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How to use profile_methods method in Airtest

Best Python code snippet using Airtest

generate_method_whitelist.py

Source:generate_method_whitelist.py

1# Copyright 2015 The Chromium Authors. All rights reserved.2# Use of this source code is governed by a BSD-style license that can be3# found in the LICENSE file.4#5# Script to generate mods/android/frameworks/base/compiled-methods.6"""7In order to generate all the files needed to run this script:8* Obtain the list of compilable methods:9  - Compile ARC in debug mode without method whitelist:10    `./configure -t=nx --disable-method-whitelist && ninja -j200`11  - Extract the symbols from boot.oat:12    `nm -a out/target/nacl_x86_64_dbg_intermediates/boot_art/boot.oat` and save13    that to a file.14* Obtain the list of methods used during boot:15  - Compile ARC without AOT:16    `./configure -t=nx --disable-art-aot --logging=java-methods && ninja -j200`17  - Run HelloAndroid and redirect stderr to a file.18* Profile several Android applications:19  - Compile ARC in release mode:20    `./configure -t=nx --official-build && ninja -j200`21  - Follow `docs/profiling.md`, "Profiling Java code" section to obtain22    profiles and process the results.23* After compiling boot.oat with the updated whitelist, be sure to update the24  address at which the image will be loaded in mods/android/art/config.py.25"""26import argparse27import collections28import re29import sys30_JAVA_METHOD_ENTER = re.compile('^java_methods:\s+\d+\s+->\s+(.*?)$')31_SYMBOL_RE = re.compile(r'^([0-9a-f]+) T (.*?)(?: \[ DEDUPED \])?$')32_TRACE_RE = re.compile(r'^\s+(\d+)\s+(\d+\.\d+)\s+(\d+\.\d+)\s+\[\d+\]\s+' +33                       r'([^ \t]+)\s+\(([^)]*)\)([^ \t]+)')34_TYPE_SIGNATURE_TO_JAVA_TYPE = {'B': 'byte',35                                'C': 'char',36                                'D': 'double',37                                'F': 'float',38                                'I': 'int',39                                'J': 'long',40                                'S': 'short',41                                'V': 'void',42                                'Z': 'boolean'}43def _parse_java_type_signature(s, idx=0):44  type_signature = s[idx]45  java_type = _TYPE_SIGNATURE_TO_JAVA_TYPE.get(type_signature)46  if java_type is not None:47    return (java_type, idx + 1)48  elif type_signature == 'L':49    semicolon = s.find(';', idx)50    return (s[idx + 1:semicolon].replace('/', '.'), semicolon + 1)51  else:52    assert type_signature == '[', s53    result, idx = _parse_java_type_signature(s, idx + 1)54    return (result + '[]', idx)55def _parse_java_type_signature_list(s):56  typelist = []57  idx = 058  l = len(s)59  while idx < l:60    typename, idx = _parse_java_type_signature(s, idx)61    typelist.append(typename)62  return typelist63def _read_symbol_file(symbol_file):64  method_addresses = collections.defaultdict(list)65  compilable_methods = set()66  with open(symbol_file) as f:67    for line in f:68      address, method_name = _SYMBOL_RE.match(line).groups()69      compilable_methods.add(method_name)70      method_addresses[address].append(method_name)71  return compilable_methods, method_addresses72def _deduplicate_methods(method_addresses):73  """Obtain a set of all de-duplicated methods.74  ART has a mechanism to save space by emitting identical code just once and75  making all symbols/callers point to that version. That means that for no76  additional cost, we can have more methods be compiled. In practice, only about77  10% of the methods returned by this function will actually emit code, for a78  grand total of about 1MB."""79  compiled_methods = set()80  for address, method_names in method_addresses.iteritems():81    if len(method_names) > 1:82      # This function only considers duplicated methods, so an address should83      # have more than one assigned methods to be considered in this step.84      compiled_methods.update(method_names)85  return compiled_methods86def _read_boot_methods(boot_methods_file):87  """Parse a logfile to obtain all methods called during boot.88  Given that ARC tries to optimize boot time significantly, every single Java89  function that is called during boot time