graphs beginning with one node and branching to many. They are a hierarchical data structure that represent data by ... |
9,224 | <ASSISTANT_TASK:>
Python Code:
!pip install google-cloud-automl
!apt-get install libmagickwand-dev
!pip install pillow
!pip install --upgrade protobuf
!pip install --upgrade google-cloud-videointelligence
import sys
import os
import json
import math
from google.colab import auth
from google.colab import files
import 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: You might have to restart your runtime to load these packages.
Step2: Next, create a new GCP account (if you don't have one already), and creat... |
9,225 | <ASSISTANT_TASK:>
Python Code:
# Copyright 2018 The TensorFlow Hub Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 如何使用 TF-Hub 解决 Kaggle 上的问题
Step2: 由于本教程将使用 Kaggle 中的数据集，因此需要为您的 Kaggle 帐号创建 API 令牌，并将其上传到 Colab 环境。
Step3: 开始
Step4: 注：本竞赛的任务不是对整个评论进行评分，而是对评... |
9,226 | <ASSISTANT_TASK:>
Python Code:
import os
import zipfile
import shutil
import csv
import bcolz
os.environ["KERAS_BACKEND"] = "theano"
import keras
import numpy as np
from keras.utils.data_utils import get_file
from keras.models import load_model
from keras.layers.normalization import BatchNormalization
from keras.layers... | <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 structure
Step2: VGG16() setup boilerplate
Step3: Load in data with generators
Step4: Finetuning the model
Step5: New model architectur... |
9,227 | <ASSISTANT_TASK:>
Python Code:
# As usual, a bit of setup
import time, os, json
import numpy as np
import skimage.io
import matplotlib.pyplot as plt
from cs231n.classifiers.pretrained_cnn import PretrainedCNN
from cs231n.data_utils import load_tiny_imagenet
from cs231n.image_utils import blur_image, deprocess_image
%ma... | <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: Introducing TinyImageNet
Step2: TinyImageNet-100-A classes
Step3: Visualize Examples
Step4: Pretrained model
Step5: Pretrained model perform... |
9,228 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import os
import sys
import matplotlib.pyplot as plt
sys.path.insert(1, os.path.join(os.getcwd(), '../../'))
from glassure.core.calc import calculate_fr, calculate_sq, optimize_sq, calculate_gr
from glassure.core.utility import extrapolate_to_zero_poly, convert_density_... | <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. Effect on S(Q)
Step2: The two plots clearly show that the optimization on a not extrapolated S(Q) results in an artificial lower intensity o... |
9,229 | <ASSISTANT_TASK:>
Python Code:
print(__doc__)
import sys
from skopt.plots import plot_objective
from skopt import forest_minimize
import numpy as np
np.random.seed(123)
import matplotlib.pyplot as plt
# Here we define a function that we evaluate.
def funny_func(x):
s = 0
for i in range(len(x)):
s += (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: Objective function
Step2: Optimisation using decision trees
Step3: Partial dependence plot
Step4: It is possible to change the location of th... |
9,230 | <ASSISTANT_TASK:>
Python Code:
from ipyleaflet import Map, basemaps, basemap_to_tiles
center = (52.204793, 360.121558)
m = Map(
layers=(basemap_to_tiles(basemaps.NASAGIBS.ModisTerraTrueColorCR, "2018-11-12"), ),
center=center,
zoom=4
)
m
from ipyleaflet import Marker, Icon
icon = Icon(icon_url='https://lea... | <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: Layers
Step2: Heatmap layer
Step3: Velocity
Step4: Controls
Step5: Clean
|
9,231 | <ASSISTANT_TASK:>
Python Code:
L=json.loads(file('../json/L.json','r').read())
M=json.loads(file('../json/M.json','r').read())
N=json.loads(file('../json/N.json','r').read())
import requests
AP={}
for c in M:
if c not in AP:AP[c]={}
for i in range(len(L[c])):
AP[c][N[c][i]]=L[c][i]
baseurl='https://www... | <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: record schedules for 2 weeks, then augment count with weekly flight numbers.
