| 1 | """ |
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| 2 | This is getting really messy. |
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| 3 | |
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| 4 | All err results are going into the same dir, and it can't really be changed. |
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| 5 | """ |
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| 6 | |
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| 7 | |
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| 8 | #---------------------------------------------------------------------------- |
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| 9 | # Import necessary modules |
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| 10 | #---------------------------------------------------------------------------- |
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| 11 | |
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| 12 | # Standard modules |
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| 13 | import os |
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| 14 | from os import sep, path |
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| 15 | from csv import writer |
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| 16 | from time import localtime, strftime |
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| 17 | |
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| 18 | # Related major packages |
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| 19 | from Numeric import arange, array, zeros, Float, where, greater, less, \ |
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| 20 | compress, argmin, choose, searchsorted, sqrt, sum |
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| 21 | |
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| 22 | import project |
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| 23 | from os import sep |
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| 24 | from anuga.shallow_water.data_manager import csv2dict |
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| 25 | from anuga.utilities.numerical_tools import ensure_numeric, err, norm |
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| 26 | |
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| 27 | from slope import load_sensors |
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| 28 | from interp import interp |
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| 29 | |
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| 30 | def get_max_min_condition_array(min, max, vector): |
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| 31 | |
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| 32 | SMALL_MIN = -1e10 # Not that small, but small enough |
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| 33 | vector = ensure_numeric(vector) |
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| 34 | assert min > SMALL_MIN |
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| 35 | no_maxs = where(less(vector,max), vector, SMALL_MIN) |
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| 36 | #print "no_maxs", no_maxs |
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| 37 | band_condition = greater(no_maxs, min) |
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| 38 | return band_condition |
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| 39 | |
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| 40 | def auto_rrms(outputdir_tag, scenarios, quantity, y_location_tag=':0.0'): |
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| 41 | """ |
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| 42 | Given a bunch of scenarios that have CSV guage files, calc the |
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| 43 | err, Number_of_samples and rmsd for all gauges in each scenario. |
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| 44 | Write this info to a file for each scenario. |
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| 45 | """ |
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| 46 | for run_data in scenarios: |
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| 47 | # Don't need to do this. The simulation .csv files are already |
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| 48 | # anuga_times |
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| 49 | #anuga_start_stop_times = [] |
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| 50 | #for time in run_data['wave_start_stop_times']: |
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| 51 | # anuga_start_stop_times.append( \ |
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| 52 | # time - run_data['ANUGA_start_time']) |
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| 53 | |
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| 54 | location_sims = [] |
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| 55 | location_exps = [] |
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| 56 | for gauge_x in run_data['gauge_x']: |
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| 57 | gauge_x = str(gauge_x) |
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| 58 | location_sims.append(gauge_x + y_location_tag) |
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| 59 | location_exps.append(gauge_x) |
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| 60 | |
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| 61 | id = run_data['scenario_id'] |
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| 62 | outputdir_name = id + outputdir_tag |
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| 63 | pro_instance = project.Project(['data','flumes','Hinwood_2008'], |
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| 64 | outputdir_name=outputdir_name) |
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| 65 | end = id + ".csv" |
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| 66 | file_sim = pro_instance.outputdir + quantity + "_" + end |
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| 67 | file_exp = pro_instance.raw_data_dir + sep + id + 'pressfilt_exp_' \ |
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| 68 | + quantity + '.csv' |
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| 69 | file_err = pro_instance.rmsd_dir + sep + outputdir_name + "_" \ |
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| 70 | + quantity + "_err.csv" |
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| 71 | |
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| 72 | |
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| 73 | simulation, _ = csv2dict(file_sim) |
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| 74 | experiment, _ = csv2dict(file_exp) |
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| 75 | |
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| 76 | time_sim = [float(x) for x in simulation['time']] |
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| 77 | time_exp = [float(x) for x in experiment['Time']] |
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| 78 | time_sim = ensure_numeric(time_sim) |
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| 79 | time_exp = ensure_numeric(time_exp) |
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| 80 | #print "min(time_exp)", min(time_exp) |
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| 81 | #print "max(time_exp)", max(time_exp) |
