1 | """Classes for implementing damage curves and calculating financial damage |
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2 | |
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3 | Duncan Gray, Ole Nielsen, Jane Sexton, Nick Bartzis |
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4 | Geoscience Australia, 2006 |
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5 | """ |
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6 | import os |
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7 | from math import sqrt |
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8 | from Scientific.Functions.Interpolation import InterpolatingFunction |
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9 | from random import choice |
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10 | |
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11 | import numpy as num |
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12 | |
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13 | |
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14 | try: |
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15 | import kinds |
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16 | except ImportError: |
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17 | # Hand-built mockup of the things we need from the kinds package, since it |
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18 | # was recently removed from the standard numeric distro. Some users may |
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19 | # not have it by default. |
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20 | class _bunch: |
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21 | pass |
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22 | |
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23 | class _kinds(_bunch): |
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24 | default_float_kind = _bunch() |
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25 | default_float_kind.MIN = 2.2250738585072014e-308 #smallest +ve number |
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26 | default_float_kind.MAX = 1.7976931348623157e+308 |
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27 | |
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28 | kinds = _kinds() |
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29 | |
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30 | |
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31 | from anuga.utilities.numerical_tools import ensure_numeric |
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32 | from exposure import Exposure |
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33 | from anuga.abstract_2d_finite_volumes.util import file_function |
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34 | from anuga.geospatial_data.geospatial_data import ensure_absolute |
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35 | from anuga.utilities.numerical_tools import NAN |
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36 | import anuga.utilities.log as log |
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37 | from config import epsilon |
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38 | depth_epsilon = epsilon |
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39 | |
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40 | # Change these if the ouput from nexix changes |
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41 | SHORE_DIST_LABEL = 'SHORE_DIST' |
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42 | WALL_TYPE_LABEL = 'WALL_TYPE' |
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43 | STR_VALUE_LABEL = 'STR_VALUE' |
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44 | CONT_VALUE_LABEL = 'CONT_VALUE' |
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45 | |
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46 | def inundation_damage(sww_base_name, exposure_files_in, |
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47 | exposure_file_out_marker=None, |
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48 | ground_floor_height=0.3, |
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49 | overwrite=False, verbose=True, |
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50 | use_cache = True): |
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51 | """ |
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52 | This is the main function for calculating tsunami damage due to |
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53 | inundation. It gets the location of structures from the exposure |
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54 | file and gets the inundation of these structures from the |
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55 | sww file. |
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56 | |
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57 | It then calculates the damage loss. |
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58 | |
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59 | Note, structures outside of the sww file get the minimum inundation |
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60 | (-ground_floor_height). |
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61 | |
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62 | These calculations are done over all the sww files with the sww_base_name |
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63 | in the specified directory. |
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64 | |
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65 | exposure_files_in - a file or a list of files to input from |
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66 | exposure_file_out_marker - this string will be added to the input file |
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67 | name to get the output file name |
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68 | """ |
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69 | if isinstance(exposure_files_in, basestring): |
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70 | exposure_files_in = [exposure_files_in] |
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71 | |
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72 | |
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73 | for exposure_file_in in exposure_files_in: |
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74 | csv = Exposure(exposure_file_in, |
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75 | title_check_list=[SHORE_DIST_LABEL,WALL_TYPE_LABEL, |
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76 | STR_VALUE_LABEL,CONT_VALUE_LABEL]) |
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77 | geospatial = csv.get_location() |
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78 | geospatial = ensure_absolute(geospatial) |
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79 | max_depths, max_momentums = calc_max_depth_and_momentum(sww_base_name, |
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80 | geospatial, |
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81 | ground_floor_height=ground_floor_height, |
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82 | verbose=verbose, |
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83 | use_cache=use_cache) |
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84 | edm = EventDamageModel(max_depths, |
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85 | csv.get_column(SHORE_DIST_LABEL), |
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86 | csv.get_column(WALL_TYPE_LABEL), |
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87 | csv.get_column(STR_VALUE_LABEL), |
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88 | csv.get_column(CONT_VALUE_LABEL) |
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89 | ) |
