1 | """ |
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2 | |
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3 | All err results are going into the same dir, and it can't really be changed. |
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4 | """ |
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5 | |
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6 | |
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7 | #---------------------------------------------------------------------------- |
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8 | # Import necessary modules |
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9 | #---------------------------------------------------------------------------- |
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10 | |
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11 | # Standard modules |
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12 | import os |
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13 | from csv import writer |
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14 | from time import localtime, strftime |
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15 | |
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16 | # Related major packages |
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17 | from Numeric import zeros, Float, where, greater, less, compress, sqrt, sum |
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18 | |
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19 | from anuga.shallow_water.data_manager import csv2dict |
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20 | from anuga.utilities.numerical_tools import ensure_numeric, err, norm |
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21 | from anuga.utilities.interp import interp |
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22 | |
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23 | def get_max_min_condition_array(min, max, vector): |
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24 | |
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25 | SMALL_MIN = -1e10 # Not that small, but small enough |
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26 | vector = ensure_numeric(vector) |
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27 | assert min > SMALL_MIN |
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28 | no_maxs = where(less(vector,max), vector, SMALL_MIN) |
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29 | band_condition = greater(no_maxs, min) |
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30 | return band_condition |
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31 | |
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32 | |
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33 | def auto_rrms(outputdir_tag, scenarios, quantity='stage', |
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34 | y_location_tag=':0.0'): |
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35 | """ |
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36 | Given a list of scenarios that have CSV guage files, calc the |
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37 | err, Number_of_samples and rmsd for all gauges in each scenario. |
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38 | Write this info to a file for each scenario. |
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39 | """ |
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40 | for run_data in scenarios: |
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41 | location_sims = [] |
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42 | location_exps = [] |
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43 | for gauge_x in run_data['gauge_x']: |
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44 | gauge_x = str(gauge_x) |
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45 | location_sims.append(gauge_x + y_location_tag) |
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46 | location_exps.append(gauge_x) |
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47 | |
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48 | id = run_data['scenario_id'] |
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49 | outputdir_name = id + outputdir_tag |
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50 | file_sim = outputdir_name + '_' + quantity + ".csv" |
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51 | file_exp = id + '_exp_' + quantity + '.csv' |
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52 | file_err = outputdir_name + "_" + quantity + "_err.csv" |
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53 | |
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54 | |
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55 | simulation, _ = csv2dict(file_sim) |
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56 | experiment, _ = csv2dict(file_exp) |
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57 | |
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58 | time_sim = [float(x) for x in simulation['time']] |
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59 | time_exp = [float(x) for x in experiment['Time']] |
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60 | time_sim = ensure_numeric(time_sim) |
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61 | time_exp = ensure_numeric(time_exp) |
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62 | |
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63 | condition = get_max_min_condition_array(run_data['wave_times'][0], |
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64 | run_data['wave_times'][1], |
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65 | time_exp) |
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66 | time_exp_cut = compress(condition, time_exp) |
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67 | |
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68 | print "Writing to ", file_err |
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69 | |
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70 | err_list = [] |
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71 | points = [] |
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72 | rmsd_list = [] |
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73 | for location_sim, location_exp in map(None, location_sims, |
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74 | location_exps): |
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75 | quantity_sim = [float(x) for x in simulation[location_sim]] |
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76 | quantity_exp = [float(x) for x in experiment[location_exp]] |
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77 | |
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78 | quantity_exp_cut = compress(condition, quantity_exp) |
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79 | |
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80 | # Now let's do interpolation |
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81 | quantity_sim_interp = interp(quantity_sim, time_sim, time_exp_cut) |
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82 | |
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83 | assert len(quantity_sim_interp) == len(quantity_exp_cut) |
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84 | norm = err(quantity_sim_interp, |
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85 | quantity_exp_cut, |
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86 | 2, relative = False) # 2nd norm (rel. RMS) |
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87 | err_list.append(norm) |
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88 | points.append(len(quantity_sim_interp)) |
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89 | rmsd_list.append(norm/sqrt(len(quantity_sim_interp))) |
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90 | assert len(location_exps) == len(err_list) |
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91 | |
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92 | # Writing the file out for one scenario |
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93 | a_writer = writer(file(file_err, "wb")) |
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94 | a_writer.writerow(["x location", "err", "Number_of_samples", "rmsd"]) |
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95 | a_writer.writerows(map(None, |
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96 | location_exps, |
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97 | err_list, |
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98 | points, |
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99 | rmsd_list)) |
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100 | |
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101 | |
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102 | |