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| 82 | |
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| 83 | condition = get_max_min_condition_array(run_data['wave_times'][0], |
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| 84 | run_data['wave_times'][1], |
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| 85 | time_exp) |
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| 86 | time_exp_cut = compress(condition, time_exp) #, axis=axis) |
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| 87 | #print "min(time_exp_cut)", min(time_exp_cut) |
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| 88 | #print "max(time_exp_cut)", max(time_exp_cut) |
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| 89 | |
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| 90 | #assert min(time_sim) < min(time_exp) |
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| 91 | |
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| 92 | print "Writing to ", file_err |
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| 93 | |
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| 94 | err_list = [] |
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| 95 | points = [] |
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| 96 | rmsd_list = [] |
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| 97 | for location_sim, location_exp in map(None, location_sims, |
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| 98 | location_exps): |
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| 99 | quantity_sim = [float(x) for x in simulation[location_sim]] |
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| 100 | quantity_exp = [float(x) for x in experiment[location_exp]] |
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| 101 | |
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| 102 | quantity_exp_cut = compress(condition, quantity_exp) |
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| 103 | |
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| 104 | # Now let's do interpolation |
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| 105 | quantity_sim_interp = interp(quantity_sim, time_sim, time_exp_cut) |
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| 106 | |
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| 107 | assert len(quantity_sim_interp) == len(quantity_exp_cut) |
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| 108 | norm = err(quantity_sim_interp, |
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| 109 | quantity_exp_cut, |
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| 110 | 2, relative = False) # 2nd norm (rel. RMS) |
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| 111 | err_list.append(norm) |
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| 112 | points.append(len(quantity_sim_interp)) |
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| 113 | rmsd_list.append(norm/sqrt(len(quantity_sim_interp))) |
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| 114 | #print "norm", norm |
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| 115 | #for i in range(len(quantity_sim_interp)): |
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| 116 | |
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| 117 | #print "quantity_sim_interp", quantity_sim_interp[i] |
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| 118 | #print "quantity_exp_cut", quantity_exp_cut[i] |
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| 119 | assert len(location_exps) == len(err_list) |
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| 120 | |
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| 121 | # Writing the file out for one scenario |
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| 122 | a_writer = writer(file(file_err, "wb")) |
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| 123 | a_writer.writerow(["x location", "err", "Number_of_samples", "rmsd"]) |
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| 124 | a_writer.writerows(map(None, |
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| 125 | location_exps, |
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| 126 | err_list, |
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| 127 | points, |
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| 128 | rmsd_list)) |
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| 129 | |
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| 130 | |
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| 131 | |
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| 132 | def plot_rrms_sensor_settings(run_data, outputdir_tags, quantity, |
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| 133 | save_as=None, |
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| 134 | is_interactive=False, max_rmsd=None): |
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| 135 | """ |
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| 136 | For a scenario, do |
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| 137 | """ |
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| 138 | from pylab import ion, plot, xlabel, ylabel, close, legend, \ |
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| 139 | savefig, title, axis, setp, subplot, grid, axvspan, figlegend |
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| 140 | from anuga.shallow_water.data_manager import csv2dict |
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| 141 | |
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| 142 | # TODO |
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| 143 | # scale the plot |
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| 144 | plot_type = ".pdf" |
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| 145 | |
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| 146 | id = run_data['scenario_id'] |
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| 147 | |
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| 148 | if is_interactive: |
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| 149 | ion() |
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| 150 | time_date = strftime('plot date: %d/%m/%Y Time: %H:%M:%S', |
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| 151 | localtime()) |
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| 152 | subplot(212) |
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| 153 | plot_title = id + " Root Mean Square Deviation comparison" + '\n' \ |
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| 154 | + time_date |
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| 155 | |
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| 156 | title(plot_title) |
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| 157 | y_label = "RMSD" |
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| 158 | ylabel(y_label) |
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| 159 | xlabel("x location, m") |
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| 160 | grid(True) |
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| 161 | |
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| 162 | lines = [] |
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| 163 | for outputdir_tag in outputdir_tags: |
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| 164 | |
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| 165 | outputdir_name = id + outputdir_tag |
