Loading startop/scripts/app_startup/app_startup_runner.py +261 −8 Original line number Diff line number Diff line Loading @@ -192,7 +192,206 @@ def generate_group_run_combinations(run_combinations: Iterable[NamedTuple], dst_ (group_key_value, args_it) = args_list_it yield (group_key_value, args_it) def parse_run_script_csv_file(csv_file: TextIO) -> List[int]: 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()) def parse_run_script_csv_file_flat(csv_file: TextIO) -> List[int]: """Parse a CSV file full of integers into a flat int list.""" csv_reader = csv.reader(csv_file) arr = [] Loading @@ -202,6 +401,38 @@ def parse_run_script_csv_file(csv_file: TextIO) -> List[int]: arr.append(int(i)) return arr def parse_run_script_csv_file(csv_file: TextIO) -> DataFrame: """Parse a CSV file full of integers into a DataFrame.""" csv_reader = csv.reader(csv_file) try: header_list = next(csv_reader) except StopIteration: header_list = [] if not header_list: return None headers = [i for i in header_list] d = {} for row in csv_reader: header_idx = 0 for i in row: v = i if i: v = int(i) header_key = headers[header_idx] l = d.get(header_key, []) l.append(v) d[header_key] = l header_idx = header_idx + 1 return DataFrame(d) def make_script_command_with_temp_output(script: str, args: List[str], **kwargs)\ -> Tuple[str, TextIO]: """ Loading Loading @@ -338,27 +569,49 @@ def execute_run_combos(grouped_run_combos: Iterable[Tuple[CollectorPackageInfo, cmd, tmp_output_file = make_script_command_with_temp_output(_RUN_SCRIPT, args, count=loop_count, input=collector_tmp_output_file.name) with tmp_output_file: (passed, script_output) = execute_arbitrary_command(cmd, simulate, timeout) parsed_output = simulate and [1,2,3] or parse_run_script_csv_file(tmp_output_file) parsed_output = simulate and DataFrame({'fake_ms':[1,2,3]}) or parse_run_script_csv_file(tmp_output_file) yield (passed, script_output, parsed_output) def gather_results(commands: Iterable[Tuple[bool, str, List[int]]], key_list: List[str], value_list: List[Tuple[str, ...]]): def gather_results(commands: Iterable[Tuple[bool, str, DataFrame]], key_list: List[str], value_list: List[Tuple[str, ...]]): _debug_print("gather_results: key_list = ", key_list) yield key_list + ["time(ms)"] # yield key_list + ["time(ms)"] stringify_none = lambda s: s is None and "<none>" or s for ((passed, script_output, run_result_list), values) in itertools.zip_longest(commands, value_list): _debug_print("run_result_list = ", run_result_list) _debug_print("values = ", values) if not passed: continue for result in run_result_list: yield [stringify_none(i) for i in values] + [result] yield ["; avg(%s), min(%s), max(%s), count(%s)" %(sum(run_result_list, 0.0) / len(run_result_list), min(run_result_list), max(run_result_list), len(run_result_list)) ] # RunCommandArgs(package='com.whatever', readahead='warm', compiler_filter=None) # -> {'package':['com.whatever'], 'readahead':['warm'], 'compiler_filter':[None]} values_dict = {k:[v] for k,v in values._asdict().items()} values_df = DataFrame(values_dict) # project 'values_df' to be same number of rows as run_result_list. values_df = values_df.repeat(run_result_list.data_row_len) # the results are added as right-hand-side columns onto the existing labels for the table. values_df.merge_data_columns(run_result_list) yield values_df def eval_and_save_to_csv(output, annotated_result_values): printed_header = False csv_writer = csv.writer(output) for row in annotated_result_values: csv_writer.writerow(row) if not printed_header: headers = row.headers