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103 | def load_sensors(quantity_file): |
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104 | """ |
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105 | Load a csv file, where the first row is the column header and |
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106 | the first colum explains the rows. |
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107 | """ |
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108 | |
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109 | # Read the depth file |
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110 | dfid = open(quantity_file) |
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111 | lines = dfid.readlines() |
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112 | dfid.close() |
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113 | |
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114 | title = lines.pop(0) |
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115 | n_time = len(lines) |
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116 | n_sensors = len(lines[0].split(','))-1 # -1 to remove time |
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117 | times = zeros(n_time, Float) #Time |
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118 | depths = zeros(n_time, Float) # |
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119 | sensors = zeros((n_time,n_sensors), Float) |
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120 | quantity_locations = title.split(',') |
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121 | quantity_locations.pop(0) # remove 'time' |
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122 | |
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123 | # Doing j.split(':')[0] drops the y location |
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124 | locations = [float(j.split(':')[0]) for j in quantity_locations] |
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125 | |
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126 | for i, line in enumerate(lines): |
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127 | fields = line.split(',') |
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128 | fields = [float(j) for j in fields] |
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129 | times[i] = fields[0] |
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130 | sensors[i] = fields[1:] # 1: to remove time |
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131 | |
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132 | return times, locations, sensors |
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133 | |
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134 | |
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135 | |
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136 | |
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137 | # Return a bunch of lists |
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138 | # The err files, for all scenarios |
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139 | def err_files(scenarios, outputdir_tag, quantity='stage'): |
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140 | """ |
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141 | Create a list of err files, for a list of scenarios. |
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142 | """ |
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143 | file_errs = [] |
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144 | for scenario in scenarios: |
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145 | id = scenario['scenario_id'] |
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146 | outputdir_name = id + outputdir_tag |
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147 | file_err = outputdir_name + "_" + quantity + "_err.csv" |
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148 | file_errs.append(file_err) |
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149 | return file_errs |
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150 | |
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151 | |
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152 | def compare_different_settings(outputdir_tag, scenarios, quantity='stage'): |
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153 | """ |
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154 | Calculate the RMSD for all the tests in a scenario |
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155 | """ |
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156 | files = err_files(scenarios, outputdir_tag, quantity=quantity) |
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157 | err = 0.0 |
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158 | number_of_samples = 0 |
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159 | for run_data, file in map(None, scenarios, files): |
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160 | |
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161 | simulation, _ = csv2dict(file) |
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162 | err_list = [float(x) for x in simulation['err']] |
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163 | number_of_samples_list = [float(x) for x in \ |
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164 | simulation['Number_of_samples']] |
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165 | |
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166 | if number_of_samples is not 0: |
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167 | err_list.append(err) |
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168 | number_of_samples_list.append(number_of_samples) |
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169 | err, number_of_samples = err_addition(err_list, number_of_samples_list) |
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170 | rmsd = err/sqrt(number_of_samples) |
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171 | print outputdir_tag + " " + str(rmsd) |
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172 | |
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173 | |
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174 | |
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175 | def err_addition(err_list, number_of_samples_list): |
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176 | """ |
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177 | This function 'sums' a list of errs and sums a list of samples |
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178 | |
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179 | err is the err value (sqrt(sum_over_x&y((xi - yi)^2))) for a set of values. |
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180 | number_of_samples is the number of values associated with the err. |
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181 | |
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182 | If this function gets used alot, maybe pull this out and make it an object |
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183 | """ |
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184 | err = norm(ensure_numeric(err_list)) |
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185 | number_of_samples = sum(ensure_numeric(number_of_samples_list)) |
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186 | |
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187 | return err, number_of_samples |
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188 | |
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189 | |
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190 | #------------------------------------------------------------- |
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191 | if __name__ == "__main__": |
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192 | """ |
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193 | """ |
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194 | from scenarios import scenarios |
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195 | |
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196 | #scenarios = [scenarios[0]] # !!!!!!!!!!!!!!!!!!!!!! |
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197 | |
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198 | outputdir_tag = "_nolmts_wdth_0.1_z_0.0_ys_0.01_mta_0.01" |
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199 | calc_norms = True |
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200 | #calc_norms = False |
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201 | if calc_norms: |
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202 | auto_rrms(outputdir_tag, scenarios, "stage", y_location_tag=':0.0') |
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203 | compare_different_settings(outputdir_tag, scenarios, "stage") |
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204 | |
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