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From good...@apache.org
Subject svn commit: r1509413 - in /incubator/climate/branches/RefactorInput/ocw: plotter.py tests/test_plotter.py
Date Thu, 01 Aug 2013 20:15:48 GMT
Author: goodman
Date: Thu Aug  1 20:15:48 2013
New Revision: 1509413

URL: http://svn.apache.org/r1509413
Log:
CLIMATE-239 - Remove Plotter class from plotter.py

Modified:
    incubator/climate/branches/RefactorInput/ocw/plotter.py
    incubator/climate/branches/RefactorInput/ocw/tests/test_plotter.py

Modified: incubator/climate/branches/RefactorInput/ocw/plotter.py
URL: http://svn.apache.org/viewvc/incubator/climate/branches/RefactorInput/ocw/plotter.py?rev=1509413&r1=1509412&r2=1509413&view=diff
==============================================================================
--- incubator/climate/branches/RefactorInput/ocw/plotter.py (original)
+++ incubator/climate/branches/RefactorInput/ocw/plotter.py Thu Aug  1 20:15:48 2013
@@ -15,13 +15,6 @@
 # specific language governing permissions and limitations
 # under the License.
 
-'''
-Classes: 
-    Plotter - Visualizes pre-calculated metrics
-'''
-
-import os
-import logging
 from tempfile import TemporaryFile
 import matplotlib as mpl
 import matplotlib.pyplot as plt
@@ -31,170 +24,7 @@ import scipy.stats.mstats as mstats
 import numpy as np
 import numpy.ma as ma
 from utils.taylor import TaylorDiagram
-#from toolkit import plots
 
