add plot_2d_scatter + docs

README.rst

@@ -19,12 +19,12 @@ Description
 GetDist is a Python package for analysing Monte Carlo samples, including correlated samples
 from Markov Chain Monte Carlo (MCMC).
 
-* **Point and click GUI** - select chain files, view plots, marginalized constraints, latex tables and more
+* **Point and click GUI** - select chain files, view plots, marginalized constraints, LaTeX tables and more
 * **Plotting library** - make custom publication-ready 1D, 2D, 3D-scatter, triangle and other plots
-* **Named parameters** - simple handling of many parameters using parameter names, including LaTex labels and prior bounds
+* **Named parameters** - simple handling of many parameters using parameter names, including LaTeX labels and prior bounds
 * **Optimized Kernel Density Estimation** - automated optimal bandwidth choice for 1D and 2D densities (Botev et al. Improved Sheather-Jones method), with boundary and bias correction
 * **Convergence diagnostics** - including correlation length and diagonalized Gelman-Rubin statistics
-* **Latex tables** for marginalized 1D constraints
+* **LaTeX tables** for marginalized 1D constraints
 
 See the `Plot Gallery and tutorial <http://getdist.readthedocs.org/en/latest/plot_gallery.html>`_
 and `GetDist API reference <http://getdist.readthedocs.org/en/latest/index.html>`_.
@@ -55,9 +55,9 @@ Dependencies
 * matplotlib
 * scipy
 * PySide (optional, only needed for GUI)
-* Working latex installation (for some plotting/table functions)
+* Working LaTeX installation (for some plotting/table functions)
 
-Python distributions like Anaconda have most of what you need (except for latex). To install binary backages on Linux-like systems
+Python distributions like Anaconda have most of what you need (except for LaTeX). To install binary backages on Linux-like systems
 install pacakages *py-matplotlib, py-scipy, py-pyside, texlive-latex-extra, texlive-fonts-recommended, dvipng*. 
 For example on a Mac using Python 2.7 from `MacPorts <https://www.macports.org/install.php>`_::
 
@@ -92,13 +92,13 @@ In general there are a set of plain text files of the form::
 
 where "xxx" is some root file name.
 
-The .txt files are separate chain files (there can also be just one xxx.txt file). Each row of each sample .txt files is in the format
+The .txt files are separate chain files (there can also be just one xxx.txt file). Each row of each sample .txt file is in the format
 
   *weight like param1 param2 param3* ...
 
 The *weight* gives the number of samples (or importance weight) with these parameters. *like* gives -log(likelihood), and *param1, param2...* are the values of the parameters at the sample point. The first two columns can be 1 and 0 if they are not known or used.
 
-The .paramnames file lists the names of the parameters, one per line, optionally followed by a latex label. Names cannot include spaces, and if they end in "*" they are interpreted as derived (rather than MCMC) parameters, e.g.::
+The .paramnames file lists the names of the parameters, one per line, optionally followed by a LaTeX label. Names cannot include spaces, and if they end in "*" they are interpreted as derived (rather than MCMC) parameters, e.g.::
 
  x1   x_1
  y1   y_1
@@ -124,26 +124,24 @@ To load an MCSamples object from text files do::
 	 samples = loadMCSamples('/path/to/xxx', settings={'ignore_rows':0.3})
 
 Here *settings* gives optional parameter settings for the analysis. *ignore_rows* is useful for MCMC chains where you want to
-discard some fraction from the start of each chain as burn in (use a number >0 to discard a fixed number of sample lines rather than a fraction).
-The MCSamples object can be passed to plot functions, or used to get many results. For example to plot marginalized parameter densities 
+discard some fraction from the start of each chain as burn in (use a number >1 to discard a fixed number of sample lines rather than a fraction).
+The MCSamples object can be passed to plot functions, or used to get many results. For example, to plot marginalized parameter densities 
 for parameter names *x1* and *x2*::
 
     from getdist import plots
     g = plots.getSinglePlotter()
     g.plot_2d(samples, ['x1', 'x2'])
 
