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# Numpy z transform

Transfer functions from difference equations ¶. For a first order difference equation (the discrete equivalent of a first order differential equation): y(k) + a1y(k − 1) = b1u(k − 1) If we interpret z − n as an n time step delay, can write. Z[y(k − n)] = Y(z)z − n. This transforms our difference equation to import numpy as np from scipy.stats import zprob def z_transform (r, n): z = np.log ((1 + r) / (1 - r)) * (np.sqrt (n - 3) / 2) p = zprob (-z) return p correlation sample-size python fisher-transform

def H(z): num = z**5 - z**4 + z**3 - z**2 denom = z**5 + 0.54048*z**4 - 0.62519*z**3 - 0.66354*z**2 + 0.60317*z + 0.69341 return num/denom import numpy as np import matplotlib.pyplot as plt w_range = np.linspace(0, 2*np.pi, 1000) plt.plot(w_range, np.abs(H(np.exp(1j*w_range))) scipy.signal.freqz(b, a=1, worN=512, whole=False, plot=None, fs=6.283185307179586, include_nyquist=False) [source] ¶. Compute the frequency response of a digital filter. Given the M-order numerator b and N-order denominator a of a digital filter, compute its frequency response

scipy.signal.iirfilter. ¶. IIR digital and analog filter design given order and critical points. Design an Nth-order digital or analog filter and return the filter coefficients. The order of the filter. A scalar or length-2 sequence giving the critical frequencies. For digital filters, Wn are in the same units as fs where m is the output sample number and n=max (len (a),len (b)) is the model order. The rational transfer function describing this filter in the z-transform domain is: -1 -nb b  + b z + + b [nb] z Y (z) = ---------------------------------- X (z) -1 -na a  + a z + + a [na] z Chirp z-Transform. As described in: Rabiner, L.R., R.W. Schafer and C.M. Rader. The Chirp z-Transform Algorithm. IEEE Transactions on Audio and Electroacoustics, AU-17(2):86--92, 1969 import numpy as np: def chirpz (x, A, W, M): Compute the chirp z-transform. The discrete z-transform, X(z) = \sum_{n=0}^{N-1} x_n z^{-n} is calculated at M points

### 51. The $$z$$-transform — Dynamics and Control with ..

1. Set the underlying transformation matrix from a 3x3 numpy array: a c e b d f 0 0 1. skew (self, xShear, yShear) [source] ¶ Add a skew in place. xShear and yShear are the shear angles along the x- and y-axes, respectively, in radians. Returns self, so this method can easily be chained with more calls to rotate(), rotate_deg(), translate() and scale(). skew_deg (self, xShear, yShear) [source.
2. z-Transformation Definition. Durch eine z-Transformation bzw.Standardisierung von Merkmalen / Variablen werden diese in der Statistik in eine andere Form verwandelt, um sie vergleichbar zu machen.. Dazu subtrahiert man von jedem Messwert den arithmetischen Mittelwert, teilt die resultierende Differenz durch die Standardabweichung und erhält dadurch die sog
3. If False, try to avoid a copy and do inplace scaling instead. This is not guaranteed to always work inplace; e.g. if the data is not a NumPy array or scipy.sparse CSR matrix, a copy may still be returned. with_mean bool, default=True. If True, center the data before scaling. This does not work (and will raise an exception) when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory
4. import matplotlib.pyplot as plt import scipy from scipy.fftpack import fftshift import numpy as np x = np.arange(-3, 3, 0.01) y = np.zeros(len(x)) y[150:450] = 1 plt.plot(x, y) # plot of the step function yShift = fftshift(y) # shift of the step function Fourier = scipy.fft(yShift) # Fourier transform of y implementing the FFT Fourier = fftshift(Fourier) # inverse shift of the Fourier Transform plt.plot(Fourier) # plot of the Fourier transform
5. endolith commented on Dec 17, 2014. As mentioned in numpy/numpy/issues/1177, Chirp Z-transform and special cases of it have been submitted to SciPy under public domain by Paul Kienzle and Nadav Horesh: [SciPy-user] Chirp Z transform. The special cases of CZT are zoom FFT and scaled FFT (not sure what that does)