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| 166 | pro_instance = project.Project(['data','flumes','Hinwood_2008'], |
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| 167 | outputdir_name=outputdir_name) |
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| 168 | |
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| 169 | file_err = pro_instance.rmsd_dir + sep + outputdir_name + "_" \ |
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| 170 | + quantity + "_err.csv" |
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| 171 | |
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| 172 | simulation, _ = csv2dict(file_err) |
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| 173 | locations = [float(x) for x in simulation['x location']] |
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| 174 | rmsd_list = [float(x) for x in simulation['rmsd']] |
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| 175 | lines.append(plot(locations, rmsd_list)) |
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| 176 | |
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| 177 | if max_rmsd is not None: |
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| 178 | #print "setting axis" |
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| 179 | axis(ymin=0, ymax=max_rmsd) |
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| 180 | |
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| 181 | for break_x in run_data['break_xs']: |
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| 182 | axvspan(break_x-0.001,break_x+0.001, facecolor='g') |
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| 183 | |
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| 184 | figlegend(lines, outputdir_tags,'upper left') |
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| 185 | |
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| 186 | if is_interactive: |
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| 187 | # Wait for enter pressed |
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| 188 | raw_input() |
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| 189 | |
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| 190 | save_as = pro_instance.plots_dir + sep + \ |
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| 191 | id + "_rmsd" + plot_type |
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| 192 | if save_as is not None: |
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| 193 | savefig(save_as) |
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| 194 | |
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| 195 | #Need to close this plot |
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| 196 | close() |
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| 197 | |
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| 198 | # Return a bunch of lists |
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| 199 | # The err files, for all scenarios |
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| 200 | def err_files(scenarios, outputdir_tag, rmsd_dir, quantity): |
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| 201 | """ |
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| 202 | The err files, for a list of scenarios |
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| 203 | """ |
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| 204 | file_errs = [] |
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| 205 | for scenario in scenarios: |
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| 206 | id = scenario['scenario_id'] |
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| 207 | outputdir_name = id + outputdir_tag |
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| 208 | file_err = rmsd_dir + sep + outputdir_name + "_" \ |
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| 209 | + quantity + "_err.csv" |
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| 210 | file_errs.append(file_err) |
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| 211 | return file_errs |
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| 212 | |
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| 213 | |
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| 214 | def compare_different_settings(outputdir_tags, scenarios, quantity, |
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| 215 | save_as=None, |
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| 216 | is_interactive=False): |
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| 217 | |
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| 218 | # A bit hacky. Getting a pro_instance to get the rmsd_dir. |
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| 219 | outputdir_name = scenarios[0]['scenario_id'] + outputdir_tags[0] |
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| 220 | pro_instance = project.Project(['data','flumes','Hinwood_2008'], |
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| 221 | outputdir_name=outputdir_name) |
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| 222 | |
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| 223 | |
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| 224 | settings = {} # keys are different settings. |
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| 225 | # For each setting there will be err and amount |
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| 226 | |
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| 227 | for outputdir_tag in outputdir_tags: |
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| 228 | files = err_files(scenarios, outputdir_tag, pro_instance.rmsd_dir, |
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| 229 | quantity) |
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| 230 | sim = {} |
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| 231 | for run_data, file in map(None, scenarios, files): |
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| 232 | |
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| 233 | simulation, _ = csv2dict(file) |
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| 234 | #locations = [float(x) for x in simulation['x location']] |
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| 235 | err_list = [float(x) for x in simulation['err']] |
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| 236 | amount_list = [float(x) for x in simulation['Number_of_samples']] |
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| 237 | |
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| 238 | if sim.has_key('err'): |
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| 239 | err_list.append(sim['err']) |
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| 240 | amount_list.append(sim['amount']) |
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| 241 | sim['err'], sim['amount'] = err_addition(err_list, |
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| 242 | amount_list) |
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| 243 | sim['rmsd'] = sim['err']/sqrt(sim['amount']) |
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| 244 | settings[outputdir_tag] = sim |
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| 245 | #print "settings", settings |
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| 246 | |
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| 247 | aux = [(settings[k]['rmsd'], k) for k in settings.keys()] |
