1 | """Least squares interpolation. |
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2 | |
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3 | Implements a least-squares interpolation. |
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4 | Putting mesh data onto points. |
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5 | |
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6 | Ole Nielsen, Stephen Roberts, Duncan Gray, Christopher Zoppou |
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7 | Geoscience Australia, 2004. |
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8 | |
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9 | DESIGN ISSUES |
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10 | * what variables should be global? |
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11 | - if there are no global vars functions can be moved around alot easier |
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12 | |
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13 | * The public interface to Interpolate |
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14 | __init__ |
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15 | interpolate |
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16 | interpolate_block |
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17 | |
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18 | """ |
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19 | |
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20 | import time |
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21 | import os |
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22 | import sys |
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23 | from warnings import warn |
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24 | from math import sqrt |
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25 | from csv import writer, DictWriter |
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26 | |
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27 | from anuga.caching.caching import cache |
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28 | from anuga.abstract_2d_finite_volumes.neighbour_mesh import Mesh |
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29 | from anuga.utilities.sparse import Sparse, Sparse_CSR |
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30 | from anuga.utilities.cg_solve import conjugate_gradient, VectorShapeError |
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31 | from anuga.coordinate_transforms.geo_reference import Geo_reference |
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32 | from anuga.utilities.numerical_tools import ensure_numeric, mean, NAN |
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33 | from anuga.utilities.polygon import in_and_outside_polygon |
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34 | from anuga.geospatial_data.geospatial_data import Geospatial_data |
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35 | from anuga.geospatial_data.geospatial_data import ensure_absolute |
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36 | from anuga.fit_interpolate.search_functions import search_tree_of_vertices |
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37 | from anuga.fit_interpolate.general_fit_interpolate import FitInterpolate |
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38 | from anuga.abstract_2d_finite_volumes.util import file_function |
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39 | from anuga.config import netcdf_mode_r, netcdf_mode_w, netcdf_mode_a |
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40 | from utilities.polygon import interpolate_polyline |
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41 | import anuga.utilities.log as log |
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42 | |
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43 | import numpy as num |
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44 | |
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45 | |
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46 | # Interpolation specific exceptions |
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47 | |
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48 | class Modeltime_too_late(Exception): pass |
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49 | class Modeltime_too_early(Exception): pass |
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50 | |
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51 | |
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52 | ## |
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53 | # @brief Interpolate vertex_values to interpolation points. |
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54 | # @param vertex_coordinates List of coordinate pairs making a mesh. |
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55 | # @param triangles Iterable of 3-tuples representing indices of mesh vertices. |
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56 | # @param vertex_values Array of data at mesh vertices. |
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57 | # @param interpolation_points Points to interpolate to. |
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58 | # @param mesh_origin A geo_ref object or 3-tuples of UTMzone, easting, northing. |
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59 | # @param max_vertices_per_cell Max number of vertices before splitting cell. |
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60 | # @param start_blocking_len Block if # of points greater than this. |
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61 | # @param use_cache If True, cache. |
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62 | # @param verbose True if this function is to be verbose. |
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63 | def interpolate(vertex_coordinates, |
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64 | triangles, |
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65 | vertex_values, |
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66 | interpolation_points, |
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67 | mesh_origin=None, |
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68 | max_vertices_per_cell=None, |
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69 | start_blocking_len=500000, |
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70 | use_cache=False, |
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71 | verbose=False, |
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72 | output_centroids=False): |
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73 | """Interpolate vertex_values to interpolation points. |
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74 | |
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75 | Inputs (mandatory): |
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76 | |
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77 | |
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78 | vertex_coordinates: List of coordinate pairs [xi, eta] of |
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79 | points constituting a mesh |
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80 | (or an m x 2 numeric array or |
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81 | a geospatial object) |
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82 | Points may appear multiple times |
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83 | (e.g. if vertices have discontinuities) |
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84 | |
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85 | triangles: List of 3-tuples (or a numeric array) of |
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86 | integers representing indices of all vertices |
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87 | in the mesh. |
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88 | |
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89 | vertex_values: Vector or array of data at the mesh vertices. |
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90 | If array, interpolation will be done for each column as |
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91 | per underlying matrix-matrix multiplication |
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92 | |
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93 | interpolation_points: Interpolate mesh data to these positions. |
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94 | List of coordinate pairs [x, y] of |
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95 | data points or an nx2 numeric array or a |
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96 | Geospatial_data object |
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97 | |
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98 | Inputs (optional) |
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99 | |
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100 | mesh_origin: A geo_reference object or 3-tuples consisting of |
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101 | UTM zone, easting and northing. |
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102 | If specified vertex coordinates are assumed to be |
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103 | relative to their respective origins. |
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104 | |
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105 | max_vertices_per_cell: Number of vertices in a quad tree cell |
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106 | at which the cell is split into 4. |
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107 | |
