markdown stringlengths 0 1.02M | code stringlengths 0 832k | output stringlengths 0 1.02M | license stringlengths 3 36 | path stringlengths 6 265 | repo_name stringlengths 6 127 |
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In many cases it is useful to have both a high quality movie and a lower resolution gif of the same animation. If that is desired, just deactivate the `remove_movie` option and give a filename with `.gif`. xmovie will first render a high quality movie and then convert it to a gif, without removing the movie afterwards.... | mov.save('movie_combo.gif', remove_movie=False, progress=True) | _____no_output_____ | MIT | docs/examples/quickstart.ipynb | zmoon/xmovie |
Modify the framerate of the output with the keyword arguments `framerate` (for movies) and `gif_framerate` (for gifs). | mov.save('movie_fast.gif', remove_movie=False, progress=True, framerate=20, gif_framerate=20)
mov.save('movie_slow.gif', remove_movie=False, progress=True, framerate=5, gif_framerate=5) | _____no_output_____ | MIT | docs/examples/quickstart.ipynb | zmoon/xmovie |
  Frame dimension selection By default, the movie passes through the `'time'` dimension of the DataArray, but this can be easily changed with the `framedim` argument: | mov = Movie(ds.air, framedim='lon')
mov.save('lon_movie.gif') | Movie created at lon_movie.mp4
GIF created at lon_movie.gif
| MIT | docs/examples/quickstart.ipynb | zmoon/xmovie |
 Modifying plots Rotating globe (preset) | from xmovie.presets import rotating_globe
mov = Movie(ds.air, plotfunc=rotating_globe)
mov.save('movie_rotating.gif', progress=True) | _____no_output_____ | MIT | docs/examples/quickstart.ipynb | zmoon/xmovie |
 | mov = Movie(ds.air, plotfunc=rotating_globe, style='dark')
mov.save('movie_rotating_dark.gif', progress=True) | _____no_output_____ | MIT | docs/examples/quickstart.ipynb | zmoon/xmovie |
 Specifying xarray plot method to be used Change the plotting function with the parameter `plotmethod`. | mov = Movie(ds.air, rotating_globe, plotmethod='contour')
mov.save('movie_cont.gif')
mov = Movie(ds.air, rotating_globe, plotmethod='contourf')
mov.save('movie_contf.gif') | Movie created at movie_cont.mp4
GIF created at movie_cont.gif
Movie created at movie_contf.mp4
GIF created at movie_contf.gif
| MIT | docs/examples/quickstart.ipynb | zmoon/xmovie |
 Changing preset settings | import numpy as np
ds = xr.tutorial.open_dataset('rasm', decode_times=False).Tair # 36 times in total
# Interpolate time for smoother animation
ds['time'].values[:] = np.arange(len(ds['time']))
ds = ds.interp(time=np.linspace(0, 10, 60))
# `Movie` accepts keywords for the xarray plotting interface and provides a set... | _____no_output_____ | MIT | docs/examples/quickstart.ipynb | zmoon/xmovie |
 User-provided Besides the presets, xmovie is designed to animate any custom plot which can be wrapped in a function acting on a matplotlib figure. This can contain xarray plotting commands, 'pure' matplotlib or a combination of both. This can come in handy when you want to animate a complex static ... | ds = xr.tutorial.open_dataset('rasm', decode_times=False).Tair
fig = plt.figure(figsize=[10,5])
tt = 30
station = dict(x=100, y=150)
ds_station = ds.sel(**station)
(ax1, ax2) = fig.subplots(ncols=2)
ds.isel(time=tt).plot(ax=ax1)
ax1.plot(station['x'], station['y'], marker='*', color='k' ,markersize=15)
ax1.text(stati... | _____no_output_____ | MIT | docs/examples/quickstart.ipynb | zmoon/xmovie |
All you need to do is wrap your plotting calls into a functions `func(ds, fig, frame)`, where ds is an xarray dataset you pass to `Movie`, fig is a matplotlib.figure handle and tt is the movie frame. | def custom_plotfunc(ds, fig, tt, *args, **kwargs):
# Define station location for timeseries
station = dict(x=100, y=150)
ds_station = ds.sel(**station)
(ax1, ax2) = fig.subplots(ncols=2)
# Map axis
# Colorlimits need to be fixed or your video is going to cause seizures.
# This is the o... | _____no_output_____ | MIT | docs/examples/quickstart.ipynb | zmoon/xmovie |
수학 기호 연습수식 기호들을 집어 넣는 연습을 해봅시다. $\theta = 1$ $1 \le 5 $ $\sum_{i=1}^{n} i^2 $ $$\sum_{i=1}^{n} \frac{1}{i} $$ | 1+1
| _____no_output_____ | MIT | math_symbol_prac.ipynb | Sumi-Lee/testrepository |
Summarize titers and sequences by dateCreate a single histogram on the same scale for number of titer measurements and number of genomic sequences per year to show the relative contribution of each data source. | import Bio
import Bio.SeqIO
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
%matplotlib inline
# Configure matplotlib theme.
fontsize = 14
matplotlib_params = {
'axes.labelsize': fontsize,
'font.size': fontsize,
'legend.fontsize': 12,
'xtick.labelsize': fontsize,... | _____no_output_____ | MIT | analyses/2018-11-07-summarize-titers-and-sequences-by-date.ipynb | blab/flu-forecasting |
Load sequences | ls ../../seasonal-flu/data/*.fasta
# Open FASTA of HA sequences for H3N2.
sequences = Bio.SeqIO.parse("../../seasonal-flu/data/h3n2_ha.fasta", "fasta")
# Get strain names from sequences.
distinct_strains_with_sequences = pd.Series([sequence.name.split("|")[0].replace("-egg", "")
... | _____no_output_____ | MIT | analyses/2018-11-07-summarize-titers-and-sequences-by-date.ipynb | blab/flu-forecasting |
Load titers | # Read titers into a data frame.
