class_docstr: Estimates loader that loads data that needs to be split-adjusted.
  
  Parameters
  ----------
  split_adjustments_loader : SQLiteAdjustmentReader
      The loader to use for reading split adjustments.
  split_adjusted_column_names : iterable of str
      The column names that should be split-adjusted.
  split_adjusted_asof : pd.Timestamp
      The date that separates data into 2 halves: the first half is the set
      of dates up to and including the split_adjusted_asof date. All
      adjustments occurring during this first half are applied  to all
      dates in this first half. The second half is the set of dates after
      the split_adjusted_asof date. All adjustments occurring during this
      second half are applied sequentially as they appear in the timeline.
class_docstr_tok: ["Estimates","loader","that","loads","data","that","needs","to","be","split-adjusted",".","Parameters","--","--","--","--","--","split_adjustments_loader",":","SQLiteAdjustmentReader","The","loader","to","use","for","reading","split","adjustments",".","split_adjusted_column_names",":","iterable","of","str","The","column","names","that","should","be","split-adjusted",".","split_adjusted_asof",":","pd.Timestamp","The","date","that","separates","data","into","2","halves",":","the","first","half","is","the","set","of","dates","up","to","and","including","the","split_adjusted_asof","date",".","All","adjustments","occurring","during","this","first","half","are","applied","to","all","dates","in","this","first","half",".","The","second","half","is","the","set","of","dates","after","the","split_adjusted_asof","date",".","All","adjustments","occurring","during","this","second","half","are","applied","sequentially","as","they","appear","in","the","timeline","."]
docstr: Collects both overwrites and adjustments for a particular sid.
  
  Parameters
  ----------
  split_adjusted_asof_idx : int
      The integer index of the date on which the data was split-adjusted.
  split_adjusted_cols_for_group : list of str
      The names of requested columns that should also be split-adjusted.
docstr_tok: ["Collects","both","overwrites","and","adjustments","for","a","particular","sid",".","Parameters","--","--","--","--","--","split_adjusted_asof_idx",":","int","The","integer","index","of","the","date","on","which","the","data","was","split-adjusted",".","split_adjusted_cols_for_group",":","list","of","str","The","names","of","requested","columns","that","should","also","be","split-adjusted","."]
code: def get_adjustments_for_sid(
      self,
      group,
      dates,
      requested_qtr_data,
      last_per_qtr,
      sid_to_idx,
      columns,
      col_to_all_adjustments,
      split_adjusted_asof_idx=None,
      split_adjusted_cols_for_group=None,
  ):
      """
      Collects both overwrites and adjustments for a particular sid.
  
      Parameters
      ----------
      split_adjusted_asof_idx : int
          The integer index of the date on which the data was split-adjusted.
      split_adjusted_cols_for_group : list of str
          The names of requested columns that should also be split-adjusted.
      """
      all_adjustments_for_sid = {}
      sid = int(group.name)
      self.collect_overwrites_for_sid(
          group,
          dates,
          requested_qtr_data,
          last_per_qtr,
          sid_to_idx[sid],
          columns,
          all_adjustments_for_sid,
          sid,
      )
      (
          pre_adjustments,
          post_adjustments,
      ) = self.retrieve_split_adjustment_data_for_sid(
          dates, sid, split_adjusted_asof_idx
      )
      sid_estimates = self.estimates[
          self.estimates[SID_FIELD_NAME] == sid
      ]
      # We might not have any overwrites but still have
      # adjustments, and we will need to manually add columns if
      # that is the case.
      for col_name in split_adjusted_cols_for_group:
          if col_name not in all_adjustments_for_sid:
              all_adjustments_for_sid[col_name] = {}
  
      self.collect_split_adjustments(
          all_adjustments_for_sid,
          requested_qtr_data,
          dates,
          sid,
          sid_to_idx[sid],
          sid_estimates,
          split_adjusted_asof_idx,
          pre_adjustments,
          post_adjustments,
          split_adjusted_cols_for_group,
      )
      self.merge_into_adjustments_for_all_sids(
          all_adjustments_for_sid, col_to_all_adjustments
      )
  
imports: from abc import abstractmethod, abstractproperty
  import numpy
  import pandas
  from six import viewvalues
  from toolz import groupby
  from catalyst.lib.adjusted_array import AdjustedArray
  from catalyst.lib.adjustment import Datetime641DArrayOverwrite, Datetime64Overwrite, Float641DArrayOverwrite, Float64Multiply, Float64Overwrite
  from catalyst.pipeline.common import EVENT_DATE_FIELD_NAME, FISCAL_QUARTER_FIELD_NAME, FISCAL_YEAR_FIELD_NAME, SID_FIELD_NAME, TS_FIELD_NAME
  from catalyst.pipeline.loaders.base import PipelineLoader
  from catalyst.utils.numpy_utils import datetime64ns_dtype, float64_dtype
  from catalyst.pipeline.loaders.utils import ffill_across_cols, last_in_date_group
label_desc: Use image node_id 3 for calling the SplitAdjustedEstimatesLoader obj's underlying member method code with example usage: obj.get_adjustments_for_sid(group, dates, requested_qtr_data, last_per_qtr, sid_to_idx, columns, col_to_all_adjustments, split_adjusted_asof_idx, split_adjusted_cols_for_group) without return types