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Description:
Step1: Decorators with arguments
Step2: Decorators with arguments
Step3: Debug with arguments
Step4: Decorators with arguments
Step5: Class decorat... | <ASSISTANT_TASK:>
Python Code:
from functools import wraps
def debug(func):
msg = func.__name__
# wraps is used to keep the metadata of the original function
@wraps(func)
def wrapper(*args, **kwargs):
print(msg)
return func(*args, **kwargs)
return wrapper
@debug
def add(x,y):
ret... |
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Description:
Step1: Load the arm library-- see the Chapter 3.1 notebook if you need help.
Step2: Regression-- to demonstrate reports of fit, Pg38
Step3: Display, ... | <ASSISTANT_TASK:>
Python Code:
%%R
# I had to import foreign to get access to read.dta
library("foreign")
kidiq <- read.dta("../../ARM_Data/child.iq/kidiq.dta")
# I won't attach kidiq-- i generally don't attach to avoid confusion(s)
#attach(kidiq)
%%R
library("arm")
%%R
fit.3 <- lm(kidiq$kid_score ~ kidiq$mom_hs + ki... |
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Description:
Step3: Create the Code
Step4: Create the Nose Tests
Step5: Run the Nose Tests
| <ASSISTANT_TASK:>
Python Code:
!pip install nose
%%file type_util.py
class TypeUtil:
@classmethod
def is_iterable(cls, obj):
Determines if obj is iterable.
Useful when writing functions that can accept multiple types of
input (list, tuple, ndarray, iterator). Pairs well with
co... |
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Description:
Step1: Use xmltodict
Step2: Use untangle
| <ASSISTANT_TASK:>
Python Code:
# import the appropriate libraries
import xml.etree.ElementTree as ET # xml processing
# read the XML file
tree = ET.parse('input/menu.xml')
print('tree element:\t', tree)
# get the root of the tree
root = tree.getroot()
print 'root element:\t ', root
# here is the name of the root elemen... |
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Description:
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Python Code:
from scipy import sparse
V = sparse.random(10, 10, density = 0.05, format = 'coo', random_state = 42)
x = 100
y = 99
V = V.copy()
V.data += x
V.eliminate_zeros()
V.data += y
V.eliminate_zeros()
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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... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'nerc', 'ukesm1-0-ll', 'seaice')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "e... |
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Description:
Step1: Loading Model Results
Step2: Using os.path.join and os.path.basename
Step3: Iterating through model run directories
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Python Code:
# Imports
import matplotlib.pyplot as plt
import numpy
import pandas
import scipy
import scipy.stats
import os
# Using os.listdir to show the current directory
os.listdir("./")
# Using os.listdir to show the output directory
os.listdir("output")[0:5]
import glob
# Using glob to list the ... |
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Description:
Step1: KISS-GP for 1D Data
Step2: Set up the model
Step3: Train the model hyperparameters
Step4: Make predictions
Step5: KISS-GP for 2D-4D Data
Ste... | <ASSISTANT_TASK:>
Python Code:
import math
import torch
import gpytorch
from matplotlib import pyplot as plt
# Make plots inline
%matplotlib inline
train_x = torch.linspace(0, 1, 1000)
train_y = torch.sin(train_x * (4 * math.pi) + torch.randn(train_x.size()) * 0.2)
class GPRegressionModel(gpytorch.models.ExactGP):
... |
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Description:
Step3: The notMNIST dataset is too large for many computers to handle. It contains 500,000 images for just training. You'll be using a subset of this... | <ASSISTANT_TASK:>
Python Code:
import hashlib
import os
import pickle
from urllib.request import urlretrieve
import numpy as np
from PIL import Image
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelBinarizer
from sklearn.utils import resample
from tqdm import tqdm
from zipfil... |
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Description:
Step1: <font size="4">Load the Data</font>
Step2: <font size="4">1. Model</font>
Step3: <font size="4">2. Identify</font>
Step4: <font size="4">3. E... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
import dowhy
from dowhy import CausalModel
from dowhy import causal_estimators
# Config dict to set the logging level
import logging.config
DEFAULT_LOGGING = {
'version': 1,
'disable_existing_loggers': False,
'loggers': {
'': {
... |
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Description:
Step1: Perplexity on Each Dataset
Step2: Loss vs. Epoch
Step3: Perplexity vs. Epoch
Step4: Generations
Step5: BLEU Analysis
Step6: N-pairs BLEU An... | <ASSISTANT_TASK:>
Python Code:
report_file = '/Users/bking/IdeaProjects/LanguageModelRNN/reports/encdec_noing_250_512_025dr.json'
log_file = '/Users/bking/IdeaProjects/LanguageModelRNN/logs/encdec_noing_250_512_025dr_logs.json'
import json
import matplotlib.pyplot as plt
with open(report_file) as f:
report = json.l... |
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Description:
Step1: 주요 내용
Step2: 아니면, 반복문을 활용할 수 있다.
Step3: for 반복문
Step4: 예제
Step5: 반면에 반복문을 활용하는 것은 언제든지 가능하다.
Step6: 0과 20 사이의 홀수들의 리스트는 다음과 같다.
Step7: 이제... | <ASSISTANT_TASK:>
Python Code:
from __future__ import division, print_function
odd_20 = [1, 3, 5, 7, 9, 11, 13, 15, 17, 19]
i = 0
odd_20 = []
while i <= 20:
if i % 2 == 1:
odd_20.append(i)
i += 1
print(odd_20)
odd_20 = []
for i in range(21):
if i % 2 == 1:
odd_20.append(i... |
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Description:
Step1: Processing the dataset
Step2: In each directory, there is one or more images corresponding to the identity. We map each image path with an inte... | <ASSISTANT_TASK:>
Python Code:
import tensorflow as tf
# If you have a GPU, execute the following lines to restrict the amount of VRAM used:
gpus = tf.config.experimental.list_physical_devices('GPU')
if len(gpus) > 1:
print("Using GPU {}".format(gpus[0]))
tf.config.experimental.set_visible_devices(gpus[0], 'GPU... |
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Description:
Step1: Biomodels repository hosts a number of published models.