$\begingroup$ well, you gotta know python.i s'pose you can do the same with MATLAB of Octave. i think it might be easier to just write your digital filter in C. i can give you some generic C code if you want (send me an email) How to do inverse transformation sampling in scipy and numpy 16 Dec 2013. Let's say you have some data which follows a certain probability distribution. You can create a histogram and visualize the probability distribution, but now you want to sample from it. How do you go about doing this with python? The short answer: import numpy as np import scipy.interpolate as interpolate def inverse. The Z-transform is defined as. so if we apply that to our FIR filter friend up above, we get. Our transfer function that tells us what the filter does is in the Z domain. The FIR filter makes it so easy, we just divide by on both sides and we get In statistics, the Fisher transformation (aka Fisher z-transformation) can be used to test hypotheses about the value of the population correlation coefficient ρ between variables X and Y. This is because, when the transformation is applied to the sample correlation coefficient, the sampling distribution of the resulting variable is approximately normal, with a variance that is stable over. s = control.tf( [1, 0], 1) ts = numpy.linspace(0, 5) : def plot_step_response(G): t, y = control.step_response(G, ts) # Add some action before time zero so that the initial step is visible t = numpy.concatenate( [ [-1, 0], t]) y = numpy.concatenate( [ [0, 0], y]) plt.plot(t, y) : K_C = 1

### correlation - Fisher's z-transform in Python? - Cross

• Matrices (M) can be inverted using numpy.linalg.inv(M), be concatenated using numpy.dot(M0, M1), or transform homogeneous coordinate arrays (v) using numpy.dot(M, v) for shape (4, -1) column vectors, respectively numpy.dot(v, M.T) for shape (-1, 4) row vectors (array of points). This module follows the column vectors on the right and row major storage (C contiguous.
• Fourier analysis is fundamentally a method for expressing a function as a sum of periodic components, and for recovering the function from those components. When both the function and its Fourier transform are replaced with discretized counterparts, it is called the discrete Fourier transform (DFT). The DFT has become a mainstay of numerical.
• Affine transform of an image. ¶. Prepending an affine transformation ( Affine2D) to the data transform of an image allows to manipulate the image's shape and orientation. This is an example of the concept of transform chaining. The image of the output should have its boundary match the dashed yellow rectangle. import numpy as np import.
• import numpy as np: import matplotlib. pyplot as plt: from matplotlib import patches: from matplotlib. pyplot import axvline, axhline: from collections import defaultdict: def zplane (z, p, filename = None): Plot the complex z-plane given zeros and poles. # get a figure/plot: ax = plt. subplot (2, 2, 1) # TODO: should just inherit.
• from numpy import pi, exp, tan, sqrt, log10: import numpy as np: from scipy import signal: import matplotlib. pyplot as plt: def lp2_mzti (w0 = None, K = None): Digital 2nd order lowpass filter design using: improved Matched Z transform (MZTi) Analog prototype is: 1: H(s) = -----(s/w0)^2 + K*(s/w0) + 1: Returns coeffs of biquadratic digital. The Z-transform will have the below structure, based on Rational Functions: X ( z ) = P ( z ) Q ( z ) The two polynomials, P ( z ) and Q ( z ) , allow us to find the poles and zeros of the Z-Transform The need of NumPy. With the revolution of data science, data analysis libraries like NumPy, SciPy, Pandas, etc. have seen a lot of growth. With a much easier syntax than other programming languages, python is the first choice language for the data scientist. NumPy provides a convenient and efficient way to handle the vast amount of data. NumPy is also very convenient with Matrix multiplication and data reshaping. NumPy is fast which makes it reasonable to work with a large set of data Mars ⭐ 2,141. Mars is a tensor-based unified framework for large-scale data computation which scales Numpy, pandas, Scikit-learn and Python functions. Orjson ⭐ 2,095. Fast, correct Python JSON library supporting dataclasses, datetimes, and numpy. Xarray ⭐ 2,092. N-D labeled arrays and datasets in Python pandas.DataFrame.transform¶ DataFrame. transform (func, axis = 0, * args, ** kwargs) [source] ¶ Call func on self producing a DataFrame with transformed values.. Produced DataFrame will have same axis length as self. Parameters func function, str, list-like or dict-like. Function to use for transforming the data I ask as for discrete signals it is much easier to use the z-transform (which is why it exists). Can you give us more detail of what practical problem you are trying to solve as perhaps you're one layer to deep. $\endgroup$ - Dan Boschen Apr 13 '20 at 22:3 Browse other questions tagged control-theory z-transform linear-control or ask your own question. Featured on Meta Testing three-vote close and reopen on 13 network site numpy.ndarray. z-transform of input array. hybridLFPy.helpers.powerspec (data, tbin, Df = None, units = False, pointProcess = False) [source] ¶ Calculate (smoothed) power spectra of all timeseries in data. If units=True, power spectra are averaged across units. Note that averaging is done on power spectra rather than data. If pointProcess is True, power spectra are normalized by the length T.