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108 | Note: Don't supply a vertex coords as a geospatial |
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109 | object and a mesh origin, since geospatial has its |
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110 | own mesh origin. |
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111 | |
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112 | start_blocking_len: If the # of points is more or greater than this, |
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113 | start blocking |
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114 | |
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115 | use_cache: True or False |
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116 | |
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117 | |
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118 | Output: |
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119 | |
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120 | Interpolated values at specified point_coordinates |
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121 | |
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122 | Note: This function is a simple shortcut for case where |
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123 | interpolation matrix is unnecessary |
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124 | Note: This function does not take blocking into account, |
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125 | but allows caching. |
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126 | |
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127 | """ |
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128 | |
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129 | # FIXME(Ole): Probably obsolete since I is precomputed and |
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130 | # interpolate_block caches |
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131 | |
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132 | from anuga.caching import cache |
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133 | |
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134 | # Create interpolation object with matrix |
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135 | args = (ensure_numeric(vertex_coordinates, num.float), |
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136 | ensure_numeric(triangles)) |
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137 | kwargs = {'mesh_origin': mesh_origin, |
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138 | 'max_vertices_per_cell': max_vertices_per_cell, |
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139 | 'verbose': verbose} |
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140 | |
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141 | if use_cache is True: |
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142 | if sys.platform != 'win32': |
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143 | I = cache(Interpolate, args, kwargs, verbose=verbose) |
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144 | else: |
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145 | # Messy wrapping of Interpolate to deal with win32 error |
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146 | def wrap_Interpolate(args,kwargs): |
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147 | I = apply(Interpolate, args, kwargs) |
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148 | return I |
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149 | I = cache(wrap_Interpolate, (args, kwargs), {}, verbose=verbose) |
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150 | else: |
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151 | I = apply(Interpolate, args, kwargs) |
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152 | |
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153 | # Call interpolate method with interpolation points |
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154 | result = I.interpolate_block(vertex_values, interpolation_points, |
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155 | use_cache=use_cache, |
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156 | verbose=verbose, |
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157 | output_centroids=output_centroids) |
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158 | |
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159 | return result |
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160 | |
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161 | |
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162 | ## |
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163 | # @brief |
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164 | class Interpolate (FitInterpolate): |
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165 | |
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166 | ## |
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167 | # @brief Build interpolation matrix. |
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168 | # @param vertex_coordinates List of pairs [xi, eta] of points making a mesh. |
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169 | # @param triangles List of 3-tuples of indices of all vertices in the mesh. |
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170 | # @param mesh_origin A geo_ref object (UTM zone, easting and northing). |
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171 | # @param verbose If True, this function is to be verbose. |
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172 | # @param max_vertices_per_cell Split quadtree cell if vertices >= this. |
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173 | def __init__(self, |
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174 | vertex_coordinates, |
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175 | triangles, |
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176 | mesh_origin=None, |
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177 | verbose=False, |
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178 | max_vertices_per_cell=None): |
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179 | |
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180 | """ Build interpolation matrix mapping from |
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181 | function values at vertices to function values at data points |
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182 | |
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183 | Inputs: |
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184 | vertex_coordinates: List of coordinate pairs [xi, eta] of |
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185 | points constituting a mesh (or an m x 2 numeric array or |
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186 | a geospatial object) |
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187 | Points may appear multiple times |
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188 | (e.g. if vertices have discontinuities) |
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189 | |
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190 | triangles: List of 3-tuples (or a numeric array) of |
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191 | integers representing indices of all vertices in the mesh. |
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192 | |
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193 | mesh_origin: A geo_reference object or 3-tuples consisting of |
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194 | UTM zone, easting and northing. |
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195 | If specified vertex coordinates are assumed to be |
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196 | relative to their respective origins. |
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197 | |
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198 | max_vertices_per_cell: Number of vertices in a quad tree cell |
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199 | at which the cell is split into 4. |
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200 | |
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201 | Note: Don't supply a vertex coords as a geospatial object and |
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202 | a mesh origin, since geospatial has its own mesh origin. |
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203 | """ |
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204 | |
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205 | # FIXME (Ole): Need an input check |
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206 | |
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207 | FitInterpolate.__init__(self, |
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208 | vertex_coordinates=vertex_coordinates, |
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209 | triangles=triangles, |
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210 | mesh_origin=mesh_origin, |
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211 | verbose=verbose, |
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212 | max_vertices_per_cell=max_vertices_per_cell) |
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213 | |
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214 | # Initialise variables |
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215 | self._A_can_be_reused = False # FIXME (Ole): Probably obsolete |
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216 | self._point_coordinates = None # FIXME (Ole): Probably obsolete |
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217 | self.interpolation_matrices = {} # Store precomputed matrices |
