############################################################################### # Copyright (c) 2012 Trimble Navigation Ltd # $Id: NoPiSD_Process.py,v 1.1 2012/02/04 01:08:47 shankar Exp $ ############################################################################### # # NoPiSD_Process.py # # This file contains functions used to read stats_combo files and # generate long term trend plots for each metric ############################################################################### from pylab import * from NoPiSD_Plot import * from NoPiSD_Utils import * add_utils_dir_to_path() from NoPiUT_Common_Meas import get_metrics_list, get_metrics_column from NoPiUT_Common_Display import * global metric_list, num_metric metric_list = get_metrics_list() num_metrics = len( metric_list ) ############################################################################### # Wrapper function to generate long term trends using method 1. Generates one # plot per metric/meas_type/combo/receiver pair baseline. Both auto scaled and # fixed scaled plots are generated. ############################################################################### def gen_trends_method_1( concat_summ, data_path ) : method = 1 # loop over all receiver pair baselines read from concat summary file for baseline_idx in range( concat_summ.num_baselines ) : [orig_baseline_name, new_baseline_name] = update_baseline_name( concat_summ, baseline_idx ) # check if trend plots output directory exists trend_dir_name = create_trends_dir( method, baseline = new_baseline_name ) # loop over combo/mtype/metric to generate trends for combo_idx in range( concat_summ.num_combos ) : combo_name = concat_summ.combo[ combo_idx ].combo_name combo_sd_amb = concat_summ.combo[ combo_idx ].resolve_sdiff # Read concatenated statistics file [ stats_in, load_ok ] = load_stats_data( concat_summ, data_path, baseline_idx, combo_idx ) # Can proceed further only if the file was found and parsed if ( load_ok ) : # loop over the three measurement types for mtype in range( 3 ) : index_mtype = find( stats_in[ :, 2 ] == mtype ) if ( mtype == 0 and combo_sd_amb == 1 ) : stats_in[index_mtype, :] = scale_mm_to_mcycles( stats_in[index_mtype,:], combo_name ) # loop over each metric for idx, metric in enumerate( metric_list ) : # generate trend plots for each metric metric_col = get_data_column( metric ) gen_trend_plots( stats_in[ index_mtype, : ], concat_summ, metric, fig_num = idx, label = get_legend_name( orig_baseline_name ) ) save_and_close_fig( concat_summ, trend_dir_name, method, fig_num = idx, metric = metric, mtype = mtype, baseline = new_baseline_name, combo_idx = combo_idx ) ############################################################################### # Wrapper function to generate long term trends using method 2. Generates one # plot per metric/meas_type/combo/physical baseline. Physical baseline can be # one of three: # 1. physical zero baseline # 2. physical short baseline # 3. antenna switch setup receiver pairs # Both auto scaled and fixed scaled plots are generated. ############################################################################### def gen_trends_method_2( concat_summ, data_path ) : method = 2 [zero_base_idx, short_base_idx, relock_idx] = get_phy_baseline_idx( concat_summ ) # generate trends for each combo for combo_idx in range( concat_summ.num_combos ) : print 'Generating trends for combo: %s' % concat_summ.combo[combo_idx].combo_name # Loop over physical baselines and include all receiver pair belonging to a # particular physical baseline in the same plot # physical zero baseline for baseline_idx in zero_base_idx : gen_individual_trends( concat_summ, data_path, method, baseline_idx = baseline_idx, combo_idx = combo_idx ) trend_dir_name = create_trends_dir( method, baseline = 'Zero_Baseline' ) save_and_close_individual_plots( concat_summ, trend_dir_name, method, baseline = 'Zero_Baseline', combo_idx = combo_idx ) # physical short baseline for baseline_idx in short_base_idx : gen_individual_trends( concat_summ, data_path, method, baseline_idx = baseline_idx, combo_idx = combo_idx ) trend_dir_name = create_trends_dir( method, baseline = 'Short_Baseline' ) save_and_close_individual_plots( concat_summ, trend_dir_name, method, baseline = 'Short_Baseline', combo_idx = combo_idx ) # antenna switch receiver pairs for baseline_idx in relock_idx : gen_individual_trends( concat_summ, data_path, method, baseline_idx = baseline_idx, combo_idx = combo_idx ) trend_dir_name = create_trends_dir( method, baseline = 