198 lines
6.2 KiB
Python
198 lines
6.2 KiB
Python
# Copyright 2015-2017 ARM Limited
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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"""Process the output of the cpu_cooling devices in the current
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directory's trace.dat"""
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import pandas as pd
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from trappy.base import Base
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from trappy.dynamic import register_ftrace_parser
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def pivot_with_labels(dfr, data_col_name, new_col_name, mapping_label):
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"""Pivot a :mod:`pandas.DataFrame` row into columns
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:param dfr: The :mod:`pandas.DataFrame` to operate on.
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:param data_col_name: The name of the column in the :mod:`pandas.DataFrame`
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which contains the values.
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:param new_col_name: The name of the column in the :mod:`pandas.DataFrame` that will
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become the new columns.
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:param mapping_label: A dictionary whose keys are the values in
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new_col_name and whose values are their
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corresponding name in the :mod:`pandas.DataFrame` to be returned.
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:type dfr: :mod:`pandas.DataFrame`
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:type data_col_name: str
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:type new_col_name: str
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:type mapping_label: dict
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Example:
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>>> dfr_in = pd.DataFrame({'cpus': ["000000f0",
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>>> "0000000f",
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>>> "000000f0",
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>>> "0000000f"
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>>> ],
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>>> 'freq': [1, 3, 2, 6]})
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>>> dfr_in
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cpus freq
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0 000000f0 1
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1 0000000f 3
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2 000000f0 2
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3 0000000f 6
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>>> map_label = {"000000f0": "A15", "0000000f": "A7"}
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>>> power.pivot_with_labels(dfr_in, "freq", "cpus", map_label)
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A15 A7
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0 1 NaN
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1 1 3
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2 2 3
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3 2 6
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"""
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# There has to be a more "pandas" way of doing this.
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col_set = set(dfr[new_col_name])
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ret_series = {}
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for col in col_set:
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try:
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label = mapping_label[col]
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except KeyError:
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available_keys = ", ".join(mapping_label.keys())
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error_str = '"{}" not found, available keys: {}'.format(col,
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available_keys)
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raise KeyError(error_str)
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data = dfr[dfr[new_col_name] == col][data_col_name]
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ret_series[label] = data
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return pd.DataFrame(ret_series).fillna(method="pad")
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def num_cpus_in_mask(mask):
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"""Return the number of cpus in a cpumask"""
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mask = mask.replace(",", "")
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value = int(mask, 16)
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return bin(value).count("1")
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class CpuOutPower(Base):
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"""Process the cpufreq cooling power actor data in a ftrace dump"""
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unique_word = "thermal_power_cpu_limit"
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"""The unique word that will be matched in a trace line"""
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name = "cpu_out_power"
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"""The name of the :mod:`pandas.DataFrame` member that will be created in a
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:mod:`trappy.ftrace.FTrace` object"""
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pivot = "cpus"
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"""The Pivot along which the data is orthogonal"""
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def get_all_freqs(self, mapping_label):
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"""Get a :mod:`pandas.DataFrame` with the maximum frequencies allowed by the governor
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:param mapping_label: A dictionary that maps cpumasks to name
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of the cpu.
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:type mapping_label: dict
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:return: freqs are in MHz
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"""
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dfr = self.data_frame
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return pivot_with_labels(dfr, "freq", "cpus", mapping_label) / 1000
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register_ftrace_parser(CpuOutPower, "thermal")
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class CpuInPower(Base):
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"""Process the cpufreq cooling power actor data in a ftrace dump
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"""
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unique_word = "thermal_power_cpu_get"
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"""The unique word that will be matched in a trace line"""
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name = "cpu_in_power"
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"""The name of the :mod:`pandas.DataFrame` member that will be created in a
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:mod:`trappy.ftrace.FTrace` object"""
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pivot = "cpus"
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"""The Pivot along which the data is orthogonal"""
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def _get_load_series(self):
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"""get a :mod:`pandas.Series` with the aggregated load"""
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dfr = self.data_frame
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load_cols = [s for s in dfr.columns if s.startswith("load")]
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load_series = dfr[load_cols[0]].copy()
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for col in load_cols[1:]:
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load_series += dfr[col]
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return load_series
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def get_load_data(self, mapping_label):
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"""Return :mod:`pandas.DataFrame` suitable for plot_load()
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:param mapping_label: A Dictionary mapping cluster cpumasks to labels
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:type mapping_label: dict
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"""
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dfr = self.data_frame
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load_series = self._get_load_series()
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load_dfr = pd.DataFrame({"cpus": dfr["cpus"], "load": load_series})
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return pivot_with_labels(load_dfr, "load", "cpus", mapping_label)
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def get_normalized_load_data(self, mapping_label):
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"""Return a :mod:`pandas.DataFrame` for plotting normalized load data
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:param mapping_label: should be a dictionary mapping cluster cpumasks
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to labels
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:type mapping_label: dict
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"""
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dfr = self.data_frame
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load_series = self._get_load_series()
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load_series *= dfr['freq']
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for cpumask in mapping_label:
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num_cpus = num_cpus_in_mask(cpumask)
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idx = dfr["cpus"] == cpumask
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max_freq = max(dfr[idx]["freq"])
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load_series[idx] = load_series[idx] / (max_freq * num_cpus)
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load_dfr = pd.DataFrame({"cpus": dfr["cpus"], "load": load_series})
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return pivot_with_labels(load_dfr, "load", "cpus", mapping_label)
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def get_all_freqs(self, mapping_label):
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"""get a :mod:`pandas.DataFrame` with the "in" frequencies as seen by the governor
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.. note::
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Frequencies are in MHz
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"""
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dfr = self.data_frame
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return pivot_with_labels(dfr, "freq", "cpus", mapping_label) / 1000
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register_ftrace_parser(CpuInPower, "thermal")
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