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218 | |
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219 | |
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220 | |
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221 | ## |
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222 | # @brief Interpolate mesh data f to determine values, z, at points. |
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223 | # @param f Data on the mesh vertices. |
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224 | # @param point_coordinates Interpolate mesh data to these positions. |
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225 | # @param start_blocking_len Block if # points >= this. |
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226 | # @param verbose True if this function is to be verbose. |
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227 | # FIXME: What is a good start_blocking_len value? |
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228 | def interpolate(self, |
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229 | f, |
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230 | point_coordinates=None, |
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231 | start_blocking_len=500000, |
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232 | verbose=False, |
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233 | output_centroids=False): |
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234 | """Interpolate mesh data f to determine values, z, at points. |
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235 | |
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236 | f is the data on the mesh vertices. |
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237 | |
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238 | The mesh values representing a smooth surface are |
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239 | assumed to be specified in f. |
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240 | |
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241 | Inputs: |
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242 | f: Vector or array of data at the mesh vertices. |
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243 | If f is an array, interpolation will be done for each column as |
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244 | per underlying matrix-matrix multiplication |
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245 | |
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246 | point_coordinates: Interpolate mesh data to these positions. |
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247 | List of coordinate pairs [x, y] of |
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248 | data points or an nx2 numeric array or a Geospatial_data object |
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249 | |
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250 | If point_coordinates is absent, the points inputted last time |
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251 | this method was called are used, if possible. |
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252 | |
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253 | start_blocking_len: If the # of points is more or greater than this, |
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254 | start blocking |
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255 | |
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256 | Output: |
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257 | Interpolated values at inputted points (z). |
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258 | """ |
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259 | |
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260 | # FIXME (Ole): Why is the interpolation matrix rebuilt everytime the |
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261 | # method is called even if interpolation points are unchanged. |
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262 | # This really should use some kind of caching in cases where |
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263 | # interpolation points are reused. |
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264 | # |
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265 | # This has now been addressed through an attempt in interpolate_block |
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266 | |
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267 | if verbose: log.critical('Build intepolation object') |
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268 | if isinstance(point_coordinates, Geospatial_data): |
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269 | point_coordinates = point_coordinates.get_data_points(absolute=True) |
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270 | |
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271 | # Can I interpolate, based on previous point_coordinates? |
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272 | if point_coordinates is None: |
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273 | if self._A_can_be_reused is True \ |
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274 | and len(self._point_coordinates) < start_blocking_len: |
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275 | z = self._get_point_data_z(f, verbose=verbose) |
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276 | elif self._point_coordinates is not None: |
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277 | # if verbose, give warning |
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278 | if verbose: |
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279 | log.critical('WARNING: Recalculating A matrix, ' |
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280 | 'due to blocking.') |
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281 | point_coordinates = self._point_coordinates |
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282 | else: |
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283 | # There are no good point_coordinates. import sys; sys.exit() |
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284 | msg = 'ERROR (interpolate.py): No point_coordinates inputted' |
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285 | raise Exception(msg) |
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286 | |
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287 | if point_coordinates is not None: |
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288 | self._point_coordinates = point_coordinates |
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289 | if len(point_coordinates) < start_blocking_len \ |
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290 | or start_blocking_len == 0: |
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291 | self._A_can_be_reused = True |
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292 | z = self.interpolate_block(f, point_coordinates, |
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293 | verbose=verbose, output_centroids=output_centroids) |
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294 | else: |
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295 | # Handle blocking |
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296 | self._A_can_be_reused = False |
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297 | start = 0 |
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298 | # creating a dummy array to concatenate to. |
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299 | |
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300 | f = ensure_numeric(f, num.float) |
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301 | if len(f.shape) > 1: |
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302 | z = num.zeros((0, f.shape[1]), num.int) #array default# |
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303 | else: |
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304 | z = num.zeros((0,), num.int) #array default# |
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305 | |
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306 | for end in range(start_blocking_len, |
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307 | len(point_coordinates), |
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308 | start_blocking_len): |
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309 | t = self.interpolate_block(f, point_coordinates[start:end], |
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310 | verbose=verbose, output_centroids=output_centroids) |
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311 | z = num.concatenate((z, t), axis=0) #??default# |
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312 | start = end |
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313 | |
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314 | end = len(point_coordinates) |
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315 | t = self.interpolate_block(f, point_coordinates[start:end], |
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316 | verbose=verbose, output_centroids=output_centroids) |
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317 | z = num.concatenate((z, t), axis=0) #??default# |
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318 | return z |
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319 | |
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320 | |
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321 | ## |
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322 | # @brief Interpolate a block of vertices |
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323 | # @param f Array of arbitrary data to be interpolated |
