1.3.3+ds-1 (patches unapplied)import/1.3.3+ds-1 ubuntu/focal-proposed ubuntu/focal-devel ubuntu/focal

Imported using git-ubuntu import.
author: Andrius Merkys <merkys@debian.org> 2020-01-23 10:27:44 -0500
committer: git-ubuntu importer <ubuntu-devel-discuss@lists.ubuntu.com> 2020-01-23 22:39:31 +0000
commit: 2259b1ce787198ef0ac75b03854b26bd34ff6c7e (patch)
tree: bf3785447aa7a05d918550126fbf87a0489a24cd
parent: 9a0bb7160a6a279de9753bacc48438e47a2be50e (diff)
12 files changed, 982 insertions, 45 deletions
diff --git a/MANIFEST.in b/MANIFEST.in
index 7dee161..795e57f 100644
--- a/MANIFEST.in
+++ b/MANIFEST.in
@@ -1,5 +1,5 @@
 include LICENSE.txt README.rst setup.py
-include tests/baseline tests/noise
+include tests/baseline tests/noise tests/exp tests/tutorial.ipynb
 recursive-include peakutils *.py
 recursive-include tests *.py
 recursive-include docs *
diff --git a/PKG-INFO b/PKG-INFO
index 63d73ef..5ecec7d 100644
--- a/PKG-INFO
+++ b/PKG-INFO
@@ -1,6 +1,6 @@
 Metadata-Version: 2.1
 Name: PeakUtils
-Version: 1.3.2
+Version: 1.3.3
 Summary: Peak detection utilities for 1D data
 Home-page: https://bitbucket.org/lucashnegri/peakutils
 Author: Lucas Hermann Negri
diff --git a/debian/changelog b/debian/changelog
index f7741f9..ba9a045 100644
--- a/debian/changelog
+++ b/debian/changelog
@@ -1,3 +1,10 @@
+python-peakutils (1.3.3+ds-1) unstable; urgency=medium
+
+  * New upstream version 1.3.3+ds
+  * Dropping no longer needed remove-test_conditioning.patch.
+
+ -- Andrius Merkys <merkys@debian.org>  Thu, 23 Jan 2020 10:27:44 -0500
+
 python-peakutils (1.3.2+ds-2) unstable; urgency=medium
 
   * Adding autopkgtest.
diff --git a/debian/patches/remove-test_conditioning.patch b/debian/patches/remove-test_conditioning.patch
deleted file mode 100644
index 697f32c..0000000
--- a/debian/patches/remove-test_conditioning.patch
+++ /dev/null
@@ -1,24 +0,0 @@
-Description: Removing test case failing due to a missing file.
-Author: Andrius Merkys <merkys@debian.org>
-Bug: https://bitbucket.org/lucashnegri/peakutils/issues/36
---- a/tests/peakutils_test.py
-+++ b/tests/peakutils_test.py
-@@ -135,18 +135,6 @@
- 
-     """Tests the conditioning of the lsqreg in the implementation"""
- 
--    def test_conditioning(self):
--        data = data = load('exp')
--        y = data[:, 1]
--        mult = 1e-6
--
--        while mult < 100001:
--            ny = y * mult
--            base = peakutils.baseline(ny, 9) / mult
--            self.assertTrue(0.8 < base.max() < 1.0)
--            self.assertTrue(-0.1 <= base.min() < 0.1)
--            mult *= 10
--
-     def test_negative(self):
-         data = np.array([-1, -2, -3, -4, -3, -2, -1] * 10)
-         base = peakutils.baseline(data)
diff --git a/debian/patches/series b/debian/patches/series
deleted file mode 100644
index 0b89081..0000000
--- a/debian/patches/series
+++ /dev/null
@@ -1 +0,0 @@
-remove-test_conditioning.patch
diff --git a/docs/conf.py b/docs/conf.py
index 001dff0..fb0d9b4 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -25,7 +25,7 @@ master_doc = 'index'
 
