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## Vereadores mais votados do município do Rio de Janeiro por zona
Esse programa lê o banco de dados oferecido pelo TSE para votações estaduais e seleciona os vereadores de um determinado município, filtrando apenas as variáveis desejadas, e gera um novo arquivo *.csv*.
Esses dados podem ser encontrados no repositóri... | github_jupyter |
## Import Required Packages
```
import tensorflow as tf
import tensorflow_addons as tfa
from tqdm import tqdm
import pandas as pd
import sklearn
from sklearn import metrics
import re
import numpy as np
import pickle as pkl
import PIL
import datetime
import os
import random
import shutil
import statistics
import time
i... | github_jupyter |
Cross-validation is one among the foremost powerful tools of machine learning and every Data Scientist should be conversant in it. In real world , you can’t finish the project without cross-validating a model. However, It’s worth mentioning that sometimes performing cross-validation could be a touch tricky task.
For e... | github_jupyter |
<br>
<br>
<font size='6'><u><b>Gravitational Lensing</b></u></font>
<br>
_**Written by A. Bolton, 2017**_
_**Updated 2018: Elliot Kisiel and Connie Walker**_
_**Revised by Andres Jaramillo**_
You have learned about how we can measure the mass of a galaxy based on the gravitational lensing of a foreground galaxy. Th... | github_jupyter |
# Array Compare $\psi_4$ data with low and high level MMRDNS Models
### Setup The Enviroment
```
# Low-level import
from numpy import array,loadtxt,linspace,zeros,exp,ones,unwrap,angle,pi
# Setup ipython environment
%load_ext autoreload
%autoreload 2
# %matplotlib inline
# Import useful things from kerr
from kerr.... | github_jupyter |
# Introduction
Structured Query Language, or **SQL**, is the programming language used with databases, and it is an important skill for any data scientist. In this course, you'll build your SQL skills using **BigQuery**, a web service that lets you apply SQL to huge datasets.
In this lesson, you'll learn the basics o... | github_jupyter |
### Overall Strategy
What is the **want** we want to plot the price of a product (brand/format) over the year, for one year. What we have done so far is identified the UPCs for a particular product (brand/format), now we just need to connect it with the scanner data set and work towards our want.
Let me first discus... | github_jupyter |
<a href="https://colab.research.google.com/github/Educat8n/Reinforcement-Learning-for-Game-Playing-and-More/blob/main/Module2/Module_2.1_Introduction_to_gym.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
```
# In colab please uncomment this to inst... | github_jupyter |
# Skip-gram word2vec
In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in natural language processing. This will come in handy when dealing with things like machine translation.... | github_jupyter |
# Neste notebook vamos avaliar o atraso de grupo
```
# importar as bibliotecas necessárias
import numpy as np # arrays
import matplotlib.pyplot as plt # plots
plt.rcParams.update({'font.size': 14})
import IPython.display as ipd # to play signals
import sounddevice as sd
import soundfile as sf
# Os próximos módulos são... | github_jupyter |
# Softmax exercise
*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](http://vision.stanford.edu/teaching/cs231n/assignments.html) on the course website.*
This exercise is ... | github_jupyter |
# Pythonで基礎から機械学習 重回帰分析
今回は、前回の単回帰分析を読んだことを前提の内容となっております。ご了承ください。
重回帰分析は、単回帰分析の入力変数が1つだったのが、複数（n）になったものです。それにより、単回帰から、以下のような変化があります。
- 行列を使った計算が増える（複雑になる）
- 複数の入力変数の粒度を揃えるために正規化が必要
- 単回帰と同様の計算に対して、入力変数の数に応じた補正が必要になる場合がある
- 入力変数同士の相関が強い（線形従属）の場合は、うまくモデル化できないので、正則化・次元削減といった対策が必要
これらに注意して、実際に手を動かしながら確認していきましょう。
## デ... | github_jupyter |
# Apartado 3 - Bucles
- Iteraciones | `for`
- `range` & `enumerate`
- `while`
- `break` & `continue`
--------------------------------------------------------------------------------------
## Iteraciones | bucle `for`
```
lista = ["red", 2, "blue", 4.0]
lista2 = [2, 4, 6, 8, 10, 12, 9518591859]
for x in lista2:
... | github_jupyter |
##### Copyright 2019 The TensorFlow Authors.
Licensed under the Apache License, Version 2.0 (the "License");
```
#@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.o... | github_jupyter |
```
import numpy as np
import pandas as pd
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from matplotlib import pyplot as plt
%matplotlib inline
torch.backends... | github_jupyter |
# Dispersion
By Evgenia "Jenny" Nitishinskaya and Delaney Granizo-Mackenzie
Notebook released under the Creative Commons Attribution 4.0 License.
---
Dispersion measures how spread out a set of data is. This corresponds to risk when our data set is returns over time. Data with low dispersion is heavily clustered arou... | github_jupyter |
# Audio Playback and Recording
[back to main page](../index.ipynb)
There are many libraries for audio playback and/or recording available for Python.
They greatly differ in features, API, requirements, quality, ...
This is just a random selection.
See also https://wiki.python.org/moin/Audio and https://wiki.python.... | github_jupyter |
# Queue Runners
```
import tensorflow as tf
```
## Import csv files
<hr/>
### first
```python
filename_queue = tf.train.string_input_producer(['data-01-test-score.csv',
'data-02-stock_daily.csv',
'data-03-diabetes.csv'... | github_jupyter |
# Norman 2019 Training Demo
```
import sys
#if branch is stable, will install via pypi, else will install from source
branch = "stable"
IN_COLAB = "google.colab" in sys.modules
if IN_COLAB and branch == "stable":
!pip install cpa-tools
elif IN_COLAB and branch != "stable":
!pip install --quiet --upgrade jsons... | github_jupyter |
```
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file... | github_jupyter |
# MINI PROJECT 1
```
# solusi tanpa menggunakan set
def remove_duplicate(obj_list):
temp = sorted(obj_list)
new_list = []
for i in temp:
if i not in new_list:
new_list.append(i)
return new_list
# solusi dengan menggunakan set
def remove_duplicate_with_set(obj_list):
new_list =... | github_jupyter |
```
# %pip install iteround
# %pip install pairing
# %pip install scikit-multilearn
# %pip install arff
# %pip install category_encoders
import sys
sys.path.append("../")
from bandipy import simulation
import numpy as np
## For synthetic data generation
import keras
from keras.models import Sequential
from keras.layers... | github_jupyter |
# Data wrangling
This notebook is adapated from Joris Van den Bossche tutorial:
* https://github.com/paris-saclay-cds/python-workshop/blob/master/Day_1_Scientific_Python/02-pandas_introduction.ipynb
```
%matplotlib inline
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
pd.options.display.max_... | github_jupyter |
```
# reload packages
%load_ext autoreload
%autoreload 2
```
### Choose GPU (this may not be needed on your computer)
```
%env CUDA_DEVICE_ORDER=PCI_BUS_ID
%env CUDA_VISIBLE_DEVICES=1
```
### load packages
```
from tfumap.umap import tfUMAP
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
... | github_jupyter |
```
# Run the command below if necessary, for example with Google Colab
#!python3 -m pip install mxnet-cu110
# Global Libs
import matplotlib.pyplot as plt
import mxnet as mx
import numpy as np
import pandas as pd
import pickle
import random
from sklearn import datasets, metrics
# Local libs
import model
with open("cla... | github_jupyter |
# Custom TF-Hub Word Embedding with text2hub
**Learning Objectives:**
1. Learn how to deploy AI Hub Kubeflow pipeline
1. Learn how to configure the run parameters for text2hub
1. Learn how to inspect text2hub generated artifacts and word embeddings in TensorBoard
1. Learn how to run TF 1.x generated hub module... | github_jupyter |
## Download Future Contract data
Make sure to download this file only once after market closing. No need to run the cells more than once otherwise multiple files may get created.