csv_writer.writerow(headers) printed_header = True # TODO: what about when headers change? for data_row in row.data_table: csv_writer.writerow(data_row) output.flush() # see the output live. def main(): Loading startop/scripts/app_startup/app_startup_runner_test.py +125 −4 Original line number Diff line number Diff line Loading @@ -192,19 +192,140 @@ def test_make_script_command_with_temp_output(): with tmp_file: assert cmd_str == ["fake_script", "a", "b", "--count", "2", "--output", tmp_file.name] def test_parse_run_script_csv_file(): def test_parse_run_script_csv_file_flat(): # empty file -> empty list f = io.StringIO("") assert asr.parse_run_script_csv_file(f) == [] assert asr.parse_run_script_csv_file_flat(f) == [] # common case f = io.StringIO("1,2,3") assert asr.parse_run_script_csv_file(f) == [1,2,3] assert asr.parse_run_script_csv_file_flat(f) == [1,2,3] # ignore trailing comma f = io.StringIO("1,2,3,4,5,") assert asr.parse_run_script_csv_file(f) == [1,2,3,4,5] assert asr.parse_run_script_csv_file_flat(f) == [1,2,3,4,5] def test_data_frame(): # trivial empty data frame df = asr.DataFrame() assert df.headers == [] assert df.data_table == [] assert df.data_table_transposed == [] # common case, same number of values in each place. df = asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[4,5,6]}) assert df.headers == ['TotalTime_ms', 'Displayed_ms'] assert df.data_table == [[1, 4], [2, 5], [3, 6]] assert df.data_table_transposed == [(1, 2, 3), (4, 5, 6)] # varying num values. df = asr.DataFrame({'many':[1,2], 'none': []}) assert df.headers == ['many', 'none'] assert df.data_table == [[1, None], [2, None]] assert df.data_table_transposed == [(1, 2), (None, None)] df = asr.DataFrame({'many':[], 'none': [1,2]}) assert df.headers == ['many', 'none'] assert df.data_table == [[None, 1], [None, 2]] assert df.data_table_transposed == [(None, None), (1, 2)] # merge multiple data frames df = asr.DataFrame() df.concat_rows(asr.DataFrame()) assert df.headers == [] assert df.data_table == [] assert df.data_table_transposed == [] df = asr.DataFrame() df2 = asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[4,5,6]}) df.concat_rows(df2) assert df.headers == ['TotalTime_ms', 'Displayed_ms'] assert df.data_table == [[1, 4], [2, 5], [3, 6]] assert df.data_table_transposed == [(1, 2, 3), (4, 5, 6)] df = asr.DataFrame({'TotalTime_ms':[1,2]}) df2 = asr.DataFrame({'Displayed_ms':[4,5]}) df.concat_rows(df2) assert df.headers == ['TotalTime_ms', 'Displayed_ms'] assert df.data_table == [[1, None], [2, None], [None, 4], [None, 5]] df = asr.DataFrame({'TotalTime_ms':[1,2]}) df2 = asr.DataFrame({'TotalTime_ms': [3, 4], 'Displayed_ms':[5, 6]}) df.concat_rows(df2) assert df.headers == ['TotalTime_ms', 'Displayed_ms'] assert df.data_table == [[1, None], [2, None], [3, 5], [4, 6]] # data_row_at df = asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[4,5,6]}) assert df.data_row_at(-1) == [3,6] assert df.data_row_at(2) == [3,6] assert df.data_row_at(1) == [2,5] # repeat df = asr.DataFrame({'TotalTime_ms':[1], 'Displayed_ms':[4]}) df2 = asr.DataFrame({'TotalTime_ms':[1,1,1], 'Displayed_ms':[4,4,4]}) assert df.repeat(3) == df2 # repeat df = asr.DataFrame({'TotalTime_ms':[1,1,1], 'Displayed_ms':[4,4,4]}) assert df.data_row_len == 3 df = asr.DataFrame({'TotalTime_ms':[1,1]}) assert df.data_row_len == 2 # repeat df = asr.DataFrame({'TotalTime_ms':[1,1,1], 'Displayed_ms':[4,4,4]}) assert df.data_row_len == 3 df = asr.DataFrame({'TotalTime_ms':[1,1]}) assert df.data_row_len == 2 # data_row_reduce df = asr.DataFrame({'TotalTime_ms':[1,1,1], 'Displayed_ms':[4,4,4]}) df_sum = asr.DataFrame({'TotalTime_ms':[3], 'Displayed_ms':[12]}) assert df.data_row_reduce(sum) == df_sum # merge_data_columns df = asr.DataFrame({'TotalTime_ms':[1,2,3]}) df2 = asr.DataFrame({'Displayed_ms':[3,4,5,6]}) df.merge_data_columns(df2) assert df == asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[3,4,5,6]}) df = asr.DataFrame({'TotalTime_ms':[1,2,3]}) df2 = asr.DataFrame({'Displayed_ms':[3,4]}) df.merge_data_columns(df2) assert df == asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[3,4]}) df = asr.DataFrame({'TotalTime_ms':[1,2,3]}) df2 = asr.DataFrame({'TotalTime_ms':[10,11]}) df.merge_data_columns(df2) assert df == asr.DataFrame({'TotalTime_ms':[10,11,3]}) df = asr.DataFrame({'TotalTime_ms':[]}) df2 = asr.DataFrame({'TotalTime_ms':[10,11]}) df.merge_data_columns(df2) assert df == asr.DataFrame({'TotalTime_ms':[10,11]}) def test_parse_run_script_csv_file(): # empty file -> empty list f = io.StringIO("") assert asr.parse_run_script_csv_file(f) == None # common case f = io.StringIO("TotalTime_ms,Displayed_ms\n1,2") df = asr.DataFrame({'TotalTime_ms': [1], 'Displayed_ms': [2]}) pf = asr.parse_run_script_csv_file(f) assert pf == df if __name__ == '__main__': pytest.main() startop/scripts/app_startup/launch_application +2 −15 Original line number Diff line number Diff line Loading @@ -39,22 +39,9 @@ launch_application() { # if the $ is not escaped, adb shell will try to evaluate $HomeActivity to a variable. activity=${activity//\$/\\$} local am_output="$(adb shell am start -S -W "$package"/"$activity")" verbose_print adb shell am start -S -W "$package"/"$activity" if [[ $? -ne 0 ]]; then echo "am start failed" >&2 adb shell am start -S -W "$package"/"$activity" return 1 fi # for everything else use the am start "TotalTime" output. verbose_print "$am_output" local total_time="$(echo "$am_output" | grep 'TotalTime:' | sed 's/TotalTime: //g')" verbose_print "total time: $total_time" # TODO: Extract alternative metrics such as the #reportFullyDrawn. echo "$total_time" # pipe this into 'parse_metrics' to parse the output. } if [[ $# -lt 2 ]]; then Loading startop/scripts/app_startup/lib/common +46 −12 Original line number Diff line number Diff line Loading @@ -83,17 +83,7 @@ logcat_save_timestamp() { # # First use 'logcat_save_timestamp' # Then do whatever action you want. # Then us 'logcat_from_timestamp $timestamp' logcat_from_timestamp() { local timestamp="$1" shift # drop timestamp from args. echo "DONT CALL THIS FUNCTION" >&2 exit 1 verbose_print adb logcat -T \"$timestamp\" \"$@\" adb logcat -T "$timestamp" "$@" } # Then use 'logcat_from_timestamp_bg $timestamp' logcat_from_timestamp_bg() { local timestamp="$1" shift # drop timestamp from args. Loading @@ -104,9 +94,10 @@ logcat_from_timestamp_bg() { # Starting at timestamp $2, wait until we seen pattern $3 # or until a timeout happens in $1 seconds. # If successful, also echo the line that matched the pattern. # # Set VERBOSE_LOGCAT=1 to debug every line of logcat it tries to parse. logcat_wait_for_pattern() { logcat_select_pattern() { local timeout="$1" local timestamp="$2" local pattern="$3" Loading Loading @@ -143,6 +134,7 @@ logcat_wait_for_pattern() { if [[ "$logcat_output:" == *"$pattern"* ]]; then verbose_print "LOGCAT: " "$logcat_output" verbose_print "WE DID SEE PATTERN" '<<' "$pattern" '>>.' echo "$logcat_output" return_code=0 break fi Loading @@ -162,3 +154,45 @@ logcat_wait_for_pattern() { return $return_code } # Starting at timestamp $2, wait until we seen pattern $3 # or until a timeout happens in $1 seconds. # # Set VERBOSE_LOGCAT=1 