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| 248 | aux.sort() |
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| 249 | for val, key in aux: |
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| 250 | print key + " " + str(val) |
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| 251 | |
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| 252 | |
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| 253 | |
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| 254 | def err_addition(err_list, amount_list): |
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| 255 | """ |
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| 256 | err1 is the err value (sqrt(sum_over_x&y((xi - yi)^2))) for a set of values |
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| 257 | amount1 is the number of values associated with the err. |
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| 258 | |
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| 259 | If this function gets used alot, maybe pull this out and make it an object |
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| 260 | """ |
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| 261 | err = norm(ensure_numeric(err_list)) |
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| 262 | amount = sum(ensure_numeric(amount_list)) |
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| 263 | |
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| 264 | return err, amount |
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| 265 | |
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| 266 | |
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| 267 | def calc_max_rmsd(scenarios, outputdir_tags, quantity): |
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| 268 | |
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| 269 | max_rmsd = 0 |
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| 270 | |
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| 271 | for run_data in scenarios: |
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| 272 | id = run_data['scenario_id'] |
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| 273 | for outputdir_tag in outputdir_tags: |
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| 274 | |
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| 275 | outputdir_name = id + outputdir_tag |
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| 276 | pro_instance = project.Project(['data','flumes','Hinwood_2008'], |
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| 277 | outputdir_name=outputdir_name) |
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| 278 | |
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| 279 | file_err = pro_instance.rmsd_dir + sep + outputdir_name + "_" \ |
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| 280 | + quantity + "_err.csv" |
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| 281 | |
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| 282 | simulation, _ = csv2dict(file_err) |
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| 283 | rmsd_list = [float(x) for x in simulation['rmsd']] |
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| 284 | max_rmsd = max(max(rmsd_list), max_rmsd) |
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| 285 | return max_rmsd |
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| 286 | |
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| 287 | |
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| 288 | def auto_plot_rrms_sensor_settings(outputdir_tags, scenarios, quantity, |
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| 289 | save_as=None, |
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| 290 | is_interactive=False): |
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| 291 | max_rmsd = calc_max_rmsd(scenarios, outputdir_tags, quantity) |
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| 292 | print "max_rmsd", max_rmsd |
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| 293 | for run_data in scenarios: |
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| 294 | plot_rrms_sensor_settings(run_data, outputdir_tags, quantity, |
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| 295 | max_rmsd=max_rmsd) |
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| 296 | |
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| 297 | #------------------------------------------------------------- |
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| 298 | if __name__ == "__main__": |
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| 299 | """ |
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| 300 | """ |
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| 301 | from scenarios import scenarios |
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| 302 | |
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| 303 | outputdir_tags = [] |
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| 304 | #outputdir_tags.append("_nolmts_wdth_0.1_z_0.012_ys_0.01_mta_0.01_H") |
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| 305 | outputdir_tags.append("_nolmts_wdth_0.1_z_0.0_ys_0.01_mta_0.01_G") |
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| 306 | #outputdir_tag = "_good_tri_area_0.01_D" |
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| 307 | outputdir_tags.append("_nolmts_wdth_0.1_z_0.012_ys_0.01_mta_0.01_G") |
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| 308 | outputdir_tags.append("_lmts_wdth_0.1_z_0.012_ys_0.01_mta_0.01_G") |
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| 309 | outputdir_tags.append("_nolmts_wdth_0.1_z_0.012_ys_0.05_mta_1e-05_G") |
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| 310 | outputdir_tags.append("_nolmts_wdth_0.1_z_0.012_ys_0.01_mta_0.001_G") |
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| 311 | outputdir_tags.append("_nolmts_wdth_0.01_z_0.012_ys_0.01_mta_1e-05_G") |
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| 312 | outputdir_tags = [] |
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| 313 | outputdir_tags.append("_nolmts_wdth_0.1_z_0.0_ys_0.01_mta_0.01_G") |
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| 314 | |
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| 315 | #outputdir_tag = "_good_lmts_wdth_1.0_z_0.012_ys_0.01_mta_0.001_H" |
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| 316 | #outputdir_tag = "_test_limiterC" |
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| 317 | #scenarios = [scenarios[0]] # !!!!!!!!!!!!!!!!!!!!!! |
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| 318 | #scenarios = scenarios[4:] # !!!!!!!!!!!!!!!!!!!!!! |
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| 319 | #for outputdir_tag in outputdir_tags: |
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| 320 | # auto_rrms(outputdir_tag, scenarios, "stage", y_location_tag=':0.0') |
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| 321 | |
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| 322 | #scenarios = [scenarios[0]] # !!!!!!!!!!!!!!!!!!!!!! |
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| 323 | auto_plot_rrms_sensor_settings(outputdir_tags, scenarios, "stage") |
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| 324 | #compare_different_settings(outputdir_tags, scenarios, "stage") |
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