content: #
  # Copyright 2013 Quantopian, Inc.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  import datetime
  
  import numpy as np
  import pandas as pd
  from copy import deepcopy
  
  
  def _ensure_index(x):
      if not isinstance(x, pd.Index):
          x = pd.Index(sorted(x))
  
      return x
  
  
  class RollingPanel(object):
      """
      Preallocation strategies for rolling window over expanding data set
  
      Restrictions: major_axis can only be a DatetimeIndex for now
      """
  
      def __init__(self,
                   window,
                   items,
                   sids,
                   cap_multiple=2,
                   dtype=np.float64,
                   initial_dates=None):
  
          self._pos = window
          self._window = window
  
          self.items = _ensure_index(items)
          self.minor_axis = _ensure_index(sids)
  
          self.cap_multiple = cap_multiple
  
          self.dtype = dtype
          if initial_dates is None:
              self.date_buf = np.empty(self.cap, dtype='M8[ns]') * pd.NaT
          elif len(initial_dates) != window:
              raise ValueError('initial_dates must be of length window')
          else:
              self.date_buf = np.hstack(
                  (
                      initial_dates,
                      np.empty(
                          window * (cap_multiple - 1),
                          dtype='datetime64[ns]',
                      ),
                  ),
              )
  
          self.buffer = self._create_buffer()
  
      @property
      def cap(self):
          return self.cap_multiple * self._window
  
      @property
      def _start_index(self):
          return self._pos - self._window
  
      @property
      def start_date(self):
          return self.date_buf[self._start_index]
  
      def oldest_frame(self, raw=False):
          """
          Get the oldest frame in the panel.
          """
          if raw:
              return self.buffer.values[:, self._start_index, :]
          return self.buffer.iloc[:, self._start_index, :]
  
      def set_minor_axis(self, minor_axis):
          self.minor_axis = _ensure_index(minor_axis)
          self.buffer = self.buffer.reindex(minor_axis=self.minor_axis)
  
      def set_items(self, items):
          self.items = _ensure_index(items)
          self.buffer = self.buffer.reindex(items=self.items)
  
      def _create_buffer(self):
          panel = pd.Panel(
              items=self.items,
              minor_axis=self.minor_axis,
              major_axis=range(self.cap),
              dtype=self.dtype,
          )
          return panel
  
      def extend_back(self, missing_dts):
          """
          Resizes the buffer to hold a new window with a new cap_multiple.
          If cap_multiple is None, then the old cap_multiple is used.
          """
          delta = len(missing_dts)
  
          if not delta:
              raise ValueError(
                  'missing_dts must be a non-empty index',
              )
  
          self._window += delta
  
          self._pos += delta
  
          self.date_buf = self.date_buf.copy()
          self.date_buf.resize(self.cap)
          self.date_buf = np.roll(self.date_buf, delta)
  
          old_vals = self.buffer.values
          shape = old_vals.shape
          nan_arr = np.empty((shape[0], delta, shape[2]))
          nan_arr.fill(np.nan)
  
          new_vals = np.column_stack(
              (nan_arr,
               old_vals,
               np.empty((shape[0], delta * (self.cap_multiple - 1), shape[2]))),
          )
  
          self.buffer = pd.Panel(
              data=new_vals,
              items=self.items,
              minor_axis=self.minor_axis,
              major_axis=np.arange(self.cap),
              dtype=self.dtype,
          )
  
          # Fill the delta with the dates we calculated.
          where = slice(self._start_index, self._start_index + delta)
          self.date_buf[where] = missing_dts
  
      def add_frame(self, tick, frame, minor_axis=None, items=None):
          """
          """
          if self._pos == self.cap:
              self._roll_data()
  
          values = frame
          if isinstance(frame, pd.DataFrame):
              values = frame.values
  
          self.buffer.values[:, self._pos, :] = values.astype(self.dtype)
          self.date_buf[self._pos] = tick
  
          self._pos += 1
  
      def get_current(self, item=None, raw=False, start=None, end=None):
          """
          Get a Panel that is the current data in view. It is not safe to persist
          these objects because internal data might change
          """
          item_indexer = slice(None)
          if item:
              item_indexer = self.items.get_loc(item)
  
          start_index = self._start_index
          end_index = self._pos
  
          # get inital date window
          where = slice(start_index, end_index)
          current_dates = self.date_buf[where]
  
          def convert_datelike_to_long(dt):
              if isinstance(dt, pd.Timestamp):
                  return dt.asm8
              if isinstance(dt, datetime.datetime):
                  return np.datetime64(dt)
              return dt
  
          # constrict further by date
          if start:
              start = convert_datelike_to_long(start)
              start_index += current_dates.searchsorted(start)
  
          if end:
              end = convert_datelike_to_long(end)
              _end = current_dates.searchsorted(end, 'right')
              end_index -= len(current_dates) - _end
  
          where = slice(start_index, end_index)
  
          values = self.buffer.values[item_indexer, where, :]
          current_dates = self.date_buf[where]
  
          if raw:
              # return copy so we can change it without side effects here
              return values.copy()
  
          major_axis = pd.DatetimeIndex(deepcopy(current_dates), tz='utc')
          if values.ndim == 3:
              return pd.Panel(values, self.items, major_axis, self.minor_axis,
                              dtype=self.dtype)
  
          elif values.ndim == 2:
              return pd.DataFrame(values, major_axis, self.minor_axis,
                                  dtype=self.dtype)
  
      def set_current(self, panel):
          """
          Set the values stored in our current in-view data to be values of the
          passed panel.  The passed panel must have the same indices as the panel
          that would be returned by self.get_current.
          """
          where = slice(self._start_index, self._pos)
          self.buffer.values[:, where, :] = panel.values
  
      def current_dates(self):
          where = slice(self._start_index, self._pos)
          return pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='utc')
  
      def _roll_data(self):
          """
          Roll window worth of data up to position zero.
          Save the effort of having to expensively roll at each iteration
          """
  
          self.buffer.values[:, :self._window, :] = \
              self.buffer.values[:, -self._window:, :]
          self.date_buf[:self._window] = self.date_buf[-self._window:]
          self._pos = self._window
  
      @property
      def window_length(self):
          return self._window
  
  
  class MutableIndexRollingPanel(object):
      """
      A version of RollingPanel that exists for backwards compatibility with
      batch_transform. This is a copy to allow behavior of RollingPanel to drift
      away from this without breaking this class.
  
