Дим Щ sherdim

## README.md

      
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                lgersman
                / README.md
            
            
              Last active
              October 19, 2022 12:15
            
              
                improved version of my d3.js selection frame example (https://gist.github.com/lgersman/5310854) supporting draggable/selectable nodes
              
          
Click into the drawing area to start the selection frame
move the mouse to resize the selection frame
Release the mouse button to resize the selection frame

new features :

circles are draggable
circles can be selected (multiple selections possible by pressing CTRL while clicking a circle)
the selection frame selects all circles within the frame (by pressing CTRL the selected circles will be appended to current selection)
multiple selected circles will be dragged simultaneous


## output.png

      
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              26 forks
            
          
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              76 stars
            
          
                endolith
                / output.png
            
            
              Last active
              May 21, 2024 23:58
            
              
                Detecting rotation and line spacing of image of page of text using Radon transform
              
          
## docker-compose.yml
jupyter:
  image: jupyter/datascience-notebook
  environment:
    - PASSWORD=${PASSWORD}


nginx:
  image: nginx
  links:
    - jupyter

## signal_quality.py
import mne
import numpy as np
from scipy.stats import norm
from matplotlib import pyplot as plt

# Load required sample data
data_path = mne.datasets.sample.data_path()
subjects_dir = data_path + '/subjects'
evoked = mne.read_evokeds(data_path + '/MEG/sample/sample_audvis-ave.fif')[0]
fwd = mne.read_forward_solution(data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif', surf_ori=False, force_fixed=True)

## lda_ridge_pcagnb.ipynb

      
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                kingjr
                / lda_ridge_pcagnb.ipynb
            
            
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              August 2, 2017 16:10
            
              
                This illustrate that LDA, Ridge and PCA+GNB are equivalent for binary y
              
          
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## ctc_decoder.py
"""
Author: Awni Hannun

This is an example CTC decoder written in Python. The code is
intended to be a simple example and is not designed to be
especially efficient.

The algorithm is a prefix beam search for a model trained
with the CTC loss function.

## engines.json
[
	{
		"name": "CrossRef Lookup",
		"alias": "CrossRef",
		"icon": "file:///C:/Users/zumstein/AppData/Roaming/Mozilla/Firefox/Profiles/fo682pah.default/zotero/locate/CrossRef%20Lookup.gif",
		"_urlTemplate": "http://crossref.org/openurl?{z:openURL}&pid=zter:zter321",
		"description": "CrossRef Search Engine",
		"hidden": false,
		"_urlParams": [],
		"_urlNamespaces": {

## effective_data_viz_6.py
# Correlation Matrix Heatmap
f, ax = plt.subplots(figsize=(10, 6))
corr = wines.corr()
hm = sns.heatmap(round(corr,2), annot=True, ax=ax, cmap="coolwarm",fmt='.2f',
                 linewidths=.05)
f.subplots_adjust(top=0.93)
t= f.suptitle('Wine Attributes Correlation Heatmap', fontsize=14)
	jupyter:
	image: jupyter/datascience-notebook
	environment:
	- PASSWORD=${PASSWORD}


	nginx:
	image: nginx
	links:
	- jupyter
	import mne
	import numpy as np
	from scipy.stats import norm
	from matplotlib import pyplot as plt

	# Load required sample data
	data_path = mne.datasets.sample.data_path()
	subjects_dir = data_path + '/subjects'
	evoked = mne.read_evokeds(data_path + '/MEG/sample/sample_audvis-ave.fif')[0]
	fwd = mne.read_forward_solution(data_path + '/MEG/sample/sample_audvis-meg-eeg-oct-6-fwd.fif', surf_ori=False, force_fixed=True)
	"""
	Author: Awni Hannun

	This is an example CTC decoder written in Python. The code is
	intended to be a simple example and is not designed to be
	especially efficient.

	The algorithm is a prefix beam search for a model trained
	with the CTC loss function.
	[
	{
	"name": "CrossRef Lookup",
	"alias": "CrossRef",
	"icon": "file:///C:/Users/zumstein/AppData/Roaming/Mozilla/Firefox/Profiles/fo682pah.default/zotero/locate/CrossRef%20Lookup.gif",
	"_urlTemplate": "http://crossref.org/openurl?{z:openURL}&pid=zter:zter321",
	"description": "CrossRef Search Engine",
	"hidden": false,
	"_urlParams": [],
	"_urlNamespaces": {
	# Correlation Matrix Heatmap
	f, ax = plt.subplots(figsize=(10, 6))
	corr = wines.corr()
	hm = sns.heatmap(round(corr,2), annot=True, ax=ax, cmap="coolwarm",fmt='.2f',
	linewidths=.05)
	f.subplots_adjust(top=0.93)
	t= f.suptitle('Wine Attributes Correlation Heatmap', fontsize=14)