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* major update to the gui * bug fix in gui * function to generate gui config file * switched from argparser to sys.argv * docstring added for gui_config.py * bug fixs * bumping version number and editing README GUI description * gui configuration files for released models * flake8 tidy * bug fix in gui_config/update to T_release gui_config file * kwarg catches added to gui_config and relevant doc strings to docs * bug fixes and tests for gui_config * bug fix in tests
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,8 @@ | ||
| names,mins,maxs,label_min,label_max,logs,xHI | ||
| $\log(f_*)$,-3.4579971262630043,-0.3010299956639812,-246.84562,32.171596,0,False | ||
| $\log(V_c)$,0.6232492903979004,1.8836614351536176,,,1, | ||
| $\log(f_X)$,-6.0,0.9977593286204041,,,2, | ||
| $\tau$,0.05550117,0.09999531,,,--, | ||
| $\alpha$,1.0,1.5,,,--, | ||
| $\nu_\mathrm{min}$,0.1,3.0,,,--, | ||
| $R_\mathrm{mfp}$,10.0,50.0,,,--, |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,129 @@ | ||
| """ | ||
| This function can be used to generate a configurate file for the GUI that | ||
| is specific to a given trained model. The file gets saved into the supplied | ||
| ``base_dir`` which should contain the relevant trained model. The user | ||
| also needs to supply a path to the ``data_dir`` that contains the relevant | ||
| testing and training data. Additional arguments are described below. | ||
| A GUI config file is required to be able to visualise the signals with the | ||
| GUI and once generated the gui can be run from the command line | ||
| .. code:: bash | ||
| globalemu /path/to/base_dir/containing/model/and/config/ | ||
| """ | ||
|
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| import numpy as np | ||
| import pandas as pd | ||
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| class config(): | ||
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| r""" | ||
| **Parameters:** | ||
| base_dir: **string** | ||
| | The path to the file containing the trained tensorflow model | ||
| that the user wishes to visualise with the GUI. Must end | ||
| in '/'. | ||
| paramnames: **list of strings** | ||
| | This should be a list of parameter names in the correct input | ||
| order. For example for the released global signal model this | ||
| would correspond to | ||
| .. code: python | ||
| paramnames = [r'$\log(f_*)$', r'$\log(V_c)$', | ||
| r'$\log(f_X)$', | ||
| r'$\tau$', r'$\alpha$', | ||
| r'$\nu_\mathrm{min}$', | ||
| r'$R_\mathrm{mfp}$'] | ||
| Latex strings can be provided as above. | ||
| data_dir: **string** | ||
| | The file path to the training and test data which is used to set | ||
| the y lims of the GUI graph and ranges/intervals of GUI | ||
| sliders. | ||
| **Kwargs:** | ||
| xHI: **Bool / default: False** | ||
| | If True then ``globalemu`` will act as if it is evaluating a | ||
| neutral fraction history emulator. | ||
| logs: **list / default: [0, 1, 2]** | ||
| | The indices corresponding to the astrophysical | ||
| parameters that | ||
| were logged during training. The default assumes | ||
| that the first three columns in "train_data.txt" are | ||
| :math:`{f_*}` (star formation efficiency), | ||
| :math:`{V_c}` (minimum virial circular velocity) and | ||
| :math:`{f_x}` (X-ray efficieny). | ||
| """ | ||
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| def __init__(self, base_dir, paramnames, data_dir, **kwargs): | ||
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| for key, values in kwargs.items(): | ||
| if key not in set( | ||
| ['xHI', 'logs']): | ||
| raise KeyError("Unexpected keyword argument in process()") | ||
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| self.base_dir = base_dir | ||
| self.paramnames = paramnames | ||
| self.data_dir = data_dir | ||
| self.logs = kwargs.pop('logs', [0, 1, 2]) | ||
| self.xHI = kwargs.pop('xHI', False) | ||
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| file_kwargs = [self.base_dir, self.data_dir] | ||
| file_strings = ['base_dir', 'data_dir'] | ||
| for i in range(len(file_kwargs)): | ||
| if type(file_kwargs[i]) is not str: | ||
| raise TypeError("'" + file_strings[i] + "' must be a sting.") | ||
| elif file_kwargs[i].endswith('/') is False: | ||
| raise KeyError("'" + file_strings[i] + "' must end with '/'.") | ||
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| if type(self.paramnames) is not list: | ||
| raise TypeError("'paramnames' must be a list of strings.") | ||
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| if type(self.xHI) is not bool: | ||
| raise TypeError("'xHI' must be a bool.") | ||
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| if type(self.logs) is not list: | ||
| raise TypeError("'logs' must be a list.") | ||
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| test_data = np.loadtxt(data_dir + 'test_data.txt') | ||
| test_labels = np.loadtxt(data_dir + 'test_labels.txt') | ||
| for i in range(test_data.shape[1]): | ||
| if i in self.logs: | ||
| for j in range(test_data.shape[0]): | ||
| if test_data[j, i] == 0: | ||
| test_data[j, i] = 1e-6 | ||
| test_data[:, i] = np.log10(test_data[:, i]) | ||
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| data_mins = test_data.min(axis=0) | ||
| data_maxs = test_data.max(axis=0) | ||
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| full_logs = [] | ||
| for i in range(len(data_maxs)): | ||
| if i in set(self.logs): | ||
| full_logs.append(i) | ||
| else: | ||
| full_logs.append('--') | ||
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| df = pd.DataFrame({'names': self.paramnames, | ||
| 'mins': data_mins, | ||
| 'maxs': data_maxs, | ||
| 'label_min': | ||
| [test_labels.min()] + ['']*(len(data_maxs)-1), | ||
| 'label_max': | ||
| [test_labels.max()] + ['']*(len(data_maxs)-1), | ||
| 'logs': full_logs, | ||
| 'xHI': | ||
| [self.xHI] + ['']*(len(data_maxs)-1)}) | ||
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| df.to_csv(base_dir + 'gui_configuration.csv', index=False) |
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