Step2: This model can be parsed into MEANS Model object using means.io.read_sbml funct... | <ASSISTANT_TASK:>
Python Code:
import means
import urllib
__ = urllib.urlretrieve("http://www.ebi.ac.uk/biomodels/models-main/publ/"
"BIOMD0000000010/BIOMD0000000010.xml.origin",
filename="autoreg.xml")
# Requires: libsbml
autoreg_model, autoreg_parameters, autoreg_ini... |
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Description:
Step1: A DataFrame also performs automatic alignment of the data for each Series passed in by a dictionary. For example, the following code adds a thir... | <ASSISTANT_TASK:>
Python Code:
# import NumPy and pandas
import numpy as np
import pandas as pd
# set some pandas options
pd.set_option('display.notebook_repr_html', False)
pd.set_option('display.max_columns', 10)
pd.set_option('display.max_rows',10)
# create a DataFrame from a 2-d array
pd.DataFrame(np.array([[10,11],... |
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Description:
Step1: Pulso cuadrado
Step2: Definimos 1000 puntos en el intervalo $[-\pi,\pi]$
Step3: Función Guassiana
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Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
from ipywidgets import interact
plt.style.use('classic')
def p(x,a):
if abs(x)<a:
return 1.
else:
return 0.
pulso = np.vectorize(p) #vectorizando la función pulso
x = np.linspace(-10,10,1000)
k... |
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Description:
Step1: Note
Step2: Load in the stopwords file. These are common words which we wish to exclude when performing comparisons (a, an, the, etc). Every ... | <ASSISTANT_TASK:>
Python Code:
import re
from gensim import models
from scipy import spatial
import numpy as np
import os.path
import urllib
import gzip
import json
import pandas as pd
def search_tags(entity, search):
This function searches through all the 'tags' (semantic content) of a data set
and return... |
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Description:
Step1: Now let's load the Iris Dataset for a demo.
Step2: Let's just assume class 2 as the anomaly for test purposes.
Step3: Now we have 100 normal e... | <ASSISTANT_TASK:>
Python Code:
import math
import numpy as np
import scipy
class AnomalyDetection():
def __init__(self, multi_variate=False):
# if multi_variate is True, we will use multivariate Gaussian distribution
# to estimate the probabilities
self.multi_variate = multi_variate
... |
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Description:
Step1: Load some house value vs. crime rate data
Step2: Exploring the data
Step3: Fit the regression model using crime as the feature
Step4: Let's s... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import graphlab
sales = graphlab.SFrame.read_csv('Philadelphia_Crime_Rate_noNA.csv/')
sales
graphlab.canvas.set_target('ipynb')
sales.show(view="Scatter Plot", x="CrimeRate", y="HousePrice")
crime_model = graphlab.linear_regression.create(sales, target='HousePrice', ... |
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Description:
Step1: Scatter Plots with plt.plot
Step2: The third argument in the function call is a character that represents the type of symbol used for the plott... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn-whitegrid')
import numpy as np
x = np.linspace(0, 10, 30)
y = np.sin(x)
plt.plot(x, y, 'o', color='black');
rng = np.random.RandomState(0)
for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']:
... |
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Description:
Step1: Specify list of dates
Step2: Enter Research Data Archive (NCAR) credentials
Step3: Create data fetcher
Step4: Access data
Step5: Plot temper... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
plt.rcParams['figure.dpi'] = 150
from getpass import getpass
import pandas as pd
from skdaccess.framework.param_class import *
from skdaccess.geo.era_interim.cache import DataFetcher as EDF
date_list = pd.date_range('2015-06-06 00:00:00'... |
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Description:
Step1: TODO
Step2: Convert the data to Web Mercator
Step3: Contextily helper function
Step4: Add background tiles to plot
Step5: Save selected depa... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import geopandas as gpd
df = gpd.read_file("communes-20181110.shp")
!head test.csv
# https://gis.stackexchange.com/questions/114066/handling-kml-csv-with-geopandas-drivererror-unsupported-driver-ucsv
df_tracks = pd.read_csv("test.csv", skiprows=3)
df_tracks.head()
df_... |
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Description:
Step1: we'll use the gutenberg corpus as test data, which is available through the nltk library.
Step2: files in test data
Step3: creating test corpu... | <ASSISTANT_TASK:>
Python Code:
import os
import json
from nltk.corpus import gutenberg
import corpushash as ch
import base64
import hashlib
import random
import nltk
#nltk.download('gutenberg') # comment (uncomment) if you have (don't have) the data
gutenberg.fileids()
base_path = os.getcwd()
base_path
corpus_path ... |
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Description:
Step1: We can compute a derivative symbolically, but it is of course horrendous (see below). Think of how much worse it would be if we chose a functio... | <ASSISTANT_TASK:>
Python Code:
from math import sin, cos
def func(x):
y = x
for i in range(30):
y = sin(x + y)
return y
from sympy import diff, Symbol, sin
from __future__ import print_function
x = Symbol('x')
dexp = diff(func(x), x)
print(dexp)
xpt = 0.1
dfdx = dexp.subs(x, xpt)
print('dfdx =', d... |
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Description:
Step2: Synthesizing fake data
Step3: Setting up a factor analysis pipeline
Step4: Demo
Step5: Demo
Step6: Demo
Step7: Demo
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Python Code:
import os
import sys
sys.path.append(os.path.pardir)
%matplotlib inline
import numpy as np
from fa_kit import FactorAnalysis
from fa_kit import plotting as fa_plotting
def make_random_data(n_samp=10000, n_feat=100):
make some random data with correlated features
data = ... |
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Description:
Step1: 1. Regular linear regression
Step2: 1b. Outliers and normality of errors
Step3: 1c. Importance of independence of samples
Step5: 2. Multiple ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import statsmodels.formula.api as smf
import pandas as pd
import scipy as sp
%matplotlib notebook
%config InlineBackend.figure_format = 'retina'
%matplotlib inline
import matplotlib.pyplot as plt
# Define true statistics relating x and y
N_points = 10
true_beta0 = 0
tr... |
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Description:
Step1: Problem 1c
Step2: Probelm 2) Fitting a Line to Data
Step3: There is a very good chance, though, again, I am not specifically assuming anything... | <ASSISTANT_TASK:>
Python Code:
y = np.array([203, 58, 210, 202, 198, 158,
165, 201, 157, 131, 166, 160,
186, 125, 218, 146])
x = np.array([495, 173, 479, 504, 510, 416,
393, 442, 317, 311, 400, 337,
423, 334, 533, 344])
plt.scatter( # complete
# complete
# co... |
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Description:
Step1: In order to get you familiar with graph ideas,
Step2: Firstly, we need to unzip the dataset
Step3: Now, let's load in both tables.