```
from datetime import date
from nsepy import get_history
import pandas as pd
import os
import datetime
import tqdm
exdates = pd.read_csv... | github_jupyter |
## Documentation:
* https://snap.stanford.edu/snappy/doc/index.html
## Data:
* http://snap.stanford.edu/data/wiki-Vote.html
* http://snap.stanford.edu/class/cs224w-data/hw0/stackoverflow-Java.txt.gz
```
import snap
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from collections import defaultd... | github_jupyter |
# Введение в математику для МЛ
```
import numpy as np
import scipy as sc
```
### Вектор
Вектор $\mathbf{v}$ (или $\vec{v}$) - это элемент векторного пространства $\mathrm{V}$.
Для него определены операции сложения друг с другом и умножения на число (скаляр). Эти операции подчинены аксиомам, которые мы скоро увидим... | github_jupyter |
```
%matplotlib notebook
# use ``%matplotlib widget`` in Jupyter Lab
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import pathlib
import os
import pwd
def get_home():
return os.path.expanduser("~")
home = get_home()
run_dir = pathlib.Path(rf"{home}/LEC/") #path to your ... | github_jupyter |
## Content Based Filtering by hand
This lab illustrates how to implement a content based filter using low level Tensorflow operations.
The code here follows the technique explained in Module 2 of Recommendation Engines: Content Based Filtering.
```
!pip install tensorflow==2.1
```
Make sure to restart your kernel ... | github_jupyter |
Deep Learning Models -- A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks.
- Author: Sebastian Raschka
- GitHub Repository: https://github.com/rasbt/deeplearning-models
```
%load_ext watermark
%watermark -a 'Sebastian Raschka' -v -p torch
```
- Runs ... | github_jupyter |
# Errori di sintassi ed eccezioni #
In python possono capitare due tipi di errori principalmente:
- Errori di sintassi : errori dovuti alla scrittura sbagliata di comandi nel codice
- eccezioni : errori la cui natura è logica e non simile alla precedente
Fortunatamente python fornisce dei possibili metodi per risolvere... | github_jupyter |
```
!pip install html2text #ONLY if html2text not installed
import numpy as np
import pandas as pd
from __future__ import print_function
import sys
import io
import random
# NLP and text
from html2text import html2text
import re
import string
import nltk
from nltk.data import find
import gensim
from gensim.models impo... | github_jupyter |
# OSM COMPETITION: A Meta Model that optimally combines the outputs of other models.
The aim of the competition is to develop a computational model that predicts which molecules will block the malaria parasite's ion pump, PfATP4.
Submitted by James McCulloch - james.duncan.mcculloch@gmail.com
## Final Results. The D... | github_jupyter |
```
import numpy as np
import scipy
import matplotlib
import pandas as pd
from numpy import genfromtxt
import json
import sys
import pandas
import matplotlib.pyplot as plt
import os
%matplotlib inline
# os.environ["PATH"] += os.pathsep + '/usr/local/texlive/2019/bin/x86_64-darwin'
print(os.getenv("PATH"))
lgndsize = '... | github_jupyter |
# Components of StyleGAN
### Goals
In this notebook, you're going to implement various components of StyleGAN, including the truncation trick, the mapping layer, noise injection, adaptive instance normalization (AdaIN), and progressive growing.
### Learning Objectives
1. Understand the components of StyleGAN that... | github_jupyter |
```
%run Common.ipynb
import numpy as np
import matplotlib.pyplot as plt
from azure.cognitiveservices.vision.customvision.training import CustomVisionTrainingClient
from azure.cognitiveservices.vision.customvision.training.models import ImageFileCreateEntry
from PIL import Image, ImageFilter
%matplotlib inline
```
##... | github_jupyter |
# Pipeline
In this part, we present the originally complex steps using the way of pipeline. Due to the complexity of the project, we needed to customize more functions for functional implementation.
### Predefined function
This section contains all of the functionality we implemented earlier. We show all the functions... | github_jupyter |
```
#r "nuget:Microsoft.Data.Analysis,0.1.0"
using Microsoft.Data.Analysis;
PrimitiveDataFrameColumn<DateTime> dateTimes = new PrimitiveDataFrameColumn<DateTime>("DateTimes"); // Default length is 0.