'Antenna_Switch' ) save_and_close_individual_plots( concat_summ, trend_dir_name, method, baseline = 'Antenna_Switch', combo_idx = combo_idx ) ############################################################################### # Wrapper function to generate long term trends using method 3. Generates one # plot per metric/meas_type/receiver pair baseline. GLONASS Carrier phase # results are scaled from mm to mcycles Both auto scaled and fixed scaled plots # are generated. ############################################################################### def gen_trends_method_3( concat_summ, data_path ) : method = 3 for baseline_idx in range( concat_summ.num_baselines ) : [orig_baseline_name, new_baseline_name] = update_baseline_name( concat_summ, baseline_idx ) for combo_idx in range( concat_summ.num_combos ) : gen_individual_trends( concat_summ, data_path, method, baseline_idx, combo_idx ) # Check if output directory exists trend_dir_name = create_trends_dir( method, baseline = new_baseline_name ) save_and_close_individual_plots( concat_summ, trend_dir_name, method, baseline = new_baseline_name ) ############################################################################### # Wrapper function to generate long term trends using method 4. Generates one # plot per metric/meas_type/physical baseline. GLONASS Carrier phase data are # scaled from mm to mcycles. Both auto scaled and fixed scaled plots are # generated. ############################################################################### def gen_trends_method_4( concat_summ, data_path ) : method = 4 [zero_base_idx, short_base_idx, relock_idx] = get_phy_baseline_idx( concat_summ ) # Loop over each all receiver pairs belonging to a particular physical # baseline setup. Display trends for all combos on a single plot # Physical Zero Baseline for baseline_idx in zero_base_idx : update_baseline_name( concat_summ, baseline_idx ) for combo_idx in range( concat_summ.num_combos ) : gen_individual_trends( concat_summ, data_path, method, baseline_idx = baseline_idx, combo_idx = combo_idx ) trend_dir_name = create_trends_dir( method, baseline = 'Zero_Baseline' ) save_and_close_individual_plots( concat_summ, trend_dir_name, method, baseline = 'Zero_Baseline' ) # Physical Short Baseline for baseline_idx in short_base_idx : update_baseline_name( concat_summ, baseline_idx ) for combo_idx in range( concat_summ.num_combos ) : gen_individual_trends( concat_summ, data_path, method, baseline_idx = baseline_idx, combo_idx = combo_idx ) trend_dir_name = create_trends_dir( method, baseline = 'Short_Baseline' ) save_and_close_individual_plots( concat_summ, trend_dir_name, method, baseline = 'Short_Baseline' ) # Antenna Switch Receiver Pairs for baseline_idx in relock_idx : update_baseline_name( concat_summ, baseline_idx ) for combo_idx in range( concat_summ.num_combos ) : gen_individual_trends( concat_summ, data_path, method, baseline_idx = baseline_idx, combo_idx = combo_idx ) trend_dir_name = create_trends_dir( method, baseline = 'Antenna_Switch' ) save_and_close_individual_plots( concat_summ, trend_dir_name, method, baseline = 'Antenna_Switch' ) ############################################################################### # Wrapper function to generate long term trends using method 5. Generates one # plot per metric/meas_type. GLONASS Carrier phase data are scaled from mm to # mcycles. All combos and receiver pairs are plotting on a single plot. # Both auto scaled and fixed scaled plots are generated. ############################################################################### def gen_trends_method_5( concat_summ, data_path ) : method = 5 trend_dir_name = create_trends_dir( method, baseline = '' ) # Loop over each metric and meas_type for metric_idx, metric in enumerate( metric_list ) : print 'Processing Metric: %s' % metric for baseline_idx in range( concat_summ.num_baselines ) : for combo_idx in range( concat_summ.num_combo ) : combo_name = concat_summ.combo[ combo_idx ].combo_name combo_sd_amb = concat_summ.combo[ combo_idx ].resolve_sdiff [ label, color ] = get_plot_label_color( concat_summ, method, baseline_idx, combo_idx ) [stats_in, load_ok ] = load_stats_data( concat_summ, data_path, baseline_idx, combo_idx ) if ( load_ok ) : for mtype in range( 3 ) : index_mtype = find( stats_in[ :, 2 ] == mtype ) if ( mtype == 0 and combo_sd_amb == 1 ) : stats_in[index_mtype,:] = scale_mm_to_mcycles( stats_in[index_mtype,:], combo_name ) gen_trend_plots( stats_in[ index_mtype, : ], concat_summ, metric, fig_num = mtype, label = label, color = color ) # Save and close trends for each metric prior to incrementing to the next # metric for mtype in range( 3 ) : save_and_close_fig( concat_summ, trend_dir_name, method, fig_num = mtype, metric = metric, mtype = mtype ) ############################################################################### # Common wrapper function to loop over all measurement types and metrics and # generate trends based on the specified method ############################################################################### def gen_individual_trends( concat_summ, data_path, method, baseline_idx, combo_idx ) : # Work out plot color and legend label based on method specified [ label, color ] = get_plot_label_color( concat_summ, method, baseline_idx, combo_idx ) # Read concatenated stats data if the file exists [stats_in, load_ok ] = load_stats_data( concat_summ, data_path, baseline_idx, combo_idx ) # If data was read, generate trends using the method specified if ( load_ok ) : # Loop over all three measurement types for mtype in range( 3 ) : idx_mtype = find( stats_in[ :, 2 ] == mtype ) combo_name = concat_summ.combo[ combo_idx ].combo_name combo_sd_amb = concat_summ.combo[ combo_idx ].resolve_sdiff # Scale S.D. Ambiguity resolved carrier phase results from mm to # mcycles. This ensures different combos have the same scale when # displaying all combos in a single plot if ( mtype == 0 and combo_sd_amb == 1 ) : stats_in[ idx_mtype,: ] = scale_mm_to_mcycles( stats_in[idx_mtype,:], combo_name ) # Loop over each metric for metric_idx, metric in enumerate( metric_list ) : gen_trend_plots( stats_in[ idx_mtype, : ], concat_summ, metric, fig_num = ( metric_idx + mtype*num_metrics ), label = label, color = color ) ############################################################################### # Wrapper function to invoke save_and_close_fig() defined in NoPiSD_Plot.py # Optional parameters are passed in as a dictionary ############################################################################### def save_and_close_individual_plots( concat_summ, trend_dir_name, method, **kwargs ) : for mtype in range( 3 ) : for metric_idx, metric in enumerate( metric_list ) : save_and_close_fig( concat_summ, trend_dir_name, method, fig_num = ( metric_idx + mtype*num_metrics ), metric = metric, mtype = mtype, **kwargs ) ############################################################################### # Wrapper function to scale S.D. ambiguity resolution carrier phase results # from mm to mcycles. Scaling is only required for # Mean/Median/Std. Dev/MADN. The remaining metrics are scale invariant ############################################################################### def scale_mm_to_mcycles( stats_in, combo_name ) : scalable_metrics = ['MEAN', 'MEDIAN', 'STD', 'MADN'] for metric_idx, metric in enumerate( metric_list ) : if ( metric.upper( ) in scalable_metrics ) : metric_col = get_concat_file_metric_column( metric ) stats_in[:,metric_col ] = convert_mm_to_mcycles( stats_in[:,metric_col], combo_name ) return( stats_in ) ############################################################################### # Work out plot legend name and line color ############################################################################### def get_plot_label_color( concat_summ, method, baseline_idx, combo_idx ) : if ( method == 2 ) : baseline_name = concat_summ.baseline[ baseline_idx ].baseline_dir label = get_legend_name( baseline_name ) color = get_colour( baseline_idx ) else : combo_name = concat_summ.combo[ combo_idx ].combo_name combo_short_name = get_combo_short_name( combo_name ) label = combo_short_name color = get_stats_colour( baseline_idx*concat_summ.num_combos + combo_idx ) return( label, color ) ############################################################################### # Inform user about current processing state. Apollo Zero baseline is named as # '' in concatenation summary file. Change name from '' to 'Apollo_Zero' for # user readability # Common to methods 1,3 and 4 ############################################################################### def update_baseline_name( concat_summ, baseline_idx ) : orig_baseline_name = concat_summ.baseline[baseline_idx].baseline_dir # Update baseline name for the Apollo Zero baseline. new_baseline_name = orig_baseline_name if ( orig_baseline_name == '' ) : new_baseline_name = 'Apollo_Zero' print 'Processing Baseline: ' + new_baseline_name print '------------------------------------------------------------' return ( orig_baseline_name, new_baseline_name )