Step2: good dates
Step3: Save
|
9,232 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'cccr-iitm', 'sandbox-3', 'land')
# 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: 1... |
9,233 | <ASSISTANT_TASK:>
Python Code:
%load_ext sql
%sql mysql://steinam:steinam@localhost/sommer_2015
%%sql
%sql select count(*) as AnzahlFahrten from fahrten
%sql select k.kd_id, k.`kd_firma`, k.`kd_plz`,
count(a.Au_ID) as AnzAuftrag,
count(f.f_id) as AnzFahrt,
sum(ts.ts_strecke) as SumStrecke
from kun... | <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: Sommer 2015
Step2: Warum geht kein Join ??
Step3: Der Ansatz mit Join funktioniert in dieser Form nicht, da spätestens beim 2. Join die Firma ... |
9,234 | <ASSISTANT_TASK:>
Python Code:
__author__ = 'Nick Dingwall'
from average_precision_post_code import *
precision_scores = np.mean(
[1.00, 1.00, 1.00, 0.67, 0.75, 0.60,
0.67, 0.71, 0.62, 0.56, 0.50])
print("Mean precision: {:4.4f}".format(precision_scores))
%matplotlib inline
ranked_predictions = [1,1,0,1,0,... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: TL;DR Interpolated average precision is a common metric for classification tasks. However, interpolating linearly between operating points, as i... |
9,235 | <ASSISTANT_TASK:>
Python Code:
#import the necessary packages
import pandas
import nltk
from nltk import word_tokenize
import string
#read the Music Reviews corpus into a Pandas dataframe
df = pandas.read_csv("../Data/BDHSI2016_music_reviews.csv", encoding='utf-8', sep = '\t')
#view the dataframe
df
#first create a ne... | <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 next step is to create a new column in our dataset that contains tokenized words with all the pre-processing steps.
Step2: Pre-processing i... |
9,236 | <ASSISTANT_TASK:>
Python Code:
# Pure python modules and jupyter notebook functionality
# first you should import the third-party python modules which you'll use later on
# the first line enables that figures are shown inline, directly in the notebook
%pylab inline
import os
import datetime as dt
import pandas as pd
fr... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The Shyft Environment
Step2: 2. Configuration of a SHyFT calibration
Step3: Now that we have the initial state, we'll run the calibration (thi... |
9,237 | <ASSISTANT_TASK:>
Python Code:
import copy
# open the file you have downloaded
# these files are organized
file = open("amazon.txt")
# this returns an array with one entry for each line ni the file
lines = file.readlines()
print len(lines)
# Note: the format of the snap files is to list a node (identified by a unique ... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: Next, let's find if there exists clusters(connected components)
Step2: Visualization
Step3: Power Law Property
Step4: Directed Graphs
|
9,238 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import print_function
import numpy as np
import matplotlib.pyplot as plt
import simtk.openmm as mm
from msmbuilder.decomposition import tICA, PCA
def propagate(n_steps=10000):
"Simulate some dynamics"
system = mm.System()
system.addParticle(... | <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, let's use OpenMM to run some dynamics on the 3D potential energy function
Step2: Okay, let's run the dynamics. The first plot below sho... |
9,239 | <ASSISTANT_TASK:>
Python Code:
from allensdk.core.cell_types_cache import CellTypesCache
# Instantiate the CellTypesCache instance. The manifest_file argument
# tells it where to store the manifest, which is a JSON file that tracks
# file paths. If you supply a relative path (like this), it will go
# into your curren... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: The data_set variable is an NwbDataSet instance, which has some methods we can use to access the injected current stimulus waveform and the volt... |
9,240 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from __future__ import division
import os
import sys
import tensorflow as tf
import skimage.io as io
import numpy as np
sys.path.append("/home/aakash-sinha/Documents/Tensorflow/tf-image-segmentation/")
sys.path.append("/home/aakash-sinha/Documents/Tensorflow/models/slim... | <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 display the look up table with mapping from class number to the name of the PASCAL VOC class
Step2: Now, let's create a contour for our s... |
9,241 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
def tokenize(s, stop_words=None, punctuation='`~!@#$%^&*()_-+={[}]|\:;"<,>.?/}\t'):
Split a string into a list of words, removing punctuation and stop words.