PrimitiveDataFrameColumn<int> ints = new PrimitiveDataFrameColumn<int>("Ints", 3); // Makes a column of length 3. Filled... | github_jupyter |
```
#imports
from datasets import load_dataset, load_metric
from sklearn.metrics import accuracy_score, precision_recall_fscore_support
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification, TrainingArguments, Trainer
from thai2transformers.preprocess import process_transfor... | github_jupyter |
# SIFTS Data Demo
This demo shows how to query PDB annotations from the SIFTS project.
The "Structure Integration with Function, Taxonomy and Sequence" is the authoritative source of up-to-date residue-level annotation of structures in the PDB with data available in Uniprot, IntEnz, CATH, SCOP, GO, InterPro, Pfam and... | github_jupyter |
# Deploying pre-trained PyTorch vision models with Amazon SageMaker Neo
Neo is a capability of Amazon SageMaker that enables you to compile machine learning models to optimize them for our choice of hardward targets. Currently, Neo supports pre-trained PyTorch models from [TorchVision](https://pytorch.org/docs/stable/... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib notebook
plt.style.use('seaborn-notebook')
#The Fuel of the system, DATASETS!
df1 = pd.read_csv('F:/Akshay Files/DataSets/kanpur.csv')
df0 = pd.read_csv('F:/Akshay Files/DataSets/bengaluru.csv')
#what does it look like
df0.tail()
#C... | github_jupyter |
```
import json
from dateutil.parser import parse
import pprint
f = open('../data/fhir/Abe604_Veum823_e841a5e8-9ace-437b-be32-b37d006aef87.json', 'r')
text = f.read()
f.close()
print(type(text))
with open('../data/fhir/Abe604_Veum823_e841a5e8-9ace-437b-be32-b37d006aef87.json') as f:
bundle = json.load(f)
print(type... | github_jupyter |
<table class="ee-notebook-buttons" align="left">
<td><a target="_blank" href="https://github.com/giswqs/earthengine-py-notebooks/tree/master/Algorithms/CloudMasking/modis_surface_reflectance_qa_band.ipynb"><img width=32px src="https://www.tensorflow.org/images/GitHub-Mark-32px.png" /> View source on GitHub</a></td... | github_jupyter |
# ISB-CGC Community Notebooks
Check out more notebooks at our [Community Notebooks Repository](https://github.com/isb-cgc/Community-Notebooks)!
```
Title: How to make t-SNE and UMAP plots
Author: David L Gibbs
Created: 2019-10-15
Purpose: Demonstrate how to perform dimension reduction with PCA, t-SNE, and UMAP, an... | github_jupyter |
## Using Scikit-Learn and NLTK to build a Naive Bayes Classifier that identifies subtweets
#### In all tables, assume:
* "➊" represents a single URL
* "➋" represents a single mention of a username (e.g. "@noah")
* "➌" represents a single mention of an English first name
#### Import libraries
```
%matplotlib inline
f... | github_jupyter |
# A step-by-step look at the Simulation class
The simplest way to solve a model is to use the `Simulation` class. This automatically processes the model (setting of parameters, setting up the mesh and discretisation, etc.) for you, and provides built-in functionality for solving and plotting. Changing things such as pa... | github_jupyter |
```
from pydgrid import grid
from pydgrid.pydgrid import phasor2time, pq
from pydgrid.pf import pf_eval,time_serie
from pydgrid.electric import bess_vsc, bess_vsc_eval
from pydgrid.simu import simu, f_eval, ini_eval, run_eval
import matplotlib.pyplot as plt
import numpy as np
plt.style.use('presentation.mplstyle') # co... | github_jupyter |
<a href="https://colab.research.google.com/github/NeuromatchAcademy/course-content-dl/blob/main/tutorials/W1D3_MultiLayerPerceptrons/W1D3_Tutorial2.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Tutorial 2: Deep MLPs
**Week 1, Day 3: Multi Layer... | github_jupyter |
<center>
<img src="https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DA0101EN-SkillsNetwork/labs/Module%205/images/IDSNlogo.png" width="300" alt="cognitiveclass.ai logo" />
</center>
# Model Evaluation and Refinement
Estimated time needed: **30** minutes
## Objectives
... | github_jupyter |
```
import pandas as pd
import numpy as np
from matplotlib import pyplot as plt
import seaborn as sns
import geopandas as gpd
%matplotlib inline
from urbansim_templates import modelmanager as mm
from urbansim_templates.models import LargeMultinomialLogitStep
import orca
import os; os.chdir('../')
import warnings;warnin... | github_jupyter |
```
# to run the script, you need to start pathway tools form the command line
# using the -lisp -python options. Example (from the pathway tools github repository)
import os
# os.system('nohup /opt/pathway-tools/pathway-tools -lisp -python &')
os.system('nohup pathway-tools -lisp -python-local-only &') # added cyber... | github_jupyter |
# Enabling App Insights for Services in Production
With this notebook, you can learn how to enable App Insights for standard service monitoring, plus, we provide examples for doing custom logging within a scoring files in a model.
