remove old data and notebooks. add some code and revamp license and r…

…eadme.

.gitignore

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+*.pyc
+.coverage
+build
+dist
+*.egg-info

LICENSE.txt

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+MIT License
+
+Copyright (c) 2021 Berkeley Radio Astronomy Lab
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# <span style="font-variant:small-caps;">calamity</span>
+<span style="font-variant:small-caps;">calamity</span> is a frequency regulated self-cal strategy that simultaneously fits for calibration solutions with foregrounds that are described by a well understood / behaved set of  basis vectors. Examples of basis sets that do not incorporate inter-baseline correlations are DPSS vectors and DFT vectors. <span style="font-variant:small-caps;">calamity</span> can also handle fitting modes with inter-baseline correlations to reduce sample variance and potentially recover modes inside of the wedge.

calamity/calamity.py

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+import numpy as np
+from uvtools import dspec
+import tensorflow as tf
+from pyuvdata import UVData, UVCal
+
+OPTIMIZERS = {'Adadelta': tf.optimizers.Adadelta, 'Adam': tf.optimizers.Adam, 'Adamax':tf.optimizers.Adamax,
+              'Ftrl': tf.optimizers.Ftrl, 'Nadam':tf.optimizers.Nadam, 'SGD':tf.optimizers.SGD, 'RMSprop': tf.optimizers.RMSprop}
+
+
+def calibrate_data(uvdata, foreground_basis_vectors, fg0=None, g0=None, weights=None,
+                   foreground_coefficients=None, optimizer='Adamax', tol=1e-14, maxsteps=10000, **opt_kwargs):
+    """A foreground loss function
+
+    Parameters
+    ----------
+    uvdata: UVData object
+        uvdata objet of data to be calibrated.
+    foreground_coefficients: array-like
+        Nfg foreground coefficients
+    foreground_basis_vectros: array-like
+        (Nbls x Nfrequency) x Nfg 2d tensor of foreground basis vectors.
+    fg0: array-like
+        Nfg len complex 1d array of coefficients to serve as initial values for fg
+        optimization.
+    g0: array-like
+        Nant x Nfreqs len complex 1d array of initial gain values.
+    optimizer: string
+        Name of optimizer. See OPTIMIZERS dictionary
+        default is 'Adamax'
+    tol: float, optional
+        halting condition for optimizer loop. Stop loop when the change in the cost function falls
+        below tol.
+        default is 1e-14
+    maxsteps: int, optional
+        maximum number of opt.minimize calls before halting.
+        default is 10000
+
+    Returns
+    -------
+    uvdata_model: UVData object
+        uvdata object containing model of the foregrounds multiplied by the gains
+        (uncalibrated data). This model is mean to be subtracted from the data
+        before applying gain solutions.
+    uvcal_model: UVCal object
+        uvcal object containing estimates of the gain solutions. These solutions
+        are not referenced to any sky model and are likely orders of
+    fitting_info:
+        dictionary containing fit history with fields:
+        'loss_history': list of values of the loss function in each minimization iteration.
+        if record_var_history is True:
+            each of the following fields is included which points to an Nstep x Nvar array of values
+            'fg_r': real part of foreground coefficients
+            'fg_i': imag part of foreground coefficients
+            'gr': real part of gains.
+            'gi': imag part of gains
+    """