w = []
for line in s.splitlines(): #uses th... | <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: Word counting
Step4: Write a function count_words that takes a list of words and returns a dictionary where the keys in the dictionary are the ... |
9,242 | <ASSISTANT_TASK:>
Python Code:
#!pip install -I "phoebe>=2.3,<2.4"
import phoebe
print(phoebe.mpi.enabled)
print(phoebe.mpi.mode)
phoebe.mpi_on()
print(phoebe.mpi.enabled)
print(phoebe.mpi.mode)
print(phoebe.mpi.myrank)
print(phoebe.mpi.nprocs)
print(phoebe.mpi.within_mpirun)
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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: MPI Modes
Step2: PHOEBE determines whether the current script is running within an MPI environment by checking for environment variables set by... |
9,243 | <ASSISTANT_TASK:>
Python Code:
from IPython.display import Image
Image(filename='pagerank1.jpeg')
import numpy as np
# Adjacency matrix
# m1 = [ 0, 0, 0]
# [0.5, 0, 0]
# [0.5, 1, 1]
m1 = np.matrix([[0, 0, 0],[0.5, 0, 0],[0.5, 1, 1]])
beta = 0.7
# r = beta * m1 * r + ((1-beta)/N)
def r_p(r):
return beta... | <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: Suppose we compute PageRank with a β of 0.7, and we introduce the additional constraint that the sum of the PageRanks of the three pages must be... |
9,244 | <ASSISTANT_TASK:>
Python Code:
import nexradaws
conn = nexradaws.NexradAwsInterface()
years = conn.get_avail_years()
print(years)
months = conn.get_avail_months('2013')
print(months)
days = conn.get_avail_days('2013','05')
print(days)
radars = conn.get_avail_radars('2013','05','31')
print(radars)
availscans = conn... | <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: Query methods
Step2: Get available months in a year
Step3: Get available days in a given year and month
Step4: Get available radars in a give... |
9,245 | <ASSISTANT_TASK:>
Python Code:
import os
import numpy as np
from astropy.table import QTable
os.listdir()
planet_table = QTable.read('Planets.csv', format='ascii.csv')
planet_table
print(planet_table)
planet_table.rename_column('col2', 'ecc')
print(planet_table)
planet_table['Name']
planet_table['Name'][0]
planet_ta... | <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 AstroPy package - QTable
Step2: Renaming columns
Step3: Sorting
Step4: Masking
Step5: Adding a column to the Table
Step6: Saving a tabl... |
9,246 | <ASSISTANT_TASK:>
Python Code:
import dx
import datetime as dt
import pandas as pd
import seaborn as sns; sns.set()
r = dx.constant_short_rate('r', 0.01)
me_1 = dx.market_environment('me', dt.datetime(2015, 1, 1))
me_1.add_constant('initial_value', 100.)
# starting value of simulated processes
me_1.add_constant('vo... | <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: Risk Factor Models
Step2: We then define a market environment containing the major parameter specifications needed,
Step3: Next, the model obj... |
9,247 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import configparser
import os
import requests
from tqdm import tqdm
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from scipy import sparse, stats, spatial
import scipy.sparse.linalg
from sklearn import preprocessing, decomposition
import librosa... | <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. Parsing example
Step2: 2. Determine different genres
Step3: 3. Create vector of genres for each movie and a dataframe
Step4: Observe the r... |
9,248 | <ASSISTANT_TASK:>
Python Code:
targets = ['ENSG00000069696', 'ENSG00000144285']
targets_string = ', '.join('"{0}"'.format(t) for t in targets)
url = 'https://www.targetvalidation.org/api/latest/public/association/filter'
headers = {"Accept": "application/json"}
# There may be an easier way of building these parameters... | <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: Make the API call with our list of targets to find the associations. Set facets to true.
Step2: Print out all the json returned just for refere... |
9,249 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from scipy.sparse import csr_matrix
arr = np.random.rand(4, 4)
M = csr_matrix(arr)
result = M.A.diagonal(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:
|
9,250 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
dummy_fields = ['seas... | <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 and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... |
9,251 | <ASSISTANT_TASK:>
Python Code:
import graphlab
sales = graphlab.SFrame('kc_house_data_small.gl/')
import numpy as np # note this allows us to refer to numpy as np instead
(train_and_validation, test) = sales.random_split(.8, seed=1) # initial train/test split
(train, validation) = train_and_validation.random_split(.... | <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 in house sales data
Step2: Import useful functions from previous notebooks
Step3: We will also need the normalize_features() function fro... |
9,252 | <ASSISTANT_TASK:>
Python Code:
import mesh.patch as patch
import mesh.boundary as bnd
import numpy as np
g = patch.Grid2d(16, 16, ng=2)
print(g)
bc = bnd.BC(xlb="periodic", xrb="periodic", ylb="reflect", yrb="outflow")
print(bc)
d = patch.CellCenterData2d(g)
d.register_var("a", bc)
d.create()
print(d)
a = d.get_var("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: Grids
Step2: Data is stored as an ArrayIndexer object, which makes it easy to implement differencing on the entire array.