titers = pd.read_table(
"../../seasonal-flu/data/cdc_h3n2_egg_hi_titers.tsv",
header=None,
index_col=False,
names=["test", "reference", "serum", "source", "titer", "assay"]
)
titers.head()
titers["test_year"] = titers["test"].apply(lambda strain: int(strain.replace("-egg... | _____no_output_____ | MIT | analyses/2018-11-07-summarize-titers-and-sequences-by-date.ipynb | blab/flu-forecasting |
Plot sequence and titer strains by year | sequence_years.min()
sequence_years.max()
[sequence_years, titer_years]
sequence
fig, ax = plt.subplots(1, 1)
bins = np.arange(1968, 2019)
ax.hist([sequence_years, titer_years], bins, histtype="bar", label=["HA sequence", "HI titer"])
legend = ax.legend(
loc="upper left",
ncol=1,
frameon=False,
handlel... | _____no_output_____ | MIT | analyses/2018-11-07-summarize-titers-and-sequences-by-date.ipynb | blab/flu-forecasting |
HSMfile examples The [hsmfile module](https://github.com/hadfieldnz/hsmfile) is modelled on my IDL mgh_san routines and provides user-customisable access to remote (slow-access) and local (fast-access) files.This notebook exercises various aspects of the hsmfile module.Change history:MGH 2019-08-15 - afile is now ... | import os
import hsmfile | _____no_output_____ | MIT | examples/HSMfile_examples.ipynb | hadfieldnz/notebooks |
The following cell should be executed whenever the hsmfile module code has been changed. | from importlib import reload
reload(hsmfile); | _____no_output_____ | MIT | examples/HSMfile_examples.ipynb | hadfieldnz/notebooks |
Print the volumes supported by the hsmfile module on this platform | print(hsmfile.volume.keys()) | _____no_output_____ | MIT | examples/HSMfile_examples.ipynb | hadfieldnz/notebooks |
Specify the files for which we will search (Cook Strait Narrows 1 km run). Normally | vol = '/nesi/nobackup/niwa00020/hadfield'
sub = 'work/cook/roms/sim34/run'
pattern = 'bran-2009-2012-nzlam-1.20-detide/roms_avg_????.nc' | _____no_output_____ | MIT | examples/HSMfile_examples.ipynb | hadfieldnz/notebooks |
The hsmfile.path function returns a pathlib Path object. Here we construct the path names for the base directory on the remote, or master, volume (mirror = False) and the local, or mirror, volume (mirror = True) | hsmfile.path(sub=sub,vol=vol,mirror=False)
if 'mirror' in hsmfile.volume[vol]:
print(repr(hsmfile.path(sub=sub,vol=vol,mirror=True)))
else:
print('Volume has no mirror') | _____no_output_____ | MIT | examples/HSMfile_examples.ipynb | hadfieldnz/notebooks |
The hsmfile.search function uses the Path's glob function to create a generator object and from that generates and returns a sorted list of Path objects relative to the base. | match = hsmfile.search(pattern,sub=sub,vol=vol); match | _____no_output_____ | MIT | examples/HSMfile_examples.ipynb | hadfieldnz/notebooks |
The hsmfile.file function constructs and returns a list of path objects representing actual files. It checks for existence and copies from master to mirror as necessary. | file = [hsmfile.file(m,sub=sub,vol=vol) for m in match]; file | _____no_output_____ | MIT | examples/HSMfile_examples.ipynb | hadfieldnz/notebooks |
参数| | || --------- | ---------------------------------------------------------------------- || 参数 | 描述 || `pattern` | 匹配的正则表达式 ... | # re.sub(pattern, repl, string, count=0, flags=0)
# pattern 正则中的模式字符串。
# repl 替换的字符串，也可为一个函数。
# string 要被查找替换的原始字符串。
# count 模式匹配后替换的最大次数，默认 0 表示替换所有的匹配。
phone = "123-456-789 # 这是一个电话号码"
print(re.sub(r'#.*$', "", phone))
... | _____no_output_____ | MIT | _note_/内置包/re_正则处理.ipynb | By2048/_python_ |
其他```re.RegexObjectre.compile()返回RegexObject对象。re.MatchObjectgroup()返回被RE匹配的字符串。``` | dytt_title = ".*\[(.*)\].*"
name_0 = r"罗拉快跑BD国德双语中字[电影天堂www.dy2018.com].mkv"
name_1 = r"[电影天堂www.dy2018.com]罗拉快跑BD国德双语中字.mkv"
print(1, re.findall(dytt_title, name_0))
print(1, re.findall(dytt_title, name_1))
data = "xxxxxxxxxxxentry某某内容for-----------"
result = re.findall(".*entry(.*)for.*", data)
print(3, result)
| _____no_output_____ | MIT | _note_/内置包/re_正则处理.ipynb | By2048/_python_ |
Wizualizacja danych | df.price_value.hist(bins=100);
df.price_value.max()
df.price_value.describe()
df.groupby(['param_marka-pojazdu'])['price_value'].mean()
(
df
.groupby(['param_marka-pojazdu'])['price_value']
.agg(np.mean)
.sort_values(ascending=False)
.head(50)
).plot(kind='bar', figsize=(20,5))
(
df
.groupby(['param_marka-poja... | _____no_output_____ | MIT | day2_visualization.ipynb | wudzitsu/dw_matrix_car |
1- Class Activation Map with convolutionsIn this firt part, we will code class activation map as described in the paper [Learning Deep Features for Discriminative Localization](http://cnnlocalization.csail.mit.edu/)There is a GitHub repo associated with the paper:https://github.com/zhoubolei/CAMAnd even a demo in PyTo... | import io
import requests
from PIL import Image
import torch
import torch.nn as nn
from torchvision import models, transforms
from torch.nn import functional as F
import torch.optim as optim
import numpy as np
import cv2
import pdb
from matplotlib.pyplot import imshow
# input image
LABELS_URL = 'https://s3.amazonaws.... | _____no_output_____ | Apache-2.0 | HW2/HW2_CAM_Adversarial.ipynb | Hmkhalla/notebooks |
As in the demo, we will use the Resnet18 architecture. In order to get CAM, we need to transform this network in a fully convolutional network: at all layers, we need to deal with images, i.e. with a shape $\text{Number of channels} \times W\times H$ . In particular, we are interested in the last images as shown here:!... | net = models.resnet18(pretrained=True)
net.eval()
x = torch.randn(5, 3, 224, 224)
y = net(x)
y.shape
n_mean = [0.485, 0.456, 0.406]
n_std = [0.229, 0.224, 0.225]
normalize = transforms.Normalize(
mean=n_mean,
std=n_std
)
preprocess = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),... | _____no_output_____ | Apache-2.0 | HW2/HW2_CAM_Adversarial.ipynb | Hmkhalla/notebooks |
2- Adversarial examples In this second part, we will look at [adversarial examples](https://arxiv.org/abs/1607.02533): "An adversarial example is a sample of input data which has been modified very slightly in a way that is intended to cause a machine learning classifier to misclassify it. In many cases, these modific... | # Image under attack!