 # General information about the project.
 project = 'PeakUtils'
-copyright = '2014 - 2019, Lucas Hermann Negri'
+copyright = '2014 - 2020, Lucas Hermann Negri'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
diff --git a/peakutils/__init__.py b/peakutils/__init__.py
index f2d3349..9694445 100644
--- a/peakutils/__init__.py
+++ b/peakutils/__init__.py
@@ -2,4 +2,4 @@ from .baseline import *
 from .peak import *
 from .prepare import *
 
-__version__ = '1.3.2'
+__version__ = '1.3.3'
diff --git a/peakutils/peak.py b/peakutils/peak.py
index 31a1c00..b330519 100644
--- a/peakutils/peak.py
+++ b/peakutils/peak.py
@@ -8,6 +8,7 @@ from scipy.integrate import simps
 
 eps = np.finfo(float).eps
 
+
 def indexes(y, thres=0.3, min_dist=1, thres_abs=False):
     """Peak detection routine.
 
@@ -32,22 +33,23 @@ def indexes(y, thres=0.3, min_dist=1, thres_abs=False):
     Returns
     -------
     ndarray
-        Array containing the numeric indexes of the peaks that were detected
+        Array containing the numeric indexes of the peaks that were detected.
+        When using with Pandas DataFrames, iloc should be used to access the values at the returned positions.
     """
     if isinstance(y, np.ndarray) and np.issubdtype(y.dtype, np.unsignedinteger):
         raise ValueError("y must be signed")
 
     if not thres_abs:
         thres = thres * (np.max(y) - np.min(y)) + np.min(y)
-        
+
     min_dist = int(min_dist)
 
     # compute first order difference
     dy = np.diff(y)
 
     # propagate left and right values successively to fill all plateau pixels (0-value)
-    zeros,=np.where(dy == 0)
-    
+    zeros, = np.where(dy == 0)
+
     # check if the signal is totally flat
     if len(zeros) == len(y) - 1:
         return np.array([])
@@ -79,9 +81,11 @@ def indexes(y, thres=0.3, min_dist=1, thres_abs=False):
             dy[plateau[plateau >= median]] = dy[plateau[-1] + 1]
 
     # find the peaks by using the first order difference
-    peaks = np.where((np.hstack([dy, 0.]) < 0.)
-                     & (np.hstack([0., dy]) > 0.)
-                     & (np.greater(y, thres)))[0]
+    peaks = np.where(
+        (np.hstack([dy, 0.0]) < 0.0)
+        & (np.hstack([0.0, dy]) > 0.0)
+        & (np.greater(y, thres))
+    )[0]
 
     # handle multiple peaks, respecting the minimum distance
     if peaks.size > 1 and min_dist > 1:
@@ -118,7 +122,8 @@ def centroid(x, y):
     """
     return np.sum(x * y) / np.sum(y)
 
-def centroid2(y, x=None, dx=1.):
+
+def centroid2(y, x=None, dx=1.0):
     """Computes the centroid for the specified data.
     Not intended to be used
 
@@ -136,13 +141,14 @@ def centroid2(y, x=None, dx=1.):
     yt = np.array(y)
 
     if x is None:
-        x = np.arange(yt.size, dtype='float') * dx
+        x = np.arange(yt.size, dtype="float") * dx
 
     normaliser = simps(yt, x)
     centroid = simps(x * yt, x) / normaliser
     var = simps((x - centroid) ** 2 * yt, x) / normaliser
     return centroid, np.sqrt(var)
 
+
 def gaussian(x, ampl, center, dev):
     """Computes the Gaussian function.
 
@@ -164,6 +170,7 @@ def gaussian(x, ampl, center, dev):
     """
     return ampl * np.exp(-(x - float(center)) ** 2 / (2.0 * dev ** 2 + eps))
 
+
 def gaussian_fit(x, y, center_only=True):
     """Performs a Gaussian fitting of the specified data.
 
