Mercurial > dive4elements > river
annotate flys-artifacts/src/main/java/de/intevation/flys/artifacts/model/Parameters.java @ 4837:9e25c7523485
Fixed calculation of effective width in MINFO SQ relation.
* Get all (including empty datasets) from db.
* Filter empty datasets when processing data of the same date.
* Added debug outputs.
author | Raimund Renkert <rrenkert@intevation.de> |
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date | Wed, 23 Jan 2013 11:14:41 +0100 |
parents | 1df6984628c3 |
children | bcf25d8c183e |
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1 package de.intevation.flys.artifacts.model; |
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2 |
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Parameters: Linear interpolation of parameters.
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3 import de.intevation.flys.artifacts.math.Linear; |
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4 |
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5 import gnu.trove.TDoubleArrayList; |
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6 |
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7 import java.io.Serializable; |
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8 |
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9 import org.apache.log4j.Logger; |
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10 |
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11 public class Parameters |
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12 implements Serializable |
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13 { |
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14 private static Logger log = Logger.getLogger(Parameters.class); |
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15 |
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16 public interface Visitor { |
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17 |
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18 void visit(double [] row); |
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19 |
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20 } // interface Visitor |
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21 |
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22 public static final double EPSILON = 1e-4; |
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23 |
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24 protected String [] columnNames; |
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25 protected TDoubleArrayList [] columns; |
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26 |
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27 public Parameters() { |
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28 } |
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29 |
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30 public Parameters(String [] columnNames) { |
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31 if (columnNames == null || columnNames.length < 1) { |
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32 throw new IllegalArgumentException("columnNames too short."); |
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33 } |
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34 this.columnNames = columnNames; |
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35 columns = new TDoubleArrayList[columnNames.length]; |
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36 for (int i = 0; i < columns.length; ++i) { |
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37 columns[i] = new TDoubleArrayList(); |
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38 } |
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39 } |
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40 |
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41 public int columnIndex(String name) { |
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42 for (int i = 0; i < columnNames.length; ++i) { |
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43 if (columnNames[i].equals(name)) { |
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44 return i; |
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45 } |
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46 } |
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47 if (log.isDebugEnabled()) { |
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48 log.debug("columnIndex: " + name + " not found in columnNames"); |
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49 } |
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50 return -1; |
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51 } |
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52 |
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53 public int newRow() { |
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54 |
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55 int N = columns[0].size(); |
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56 |
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57 for (int i = 0; i < columns.length; ++i) { |
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58 columns[i].add(Double.NaN); |
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59 } |
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60 |
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61 return N; |
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62 } |
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63 |
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64 public double get(int row, int index) { |
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65 return columns[index].getQuick(row); |
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66 } |
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67 |
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68 public double get(int i, String columnName) { |
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69 int index = columnIndex(columnName); |
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70 return index >= 0 |
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71 ? columns[index].getQuick(i) |
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72 : Double.NaN; |
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73 } |
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74 |
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75 public void set(int row, int index, double value) { |
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76 columns[index].setQuick(row, value); |
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77 } |
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78 |
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79 public void set(int i, String columnName, double value) { |
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80 int idx = columnIndex(columnName); |
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81 if (idx >= 0) { |
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82 columns[idx].setQuick(i, value); |
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83 } |
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84 } |
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85 |
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86 public boolean set(int row, int [] indices, double [] values) { |
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87 boolean invalid = false; |
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88 for (int i = 0; i < indices.length; ++i) { |
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89 double v = values[i]; |
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90 if (Double.isNaN(v)) { |
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91 invalid = true; |
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92 } |
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93 else { |
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94 columns[indices[i]].setQuick(row, v); |
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95 } |
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96 } |
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97 return invalid; |
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98 } |
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99 |
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100 public boolean set(int row, String [] names, double [] values) { |
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101 boolean success = true; |
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102 for (int i = 0; i < names.length; ++i) { |
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103 int idx = columnIndex(names[i]); |
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104 if (idx >= 0) { |
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105 columns[idx].setQuick(row, values[i]); |
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106 } |
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107 else { |
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108 success = false; |
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109 } |
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110 } |
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111 return success; |