## What does Application Insights monitor?
It monitors request rates, response times, f... | github_jupyter |
```
from random import seed
from random import randrange
from csv import reader
def load_csv(filename):
dataset = list()
with open(filename, 'r') as file:
csv_reader = reader(file)
for row in csv_reader:
if not row:
continue
dataset.append(row)
return dataset
def str_column_to_float(dataset, column... | github_jupyter |
```
# for numbers
import xarray as xr
import numpy as np
import pandas as pd
# for figures
import matplotlib as mpl
import matplotlib.pyplot as plt
import cartopy.crs as ccrs
# An "anonymous function" to print the max value of an Xarray DataArray
printMax = lambda x: print(np.asscalar(x.max().values))
def quick_map(lo... | github_jupyter |
```
import pandas as pd
from matplotlib import pyplot as plt
import os
```
# Preparation
## Get T1 image
18 controls and 11 patients. All images are bias corrected using fsl_anat.
```
!ls *.nii.gz
# check images
# !slicesdir `imglob *`
```
## Create template list
choose 11 control's image and 11 patient's iamge
```... | github_jupyter |
# Locate P, Q, S and T waves in ECG
This example shows how to use Neurokit to delineate the ECG peaks in Python using NeuroKit. This means detecting and locating all components of the QRS complex, including **P-peaks** and **T-peaks**, as well their **onsets** and **offsets** from an ECG signal.
```
# Load NeuroKit a... | github_jupyter |
# CNT orientation detection using TDA
The following shows scanning electron microscopy (SEM) images of carbon nanotube (CNT) samples of different alignment degree.
<table><tr>
<td> <img src="SEM/00.PNG" style="width:100%"/> </td>
<td> <img src="SEM/10.PNG" style="width:100%"/> </td>
</tr></table>
<table><tr>
... | github_jupyter |
在这个教程中，你将会学到如何使用python的pandas包对出租车GPS数据进行数据清洗，识别出行OD
<div class="alert alert-info"><h2>提供的基础数据是：</h2><p> 数据：<br>
1.出租车原始GPS数据(在data-sample文件夹下，原始数据集的抽样500辆车的数据)</p></div>
[pandas包的简介](https://baike.baidu.com/item/pandas/17209606?fr=aladdin)
# 读取数据
首先，读取出租车数据
```
import pandas as pd
#读取数据
data = pd.read_cs... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
%matplotlib inline
from argparse import Namespace
import pbio.misc.logging_utils as logging_utils
args = Namespace()
logger = logging_utils.get_ipython_logger()
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns; sns.set(style='white')
... | github_jupyter |
## SON
Burda yapılan tüm işlemler özetlenerek tek bir dataframe üzerinde performans testleri yapıldı. Diğer .ipynb uzantılı sayfalarda ise burdaki algoritmalar açıklandı.