### Is there a way to do inverse z-transforms in Python

1. Function to normalize data to have mean 0 and unity standard deviation (also called z-transform) Parameters: data: numpy.ndarray. Returns: numpy.ndarray. z-transform of input array. hybridLFPy.helpers.powerspec (data, tbin, Df=None, units=False, pointProcess=False) [source] ¶ Calculate (smoothed) power spectra of all timeseries in data. If units=True, power spectra are averaged across units.
2. Right, I meant z-transform. > I'm guessing you are talking about code that allows you to use the Bluestein > algorithm also for non-prime sizes where it makes sense, for example to > speed up the second case of this: > > In : x = np.random.random(5879) # a large prime This is for the real transform case, where the reindexing step requires to find a generator of Z/nZ
3. If you want to scale the entire matrix (not column wise), then remove the axis=0 and change the lines denom [denom==0] = 1 for denom = denom + (denom is 0). Suppose you have an array arr. You can normalize it like this: You first subtract the mean to center it around 0, then divide by the max to scale it to [ − 1, 1]
4. from mpl_toolkits import mplot3d import numpy as np import matplotlib.pyplot as plt fig = plt.figure() ax = plt.axes(projection='3d') z = np.linspace(0, 1, 100) x = z * np.sin(20 * z) y = z * np.cos(20 * z) ax.plot3D(x, y, z, 'gray') ax.set_title('3D line plot') plt.show() We can now plot a variety of three-dimensional plot types. The most basic three-dimensional plot is a 3D line plot created.
5. First, we create an abs_scaler with the MaxAbsScaler class. Then, we use the fit method to learn the required parameters for scaling the data (the maximum absolute value of each feature). Finally, we transform the data using those parameters. As you can observe, we obtain the same results using Pandas and Scikit-learn
6. >>> import pysal >>> import numpy as np >>> np. random. seed (12345) We create the binary weights matrix for the 4 x 4 lattice and generate the observation vector y: >>> w = pysal. lat2W (4, 4) >>> y = np. ones (16) >>> y [0: 8] = 0. The Gamma index function has five arguments, three of which are optional. The first two arguments are the vector of observations (y) and the spatial weights.

# sampling a sine wave programmatically import numpy as np import matplotlib.pyplot as plt plt.style.use('ggplot') # sampling information Fs = 44100 # sample rate T = 1 /Fs # sampling period t = 0.1 # seconds of sampling N = Fs*t # total points in signal # signal information freq = 100 # in hertz, the desired natural frequency omega = 2 *np.pi*freq # angular frequency for sine waves t_vec = np. NumPy: needed for doing some maths, such as finding the LSFs from a filter or roots from a polynomial; MatPlotLib: (besides the single pole) and _exp denotes the exponential approximation from a matched Z-Transform pole value from the equivalent or mirrored analog filter equation (Laplace). The absence of such suffix denotes it was designed directly as a digital filter. lazy_io: New api. import numpy as np import matplotlib.pyplot as plt angle = np.linspace( 0 , 2 * np.pi , 150 ) radius = 0.4 x = radius * np.cos( angle ) y = radius * np.sin( angle ) figure, axes = plt.subplots( 1 ) axes.plot( x, y ) axes.set_aspect( 1 ) plt.title( 'Parametric Equation Circle' ) plt.show() Output & Explanation: Output. In this example, we used the parametric equation of the circle to plot the.