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324 | # @param point_coordinates List of vertices to intersect with the mesh |
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325 | # @param use_cache True if caching should be used to accelerate the calculations |
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326 | # @param verbose True if this function is verbose. |
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327 | # @return ?? |
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328 | def interpolate_block(self, f, point_coordinates, |
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329 | use_cache=False, verbose=False, output_centroids=False): |
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330 | """ |
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331 | Call this if you want to control the blocking or make sure blocking |
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332 | doesn't occur. |
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333 | |
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334 | Return the point data, z. |
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335 | |
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336 | See interpolate for doc info. |
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337 | """ |
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338 | |
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339 | # FIXME (Ole): I reckon we should change the interface so that |
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340 | # the user can specify the interpolation matrix instead of the |
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341 | # interpolation points to save time. |
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342 | |
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343 | if isinstance(point_coordinates, Geospatial_data): |
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344 | point_coordinates = point_coordinates.get_data_points(absolute=True) |
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345 | |
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346 | # Convert lists to numeric arrays if necessary |
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347 | point_coordinates = ensure_numeric(point_coordinates, num.float) |
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348 | f = ensure_numeric(f, num.float) |
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349 | |
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350 | from anuga.caching import myhash |
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351 | import sys |
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352 | |
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353 | if use_cache is True: |
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354 | if sys.platform != 'win32': |
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355 | # FIXME (Ole): (Why doesn't this work on windoze?) |
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356 | # Still absolutely fails on Win 24 Oct 2008 |
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357 | |
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358 | X = cache(self._build_interpolation_matrix_A, |
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359 | args=(point_coordinates, output_centroids), |
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360 | kwargs={'verbose': verbose}, |
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361 | verbose=verbose) |
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362 | else: |
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363 | # FIXME |
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364 | # Hash point_coordinates to memory location, reuse if possible |
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365 | # This will work on Linux as well if we want to use it there. |
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366 | key = myhash(point_coordinates) |
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367 | |
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368 | reuse_A = False |
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369 | |
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370 | if self.interpolation_matrices.has_key(key): |
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371 | X, stored_points = self.interpolation_matrices[key] |
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372 | if num.alltrue(stored_points == point_coordinates): |
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373 | reuse_A = True # Reuse interpolation matrix |
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374 | |
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375 | if reuse_A is False: |
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376 | X = self._build_interpolation_matrix_A(point_coordinates, |
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377 | output_centroids, |
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378 | verbose=verbose) |
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379 | self.interpolation_matrices[key] = (X, point_coordinates) |
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380 | else: |
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381 | X = self._build_interpolation_matrix_A(point_coordinates, output_centroids, |
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382 | verbose=verbose) |
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383 | |
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384 | # Unpack result |
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385 | self._A, self.inside_poly_indices, self.outside_poly_indices = X |
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386 | |
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387 | # Check that input dimensions are compatible |
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388 | msg = 'Two columns must be specified in point coordinates. ' \ |
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389 | 'I got shape=%s' % (str(point_coordinates.shape)) |
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390 | assert point_coordinates.shape[1] == 2, msg |
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391 | |
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392 | msg = 'The number of rows in matrix A must be the same as the ' |
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393 | msg += 'number of points supplied.' |
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394 | msg += ' I got %d points and %d matrix rows.' \ |
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395 | % (point_coordinates.shape[0], self._A.shape[0]) |
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396 | assert point_coordinates.shape[0] == self._A.shape[0], msg |
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397 | |
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398 | msg = 'The number of columns in matrix A must be the same as the ' |
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399 | msg += 'number of mesh vertices.' |
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400 | msg += ' I got %d vertices and %d matrix columns.' \ |
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401 | % (f.shape[0], self._A.shape[1]) |
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402 | assert self._A.shape[1] == f.shape[0], msg |
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403 | |
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404 | # Compute Matrix vector product and return |
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405 | return self._get_point_data_z(f) |
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406 | |
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407 | |
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408 | ## |
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409 | # @brief Get interpolated data at given points. |
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410 | # Applies a transform to all points to calculate the |
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411 | # interpolated values. Points outside the mesh are returned as NaN. |
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412 | # @note self._A matrix must be valid |
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413 | # @param f Array of arbitrary data |
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414 | # @param verbose True if this function is to be verbose. |
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415 | # @return f transformed by interpolation matrix (f') |
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416 | def _get_point_data_z(self, f, verbose=False): |
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417 | """ |
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418 | Return the point data, z. |
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419 | |
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420 | Precondition: The _A matrix has been created |
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421 | """ |
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422 | |
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423 | z = self._A * f |
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424 | |
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425 | # Taking into account points outside the mesh. |
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426 | for i in self.outside_poly_indices: |
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427 | z[i] = NAN |
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428 | return z |
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429 | |
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430 | |
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431 | ## |