```
import nltk # Python un NLP kütüphanesini ekledik
from nltk.corpus import twitter_samples # Twitter veriseti... | github_jupyter |
##### Copyright 2018 The TensorFlow Authors.
```
#@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 ... | github_jupyter |
<a href="https://colab.research.google.com/github/livinNector/deep-learning-tools-lab/blob/main/3%20-%20Deep%20Neural%20Networks.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# 3 - Deep Neural Networks
## Classification using Deep Neural Networks... | github_jupyter |
# Loading packages
```
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import statsmodels.api as sm
from scipy.stats import norm,gamma,lognorm,pareto,spearmanr,pearsonr
import seaborn as sns
from scipy.interpolate import interp1d
import itertools
#from matplotlib import colors
plt.style.use('ggp... | github_jupyter |
```
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from tqdm import tqdm as tqdm
%matplotlib inline
import torch
import torchvision
import torchvision.transforms as transforms
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import random
transform = trans... | github_jupyter |
# sanity checks on `models.NMF` with emulator
```
import numpy as np
from provabgs import models as Models
# --- plotting ---
import corner as DFM
import matplotlib as mpl
import matplotlib.pyplot as plt
mpl.rcParams['text.usetex'] = True
mpl.rcParams['font.family'] = 'serif'
mpl.rcParams['axes.linewidth'] = 1.5
mpl... | github_jupyter |
# Encoders: Binary Example
One of the interesting Neural Net Architectures are auto-encoders. Auto-encoders are networks designed to predict their own input. An auto-encoder consists of an `encoder` which encodes the input to a set of __latent variables__ and a `decoder` which decodes the latent variables and tries to ... | github_jupyter |
```
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sns
import random
import time
import collections
from tqdm import tqdm
from tensor2tensor.utils import beam_search
sns.set()
with open('shakespeare.txt') as fopen:
shakespeare = fopen.read()
char2idx = {c: i+3 for i, c ... | github_jupyter |
# How to generate text: using different decoding methods for language generation with Transformers
https://huggingface.co/blog/how-to-generate
```
!pip install -q git+https://github.com/huggingface/transformers.git
!pip install -q tensorflow
import tensorflow as tf
from transformers import TFGPT2LMHeadModel, GPT2Token... | github_jupyter |
<a href="https://colab.research.google.com/github/PUC-RecSys-Class/RecSysPUC-2020/blob/master/practicos/pyRecLab_iKNN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>
# Práctica de Sistemas Recomendadores: pyreclab - iKNN
En este tutorial vamos a ut... | github_jupyter |
```
import tensorflow as tf
import numpy as np
import pandas as pd
import math
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import seaborn as sns
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Con... | github_jupyter |
# DMFT calculation with IPT
Author: [Fumiya KAKIZAWA](mailto:f.kakizawa.178@ms.saitama-u.ac.jp), [Rihito SAKURAI](mailto:sakurairihito@gmail.com), [Hiroshi SHINAOKA](mailto:h.shinaoka@gmail.com)
## Theory
### Self-consistent equation
We will solve the Hubbard model using the dynamical mean-field theory (DMFT) [1].
We... | github_jupyter |
# Preface
The locations requiring configuration for your experiment are commented in capital text.
# Setup
**Installations**
```
!pip install apricot-select
!pip install sphinxcontrib-napoleon
!pip install sphinxcontrib-bibtex
!git clone https://github.com/decile-team/distil.git
!git clone https://github.com/circu... | github_jupyter |
# Single-cell RNA-seq analysis workflow using Scanpy on CPU
Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
NVIDIA CORPORATION and its licensors retain all intellectual property
and proprietary rights in and to this software, related documentation
and any modifications thereto. Any use, reproduction, d... | github_jupyter |
# Introduction
Welcome to the first example of **Data Science for Everyone**. The following example will be about introduction to data visualization with **Python** and plotting line charts.
In the introductory examples, we'll use Google's CoLab which provides all the libr... | github_jupyter |
# The Electron Collection
The electron collection is a lot like the jet collection other than there are working points (loose, medium, tight) that are defined by the Egamma working group.