url_car = 'https://cdn130.picsart.com/263132982003202.jpg?type=webp&to=min&r=640'
response = requests.get(url_car)
img_pil = Image.open(io.BytesIO(response.content))
imshow(img_pil);
# same as above
preprocess = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
nor... | _____no_output_____ | Apache-2.0 | HW2/HW2_CAM_Adversarial.ipynb | Hmkhalla/notebooks |
3- Transforming a car into a catWe now implement the *Iterative Target Class Method (ITCM)* as defined by equation (4) in [Adversarial Attacks and Defences Competition](https://arxiv.org/abs/1804.00097)To test it, we will transform the car (labeled minivan by our `resnet18`) into a [Tabby cat](https://en.wikipedia.org... | x = preprocess(img_pil).clone()
xd = preprocess(img_pil).clone()
xd.requires_grad = True
idx = 281 #tabby
optimizer = optim.SGD([xd], lr=0.01)
for i in range(200):
#TODO: your code here
optimizer.zero_grad()
loss.backward()
optimizer.step()
print(loss.item())
_ = print_preds(output)
... | _____no_output_____ | Apache-2.0 | HW2/HW2_CAM_Adversarial.ipynb | Hmkhalla/notebooks |
Introduction to Deep Learning with PyTorchIn this notebook, you'll get introduced to [PyTorch](http://pytorch.org/), a framework for building and training neural networks. PyTorch in a lot of ways behaves like the arrays you love from Numpy. These Numpy arrays, after all, are just tensors. PyTorch takes these tensors ... | # First, import PyTorch
import torch
def activation(x):
""" Sigmoid activation function
Arguments
---------
x: torch.Tensor
"""
return 1/(1+torch.exp(-x))
### Generate some data
torch.manual_seed(7) # Set the random seed so things are predictable
# Features are 3 random normal... | _____no_output_____ | MIT | intro-to-pytorch/Part 1 - Tensors in PyTorch (Exercises).ipynb | Yasel-Garces/deep-learning-v2-pytorch |
Above I generated data we can use to get the output of our simple network. This is all just random for now, going forward we'll start using normal data. Going through each relevant line:`features = torch.randn((1, 5))` creates a tensor with shape `(1, 5)`, one row and five columns, that contains values randomly distrib... | ## Calculate the output of this network using the weights and bias tensors
activation(torch.sum(weights*features) + bias) | _____no_output_____ | MIT | intro-to-pytorch/Part 1 - Tensors in PyTorch (Exercises).ipynb | Yasel-Garces/deep-learning-v2-pytorch |
You can do the multiplication and sum in the same operation using a matrix multiplication. In general, you'll want to use matrix multiplications since they are more efficient and accelerated using modern libraries and high-performance computing on GPUs.Here, we want to do a matrix multiplication of the features and the... | ## Calculate the output of this network using matrix multiplication
torch.matmul(features,weights.reshape(5,1))+bias | _____no_output_____ | MIT | intro-to-pytorch/Part 1 - Tensors in PyTorch (Exercises).ipynb | Yasel-Garces/deep-learning-v2-pytorch |
Stack them up!That's how you can calculate the output for a single neuron. The real power of this algorithm happens when you start stacking these individual units into layers and stacks of layers, into a network of neurons. The output of one layer of neurons becomes the input for the next layer. With multiple input un... | ### Generate some data
torch.manual_seed(7) # Set the random seed so things are predictable
# Features are 3 random normal variables
features = torch.randn((1, 3))
# Define the size of each layer in our network
n_input = features.shape[1] # Number of input units, must match number of input features
n_hidden = 2 ... | _____no_output_____ | MIT | intro-to-pytorch/Part 1 - Tensors in PyTorch (Exercises).ipynb | Yasel-Garces/deep-learning-v2-pytorch |
> **Exercise:** Calculate the output for this multi-layer network using the weights `W1` & `W2`, and the biases, `B1` & `B2`. | ## Your solution here
activation(torch.matmul(activation(torch.matmul(features,W1) + B1),W2) + B2) | _____no_output_____ | MIT | intro-to-pytorch/Part 1 - Tensors in PyTorch (Exercises).ipynb | Yasel-Garces/deep-learning-v2-pytorch |
If you did this correctly, you should see the output `tensor([[ 0.3171]])`.The number of hidden units a parameter of the network, often called a **hyperparameter** to differentiate it from the weights and biases parameters. As you'll see later when we discuss training a neural network, the more hidden units a network h... | import numpy as np
a = np.random.rand(4,3)
a
b = torch.from_numpy(a)
b
b.numpy() | _____no_output_____ | MIT | intro-to-pytorch/Part 1 - Tensors in PyTorch (Exercises).ipynb | Yasel-Garces/deep-learning-v2-pytorch |
The memory is shared between the Numpy array and Torch tensor, so if you change the values in-place of one object, the other will change as well. | # Multiply PyTorch Tensor by 2, in place
b.mul_(2)
# Numpy array matches new values from Tensor
a | _____no_output_____ | MIT | intro-to-pytorch/Part 1 - Tensors in PyTorch (Exercises).ipynb | Yasel-Garces/deep-learning-v2-pytorch |
Passive Membrane Tutorial This is a tutorial which is designed to allow users to explore the passive responses of neuron membrane potentials and how it changes under various conditions such as current injection, ion concentration (both inside and outside the cell), change in membrane capacitance and passive conductanc... | dt = 1e-4 #Integration time step. Reduce if you encounter NaN errors.
t_sim = 0.5 #Total time plotted. Increase as desired.