@@ -224,15 +231,15 @@ def interpolate(x, y, ind=None, width=10, func=gaussian_fit):
         Array with the adjusted peak positions (in *x*)
     """
     assert x.shape == y.shape
-    
+
     if ind is None:
         ind = indexes(y)
 
     out = []
-    
+
     for i in ind:
         slice_ = slice(i - width, i + width + 1)
-        
+
         try:
             best_idx = func(x[slice_], y[slice_])
         except RuntimeError as e:
diff --git a/peakutils/plot.py b/peakutils/plot.py
index 1f66f8d..67c65a2 100644
--- a/peakutils/plot.py
+++ b/peakutils/plot.py
@@ -14,7 +14,10 @@ def plot(x, y, ind):
     ind : array-like
         Indexes of the identified peaks
     """
-    plt.plot(x, y, '--')
-    plt.plot(x[ind], y[ind], 'r+', ms=5, mew=2,
-             label='{} peaks'.format(len(ind)))
+    plt.plot(x, y, "--")
+
+    marker_x = x.iloc[ind] if hasattr(x, "iloc") else x[ind]
+    marker_y = y.iloc[ind] if hasattr(y, "iloc") else y[ind]
+
+    plt.plot(marker_x, marker_y, "r+", ms=5, mew=2, label="{} peaks".format(len(ind)))
     plt.legend()
diff --git a/setup.py b/setup.py
index 22e174e..ebc8b3e 100644
--- a/setup.py
+++ b/setup.py
@@ -5,7 +5,7 @@ with open('README.rst') as readme:
 