Step4: Now... | <ASSISTANT_TASK:>
Python Code:
from IPython.display import YouTubeVideo
YouTubeVideo(id="3sJnTpeFXZ4", width="100%")
from pyprojroot import here
import zipfile
import os
from nams.load_data import datasets
# This block of code checks to make sure that a particular directory is present.
if "divvy_2013" not in os.listd... |
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Description:
Step1: From now on, we will refer to this table using this variable ($miRNA_BQtable), but we could just as well explicitly give the table name each tim... | <ASSISTANT_TASK:>
Python Code:
import gcp.bigquery as bq
miRNA_BQtable = bq.Table('isb-cgc:tcga_201607_beta.miRNA_Expression')
%bigquery schema --table $miRNA_BQtable
%%sql --module count_unique
DEFINE QUERY q1
SELECT COUNT (DISTINCT $f, 25000) AS n
FROM $t
fieldList = ['ParticipantBarcode', 'SampleBarcode', 'Aliquot... |
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Description:
Step1: Lesson
Step2: Project 1
Step3: Transforming Text into Numbers
Step4: Project 2
Step5: Project 3
Step6: Understanding Neural Noise
Step7: P... | <ASSISTANT_TASK:>
Python Code:
def pretty_print_review_and_label(i):
print(labels[i] + "\t:\t" + reviews[i][:80] + "...")
g = open('reviews.txt','r') # What we know!
reviews = list(map(lambda x:x[:-1],g.readlines()))
g.close()
g = open('labels.txt','r') # What we WANT to know!
labels = list(map(lambda x:x[:-1].uppe... |
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Description:
Step1: Install the latest version of the Google Cloud Storage library.
Step2: Restart the kernel
Step3: Before you begin
Step4: Otherwise, set your ... | <ASSISTANT_TASK:>
Python Code:
import os
# The Google Cloud Notebook product has specific requirements
IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists("/opt/deeplearning/metadata/env_version")
# Google Cloud Notebook requires dependencies to be installed with '--user'
USER_FLAG = ""
if IS_GOOGLE_CLOUD_NOTEBOOK:
USER_FLAG... |
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Description:
Step1: Next, let's define a vertical coordinate system that minimises missing data values, and gives good resolution at the (orographic) surface.
Step2... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
nx, ny = 6, 3
np.random.seed(0)
orography = np.random.normal(1000, 600, size=(ny, nx)) - 400
sea_level_temp = np.random.normal(290, 5, size=(ny, nx))
# Now visualise:
import matplotlib.pyplot as plt
plt.set_cmap('viridis')
fig = plt.figure(figsize=(8,... |
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Description:
Step1: Now, let use the Neural Network with 1 hidden layers. The number of neurons in each layer is X_train.shape[1] which is 400 in our example (exclu... | <ASSISTANT_TASK:>
Python Code:
data = pd.read_csv('fer2013/fer2013.csv')
df = shuffle(df)
X = data['pixels']
y = data['emotion']
X = pd.Series([np.array(x.split()).astype(int) for x in X])
# convert one column as list of ints into dataframe where each item in array is a column
X = pd.DataFrame(np.matrix(X.tolist()))
df... |
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Description:
Step1: Displaying widgets
Step2: Widgets have many properties to modify their appearance and behavior
Step3: Layout
Step4: Events
Step5: Compound w... | <ASSISTANT_TASK:>
Python Code:
%gui asyncio
from flexx import flx
flx.init_notebook()
b = flx.Button(text='foo')
b
b.set_text('click me!')
None # suppress output
with flx.HBox() as hbox:
slider = flx.Slider(flex=1)
progress = flx.ProgressBar(flex=3, value=0.7)
hbox
@slider.reaction('value')
def show_slider... |
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Description:
Step1: Extract Images
Step2: Image Properties
Step3: This tells us that this image is 24x24 pixels in size, and that the datatype of the values it st... | <ASSISTANT_TASK:>
Python Code:
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.cm as cm
%matplotlib inline
rect_image = cv2.imread('data/I/27.png', cv2.IMREAD_GRAYSCALE)
circle_image = cv2.imread('data/O/11527.png', cv2.IMREAD_GRAYSCALE)
queen_image = cv2.imread('data/Q/18027.png'... |
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Description:
Step1: Overview
Step2: b)
Step3: Model 2
Step4: b)
Step5: Model 3
Step6: b)
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Python Code:
from symbulate import *
%matplotlib inline
# Type all of your code for this problem in this cell.
# Feel free to add additional cells for scratch work, but they will not be graded.
# Type all of your code for this problem in this cell.
# Feel free to add additional cells for scratch wor... |
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Description:
Step1: Validate lab package version installation
Step2: Note
Step3: Note
Step4: The config.py module configures the default values for the environme... | <ASSISTANT_TASK:>
Python Code:
import yaml
# Set `PATH` to include the directory containing TFX CLI and skaffold.
PATH=%env PATH
%env PATH=/home/jupyter/.local/bin:{PATH}
!python -c "import tensorflow; print('TF version: {}'.format(tensorflow.__version__))"
!python -c "import tfx; print('TFX version: {}'.format(tfx.__... |
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Description:
Step1: For $\alpha=1$, sRVI does not converge on the (periodic) 3-loop problem.
