示例#1
0
    def _process_and_display_trends(self, query, objects_expected):
        from qtools.lib.nstats import moving_average_by_interval
        from numpy import histogram
        import numpy as np

        group_by_plate = self.form_result['group_by_plate']
        if objects_expected:
            results = execute_built_query(query, group_by_plate, True, self.form_result['metric'][1])
        else:
            results = execute_built_query(query, group_by_plate, False)

        epoch_results = [(float(stat), time.mktime(dt.timetuple()), id, name) for stat, dt, id, name in results if stat is not None]

        exclude_func = create_exclude_function(self.form_result)
        filtered_results = [tup for tup in epoch_results if exclude_func(tup[0])]

        c.yaxis_title = fl.comparable_metric_display(MetricPattern.from_python(self.form_result['metric']))
        c.mean_value = 'N/A'
        c.std_value  = 'N/A'
        c.metric_name = fl.comparable_metric_display(MetricPattern.from_python(self.form_result['metric']))
        c.table_data =[]

        if group_by_plate:
            moving_average = 10
            epoch_url_results = [(stat, stamp*1000, url(controller='plate', action='view', id=id), name) for stat, stamp, id, name in filtered_results]
        else:
            moving_average = 50
            epoch_url_results = [(stat, stamp*1000, url(controller='well', action='view', id=id), name) for stat, stamp, id, name in filtered_results]

        if epoch_url_results:
            c.stats = {}
            stats, time_points, urls, names = zip(*epoch_url_results)
            c.stats = h.literal(json.dumps(zip(time_points, stats)))

            c.table_data = zip( names, stats, time_points )
            #update if we can.
            c.mean_value = str( np.mean( stats ) )
            c.std_value  = str( np.std( stats ) )
            
            INTERVAL = 24*60*60*1000
            ma = moving_average_by_interval(stats, time_points, INTERVAL, 10)
            c.moving_avg = h.literal(json.dumps(ma))
            c.urls = h.literal(json.dumps(urls))
            c.names = h.literal(json.dumps(names))

            # get histogram of points by day
            #raise Exception, len(ma)
            density, edges = histogram(time_points, bins=len(ma), range=(ma[0][0], ma[-1][0]))
            c.densities = h.literal(json.dumps(zip([m[0] for m in ma], [int(d) for d in density])))
            c.max_density_axis = 3*max(density)
        else:
            c.stats = {}
            c.moving_avg = {}
            c.urls = {}
            c.names = {}
            c.densities = {}
            c.max_density_axis = 3

        return render('/trend/results.html')
示例#2
0
    def _process_and_display_qcc(self, query, objects_expected):
        """
        This procuces the data for java scirpt display of qcc
        """
        import numpy as np

        #average results across plates?
        group_by_plate = self.form_result['group_by_plate']
        
        if objects_expected:
            results = execute_built_query(query, group_by_plate, True, self.form_result['metric'][1])
        else:
            results = execute_built_query(query, group_by_plate, False)

        epoch_results = [(float(stat), time.mktime(dt.timetuple()), id, name, DR) for stat, dt, id, name, DR in results if stat is not None]

        exclude_func = create_exclude_function(self.form_result)
        filtered_results = [tup for tup in epoch_results if exclude_func(tup[0])]

        c.yaxis_title = fl.comparable_metric_display(MetricPattern.from_python(self.form_result['metric']))
        c.y_label = fl.comparable_metric_display(MetricPattern.from_python(self.form_result['metric']))    
        c.explanation, c.x_label = self._get_qcc_text()

        if group_by_plate:
            epoch_url_results = [];
            for i in range( len( filtered_results ) ):
                stat, stamp, id, name, DR = filtered_results[i]
                url_str =  url(controller='plate', action='view', id=id)
                epoch_url_results.append( (stat,i,url_str,name,DR) )
            #epoch_url_results = [(stat, stamp, url(controller='plate', action='view', id=id), name) for stat, stamp, id, name in filtered_results]
        else:
            epoch_url_results = [];
            for i in range( len( filtered_results ) ):
                stat, stamp, id, name, plate = filtered_results[i]
                url_str =  url(controller='well', action='view', id=id)
                epoch_url_results.append( (stat,i,url_str,name,plate) )
            #epoch_url_results = [(stat, stamp, url(controller='well', action='view', id=id), name) for stat, stamp, id, name in filtered_results]

        if epoch_url_results:
            c.stats = {}
            stats, time_points, urls, names, xTickLabels = zip(*epoch_url_results)

            xTickLabels = [ x + "   " for x in xTickLabels]

            c.stats = h.literal(json.dumps(zip(time_points, stats)))
            
            stat_mean  = np.mean( stats )
            stat_standard_dev = np.std( stats ) 
            #stat_standard_error = np.std( stats ) / np.sqrt( len(stats) )
            ucl = stat_mean + 3 * stat_standard_dev
            lcl = stat_mean - 3 * stat_standard_dev
            
            mv = [[epoch_url_results[0][1],stat_mean],[epoch_url_results[-1][1],stat_mean]]
            c.mean_value = h.literal(json.dumps(mv))
        
            xaxis_start = epoch_url_results[0][1]
            xaxis_end   = epoch_url_results[-1][1]
            c.ucl =  h.literal(json.dumps([[xaxis_start,ucl],[xaxis_end,ucl]] ))
            c.lcl =  h.literal(json.dumps([[xaxis_start,lcl],[xaxis_end,lcl]] ))

            axis_offset = 0.05*(max(stats)-min(stats)) 

            if self.form_result['lower_yaxis']:
                c.min_y_axis = float( self.form_result['lower_yaxis'] )
            else:
                c.min_y_axis =  min( stats ) - axis_offset
        
            if self.form_result['upper_yaxis']:
                c.max_y_axis = float( self.form_result['upper_yaxis'] )
            else:
                c.max_y_axis =  max( stats ) + axis_offset

            c.mv_label  = "Center Line (mean): %f" % stat_mean
            c.ucl_label = "UCL (mean + 3std): %f" % ucl
            c.lcl_label = "LCL (mean - 3std): %f" % lcl

            # add user defined limits if presented
            if self.form_result['upper_spec']:
                upper_spec = float(self.form_result['upper_spec'])
                c.upper_spec = h.literal(json.dumps([[xaxis_start,upper_spec],[xaxis_end,upper_spec]] ))
                c.upper_spec_label = 'User defined upper line: %f' % upper_spec 
            else:
                c.upper_spec = 'null'
                c.upper_spec_label = ''           
 
            if self.form_result['lower_spec']:
                lower_spec = float(self.form_result['lower_spec'])
                c.lower_spec = h.literal(json.dumps([[xaxis_start,lower_spec],[xaxis_end,lower_spec]] ))
                c.lower_spec_label = 'User defined lower line: %f' % lower_spec
            else:
                c.lower_spec = 'null'
                c.lower_spec_label = ''

            c.urls = h.literal(json.dumps(urls))
            c.names = h.literal(json.dumps(names))
            c.TickData = h.literal(json.dumps(zip(range(len(xTickLabels)),xTickLabels)))
        else:
            c.stats = {}
            c.mean_value = {}
            c.ucl = {}
            c.lcl = {}
            c.mv_label = ''
            c.ucl_label = ''
            c.lcl_label = ''
            c.urls = {}
            c.names = {}
            c.TickData = {}
            c.min_y_axis = 0
            c.max_y_axis = 3
            c.upper_spec = 'null'
            c.upper_spec_label = ''
            c.lower_spec = 'null'
            c.lower_spec_label = ''


        return render('/qcCharts/results.html')