1 | 1. Use Case 1: 1 data set and 1 model |
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2 | |
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3 | This use case describes an application (the client) performing a fit on a single |
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4 | data set with a single model. |
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5 | |
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6 | 1.1. Flow of Events |
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7 | 1.1.1. Basic Flow |
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8 | 1.1.1.1. The client chooses a data set to fit. |
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9 | 1.1.1.2. The client chooses a model to use. |
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10 | 1.1.1.3. The client enters the initial parameters of the model. |
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11 | 1.1.1.4. The client starts the fit. |
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12 | 1.1.1.5. The client reads back the fit results from the fitting object. |
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13 | |
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14 | |
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15 | 1.1.2. Alternative Flows |
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16 | |
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17 | Any of the following actions can take place: |
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18 | |
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19 | 1.1.2.1. The client does not enter initial parameters |
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20 | |
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21 | |
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22 | 1.1.3. Error Conditions |
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23 | 1.1.3.1. The user does not choose a data set before starting the fit. |
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24 | 1.1.3.2. The user does not choose a model before starting the fit. |
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25 | |
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26 | |
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27 | 1.2. Special requirements |
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28 | 1.2.1. Preconditions |
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29 | None |
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30 | |
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31 | 1.2.2. Post Conditions |
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32 | The client now has a fitting object that contains the results of his fit. |
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33 | He can modify that object and perform another fit. |
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34 | It will be possible for him to use the fit output as the initial parameters |
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35 | for any subsequent fit. |
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36 | |
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37 | |
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38 | 2. Use Case 2: 2 data sets and 1 model |
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39 | |
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40 | This use case describes an application (the client) performing a fit on multiple |
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41 | data sets with a single model. |
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42 | |
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43 | 2.1. Flow of Events |
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44 | 2.1.1. Basic Flow |
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45 | 2.1.1.1. The client chooses two data sets to fit. |
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46 | 2.1.1.2. The client chooses a model to use. |
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47 | 2.1.1.3. The client enters the initial parameters of the model. |
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48 | 2.1.1.4. The client starts the fit. |
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49 | 2.1.1.5. The client reads back the fit results from the fitting object. |
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50 | |
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51 | |
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52 | 2.1.2. Alternative Flows |
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53 | |
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54 | Any of the following actions can take place: |
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55 | |
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56 | 2.1.2.1. The client does not enter initial parameters |
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57 | |
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58 | |
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59 | 2.1.3. Error Conditions |
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60 | 2.1.3.1. The user does not choose a data set before starting the fit. |
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61 | 2.1.3.2. The user does not choose a model before starting the fit. |
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62 | |
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63 | |
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64 | 2.2. Special requirements |
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65 | 2.2.1. Preconditions |
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66 | None |
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67 | |
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68 | 2.2.2. Post Conditions |
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69 | The client now has a fitting object that contains the results of his fit. |
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70 | He can modify that object and perform another fit. |
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71 | It will be possible for him to use the fit output as the initial parameters |
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72 | for any subsequent fit. |
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73 | |
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74 | |
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75 | 3. Use Case 3: 2 data sets, 2 models and one constraint |
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76 | |
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77 | This use case describes an application (the client) performing a fit on multiple |
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78 | data sets with a single model. |
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79 | |
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80 | 3.1. Flow of Events |
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81 | 3.1.1. Basic Flow |
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82 | 3.1.1.1. The client chooses two data sets to fit. |
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83 | 3.1.1.2. The client chooses one model to use with each data set. |
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84 | 3.1.1.3. The client enters the initial parameters of the models. |
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85 | 3.1.1.4 The client enters a string describing a condition between the two models. |
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86 | 3.1.1.5. The client starts the fit. |
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87 | 3.1.1.6. The client reads back the fit results from the fitting object. |
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88 | |
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89 | |
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90 | 3.1.2. Alternative Flows |
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91 | |
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92 | Any of the following actions can take place: |
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93 | |
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94 | 3.1.2.1. The client does not enter initial parameters for one or both models |
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95 | |
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96 | |
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97 | 3.1.3. Error Conditions |
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98 | 3.1.3.1. The user does not choose a data set before starting the fit. |
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99 | 3.1.3.2. The user does not choose a model before starting the fit. |
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100 | 3.1.3.3. The user does not choose a model for one of the data sets. |
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101 | |
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102 | |
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103 | 3.2. Special requirements |
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104 | 3.2.1. Preconditions |
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105 | None |
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106 | |
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107 | 3.2.2. Post Conditions |
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108 | The client now has a fitting object that contains the results of his fit. |
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109 | He can modify that object and perform another fit. |
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110 | It will be possible for him to use the fit output as the initial parameters |
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111 | for any subsequent fit. |
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112 | |
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113 | Note: The test-case for this should use the testdata_generator module in the |
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114 | test directory and use two models: |
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115 | 1- A Line: Y = A*x + B for the file "testdata_line.txt" |
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116 | 2- A constant: Y = A for the file "testdata_cst.txt" |
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117 | The constraint should be that parameter "A" has the same value for both models. |
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118 | |
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119 | |
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120 | |
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121 | |
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