I am trying to make sense of the generic_filter1d() function in scipy.ndimage. My understanding is that this function would allow a function being applied to a 1d array by 'extracting' a subset of this array and applying a function that returns a single value (analogous to the generic_filter()?). Is this correct? I have the following simple code:
def test(x,y):
return np.sum(x/y)
and
import numpy as np
from scipy.ndimage import generic_filter1d
dummy_data= np.arange(10)
size = 5
y= np.linspace(0.1, size*0.1, size)
rstl = generic_filter1d(dummy_data, test, filter_size= size, extra_arguments =(y,))
And it fails. It says that I have an extra argument.
Related
I got the following error:
<lambdifygenerated-1>:2: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.return numpy.array((A1exp(-1/2(x - xc1)**2/sigma1**2), 0, 0))
Here I have just one model but this code is written for model combination in fitting by the lmfit Please kindly let me know about it.
import matplotlib.pyplot as plt
import numpy as np
import sympy
from sympy.parsing import sympy_parser
import lmfit
gauss_peak1 = sympy_parser.parse_expr('A1*exp(-(x-xc1)**2/(2*sigma1**2))')
gauss_peak2 = 0
exp_back = 0
model_list = sympy.Array((gauss_peak1, gauss_peak2, exp_back))
model = sum(model_list)
print(model)
model_list_func = sympy.lambdify(list(model_list.free_symbols), model_list)
model_func = sympy.lambdify(list(model.free_symbols), model)
np.random.seed(1)
x = np.linspace(0, 10, 40)
param_values = dict(x=x, A1=2, sigma1=1, xc1=2)
y = model_func(**param_values)
yi = model_list_func(**param_values)
yn = y + np.random.randn(y.size)*0.4
plt.plot(x, yn, 'o')
plt.plot(x, y)
lm_mod = lmfit.Model(model_func, independent_vars=('x'))
res = lm_mod.fit(data=yn, **param_values)
res.plot_fit()
plt.plot(x, y, label='true')
plt.legend()
plt.show()
lmfit.Model takes a model function that is a Python function. It parses the function arguments and expects those to be the Parameters for the model.
I don't think using sympy-created functions will do that. Do you need to use sympy here? I don't see why. The usage here seems designed to make the code more complex than it needs to be. It seems you want to make a model with a Gaussian-like peak, and a constant(?) background. If so, why not do
from lmfit.Models import GaussianModel, ConstantModel
model = GaussianModel(prefix='p1_') + ConstantModel()
params = model.make_params(p1_amplitude=2, p1_center=2, p1_sigma=1, c=0)
That just seems way easier to me, and it is very easy to add a second Gaussian peak to that model.
But even if you have your own preferred mathematical expression, don't use that as a sympy string, use it as Python code:
def myfunction(x, A1, xc1, sigma1):
return A1*exp(-(x-xc1)**2/(2*sigma1**2))
and then
from lmfit import Model
mymodel = Model(myfunction)
params = mymodel.guess(A1=2, xc1=2, sigma1=1)
In short: sympy is an amazing tool, but lmfit does not use it.
I'm experimenting with numpy and I'd like to ask a solution for the following code. I'd like to, actually, generate a 256x256 image, from start using a random rgb schema -- probably that would be the way to go. Any numpy insights would be welcome!
# -*- coding: utf-8 -*-
from PIL import Image
import numpy as np
def transform_matrice(data):
aux_data = []
for e in data:
aux = []
for a in e:
aux.append(np.array([[random.randrange(255), random.randrange(255), random.randrange(255)]]))
aux_data.append(aux)
return aux_data
w, h = 250, 250
data = np.zeros((h, w, 3), dtype=np.uint8)
ret = transform_matrice(data)
img = Image.fromarray(ret, 'RGB')
img.save('eg.png')
img.show()
with this code I got the following error:
AttributeError: 'list' object has no attribute '__array_interface__'
You do not need to create a empty data table neither you need to use for loops, numpy can do it for you!
np.random.randint will create you a 3D matrix of size (w,h,3) with integers from 0 to 255 using the following command:
def transform_matrice(w,h):
return np.random.randint(0,256,size=(w,h,3)).astype('uint8')
ret = transform_matrice(250,250)
None that I put 256 and not 255 as second parameter since the parameter is one above the largest integer you want
I am trying to use TF.dataset.map to port over this old code because I get a deprecation warning.