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432 | # @brief Build NxM interpolation matrix. |
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433 | # @param point_coordinates Points to sample at |
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434 | # @param output_centroids set to True to always sample from the centre |
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435 | # of the intersected triangle, instead of the intersection |
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436 | # point. |
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437 | # @param verbose True if this function is to be verbose. |
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438 | # @return Interpolation matrix A, plus lists of the points inside and outside the mesh. |
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439 | def _build_interpolation_matrix_A(self, |
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440 | point_coordinates, |
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441 | output_centroids=False, |
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442 | verbose=False): |
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443 | """Build n x m interpolation matrix, where |
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444 | n is the number of data points and |
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445 | m is the number of basis functions phi_k (one per vertex) |
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446 | |
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447 | This algorithm uses a quad tree data structure for fast binning |
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448 | of data points |
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449 | origin is a 3-tuple consisting of UTM zone, easting and northing. |
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450 | If specified coordinates are assumed to be relative to this origin. |
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451 | |
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452 | This one will override any data_origin that may be specified in |
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453 | instance interpolation |
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454 | |
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455 | Preconditions: |
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456 | Point_coordindates and mesh vertices have the same origin. |
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457 | """ |
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458 | |
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459 | if verbose: log.critical('Building interpolation matrix') |
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460 | |
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461 | # Convert point_coordinates to numeric arrays, in case it was a list. |
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462 | point_coordinates = ensure_numeric(point_coordinates, num.float) |
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463 | |
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464 | if verbose: log.critical('Getting indices inside mesh boundary') |
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465 | |
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466 | inside_poly_indices, outside_poly_indices = \ |
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467 | in_and_outside_polygon(point_coordinates, |
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468 | self.mesh.get_boundary_polygon(), |
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469 | closed=True, verbose=verbose) |
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470 | |
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471 | # Build n x m interpolation matrix |
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472 | if verbose and len(outside_poly_indices) > 0: |
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473 | log.critical('WARNING: Points outside mesh boundary.') |
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474 | |
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475 | # Since you can block, throw a warning, not an error. |
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476 | if verbose and 0 == len(inside_poly_indices): |
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477 | log.critical('WARNING: No points within the mesh!') |
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478 | |
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479 | m = self.mesh.number_of_nodes # Nbr of basis functions (1/vertex) |
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480 | n = point_coordinates.shape[0] # Nbr of data points |
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481 | |
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482 | if verbose: log.critical('Number of datapoints: %d' % n) |
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483 | if verbose: log.critical('Number of basis functions: %d' % m) |
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484 | |
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485 | A = Sparse(n,m) |
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486 | |
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487 | n = len(inside_poly_indices) |
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488 | |
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489 | # Compute matrix elements for points inside the mesh |
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490 | if verbose: log.critical('Building interpolation matrix from %d points' |
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491 | % n) |
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492 | |
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493 | for d, i in enumerate(inside_poly_indices): |
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494 | # For each data_coordinate point |
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495 | if verbose and d%((n+10)/10)==0: log.critical('Doing %d of %d' |
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496 | %(d, n)) |
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497 | |
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498 | x = point_coordinates[i] |
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499 | element_found, sigma0, sigma1, sigma2, k = \ |
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500 | search_tree_of_vertices(self.root, x) |
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501 | |
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502 | # Update interpolation matrix A if necessary |
---|
503 | if element_found is True: |
---|
504 | # Assign values to matrix A |
---|
505 | j0 = self.mesh.triangles[k,0] # Global vertex id for sigma0 |
---|
506 | j1 = self.mesh.triangles[k,1] # Global vertex id for sigma1 |
---|
507 | j2 = self.mesh.triangles[k,2] # Global vertex id for sigma2 |
---|
508 | js = [j0, j1, j2] |
---|
509 | |
---|
510 | if output_centroids is False: |
---|
511 | # Weight each vertex according to its distance from x |
---|
512 | sigmas = {j0:sigma0, j1:sigma1, j2:sigma2} |
---|
513 | for j in js: |
---|
514 | A[i, j] = sigmas[j] |
---|
515 | else: |
---|
516 | # If centroids are needed, weight all 3 vertices equally |
---|
517 | for j in js: |
---|
518 | A[i, j] = 1.0/3.0 |
---|
519 | else: |
---|
520 | msg = 'Could not find triangle for point', x |
---|
521 | raise Exception(msg) |
---|
522 | return A, inside_poly_indices, outside_poly_indices |
---|
523 | |
---|
524 | |
---|
525 | |
---|
526 | |
---|
527 | |
---|
528 | |
---|
529 | ## |
---|
530 | # @brief ?? |
---|
531 | # @param vertices ?? |
---|
532 | # @param vertex_attributes ?? |
---|
533 | # @param triangles ?? |
---|
534 | # @param points ?? |
---|
535 | # @param max_points_per_cell ?? |
---|
536 | # @param start_blocking_len ?? |
---|
537 | # @param mesh_origin ?? |
---|
538 | def benchmark_interpolate(vertices, |
---|
539 | vertex_attributes, |
---|
540 | triangles, points, |
---|
541 | max_points_per_cell=None, |
---|
542 | start_blocking_len=500000, |
---|
543 | mesh_origin=None): |
---|
544 | """ |
---|
545 | points: Interpolate mesh data to these positions. |
---|
546 | List of coordinate pairs [x, y] of |
---|
547 | data points or an nx2 numeric array or a Geospatial_data object |
---|
548 | |
---|
549 | No test for this yet. |
---|
550 | Note, this has no time the input data has no time dimension. Which is |
---|
551 | different from most of the data we interpolate, eg sww info. |
---|
552 | |
---|
553 | Output: |
---|
554 | Interpolated values at inputted points. |
---|
555 | """ |
---|
556 | |
---|
557 | interp = Interpolate(vertices, |
---|
558 | triangles, |
---|
559 | max_vertices_per_cell=max_points_per_cell, |
---|
560 | mesh_origin=mesh_origin) |
---|
561 | |
---|
562 | calc = interp.interpolate(vertex_attributes, |
---|
563 | points, |
---|
564 | start_blocking_len=start_blocking_len) |
---|
565 | |
---|
566 | |
---|
567 | ## |
---|
568 | # @brief Interpolate quantities at given locations (from .SWW file). |
---|
569 | # @param sww_file Input .SWW file. |
---|
570 | # @param points A list of the 'gauges' x,y location. |
---|
571 | # @param depth_file The name of the output depth file. |
---|
572 | # @param velocity_x_file Name of the output x velocity file. |
---|
573 | # @param velocity_y_file Name of the output y velocity file. |
---|
574 | # @param stage_file Name of the output stage file. |
---|
575 | # @param froude_file |
---|
576 | # @param time_thinning Time thinning step to use. |
---|
577 | # @param verbose True if this function is to be verbose. |
---|
578 | # @param use_cache True if we are caching. |
---|
579 | def interpolate_sww2csv(sww_file, |