to debug every line of logcat it tries to parse. logcat_wait_for_pattern() { logcat_select_pattern "$@" > /dev/null } # Starting at timestamp $2, wait until we seen pattern $3 # or until a timeout happens in $1 seconds. # If successful, extract with the regular expression pattern in #4 # and return the first capture group. # # Set VERBOSE_LOGCAT=1 to debug every line of logcat it tries to parse. logcat_extract_pattern() { local timeout="$1" local timestamp="$2" local pattern="$3" local re_pattern="$4" local result local exit_code result="$(logcat_select_pattern "$@")" exit_code=$? if [[ $exit_code -ne 0 ]]; then return $exit_code fi echo "$result" | sed 's/'"$re_pattern"'/\1/g' } # Join array # FOO=(a b c) # join_by , "${FOO[@]}" #a,b,c join_by() { local IFS="$1" shift echo "$*" } startop/scripts/app_startup/metrics/com.google.android.GoogleCamera 0 → 100755 +25 −0 Original line number Diff line number Diff line #!/bin/bash # # Copyright 2019, The Android Open Source Project # # 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. # 05-06 14:51:17.688 29691 29897 I CAM_Timing: CameraActivity: START -> ACTIVITY_FIRST_PREVIEW_FRAME_RECEIVED: 385ms pattern="CAM_Timing: CameraActivity: START -> ACTIVITY_FIRST_PREVIEW_FRAME_RECEIVED:" re_pattern='.*ACTIVITY_FIRST_PREVIEW_FRAME_RECEIVED: \([[:digit:]]\+\)ms' parse_metric_from_logcat "ACTIVITY_FIRST_PREVIEW_FRAME_RECEIVED_ms" "$pattern" "$re_pattern" # 05-06 14:51:17.724 29691 29691 I CAM_Timing: CameraActivity: START -> ACTIVITY_FIRST_PREVIEW_FRAME_RENDERED: 421ms pattern="CAM_Timing: CameraActivity: START -> ACTIVITY_FIRST_PREVIEW_FRAME_RENDERED:" re_pattern='.*ACTIVITY_FIRST_PREVIEW_FRAME_RENDERED: \([[:digit:]]\+\)ms' parse_metric_from_logcat "ACTIVITY_FIRST_PREVIEW_FRAME_RENDERED_ms" "$pattern" "$re_pattern" Loading
startop/scripts/app_startup/app_startup_runner.py +261 −8 Original line number Diff line number Diff line Loading @@ -192,7 +192,206 @@ def generate_group_run_combinations(run_combinations: Iterable[NamedTuple], dst_ (group_key_value, args_it) = args_list_it yield (group_key_value, args_it) def parse_run_script_csv_file(csv_file: TextIO) -> List[int]: 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()) def parse_run_script_csv_file_flat(csv_file: TextIO) -> List[int]: """Parse a CSV file full of integers into a flat int list.""" csv_reader = csv.reader(csv_file) arr = [] Loading @@ -202,6 +401,38 @@ def parse_run_script_csv_file(csv_file: TextIO) -> List[int]: arr.append(int(i)) return arr def parse_run_script_csv_file(csv_file: TextIO) -> DataFrame: """Parse a CSV file full of integers into a DataFrame.""" csv_reader = csv.reader(csv_file) try: header_list = next(csv_reader) except StopIteration: header_list = [] if not header_list: return None headers = [i for i in header_list] d = {} for row in csv_reader: header_idx = 0 for i in row: v = i if i: v = int(i) header_key = headers[header_idx] l = d.get(header_key, []) l.append(v) d[header_key] = l header_idx = header_idx + 1 return DataFrame(d) def make_script_command_with_temp_output(script: str, args: List[str], **kwargs)\ -> Tuple[str, TextIO]: """ Loading Loading @@ -338,27 +569,49 @@ def execute_run_combos(grouped_run_combos: Iterable[Tuple[CollectorPackageInfo, cmd, tmp_output_file = make_script_command_with_temp_output(_RUN_SCRIPT, args, count=loop_count, input=collector_tmp_output_file.name) with tmp_output_file: (passed, script_output) = execute_arbitrary_command(cmd, simulate, timeout) parsed_output = simulate and [1,2,3] or parse_run_script_csv_file(tmp_output_file) parsed_output = simulate