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Python Code:
alphas = [1.0, 0.999, 0.99, 0.9, 0.7, 0.5, 0.3, 0.1, 0.01, 0.001]
max_iters = 50000
epsilon = 0.001
init_v = np.zeros(env.num_states())
init_r_bar_scalar = 0
convergence_flags = np.zeros(alphas.__len__())
for i, alpha in enumerate(alphas):
alg = RVI_Evaluation(env, init_v, alpha, ref_... |
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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: Exercise 1
Step2: b. Spearman Rank Correlation
Step3: Check your results against scipy's Spearman rank function. stats.spearmanr
Step4: Exerc... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pandas as pd
import scipy.stats as stats
import matplotlib.pyplot as plt
import math
n = 100
x = np.linspace(1, n, n)
y = x**5
#Your code goes here
#Your code goes here
# Your code goes here
n = 100
a = np.random.normal(0, 1, n)
#Your code goes here
n = 100
... |
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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: The spike times of all descending commands along the 5000 ms of simulation is shown in Fig. \ref{fig
Step2: The spike times of the MNs along th... | <ASSISTANT_TASK:>
Python Code:
import sys
sys.path.insert(0, '..')
import time
import matplotlib.pyplot as plt
%matplotlib inline
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('pdf', 'png')
plt.rcParams['savefig.dpi'] = 75
plt.rcParams['figure.autolayout'] = False
plt.rcParams['figure.figs... |
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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: Exercise
Step2: No more setup needed! We can run the simulation and plot our observables.
Step4: The Mean Square Displacement of an active par... | <ASSISTANT_TASK:>
Python Code:
import tqdm
import numpy as np
import espressomd.observables
import espressomd.accumulators
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 18})
%matplotlib inline
espressomd.assert_features(
["ENGINE", "ROTATION", "MASS", "ROTATIONAL_INERTIA", "CUDA"])
ED_PARAMS = {... |
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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: TF简介
Step2: LR算法
Step3: LR算法简介：
Step4: 添加一个隐藏层
Step5: 比较
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Python Code:
import numpy as np
import pandas as pd
import tensorflow as tf
from sklearn.model_selection import train_test_split
# 例1: a+b
a = tf.placeholder(dtype=tf.float32, shape=[2]) # 定义占位符，可以feed满足相应条件的数据
b = tf.placeholder(dtype=tf.float32, shape=[2])
c = a + b
with tf.Session() as sess: # 创... |
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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: The cyc1.gb sequence file only contains the ORF, so we can use it directly. The sequence file can be inspected using the ling above.
Step2: The... | <ASSISTANT_TASK:>
Python Code:
from pydna.readers import read
cyc1 = read("cyc1.gb")
cyc1
cyc1.isorf()
pUG35 = read("pUG35.gb")
pUG35
p426GPD = read("p426GPD.gb")
p426GPD
pUG35.list_features()
gfp=pUG35.extract_feature(5)
gfp.seq
gfp.isorf()
from Bio.Restriction import SmaI
linear_vector= p426GPD.linearize(SmaI)
li... |
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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: 1. import and plot data
Step2: 2. Create a timeseries model
Step3: 3. Adding river water levels
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Python Code:
import pandas as pd
import pastas as ps
import matplotlib.pyplot as plt
ps.show_versions()
ps.set_log_level("INFO")
oseries = pd.read_csv("../data/nb5_head.csv", parse_dates=True,
squeeze=True, index_col=0)
rain = pd.read_csv("../data/nb5_prec.csv", parse_dates=Tru... |
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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: Lab 1
Step2: $\newcommand{\bPhi}{\mathbf{\Phi}}$
Step3: 1.2 Polynomial regression (10 points)
Step4: 1.3 Plot (5 points)
Step5: 1.4 Regulari... | <ASSISTANT_TASK:>
Python Code:
NAME = "Michelle Appel"
NAME2 = "Verna Dankers"
NAME3 = "Yves van Montfort"
EMAIL = "michelle.appel@student.uva.nl"
EMAIL2 = "verna.dankers@student.uva.nl"
EMAIL3 = "yves.vanmontfort@student.uva.nl"
%pylab inline
plt.rcParams["figure.figsize"] = [20,10]
import numpy as np
import matplot... |
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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: You can add a header row like this
Step2: Table also accepts dicts (or any mapping) with keys as column headers and values as column contents. ... | <ASSISTANT_TASK:>
Python Code:
Table((4, 1, 8),
(9, 7, 3),
(5, 2, 6))
Table(TableHeaderRow('a','b','c'),
(1, 2, 3),
(2, 4, 6),
)
Table({'a': (1, 2),
'b': (2, 4),
'c': (3, 6)})
Table({'a': (1, 2),
'b': (2,),
'c': (3, 6)})
# Computing values
t = Table(Table... |
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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: Training and Test Data Sets
Step2: Linear Support Vector Machine Classification
Step3: Evaluating performance of the model
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Python Code:
import numpy as np
import pandas as pd
def load_data(filename):
import csv
with open(filename, 'rb') as csvfile:
csvreader = csv.reader(csvfile, delimiter=',')
df = pd.DataFrame([[-1 if el == '?' else int(el) for el in r] for r in csvreader])
df.columns=["p... |
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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: D3.js
Step2: What's D3.js?
Step3: A website inside a web presentation
Step4: What is D3.js
Step5: Scott Murray (@alignedleft)
Step6: Jérôme... | <ASSISTANT_TASK:>
Python Code:
# Some styling
from IPython.display import display, HTML
from IPython.display import IFrame, Image
s=
<style>
.rendered_html h1{
font-family: "Roboto", helvetica;
color: #8896B4; !important
}
.rendered_html h2{
font-family: "Roboto", helvetica;
color: #5C6E95; !important... |
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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: Lets define a domain from -5 to 5, of 100 points, and plot some XY curves that show some functions.
Step2: Now that we've plotted some data, le... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
domain = np.linspace(-5.0,5.0,100)
y = np.power(domain, 2)
%matplotlib inline
# "magic" command telling Jupyter NB to embed plots
# always label and title your plot, at minimum
plt.xlabel("X")
plt.ylabel("Y")
p... |
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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: Here we have a set of values, $X$, and another set of values $y$. The values of $X$ are related to $y$ by a function $f(x)$, which is described ... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
rng = np.random.RandomState(1999)
n_samples = 1000
X = rng.rand(n_samples)
y = np.sin(20 * X) + .05 * rng.randn(X.shape[0])
X_t = np.linspace(0, 1, 100)
y_t = np.sin(20 * X_t)
plt.scatter(X, y, color='steelblue', label=... |
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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: 1. ANALYTICAL SOLUTION VS. NUMERICAL SOLUTION
Step2: 2. EMPTYING AND FILLING THE UNSATURATED ZONE
Step3: 3. RANDOM FORCINGS
Step4: Compare pe... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import matplotlib.pyplot as plt
import timeit
import pstats, cProfile
from GWTSA import *
print 'packages succesfully imported!'