Step3: Running
Step4... |
9,253 | <ASSISTANT_TASK:>
Python Code:
traj = pt.Trajectory('step5_production.dcd', '../step3_pbcsetup.xplor.ext.psf')
print(traj)
goo = pt.rdf(traj, solvent_mask=':TIP3@OH2', solute_mask=':TIP3@OH2', bin_spacing=0.05, maximum=8.)
goh1 = pt.rdf(traj, solvent_mask=':TIP3@OH2', solute_mask=':TIP3@H1', bin_spacing=0.05, maximu... | <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: Calculate the water-water radial distribution function. In statistical mechanics, the radial distribution function, (or pair correlation functio... |
9,254 | <ASSISTANT_TASK:>
Python Code:
import espressomd
import espressomd.magnetostatics
import espressomd.magnetostatic_extensions
espressomd.assert_features('DIPOLES', 'LENNARD_JONES')
import numpy as np
# Lennard-Jones parameters
LJ_SIGMA = 1.
LJ_EPSILON = 1.
LJ_CUT = 2**(1. / 6.) * LJ_SIGMA
# Particles
N_PART = 700
# Are... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: and set up the simulation parameters where we introduce a new dimensionless parameter
Step2: Now we set up the system. As in part I, the orien... |
9,255 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
#generate some random numbers with values between -0.5 and 0.5, which we'll call "noise"
noise = (np.random.rand(11)-0.5)
noise
#plot simple relationship y=2x with this noise added
x = np.arange(11)
plt.plot(x,2*x+noise... | <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 usually called "least-squares" fitting
Step2: Overfitting
Step3: General rule of thumb
Step4: Weighted Least Squares
Step5: Oops wha... |
9,256 | <ASSISTANT_TASK:>
Python Code:
class Set:
def __init__(self):
self.mKey = None
self.mLeft = None
self.mRight = None
self.mHeight = 0
def isEmpty(self):
return self.mKey is None
Set.isEmpty = isEmpty
Set.__bool__ = isEmpty
def __bool__(self):
return self.mKey is not Non... | <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: Given an ordered binary tree $t$, the expression $t.\texttt{isEmpty}()$ checks whether $t$ is the empty tree.
Step2: Given an ordered binary tr... |
9,257 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
data_path = 'Bike-Sharing-Dataset/hour.csv'
rides = pd.read_csv(data_path)
rides.head()
rides[:24*10].plot(x='dteday', y='cnt')
dummy_fields = ['seas... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Load and prepare the data
Step2: Checking out the data
Step3: Dummy variables
Step4: Scaling target variables
Step5: Splitting the data into... |
9,258 | <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
def extract_reference_time(API_data_loc):
Find reference time that corresponds to most complete forecast. ... | <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: Planet OS API Demo for Model Comparison
Step2: Let's choose a location near Oahu, Hawaii, to make use of the regional SWAN model we have availa... |
9,259 | <ASSISTANT_TASK:>
Python Code:
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, sof... | <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: Regression Project
Step2: Exercise 2
Step3: Modeling
|
9,260 | <ASSISTANT_TASK:>
Python Code:
from regraph import NXGraph, NXHierarchy, Rule
from regraph import plot_graph, plot_instance, plot_rule
%matplotlib inline
# Define graph G
g = NXGraph()
g.add_nodes_from(["protein", "binding", "region", "compound"])
g.add_edges_from([("region", "protein"), ("protein", "binding"), ("regi... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 1. Creating and modifying a hierarchy object
Step2: The method get_graph returns the graph object corresponding to the provided graph id.