### scipy.signal.lfilter — SciPy v1.6.3 Reference Guid

Z-Transform and Transfer function. In signal processing, delaying a signal . by . sample period (unit delay) is equivalent to multiplying the Z-transform . by . By applying this idea, we can find the Z-transform of the -point moving average filter in equation (2) as. Similarly, the Z-transform of the generic -sample Moving Average filter of equation (1) is . The transfer function describes the. Compute the Z-transform of exp (m+n). By default, the independent variable is n and the transformation variable is z. syms m n f = exp (m+n); ztrans (f) ans = (z*exp (m))/ (z - exp (1)) Specify the transformation variable as y. If you specify only one variable, that variable is the transformation variable. The independent variable is still n In addition, chirp z transform is broader then zoom fft. There was someone on this list that was interested especially in zoom fft, so I was wondered if there is a code for it. Anyway, I can use my old code again. Nadav -----הודעה מקורית----- מאת: [hidden email] בשם St?fan van der Walt נשלח: ב 05-ינואר-09 10:25 אל: Discussion of Numerical Python נושא: Re. y = czt(x,m,w,a) returns the length-m chirp Z-transform (CZT) of x along the spiral contour on the z-plane defined by w and a through z = a*w.^-(0:m-1).. With the default values of m, w, and a, czt returns the Z-transform of x at m equally spaced points around the unit circle, a result equivalent to the discrete Fourier transform (DFT) of x as given by fft(x)

### scipy.spatial.transform.Rotation — SciPy v1.6.3 Reference ..

1. Many machine learning algorithms perform better when numerical input variables are scaled to a standard range. This includes algorithms that use a weighted sum of the input, like linear regression, and algorithms that use distance measures, like k-nearest neighbors. The two most popular techniques for scaling numerical data prior to modeling are normalization and standardization
2. In the previous post, we calculated the area under the standard normal curve using Python and the erf() function from the math module in Python's Standard Library. In this post, we will construct a plot that illustrates the standard normal curve and the area we calculated. To build the Gaussian normal curve, we are going to use Python, Matplotlib, and a module called SciPy
3. SymPy uses mpmath in the background, which makes it possible to perform computations using arbitrary-precision arithmetic. That way, some special constants, like , , (Infinity), are treated as symbols and can be evaluated with arbitrary precision: >>> sym. pi **
4. scipy.stats.skew(array, axis=0, bias=True) function calculates the skewness of the data set. skewness = 0 : normally distributed.skewness > 0 : more weight in the left tail of the distribution.skewness < 0 : more weight in the right tail of the distribution. Its formula - Parameters : array : Input array or object having the elements. axis : Axis along which the skewness value is to be measured
5. The (unilateral) Z-transform is defined as: Z {x [n]} = cos, sqrt, pi import numpy as np import matplotlib.pyplot as plt # Function for calculating the cut-off frequency of a moving average filter def get_sma_cutoff (N, ** kwargs): func = lambda w: sin (N * w / 2) -N / sqrt(2) * sin(w / 2) # |H(e^jω)| = √2/2 deriv = lambda w: cos(N * w / 2) * N / 2-N / sqrt(2) * cos(w / 2) / 2 # dfunc.
6. $z$-transform and lag operator methods, or ; matrix decompositions applied to linear systems of first-order conditions for optimum problems. In this lecture and the sequel Classical Filtering with Linear Algebra, we mostly rely on elementary linear algebra. The main tool from linear algebra we'll put to work here is LU decomposition. We'll begin with discrete horizon problems. Then we.
7. Variant transforms¶. In addition to the basic 1, 2 and 3 dimensional DT-CWT, this library also supports a selection of variant transforms

This means that we call the function using numpy arrays, it will calculate using numpy array expressions, doing the whole calculation in a single call. We now have a function that we can directly call: In : print (sympy solution at t=0: {}. format (ode_function (0.0))) print (sympy solution at t=0.5: {}. format (ode_function (0.5))) sympy solution at t=0: 1.0 sympy solution at t=0.5: 0. Introduction. The Transform function in Pandas (Python) can be slightly difficult to understand, especially if you're coming from an Excel background. Honestly, most data scientists don't use it right off the bat in their learning journey. But Pandas' transform function is actually quite a handy tool to have as a data scientist The standardization method uses this formula: z = (x - u) / s. Where z is the new value, x is the original value, u is the mean and s is the standard deviation. If you take the weight column from the data set above, the first value is 790, and the scaled value will be: (790 - 1292.23) / 238.74 = -2.1. If you take the volume column from the data.

