More unauthorized work

doc/source/_static/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

panel/iv/covariance.py

@@ -347,7 +347,7 @@ class IVGMMCovariance(HomoskedasticCovariance):
         Weighting matrix used in GMM estimation
     """
 
-    def __init__(self, x, y, z, params, w):
+    def __init__(self, x, y, z, params, w, **cov_config):
         super(IVGMMCovariance, self).__init__(x, y, z, params, False)
         self.w = w
 

panel/iv/data.py

@@ -0,0 +1,95 @@
+import numpy as np
+import pandas as pd
+import xarray as xr
+
+dim_err = '{0} has too many dims.  Maximum is 2, actual is {2}'
+type_err = 'Only ndarrays, DataArrays and Series and DataFrames are permitted'
+
+
+def convert_columns(s):
+    if pd.api.types.is_categorical(s):
+        out = pd.get_dummies(s, drop_first=True)
+        out.columns = [s.name + '.' + c for c in out]
+        return out
+    return s
+
+
+def expand_categoricals(x):
+    if isinstance(x, pd.Series):
+        return convert_columns(x)
+    if isinstance(x, pd.DataFrame):
+        return pd.concat([convert_columns(x[c]) for c in x.columns], axis=1)
+    elif isinstance(x, pd.Panel):
+        raise NotImplementedError
+
+
+class DataHandler(object):
+    def __init__(self, x, var_name='x'):
+
+        if isinstance(x, DataHandler):
+            x = x.original
+
+        self.original = x
+        xndim = x.ndim
+        if xndim > 2:
+            raise ValueError(dim_err.format(var_name, xndim))
+
+        if isinstance(x, np.ndarray):
+            x = x.view()
+            if xndim == 1:
+                x.shape = (x.shape[0], -1)
+
+            self._ndarray = x
+            index = list(range(x.shape[0]))
+            cols = [var_name + '.{0}'.format(i) for i in range(x.shape[1])]
+            self._pandas = pd.DataFrame(x, index=index, columns=cols)
+            self._labels = {0: index,
+                            1: cols}
+
+        elif isinstance(x, (pd.Series, pd.DataFrame)):
+            dts = [x.dtype] if xndim == 1 else x.dtypes
+            for dt in dts:
+                if not (pd.api.types.is_numeric_dtype(dt)
+                        or pd.api.types.is_categorical_dtype(dt)):
+                    raise ValueError('Only numeric or categorical data permitted')
+
+            x = expand_categoricals(x)
+            if x.ndim == 1:
+                x = pd.DataFrame({var_name: x})
+
+            self._pandas = x
+            self._ndarray = self._pandas.values
+            self._labels = {i: list(label) for i, label in zip(range(x.ndim), x.axes)}
+
+        elif isinstance(x, xr.DataArray):
+            raise NotImplementedError('Not implemented yet.')
+        else:
+            raise TypeError(type_err)
+
+    @property
+    def pandas(self):
+        return self._pandas
+
+    @property
+    def ndarray(self):
+        return self._ndarray
+
+    @property
+    def shape(self):
+        return self._ndarray.shape
+
+    @property
+    def ndim(self):
+        return self._ndarray.ndim
+
+    @property
+    def cols(self):
+        return self._labels[1]
+
+    @property
+    def rows(self):
+        return self._labels[0]
+
+    @property
+    def labels(self):
+        return self._labels