Step3... |
9,261 | <ASSISTANT_TASK:>
Python Code:
import hamnonlineng as hnle
letters = 'abcde'
resonant = [hnle.Monomial(1, 'aabbEEC'), hnle.Monomial(1,'abddEEC')]
op_sum = hnle.operator_sum(letters)
sine_exp = (
hnle.sin_terms(op_sum, 3)
+hnle.sin_terms(op_sum, 5)
+hnle.sin_terms(op_sum, 7)
... | <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: Try to solve (takes around a minute)
Step2: Remove constraints on terms of the form $\hat{a}^2\hat{b}^2\dots$ or $\hat{a}\hat{b}\dots$ or those... |
9,262 | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
np.random.seed(1)
df = pd.DataFrame({
'A' : ['one', 'one', 'two', 'three'] * 6,
'B' : ['A', 'B', 'C'] * 8,
'C' : ['foo', 'foo', 'foo', 'bar', 'bar', 'bar'] * 4,
'D' : np.random.randn(24),
'E' : np.ran... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
|
9,263 | <ASSISTANT_TASK:>
Python Code:
## Read in the Training Data and Instantiating the Photo-z Algorithm
%matplotlib inline
from astropy.table import Table
import numpy as np
import matplotlib.pyplot as plt
#data = Table.read('GTR-ADM-QSO-ir-testhighz_findbw_lup_2016_starclean.fits')
#JT PATH ON TRITON to training set after... | <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: Since we are running on separate test data, we don't need to do a train_test_split here. But we will scale the data. Need to remember to scale... |
9,264 | <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
Step6: Implement Preprocessing Function
Step8: Preprocess all the data and save it
Step10: Chec... |
9,265 | <ASSISTANT_TASK:>
Python Code:
%pylab inline
import numpy as np
from timeit import default_timer as timer
def mandel(x, y, max_iters):
Given the real and imaginary parts of a complex number,
determine if it is a candidate for membership in the Mandelbrot
set given a fixed number of iterations.
c = ... | <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: The mandel function performs the Mandelbrot set calculation for a given (x,y) position on the imaginary plane. It returns the number of iteratio... |
9,266 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: 使用 int16 激活值进行训练后整数量化
Step2: 检查 16x8 量化模式是否可用
Step3: 训练并导出模型
Step4: 在此示例中，您只对模型进行了一个周期的训练，因此只训练到约 96% 的准确率。
Step5: 将其写入 .tflite 文件：
Step6: ... |
9,267 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'dwd', 'sandbox-2', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "email"... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
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<USER_TASK:>
Description:
Step1: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... |
9,268 | <ASSISTANT_TASK:>
Python Code:
# Import all libraries needed for the tutorial
# General syntax to import specific functions in a library:
##from (library) import (specific library function)
from pandas import DataFrame, read_csv
# General syntax to import a library but no functions:
##import (library) as (give the li... | <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 Data
Step2: To merge these two lists together we will use the zip function.
Step3: We are basically done creating the data set. We now ... |
9,269 | <ASSISTANT_TASK:>
Python Code:
import pyConTextNLP.pyConTextGraph as pyConText
import pyConTextNLP.itemData as itemData
import networkx as nx
reports = [
IMPRESSION: Evaluation limited by lack of IV contrast; however, no evidence of
bowel obstruction or mass identified within the abdomen or pelvis. Non-speci... | <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:
Step7: pyConTextGraph contains the bulk of the pyConTextNLP functionality, including basic class definitions such as the ConTextMarkup class that repre... |
9,270 | <ASSISTANT_TASK:>
Python Code:
import datetime
from collections import Counter
start = datetime.date(2001, 1, 1)
end = datetime.date(2100, 1, 1) - datetime.timedelta(days=1)
d = start
anarchy_dates = []
delta = datetime.timedelta(days=1)
while d <= end:
if d.day * d.month == d.year % 100:
anarchy_dates.appe... | <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: How many attacks will happen between the beginning of 2001 and the end of 2099
Step2: What year will see the most vandalism?
Step3: The least?... |
9,271 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as pyplot
from km3net.kernels import QuadraticDifferenceSparse, PurgingSparse
import km3net.util as util
window_width = 1500
N,x,y,z,ct = util.get_real_input_data("sample1.txt")
print ("Read", N, "hits from file")
context, cc... | <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 also initialize the GPU, and instantiate the Python interfaces to the GPU codes to get the GPU kernels compiled.