### python - Frequency Response Scipy

1. Overview¶. This is a sequel to the earlier lecture Classical Control with Linear Algebra.. That lecture used linear algebra - in particular, the LU decomposition - to formulate and solve a class of linear-quadratic optimal control problems.. In this lecture, we'll be using a closely related decomposition, the Cholesky decomposition, to solve linear prediction and filtering problems
2. Is there a ready made function in numpy/scipy to compute the correlation y=mx+o of an X and Y fast: m, m-err, o, o-err, r-coef,r-coef-err ? numpy and scipy questions are best asked on their lists, not here. There are a number of people who know the capabilities of numpy and scipy through and through, but most of them don't hang out on comp.lang.
3. import numpy as np import gdal from gdalconst import * from osgeo import osr def GetGeoInfo(FileName): SourceDS = gdal.Open(FileName, GA_ReadOnly) GeoT = SourceDS.GetGeoTransform() Projection = osr.SpatialReference() Projection.ImportFromWkt(SourceDS.GetProjectionRef()) return GeoT, Projection def CreateGeoTiff(Name, Array, driver, xsize, ysize, GeoT, Projection): DataType = gdal.GDT_Float32.
4. Lab3_Filter_Design_Problems_2021 March 9, 2021 In : %matplotlib inline import numpy as np import matplotlib.pyplot as plt 1 A Notch Digital Filter (5 marks, 2/2/1) A notch filter is a filter that passes almost all frequencies with unit amplitude, except for a narrow range of frequencies centered on the rejection frequency f0 (at which frequency nothing is passed)
5. Module contents ¶. complex_to_seq(z) ¶. Transform a complex number into a 4-element sequence. Parameters: z - a number. If z = x + yj, then an array of the form [ [x,-y], [y,x]] can be used to represent z in matrix computations. Examples :: >>> import numpy >>> z = 1 + 2j >>> function.complex_to_seq(z) (1.0, -2.0, 2.0, 1.0

### scipy.signal.freqz — SciPy v1.6.3 Reference Guid

% matplotlib inline import matplotlib.pyplot as plt import numpy as np import numpy.ma as ma from scipy.stats import norm from scipy.optimize import curve_fit from scipy.integrate import trapz, simps from skimage.io import imread from aloe.plots import plot_image from PIL import Image # for fitting probability densities to a normal distribution def gauss (x, a, x0, sigma): gaussian. Die schnelle Fourier-Transformation (englisch fast Fourier transform, daher meist FFT abgekürzt) ist ein Algorithmus zur effizienten Berechnung der diskreten Fourier-Transformation (DFT). Mit ihr kann ein zeitdiskretes Signal in seine Frequenzanteile zerlegt und dadurch analysiert werden.. Analog gibt es für die diskrete inverse Fourier-Transformation die inverse schnelle Fourier. I am trying to re-project/resample with the GDAL python bindings, but am getting slightly different results compared to those from the command line utility gdalwarp. See update below for shorte In der numerischen Mathematik bezeichnet numerische Integration (traditionell auch als numerische Quadratur bezeichnet) die näherungsweise Berechnung von Integralen.. Oft kann man Integrale nicht geschlossen lösen, weil für den Integranden keine Stammfunktion angegeben werden kann oder er nur durch diskrete Werte, etwa Messungen, gegeben ist

### scipy.signal.iirfilter — SciPy v1.6.3 Reference Guid

UConn HKN's Andrew Finelli shows how to perform an inverse fourier transform on a rectangular function.Check out our Signals and Systems playlist for more sklearn.metrics. .roc_curve. ¶. Compute Receiver operating characteristic (ROC). Note: this implementation is restricted to the binary classification task. Read more in the User Guide. True binary labels. If labels are not either {-1, 1} or {0, 1}, then pos_label should be explicitly given