 setup(
     name='PeakUtils',
-    version='1.3.2',
+    version='1.3.3',
     description='Peak detection utilities for 1D data',
     long_description=long_description,
     long_description_content_type="text/x-rst",
diff --git a/tests/exp b/tests/exp
new file mode 100644
index 0000000..6f32b65
--- /dev/null
+++ b/tests/exp
@@ -0,0 +1,687 @@
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diff --git a/tests/tutorial.ipynb b/tests/tutorial.ipynb
new file mode 100644
index 0000000..430bc59
--- /dev/null
+++ b/tests/tutorial.ipynb
@@ -0,0 +1,258 @@
+{
+ "metadata": {
+  "name": "",
+  "signature": "sha256:09ff63367d20eff3d4d55e9d7dc9ce71652a9cf98b3e5529fa2d615fb7b98ae3"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+  {
+   "cells": [
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "# PeakUtils tutorial\n",
+      "\n",
+      "This tutorial shows the basic usage of PeakUtils to detect the peaks of 1D data."
+     ]
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Importing the libraries"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "import numpy\n",
+      "import peakutils\n",
+      "from peakutils.plot import plot as pplot\n",
+      "from matplotlib import pyplot\n",
+      "%matplotlib inline"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Preparing the data\n",
+      "\n",
+      "Now, lets generate some noisy data with two gaussians:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "centers = (30.5, 72.3)\n",
+      "x = numpy.linspace(0, 120, 121)\n",
+      "y = peakutils.gaussian(x, 5, centers[0], 3) + peakutils.gaussian(x, 7, centers[1], 10) + numpy.random.rand(x.size)\n",
+      "pyplot.figure(figsize=(10,6))\n",
+      "pyplot.plot(x, y)\n",
+      "pyplot.title(\"Data with noise\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 2,
+       "text": [
+        "<matplotlib.text.Text at 0x7f5eaec6d278>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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MH67VJg689B72OVEuY3CilK1fD7Rtm74XOG7V+c/WrUBJCXDeeW6vhJJx3HEc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\nRERENjE4EREREdnE4ERERERk0/8HtV1CDAnsoBgAAAAASUVORK5CYII=\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f5eb0c9ecc0>"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Getting a first estimate of the peaks\n",
+      "\n",
+      "By using peakutils.indexes, we can get the indexes of the peaks from the data. Due to noise, it will be just a rough approximation."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "indexes = peakutils.indexes(y, thres=0.5, min_dist=30)\n",
+      "print(indexes)\n",
+      "print(x[indexes], y[indexes])\n",
+      "pyplot.figure(figsize=(10,6))\n",
+      "pplot(x, y, indexes)\n",
+      "pyplot.title('First estimate')"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[31 74]\n",
+        "[ 31.  74.] [ 5.67608909  7.79403394]\n"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 8,
+       "text": [
+        "<matplotlib.text.Text at 0x7f5eaebf6198>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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7CkpXTJpSR8eOLIJJ6Y296sj3+vcH7rwTaNTIdiREFI0xwIEHAvPmAQ0b2o6G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\nAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f5eaec61710>"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Enhancing the resolution by interpolation\n",
+      "\n",
+      "We can enhance the resolution by interpolation. Here, we will try to fit a Gaussian near each peak that we have just detected."
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "peaks_x = peakutils.interpolate(x, y, ind=indexes)\n",
+      "print(peaks_x)"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "[ 30.58270223  72.34348214]\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Estimating and removing the baseline\n",
+      "\n",
+      "It is common for data to have a baseline that may not be desired. *PeakUtils* implements one function for estimating the baseline by using an iterative polynomial regression algorithm:"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "y2 = y + numpy.polyval([0.002,-0.08,5], x)\n",
+      "pyplot.figure(figsize=(10,6))\n",
+      "pyplot.plot(x, y2)\n",
+      "pyplot.title(\"Data with baseline\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 10,
+       "text": [
+        "<matplotlib.text.Text at 0x7f5eaeb73400>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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1ahR3NQDiRJcfAFTTLbd4dx9hCkBUrJQOoMZZs0Z64QVpxAipT5+4qwGQC2ih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\nACAiAhUAAEBEBCoAAICI/h89/rgbySA39QAAAABJRU5ErkJggg==\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f5eaebedc88>"
+       ]
+      }
+     ],
+     "prompt_number": 10
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "base = peakutils.baseline(y2, 2)\n",
+      "pyplot.figure(figsize=(10,6))\n",
+      "pyplot.plot(x, y2-base)\n",
+      "pyplot.title(\"Data with baseline removed\")"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "metadata": {},
+       "output_type": "pyout",
+       "prompt_number": 11,
+       "text": [
+        "<matplotlib.text.Text at 0x7f5eae14e940>"
+       ]
+      },
+      {
+       "metadata": {},
+       "output_type": "display_data",
+       "png": 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V5Dmvyn8AkyoKj6W/9NGrFzB/vt9REKUGkyry1PbtwJEjQIMG3hyfSRWFw6Qq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\nAAAaKFUAAA2UKgCABkoVAECDBzat0pvhdkICAAAAAElFTkSuQmCC\n",
+       "text": [
+        "<matplotlib.figure.Figure at 0x7f5eaeb1d358>"
+       ]
+      }
+     ],
+     "prompt_number": 11
+    },
+    {
+     "cell_type": "markdown",
+     "metadata": {},
+     "source": [
+      "## Related functionality in Scipy\n",
+      "\n",
+      "Scipy also implements functions that can be used for peak detection. Some examples:\n",
+      "\n",
+      "* [scipy.signal.find_peaks_cwt](http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks_cwt.html)\n",
+      "* [scipy.signal.savgol_filter](http://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.savgol_filter.html)"
+     ]
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [],
+     "language": "python",
+     "metadata": {},
+     "outputs": []
+    }
+   ],
+   "metadata": {}
+  }
+ ]
+}
+\ No newline at end of file
author	Andrius Merkys <merkys@debian.org>	2020-01-23 10:27:44 -0500
committer	git-ubuntu importer <ubuntu-devel-discuss@lists.ubuntu.com>	2020-01-23 22:39:31 +0000
commit	2259b1ce787198ef0ac75b03854b26bd34ff6c7e (patch)
tree	bf3785447aa7a05d918550126fbf87a0489a24cd
parent	9a0bb7160a6a279de9753bacc48438e47a2be50e (diff)