and DataFrame({'fake_ms':[1,2,3]}) or parse_run_script_csv_file(tmp_output_file) yield (passed, script_output, parsed_output) def gather_results(commands: Iterable[Tuple[bool, str, List[int]]], key_list: List[str], value_list: List[Tuple[str, ...]]): def gather_results(commands: Iterable[Tuple[bool, str, DataFrame]], key_list: List[str], value_list: List[Tuple[str, ...]]): _debug_print("gather_results: key_list = ", key_list) yield key_list + ["time(ms)"] # yield key_list + ["time(ms)"] stringify_none = lambda s: s is None and "<none>" or s for ((passed, script_output, run_result_list), values) in itertools.zip_longest(commands, value_list): _debug_print("run_result_list = ", run_result_list) _debug_print("values = ", values) if not passed: continue for result in run_result_list: yield [stringify_none(i) for i in values] + [result] yield ["; avg(%s), min(%s), max(%s), count(%s)" %(sum(run_result_list, 0.0) / len(run_result_list), min(run_result_list), max(run_result_list), len(run_result_list)) ] # RunCommandArgs(package='com.whatever', readahead='warm', compiler_filter=None) # -> {'package':['com.whatever'], 'readahead':['warm'], 'compiler_filter':[None]} values_dict = {k:[v] for k,v in values._asdict().items()} values_df = DataFrame(values_dict) # project 'values_df' to be same number of rows as run_result_list. values_df = values_df.repeat(run_result_list.data_row_len) # the results are added as right-hand-side columns onto the existing labels for the table. values_df.merge_data_columns(run_result_list) yield values_df def eval_and_save_to_csv(output, annotated_result_values): printed_header = False csv_writer = csv.writer(output) for row in annotated_result_values: csv_writer.writerow(row) if not printed_header: headers = row.headers csv_writer.writerow(headers) printed_header = True # TODO: what about when headers change? for data_row in row.data_table: csv_writer.writerow(data_row) output.flush() # see the output live. def main(): Loading
startop/scripts/app_startup/app_startup_runner_test.py +125 −4 Original line number Diff line number Diff line Loading @@ -192,19 +192,140 @@ def test_make_script_command_with_temp_output(): with tmp_file: assert cmd_str == ["fake_script", "a", "b", "--count", "2", "--output", tmp_file.name] def test_parse_run_script_csv_file(): def test_parse_run_script_csv_file_flat(): # empty file -> empty list f = io.StringIO("") assert asr.parse_run_script_csv_file(f) == [] assert asr.parse_run_script_csv_file_flat(f) == [] # common case f = io.StringIO("1,2,3") assert asr.parse_run_script_csv_file(f) == [1,2,3] assert asr.parse_run_script_csv_file_flat(f) == [1,2,3] # ignore trailing comma f = io.StringIO("1,2,3,4,5,") assert asr.parse_run_script_csv_file(f) == [1,2,3,4,5] assert asr.parse_run_script_csv_file_flat(f) == [1,2,3,4,5] def test_data_frame(): # trivial empty data frame df = asr.DataFrame() assert df.headers == [] assert df.data_table == [] assert df.data_table_transposed == [] # common case, same number of values in each place. df = asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[4,5,6]}) assert df.headers == ['TotalTime_ms', 'Displayed_ms'] assert df.data_table == [[1, 4], [2, 5], [3, 6]] assert df.data_table_transposed == [(1, 2, 3), (4, 5, 6)] # varying num values. df = asr.DataFrame({'many':[1,2], 'none': []}) assert df.headers == ['many', 'none'] assert df.data_table == [[1, None], [2, None]] assert df.data_table_transposed == [(1, 2), (None, None)] df = asr.DataFrame({'many':[], 'none': [1,2]}) assert df.headers == ['many', 'none'] assert df.data_table == [[None, 