-class Plotter:
-    '''
-    Visualizes pre-calculated metrics
-    
-    This class is used for quickly plotting metrics as processed by an 
-    :class:`evaluation.Evaluation` object. Different plots are
-    generated depending on the type of metric. For example, spatial 
-    correlation (2D) would be contoured over a map projection region.
-    On the other hand, a time series of annual averages would be
-    drawn as a line plot. Each plot is saved in a supported format
-    (eg png, eps, pdf) to a user-specified directory.
-    
-    If the given evaluation contains multiple target datasets (eg multiple
-    models), subplots will be drawn for each dataset in a single file.
-    '''
-    
-    def __init__(self, evaluation, workdir=None, fmt='png'):
-        '''
-        Plotter Constructor.
-        
-        :param evaluation: An evaluation that has been previously ran for a
-            series of datasets.
-        :type evaluation: Evaluation 
-        :param workdir: The working directory to save generated figures.
-        	If set to None, it will be set to your current working directory.
-        :type workdir: [optional] str or None
-        :param fmt: The file type to save figures. Supported formats include
-            png, eps, and pdf.
-        :type fmt: [optional] str 
-        '''
-        if workdir is None:
-            workdir = os.getcwd()
-            
-        self.workdir = workdir
-        self.fmt = fmt
-        self._plot_data = []
-        self.assign_plot_data(evaluation)
-    
-    def assign_plot_data(self, evaluation):
-        '''
-        Builds a list of plotting functions and corresponding arguments based
-        on the shape of each result in the evaluation.
-        
-        Plotting function shapes are based on the length of the following 
-        evaluation attributes (X denotes an arbitrary value):
-        Contour Map: datasets, lats, lons
-        Portrait Diagram: X, subregions, datasets (not supported)
-        Time Series: subregions, datasets, times
-        Taylor Diagram: datasets, 2
-        
-        Arguments:
-        :param evaluation: A previously ran Evaluation instance
-        :type evaluation: Evaluation
-        '''
-        # Check if the evaluation was ran previously. 
-        try:
-            results = evaluation.results
-        except AttributeError:
-            raise ValueError('evaluation must be ran before being processed')
-        
-        # Load dataset attributes
-        names = [dataset.name for dataset in evaluation.target_datasets]
-        lats = evaluation.ref_dataset.lats
-        lons = evaluation.ref_dataset.lons
-        variable = evaluation.ref_dataset.variable
-        times = evaluation.ref_dataset.times        
-        mtimes = [t for t in times if t.day == 1][:12]
-        ytimes = [t for t in times if t.month == 1 and t.day == 1]
-        
-        # For default plotting always assume two columns
-        gridshape = len(names), 2
-        
-        # Process results
-        for result, metric in zip(results, evaluation.metrics):
-            
-            # Convert result to numpy array for easier processing
-            result = np.array(result)
-            
-            # Plot title
-            ptitle = '%s_%s' %(variable, metric.__name__)
-        
-            # Contour Map 
-            if result.shape == (len(names), len(lats), len(lons)):
-                plotfunc = draw_contour_map
-                fname = os.path.join(self.workdir, '%s_contour_map' 
-                                     %(metric.__name__))
-                args = result, lats, lons, fname
-                kwargs = {'fmt': self.fmt, 
-                          'subtitles': names, 
-                          'gridshape': gridshape,
-                          'ptitle': title
-                          }
-            
-            # Taylor Diagram    
-            elif result.shape == (len(names), 2):
-                plotfunc = draw_taylor_diagram
-                fname = os.path.join(self.workdir, '%s_taylor_diagram'
-                                     %(metric.name))
-                refname = self.ref_dataset.name
-                args = result, names, refname
-                kwargs = {'fmt': self.fmt, 'ptitle': title}
-                
-            # Time Series
-            elif result.shape[2] == len(mtimes) or result.shape[2] == len(ytimes):
-                plotfunc = draw_time_series
-                fname = os.path.join(self.workdir, '%s_time_series'
-                                     %(metric.name))
-                
-                # Arguments vary slightly depending on whether the time series
-                # is monthly or annual
-                if result.shape[2] == len(mtimes):
-                    ptimes = mtimes
-                    label_month = True
-                else:
-                    ptimes = ytimes
-                    label_month = False
-                
-                # Number of datasets is always the first dimension in result
-                # so make it the second dimension to be compatible with the
-                # plotting function.
-                result = result.swapaxes(1, 0)
-                args = result, ptimes, names, fname
-                kwargs = {'fmt': self.fmt, 
-                          'gridshape': gridshape,
-                          'ptitle': title,
-                          'label_month': label_month
-                           # TODO: Subplot Titles by subregion?
-                          }
-                
-            # Automatic plotting currently not supported for given result
-            else:
-                warning = 'No plotting function found for metric %s.' \
-                          'Consider assigning one manually and rerun the' \
-                          'evaluation.' %(metric.__name__)
-                logging.warning(warning)
-                continue
-                
-            self.add_plot_func(plotfunc, *args, **kwargs)
-            
-    def add_plot_func(self, plotfunc, *args, **kwargs):
-        '''
-        Add a plotting function to the plotter with given positional and
-        keyword arguments.
-        
-        :param plotfunc: Reference to a plotting function. 
-        :type plotfunc: function
-        '''
-        self._plot_data.append((plotfunc, args, kwargs))
-    
-    def make_plots(self):
-        '''
-        Calls the stored plotting functions
-        '''
-        if not self._plot_data:
-            raise AttributeError('No compatible plotting functions found!')
-        
-        for plotfunc, args, kwargs in self._plot_data:
-            plotfunc(*args, **kwargs)
-
-    
-# Plotting function library. Originally from a separate file, but placed here
-# for now so that the original rcmet code in this branch still works
 def _nice_intervals(data, nlevs):
     '''
     Purpose::

Modified: incubator/climate/branches/RefactorInput/ocw/tests/test_plotter.py
URL: http://svn.apache.org/viewvc/incubator/climate/branches/RefactorInput/ocw/tests/test_plotter.py?rev=1509413&r1=1509412&r2=1509413&view=diff
==============================================================================
--- incubator/climate/branches/RefactorInput/ocw/tests/test_plotter.py (original)
+++ incubator/climate/branches/RefactorInput/ocw/tests/test_plotter.py Thu Aug  1 20:15:48
2013
@@ -18,32 +18,9 @@
 '''Unit tests for the plotter.py module'''
 
 import unittest
-import os
-import datetime
-import metrics
-import numpy as np
-from dataset import Dataset
-from evaluation import Evaluation
-from plotter import Plotter
 
 class TestPlotter(unittest.TestCase):
-    def setUp(self):
-        pass
-        
-    def test_init(self):
-        pass
-
-    def test_assign_plot_data_contour_map(self):
-        pass
-    
-    def test_assign_plot_data_time_series(self):
-        pass
-    
-    def test_assign_plot_data_taylor_diagram(self):
-        pass
-        
-    def test_assign_plot_data_portrait_diagram(self):
-        pass
+    pass
 
 if __name__  == '__main__':
     unittest.main()
\ No newline at end of file



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