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112 } |
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113 |
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114 public int size() { |
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115 return columns[0].size(); |
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116 } |
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117 |
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118 public int getNumberColumns() { |
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119 return columnNames.length; |
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120 } |
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121 |
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122 public String [] getColumnNames() { |
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123 return columnNames; |
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124 } |
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125 |
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126 public void removeNaNs() { |
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127 W.removeNaNs(columns); |
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128 } |
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129 |
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130 public int [] columnIndices(String [] columns) { |
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131 int [] indices = new int[columns.length]; |
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132 for (int i = 0; i < columns.length; ++i) { |
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133 indices[i] = columnIndex(columns[i]); |
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134 } |
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135 return indices; |
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136 } |
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137 |
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138 public double getValue(int row, String column) { |
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139 int idx = columnIndex(column); |
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140 return idx >= 0 |
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141 ? columns[idx].getQuick(row) |
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142 : Double.NaN; |
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143 } |
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144 |
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145 public double [] get(int row, String [] columns) { |
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146 return get(row, columns, new double[columns.length]); |
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147 } |
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148 |
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149 public double [] get(int row, String [] columns, double [] values) { |
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150 for (int i = 0; i < columns.length; ++i) { |
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151 int idx = columnIndex(columns[i]); |
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152 values[i] = idx < 0 |
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153 ? Double.NaN |
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154 : this.columns[idx].getQuick(row); |
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155 } |
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156 |
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157 return values; |
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158 } |
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159 |
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160 public void get(int row, int [] columnIndices, double [] values) { |
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161 for (int i = 0; i < columnIndices.length; ++i) { |
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162 int index = columnIndices[i]; |
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163 values[i] = index >= 0 && index < columns.length |
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164 ? columns[index].getQuick(row) |
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165 : Double.NaN; |
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166 } |
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167 } |
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168 |
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169 public int binarySearch(String columnName, double value) { |
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170 return binarySearch(columnIndex(columnName), value); |
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171 } |
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172 |
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173 /** |
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174 * Performes a binary search in the column identified by its |
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175 * index. |
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176 * @return Index of found element or negative insertion point (shifted by one) |
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177 */ |
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178 public int binarySearch(int columnIndex, double value) { |
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179 TDoubleArrayList column = columns[columnIndex]; |
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180 return column.binarySearch(value); |
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181 } |
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182 |
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183 public int binarySearch(String columnName, double value, double epsilon) { |
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184 return binarySearch(columnIndex(columnName), value, epsilon); |
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185 } |
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186 |
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187 public int binarySearch(int columnIndex, double value, double epsilon) { |
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188 if (epsilon < 0d) epsilon = -epsilon; |
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189 double vl = value - epsilon; |
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190 double vh = value + epsilon; |
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191 |
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192 TDoubleArrayList column = columns[columnIndex]; |
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193 int lo = 0, hi = column.size()-1; |
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194 while (hi >= lo) { |
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195 int mid = (lo + hi) >> 1; |
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196 double v = column.getQuick(mid); |
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197 if (v < vl) lo = mid + 1; |
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198 else if (v > vh) hi = mid - 1; |
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199 else return mid; |
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200 } |
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201 |
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202 return -(lo + 1); |
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203 } |
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204 |
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205 public double [] interpolate(int columnIndex, double key) { |
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206 return interpolate(columnIndex, key, new double[columns.length]); |
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207 } |
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208 |
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209 public double [] interpolate(String columnName, double key) { |
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210 return interpolate( |
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211 columnIndex(columnName), key, new double[columns.length]); |
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212 } |
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213 |
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214 public double [] interpolate( |
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215 String columnName, |
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216 double key, |
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217 double [] values |
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218 ) { |
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219 return interpolate(columnIndex(columnName), key, values); |
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220 } |
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221 |
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222 public double [] interpolate( |
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223 int columnIndex, |
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224 double key, |
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225 double [] values |