1], [None, 2]] assert df.data_table_transposed == [(None, None), (1, 2)] # merge multiple data frames df = asr.DataFrame() df.concat_rows(asr.DataFrame()) assert df.headers == [] assert df.data_table == [] assert df.data_table_transposed == [] df = asr.DataFrame() df2 = asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[4,5,6]}) df.concat_rows(df2) assert df.headers == ['TotalTime_ms', 'Displayed_ms'] assert df.data_table == [[1, 4], [2, 5], [3, 6]] assert df.data_table_transposed == [(1, 2, 3), (4, 5, 6)] df = asr.DataFrame({'TotalTime_ms':[1,2]}) df2 = asr.DataFrame({'Displayed_ms':[4,5]}) df.concat_rows(df2) assert df.headers == ['TotalTime_ms', 'Displayed_ms'] assert df.data_table == [[1, None], [2, None], [None, 4], [None, 5]] df = asr.DataFrame({'TotalTime_ms':[1,2]}) df2 = asr.DataFrame({'TotalTime_ms': [3, 4], 'Displayed_ms':[5, 6]}) df.concat_rows(df2) assert df.headers == ['TotalTime_ms', 'Displayed_ms'] assert df.data_table == [[1, None], [2, None], [3, 5], [4, 6]] # data_row_at df = asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[4,5,6]}) assert df.data_row_at(-1) == [3,6] assert df.data_row_at(2) == [3,6] assert df.data_row_at(1) == [2,5] # repeat df = asr.DataFrame({'TotalTime_ms':[1], 'Displayed_ms':[4]}) df2 = asr.DataFrame({'TotalTime_ms':[1,1,1], 'Displayed_ms':[4,4,4]}) assert df.repeat(3) == df2 # repeat df = asr.DataFrame({'TotalTime_ms':[1,1,1], 'Displayed_ms':[4,4,4]}) assert df.data_row_len == 3 df = asr.DataFrame({'TotalTime_ms':[1,1]}) assert df.data_row_len == 2 # repeat df = asr.DataFrame({'TotalTime_ms':[1,1,1], 'Displayed_ms':[4,4,4]}) assert df.data_row_len == 3 df = asr.DataFrame({'TotalTime_ms':[1,1]}) assert df.data_row_len == 2 # data_row_reduce df = asr.DataFrame({'TotalTime_ms':[1,1,1], 'Displayed_ms':[4,4,4]}) df_sum = asr.DataFrame({'TotalTime_ms':[3], 'Displayed_ms':[12]}) assert df.data_row_reduce(sum) == df_sum # merge_data_columns df = asr.DataFrame({'TotalTime_ms':[1,2,3]}) df2 = asr.DataFrame({'Displayed_ms':[3,4,5,6]}) df.merge_data_columns(df2) assert df == asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[3,4,5,6]}) df = asr.DataFrame({'TotalTime_ms':[1,2,3]}) df2 = asr.DataFrame({'Displayed_ms':[3,4]}) df.merge_data_columns(df2) assert df == asr.DataFrame({'TotalTime_ms':[1,2,3], 'Displayed_ms':[3,4]}) df = asr.DataFrame({'TotalTime_ms':[1,2,3]}) df2 = asr.DataFrame({'TotalTime_ms':[10,11]}) df.merge_data_columns(df2) assert df == asr.DataFrame({'TotalTime_ms':[10,11,3]}) df = asr.DataFrame({'TotalTime_ms':[]}) df2 = asr.DataFrame({'TotalTime_ms':[10,11]}) df.merge_data_columns(df2) assert df == asr.DataFrame({'TotalTime_ms':[10,11]}) def test_parse_run_script_csv_file(): # empty file -> empty list f = io.StringIO("") assert asr.parse_run_script_csv_file(f) == None # common case f = io.StringIO("TotalTime_ms,Displayed_ms\n1,2") df = asr.DataFrame({'TotalTime_ms': [1], 'Displayed_ms': [2]}) pf = asr.parse_run_script_csv_file(f) assert pf == df if __name__ == '__main__': pytest.main()
startop/scripts/app_startup/launch_application +2 −15 Original line number Diff line number Diff line Loading @@ -39,22 +39,9 @@ launch_application() { # if the $ is not escaped, adb shell will try to evaluate $HomeActivity to a variable. activity=${activity//\$/\\$} local am_output="$(adb shell am start -S -W "$package"/"$activity")" verbose_print adb shell am start -S -W "$package"/"$activity" if [[ $? -ne 0 ]]; then echo "am start failed" >&2 adb shell am start -S -W "$package"/"$activity" return 1 fi # for everything else use the am start "TotalTime" output. verbose_print "$am_output" local total_time="$(echo "$am_output" | grep 'TotalTime:' | sed 's/TotalTime: //g')" verbose_print "total time: $total_time" # TODO: Extract alternative metrics such as the #reportFullyDrawn. echo "$total_time" # pipe this into 'parse_metrics' to parse the output. } if [[ $# -lt 2 ]]; then Loading