---|
580 | points, |
---|
581 | depth_file, |
---|
582 | velocity_x_file, |
---|
583 | velocity_y_file, |
---|
584 | stage_file=None, |
---|
585 | froude_file=None, |
---|
586 | time_thinning=1, |
---|
587 | verbose=True, |
---|
588 | use_cache = True): |
---|
589 | """ |
---|
590 | Interpolate the quantities at a given set of locations, given |
---|
591 | an sww file. |
---|
592 | The results are written to csv files. |
---|
593 | |
---|
594 | sww_file is the input sww file. |
---|
595 | points is a list of the 'gauges' x,y location. |
---|
596 | depth_file is the name of the output depth file |
---|
597 | velocity_x_file is the name of the output x velocity file. |
---|
598 | velocity_y_file is the name of the output y velocity file. |
---|
599 | stage_file is the name of the output stage file. |
---|
600 | |
---|
601 | In the csv files columns represents the gauges and each row is a |
---|
602 | time slice. |
---|
603 | |
---|
604 | Time_thinning_number controls how many timesteps to use. Only |
---|
605 | timesteps with index%time_thinning_number == 0 will used, or |
---|
606 | in other words a value of 3, say, will cause the algorithm to |
---|
607 | use every third time step. |
---|
608 | |
---|
609 | In the future let points be a points file. |
---|
610 | And let the user choose the quantities. |
---|
611 | |
---|
612 | This is currently quite specific. |
---|
613 | If it is need to be more general, change things. |
---|
614 | """ |
---|
615 | |
---|
616 | quantities = ['stage', 'elevation', 'xmomentum', 'ymomentum'] |
---|
617 | points = ensure_absolute(points) |
---|
618 | point_count = len(points) |
---|
619 | callable_sww = file_function(sww_file, |
---|
620 | quantities=quantities, |
---|
621 | interpolation_points=points, |
---|
622 | verbose=verbose, |
---|
623 | time_thinning=time_thinning, |
---|
624 | use_cache=use_cache) |
---|
625 | |
---|
626 | depth_writer = writer(file(depth_file, "wb")) |
---|
627 | velocity_x_writer = writer(file(velocity_x_file, "wb")) |
---|
628 | velocity_y_writer = writer(file(velocity_y_file, "wb")) |
---|
629 | if stage_file is not None: |
---|
630 | stage_writer = writer(file(stage_file, "wb")) |
---|
631 | if froude_file is not None: |
---|
632 | froude_writer = writer(file(froude_file, "wb")) |
---|
633 | |
---|
634 | # Write heading |
---|
635 | heading = [str(x[0])+ ':' + str(x[1]) for x in points] |
---|
636 | heading.insert(0, "time") |
---|
637 | depth_writer.writerow(heading) |
---|
638 | velocity_x_writer.writerow(heading) |
---|
639 | velocity_y_writer.writerow(heading) |
---|
640 | if stage_file is not None: |
---|
641 | stage_writer.writerow(heading) |
---|
642 | if froude_file is not None: |
---|
643 | froude_writer.writerow(heading) |
---|
644 | |
---|
645 | for time in callable_sww.get_time(): |
---|
646 | depths = [time] |
---|
647 | velocity_xs = [time] |
---|
648 | velocity_ys = [time] |
---|
649 | if stage_file is not None: |
---|
650 | stages = [time] |
---|
651 | if froude_file is not None: |
---|
652 | froudes = [time] |
---|
653 | for point_i, point in enumerate(points): |
---|
654 | quantities = callable_sww(time,point_i) |
---|
655 | |
---|
656 | w = quantities[0] |
---|
657 | z = quantities[1] |
---|
658 | momentum_x = quantities[2] |
---|
659 | momentum_y = quantities[3] |
---|
660 | depth = w - z |
---|
661 | |
---|
662 | if w == NAN or z == NAN or momentum_x == NAN: |
---|
663 | velocity_x = NAN |
---|
664 | else: |
---|
665 | if depth > 1.e-30: # use epsilon |
---|
666 | velocity_x = momentum_x / depth #Absolute velocity |
---|
667 | else: |
---|
668 | velocity_x = 0 |
---|
669 | |
---|
670 | if w == NAN or z == NAN or momentum_y == NAN: |
---|
671 | velocity_y = NAN |
---|
672 | else: |
---|
673 | if depth > 1.e-30: # use epsilon |
---|
674 | velocity_y = momentum_y / depth #Absolute velocity |
---|
675 | else: |
---|
676 | velocity_y = 0 |
---|
677 | |
---|
678 | if depth < 1.e-30: # use epsilon |
---|
679 | froude = NAN |
---|
680 | else: |
---|
681 | froude = sqrt(velocity_x*velocity_x + velocity_y*velocity_y)/ \ |
---|
682 | sqrt(depth * 9.8066) # gravity m/s/s |
---|
683 | |
---|
684 | depths.append(depth) |
---|
685 | velocity_xs.append(velocity_x) |
---|
686 | velocity_ys.append(velocity_y) |
---|
687 | |
---|
688 | if stage_file is not None: |
---|
689 | stages.append(w) |
---|
690 | if froude_file is not None: |
---|
691 | froudes.append(froude) |
---|
692 | |
---|
693 | depth_writer.writerow(depths) |
---|
694 | velocity_x_writer.writerow(velocity_xs) |
---|
695 | velocity_y_writer.writerow(velocity_ys) |
---|
696 | |
---|
697 | if stage_file is not None: |
---|
698 | stage_writer.writerow(stages) |
---|
699 | if froude_file is not None: |
---|
700 | froude_writer.writerow(froudes) |
---|
701 | |
---|
702 | |
---|
703 | ## |
---|
704 | # @brief |
---|
705 | class Interpolation_function: |
---|
706 | """Interpolation_interface - creates callable object f(t, id) or f(t,x,y) |
---|
707 | which is interpolated from time series defined at vertices of |
---|
708 | triangular mesh (such as those stored in sww files) |
---|
709 | |
---|
710 | Let m be the number of vertices, n the number of triangles |
---|
711 | and p the number of timesteps. |
---|
712 | Also, let N be the number of interpolation points. |
---|
713 | |
---|
714 | Mandatory input |
---|
715 | time: px1 array of monotonously increasing times (float) |
---|
716 | quantities: Dictionary of arrays or 1 array (float) |
---|
717 | The arrays must either have dimensions pxm or mx1. |
---|
718 | The resulting function will be time dependent in |
---|
719 | the former case while it will be constant with |
---|
720 | respect to time in the latter case. |
---|
721 | |
---|
722 | Optional input: |
---|
723 | quantity_names: List of keys into the quantities dictionary for |
---|
724 | imposing a particular order on the output vector. |
---|
725 | vertex_coordinates: mx2 array of coordinates (float) |
---|
726 | triangles: nx3 array of indices into vertex_coordinates (int) |
---|
727 | interpolation_points: Nx2 array of coordinates to be interpolated to |
---|
728 | verbose: Level of reporting |
---|
729 | |
---|
730 | The quantities returned by the callable object are specified by |
---|
731 | the list quantities which must contain the names of the |
---|
732 | quantities to be returned and also reflect the order, e.g. for |
---|
733 | the shallow water wave equation, on would have |
---|
734 | quantities = ['stage', 'xmomentum', 'ymomentum'] |
---|
735 | |
---|
736 | The parameter interpolation_points decides at which points interpolated |
---|
737 | quantities are to be computed whenever object is called. |
---|
738 | If None, return average value |
---|
739 | |
---|
740 | FIXME (Ole): Need to allow vertex coordinates and interpolation points to |
---|
741 | be geospatial data objects |
---|
742 | |
---|
743 | (FIXME (Ole): This comment should be removed) |
---|
744 | Time assumed to be relative to starttime |
---|
745 | All coordinates assume origin of (0,0) - e.g. georeferencing must be |
---|
746 | taken care of outside this function |
---|
747 | """ |
---|
748 | |
---|
749 | ## |
---|
750 | # @brief ?? |
---|
751 | # @param time ?? |
---|
752 | # @param quantities ?? |
---|
753 | # @param quantity_names ?? |
---|
754 | # @param vertex_coordinates ?? |
---|
755 | # @param triangles ?? |
---|
756 | # @param interpolation_points ?? |
---|
757 | # @param time_thinning ?? |
---|
758 | # @param verbose ?? |
---|
759 | # @param gauge_neighbour_id ?? |
---|
760 | def __init__(self, |
---|
761 | time, |
---|
762 | quantities, |
---|
763 | quantity_names=None, |
---|
764 | vertex_coordinates=None, |
---|
765 | triangles=None, |
---|
766 | interpolation_points=None, |
---|
767 | time_thinning=1, |
---|
768 | verbose=False, |
---|
769 | gauge_neighbour_id=None, |
---|
770 | output_centroids=False): |
---|
771 | """Initialise object and build spatial interpolation if required |
---|
772 | |
---|
773 | Time_thinning_number controls how many timesteps to use. Only timesteps |
---|
774 | with index%time_thinning_number == 0 will used, or in other words a |
---|
775 | value of 3, say, will cause the algorithm to use every third time step. |
---|
776 | """ |
---|
777 | |
---|
778 | from anuga.config import time_format |
---|
779 | import types |
---|
780 | |
---|
781 | if verbose is True: |
---|
782 | log.critical('Interpolation_function: input checks') |
---|
783 | |
---|
784 | # Check temporal info |
---|
785 | time = ensure_numeric(time) |
---|
786 | |
---|
787 | if not num.alltrue(time[1:] - time[:-1] >= 0): |
---|
788 | # This message is time consuming to form due to the conversion of |
---|
789 | msg = 'Time must be a monotonuosly increasing sequence %s' % time |
---|
790 | raise Exception, msg |
---|
791 | |
---|
792 | # Check if quantities is a single array only |
---|
793 | if type(quantities) != types.DictType: |
---|
794 | quantities = ensure_numeric(quantities) |
---|
795 | quantity_names = ['Attribute'] |
---|
796 | |
---|
797 | # Make it a dictionary |
---|
798 | quantities = {quantity_names[0]: quantities} |
---|
799 | |
---|
800 | # Use keys if no names are specified |
---|
801 | if quantity_names is None: |
---|
802 | quantity_names = quantities.keys() |
---|
803 | |
---|
804 | # Check spatial info |
---|
805 | if vertex_coordinates is None: |
---|
806 | self.spatial = False |
---|
807 | else: |
---|
808 | # FIXME (Ole): Try ensure_numeric here - |
---|
809 | # this function knows nothing about georefering. |
---|
810 | vertex_coordinates = ensure_absolute(vertex_coordinates) |
---|
811 | |
---|
812 | if triangles is not None: |
---|
813 | triangles = ensure_numeric(triangles) |
---|
814 | self.spatial = True |
---|
815 | |
---|
816 | if verbose is True: |
---|
817 | log.critical('Interpolation_function: thinning by %d' |