Na_in = 13 #Sodium ion concentration inside the cell. Default = 13 (in mM)
Na_out = 120 #Sodium ion concentration outside the cell. Default = 120 (in mM)
K_in = 140 #Potassium i... | _____no_output_____ | MIT | Passive_Membrane_tutorial.ipynb | zbpvarun/Neuron |
Nernst Potential Equations: | import math as ma
Ena = -0.058*ma.log10(Na_in/Na_out);
Ek = -0.058*ma.log10(K_in/K_out); | _____no_output_____ | MIT | Passive_Membrane_tutorial.ipynb | zbpvarun/Neuron |
If you wish to use pre-determined ENa and EK values, set them here and convert this cell into code from Markdown:Ena = ??;Ek = ??; | import numpy as np
niter = int(t_sim//dt) #Total number of integration steps (constant).
#Output variables:
Vm = np.zeros(niter)
Ie = np.zeros(niter)
#Starting values: You can change the initial conditions of each simulation here:
Vm[0] = -0.070; | _____no_output_____ | MIT | Passive_Membrane_tutorial.ipynb | zbpvarun/Neuron |
Current Injection | I_inj =-5e-8 #Current amplitude. Default = 50 nA.
t_start = 0.150 #Start time of current injection.
t_end = 0.350 #End time of current injection.
Ie[int(t_start//dt):int(t_end//dt)] = I_inj | _____no_output_____ | MIT | Passive_Membrane_tutorial.ipynb | zbpvarun/Neuron |
Calculation - do the actual computation here: | #Integration steps - do not change:
for i in np.arange(niter-1):
Vm[i+1] = Vm[i] + dt/Cm*(Ie[i] - gNa*(Vm[i] - Ena) - gK*(Vm[i] - Ek));
| _____no_output_____ | MIT | Passive_Membrane_tutorial.ipynb | zbpvarun/Neuron |
Plot results | import matplotlib.pyplot as plt
%matplotlib notebook
plt.figure()
t = np.arange(niter)*dt;
plt.plot(t,Vm);
plt.xlabel('Time in s')
plt.ylabel('Membrane Voltage in V') | _____no_output_____ | MIT | Passive_Membrane_tutorial.ipynb | zbpvarun/Neuron |
Circuit visualizeこのドキュメントでは scikit-qulacs に用意されている量子回路を可視化します。scikitqulacsには現在、以下のような量子回路を用意しています。- create_qcl_ansatz(n_qubit: int, c_depth: int, time_step: float, seed=None): [arXiv:1803.00745](https://arxiv.org/abs/1803.00745)- create_farhi_neven_ansatz(n_qubit: int, c_depth: int, seed: Optional[int] = None): [arXiv... | from skqulacs.circuit.pre_defined import create_qcl_ansatz
from qulacsvis import circuit_drawer
n_qubit = 4
c_depth = 2
time_step = 1.
ansatz = create_qcl_ansatz(n_qubit, c_depth, time_step)
circuit_drawer(ansatz._circuit,"latex") | _____no_output_____ | MIT | doc/source/notebooks/circuit_visualize.ipynb | forest1040/scikit-qulacs |
farhi_neven_ansatzcreate_farhi_neven_ansatz( n_qubit: int, c_depth: int, seed: Optional[int] = None)[arXiv:1802.06002](https://arxiv.org/abs/1802.06002) | from skqulacs.circuit.pre_defined import create_farhi_neven_ansatz
n_qubit = 4
c_depth = 2
ansatz = create_farhi_neven_ansatz(n_qubit, c_depth)
circuit_drawer(ansatz._circuit,"latex") | _____no_output_____ | MIT | doc/source/notebooks/circuit_visualize.ipynb | forest1040/scikit-qulacs |
farhi_neven_watle_ansatzfarhi_neven_ansatzを @WATLE さんが改良したものcreate_farhi_neven_watle_ansatz( n_qubit: int, c_depth: int, seed: Optional[int] = None) | from skqulacs.circuit.pre_defined import create_farhi_neven_watle_ansatz
n_qubit = 4
c_depth = 2
ansatz = create_farhi_neven_watle_ansatz(n_qubit, c_depth)
circuit_drawer(ansatz._circuit,"latex") | _____no_output_____ | MIT | doc/source/notebooks/circuit_visualize.ipynb | forest1040/scikit-qulacs |
ibm_embedding_circuitcreate_ibm_embedding_circuit(n_qubit: int)[arXiv:1802.06002](https://arxiv.org/abs/1802.06002) | from skqulacs.circuit.pre_defined import create_ibm_embedding_circuit
n_qubit = 4
circuit = create_ibm_embedding_circuit(n_qubit)
circuit_drawer(circuit._circuit,"latex") | _____no_output_____ | MIT | doc/source/notebooks/circuit_visualize.ipynb | forest1040/scikit-qulacs |
shirai_ansatzcreate_shirai_ansatz( n_qubit: int, c_depth: int = 5, seed: int = 0)[arXiv:2111.02951](https://arxiv.org/abs/2111.02951) | from skqulacs.circuit.pre_defined import create_shirai_ansatz
n_qubit = 4
c_depth = 2
ansatz = create_shirai_ansatz(n_qubit, c_depth)
circuit_drawer(ansatz._circuit,"latex") | _____no_output_____ | MIT | doc/source/notebooks/circuit_visualize.ipynb | forest1040/scikit-qulacs |
npqcd_ansatzcreate_npqcd_ansatz( n_qubit: int, c_depth: int, c: float = 0.1)[arXiv:2108.01039](https://arxiv.org/abs/2108.01039) | from skqulacs.circuit.pre_defined import create_npqc_ansatz
n_qubit = 4
c_depth = 2
ansatz = create_npqc_ansatz(n_qubit, c_depth)
circuit_drawer(ansatz._circuit,"latex") | _____no_output_____ | MIT | doc/source/notebooks/circuit_visualize.ipynb | forest1040/scikit-qulacs |
yzcx_ansatzcreate_yzcx_ansatz( n_qubit: int, c_depth: int = 4, c: float = 0.1, seed: int = 9)[arXiv:2108.01039](https://arxiv.org/abs/2108.01039) | from skqulacs.circuit.pre_defined import create_yzcx_ansatz
n_qubit = 4
c_depth = 2
ansatz = create_yzcx_ansatz(n_qubit, c_depth)
circuit_drawer(ansatz._circuit,"latex") | _____no_output_____ | MIT | doc/source/notebooks/circuit_visualize.ipynb | forest1040/scikit-qulacs |
qcnn_ansatzcreate_qcnn_ansatz(n_qubit: int, seed: int = 0)Creates circuit used in https://www.tensorflow.org/quantum/tutorials/qcnn?hl=en, Section 1. | from skqulacs.circuit.pre_defined import create_qcnn_ansatz