-For plotting, when you have many different chain files in the same directory, 
-you can work directly with the root names. For example to compare *x* and *y* constraints
+When you have many different chain files in the same directory, 
+plotting can work directly with the root file names. For example to compare *x* and *y* constraints
 from two chains with root names *xxx* and *yyy*::
 
 	from getdist import plots
-	
 	g = plots.getSinglePlotter(chain_dir='/path/to/', analysis_settings={'ignore_rows':0.3})
-
 	g.plot_2d(['xxx','yyy], ['x', 'y'])
 
 
-MCSamples objects can also be constructed directly from numpy arrays in memory, see the example in the `Plot Gallery <https://github.com/cmbant/getdist/blob/master/docs/plot_gallery.ipynb>`_.
+MCSamples objects can also be constructed directly from numpy arrays in memory, see the example in the `Plot Gallery <http://getdist.readthedocs.org/en/latest/plot_gallery.html>`_.
 
 GetDist script
 ===================

docs/source/plots.rst

@@ -3,6 +3,15 @@ getdist.plots
 
 .. currentmodule:: getdist.plots
 
+This module is used for making plots from samples. The :class:`~getSinglePlotter` and :class:`~getSubplotPlotter` functions are used to make a plotter instance,
+which is then used to make and export plots. 
+
+Many plotter functions take a **roots** argument, which is either a root name for
+some chain files, or an in-memory :class:`~.mcsamples.MCSamples` instance. You can also make comparison plots by giving a list of either of these.
+
+Parameter are referenced simply by name (as specified in the .paramnames file when loading from file, or set in the :class:`~.mcsamples.MCSamples` instance). 
+For functions that takes lists of parameters, these can be just lists of names. 
+You can also use glob patterns to match specific subsets of parameters (e.g. *x\** to match all parameters with names starting with *x*).
 
 .. autosummary::
    :toctree: _summaries

getdist/gui/mainwindow.py

@@ -724,7 +724,7 @@ def about(self):
         """
         QMessageBox.about(
             self, "About GetDist GUI",
-            "GetDist GUI v " + getdist.__version__ + "\ncosmologist.info/cosmomc/\n" +
+            "GetDist GUI v " + getdist.__version__ + "\nhttps://github.com/cmbant/getdist/\n" +
             "\nPython: " + sys.version +
             "\nMatplotlib: " + matplotlib.__version__ +
             "\nSciPy: " + scipy.__version__ +

getdist/mcsamples.py

@@ -1238,13 +1238,13 @@ def get1DDensityGridData(self, j, writeDataToFile=False, get_density=False, para
         :param get_density: return a :class:`~.densities.Density1D` instance only, does not write out or calculate mean likelihoods for plots
         :param paramConfid: optional cached :class:`ParamConfidenceData` instance
         :param meanlikes: include mean likelihoods
-        :param kwargs: optional settings to override instance settings of the same name:
+        :param kwargs: optional settings to override instance settings of the same name (see `analysis_settings`):
         
-               - *smooth_scale_1D*
-               - *boundary_correction_order*
-               - *mult_bias_correction_order*
-               - *fine_bins*
-               - *num_bins*
+               - **smooth_scale_1D**
+               - **boundary_correction_order**
+               - **mult_bias_correction_order**
+               - **fine_bins**
+               - **num_bins**
         :return: A :class:`~.densities.Density1D` instance
         """
         j = self._parAndNumber(j)[0]
@@ -1462,7 +1462,7 @@ def get2DDensity(self, x, y, normalized=False, **kwargs):
 