---|
818 | % time_thinning) |
---|
819 | |
---|
820 | |
---|
821 | # Thin timesteps if needed |
---|
822 | # Note array() is used to make the thinned arrays contiguous in memory |
---|
823 | self.time = num.array(time[::time_thinning]) |
---|
824 | for name in quantity_names: |
---|
825 | if len(quantities[name].shape) == 2: |
---|
826 | quantities[name] = num.array(quantities[name][::time_thinning,:]) |
---|
827 | |
---|
828 | if verbose is True: |
---|
829 | log.critical('Interpolation_function: precomputing') |
---|
830 | |
---|
831 | # Save for use with statistics |
---|
832 | self.quantities_range = {} |
---|
833 | for name in quantity_names: |
---|
834 | q = quantities[name][:].flatten() |
---|
835 | self.quantities_range[name] = [min(q), max(q)] |
---|
836 | |
---|
837 | self.quantity_names = quantity_names |
---|
838 | self.vertex_coordinates = vertex_coordinates |
---|
839 | self.interpolation_points = interpolation_points |
---|
840 | |
---|
841 | self.index = 0 # Initial time index |
---|
842 | self.precomputed_values = {} |
---|
843 | |
---|
844 | # Precomputed spatial interpolation if requested |
---|
845 | if interpolation_points is not None: |
---|
846 | #no longer true. sts files have spatial = True but |
---|
847 | #if self.spatial is False: |
---|
848 | # raise 'Triangles and vertex_coordinates must be specified' |
---|
849 | # |
---|
850 | try: |
---|
851 | self.interpolation_points = \ |
---|
852 | interpolation_points = ensure_numeric(interpolation_points) |
---|
853 | except: |
---|
854 | msg = 'Interpolation points must be an N x 2 numeric array ' \ |
---|
855 | 'or a list of points\n' |
---|
856 | msg += 'Got: %s.' %(str(self.interpolation_points)[:60] + '...') |
---|
857 | raise msg |
---|
858 | |
---|
859 | # Ensure 'mesh_boundary_polygon' is defined |
---|
860 | mesh_boundary_polygon = None |
---|
861 | |
---|
862 | if triangles is not None and vertex_coordinates is not None: |
---|
863 | # Check that all interpolation points fall within |
---|
864 | # mesh boundary as defined by triangles and vertex_coordinates. |
---|
865 | from anuga.abstract_2d_finite_volumes.neighbour_mesh import Mesh |
---|
866 | from anuga.utilities.polygon import outside_polygon |
---|
867 | |
---|
868 | # Create temporary mesh object from mesh info passed |
---|
869 | # into this function. |
---|
870 | mesh = Mesh(vertex_coordinates, triangles) |
---|
871 | mesh_boundary_polygon = mesh.get_boundary_polygon() |
---|
872 | |
---|
873 | indices = outside_polygon(interpolation_points, |
---|
874 | mesh_boundary_polygon) |
---|
875 | |
---|
876 | # Record result |
---|
877 | #self.mesh_boundary_polygon = mesh_boundary_polygon |
---|
878 | self.indices_outside_mesh = indices |
---|
879 | |
---|
880 | # Report |
---|
881 | if len(indices) > 0: |
---|
882 | msg = 'Interpolation points in Interpolation function fall ' |
---|
883 | msg += 'outside specified mesh. Offending points:\n' |
---|
884 | out_interp_pts = [] |
---|
885 | for i in indices: |
---|
886 | msg += '%d: %s\n' % (i, interpolation_points[i]) |
---|
887 | out_interp_pts.append( |
---|
888 | ensure_numeric(interpolation_points[i])) |
---|
889 | |
---|
890 | if verbose is True: |
---|
891 | import sys |
---|
892 | if sys.platform == 'win32': |
---|
893 | # FIXME (Ole): Why only Windoze? |
---|
894 | from anuga.utilities.polygon import plot_polygons |
---|
895 | title = ('Interpolation points fall ' |
---|
896 | 'outside specified mesh') |
---|
897 | plot_polygons([mesh_boundary_polygon, |
---|
898 | interpolation_points, |
---|
899 | out_interp_pts], |
---|
900 | ['line', 'point', 'outside'], |
---|
901 | figname='points_boundary_out', |
---|
902 | label=title, |
---|
903 | verbose=verbose) |
---|
904 | |
---|
905 | # Joaquim Luis suggested this as an Exception, so |
---|
906 | # that the user can now what the problem is rather than |
---|
907 | # looking for NaN's. However, NANs are handy as they can |
---|
908 | # be ignored leaving good points for continued processing. |
---|
909 | if verbose: |
---|
910 | log.critical(msg) |
---|
911 | #raise Exception(msg) |
---|
912 | |
---|
913 | elif triangles is None and vertex_coordinates is not None: #jj |
---|
914 | #Dealing with sts file |
---|
915 | pass |
---|
916 | else: |
---|
917 | raise Exception('Sww file function requires both triangles and ' |
---|
918 | 'vertex_coordinates. sts file file function ' |
---|
919 | 'requires the latter.') |
---|
920 | |
---|
921 | # Plot boundary and interpolation points, |
---|
922 | # but only if if 'mesh_boundary_polygon' has data. |
---|
923 | if verbose is True and mesh_boundary_polygon is not None: |
---|
924 | import sys |
---|
925 | if sys.platform == 'win32': |
---|
926 | from anuga.utilities.polygon import plot_polygons |
---|
927 | title = ('Interpolation function: ' |
---|
928 | 'Polygon and interpolation points') |
---|
929 | plot_polygons([mesh_boundary_polygon, |
---|
930 | interpolation_points], |
---|
931 | ['line', 'point'], |
---|
932 | figname='points_boundary', |
---|
933 | label=title, |
---|
934 | verbose=verbose) |
---|
935 | |
---|
936 | m = len(self.interpolation_points) |
---|
937 | p = len(self.time) |
---|
938 | |
---|
939 | for name in quantity_names: |
---|
940 | self.precomputed_values[name] = num.zeros((p, m), num.float) |
---|
941 | |
---|
942 | if verbose is True: |
---|
943 | log.critical('Build interpolator') |
---|
944 | |
---|
945 | |
---|
946 | # Build interpolator |
---|
947 | if triangles is not None and vertex_coordinates is not None: |
---|
948 | if verbose: |
---|
949 | msg = 'Building interpolation matrix from source mesh ' |
---|
950 | msg += '(%d vertices, %d triangles)' \ |
---|
951 | % (vertex_coordinates.shape[0], |
---|
952 | triangles.shape[0]) |
---|
953 | log.critical(msg) |
---|
954 | |
---|
955 | # This one is no longer needed for STS files |
---|
956 | interpol = Interpolate(vertex_coordinates, |
---|
957 | triangles, |
---|
958 | verbose=verbose) |
---|
959 | |
---|
960 | elif triangles is None and vertex_coordinates is not None: |
---|
961 | if verbose: |
---|
962 | log.critical('Interpolation from STS file') |
---|
963 | |
---|
964 | |
---|
965 | |
---|
966 | if verbose: |
---|
967 | log.critical('Interpolating (%d interpolation points, %d timesteps).' |
---|
968 | % (self.interpolation_points.shape[0], self.time.shape[0])) |
---|
969 | |
---|
970 | if time_thinning > 1: |
---|
971 | log.critical('Timesteps were thinned by a factor of %d' |
---|
972 | % time_thinning) |
---|
973 | else: |
---|
974 | log.critical() |
---|
975 | |
---|
976 | for i, t in enumerate(self.time): |
---|
977 | # Interpolate quantities at this timestep |
---|
978 | if verbose and i%((p+10)/10) == 0: |
---|
979 | log.critical(' time step %d of %d' % (i, p)) |
---|
980 | |
---|
981 | for name in quantity_names: |
---|
982 | if len(quantities[name].shape) == 2: |
---|
983 | Q = quantities[name][i,:] # Quantities at timestep i |
---|
984 | else: |
---|
985 | Q = quantities[name][:] # No time dependency |
---|
986 | |
---|
987 | if verbose and i%((p+10)/10) == 0: |
---|
988 | log.critical(' quantity %s, size=%d' % (name, len(Q))) |
---|
989 | |
---|
990 | # Interpolate |
---|
991 | if triangles is not None and vertex_coordinates is not None: |
---|
992 | result = interpol.interpolate(Q, |
---|
993 | point_coordinates=\ |
---|
994 | self.interpolation_points, |
---|
995 | verbose=False, |
---|
996 | output_centroids=output_centroids) |
---|
997 | elif triangles is None and vertex_coordinates is not None: |
---|
998 | result = interpolate_polyline(Q, |
---|
999 | vertex_coordinates, |
---|
1000 | gauge_neighbour_id, |
---|
1001 | interpolation_points=\ |
---|
1002 | self.interpolation_points) |
---|
1003 | |
---|
1004 | #assert len(result), len(interpolation_points) |
---|
1005 | self.precomputed_values[name][i, :] = result |
---|
1006 | |
---|
1007 | # Report |
---|
1008 | if verbose: |
---|
1009 | log.critical(self.statistics()) |
---|
1010 | else: |
---|
1011 | # Store quantitites as is |
---|
1012 | for name in quantity_names: |
---|
1013 | self.precomputed_values[name] = quantities[name] |
---|
1014 | |
---|
1015 | ## |
---|
1016 | # @brief Override object representation method. |
---|
1017 | def __repr__(self): |
---|
1018 | # return 'Interpolation function (spatio-temporal)' |
---|
1019 | return self.statistics() |
---|
1020 | |
---|
1021 | ## |
---|
1022 | # @brief Evaluate interpolation function |
---|
1023 | # @param t Model time - must lie within existing timesteps. |
---|
1024 | # @param point_id Index of one of the preprocessed points. |
---|
1025 | # @param x ?? |
---|
1026 | # @param y ?? |
---|
1027 | # @return ?? |
---|
1028 | def __call__(self, t, point_id=None, x=None, y=None): |
---|
1029 | """Evaluate f(t) or f(t, point_id) |
---|
1030 | |
---|
1031 | Inputs: |
---|
1032 | t: time - Model time. Must lie within existing timesteps |
---|
1033 | point_id: index of one of the preprocessed points. |
---|
1034 | |
---|
1035 | If spatial info is present and all of point_id |
---|
1036 | are None an exception is raised |
---|
1037 | |
---|
1038 | If no spatial info is present, point_id arguments are ignored |
---|
1039 | making f a function of time only. |
---|
1040 | |
---|
1041 | FIXME: f(t, x, y) x, y could overrided location, point_id ignored |
---|
1042 | FIXME: point_id could also be a slice |
---|
1043 | FIXME: What if x and y are vectors? |
---|
1044 | FIXME: What about f(x,y) without t? |
---|
1045 | """ |
---|
1046 | |
---|
1047 | from math import pi, cos, sin, sqrt |
---|
1048 | from anuga.abstract_2d_finite_volumes.util import mean |
---|
1049 | |
---|
1050 | if self.spatial is True: |
---|
1051 | if point_id is None: |
---|
1052 | if x is None or y is None: |
---|
1053 | msg = 'Either point_id or x and y must be specified' |
---|
1054 | raise Exception(msg) |
---|