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90 | results_dic = edm.calc_damage_and_costs(verbose_csv=True, |
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91 | verbose=verbose) |
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92 | for title, value in results_dic.iteritems(): |
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93 | csv.set_column(title, value, overwrite=overwrite) |
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94 | |
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95 | # Save info back to csv file |
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96 | if exposure_file_out_marker == None: |
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97 | exposure_file_out = exposure_file_in |
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98 | else: |
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99 | # split off extension, in such a way to deal with more than one '.' in the name of file |
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100 | split_name = exposure_file_in.split('.') |
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101 | exposure_file_out = '.'.join(split_name[:-1]) + exposure_file_out_marker + \ |
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102 | '.' + split_name[-1] |
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103 | csv.save(exposure_file_out) |
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104 | if verbose: log.critical('Augmented building file written to %s' |
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105 | % exposure_file_out) |
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106 | |
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107 | def add_depth_and_momentum2csv(sww_base_name, exposure_file_in, |
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108 | exposure_file_out=None, |
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109 | overwrite=False, verbose=True, |
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110 | use_cache = True): |
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111 | """ |
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112 | Calculate the maximum depth and momemtum in an sww file, for locations |
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113 | specified in an csv exposure file. |
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114 | |
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115 | These calculations are done over all the sww files with the sww_base_name |
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116 | in the specified directory. |
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117 | """ |
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118 | |
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119 | csv = Exposure(exposure_file_in) |
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120 | geospatial = csv.get_location() |
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121 | max_depths, max_momentums = calc_max_depth_and_momentum(sww_base_name, |
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122 | geospatial, |
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123 | verbose=verbose, |
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124 | use_cache=use_cache) |
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125 | csv.set_column("MAX INUNDATION DEPTH (m)",max_depths, overwrite=overwrite) |
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126 | csv.set_column("MOMENTUM (m^2/s) ",max_momentums, overwrite=overwrite) |
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127 | csv.save(exposure_file_out) |
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128 | |
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129 | def calc_max_depth_and_momentum(sww_base_name, points, |
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130 | ground_floor_height=0.0, |
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131 | verbose=True, |
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132 | use_cache = True): |
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133 | """ |
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134 | Calculate the maximum inundation height above ground floor for a list |
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135 | of locations. |
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136 | |
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137 | The inundation value is in the range -ground_floor_height to |
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138 | overflow errors. |
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139 | |
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140 | These calculations are done over all the sww files with the sww_base_name |
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141 | in the specified directory. |
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142 | """ |
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143 | |
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144 | quantities = ['stage', 'elevation', 'xmomentum', 'ymomentum'] |
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145 | points = ensure_absolute(points) |
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146 | point_count = len(points) |
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147 | |
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148 | # initialise the max lists |
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149 | max_depths = [-ground_floor_height]*point_count |
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150 | max_momentums = [-ground_floor_height]*point_count |
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151 | |
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152 | # How many sww files are there? |
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153 | dir, base = os.path.split(sww_base_name) |
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154 | if base[-4:] == '.sww': |
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155 | base = base[:-4] |
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156 | if dir == "": dir = "." # Unix compatibility |
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157 | dir_ls = os.listdir(dir) |
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158 | interate_over = [x for x in dir_ls if base in x and x[-4:] == '.sww'] |
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159 | if len(interate_over) == 0: |
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160 | msg = 'No files of the base name %s.'\ |
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161 | %(sww_base_name) |
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162 | raise IOError, msg |
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163 | from os import sep |
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164 | |
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165 | for this_sww_file in interate_over: |
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166 | callable_sww = file_function(dir+sep+this_sww_file, |
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167 | quantities=quantities, |
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168 | interpolation_points=points, |
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169 | verbose=verbose, |
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170 | use_cache=use_cache) |
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171 | |
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172 | for point_i, point in enumerate(points): |
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173 | for time in callable_sww.get_time(): |
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174 | quantity_values = callable_sww(time,point_i) |
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175 | w = quantity_values[0] |
