[103a0b0] | 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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