%matplotlib inline
# Provide the forcings precipitation and potential evapotransapiration
n = 50
P = E = np.zeros(n)
# Provide the model par... |
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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: <font color=teal>2. Atomic String Function (AString) is an Integral and Composing Branch of Atomic Function up(x) (introduced in 2017 by S. Yu. ... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import pylab as pl
pl.rcParams["figure.figsize"] = 9,6
###################################################################
##This script calculates the values of Atomic Function up(x) (1971)
###################################################################
###########... |
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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: numpy.ndarray.tofile
Step2: numpy.fromfile
Step3: Then go to CUDA C++14 file binIO_playground.cu or the C++14 version (serial version), binIO_... | <ASSISTANT_TASK:>
Python Code:
import numpy
import numpy as np
# find out where we are in the file directory
import os, sys
print(os.getcwd())
datafilefolder = "./data/"
m=5
n=4
A = 11.111111*np.array(range(m*n),dtype=np.float32).reshape((m,n))
print(A)
Afilename = "A_mat_5_4.npy"
try:
A.tofile(datafilefolder+ Afi... |
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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: Some drawing routines
Step2: Pascal VOC dataset
Step3: Test SSD-300 model using TFRecords pipeline
Step4: Test SSD-300 model using sample ima... | <ASSISTANT_TASK:>
Python Code:
def colors_subselect(colors, num_classes=21):
dt = len(colors) // num_classes
sub_colors = []
for i in range(num_classes):
color = colors[i*dt]
if isinstance(color[0], float):
sub_colors.append([int(c * 255) for c in color])
else:
... |
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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: Label
Step2: Classification accuracy
Step3: Le taux de prédiction est 0.6927, ce qui à première vue peut sembler satisfaisant Mais est-ce l... | <ASSISTANT_TASK:>
Python Code:
# charger les données dans un dataframe de Pandas
import pandas as pd
col_names = ['pregnant', 'glucose', 'bp', 'skin', 'insulin', 'bmi', 'pedigree', 'age', 'label']
pima = pd.read_csv('./pima-indians-diabetes.data.txt', header=None, names=col_names)
# afficher les 5 premières lignes
pima... |
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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: Now we run this a second time, on the second (b) feature table that has removed all epithets with fewer than 27 representative documents. The re... | <ASSISTANT_TASK:>
Python Code:
import os
from sklearn import preprocessing
from sklearn.tree import DecisionTreeClassifier
from sklearn.cross_validation import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.externals import joblib
from sklearn.feature_extraction.text import CountVecto... |
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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: Select the notebook runtime environment devices / settings
Step2: There are two run modes
Step3: Data Reading
Step4: The random noise we will... | <ASSISTANT_TASK:>
Python Code:
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import os
import cntk as C
from cntk import Trainer
from cntk.layers import default_options
from cntk.device import set_default_device, gpu, cpu
from cntk.initializer import normal
from cntk.io import (MinibatchSo... |
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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: Images are 224x224 pixels, with 3 channels. Batch size is 50. This is specified in the caffemodel but not in the tf class (mynet.py)
Step2: Now... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
import tensorflow as tf
import os.path as osp
input_size = {50, 3, 224, 224}
fake_data = np.random.rand(2, 224, 224, 3)
from mynet import CaffeNet
images = tf.placeholder(tf.float32, [None, 224, 224, 3])
net = CaffeNet({'data':images})
sesh = tf.Session()
sesh.run(tf... |
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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: A full adder has three single bit inputs, and returns the sum and the carry. The sum is the exclusive or of the 3 bits, the carry is 1 if any tw... | <ASSISTANT_TASK:>
Python Code:
import magma as m
import mantle
def fulladder(A, B, C):
return A^B^C, A&B|B&C|C&A # sum, carry
assert fulladder(1, 0, 0) == (1, 0), "Failed"
assert fulladder(0, 1, 0) == (1, 0), "Failed"
assert fulladder(1, 1, 0) == (0, 1), "Failed"
assert fulladder(1, 0, 1) == (0, 1), "Failed"
asse... |
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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: 時間太長！
| <ASSISTANT_TASK:>
Python Code:
import itertools
屋子 = 第一間, _, 中間, _, _ = [1, 2, 3, 4, 5]
所有順序 = list(itertools.permutations(屋子))
所有順序
def 在右邊(h1, h2):
"h1 緊鄰 h2 的右邊."
return h1-h2 == 1
def 隔壁(h1, h2):
"h1 h2 在隔壁"
return abs(h1-h2) == 1
def zebra_puzzle():
return [locals()
for... |
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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: From SQL to DataFrames
Step2: Data Manipulation
Step3: Or it can be inspected for schema,
Step4: or further transformed locally, for example ... | <ASSISTANT_TASK:>
Python Code:
import google.datalab.bigquery as bq
import pandas as pd
%%bq query -n requests
SELECT timestamp, latency, endpoint
FROM `cloud-datalab-samples.httplogs.logs_20140615`
WHERE endpoint = 'Popular' OR endpoint = 'Recent'
%%bq sample --count 5 --query requests
df = requests.execute(output_op... |
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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: GitHub workflow
Step2: PR ready!? What now?
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Python Code:
%%bash
git status
%%bash
git log
%%bash
git show
%%writefile foo.md
Fetchez la vache
%%bash
git add foo.md
%%bash
git st
%%bash
git diff foo.md
%%bash
git diff git_intro.ipynb
%%bash
git rm -f foo.md
%%bash
git st
%%bash
git branch new_post
%%bash
git checkout new_post
%%writefile my_new... |
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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:
Step2: Lorenz system
Step4: Write a function solve_lorenz that solves the Lorenz system above for a particular initial condition $[x(0),y(0),z(0)]$. Y... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
from scipy.integrate import odeint
from IPython.html.widgets import interact, fixed
def lorentz_derivs(yvec, t, sigma, rho, beta):
Compute the the derivatives for the Lorentz system at yvec(t).
x = yvec[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:
Step1: Now, we add a plane at z=0.