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176 | z = quantity_values[1] |
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177 | uh = quantity_values[2] |
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178 | vh = quantity_values[3] |
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179 | |
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180 | #print w,z,uh,vh |
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181 | if w == NAN or z == NAN or uh == NAN or vh == NAN: |
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182 | continue |
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183 | |
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184 | # -ground_floor_height is the minimum value. |
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185 | depth = w - z - ground_floor_height |
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186 | |
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187 | if depth > max_depths[point_i]: |
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188 | max_depths[point_i] = depth |
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189 | |
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190 | momentum = sqrt(uh*uh + vh*vh) |
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191 | if momentum > max_momentums[point_i]: |
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192 | max_momentums[point_i] = momentum |
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193 | |
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194 | |
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195 | return max_depths, max_momentums |
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196 | |
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197 | class EventDamageModel: |
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198 | """ |
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199 | Object for working out the damage and cost |
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200 | |
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201 | """ |
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202 | STRUCT_LOSS_TITLE = "STRUCT_LOSS_$"#"Structure Loss ($)" |
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203 | CONTENTS_LOSS_TITLE = "CONTENTS_LOSS_$"#"Contents Loss ($)" |
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204 | CONTENTS_DAMAGE_TITLE = "CONTENTS_DAMAGE_fraction"#"Contents damaged (fraction)" |
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205 | STRUCT_DAMAGE_TITLE = "STRUCT_DAMAGE_fraction" #"Structure damaged (fraction)" |
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206 | COLLAPSE_CSV_INFO_TITLE = "COLLAPSE_CSV_INFO"#"Calculation notes" |
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207 | MAX_DEPTH_TITLE = "MAX_DEPTH_m" #"Inundation height above ground floor (m)" |
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208 | STRUCT_COLLAPSED_TITLE = "STRUCT_COLLAPSED"#"collapsed structure if 1" |
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209 | STRUCT_INUNDATED_TITLE = "STRUCT_INUNDATED"#"inundated structure if 1" |
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210 | double_brick_damage_array = num.array([[-kinds.default_float_kind.MAX, 0.0], |
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211 | [0.0-depth_epsilon, 0.0], |
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212 | [0.0,0.016], |
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213 | [0.1,0.150], |
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214 | [0.3,0.425], |
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215 | [0.5,0.449], |
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216 | [1.0,0.572], |
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217 | [1.5,0.582], |
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218 | [2.0,0.587], |
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219 | [2.5,0.647], |
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220 | [kinds.default_float_kind.MAX,64.7]]) |
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221 | double_brick_damage_curve = InterpolatingFunction( \ |
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222 | (num.ravel(double_brick_damage_array[:,0:1]),), |
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223 | num.ravel(double_brick_damage_array[:,1:])) |
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224 | |
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225 | brick_veeer_damage_array = num.array([[-kinds.default_float_kind.MAX, 0.0], |
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226 | [0.0-depth_epsilon, 0.0], |
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227 | [0.0,0.016], |
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228 | [0.1,0.169], |
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229 | [0.3,0.445], |
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230 | [0.5,0.472], |
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231 | [1.0,0.618], |
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232 | [1.5,0.629], |
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233 | [2.0,0.633], |
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234 | [2.5,0.694], |
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235 | [kinds.default_float_kind.MAX,69.4]]) |
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236 | brick_veeer_damage_curve = InterpolatingFunction( \ |
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237 | (num.ravel(brick_veeer_damage_array[:,0:1]),), |
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238 | num.ravel(brick_veeer_damage_array[:,1:])) |
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239 | struct_damage_curve = {'Double Brick':double_brick_damage_curve, |
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240 | 'Brick Veneer':brick_veeer_damage_curve} |
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241 | default_struct_damage_curve = brick_veeer_damage_curve |
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242 | |
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243 | contents_damage_array = num.array([[-kinds.default_float_kind.MAX, 0.0], |
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244 | [0.0-depth_epsilon, 0.0], |
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245 | [0.0,0.013], |
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246 | [0.1,0.102], |
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247 | [0.3,0.381], |
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248 | [0.5,0.500], |
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249 | [1.0,0.970], |
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250 | [1.5,0.976], |
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251 | [2.0,0.986], |
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252 | [kinds.default_float_kind.MAX,98.6]]) |
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253 | contents_damage_curve = InterpolatingFunction( \ |
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254 | (num.ravel(contents_damage_array[:,0:1]),), |
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255 | num.ravel(contents_damage_array[:,1:])) |
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256 | |
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257 | #building collapse probability |
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258 | # inundation depth above ground floor, m |
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259 | depth_upper_limits = [depth_epsilon, 1.0, 2.0, 3.0, 5.0, |
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260 | kinds.default_float_kind.MAX] |
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261 | # shore mistance, m |
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262 | shore_upper_limits = [125,200,250, kinds.default_float_kind.MAX] |
