startop: Rewrite app startup runner to use new python run_app_with_prefetch.

Test: pytest app_startup_runner_test.py
Test: pytest lib/args_utils_test.py
Test: pytest lib/data_frame_test.py
Test: ./app_startup_runner.py  --package com.google.android.music --readahead fadvise --readahead cold   --inodes textcache  --output output.txt -d -lc 3
Change-Id: Ide9abe4ff3d7179e6830a7866b0eb90cc67d8e40
Bug: 137216480

1 files changed, 201 insertions, 0 deletions

diff --git a/startop/scripts/app_startup/lib/data_frame.py b/startop/scripts/app_startup/lib/data_frame.py
new file mode 100644
index 000000000000..20a2308637f2
--- /dev/null
+++ b/startop/scripts/app_startup/lib/data_frame.py
@@ -0,0 +1,201 @@
+import itertools
+from typing import Dict, List
+
+class DataFrame:
+  """Table-like class for storing a 2D cells table with named columns."""
+  def __init__(self, data: Dict[str, List[object]] = {}):
+    """
+    Create a new DataFrame from a dictionary (keys = headers,
+    values = columns).
+    """
+    self._headers = [i for i in data.keys()]
+    self._rows = []
+
+    row_num = 0
+
+    def get_data_row(idx):
+      r = {}
+      for header, header_data in data.items():
+
+        if not len(header_data) > idx:
+          continue
+
+        r[header] = header_data[idx]
+
+      return r
+
+    while True:
+      row_dict = get_data_row(row_num)
+      if len(row_dict) == 0:
+        break
+
+      self._append_row(row_dict.keys(), row_dict.values())
+      row_num = row_num + 1
+
+  def concat_rows(self, other: 'DataFrame') -> None:
+    """
+    In-place concatenate rows of other into the rows of the
+    current DataFrame.
+
+    None is added in pre-existing cells if new headers
+    are introduced.
+    """
+    other_datas = other._data_only()
+
+    other_headers = other.headers
+
+    for d in other_datas:
+      self._append_row(other_headers, d)
+
+  def _append_row(self, headers: List[str], data: List[object]):
+    new_row = {k:v for k,v in zip(headers, data)}
+    self._rows.append(new_row)
+
+    for header in headers:
+      if not header in self._headers:
+        self._headers.append(header)
+
+  def __repr__(self):
+#     return repr(self._rows)
+    repr = ""
+
+    header_list = self._headers_only()
+
+    row_format = u""
+    for header in header_list:
+      row_format = row_format + u"{:>%d}" %(len(header) + 1)
+
+    repr = row_format.format(*header_list) + "\n"
+
+    for v in self._data_only():
+      repr = repr + row_format.format(*v) + "\n"
+
+    return repr
+
+  def __eq__(self, other):
+    if isinstance(other, self.__class__):
+      return self.headers == other.headers and self.data_table == other.data_table
+    else:
+      print("wrong instance", other.__class__)
+      return False
+
+  @property
+  def headers(self) -> List[str]:
+    return [i for i in self._headers_only()]
+
+  @property
+  def data_table(self) -> List[List[object]]:
+    return list(self._data_only())
+
+  @property
+  def data_table_transposed(self) -> List[List[object]]:
+    return list(self._transposed_data())
+
+  @property
+  def data_row_len(self) -> int:
+    return len(self._rows)
+
+  def data_row_at(self, idx) -> List[object]:
+    """
+    Return a single data row at the specified index (0th based).
+
+    Accepts negative indices, e.g. -1 is last row.
+    """
+    row_dict = self._rows[idx]
+    l = []
+
+    for h in self._headers_only():
+      l.append(row_dict.get(h)) # Adds None in blank spots.
+
+    return l
+
+  def copy(self) -> 'DataFrame':
+    """
+    Shallow copy of this DataFrame.
+    """
+    return self.repeat(count=0)
+
+  def repeat(self, count: int) -> 'DataFrame':
+    """
+    Returns a new DataFrame where each row of this dataframe is repeated count times.
+    A repeat of a row is adjacent to other repeats of that same row.
+    """
+    df = DataFrame()
+    df._headers = self._headers.copy()
+
+    rows = []
+    for row in self._rows:
+      for i in range(count):
+        rows.append(row.copy())
+
+    df._rows = rows
+
+    return df
+
+  def merge_data_columns(self, other: 'DataFrame'):
+    """
+    Merge self and another DataFrame by adding the data from other column-wise.
+    For any headers that are the same, data from 'other' is preferred.
+    """
+    for h in other._headers:
+      if not h in self._headers:
+        self._headers.append(h)
+
+    append_rows = []
+
+    for self_dict, other_dict in itertools.zip_longest(self._rows, other._rows):
+      if not self_dict:
+        d = {}
+        append_rows.append(d)
+      else:
+        d = self_dict
+
+      d_other = other_dict
+      if d_other:
+        for k,v in d_other.items():
+          d[k] = v
+
+    for r in append_rows:
+      self._rows.append(r)
+
+  def data_row_reduce(self, fnc) -> 'DataFrame':
+    """
+    Reduces the data row-wise by applying the fnc to each row (column-wise).
+    Empty cells are skipped.
+
+    fnc(Iterable[object]) -> object
+    fnc is applied over every non-empty cell in that column (descending row-wise).
+
+    Example:
+      DataFrame({'a':[1,2,3]}).data_row_reduce(sum) == DataFrame({'a':[6]})
+
+    Returns a new single-row DataFrame.
+    """
+    df = DataFrame()
+    df._headers = self._headers.copy()
+
+    def yield_by_column(header_key):
+      for row_dict in self._rows:
+        val = row_dict.get(header_key)
+        if val:
+          yield val
+
+    new_row_dict = {}
+    for h in df._headers:
+      cell_value = fnc(yield_by_column(h))
+      new_row_dict[h] = cell_value
+
+    df._rows = [new_row_dict]
+    return df
+
+  def _headers_only(self):
+    return self._headers
+
+  def _data_only(self):
+    row_len = len(self._rows)
+
+    for i in range(row_len):
+      yield self.data_row_at(i)
+
+  def _transposed_data(self):
+    return zip(*self._data_only())
\ No newline at end of file