Step2: By holding the shift key and hovering the mouse at the edges of the bounding box (or activate slice mode in ... | <ASSISTANT_TASK:>
Python Code:
import ipyvolume as ipv
fig = ipv.figure()
scatter = ipv.examples.gaussian(show=False)
ipv.show()
plane = ipv.plot_plane("z");
import ipywidgets as widgets
widgets.jslink((fig, 'slice_z'), (plane, 'z_offset'));
## Uncomment to try
# import vaex
# import matplotlib.pylab as plt
# import... |
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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: Spinning Symmetric Rigid Body setup
Step2: $\left[{}^\mathcal{I}\boldsymbol{\omega}^\mathcal{B}\right]_\mathcal{B}$
Step3: ${}^\mathcal{I}\bol... | <ASSISTANT_TASK:>
Python Code:
from miscpy.utils.sympyhelpers import *
init_printing()
th,psi,thd,psidd,thdd,psidd,Omega,I1,I2,t,M1,C = \
symbols('theta,psi,thetadot,psidot,thetaddot,psiddot,Omega,I_1,I_2,t,M_1,C')
diffmap = {th:thd,psi:psid,thd:thdd,psid:psidd}
bCa = rotMat(1,th);bCa
iWb_B = bCa*Matrix([0,0,psid])+ ... |
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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: We will use this helper function to write lists containing article ids, categories, and authors for each article in our database to local file.
... | <ASSISTANT_TASK:>
Python Code:
import os
import tensorflow as tf
import numpy as np
from google.cloud import bigquery
PROJECT = 'cloud-training-demos' # REPLACE WITH YOUR PROJECT ID
BUCKET = 'cloud-training-demos-ml' # REPLACE WITH YOUR BUCKET NAME
REGION = 'us-central1' # REPLACE WITH YOUR BUCKET REGION e.g. us-centr... |
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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: Test data
Step2: Regression
Step3: Test for Outliers
Step4: Figure
Step5: Create a function and test it
| <ASSISTANT_TASK:>
Python Code:
import numpy as np
import statsmodels.api as sm # For some reason this import is necessary...
import statsmodels.formula.api as smapi
import statsmodels.graphics as smgraph
import matplotlib.pyplot as plt
%matplotlib inline
x = np.arange(30, dtype=float)
# Make some y data with random no... |
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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: Modeling
Step2: Visualization
| <ASSISTANT_TASK:>
Python Code:
from ozapfdis import jdeps
deps = jdeps.read_jdeps_file(
"../dataset/jdeps_dropover.txt",
filter_regex="at.dropover")
deps.head()
deps = deps[['from', 'to']]
deps['group_from'] = deps['from'].str.split(".").str[2]
deps['group_to'] = deps['to'].str.split(".").str[2]
deps.head()
f... |
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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: record schedules for 2 weeks, then augment count with weekly flight numbers.
Step2: good dates
Step3: Save
| <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... |
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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: Hamilton (1989) switching model of GNP
Step2: We plot the filtered and smoothed probabilities of a recession. Filtered refers to an estimate of... | <ASSISTANT_TASK:>
Python Code:
%matplotlib inline
import numpy as np
import pandas as pd
import statsmodels.api as sm
import matplotlib.pyplot as plt
import requests
from io import BytesIO
# NBER recessions
from pandas_datareader.data import DataReader
from datetime import datetime
usrec = DataReader('USREC', 'fred', s... |
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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: Defining the refugee
Step2: I gave the Person class a simple constructor (see the _init_() function), which sets a number of parameters specifi... | <ASSISTANT_TASK:>
Python Code:
import random
class Person:
def __init__(self, location):
self.ill = False
self.injured = False
self.age = 35
self.location = location
self.location.numAgents += 1
# Set to true when an agent resides on a link.
self.travelling = False
def select... |
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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: Training and visualizing
Step2: Predicting classes and class probabilities
Step3: Sensitivity to training set details
Step4: Regression trees... | <ASSISTANT_TASK:>
Python Code:
# To support both python 2 and python 3
from __future__ import division, print_function, unicode_literals
# Common imports
import numpy as np
import os
# to make this notebook's output stable across runs
np.random.seed(42)
# To plot pretty figures
%matplotlib inline
import matplotlib
impo... |
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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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<USER_TASK:>
Description:
Step1: Get the model and extract the data.
Step2: Download the embeddings and the tokenizer
Step3: Load the embeddings and create functions for encod... | <ASSISTANT_TASK:>
Python Code:
from mmlspark import CNTKModel, ModelDownloader
from pyspark.sql.functions import udf, col
from pyspark.sql.types import IntegerType, ArrayType, FloatType, StringType
from pyspark.sql import Row
from os.path import abspath, join
import numpy as np
import pickle
from nltk.tokenize import s... |
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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: Getting insights
Step2: Build a model from the default schema
| <ASSISTANT_TASK:>
Python Code:
# Load PredicSis.ai SDK
from predicsis import PredicSis
prj = PredicSis.project('Outbound Mail Campaign')
mdl = prj.default_schema().fit('My first model')
mdl.auc()
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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: Programa principal
Step2: Ha funcionat a la primera? Fer un quadrat perfecte no és fàcil, i el més normal és que calga ajustar un parell de cos... | <ASSISTANT_TASK:>
Python Code:
from functions import connect, forward, stop, left, right, disconnect, next_notebook
from time import sleep
connect() # Executeu, polsant Majúscules + Enter
# avançar
# girar
# avançar
# girar
# avançar
# girar
# avançar
# girar
# parar
for i in range(4):
# avançar
# girar
# pa... |
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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: Interesting... We wanted to change two elements (line 7), but added four instead! Rather, we mutated list a with list b. "Mutability" means, sim... | <ASSISTANT_TASK:>
Python Code:
a = list(range(5))
print ("The list we created:", a, "of length", len(a))
b = list(range(6,10))
print ("The second list we created:", b, "of length", len(b))
a[1:3] = b # Line 7
print ("The first list after we changed a couple of elements is", a, "with length", len(a))
print ("hash of in... |
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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:
Step2: Problem 1) Create a galaxy class
Step3: Problem 1c
Step5: Problem 1d
Step7: Problem 2) Make a more interesting galaxy class that can evolve w... | <ASSISTANT_TASK:>
Python Code:
import matplotlib.pyplot as plt
import random
import numpy as np
%matplotlib inline
class Galaxy():
Galaxy class for simply representing a galaxy.