      This code should be considered frozen, and should not be used in the
      future. Instead, see RollingPanel.
      """
      def __init__(self, window, items, sids, cap_multiple=2, dtype=np.float64):
  
          self._pos = 0
          self._window = window
  
          self.items = _ensure_index(items)
          self.minor_axis = _ensure_index(sids)
  
          self.cap_multiple = cap_multiple
          self.cap = cap_multiple * window
  
          self.dtype = dtype
          self.date_buf = np.empty(self.cap, dtype='M8[ns]')
  
          self.buffer = self._create_buffer()
  
      def _oldest_frame_idx(self):
          return max(self._pos - self._window, 0)
  
      def oldest_frame(self, raw=False):
          """
          Get the oldest frame in the panel.
          """
          if raw:
              return self.buffer.values[:, self._oldest_frame_idx(), :]
          return self.buffer.iloc[:, self._oldest_frame_idx(), :]
  
      def set_sids(self, sids):
          self.minor_axis = _ensure_index(sids)
          self.buffer = self.buffer.reindex(minor_axis=self.minor_axis)
  
      def _create_buffer(self):
          panel = pd.Panel(
              items=self.items,
              minor_axis=self.minor_axis,
              major_axis=range(self.cap),
              dtype=self.dtype,
          )
          return panel
  
      def get_current(self):
          """
          Get a Panel that is the current data in view. It is not safe to persist
          these objects because internal data might change
          """
  
          where = slice(self._oldest_frame_idx(), self._pos)
          major_axis = pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='utc')
          return pd.Panel(self.buffer.values[:, where, :], self.items,
                          major_axis, self.minor_axis, dtype=self.dtype)
  
      def set_current(self, panel):
          """
          Set the values stored in our current in-view data to be values of the
          passed panel.  The passed panel must have the same indices as the panel
          that would be returned by self.get_current.
          """
          where = slice(self._oldest_frame_idx(), self._pos)
          self.buffer.values[:, where, :] = panel.values
  
      def current_dates(self):
          where = slice(self._oldest_frame_idx(), self._pos)
          return pd.DatetimeIndex(deepcopy(self.date_buf[where]), tz='utc')
  
      def _roll_data(self):
          """
          Roll window worth of data up to position zero.
          Save the effort of having to expensively roll at each iteration
          """
  
          self.buffer.values[:, :self._window, :] = \
              self.buffer.values[:, -self._window:, :]
          self.date_buf[:self._window] = self.date_buf[-self._window:]
          self._pos = self._window
  
      def add_frame(self, tick, frame, minor_axis=None, items=None):
          """
          """
          if self._pos == self.cap:
              self._roll_data()
  
          if isinstance(frame, pd.DataFrame):
              minor_axis = frame.columns
              items = frame.index
  
          if set(minor_axis).difference(set(self.minor_axis)) or \
                  set(items).difference(set(self.items)):
              self._update_buffer(frame)
  
          vals = frame.T.astype(self.dtype)
          self.buffer.loc[:, self._pos, :] = vals
          self.date_buf[self._pos] = tick
  
          self._pos += 1
  
      def _update_buffer(self, frame):
  
          # Get current frame as we only need to care about the data that is in
          # the active window
          old_buffer = self.get_current()
          if self._pos >= self._window:
              # Don't count the last major_axis entry if we're past our window,
              # since it's about to roll off the end of the panel.
              old_buffer = old_buffer.iloc[:, 1:, :]
  
          nans = pd.isnull(old_buffer)
  
          # Find minor_axes that have only nans
          # Note that minor is axis 2
          non_nan_cols = set(old_buffer.minor_axis[~np.all(nans, axis=(0, 1))])
          # Determine new columns to be added
          new_cols = set(frame.columns).difference(non_nan_cols)
          # Update internal minor axis
          self.minor_axis = _ensure_index(new_cols.union(non_nan_cols))
  
          # Same for items (fields)
          # Find items axes that have only nans
          # Note that items is axis 0
          non_nan_items = set(old_buffer.items[~np.all(nans, axis=(1, 2))])
          new_items = set(frame.index).difference(non_nan_items)
          self.items = _ensure_index(new_items.union(non_nan_items))
  
          # :NOTE:
          # There is a simpler and 10x faster way to do this:
          #
          # Reindex buffer to update axes (automatically adds nans)
          # self.buffer = self.buffer.reindex(items=self.items,
          #                                   major_axis=np.arange(self.cap),
          #                                   minor_axis=self.minor_axis)
          #
          # However, pandas==0.12.0, for which we remain backwards compatible,
          # has a bug in .reindex() that this triggers. Using .update() as before
          # seems to work fine.
  
          new_buffer = self._create_buffer()
          new_buffer.update(
              self.buffer.loc[non_nan_items, :, non_nan_cols])
  
          self.buffer = new_buffer
  