1055 | else: |
---|
1056 | if self.interpolation_points is None: |
---|
1057 | msg = 'Interpolation_function must be instantiated ' + \ |
---|
1058 | 'with a list of interpolation points before ' + \ |
---|
1059 | 'parameter point_id can be used' |
---|
1060 | raise Exception(msg) |
---|
1061 | |
---|
1062 | msg = 'Time interval [%.16f:%.16f]' % (self.time[0], self.time[-1]) |
---|
1063 | msg += ' does not match model time: %.16f\n' % t |
---|
1064 | if t < self.time[0]: raise Modeltime_too_early(msg) |
---|
1065 | if t > self.time[-1]: raise Modeltime_too_late(msg) |
---|
1066 | |
---|
1067 | oldindex = self.index #Time index |
---|
1068 | |
---|
1069 | # Find current time slot |
---|
1070 | while t > self.time[self.index]: self.index += 1 |
---|
1071 | while t < self.time[self.index]: self.index -= 1 |
---|
1072 | |
---|
1073 | if t == self.time[self.index]: |
---|
1074 | # Protect against case where t == T[-1] (last time) |
---|
1075 | # - also works in general when t == T[i] |
---|
1076 | ratio = 0 |
---|
1077 | else: |
---|
1078 | # t is now between index and index+1 |
---|
1079 | ratio = ((t - self.time[self.index]) / |
---|
1080 | (self.time[self.index+1] - self.time[self.index])) |
---|
1081 | |
---|
1082 | # Compute interpolated values |
---|
1083 | q = num.zeros(len(self.quantity_names), num.float) |
---|
1084 | for i, name in enumerate(self.quantity_names): |
---|
1085 | Q = self.precomputed_values[name] |
---|
1086 | |
---|
1087 | if self.spatial is False: |
---|
1088 | # If there is no spatial info |
---|
1089 | assert len(Q.shape) == 1 |
---|
1090 | |
---|
1091 | Q0 = Q[self.index] |
---|
1092 | if ratio > 0: Q1 = Q[self.index+1] |
---|
1093 | else: |
---|
1094 | if x is not None and y is not None: |
---|
1095 | # Interpolate to x, y |
---|
1096 | raise 'x,y interpolation not yet implemented' |
---|
1097 | else: |
---|
1098 | # Use precomputed point |
---|
1099 | Q0 = Q[self.index, point_id] |
---|
1100 | if ratio > 0: |
---|
1101 | Q1 = Q[self.index+1, point_id] |
---|
1102 | |
---|
1103 | # Linear temporal interpolation |
---|
1104 | if ratio > 0: |
---|
1105 | if Q0 == NAN and Q1 == NAN: |
---|
1106 | q[i] = Q0 |
---|
1107 | else: |
---|
1108 | q[i] = Q0 + ratio*(Q1 - Q0) |
---|
1109 | else: |
---|
1110 | q[i] = Q0 |
---|
1111 | |
---|
1112 | # Return vector of interpolated values |
---|
1113 | # FIXME: |
---|
1114 | if self.spatial is True: |
---|
1115 | return q |
---|
1116 | else: |
---|
1117 | # Replicate q according to x and y |
---|
1118 | # This is e.g used for Wind_stress |
---|
1119 | if x is None or y is None: |
---|
1120 | return q |
---|
1121 | else: |
---|
1122 | try: |
---|
1123 | N = len(x) |
---|
1124 | except: |
---|
1125 | return q |
---|
1126 | else: |
---|
1127 | # x is a vector - Create one constant column for each value |
---|
1128 | N = len(x) |
---|
1129 | assert len(y) == N, 'x and y must have same length' |
---|
1130 | res = [] |
---|
1131 | for col in q: |
---|
1132 | res.append(col*num.ones(N, num.float)) |
---|
1133 | |
---|
1134 | return res |
---|
1135 | |
---|
1136 | ## |
---|
1137 | # @brief Return model time as a vector of timesteps. |
---|
1138 | def get_time(self): |
---|
1139 | """Return model time as a vector of timesteps |
---|
1140 | """ |
---|
1141 | return self.time |
---|
1142 | |
---|
1143 | ## |
---|
1144 | # @brief Output statistics about interpolation_function. |
---|
1145 | # @return The statistics string. |
---|
1146 | def statistics(self): |
---|
1147 | """Output statistics about interpolation_function |
---|
1148 | """ |
---|
1149 | |
---|
1150 | vertex_coordinates = self.vertex_coordinates |
---|
1151 | interpolation_points = self.interpolation_points |
---|
1152 | quantity_names = self.quantity_names |
---|
1153 | #quantities = self.quantities |
---|
1154 | precomputed_values = self.precomputed_values |
---|
1155 | |
---|
1156 | x = vertex_coordinates[:,0] |
---|
1157 | y = vertex_coordinates[:,1] |
---|
1158 | |
---|
1159 | str = '------------------------------------------------\n' |
---|
1160 | str += 'Interpolation_function (spatio-temporal) statistics:\n' |
---|
1161 | str += ' Extent:\n' |
---|
1162 | str += ' x in [%f, %f], len(x) == %d\n'\ |
---|
1163 | %(min(x), max(x), len(x)) |
---|
1164 | str += ' y in [%f, %f], len(y) == %d\n'\ |
---|
1165 | %(min(y), max(y), len(y)) |
---|
1166 | str += ' t in [%f, %f], len(t) == %d\n'\ |
---|
1167 | %(min(self.time), max(self.time), len(self.time)) |
---|
1168 | str += ' Quantities:\n' |
---|
1169 | for name in quantity_names: |
---|
1170 | minq, maxq = self.quantities_range[name] |
---|
1171 | str += ' %s in [%f, %f]\n' %(name, minq, maxq) |
---|
1172 | #q = quantities[name][:].flatten() |
---|
1173 | #str += ' %s in [%f, %f]\n' %(name, min(q), max(q)) |
---|
1174 | |
---|
1175 | if interpolation_points is not None: |
---|
1176 | str += ' Interpolation points (xi, eta):'\ |
---|
1177 | ' number of points == %d\n' %interpolation_points.shape[0] |
---|
1178 | str += ' xi in [%f, %f]\n' %(min(interpolation_points[:,0]), |
---|
1179 | max(interpolation_points[:,0])) |
---|
1180 | str += ' eta in [%f, %f]\n' %(min(interpolation_points[:,1]), |
---|
1181 | max(interpolation_points[:,1])) |
---|
1182 | str += ' Interpolated quantities (over all timesteps):\n' |
---|
1183 | |
---|
1184 | for name in quantity_names: |
---|
1185 | q = precomputed_values[name][:].flatten() |
---|
1186 | str += ' %s at interpolation points in [%f, %f]\n'\ |
---|
1187 | %(name, min(q), max(q)) |
---|
1188 | str += '------------------------------------------------\n' |
---|
1189 | |
---|
1190 | return str |
---|
1191 | |
---|
1192 | |
---|
1193 | ## |
---|
1194 | # @brief ?? |
---|
1195 | # @param sww_file ?? |
---|
1196 | # @param time ?? |
---|
1197 | # @param interpolation_points ?? |
---|
1198 | # @param quantity_names ?? |
---|
1199 | # @param verbose ?? |
---|
1200 | # @note Obsolete. Use file_function() in utils. |
---|
1201 | def interpolate_sww(sww_file, time, interpolation_points, |
---|
1202 | quantity_names=None, verbose=False): |
---|
1203 | """ |
---|
1204 | obsolete. |
---|
1205 | use file_function in utils |
---|
1206 | """ |
---|
1207 | |
---|
1208 | #open sww file |
---|
1209 | x, y, volumes, time, quantities = read_sww(sww_file) |
---|
1210 | log.critical("x=%s" % str(x)) |
---|
1211 | log.critical("y=%s" % str(y)) |
---|
1212 | |
---|
1213 | log.critical("time=%s" % str(time)) |
---|
1214 | log.critical("quantities=%s" % str(quantities)) |
---|
1215 | |
---|
1216 | #Add the x and y together |
---|
1217 | vertex_coordinates = num.concatenate((x[:,num.newaxis], y[:,num.newaxis]), |
---|
1218 | axis=1) |
---|
1219 | |
---|
1220 | #Will return the quantity values at the specified times and locations |
---|
1221 | interp = Interpolation_interface(time, |
---|
1222 | quantities, |
---|
1223 | quantity_names=quantity_names, |
---|
1224 | vertex_coordinates=vertex_coordinates, |
---|
1225 | triangles=volumes, |
---|
1226 | interpolation_points=interpolation_points, |
---|
1227 | verbose=verbose) |
---|
1228 | |
---|
1229 | |
---|
1230 | ## |
---|
1231 | # @brief ?? |
---|
1232 | # @param file_name Name of the .SWW file to read. |
---|
1233 | def read_sww(file_name): |
---|
1234 | """ |
---|
1235 | obsolete - Nothing should be calling this |
---|
1236 | |
---|
1237 | Read in an sww file. |
---|
1238 | |
---|
1239 | Input; |
---|
1240 | file_name - the sww file |
---|
1241 | |
---|
1242 | Output; |
---|
1243 | x - Vector of x values |
---|
1244 | y - Vector of y values |
---|
1245 | z - Vector of bed elevation |
---|
1246 | volumes - Array. Each row has 3 values, representing |
---|
1247 | the vertices that define the volume |
---|
1248 | time - Vector of the times where there is stage information |
---|
1249 | stage - array with respect to time and vertices (x,y) |
---|
1250 | """ |
---|
1251 | |
---|
1252 | msg = 'Function read_sww in interpolat.py is obsolete' |
---|
1253 | raise Exception, msg |
---|
1254 | |
---|
1255 | #FIXME Have this reader as part of data_manager? |
---|
1256 | |
---|
1257 | from Scientific.IO.NetCDF import NetCDFFile |
---|
1258 | import tempfile |
---|
1259 | import sys |
---|
1260 | import os |
---|
1261 | |
---|
1262 | #Check contents |
---|
1263 | #Get NetCDF |
---|
1264 | |
---|
1265 | # see if the file is there. Throw a QUIET IO error if it isn't |
---|
1266 | # I don't think I could implement the above |
---|
1267 | |
---|
1268 | #throws prints to screen if file not present |
---|
1269 | junk = tempfile.mktemp(".txt") |
---|
1270 | fd = open(junk,'w') |
---|
1271 | stdout = sys.stdout |
---|
1272 | sys.stdout = fd |
---|
1273 | fid = NetCDFFile(file_name, netcdf_mode_r) |
---|
1274 | sys.stdout = stdout |
---|
1275 | fd.close() |
---|
1276 | #clean up |
---|
1277 | os.remove(junk) |
---|
1278 | |
---|
1279 | # Get the variables |
---|
1280 | x = fid.variables['x'][:] |
---|
1281 | y = fid.variables['y'][:] |
---|
1282 | volumes = fid.variables['volumes'][:] |
---|
1283 | time = fid.variables['time'][:] |
---|
1284 | |
---|
1285 | keys = fid.variables.keys() |
---|
1286 | keys.remove("x") |
---|
1287 | keys.remove("y") |
---|
1288 | keys.remove("volumes") |
---|
1289 | keys.remove("time") |
---|
1290 | #Turn NetCDF objects into numeric arrays |
---|
1291 | quantities = {} |
---|
1292 | for name in keys: |
---|
1293 | quantities[name] = fid.variables[name][:] |
---|
1294 | |
---|
1295 | fid.close() |
---|
1296 | return x, y, volumes, time, quantities |
---|
1297 | |
---|
1298 | |
---|
1299 | #------------------------------------------------------------- |
---|
1300 | if __name__ == "__main__": |
---|
1301 | names = ["x","y"] |
---|
1302 | someiterable = [[1,2],[3,4]] |
---|
1303 | csvwriter = writer(file("some.csv", "wb")) |
---|
1304 | csvwriter.writerow(names) |
---|
1305 | for row in someiterable: |
---|
1306 | csvwriter.writerow(row) |
---|