KernSmooth for Python
=====================
Porting popular R library KernSmooth to python.
Functions for Kernel Smoothing and Density Estimation.
Transformed R and Fortran functions into Python(2,3) code.
Attention:
----------
Please use kern-smooth 1.1.0 or newer.
Reason: found not needed log10 density transformation.
Installation:
-------------
.. code:: shell
pip install kern-smooth
Main function of the module:
----------------------------
.. code:: python
def densCols(x, y=None, nbin=128, bandwidth=None)
Produces a vector of numbers which encode the local densities at each point in dataset.
x, y : 1D numpy array with coordinates of the points density will be estimated on
nbin : [optional] int or [int, int] - number of bins along each axis
(in case of single value - [nbin, nbin] will be used). Default value 128.
bandwidth : [optional] numeric vector (len of 1 or 2) of smoothing bandwidth.
Returns: numpy array with numerical representation (in range [0,1]) of point densities.
Attention: For return value numpy.nan values are allowed in case of nan / infinite values in original dataset
Source: R::grDevices::densCols
Usage
-----
Generate data for plotting (make sure Matplotlib is installed)
.. code:: python
from matplotlib import pyplot as plt
from matplotlib import cm
import numpy as np
np.random.seed(0)
# create two 'bulbs' with normal distributions
mean1 = [0, 0]
cov1 = [[5, 0], [0, 30]] # diagonal covariance
x1, y1 = np.random.multivariate_normal(mean1, cov1, 50000).T
mean2 = [5, 17]
cov2 = [[30, 0], [0, 5]] # diagonal covariance
x2, y2 = np.random.multivariate_normal(mean2, cov2, 50000).T
x = np.hstack([x1,x2])
y = np.hstack([y1,y2])
Generate point densities:
.. code:: python
from kern_smooth import densCols
densities = densCols(x, y, nbin = 128)
Plot the result
.. code:: python
sc = plt.scatter(x, y, c=densities, s=15, edgecolors='none', alpha=0.75, cmap=cm.jet)
plt.colorbar(sc)
plt.show()
Result
------
Author
------
Alexander Butyaev
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"description": "KernSmooth for Python\n=====================\nPorting popular R library KernSmooth to python.\n\nFunctions for Kernel Smoothing and Density Estimation.\n\nTransformed R and Fortran functions into Python(2,3) code.\n\nAttention:\n----------\n\nPlease use kern-smooth 1.1.0 or newer.\nReason: found not needed log10 density transformation.\n\n\nInstallation:\n-------------\n\n.. code:: shell\n\n\t\tpip install kern-smooth\n\n\nMain function of the module:\n----------------------------\n\n.. code:: python\n \n def densCols(x, y=None, nbin=128, bandwidth=None)\n\n\nProduces a vector of numbers which encode the local densities at each point in dataset.\n\nx, y : 1D numpy array with coordinates of the points density will be estimated on\n\nnbin : [optional] int or [int, int] - number of bins along each axis\n (in case of single value - [nbin, nbin] will be used). Default value 128.\n\nbandwidth : [optional] numeric vector (len of 1 or 2) of smoothing bandwidth.\n\nReturns: numpy array with numerical representation (in range [0,1]) of point densities.\n\nAttention: For return value numpy.nan values are allowed in case of nan / infinite values in original dataset \n\nSource: R::grDevices::densCols\n\n\nUsage\n-----\n\nGenerate data for plotting (make sure Matplotlib is installed)\n\n.. code:: python\n\n from matplotlib import pyplot as plt\n from matplotlib import cm\n import numpy as np\n np.random.seed(0)\n # create two 'bulbs' with normal distributions\n mean1 = [0, 0]\n cov1 = [[5, 0], [0, 30]] # diagonal covariance\n x1, y1 = np.random.multivariate_normal(mean1, cov1, 50000).T\n mean2 = [5, 17]\n cov2 = [[30, 0], [0, 5]] # diagonal covariance\n x2, y2 = np.random.multivariate_normal(mean2, cov2, 50000).T\n x = np.hstack([x1,x2])\n y = np.hstack([y1,y2])\n\n\nGenerate point densities:\n\n.. code:: python\n\n from kern_smooth import densCols\n densities = densCols(x, y, nbin = 128)\n\n\nPlot the result\n\n.. code:: python\n\n sc = plt.scatter(x, y, c=densities, s=15, edgecolors='none', alpha=0.75, cmap=cm.jet)\n plt.colorbar(sc)\n plt.show()\n\n\nResult\n------\n \n\nAuthor\n------\nAlexander Butyaev\n",
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