Old code which reads a set of custom protos from a TFRecord file:
record_iterator = tf.python_io.tf_record_iterator(path=filename)
for record in record_iterator:
example = MyProto()
example.ParseFromString(record)
I am trying to use eager mode and map, but I get this error.
def parse_proto(string):
proto_object = MyProto()
proto_object.ParseFromString(string)
dataset = tf.data.TFRecordDataset(dataset_paths)
parsed_protos = raw_tf_dataset.map(parse_proto)
This code works:
for raw_record in raw_tf_dataset:
proto_object = MyProto()
proto_object.ParseFromString(raw_record.numpy())
But the map gives me an error:
TypeError: a bytes-like object is required, not 'Tensor'
What is the right way to take use the argument the function results of the map and treat them like a string?
You need to extract string form the tensor and use in the map function. Below are the steps to be implemented in the code to achieve this.
You have to decorate the map function with tf.py_function(get_path, [x], [tf.float32]). You can find more about tf.py_function here. In tf.py_function, first argument is the name of map function, second argument is the element to be passed to map function and final argument is the return type.
You can get your string part by using bytes.decode(file_path.numpy()) in map function.
So modify your program as below,
parsed_protos = raw_tf_dataset.map(parse_proto)
to
parsed_protos = raw_tf_dataset.map(lambda x: tf.py_function(parse_proto, [x], [function return type]))
Also modify parse_proto as below,
def parse_proto(string):
proto_object = MyProto()
proto_object.ParseFromString(string)
to
def parse_proto(string):
proto_object = MyProto()
proto_object.ParseFromString(bytes.decode(string.numpy()))
In the below simple program, we are using tf.data.Dataset.list_files to read path of the image. Next in the map function we are reading the image using load_img and later doing the tf.image.central_crop function to crop central part of the image.
Code -
%tensorflow_version 2.x
import tensorflow as tf
from keras.preprocessing.image import load_img
from keras.preprocessing.image import img_to_array, array_to_img
from matplotlib import pyplot as plt
import numpy as np
def load_file_and_process(path):
image = load_img(bytes.decode(path.numpy()), target_size=(224, 224))
image = img_to_array(image)
image = tf.image.central_crop(image, np.random.uniform(0.50, 1.00))
return image
train_dataset = tf.data.Dataset.list_files('/content/bird.jpg')
train_dataset = train_dataset.map(lambda x: tf.py_function(load_file_and_process, [x], [tf.float32]))
for f in train_dataset:
for l in f:
image = np.array(array_to_img(l))
plt.imshow(image)
Output -
Hope this answers your question. Happy Learning.
As a beginner, i was trying to simply compute the dot product of two matrices using theano.
my code is very simple.
import theano
import theano.tensor as T
import numpy as np
from theano import function
def covarience(array):
input_array=T.matrix('input_array')
deviation_matrix = T.matrix('deviation_matrix')
matrix_filled_with_1s=T.matrix('matrix_filled_with_1s')
z = T.dot(input_array, matrix_filled_with_1s)
identity=np.ones((len(array),len(array)))
f=function([array,identity],z)
# print(f)
covarience(np.array([[2,4],[6,8]]))
but the problem is each time i run this code , i get error message like "TypeError: Unknown parameter type: "
Can anyone tell me whats wrong with my code?
You cannot pass numpy array to theano function, theano functions can only be defined by theano.tensor variables. So you can always define computations with interaction of tensor/symbolic variables, and to perform actual computation on values/real data you can use functions, it doesn't make sense to define theano function itself with numpy array.
This should work:
import theano
import theano.tensor as T
import numpy as np
a = T.matrix('a')
b = T.matrix('b')
z = T.dot(a, b)
f = theano.function([a, b], z)
a_d = np.asarray([[2, 4], [6, 8]], dtype=theano.config.floatX)
b_d = np.ones(a_d.shape, dtype=theano.config.floatX)
print(f(a_d, b_d))
I am trying to fit a curve over the histogram of a Poisson distribution that looks like this
I have modified the fit function so that it resembles a Poisson distribution, with the parameter t as a variable. But the curve_fit function can not be plotted and I am not sure why.
def histo(bsize):
N = bsize
#binwidth
bw = (dt.max()-dt.min())/(N-1.)