n_qubit = 8
ansatz = create_qcnn_ansatz(n_qubit)
circuit_drawer(ansatz._circuit,"latex") | _____no_output_____ | MIT | doc/source/notebooks/circuit_visualize.ipynb | forest1040/scikit-qulacs |
**Downloading data from Google Drive** | !pip install -U -q PyDrive
import os
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
import zipfile
from google.colab import drive
# 1. Authenticate and create the PyDrive client.
auth.authenticate_user()
gauth = ... | _____no_output_____ | MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
Define working directories | working_dir = os.path.join(local_download_path, "extracted")
# defining working folders and labels
train_images_folder = os.path.join(working_dir, "train_images")
train_labels_file = os.path.join(working_dir, "train.csv")
test_images_folder = os.path.join(working_dir, "test_images")
test_labels_file = os.path.join(wo... | _____no_output_____ | MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
**Data preprocessing** Drop duplicates | train_labels.drop_duplicates("ImageId", keep="last", inplace=True) | _____no_output_____ | MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
Add to the train dataframe all non-defective images, setting None as value of EncodedPixels column | images = os.listdir(train_images_folder)
present_rows = train_labels.ImageId.tolist()
for img in images:
if img not in present_rows:
train_labels = train_labels.append({"ImageId" : img, "ClassId" : 1, "EncodedPixels" : None},
ignore_index=True)
| _____no_output_____ | MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
Change EncodedPixels column, by setting 1 if images is defected and 0 otherwise | for index, row in train_labels.iterrows():
train_labels.at[index, "EncodedPixels"] = int(train_labels.at[index, "EncodedPixels"] is not None) | _____no_output_____ | MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
In total we got 12,568 training samples | train_labels | _____no_output_____ | MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
Create data flow using ImageDataGenerator, see example here: https://medium.com/@vijayabhaskar96/tutorial-on-keras-flow-from-dataframe-1fd4493d237c | from keras_preprocessing.image import ImageDataGenerator
def create_datagen():
return ImageDataGenerator(
fill_mode='constant',
cval=0.,
rotation_range=10,
height_shift_range=0.1,
width_shift_range=0.1,
vertical_flip=True,
rescale=1./255,
zoom_range=0... | Found 10683 validated image filenames.
Found 1885 validated image filenames.
Found 5506 validated image filenames.
| MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
**Building and fiting model** | from keras.applications import InceptionResNetV2
from keras.models import Model
from keras.layers.core import Dense
from keras.layers.pooling import GlobalAveragePooling2D
from keras import optimizers
model = InceptionResNetV2(weights='imagenet', input_shape=(256,512,3), include_top=False)
#model.load_weights('/kaggle... | WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/optimizers.py:793: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py:3657: The name tf.log is deprecated. Ple... | MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
Fittting the data | STEP_SIZE_TRAIN=train_gen.n//train_gen.batch_size
STEP_SIZE_VALID=val_gen.n//val_gen.batch_size
STEP_SIZE_TEST=test_gen.n//test_gen.batch_size
model_binary.fit_generator(generator=train_gen,
steps_per_epoch=STEP_SIZE_TRAIN,
validation_data=val_gen,
validation... | Epoch 1/15
333/333 [==============================] - 637s 2s/step - loss: 0.5724 - acc: 0.7208 - val_loss: 1.1674 - val_acc: 0.3987
Epoch 2/15
333/333 [==============================] - 632s 2s/step - loss: 0.3274 - acc: 0.8580 - val_loss: 0.6656 - val_acc: 0.7275
Epoch 3/15
333/333 [==============================] - ... | MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
Predicting test labels | test_gen.reset()
pred=model_binary.predict_generator(test_gen,
steps=STEP_SIZE_TEST,
verbose=1) | 5506/5506 [==============================] - 211s 38ms/step
| MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
**Saving results** Create dataframe with probalities of having defects for each image | ids = np.array(test_labels.ImageId)
pred = np.array([p[0] for p in pred])
probabilities_df = pd.DataFrame({'ImageId': ids, 'Probability': pred}, columns=['ImageId', 'Probability'])
probabilities_df
from google.colab import files
df.to_csv('filename.csv')
files.download('filename.csv')
drive.mount('/content/gdrive')
... | _____no_output_____ | MIT | Defect_check.ipynb | franchukpetro/steel_defect_detection |
For Loop | week = ["Sunday", "Monday", "Tuesday", "Wednesday", "Thursday","Friday", "Saturday"]
for x in week:
print (x) | Sunday
Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
The Break Statement |
for x in week:
print (x)
if x == "Thursday":
break
for x in week:
if x == "Thursday":
break
print (x) | Sunday
Monday
Tuesday
Wednesday
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
Looping through string | for x in "Programmming with python":
print (x) | P
r
o
g
r
a
m
m
m
i
n
g
w
i
t