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263 | # Building collapse probability |
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264 | collapse_probability = [[0.0, 0.0, 0.0, 0.0], #Code below assumes 0.0 |
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265 | [0.05, 0.02, 0.01, 0.0], |
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266 | [0.6, 0.3, 0.1, 0.05], |
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267 | [0.8, 0.4, 0.25, 0.15], |
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268 | [0.95, 0.7, 0.5, 0.3], |
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269 | [0.99, 0.9, 0.65, 0.45]] |
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270 | |
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271 | def __init__(self,max_depths, shore_distances, walls, |
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272 | struct_costs, content_costs): |
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273 | """ |
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274 | max depth is Inundation height above ground floor (m), so |
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275 | the ground floor has been taken into account. |
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276 | """ |
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277 | self.max_depths = [float(x) for x in max_depths] |
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278 | self.shore_distances = [float(x) for x in shore_distances] |
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279 | self.walls = walls |
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280 | self.struct_costs = [float(x) for x in struct_costs] |
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281 | self.content_costs = [float(x) for x in content_costs] |
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282 | |
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283 | self.structure_count = len(self.max_depths) |
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284 | #Fixme expand |
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285 | assert self.structure_count == len(self.shore_distances) |
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286 | assert self.structure_count == len(self.walls) |
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287 | assert self.structure_count == len(self.struct_costs) |
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288 | assert self.structure_count == len(self.content_costs) |
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289 | #assert self.structure_count == len(self.) |
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290 | |
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291 | def calc_damage_and_costs(self, verbose_csv=False, verbose=False): |
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292 | """ |
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293 | This is an overall method to calculate the % damage and collapsed |
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294 | structures and then the $ loss. |
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295 | """ |
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296 | self.calc_damage_percentages() |
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297 | collapse_probability = self.calc_collapse_probability() |
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298 | self._calc_collapse_structures(collapse_probability, |
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299 | verbose_csv=verbose_csv) |
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300 | self.calc_cost() |
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301 | results_dict = {self.STRUCT_LOSS_TITLE:self.struct_loss |
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302 | ,self.STRUCT_DAMAGE_TITLE:self.struct_damage |
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303 | ,self.CONTENTS_LOSS_TITLE:self.contents_loss |
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304 | ,self.CONTENTS_DAMAGE_TITLE:self.contents_damage |
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305 | ,self.MAX_DEPTH_TITLE:self.max_depths |
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306 | ,self.STRUCT_COLLAPSED_TITLE:self.struct_collapsed |
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307 | ,self.STRUCT_INUNDATED_TITLE:self.struct_inundated |
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308 | } |
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309 | if verbose_csv: |
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310 | results_dict[self.COLLAPSE_CSV_INFO_TITLE] = self.collapse_csv_info |
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311 | return results_dict |
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312 | |
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313 | def calc_damage_percentages(self): |
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314 | """ |
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315 | Using stage curves calc the damage to structures and contents |
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316 | """ |
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317 | |
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318 | # the data being created |
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319 | struct_damage = num.zeros(self.structure_count, num.float) |
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320 | contents_damage = num.zeros(self.structure_count, num.float) |
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321 | self.struct_inundated = ['']* self.structure_count |
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322 | |
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323 | for i,max_depth,shore_distance,wall in map(None, |
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324 | range(self.structure_count), |
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325 | self.max_depths, |
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326 | self.shore_distances, |
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327 | self.walls): |
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328 | ## WARNING SKIP IF DEPTH < 0.0 |
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329 | if 0.0 > max_depth: |
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330 | continue |
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331 | |
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332 | # The definition of inundated is if the max_depth is > 0.0 |
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333 | self.struct_inundated[i] = 1.0 |
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334 | |
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335 | #calc structural damage % |
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336 | damage_curve = self.struct_damage_curve.get(wall, |
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337 | self.default_struct_damage_curve) |
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338 | struct_damage[i] = damage_curve(max_depth) |
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339 | contents_damage[i] = self.contents_damage_curve(max_depth) |
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340 | |
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341 | self.struct_damage = struct_damage |
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342 | self.contents_damage = contents_damage |
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343 | |
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344 | |
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345 | def calc_cost(self): |
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346 | """ |
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347 | Once the damage has been calculated, determine the $ cost. |
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348 | """ |
