Felix F Zimmermann fzimmermann89

## leaflet slider
<!DOCTYPE html>
<html>
<head>
	<title>Leaflet debug page</title>

	<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.0.0-beta.2/leaflet.css" />
	<script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.0.0-beta.2/leaflet.js"></script>
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<link rel="stylesheet" href="screen.css" />

## ifi.py
import numpy as np

#this accum class is ripped out of some of my other code, I think it should work standalone now
#or we just do it another way, we just need a mean^^
class accumulator:
    '''
    simple accumulator for large number of data points. allows retrieval of mean and stdev
    '''
    def __init__(self, like=None):
        self._n = 0

## radial_profile.py
def radial_profile(data, center=None, calcStd=False, os=1):
    '''
    calculates a radial profile of ND data around center. will ignore nans
    calStd: calculate standard deviation, return tuple of (profile, std)
    os: oversample by a factor. With default 1 the stepsize will be 1 pixel, with 2 it will be .5 pixels etc.
    '''
    if center is None:
        center = np.array(data.shape)//2
    if len(center) != data.ndim:
        raise TypeError('center should be of length data.ndim')

## mattermost2html.py
###### SETTINGS ######
url= '"
username = ""
password = ""
team_name = ""
channel_name = ""
#######################

import os
import requests

## run_batch2.py
from __future__ import print_function
import threading
from joblib import Parallel, delayed
import Queue
import os

# Fix print
_print = print
_rlock = threading.RLock()
def print(*args, **kwargs):

## simplecorrelator.ipynb

      
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                fzimmermann89
                / simplecorrelator.ipynb
            
            
              Last active
              July 28, 2021 14:06
            
              
                simplecorrelator
              
          
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## utilities_lv65.py
import numpy as np


### Requested pixel2q and q2pixel ####
def pixel2q(pixel, E_ev, detz_m, pixelsize_m=75e-6):
    """
    returns q in reciprocal nm
    E_ev: photon Energy in eV
    detz_m: detector distance in m
    pixelsize_m: detector pixelsize in m

## lut.py
import torch
from torch import nn
from typing import Tuple, Callable


class LUT(nn.Module):
    def __init__(self, f: Callable, dx: float, xrange: Tuple[float, float], mode: str = "linear"):
        """
        LUT of values of a function
        f: function to use, does not need to be differentiable

## mri_demo.ipynb

      
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                fzimmermann89
                / mri_demo.ipynb
            
            
              Last active
              November 14, 2022 09:36
            
              
                MRI Demo for M4AIM Presentation @ PTB Berlin. (c) Felix Zimmermann, MIT License
              
          
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## sliding_window.py
import warnings
from typing import Sequence
import torch
import numpy as np
# fzimmermann89, felix.zimmermann@ptb.de, 2024
def sliding_window(x:torch.Tensor, window_shape:int|Sequence[int], axis:None|int|Sequence[int]|=None):
    """Sliding window into the tensor x.

    Returns a view into the tensor x that represents a sliding window.
	<!DOCTYPE html>
	<html>
	<head>
	<title>Leaflet debug page</title>

	<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.0.0-beta.2/leaflet.css" />
	<script src="https://cdnjs.cloudflare.com/ajax/libs/leaflet/1.0.0-beta.2/leaflet.js"></script>
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<link rel="stylesheet" href="screen.css" />
	import numpy as np

	#this accum class is ripped out of some of my other code, I think it should work standalone now
	#or we just do it another way, we just need a mean^^
	class accumulator:
	'''
	simple accumulator for large number of data points. allows retrieval of mean and stdev
	'''
	def __init__(self, like=None):
	self._n = 0
	def radial_profile(data, center=None, calcStd=False, os=1):
	'''
	calculates a radial profile of ND data around center. will ignore nans
	calStd: calculate standard deviation, return tuple of (profile, std)
	os: oversample by a factor. With default 1 the stepsize will be 1 pixel, with 2 it will be .5 pixels etc.
	'''
	if center is None:
	center = np.array(data.shape)//2
	if len(center) != data.ndim:
	raise TypeError('center should be of length data.ndim')
	###### SETTINGS ######
	url= '"
	username = ""
	password = ""
	team_name = ""
	channel_name = ""
	#######################

	import os
	import requests
	from __future__ import print_function
	import threading
	from joblib import Parallel, delayed
	import Queue
	import os

	# Fix print
	_print = print
	_rlock = threading.RLock()
	def print(args, *kwargs):
	import numpy as np


	### Requested pixel2q and q2pixel ####
	def pixel2q(pixel, E_ev, detz_m, pixelsize_m=75e-6):
	"""
	returns q in reciprocal nm
	E_ev: photon Energy in eV
	detz_m: detector distance in m
	pixelsize_m: detector pixelsize in m
	import torch
	from torch import nn
	from typing import Tuple, Callable


	class LUT(nn.Module):
	def __init__(self, f: Callable, dx: float, xrange: Tuple[float, float], mode: str = "linear"):
	"""
	LUT of values of a function
	f: function to use, does not need to be differentiable
	import warnings
	from typing import Sequence
	import torch
	import numpy as np
	# fzimmermann89, felix.zimmermann@ptb.de, 2024
	def sliding_window(x:torch.Tensor, window_shape:int\|Sequence[int], axis:None\|int\|Sequence[int]\|=None):
	"""Sliding window into the tensor x.

	Returns a view into the tensor x that represents a sliding window.