startop/scripts/app_startup/lib/common +46 −12 Original line number Diff line number Diff line Loading @@ -83,17 +83,7 @@ logcat_save_timestamp() { # # First use 'logcat_save_timestamp' # Then do whatever action you want. # Then us 'logcat_from_timestamp $timestamp' logcat_from_timestamp() { local timestamp="$1" shift # drop timestamp from args. echo "DONT CALL THIS FUNCTION" >&2 exit 1 verbose_print adb logcat -T \"$timestamp\" \"$@\" adb logcat -T "$timestamp" "$@" } # Then use 'logcat_from_timestamp_bg $timestamp' logcat_from_timestamp_bg() { local timestamp="$1" shift # drop timestamp from args. Loading @@ -104,9 +94,10 @@ logcat_from_timestamp_bg() { # Starting at timestamp $2, wait until we seen pattern $3 # or until a timeout happens in $1 seconds. # If successful, also echo the line that matched the pattern. # # Set VERBOSE_LOGCAT=1 to debug every line of logcat it tries to parse. logcat_wait_for_pattern() { logcat_select_pattern() { local timeout="$1" local timestamp="$2" local pattern="$3" Loading Loading @@ -143,6 +134,7 @@ logcat_wait_for_pattern() { if [[ "$logcat_output:" == *"$pattern"* ]]; then verbose_print "LOGCAT: " "$logcat_output" verbose_print "WE DID SEE PATTERN" '<<' "$pattern" '>>.' echo "$logcat_output" return_code=0 break fi Loading @@ -162,3 +154,45 @@ logcat_wait_for_pattern() { return $return_code } # Starting at timestamp $2, wait until we seen pattern $3 # or until a timeout happens in $1 seconds. # # Set VERBOSE_LOGCAT=1 to debug every line of logcat it tries to parse. logcat_wait_for_pattern() { logcat_select_pattern "$@" > /dev/null } # Starting at timestamp $2, wait until we seen pattern $3 # or until a timeout happens in $1 seconds. # If successful, extract with the regular expression pattern in #4 # and return the first capture group. # # Set VERBOSE_LOGCAT=1 to debug every line of logcat it tries to parse. logcat_extract_pattern() { local timeout="$1" local timestamp="$2" local pattern="$3" local re_pattern="$4" local result local exit_code result="$(logcat_select_pattern "$@")" exit_code=$? if [[ $exit_code -ne 0 ]]; then return $exit_code fi echo "$result" | sed 's/'"$re_pattern"'/\1/g' } # Join array # FOO=(a b c) # join_by , "${FOO[@]}" #a,b,c join_by() { local IFS="$1" shift echo "$*" }
startop/scripts/app_startup/metrics/com.google.android.GoogleCamera 0 → 100755 +25 −0 Original line number Diff line number Diff line #!/bin/bash # # Copyright 2019, The Android Open Source Project # # 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. # 05-06 14:51:17.688 29691 29897 I CAM_Timing: CameraActivity: START -> ACTIVITY_FIRST_PREVIEW_FRAME_RECEIVED: 385ms pattern="CAM_Timing: CameraActivity: START -> ACTIVITY_FIRST_PREVIEW_FRAME_RECEIVED:" re_pattern='.*ACTIVITY_FIRST_PREVIEW_FRAME_RECEIVED: \([[:digit:]]\+\)ms' parse_metric_from_logcat "ACTIVITY_FIRST_PREVIEW_FRAME_RECEIVED_ms" "$pattern" "$re_pattern" # 05-06 14:51:17.724 29691 29691 I CAM_Timing: CameraActivity: START -> ACTIVITY_FIRST_PREVIEW_FRAME_RENDERED: 421ms pattern="CAM_Timing: CameraActivity: START -> ACTIVITY_FIRST_PREVIEW_FRAME_RENDERED:" re_pattern='.*ACTIVITY_FIRST_PREVIEW_FRAME_RENDERED: \([[:digit:]]\+\)ms' parse_metric_from_logcat "ACTIVITY_FIRST_PREVIEW_FRAME_RENDERED_ms" "$pattern" "$re_pattern"