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349 | # ensure_numeric does not cut it. |
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350 | self.struct_loss = self.struct_damage * \ |
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351 | ensure_numeric(self.struct_costs) |
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352 | self.contents_loss = self.contents_damage * \ |
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353 | ensure_numeric(self.content_costs) |
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354 | |
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355 | def calc_collapse_probability(self): |
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356 | """ |
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357 | return a dict of which structures have x probability of collapse. |
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358 | key is collapse probability |
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359 | value is list of struct indexes with key probability of collapse |
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360 | """ |
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361 | # I could've done this is the calc_damage_percentages and |
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362 | # Just had one loop. |
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363 | # But for ease of testing and bug finding I'm seperating the loops. |
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364 | # I'm make the outer loop for both of them the same though, |
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365 | # so this loop can easily be folded into the other loop. |
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366 | |
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367 | # dict of which structures have x probability of collapse. |
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368 | # key of collapse probability |
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369 | # value of list of struct indexes |
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370 | struct_coll_prob = {} |
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371 | |
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372 | for i,max_depth,shore_distance,wall in map(None, |
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373 | range(self.structure_count), |
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374 | self.max_depths, |
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375 | self.shore_distances, |
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376 | self.walls): |
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377 | # WARNING ASSUMING THE FIRST BIN OF DEPTHS GIVE A ZERO PROBABILITY |
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378 | depth_upper_limits = self.depth_upper_limits |
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379 | shore_upper_limits = self.shore_upper_limits |
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380 | collapse_probability = self.collapse_probability |
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381 | if max_depth <= depth_upper_limits[0]: |
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382 | continue |
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383 | start = 1 |
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384 | for i_depth, depth_limit in enumerate(depth_upper_limits[start:]): |
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385 | #Have to change i_depth so it indexes into the lists correctly |
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386 | i_depth += start |
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387 | if max_depth <= depth_limit: |
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388 | for i_shore, shore_limit in enumerate(shore_upper_limits): |
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389 | if shore_distance <= shore_limit: |
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390 | coll_prob = collapse_probability[i_depth][i_shore] |
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391 | if 0.0 == collapse_probability[i_depth][i_shore]: |
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392 | break |
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393 | struct_coll_prob.setdefault(coll_prob,[]).append(i) |
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394 | break |
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395 | break |
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396 | |
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397 | return struct_coll_prob |
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398 | |
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399 | def _calc_collapse_structures(self, collapse_probability, |
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400 | verbose_csv=False): |
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401 | """ |
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402 | Given the collapse probabilities, throw the dice |
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403 | and collapse some houses |
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404 | """ |
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405 | |
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406 | self.struct_collapsed = ['']* self.structure_count |
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407 | if verbose_csv: |
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408 | self.collapse_csv_info = ['']* self.structure_count |
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409 | #for a given 'bin', work out how many houses will collapse |
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410 | for probability, house_indexes in collapse_probability.iteritems(): |
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411 | collapse_count = round(len(house_indexes) *probability) |
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412 | |
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413 | if verbose_csv: |
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414 | for i in house_indexes: |
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415 | # This could be sped up I think |
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416 | self.collapse_csv_info[i] = str(probability) + ' prob.( ' \ |
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417 | + str(int(collapse_count)) + ' collapsed out of ' \ |
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418 | + str(len(house_indexes)) + ')' |
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419 | for _ in range(int(collapse_count)): |
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420 | house_index = choice(house_indexes) |
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421 | self.struct_damage[house_index] = 1.0 |
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422 | self.contents_damage[house_index] = 1.0 |
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423 | house_indexes.remove(house_index) |
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424 | self.struct_collapsed[house_index] = 1 |
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425 | |
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426 | # Warning, the collapse_probability list now lists |
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427 | # houses that did not collapse, (though not all of them) |
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428 | |
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429 | ############################################################################# |
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430 | if __name__ == "__main__": |
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431 | pass |
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