h
p
y
t
h
o
n
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
The range function | for x in range(10):
print (x) | 0
1
2
3
4
5
6
7
8
9
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
Nested Loops | adjective = ["red", "big", "tasty"]
fruits = ["apple","banana", "cherry"]
for x in adjective:
for y in fruits:
print (x, y) | red apple
red banana
red cherry
big apple
big banana
big cherry
tasty apple
tasty banana
tasty cherry
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
While loop | i = 10
while i > 6:
print(i)
i -= 1 #Assignment operator for subtraction i = 1 - i | 10
9
8
7
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
The break statement | i = 10
while i > 6:
print (i)
if i == 8:
break
i-=1 | 10
9
8
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
The continue statement | i = 10
while i>6:
i = i - 1
if i == 8:
continue
print (i)
| 9
7
6
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
The else statement | i = 10
while i>6:
i = i - 1
print (i)
else:
print ("i is no longer greater than 6") | 9
8
7
6
i is no longer greater than 6
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
Aplication 1 | #WHILE LOOP
x = 0
while x <= 10:
print ("Value", x)
x+=1
#FOR LOOPS
value = ["Value 1", "Value 2", "Value 3", "Value 4", "Value 5","Value 6", "Value 7", "Value 8", "Value 9", "Value 10"]
for x in value:
print (x) | Value 1
Value 2
Value 3
Value 4
Value 5
Value 6
Value 7
Value 8
Value 9
Value 10
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
Application 2 | i = 20
while i>4:
i -= 1
print (i)
else:
print ('i is no longer greater than 3') | 19
18
17
16
15
14
13
12
11
10
9
8
7
6
5
4
i is no longer greater than 3
| Apache-2.0 | Loop_Statement.ipynb | cocolleen/CPEN-21A-CPE1-2 |
PLOT FOR FOLLOWER | # THE FOLLOWERS'S VALUE AND NAME
plt.plot(markFollower[:3], [1, 1, 0])
plt.suptitle("FOLLOWER - NANO")
plt.show()
plt.plot(markFollower[1:5], [0, 1, 1,0])
plt.suptitle("FOLLOWER - MICRO")
plt.show()
plt.plot(markFollower[3:], [0, 1, 1])
plt.suptitle("FOLLOWER - MEDIUM")
plt.show()
plt.plot(markFollower[:3], [1, 1,... | _____no_output_____ | MIT | .ipynb_checkpoints/Fuzzy - Copy-checkpoint.ipynb | evanezcent/Fuzzing |
PLOT FOR LINGUSITIC | # THE LINGUISTIC'S VALUE AND NAME
markEngagement = [0, 0.6, 1.7, 4.7, 6.9, 8, 10]
plt.plot(markEngagement[:3], [1, 1, 0])
plt.suptitle("ENGAGEMENT - NANO")
plt.show()
plt.plot(markEngagement[1:4], [0, 1, 0])
plt.suptitle("ENGAGEMENT - MICRO")
plt.show()
plt.plot(markEngagement[2:6], [0, 1, 1, 0])
plt.suptitle("ENGAG... | _____no_output_____ | MIT | .ipynb_checkpoints/Fuzzy - Copy-checkpoint.ipynb | evanezcent/Fuzzing |
Fuzzification | # FOLLOWER=========================================
# membership function
def fuzzyFollower(countFol):
follower = []
# STABLE GRAPH
if (markFollower[0] <= countFol and countFol < markFollower[1]):
scoreFuzzy = 1
follower.append(Datafuzzy(scoreFuzzy, lingFollower[0]))
# GRAPH DOWN
... | _____no_output_____ | MIT | .ipynb_checkpoints/Fuzzy - Copy-checkpoint.ipynb | evanezcent/Fuzzing |
Inference | def cekDecission(follower, engagement):
temp_yes = []
temp_no = []
if (follower.decission == "NANO"):
# Get minimal score fuzzy every decision NO or YES
temp_yes.append(min(follower.score,engagement[0].score))
# if get 2 data fuzzy Engagement
if (len(engagement) > 1):
... | _____no_output_____ | MIT | .ipynb_checkpoints/Fuzzy - Copy-checkpoint.ipynb | evanezcent/Fuzzing |
Result | # Result
def getResult(resultYes, resultNo):
yes = 0
no = 0
if(resultNo):
no = max(resultNo)
if(resultYes):
yes = max(resultYes)
return yes, no | _____no_output_____ | MIT | .ipynb_checkpoints/Fuzzy - Copy-checkpoint.ipynb | evanezcent/Fuzzing |
Defuzzification | def finalDecission(yes, no):
mamdani = (((10 + 20 + 30 + 40 + 50 + 60 + 70) * no) + ((80 + 90 + 100) * yes)) / ((7 * no) + (yes * 3))
return mamdani | _____no_output_____ | MIT | .ipynb_checkpoints/Fuzzy - Copy-checkpoint.ipynb | evanezcent/Fuzzing |
Main Function | def mainFunction(followerCount, engagementRate):
follower = fuzzyFollower(followerCount)
engagement = fuzzyEngagement(engagementRate)
resultYes, resultNo = fuzzyRules(follower, engagement)
yes, no = getResult(resultYes, resultNo)
return finalDecission(yes, no)
data = pd.read_csv('influencers.csv')
... | _____no_output_____ | MIT | .ipynb_checkpoints/Fuzzy - Copy-checkpoint.ipynb | evanezcent/Fuzzing |
Road Following - Live demo (TensorRT) with collision avoidance Added collision avoidance ResNet18 TRT threshold between free and blocked is the controller - action: just a pause as long the object is in front or by time increase in speed_gain requires some small increase in steer_gain (once a slider is blue (mouse cl... | import torch
device = torch.device('cuda') | _____no_output_____ | MIT | trt-Jetbot-RoadFollowing_with_CollisionRESNet_TRT.ipynb | tomMEM/Jetbot-Project |
Load the TRT optimized models by executing the cell below | import torch
from torch2trt import TRTModule
model_trt = TRTModule()
model_trt.load_state_dict(torch.load('best_steering_model_xy_trt.pth')) # well trained road following model
model_trt_collision = TRTModule()