         :param x: index or name of x parameter
         :param y: index or name of y parameter
-        :param kwargs: arguments for the :func:`get2DDensityGridData` function
+        :param kwargs: keyword arguments for the :func:`get2DDensityGridData` function
         :param normalized: if False, is normalized so the maximum is 1, if True, density is normalized
         :return: :class:`~.densities.Density2D` instance
         """
@@ -1483,12 +1483,12 @@ def get2DDensityGridData(self, j, j2, writeDataToFile=False,
         :param num_plot_contours: number of contours to calculate and return in density.contours
         :param get_density: only get the 2D marginalized density, no additional plot data
         :param meanlikes: calculate mean likelihoods as well as marginalized density (returned as array in density.likes)
-        :param kwargs: Optional arguments to override instance settings of the same name:
+        :param kwargs: optional settings to override instance settings of the same name (see `analysis_settings`):
         
-            - fine_bins_2D,
-            - boundary_correction_order
-            - mult_bias_correction_order
-            - smooth_scale_2D
+            - **fine_bins_2D**
+            - **boundary_correction_order**
+            - **mult_bias_correction_order**
+            - **smooth_scale_2D**
         :return: a :class:`~.densities.Density2D` instance
         """
         if self.needs_update: self.updateBaseStatistics()
@@ -1686,7 +1686,7 @@ def get2DDensityGridData(self, j, j2, writeDataToFile=False,
 
     def _setLikeStats(self):
         """
-        Get and store LikeStats (see getLikeStats())
+        Get and store LikeStats (see :func:`MCSamples.getLikeStats`)
         """
         if self.loglikes is None:
             self.likeStats = None
@@ -1815,7 +1815,7 @@ def getLatex(self, params=None, limit=1):
 
         :param params: list of parameter names
         :param limit: which limit to get, 1 is the first (default 68%), 2 is the second (limits array specified by self.contours)
-        :return: list of labels, list of tex snippets
+        :return: labels, texs: a list of parameter labels, and a list of tex snippets
         """
         marge = self.getMargeStats()
         if params is None: params = marge.list()
@@ -1936,7 +1936,7 @@ def _setMargeLimits(self, par, paramConfid, max_frac_twotail=None, density1D=Non
 
     def getCorrelatedVariable2DPlots(self, num_plots=12, nparam=None):
         """
-        gets list of most correlated variable pair names
+        Gets a list of most correlated variable pair names.
 
         :param num_plots: The number of plots
         :param nparam: maximum number of pairs to get
@@ -1963,7 +1963,7 @@ def getCorrelatedVariable2DPlots(self, num_plots=12, nparam=None):
 
     def saveAsText(self, root, chain_index=None, make_dirs=False):
         """
-        Saves samples as text file, including .ranges and .paramnames
+        Saves samples as text file, including .ranges and .paramnames.
 
         :param root: The root file name to use.
         :param chain_index: optional index to be used for the filename.
@@ -1977,7 +1977,7 @@ def saveAsText(self, root, chain_index=None, make_dirs=False):
 
     def writeScriptPlots1D(self, filename, plotparams=None, ext=None):
         """
-        Write a script that generates a 1 dimensional plot. Only intended for use by GetDist.py script.
+        Write a script that generates a 1D plot. Only intended for use by GetDist.py script.
 
         :param filename: The filename to write to.
         :param plotparams: The list of parameters to plot (default: all)
@@ -2085,7 +2085,7 @@ def _WritePlotFile(self, filename, subplot_size, text, tag, ext=None):
 
 def GetChainRootFiles(rootdir):
     """
-    Gets the root names of all chain files in the root directory
+    Gets the root names of all chain files in a directory.
 
     :param rootdir: The root directory to check
     :return:  The root names
@@ -2098,7 +2098,7 @@ def GetChainRootFiles(rootdir):
 
 def GetRootFileName(rootdir):
     """
-    Gets the root name of chains in given directory (assuming only one set of chain files)
+    Gets the root name of chains in given directory (assuming only one set of chain files).
 
     :param rootdir: The directory to check
     :return: The root file name.