Step2: The next step is to ... |
9,272 | <ASSISTANT_TASK:>
Python Code:
import pymongo
from pymongo import MongoClient
import datetime
import re
from pymongo import InsertOne, DeleteOne, ReplaceOne
import datetime
client = MongoClient()
client = MongoClient('mongodb://localhost:27017/')
db = client.homework2
users = db.users
movies = db.movies
movieList = mo... | <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: Tasks
Step2: 1. Display all occupations
Step3: 2. Chose an occupation and select all users with this occupation. Only show user information an... |
9,273 | <ASSISTANT_TASK:>
Python Code:
#Obtén el cuadrado de 1
#Obtén el cuadrado de 2
#Obtén el cuadrado de 3
#Obtén el cuadrado de 4
#Obtén el cuadrado de 5
#Obtén el cuadrado de 6
#Obtén el cuadrado de 7
#Obtén el cuadrado de 8
#Obtén el cuadrado de 9
#Obtén el cuadrado de 10
for numero in range(1,21):
cuadrado = numer... | <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: Yo creo que el punto está entendido... Es tedioso estar escribiendo lo mismo 20 veces. Ahora imagina que no tienes que hacer esto 20 veces, sino... |
9,274 | <ASSISTANT_TASK:>
Python Code:
import warnings
warnings.filterwarnings('ignore')
%matplotlib inline
%pylab inline
import pandas as pd
print(pd.__version__)
import tensorflow as tf
tf.logging.set_verbosity(tf.logging.ERROR)
print(tf.__version__)
import keras
print(keras.__version__)
df = pd.read_csv('./insurance-custom... | <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 Step
Step2: Second Step
Step3: Look at all the different shapes for different kilometers per year
|
9,275 | <ASSISTANT_TASK:>
Python Code:
import pymysql
import os
import csv
ALL_WIKI_AGGREGATION_QUERY =
SELECT
timestamp AS month,
SUM(weighted_sum) AS weighted_sum,
SUM(LOG(weighted_sum)) AS weighted_log_sum,
SUM(prediction = "Stub") AS stub_n,
SUM(prediction = "Start") AS start_n,
SUM(prediction = "C") AS c_n... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step3: Queries
Step6: Database connection management object
|
9,276 | <ASSISTANT_TASK:>
Python Code:
from scipy import stats as ss
print(ss.expon.cdf(12, scale=36))
print(ss.binom.pmf(2, p=1 / 36, n=12))
print(ss.binom.pmf(2, p=1 / (3 * 365), n=365))
print(ss.poisson.pmf(2, mu=1 / 3))
print(ss.poisson.pmf(1, mu=1 / 3))
ss.binom?
result = ss.expon.ppf(0.99, scale = 24 * 60 / 2)
days = 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: 1.2 Answer
Step2: 1.3 Answer
Step3: 1.4 Answer
Step4: 1.7 Answer
Step5: 2. CLT Theory (4 Points)
Step6: 3.1 Answer
Step7: 3.2 Answer
Step8... |
9,277 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from matplotlib import pyplot as plt
import numpy as np
from IPython.html.widgets import interact, interactive, fixed
from IPython.display import display
def soliton(x, t, c, a):
Return phi(x, t) for a soliton wave with constants c and a.
return (0.5)*c*((1/np.... | <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: Using interact for animation with data
Step3: To create an animation of a soliton propagating in time, we are going to precompute the soliton d... |
9,278 | <ASSISTANT_TASK:>
Python Code:
import geopandas
path = geopandas.datasets.get_path('naturalearth_lowres')
df = geopandas.read_file(path)
# Add a column we'll use later
df['gdp_pp'] = df['gdp_md_est'] / df['pop_est']
boroughs = geopandas.read_file(geopandas.datasets.get_path('nybb')).to_crs(epsg='4326')
injurious_collis... | <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: Plotting with Geoplot
Step2: Geoplot can re-project data into any of the map projections provided by
Step3: If you want to use size as a visua... |
9,279 | <ASSISTANT_TASK:>
Python Code:
def rk2(x_0, y_0, f, step=0.001, k_max=None, method='improved_euler'):
r
Two-stage Runge-Kutta method for solving first-order ODE.