Accessing the collection is similar, at first blush, to the jet collection.
```
import matplotlib.pyplot as plt
from config impor... | github_jupyter |
## Nonlinear Sturm-Liouville Operator
### Formulation:
$L[u(x)] = f(x) \qquad x \in [0,10]$
$-[p(x) \; u_{x}]_{x} + q(x) \; u(x) + \alpha \; q(x) \; u^2(x) = f(x)$
$p(x) = 0.5 \; sin(x) + 0.1 \; sin(11 x) + 0.25 \; cos(4 x) + 3$
$q(x) = 0.6 \; sin(x+1) + 0.3 \; sin(2.5 x) + 0.2 \; cos(5x) + 1.5$
$\alpha = 0.4$
... | github_jupyter |
```
from pandas_datareader import data, wb ##Data reader to read data from web
import pandas as pd
import numpy as np
import datetime
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
```
# Data
**Name (DataFrame Name)**
<input type="checkbox"> Bank of America (BAC)
<input type="checkbox... | github_jupyter |
```
#r "nuget: TorchSharp-cpu"
open TorchSharp
open type TorchSharp.TensorExtensionMethods
open type TorchSharp.torch.distributions
open Microsoft.DotNet.Interactive.Formatting
let style = TensorStringStyle.Julia
Formatter.SetPreferredMimeTypesFor(typeof<torch.Tensor>, "text/plain")
Formatter.Register<torch.Tensor>... | github_jupyter |
```
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv), data manipulation as in SQL
import matplotlib
matplotlib.use("Agg")
%matplotlib inline
# used for plot interactive graph. I like it most for plot
import seaborn as sns # this is used for the plot the graph
from sklearn.model_selection import t... | github_jupyter |
<img src="../images/aeropython_logo.png" alt="AeroPython" style="width: 300px;"/>
# Introducción a la sintaxis de Python III: funciones
_En esta clase continuaremos con nuestra introducción a Python.
Lo más importante para programar, y no solo en Python, es saber organizar el código en piezas más pequeñas que hagan t... | github_jupyter |
```
import os
import datetime
from dotenv import load_dotenv
import pandas as pd
import altair as alt
pd.options.display.max_rows = 50
WIDTH = 650
from IPython.display import Markdown
from IPython.core.magic import register_cell_magic
@register_cell_magic
def markdown(line, cell):
return Markdown(cell.format(**glo... | github_jupyter |
# DIET PROBLEM - PYOMO
*Zuria Bauer Hartwig* ( [CAChemE](http://cacheme.org))
Original Problem: [Linear and Integer Programming](https://www.coursera.org/course/linearprogramming) (Coursera Course) - University of Colorado Boulder & University of Colorado System
Based on the Examples from the Optimization Course = [... | github_jupyter |
```
import torch
import pandas as pd
import matplotlib.pyplot as plt
import os
import subprocess
import numpy as np
os.chdir("/home/jok120/sml/proj/attention-is-all-you-need-pytorch/")
basic_train_cmd = "/home/jok120/build/anaconda3/envs/pytorch_src2/bin/python " +\
"~/sml/proj/attention-is-all-you-ne... | github_jupyter |
```
%load_ext autoreload
%autoreload 2
import sys
sys.path.append("..")