bin1 = dt.min()+ bw*np.arange(N)
#define the array to hold the occurrence count
bincount= np.array([])
for bin in bin1:
count = np.where((dt>=bin)&(dt<bin+bw))[0].size
bincount = np.append(bincount,count)
#bin center
binc = bin1+0.5*bw
plt.figure()
plt.plot(binc,bincount,drawstyle= 'steps-mid')
plt.xlabel("Interval[ticks]")
plt.ylabel("Frequency")
histo(30)
plt.xlim(0,.5e8)
plt.ylim(0,25000)
import numpy as np
from scipy.optimize import curve_fit
delta_t = 1.42e7
def func(x, t):
return t * np.exp(- delta_t/t)
popt, pcov = curve_fit(func, np.arange(0,.5e8),histo(30))
plt.plot(popt)
The problem with your code is that you do not know what the return values of curve_fit are. It is the parameters for the fit-function and their covariance matrix - not something you can plot directly.
Binned Least-Squares Fit
In general you can get everything much, much more easily:
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
from scipy.special import factorial
from scipy.stats import poisson
# get poisson deviated random numbers
data = np.random.poisson(2, 1000)
# the bins should be of integer width, because poisson is an integer distribution
bins = np.arange(11) - 0.5
entries, bin_edges, patches = plt.hist(data, bins=bins, density=True, label='Data')
# calculate bin centers
bin_centers = 0.5 * (bin_edges[1:] + bin_edges[:-1])
def fit_function(k, lamb):
'''poisson function, parameter lamb is the fit parameter'''
return poisson.pmf(k, lamb)
# fit with curve_fit
parameters, cov_matrix = curve_fit(fit_function, bin_centers, entries)
# plot poisson-deviation with fitted parameter
x_plot = np.arange(0, 15)
plt.plot(
x_plot,
fit_function(x_plot, *parameters),
marker='o', linestyle='',
label='Fit result',
)
plt.legend()
plt.show()
This is the result:
Unbinned Maximum-Likelihood fit
An even better possibility would be to not use a histogram at all
and instead to carry out a maximum-likelihood fit.
But by closer examination even this is unnecessary, because the
maximum-likelihood estimator for the parameter of the poissonian distribution is the arithmetic mean.
However, if you have other, more complicated PDFs, you can use this as example:
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import minimize
from scipy.special import factorial
from scipy import stats
def poisson(k, lamb):
"""poisson pdf, parameter lamb is the fit parameter"""
return (lamb**k/factorial(k)) * np.exp(-lamb)
def negative_log_likelihood(params, data):
"""
The negative log-Likelihood-Function
"""
lnl = - np.sum(np.log(poisson(data, params[0])))
return lnl
def negative_log_likelihood(params, data):
''' better alternative using scipy '''
return -stats.poisson.logpmf(data, params[0]).sum()
# get poisson deviated random numbers
data = np.random.poisson(2, 1000)
# minimize the negative log-Likelihood
result = minimize(negative_log_likelihood, # function to minimize
x0=np.ones(1), # start value
args=(data,), # additional arguments for function
method='Powell', # minimization method, see docs
)
# result is a scipy optimize result object, the fit parameters
# are stored in result.x
print(result)
# plot poisson-distribution with fitted parameter
x_plot = np.arange(0, 15)
plt.plot(
x_plot,
stats.poisson.pmf(x_plot, result.x),
marker='o', linestyle='',
label='Fit result',
)
plt.legend()
plt.show()
Thank you for the wonderful discussion!
You might want to consider the following:
1) Instead of computing "poisson", compute "log poisson", for better numerical behavior
2) Instead of using "lamb", use the logarithm (let me call it "log_mu"), to avoid the fit "wandering" into negative values of "mu".
So
log_poisson(k, log_mu): return k*log_mu - loggamma(k+1) - math.exp(log_mu)
Where "loggamma" is the scipy.special.loggamma function.
Actually, in the above fit, the "loggamma" term only adds a constant offset to the functions being minimized, so one can just do:
log_poisson_(k, log_mu): return k*log_mu - math.exp(log_mu)
NOTE: log_poisson_() not the same as log_poisson(), but when used for minimization in the manner above, will give the same fitted minimum (the same value of mu, up to numerical issues). The value of the function being minimized will have been offset, but one doesn't usually care about that anyway.