The function computes `k_max` iterations from the initial conditions `x_0` and `y_0` with
steps of size `step`. It yields a total of `k_m... | <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: Runge-Kutta methods
Step3: Four-stage Runge-Kutta methods implementation
Step4: Examples
Step5: As we can see from the figure above, the solu... |
9,280 | <ASSISTANT_TASK:>
Python Code:
!pip install git+https://github.com/openai/baselines >/dev/null
!pip install gym >/dev/null
import numpy as np
import random
import gym
from gym.utils import seeding
from gym import spaces
def state_name_to_int(state):
state_name_map = {
'S': 0,
'A': 1,
'B': 2... | <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: Environment
Step3: Try out Environment
Step4: Baseline
Step5: Train model
Step6: Visualizing Results
Step7: Enjoy model
Step8: Evaluation
|
9,281 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
from scipy import integrate
import math as m
def integrand(x, a):
return 1.0/(x**2 + a**2)
def integral_approx(a):
# Use the args keyword argument to feed extra arguments to your integrand... | <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: Indefinite integrals
Step2: Integral 1
Step3: Integral 2
Step4: Integral 3
Step5: Integral 4
Step6: Integral 5
|
9,282 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import collections
import matplotlib
import matplotlib.pyplot as plt
%matplotlib inline
# import seaborn as sns
# sns.set_style("whitegrid", {'axes.grid' : False})
train_categorical_iter=pd.read_csv("../data/train_categorical.csv",chunksize=100000,... | <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: Observations
|
9,283 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'ukesm1-0-ll', 'atmoschem')
# 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
<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... |
9,284 | <ASSISTANT_TASK:>
Python Code:
fig = plt.figure(figsize=(12,4))
df = pd.read_csv('rtntop.csv').set_index('date')
cols = [c for c in df.columns if 'rtn' in c]
for i, c in enumerate(cols):
ax = plt.subplot(130+(1+i))
df[['bhreturn',c]].plot(ax=ax)
ax.legend().set_visible(False)
ax.set_ylabel('Return').set... | <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: Q-learned trading performance
Step2: Random trading performance
Step3: alpha & gamma
|
9,285 | <ASSISTANT_TASK:>
Python Code:
# Everyone should know how to create (or "declare") a string by now
var = 'This is a string'
alphabet = 'abcdefghijklmnopqrstuvwxyz'
# We can get only the first element of the alphabet
# Note that a is the 0th character in the string
first_letter = alphabet[0]
print(first_letter)
# To ge... | <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: Indexing strings
Step2: This is very close to what we did last week when we looked at for statements
Step3: Question - How would you get the l... |
9,286 | <ASSISTANT_TASK:>
Python Code:
def diferencia_atras(f, x_0, x_1):
pendiente = (f(x_0) - f(x_1))/(x_0 - x_1)
return pendiente
def raiz(f, a, b):
c = b - f(b)/diferencia_atras(f, a, b)
return b, c
def secante(f, x_0, x_1):
print("{0:s} \t {1:15s} \t {2:15s} \t {3:15s}".format('i', 'x anterior', 'x a... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Implementación no vectorizada
Step2: Ejemplo 2
Step3: Ejemplo 3
|
9,287 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets('MNIST_data', validation_size=0)
img = mnist.train.images[2]
plt.imshow(img.reshape((28, 28)), cmap='... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Below I'm plotting an example image from the MNIST dataset. These are 28x28 grayscale images of handwritten digits.
Step2: We'll train an autoe... |
9,288 | <ASSISTANT_TASK:>
Python Code:
# Importing pergola modules used
import sys
# We need to set the path to run this notebook directly from ipython notebook
my_path_to_modules = "/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/"
sys.path.append(my_path_to_modules)
from pergola import jaaba_par... | <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: Data can be easily export to a csv file.
Step2: We can also load the data into an IntData object.
Step3: IntData objects
Step4: Basic data ou... |
9,289 | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mohc', 'ukesm1-0-mmh', 'ocean')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<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... |
9,290 | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
%reload_ext autoreload
%autoreload 2
from fastai.conv_learner import *
PATH = "data/cifar10/"
os.makedirs(PATH, exist_ok=True)
classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
stats = (np.array([ 0.4914 , 0.48216, 0.44653]), ... | <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 can get the data via
Step2: Look at dem der data
Step3: Fully Connected Model
Step4: From this notebook by K.Turgutlu.