model_trt_collision.load_state_dict(torch.load('best_model_trt.pth')) # anti collision model trained for o... | _____no_output_____ | MIT | trt-Jetbot-RoadFollowing_with_CollisionRESNet_TRT.ipynb | tomMEM/Jetbot-Project |
Creating the Pre-Processing Function We have now loaded our model, but there's a slight issue. The format that we trained our model doesnt exactly match the format of the camera. To do that, we need to do some preprocessing. This involves the following steps:1. Convert from HWC layout to CHW layout2. Normalize using s... | import torchvision.transforms as transforms
import torch.nn.functional as F
import cv2
import PIL.Image
import numpy as np
mean = torch.Tensor([0.485, 0.456, 0.406]).cuda().half()
std = torch.Tensor([0.229, 0.224, 0.225]).cuda().half()
def preprocess(image):
image = PIL.Image.fromarray(image)
image = transfor... | _____no_output_____ | MIT | trt-Jetbot-RoadFollowing_with_CollisionRESNet_TRT.ipynb | tomMEM/Jetbot-Project |
Awesome! We've now defined our pre-processing function which can convert images from the camera format to the neural network input format.Now, let's start and display our camera. You should be pretty familiar with this by now. | from IPython.display import display
import ipywidgets
import traitlets
from jetbot import Camera, bgr8_to_jpeg
camera = Camera()
import IPython
image_widget = ipywidgets.Image()
traitlets.dlink((camera, 'value'), (image_widget, 'value'), transform=bgr8_to_jpeg) | _____no_output_____ | MIT | trt-Jetbot-RoadFollowing_with_CollisionRESNet_TRT.ipynb | tomMEM/Jetbot-Project |
We'll also create our robot instance which we'll need to drive the motors. | from jetbot import Robot
robot = Robot() | _____no_output_____ | MIT | trt-Jetbot-RoadFollowing_with_CollisionRESNet_TRT.ipynb | tomMEM/Jetbot-Project |
Now, we will define sliders to control JetBot> Note: We have initialize the slider values for best known configurations, however these might not work for your dataset, therefore please increase or decrease the sliders according to your setup and environment1. Speed Control (speed_gain_slider): To start your JetBot incr... | speed_gain_slider = ipywidgets.FloatSlider(min=0.0, max=1.0, step=0.01, description='speed gain')
steering_gain_slider = ipywidgets.FloatSlider(min=0.0, max=1.0, step=0.01, value=0.10, description='steering gain')
steering_dgain_slider = ipywidgets.FloatSlider(min=0.0, max=0.5, step=0.001, value=0.23, description='stee... | _____no_output_____ | MIT | trt-Jetbot-RoadFollowing_with_CollisionRESNet_TRT.ipynb | tomMEM/Jetbot-Project |
Next, we'll create a function that will get called whenever the camera's value changes. This function will do the following steps1. Pre-process the camera image2. Execute the neural network3. Compute the approximate steering value4. Control the motors using proportional / derivative control (PD) | import time
import os
import math
angle = 0.0
angle_last = 0.0
angle_last_block=0
count_stops=0
go_on=1
stop_time=20 #number of frames to remain stopped
x=0.0
y=0.0
speed_value=speed_gain_slider.value
t1=0
road_following=1
speed_value_block=0
def execute(change):
global angle, angle_last, angle_last_block, bloc... | _____no_output_____ | MIT | trt-Jetbot-RoadFollowing_with_CollisionRESNet_TRT.ipynb | tomMEM/Jetbot-Project |
Cool! We've created our neural network execution function, but now we need to attach it to the camera for processing.We accomplish that with the observe function. >WARNING: This code will move the robot!! Please make sure your robot has clearance and it is on Lego or Track you have collected data on. The road follower ... | camera.observe(execute, names='value') | _____no_output_____ | MIT | trt-Jetbot-RoadFollowing_with_CollisionRESNet_TRT.ipynb | tomMEM/Jetbot-Project |
Awesome! If your robot is plugged in it should now be generating new commands with each new camera frame. You can now place JetBot on Lego or Track you have collected data on and see whether it can follow track.If you want to stop this behavior, you can unattach this callback by executing the code below. | import time
camera.unobserve(execute, names='value')
time.sleep(0.1) # add a small sleep to make sure frames have finished processing
robot.stop()
camera.stop() | _____no_output_____ | MIT | trt-Jetbot-RoadFollowing_with_CollisionRESNet_TRT.ipynb | tomMEM/Jetbot-Project |
Elastic search in CollabHad to install elastic search in colab for 'reasons' and this is the way it worked for me. Might be usefull for someone else also.Works with 7.9.2. Probably could be run also with 7.14.0, but didn't have time to debug the issues. If you want, you can try and just run the instance under the 'ela... | #7.9.1 works with ES 7.9.2
!pip install -Iv elasticsearch==7.9.1
#download ES 7.92 and extract
%%bash
wget -q https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz
wget -q https://artifacts.elastic.co/downloads/elasticsearch/elasticsearch-oss-7.9.2-linux-x86_64.tar.gz.sha512
... | /usr/local/lib/python3.7/dist-packages/requests/__init__.py:91: RequestsDependencyWarning: urllib3 (1.26.6) or chardet (3.0.4) doesn't match a supported version!
