Documentation and Metadata changes for 1.0.0 Release

CHANGELOG.rst

@@ -1,6 +1,11 @@
 Changelog
 =========
 
+:Version: 1.0.0 of 2017-09-29
+
+Some minor documentation and metadata changes. This version is included with 
+Patzelt and Bouchaud (JSTAT 2017) as an online supplement.
+
 
 :Version: 1.0-rc.1 of 2017-09-25
 

README.rst

@@ -8,17 +8,9 @@ The calculations are FFT-based for optimal performance and offer many options
 for normalisation, mean removal, averaging, and zero-padding. In particular, 
 averaging over pandas groups of different sizes (e.g. different days) is 
 supported.
-
-The algorithms to calculate three-point correlations are described in:
-
-    Patzelt, F. and Bouchaud, J-P.:
-    Nonlinear price impact from linear models. 
-    Journal of Statistical Mechanics (2017). 
-    Preprint at arXiv:1708.02411.
-
 
 ======================  ======================================================
-Command                 Synopsis
+Function                Synopsis
 ======================  ======================================================
 acorr                   Calculate autocorrelation or autocovariance
 acorr_grouped_df        Calculate acorr for pandas groups and average
@@ -36,6 +28,13 @@ xcorr_grouped_df        Calculate xcorr for pandas groups and average
 xcorrshift              Convert xcorr output so lag zero is centered
 ======================  ======================================================
 
+The algorithms to calculate three-point correlations and details of daily
+averaging over high-frequency trading data are described in:
+
+    Patzelt, F. and Bouchaud, J-P.:
+    Nonlinear price impact from linear models. 
+    Journal of Statistical Mechanics (2017, in print). 
+    Preprint at `arXiv:1708.02411 <//arxiv.org/abs/1708.02411>`_.
 
 Please find further explanations in the docstrings and in the examples 
 directory.
@@ -46,11 +45,25 @@ Installation
 
 	pip install scorr
 
-
-Dependencies
-------------
 
-	- Python 2.7
-	- NumPy
-	- SciPy
-	- Pandas
+Dependencies (automatically installed)
+--------------------------------------
+
+    - Python 2.7
+    - NumPy
+    - SciPy
+    - Pandas    
+
+
+Optional Dependencies required only for the examples (pip installable)
+----------------------------------------------------------------------
+
+    - Jupyter
+    - Matplotlib
+    - colorednoise
+
+
+Note
+----
+
+Python 3 is *NOT* supported as of yet!

examples/scorr_periodic_vs_aperiodic_vs_unbiased.ipynb

@@ -28,9 +28,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'corr2'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-3-5f737367bdef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mscorr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcorr2\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m~/Documents/uni/papers/impact_report/demo_code/packaged/testenv3/lib/python3.6/site-packages/scorr/__init__.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mcorr2\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mcorr3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__reload_submodules__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;32mimport\u001b[0m \u001b[0mhelpers\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'corr2'"
+     ]
+    }
+   ],
    "source": [
     "import scorr"
    ]
@@ -870,8 +883,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Analyse as an finite, aperiodic signal\n",
-    "Here we assume that the signal is zero everywhere outside of the interval [0, tmax]. Hence we pad with zeros (default) to demix the causal and anti-causal part."
+    "## Analyse as a finite, aperiodic signal\n",
+    "Here we assume that the signal is zero everywhere outside of the interval [0, tmax]. Hence we pad with as many zeros as data points to demix the causal and anti-causal part."
    ]
   },
   {
@@ -3524,21 +3537,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "pygments_lexer": "ipython3",
+   "version": "3.6.2"
   }
  },
  "nbformat": 4,

examples/three_point_correlations_padding.ipynb

@@ -979,7 +979,6 @@
     "ax[1].set_title('Scorr (using FFT)')\n",
     "ax[1].imshow(C3, **ikwa)\n",
     "#ax[1].imshow((C3_nofft -C3), **ikwa) # alternatively show difference\n",
-    "#ax[1].imshow(scorr.fftshift(C3), **ikwa)\n",
     "\n",
     "format_axes(ax)\n",
     "fig.tight_layout()"

examples/two_point_vs_three_point_correlations.ipynb

@@ -21,9 +21,9 @@
     "\n",
     "and the two-point correlation matrix\n",
     "\n",
-    "$C_{s c, s c}(\\ell,j)$ \n",
+    "$C_{s c, s c}(\\ell,j)$,\n",
     "\n",
-    "that was used to approximated by the prior before the developement of the spectral method implemented by `scorr.x3corr`."
+    "which was used to approximated the prior matrix before the developement of the spectral method implemented by `scorr.x3corr`."
    ]
   },
   {

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='scorr',
-    version='1.0.0-rc.1',
+    version='1.0.0',
     description=(
         'Fast and flexible two- and three-point correlation analysis '
         'for time series using spectral methods.'
@@ -20,7 +20,7 @@
         'Intended Audience :: Education',
         'Intended Audience :: Science/Research',
         'License :: OSI Approved :: MIT License',
-        'Programming Language :: Python :: 2',
+        'Programming Language :: Python :: 2 :: Only',
         'Programming Language :: Python :: 2.7',
         #'Programming Language :: Python :: 3',
         #'Programming Language :: Python :: 3.6',
@@ -34,7 +34,7 @@
     ],
     url='http://github.com/felixpatzelt/scorr',
     download_url=(
-      'https://github.com/felixpatzelt/scorr/archive/1.0.0-rc.1.tar.gz'
+      'https://github.com/felixpatzelt/scorr/archive/1.0.0.tar.gz'
     ),
     author='Felix Patzelt',
     author_email='[email protected]',