Step5: The goal i... |
9,291 | <ASSISTANT_TASK:>
Python Code:
import datetime
import collections
import getpass
import json
import os
import yaml
import pandas
import pymysql
select_stmt_base = "select id, deactivated, group_id, created_at, notes from pids where created_at < '{}'"
if os.path.exists("../conf/db.yml"):
print("Using conf/db.yml 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: Define SQL Query
Step2: Get Connection & Credentials
Step3: Running Querying
|
9,292 | <ASSISTANT_TASK:>
Python Code:
import numpy as np
N = 30000
x = np.zeros(N)
y = np.zeros(N)
z = np.zeros(N)
x1 = np.empty_like(x)
y1 = np.empty_like(y)
z1 = np.empty_like(z)
# Sierpinski triangle iterative functions
def f1(x,y,z,x1,y1,z1,c):
x1[c] = 1.0/2.0*x[c]
y1[c] = 1.0/2.0*y[c]
z1[c] = 1.0/2.0*z[c]
de... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: Now we turn separate coordinate array into triplets.
Step2: Zoom in a little
|
9,293 | <ASSISTANT_TASK:>
Python Code:
measurements = [
{'city': 'Dubai', 'temperature': 33.},
{'city': 'London', 'temperature': 12.},
{'city': 'San Francisco', 'temperature': 18.},
]
from sklearn.feature_extraction import DictVectorizer
vec = DictVectorizer()
tf_measurements = vec.fit_transform(measurements)
tf_me... | <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: Unsupervised Clustering using K-Means
Step2: Supervised Classification using Decision Trees
Step3: Now, we use a DecisionTree to learn a model... |
9,294 | <ASSISTANT_TASK:>
Python Code:
try:
import tinygp
except ImportError:
!pip install -q tinygp
from jax.config import config
config.update("jax_enable_x64", True)
import numpy as np
import matplotlib.pyplot as plt
from statsmodels.datasets import co2
data = co2.load_pandas().data
t = 2000 + (np.array(data.index.... | <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: Data
Step2: Kernel
Step3: Model fitting
Step4: Using our loss function defined above, we'll run a gradient based optimization routine from sc... |
9,295 | <ASSISTANT_TASK:>
Python Code:
from lightning import Lightning
from numpy import random, arange, asarray, corrcoef, argsort, array
import networkx as nx
from sklearn import datasets
lgn = Lightning(ipython=True, host='http://public.lightning-viz.org')
mat = random.randn(10,10)
lgn.matrix(mat)
mat = random.randn(10,2... | <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: Connect to server
Step2: <hr> Simple matrix
Step3: <hr> Different shapes
Step4: <hr> Colors
Step5: <hr> Labels
Step6: You can also turn on ... |
9,296 | <ASSISTANT_TASK:>
Python Code:
import loader as support #support library to read mnist files into memory
import gaussian_classifier as gf
import time
%pylab inline
X_train, Y_train = support.loadmnist('data/train-images-idx3-ubyte',
'data/train-labels-idx1-ubyte')
X_test, Y_test = support.loadmnist('data/t10k-images-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: As you can see the we have 60,000 training examples with 784 features. Let's see how long it takes to train and predict on this high dimensional... |
9,297 | <ASSISTANT_TASK:>
Python Code:
from flow.scenarios.loop import LoopScenario
name = "ring_example"
from flow.core.params import VehicleParams
vehicles = VehicleParams()
from flow.controllers.car_following_models import IDMController
from flow.controllers.routing_controllers import ContinuousRouter
vehicles.add("hum... | <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: This scenario, as well as all other scenarios in Flow, is parametrized by the following arguments
Step2: 2.2 VehicleParams
Step3: Once this ob... |
9,298 | <ASSISTANT_TASK:>
Python Code:
print(dir(bool))
def f():
x = 1
print(x)
x = 2
f()
print(x)
x = 2
a = [x**2 for x in range(10)]
print(a)
print(x)
import math
import numpy
print(math.pi, numpy.pi)
# don't do this at home
math.pi = 2
print(math.pi, numpy.pi)
class MyClass:
A simple example class
i = 12... | <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: A note about scopes and namespaces
Step2: List-comprehensions (and all other comprehensions) have their own scope
Step3: Note
Step5: The attr... |
9,299 | <ASSISTANT_TASK:>
Python Code:
#@title Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writin... | <SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
Step1: tf.distribute.Strategy with Training Loops
Step2: Download the fashion mnist dataset
Step3: Create a strategy to distribute the variables and ... |
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