from optimus import Optimus
op = Optimus("dask_cudf", comm=True)
df = op.load.csv("https://raw.githubusercontent.com/ironmussa/Optimus/master/examples/data/foo.csv", sep=",", header=True, infer_schema='true', charset="UTF-8").ext.cache()
df.ext.dis... | github_jupyter |
# SN1 & SN2
Fitting SN1 and SN2 cubes is very similar to fitting SN# cubes with one exception -- the machine learning algorithm used to estimate the velocity and broadening was trained on SN3, and, therefore, we cannot use it estimate these parameters in other data cubes. Thus, I have implemented another basic algorit... | github_jupyter |
```
from stepPlay import *
import numpy as np
import copy
def prn_obj(obj):
print('\n'.join(['%s:%s' % item for item in obj.__dict__.items()]))
def softmax(x):
probs = np.exp(x - np.max(x))
probs /= np.sum(probs)
return probs
n = 5
width, height = 8, 8
model_file = 'best_policy_8_8_5.model'
board = ... | github_jupyter |
# Data description & Problem statement:
I will use the Yelp Review Data Set from Kaggle. Each observation in this dataset is a review of a particular business by a particular user. The "stars" column is the number of stars (1 through 5) assigned by the reviewer to the business. (Higher stars is better.) In other words... | github_jupyter |
# StateLegiscraper: Audio Format Example Notebook
*Author*: Katherine Chang (kachang@uw.edu)
*Last Updated*: 1/3/2022
StateLegiscraper is a Python package that scrapes and processes data from U.S. state legislature websites. As of writing, the package is focused on transcribing standing committee hearings from each ... | github_jupyter |
For MS training we have 3 datasets: train, validation and holdout
```
import numpy as np
import pandas as pd
import nibabel as nib
from scipy import interp
from sklearn.utils import shuffle
from sklearn.model_selection import GroupShuffleSplit
from sklearn.metrics import confusion_matrix, roc_auc_score, roc_curve, au... | github_jupyter |
```
import sympy
from sympy import symbols , solve
x = symbols('x')
expr = x -4 - 2
sol = solve(expr)
sol
num=sol[0]
num
from sympy import symbols , solve, Eq
y = symbols('y')
eq1 = Eq(y + 3 + 8, 0)
sol = solve(eq1)
sol
y = symbols('x')
eq1 = Eq(3*x**2 - 5*x + 6, 0)
sol = solve(eq1,dict=True)
sol
from sympy... | github_jupyter |
## Data Loading Tutorial
Loading data is one crucial step in the deep learning pipeline. PyTorch makes it easy to write custom data loaders for your particular dataset. In this notebook, we're going to download and load cifar-10 dataset and return a torch "tensor" as the image and the label.
## Getting the dataset
H... | github_jupyter |
```
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
import yfinance as yf
import scipy
from sklearn.neighbors import KernelDensity
from scipy.interpolate import CubicSpline
%matplotlib inline
sns.set()
import xlrd
xlrd.xlsx.ensure_elementtree_imported(False, None)
xlrd.xlsx... | github_jupyter |
```
%matplotlib inline
#import matplotlib
#matplotlib.use('tkAgg')
import matplotlib.pyplot as plt
import sys
import math
import numpy as np
import scipy as sp
import scipy.optimize
import scipy.misc
import scipy.special
EUR_DECIMALS = 10**18
NMK_DECIMALS = 10**18
CAP = 15 * 10**8
S = -6.5
def issued(cummulative_euros)... | github_jupyter |
```
import matplotlib
import numpy as np
from scipy import stats
# matplotlib.use("macosx")
import matplotlib.pyplot as plt
#f = open("/Users/jeff/Research/Simcore/diffusion_len_10_2d.log",'r')
#f = open("/Users/jeff/Research/Simcore/diffusion_len_10_2d_2.log",'r')
f=open("/Users/jeff/Research/Simcore/rigid_diffusion_l... | github_jupyter |
# 1 Introducing neural networks
You have already been introduced to neural networks in the study materials: now you are going to have an opportunity to play with them in practice.
Neural networks can solve subtle pattern-recognition problems, which are very important in robotics. Although many of the activities are p... | github_jupyter |
# Diseño de software para cómputo científico
----
## Unidad 3: Persistencia de datos.
### Agenda de la Unidad 3
---
#### Clase 1
- Lectura y escritura de archivos.
- Persistencia de binarios en Python (`pickle`).
- Archivos INI/CFG, CSV, JSON, XML y YAML
## Lectura y escritura de archivos
- Python ofrece los obj... | github_jupyter |
# A Simple Staggered FV Code for the Navier-Stokes Equations
### Tony Saad <br/> Assistant Professor of Chemical Engineering <br/> University of Utah
```
import numpy as np
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from matpl... | github_jupyter |
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