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226 ) { |
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227 int row = binarySearch(columnIndex, key, EPSILON); |
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228 |
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229 if (row >= 0) { // direct hit |
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230 for (int i = 0; i < values.length; ++i) { |
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231 values[i] = columns[i].getQuick(row); |
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232 } |
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233 } |
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234 else { |
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235 row = -row - 1; |
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236 if (row < 1 || row >= size()) { |
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237 return null; |
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238 } |
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239 double v1 = columns[columnIndex].getQuick(row-1); |
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240 double v2 = columns[columnIndex].getQuick(row); |
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241 double factor = Linear.factor(key, v1, v2); |
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242 for (int i = 0; i < values.length; ++i) { |
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243 values[i] = Linear.weight( |
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244 factor, |
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245 columns[i].getQuick(row-1), |
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246 columns[i].getQuick(row)); |
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247 } |
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248 } |
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249 return values; |
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250 } |
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251 |
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252 |
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253 public double [] interpolate( |
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254 String keyName, |
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255 double key, |
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256 String [] columnNames |
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257 ) { |
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258 int keyIndex = columnIndex(keyName); |
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259 return keyIndex < 0 |
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260 ? null |
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261 : interpolate(keyIndex, key, columnNames); |
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262 } |
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263 |
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264 public double [] interpolate( |
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265 int keyIndex, |
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266 double key, |
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267 String [] columnNames |
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268 ) { |
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269 int row = binarySearch(keyIndex, key, EPSILON); |
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270 |
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271 if (row >= 0) { // direct match |
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272 double [] values = new double[columnNames.length]; |
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273 for (int i = 0; i < values.length; ++i) { |
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274 int ci = columnIndex(columnNames[i]); |
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275 values[i] = ci < 0 |
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276 ? Double.NaN |
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277 : columns[ci].getQuick(row); |
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278 } |
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279 return values; |
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280 } |
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281 |
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282 row = -row - 1; |
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|
283 if (row < 1 || row >= size()) { |
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FixA: Lowered log level in parameter interpolation.
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|
284 log.debug("interpolate: row is out of bounds"); |
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285 return null; |
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286 } |
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287 |
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288 double v1 = columns[keyIndex].getQuick(row-1); |
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289 double v2 = columns[keyIndex].getQuick(row); |
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290 double factor = Linear.factor(key, v1, v2); |
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291 |
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292 double [] values = new double[columnNames.length]; |
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293 |
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294 for (int i = 0; i < values.length; ++i) { |
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295 int ci = columnIndex(columnNames[i]); |
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296 values[i] = ci < 0 |
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297 ? Double.NaN |
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298 : Linear.weight( |
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299 factor, |
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300 columns[ci].getQuick(row-1), |
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301 columns[ci].getQuick(row)); |
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302 } |
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303 |
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304 return values; |
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305 } |
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306 |
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307 public boolean isSorted(String columnName) { |
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308 return isSorted(columnIndex(columnName)); |
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309 } |
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310 |
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311 public boolean isSorted(int columnIndex) { |
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312 TDoubleArrayList column = columns[columnIndex]; |
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313 for (int i = 1, N = column.size(); i < N; ++i) { |
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314 if (column.getQuick(i-1) > column.getQuick(i)) { |
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315 return false; |
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316 } |
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317 } |
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318 return true; |
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319 } |
3217
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320 |
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321 public void visit(Visitor visitor) { |
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322 visit(visitor, new double[columns.length]); |
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323 } |
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324 |
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325 public void visit(Visitor visitor, double [] data) { |
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326 for (int i = 0, R = size(); i < R; ++i) { |
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327 for (int j = 0; j < data.length; ++j) { |
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328 data[j] = columns[j].getQuick(i); |
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329 } |
79dd823733e2
FixA: Added parameter exporter.
Sascha L. Teichmann <sascha.teichmann@intevation.de>
parents:
3126
diff
changeset
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330 visitor.visit(data); |
79dd823733e2
FixA: Added parameter exporter.
Sascha L. Teichmann <sascha.teichmann@intevation.de>
parents:
3126
diff
changeset
|
331 } |
79dd823733e2
FixA: Added parameter exporter.
Sascha L. Teichmann <sascha.teichmann@intevation.de>
parents:
3126
diff
changeset
|
332 } |
2695
ae0742f92cd5
Added model to store fitting results into cache.
Sascha L. Teichmann <sascha.teichmann@intevation.de>
parents:
diff
changeset
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333 } |
ae0742f92cd5
Added model to store fitting results into cache.
Sascha L. Teichmann <sascha.teichmann@intevation.de>
parents:
diff
changeset
|
334 // vim:set ts=4 sw=4 si et sta sts=4 fenc=utf8 : |