def __init__(self, total_mass, cold_gas_mass, stellar_mass, age=0):
self.total_mass = total_mass
self.cold_... |
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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: Document Authors
Step2: Document Contributors
Step3: Document Publication
Step4: Document Table of Contents
Step5: 1.2. Model Name
Step6: 1... | <ASSISTANT_TASK:>
Python Code:
# DO NOT EDIT !
from pyesdoc.ipython.model_topic import NotebookOutput
# DO NOT EDIT !
DOC = NotebookOutput('cmip6', 'mri', 'sandbox-2', 'land')
# Set as follows: DOC.set_author("name", "email")
# TODO - please enter value(s)
# Set as follows: DOC.set_contributor("name", "email"... |
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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: Let us compute simple things like the contrast and the Doppler velocity field
Step2: Now let us compute the velocity field. To this end, we com... | <ASSISTANT_TASK:>
Python Code:
sn.set_style("dark")
f, ax = pl.subplots(figsize=(9,9))
ax.imshow(stI[:,:,0], aspect='auto', cmap=pl.cm.gray)
contrastFull = np.std(stI[:,:,0]) / np.mean(stI[:,:,0])
contrastQuiet = np.std(stI[400:,100:300,0]) / np.mean(stI[400:,100:300,0])
print("Contrast in the image : {0}%".format(con... |
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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: 1. Map
Step2: 2. Profile
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Python Code:
import numpy as np
from scipy.interpolate import interp1d
import travelmaps2 as tm
from matplotlib import pyplot as plt
tm.setup(dpi=200)
fig_x = tm.plt.figure(figsize=(tm.cm2in([11, 6])))
# Locations
MDF = [19.433333, -99.133333] # Mexico City
OAX = [16.898056, -96.414167] # Oaxaca
PES ... |
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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: Description
Step2: If the slip is 0.05, find the following quantities for this motor
Step3: $$Z_B = \frac{(R_2/(2-s) + jX_2)(jX_M)}{R_2/(2-s) ... | <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 = 0.05
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)) / (R2... |
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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
Step1: 2_ Advanced firewalking using IP options is sometimes useful to perform network enumeration. Here is a more complicated one-liner
Step2: Now th... | <ASSISTANT_TASK:>
Python Code:
send(IP(dst="1.2.3.4")/TCP(dport=502, options=[("MSS", 0)]))
ans = sr([IP(dst="8.8.8.8", ttl=(1, 8), options=IPOption_RR())/ICMP(seq=RandShort()), IP(dst="8.8.8.8", ttl=(1, 8), options=IPOption_Traceroute())/ICMP(seq=RandShort()), IP(dst="8.8.8.8", ttl=(1, 8))/ICMP(seq=RandShort())], ver... |
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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:
Step2: By series convolution
Step3: By recurrence relation
Step4: By $A, Z$ sequences
Step5: $\mathcal{C}$
Step6: $\mathcal{R}$
Step7: Co... | <ASSISTANT_TASK:>
Python Code:
from sympy import *
from sympy.abc import n, i, N, x, lamda, phi, z, j, r, k, a, t, alpha
from sequences import *
init_printing()
m = 5
d_fn, h_fn = Function('d'), Function('h')
d, h = IndexedBase('d'), IndexedBase('h')
rows, cols = 5, 5
ctor = lambda i,j: d[i,j]
Matrix(rows, cols, ctor)... |
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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: Variables to set before running
Step2: Set date index to a special DatetimeIndex and then Reindex the dataframe so
Step3: interpolate missing ... | <ASSISTANT_TASK:>
Python Code:
!wget -P ../output -qN ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02135/north/daily/data/NH_seaice_extent_final.csv
!wget -P ../output -qN ftp://sidads.colorado.edu/pub/DATASETS/NOAA/G02135/north/daily/data/NH_seaice_extent_nrt.csv
!wget -P ../output -qN ftp://sidads.colorado.edu/pub/DA... |
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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: 4.1. Checking the loss.out file.
Step2: 4.2. Checking the energy_test.out file
Step3: 4.3. Checking the force_test.out file
Step4: 4.4. Check... | <ASSISTANT_TASK:>
Python Code:
from pylab import *
loss = loadtxt('loss.out')
loglog(loss[:, 1:6])
loglog(loss[:, 7:9])
xlabel('Generation/100')
ylabel('Loss')
legend(['Total', 'L1-regularization', 'L2-regularization', 'Energy-train', 'Force-train', 'Energy-test', 'Force-test'])
tight_layout()
energy_test = loadtxt('... |
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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: Python Statistics
Step2: Some simpler exercises based on common python function
Step3: Question
Step4: Question
Step5: ``
Step6: Question
S... | <ASSISTANT_TASK:>
Python Code:
import math
import numpy as np
import pandas as pd
import re
from operator import itemgetter, attrgetter
def median(dataPoints):
"computer median of given data points"
if not dataPoints:
raise 'no datapoints passed'
sortedpoints=sorted(dataPoints)
mid=len(dataPoi... |
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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: Creating a ScaleBar object
Step2: Geographic coordinate system (degrees)
Step3: After the conversion, we can calculate the distance between th... | <ASSISTANT_TASK:>
Python Code:
import geopandas as gpd
from matplotlib_scalebar.scalebar import ScaleBar
nybb = gpd.read_file(gpd.datasets.get_path('nybb'))
nybb = nybb.to_crs(32619) # Convert the dataset to a coordinate
# system which uses meters
ax = nybb.plot()
ax.add_artist(ScaleBar(1))
from shapely.geometry.poi... |
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Description:
Step5: Copyright 2021 DeepMind Technologies Limited.