should be compiled (if possible).90  This parses a log, generated with --logging=java-methods, to create a set of91  methods that will always be invoked no matter the application being run."""92  boot_methods = set()93  with open(boot_methods_file) as f:94    for line in f:95      match = _JAVA_METHOD_ENTER.match(line)96      if not match:97        continue98      boot_methods.add(match.group(1))99  return boot_methods100def _read_profile(profile):101  """Read an Android profile summary to optimize for costly methods.102  In addition to optimize for boot time, some expensive runtime methods should103  also be included in the boot image. Currently we can add all methods found in104  all profiles without going above the size budget."""105  profile_methods = set()106  with open(profile) as f:107    for line in f:108      match = _TRACE_RE.match(line)109      if not match:110        continue111      calls, exclusive, aggregate, name, params, return_type = match.groups()112      calls = int(calls)113      exclusive = float(exclusive)114      aggregate = float(aggregate)115      params = _parse_java_type_signature_list(params)116      return_type = _parse_java_type_signature_list(return_type)[0]117      profile_methods.add('%s %s(%s)' % (return_type, name, ', '.join(params)))118  return profile_methods119def _write_methods(output, methods, name, emitted_methods):120  filtered_methods = (methods - emitted_methods)121  if filtered_methods:122    print >> output, '# %s' % name123    for method in sorted(filtered_methods):124      print >> output, method125    emitted_methods.update(filtered_methods)126def _generate_whitelist_file(parsed_args):127  print >> parsed_args.output, '# Generated with generate_method_whitelist.py.'128  print >> parsed_args.output, '# Do not edit.'129  compilable_methods, method_addresses = _read_symbol_file(130      parsed_args.symbol_file)131  emitted_methods = set()132  deduplicated_methods = _deduplicate_methods(method_addresses)133  _write_methods(parsed_args.output, deduplicated_methods,134                 'Deduplicated methods', emitted_methods)135  boot_methods = _read_boot_methods(parsed_args.boot_methods_file)136  _write_methods(parsed_args.output, boot_methods & compilable_methods,137                 'Boot methods', emitted_methods)138  profile_methods = set()139  for profile in parsed_args.profiles:140    profile_methods |= _read_profile(profile)141  _write_methods(parsed_args.output, profile_methods & compilable_methods,142                 'Profile methods', emitted_methods)143def _open_output(value):144  return open(value, 'w')145def _parse_args(argv):146  parser = argparse.ArgumentParser()147  parser.add_argument('--output', default=sys.stdout, type=_open_output,148                      help='Write the generated whitelist to a file. '149                      'The default is stdout')150  parser.add_argument('symbol_file', metavar='<symbol-file>',151                      help='A file with all the symbols in boot.oat. This can '152                      'be generated by compiling ARC in debug mode and then '153                      'running `nm -a <path to>/boot.oat`.')154  parser.add_argument('boot_methods_file', metavar='<boot-methods-file>',155                      help='A file with all the methods called during boot. '156                      'This can be generated by compiling ARC with the '157                      '--disable-art-aot and --logging=java-methods flags '158                      'and then launching HelloAndroid.')159  parser.add_argument('profiles', metavar='<profile>', nargs='*', default=[],160                      help='A file with a Java profile for an application. '161                      'This can be generated by compiling ARC in release mode '162                      'and launching it with the --java-trace-startup=<N> flag.'163                      'The trace then needs to be grabbed from the device '164                      'with `adb` and the trace needs to be processed with '165                      '`dmtracedump`. See `docs/profiling.md` for more info.')166  return parser.parse_args(argv)167def main():168  parsed_args = _parse_args(sys.argv[1:])169  return _generate_whitelist_file(parsed_args)170if __name__ == '__main__':...