RequestsDependencyWarning)
| MIT | elasticsearch_install.ipynb | xSakix/AI_colan_notebooks |
Building a Bayesian Network---In this tutorial, we introduce how to build a **Bayesian (belief) network** based on domain knowledge of the problem.If we build the Bayesian network in different ways, the built network can have different graphs and sizes, which can greatly affect the memory requirement and inference eff... | pip install pgmpy | Requirement already satisfied: pgmpy in /Users/yimei/miniforge3/lib/python3.9/site-packages (0.1.17)
Requirement already satisfied: torch in /Users/yimei/miniforge3/lib/python3.9/site-packages (from pgmpy) (1.11.0)
Requirement already satisfied: statsmodels in /Users/yimei/miniforge3/lib/python3.9/site-packages (from p... | Apache-2.0 | notebooks/bayesian-network-building.ipynb | meiyi1986/tutorials |
Then, we import the necessary modules for the Bayesian network as follows. | from pgmpy.models import BayesianNetwork
from pgmpy.factors.discrete import TabularCPD | _____no_output_____ | Apache-2.0 | notebooks/bayesian-network-building.ipynb | meiyi1986/tutorials |
Now, we build the alarm Bayesian network as follows.1. We define the network structure by specifying the four links.2. We define (estimate) the discrete conditional probability tables, represented as the `TabularCPD` class. | # Define the network structure
alarm_model = BayesianNetwork(
[
("Burglary", "Alarm"),
("Earthquake", "Alarm"),
("Alarm", "JohnCall"),
("Alarm", "MaryCall"),
]
)
# Define the probability tables by TabularCPD
cpd_burglary = TabularCPD(
variable="Burglary", variable_card=2, va... | _____no_output_____ | Apache-2.0 | notebooks/bayesian-network-building.ipynb | meiyi1986/tutorials |
We can view the nodes of the alarm network. | # Viewing nodes of the model
alarm_model.nodes() | _____no_output_____ | Apache-2.0 | notebooks/bayesian-network-building.ipynb | meiyi1986/tutorials |
We can also view the edges of the alarm network. | # Viewing edges of the model
alarm_model.edges() | _____no_output_____ | Apache-2.0 | notebooks/bayesian-network-building.ipynb | meiyi1986/tutorials |
We can show the probability tables using the `print()` method. > **NOTE**: the `pgmpy` library stores ALL the probabilities (including the last probability). This requires a bit more memory, but can save time for calculating the last probability by normalisation rule.Let's print the probability tables for **Alarm** and... | # Print the probability table of the Alarm node
print(cpd_alarm)
# Print the probability table of the MaryCalls node
print(cpd_marycall) | +------------+---------------+---------------+---------------+---------------+
| Burglary | Burglary(0) | Burglary(0) | Burglary(1) | Burglary(1) |
+------------+---------------+---------------+---------------+---------------+
| Earthquake | Earthquake(0) | Earthquake(1) | Earthquake(0) | Earthquake(1) |
+---... | Apache-2.0 | notebooks/bayesian-network-building.ipynb | meiyi1986/tutorials |
We can find all the **(conditional) independencies** between the nodes in the network. | alarm_model.get_independencies() | _____no_output_____ | Apache-2.0 | notebooks/bayesian-network-building.ipynb | meiyi1986/tutorials |
We can also find the **local (conditional) independencies of a specific node** in the network as follows. | # Checking independcies of a node
alarm_model.local_independencies("JohnCall") | _____no_output_____ | Apache-2.0 | notebooks/bayesian-network-building.ipynb | meiyi1986/tutorials |
1. Introduction | import os
import sys
module_path = os.path.abspath(os.path.join('..'))
if module_path not in sys.path:
sys.path.append(module_path)
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from prml.linear import (
LinearRegression,
RidgeRegression,
BayesianRegression
)
from prml.preprocess... | _____no_output_____ | MIT | notebooks/ch01_Introduction.ipynb | wenbos3109/PRML |
1.1. Example: Polynomial Curve Fitting | def create_toy_data(func, sample_size, std):
x = np.linspace(0, 1, sample_size)
t = func(x) + np.random.normal(scale=std, size=x.shape)
return x, t
def func(x):
return np.sin(2 * np.pi * x)
x_train, y_train = create_toy_data(func, 10, 0.25)
x_test = np.linspace(0, 1, 100)
y_test = func(x_test)
plt.sc... | _____no_output_____ | MIT | notebooks/ch01_Introduction.ipynb | wenbos3109/PRML |
Regularization | feature = PolynomialFeature(9)
X_train = feature.transform(x_train)
X_test = feature.transform(x_test)
model = RidgeRegression(alpha=1e-3)
model.fit(X_train, y_train)
y = model.predict(X_test)
y = model.predict(X_test)
plt.scatter(x_train, y_train, facecolor="none", edgecolor="b", s=50, label="training data")
plt.plo... | _____no_output_____ | MIT | notebooks/ch01_Introduction.ipynb | wenbos3109/PRML |
1.2.6 Bayesian curve fitting | model = BayesianRegression(alpha=2e-3, beta=2)
model.fit(X_train, y_train)
y, y_err = model.predict(X_test, return_std=True)
plt.scatter(x_train, y_train, facecolor="none", edgecolor="b", s=50, label="training data")
plt.plot(x_test, y_test, c="g", label="$\sin(2\pi x)$")
plt.plot(x_test, y, c="r", label="mean")
plt.f... | _____no_output_____ | MIT | notebooks/ch01_Introduction.ipynb | wenbos3109/PRML |
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