Step6: Dataset and environment
Step7: DQN learner
Step8: Training loop
Step9: Evaluation
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Python Code:
# @title Installation
!pip install dm-acme
!pip install dm-acme[reverb]
!pip install dm-acme[tf]
!pip install dm-sonnet
!pip install dopamine-rl==3.1.2
!pip install atari-py
!pip install dm_env
!git clone https://github.com/deepmind/deepmind-research.git
%cd deepmind-research
!git clone h... |
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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: Please re-run the above cell if you are getting any incompatible warnings and errors.
Step2: There are a couple of key features here
Step3: Th... | <ASSISTANT_TASK:>
Python Code:
!pip install -q --upgrade tensorflow-datasets
import pprint
import tensorflow_datasets as tfds
ratings = tfds.load("movielens/100k-ratings", split="train")
for x in ratings.take(1).as_numpy_iterator():
pprint.pprint(x)
import numpy as np
import tensorflow as tf
movie_title_lookup = tf... |
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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: Data source is http
Step2: Can also migrate it to a sqlite database
Step3: Can perform queries
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Python Code:
import Quandl
import pandas as pd
import numpy as np
import blaze as bz
with open('../.quandl_api_key.txt', 'r') as f:
api_key = f.read()
db = Quandl.get("EOD/DB", authtoken=api_key)
bz.odo(db['Rate'].reset_index(), '../data/db.bcolz')
fx = Quandl.get("CURRFX/EURUSD", authtoken=api_k... |
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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:
Step2: Number of New Followers
Step4: At First Glance...
Step8: Statuses -vs- Followers -vs- Friends
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Python Code:
df = sqlContext.read.json('/home/anaconda/md0/data/2016_potus/users_all')
df.registerTempTable('followers')
%matplotlib inline
import seaborn as sns
import matplotlib
import warnings
query =
select
candidate, count(*) as new_followers
from followers
group by candidate
dfp = sqlCont... |
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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: The setup
Step2: This sample data has way too many stations to plot all of them. The number
Step3: Now that we have the data we want, we need ... | <ASSISTANT_TASK:>
Python Code:
import cartopy.crs as ccrs
import cartopy.feature as cfeature
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from metpy.calc import reduce_point_density
from metpy.calc import wind_components
from metpy.cbook import get_test_data
from metpy.plots import add_metpy_l... |
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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: We have to define a threshold on the p-value of the statistical test to decide how many features to keep. There are several strategies implement... | <ASSISTANT_TASK:>
Python Code:
from sklearn.datasets import load_breast_cancer, load_digits
from sklearn.model_selection import train_test_split
cancer = load_breast_cancer()
# get deterministic random numbers
rng = np.random.RandomState(42)
noise = rng.normal(size=(len(cancer.data), 50))
# add noise features to the da... |
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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: Plotting 1/2 light radius from GIM2D vs NSA
Step2: Conclusion two measures of radius are comparable, expect for the NSA galaxies with very lar... | <ASSISTANT_TASK:>
Python Code:
import numpy as np
from matplotlib import pyplot as plt
%matplotlib inline
import warnings
warnings.filterwarnings('ignore')
import sys
sys.path.append("/Users/rfinn/Dropbox/pythonCode/")
sys.path.append("/anaconda/lib/python2.7/site-packages")
sys.path.append("/Users/rfinn/Ureka/variants... |
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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: 1. Model-based parametric regression
Step2: have been generated by a linear Gaussian model (i.e., with $z = T(x) = x$) with noise variance
Step... | <ASSISTANT_TASK:>
Python Code:
# Import some libraries that will be necessary for working with data and displaying plots
# To visualize plots in the notebook
%matplotlib inline
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import scipy.io # To read matlab files
import pylab
X = np.array([... |
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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: Now we will specify the site(s) we want to query for available data types.
Step2: The next code segment will query the FITS database for data o... | <ASSISTANT_TASK:>
Python Code:
import pandas as pd
import matplotlib.pyplot as plt
import datetime
import numpy as np
# Create list of all typeIDs available in the FITS database
all_type_URL = 'https://fits.geonet.org.nz/type'
all_types = pd.read_json(all_type_URL).iloc[:,0]
all_typeIDs= []
for row in all_types:
... |
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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: Data preparation
Step2: Check time sequence and inputs/outputs
Step3: Input $\delta_T$ and focused time ranges
Step4: Resample and filter dat... | <ASSISTANT_TASK:>
Python Code:
%run matt_startup
%run -i matt_utils
button_qtconsole()
#import other needed modules in all used engines
#with dview.sync_imports():
# import os
filename = 'FIWT_Exp015_20150601145005.dat.npz'
def loadData():
# Read and parse raw data
global exp_data
exp_data = np.load(file... |
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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: 选着一张空闲的卡
Step2: 1. Classifying Names with a Character-Level RNN
Step3: Now we have category_lines, a dictionary mapping each category
Step4: ... | <ASSISTANT_TASK:>
Python Code:
import torch
is_cuda = True if torch.cuda.is_available() else False
print(is_cuda)
id = 1
torch.cuda.set_device(id)
print( torch.cuda.current_device() )
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
def findFiles(path): retur... |
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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: 对于变量x来说，不需要知道它是字符串还是列表，就可以调用它的count方法—不用管它是什么类型（只要提供一个字符作为参数即可）。
Step2: 1.2 封装
Step3: 注意 尽管可能使用的是新版的Python，但一些功能不会在旧式类上起作用。为了确保类是新型的，需要在模块或者... | <ASSISTANT_TASK:>
Python Code:
'abc'.count('a')
[1,2,'a'].count('a')
1+2
'Fish '+'license'
__metaclass__=type #确定使用新式类
class Person:
def setName(self, name):
self.name=name
def getName(self):
return self.name
def greet(self):
print "Hello, world! I'm %s" % self.name
foo=Person()
... |
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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: Note that a similar transformation can be applied with compute_ems
| <ASSISTANT_TASK:>
Python Code:
# Author: Denis Engemann <denis.engemann@gmail.com>
# Jean-Remi King <jeanremi.king@gmail.com>
#
# License: BSD (3-clause)
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne import io, EvokedArray
from mne.datasets import sample
from mne.decoding import EMS, co... |
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