curtailment.py

Source:curtailment.py

1import pandas as pd2from powersimdata.scenario.scenario import Scenario3default_pmin_dict = {4    "coal": None,5    "dfo": 0,6    "geothermal": 0.95,7    "hydro": None,8    "ng": 0,9    "nuclear": 0.95,10    "other": 0,11    "solar": 0,12    "wind": 0,13    "wind_offshore": 0,14}15profile_methods = {16    "hydro": "get_hydro",17    "solar": "get_solar",18    "wind": "get_wind",19    "wind_offshore": "get_wind",20}21def temporal_curtailment(22    scenario, pmin_by_type=None, pmin_by_id=None, curtailable={"solar", "wind"}23):24    """Calculate the minimum share of potential renewable energy that will be curtailed25        due to supply/demand mismatch, assuming no storage is present.26    :param powersimdata.scenario.scenario.Scenario scenario: scenario instance.27    :param dict/pandas.Series pmin_by_type: Mapping of types to Pmin assumptions. Values28        between 0 and 1 (inclusive) are treated as shares of Pmax, and None values29        either maintain given Pmin values (for dispatchable resources) or track profiles30        (for profile resources, e.g. hydro).31    :param dict/pandas.Series pmin_by_id: Mapping of IDs to Pmin assumptions, as an32        override to the default behavior for that plant type. Values between 0 and 133        (inclusive) are treated as shares of Pmax, and None values either maintain given34        Pmin values (for dispatchable resources) or track profiles35        (for profile resources, e.g. hydro).36    :param iterable curtailable: resource types which can be curtailed.37    :return: (*float*) -- share of curtailable resources that will be curtailed.38    :raises TypeError: if inputs do not mach specified type.39    :raises ValueError: if any entries in curtailable or keys in pmin_by_type are not40        types in the grid, any keys in pmin_by_id are not plant IDs in the Grid,41        or any values in pmin_by_type/pmin_by_id are not in the range [0, 1] or None.42    """43    if not isinstance(scenario, Scenario):44        raise TypeError("scenario must be a Scenario")45    if pmin_by_type is None:46        pmin_by_type = {}47    if pmin_by_id is None:48        pmin_by_id = {}49    check_dicts = {"pmin_by_id": pmin_by_id, "pmin_by_type": pmin_by_type}50    for name, d in check_dicts.items():51        if not isinstance(d, (dict, pd.Series)):52            raise TypeError(f"{name} must be a dict or pandas Series")53        # Access values via appropriate method whether d is a dict or a pandas Series54        values = d.values() if isinstance(d, dict) else d.values55        if not all([v is None or 0 <= v <= 1 for v in values]):56            err_msg = f"all entries in {name} must be None or in the range [0, 1]"57            raise ValueError(err_msg)58    plant = scenario.state.get_grid().plant59    valid_types = plant["type"].unique()60    if not set(pmin_by_type.keys()) <= set(valid_types):61        raise ValueError("Got invalid plant type as a key to pmin_by_type")62    if not set(pmin_by_id.keys()) <= set(plant.index):63        raise ValueError("Got invalid plant id as a key to pmin_by_id")64    try:65        if not set(curtailable) <= set(valid_types):66            raise ValueError("Got invalid plant type within curtailable")67    except TypeError:68        raise TypeError("curtailable must be an iterable")69    # Get profiles, filter out plant-level overrides, then sum70    all_profiles = pd.concat(71        [getattr(scenario.state, m)() for m in set(profile_methods.values())], axis=172    )73    plant_id_mask = ~plant.index.isin(pmin_by_id.keys())74    base_plant_ids_by_type = plant.loc[plant_id_mask].groupby("type").groups75    valid_profile_types = set(base_plant_ids_by_type) & set(profile_methods)76    plant_ids_for_summed_profiles = set().union(77        *[set(base_plant_ids_by_type[g]) for g in valid_profile_types]78    )79    summed_profiles = (80        all_profiles[list(plant_ids_for_summed_profiles)]81        .groupby(plant["type"], axis=1)82        .sum()83    )84    # Build up a series of firm generation85    summed_demand = scenario.state.get_demand().sum(axis=1)86    firm_generation = pd.Series(0, index=summed_demand.index)87    # Add plants without plant-level overrides ('base' plants)88    pmin_dict = {**default_pmin_dict, **pmin_by_type}89    # Don't iterate over plant types not present in this grid90    pmin_dict = {k: pmin_dict[k] for k in base_plant_ids_by_type}91    for resource, pmin in pmin_dict.items():92        if (resource in curtailable) or (pmin == 0):93            continue94        if pmin is None:95            if resource in profile_methods:96                firm_generation += summed_profiles[resource]97            else:98                summed_pmin = plant.Pmin.loc[base_plant_ids_by_type[resource]].sum()99                firm_generation += pd.Series(summed_pmin, index=summed_demand.index)100        else:101            summed_pmin = pmin * plant.Pmax.loc[base_plant_ids_by_type[resource]].sum()102            firm_generation += pd.Series(summed_pmin, index=summed_demand.index)103    # Add plants with plant-level overrides104    for plant_id, pmin in pmin_by_id.items():105        if pmin == 0:106            continue107        if pmin is None:108            if plant.loc[plant_id, "type"] in profile_methods:109                firm_generation += all_profiles[plant_id]110            else:111                plant_pmin = plant.loc[plant_id, "Pmin"]112                firm_generation += pd.Series(plant_pmin, index=summed_demand.index)113        else:114            plant_pmin = plant.loc[plant_id, "Pmax"] * pmin115            firm_generation += pd.Series(plant_pmin, index=summed_demand.index)116    # Finally, compare this summed firm generation against summed curtailable generation117    total_curtailable = summed_profiles[list(curtailable)].sum(axis=1)118    net_demand = summed_demand - firm_generation119    curtailable_max_gen = pd.concat([net_demand, total_curtailable], axis=1).min(axis=1)120    curtailment_fraction = 1 - curtailable_max_gen.sum() / total_curtailable.sum()...

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