### sklearn.preprocessing.StandardScaler — scikit-learn 0.24.2 ..

By deﬁning g[n] = e −j 0nW n 2/2 x [n], the above equation is equivalently represented as X(ej k) = W n2/2 g[n] W −n2/2, which leads to the block diagram depicted in OSB Figure 9.26. This system isn't realizable as-is, since it is neither causal nor stable A DSP algorithm for frequency analysis. January 15, 2004 Embedded Staff. The Chirp-Z Transform (CZT), a little-known spectrum analysis algorithm, offers engineers a high-resolution FFT combined with the ability to specify bandwidth. Here's a look at how CZT works and what it has to offer. Engineers working in the field of digital signal. In elementary school, we are taught translation, rotation, re-sizing/scaling, and reflection. The first three are used heavily in computer graphics — and they're done using matrix multiplication class EnsembleKalmanFilter (object): This implements the ensemble Kalman filter (EnKF). The EnKF uses an ensemble of hundreds to thousands of state vectors that are randomly sampled around the estimate, and adds perturbations at each update and predict step. It is useful for extremely large systems such as found in hydrophysics. As such, this class is admittedly a toy as it is far too slow. >>> import numpy as np >>> import matplotlib.pyplot as plt >>> np.set_printoptions(precision=3, linewidth=50) Also, when we show the creation of plots in the transcripts, we won't show all the other plot commands (setting labels, grid lines, etc.) that were actually used to create the corresponding ﬁgure in the paper. Complete scripts for all the ﬁgures in this paper are available in the. ### numpy - Numerical Laplace transform python - Stack Overflo

Speed of single-shot vs reuse of a HankelTransform object¶. For a simple case (as in Example of single-shot transform) there are two simple forward qdht() and [inverse iqdht()] functions which can be used to calculate the [inverse] Hankel transform of a function sampled at an arbitrary set of points in radius [wave-number] space.. Here we will use the same example application as Typical usage. Definition and Usage. The transform property applies a 2D or 3D transformation to an element. This property allows you to rotate, scale, move, skew, etc., elements. To better understand the transform property, view a demo. yes. Read about animatable Try it After the Z-transform of the lower and upper bounds are calculated, we calculate the probability with SciPy's scipy. Finally, we build the plot. Note how Matplotlib's ax. The finished plot is below. Notice how the area corresponding to resistors in the given specification between the upper and lower bounds is shaded. Toggle navigation Python for Undergraduate Engineers. About Book Now Archives. NumPy; Visualization; The effect of standardization on PCA in a pattern classification task. Reading in the dataset; Dividing the dataset into a separate training and test dataset; Feature Scaling - Standardization; Dimensionality reduction via Principal Component Analysis (PCA) Training a naive Bayes classifier ; Evaluating the classification accuracy with and without standardization. 10.1.2.3. t-SNE¶. t-Distributed Stochastic Neighbor Embedding (t-SNE) is a powerful manifold learning algorithm for visualizing clusters. It finds a two-dimensional representation of your data, such that the distances between points in the 2D scatterplot match as closely as possible the distances between the same points in the original high dimensional dataset

numpy.fft 实现 czt (Chirp Z-transform) 动机. 如果对 L 2 ( R ) L^2(\R) L 2 (R) 上做Fourier变换，直接用离散FFT是不行的。需要用CZT。用于数值计算的numpy没有提供CZT，需要重新实现。本文用FFT实现CZT� •Python numpy.fft . Filtering Time Series Data 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 .5-20-10 0 10 20 0 50 100 150 200 250 300 350 400 450 500 0 500 1000 1500 2000 2500 Frequency Hz. 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 .5-15-10-5 0 5 10 15 Time (s) e filtered data original data. Original data and filtered data 0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 .5-15-10-5 0 5 10 15. Step 1: Import all the important libraries and functions that are required to understand the ROC curve, for instance, numpy and pandas. import numpy as np. import pandas as pd. import matplotlib.pyplot as plt. import seaborn as sns. from sklearn.datasets import make_classification. from sklearn.neighbors import KNeighborsClassifier # Convert to numpy array n_array = np.array( normalized_values ) # Reshape the array to its Real shape and return it. return np.reshape( n_array , shape ) Explanation of the code # Save the Real shape of the Given Data shape = data.shape # Smoothing the Given Data Values to 1 dimension data = np.reshape( data , (-1 , ) ) Some further steps: We need to Save the Real shape of the data. We need.

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