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# SPDX-License-Identifier: Apache-2.0
#
# Copyright (C) 2017, ARM Limited, Google, and contributors.
#
# 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.
#
from trace import Trace
import pandas as pd
import matplotlib.pyplot as plt
from analysis_module import AnalysisModule
from devlib.utils.misc import memoized
class BinderTransactionAnalysis(AnalysisModule):
"""
An analysis wrapper for visualizing binder transactions.
This class is currently used to plot transaction buffer
sizes and queuing delays.
"""
to_micro_second = 1000000
def __init__(self, trace):
"""
Initialized by the directory that contains systrace output
:param trace: input Trace object
:type trace: :mod:`libs.utils.Trace`
"""
super(BinderTransactionAnalysis, self).__init__(trace)
@memoized
def _dfg_alloc_df(self):
"""
Get a dataframe that captures the time spent in a transaction
allocation and the size of the buffer allocated sorted by time.
Transaction and transaction_alloc_buf dataframes are joined
on transaction(debug_id)
Example of df returned:
transaction (debug_id) | pid | delta_t | size
"""
df_start = self._dfg_trace_event("binder_transaction")
df_start["start_time"] = df_start.index
df_end = self._dfg_trace_event("binder_transaction_alloc_buf")
df_end["end_time"] = df_end.index
df = pd.merge(df_start, df_end, on="transaction")
df = df[["transaction", "__comm_x", "__pid_x",
"start_time", "end_time",
"data_size", "offsets_size"]]
df["delta_t"] = (df["end_time"] - df["start_time"]) \
* BinderTransactionAnalysis.to_micro_second
df["size"] = df["data_size"] - df["offsets_size"]
df = df.loc[df["__comm_x"] == "binderThroughpu"] \
[["transaction", "__pid_x", "delta_t", "size"]].sort("delta_t")
return df
@memoized
def _dfg_queue_df(self):
"""
Get a dataframe that captures start time, end time,
and the delta between when a transaction is issued and
when it is received by the target.
Transaction and transaction_received dataframes are joined
on transaction(debug_id)
Example df:
transaction (debug_id) | name | start | end | delta
"""
df_send = self._dfg_trace_event("binder_transaction")
df_send["start_time"] = df_send.index
df_recv = self._dfg_trace_event("binder_transaction_received")
df_recv["end_time"] = df_recv.index
df = pd.merge(df_send, df_recv, on="transaction")
df = df[["transaction", "__comm_x", "start_time", "end_time"]]
df["delta_t"] = (df["end_time"] - df["start_time"]) \
* BinderTransactionAnalysis.to_micro_second
return df
def plot_samples(self, df, y_axis, xlabel, ylabel,
ymin=0, ymax=None, x_axis="index"):
"""
Generate a plot that features the distribution of y_axis column
in the given dataframe. x_axis represents the sample points.
:param y_axis: column name of the dataframe we want to plot
:type y_axis: str
:param xlabel: label that appears on the plot's x-axis
:type xlabel: str
:param ylabel: label that appears on the plot's y-axis
:type ylabel: str
"""
df_sorted = df.sort_values(by=y_axis, ascending=True)
df_sorted[x_axis] = range(len(df_sorted.index))
df_sorted.plot(kind="scatter", x=x_axis, y=y_axis)
ax = plt.gca()
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
ax.set_ylim(ymin=ymin)
if ymax:
ax.set_ylim(ymax=ymax)
plt.show()
def plot_tasks(self, df, threshold, x_axis, y_axis, xlabel, ylabel):
"""
Generate a plot that features the tasks whose y_axis column
in the dataframe is above a certain threshold.
:param x_axis: column name of the dataframe we want to group
together and use as the x-axis index in the plot
:type x_axis: str
:param y_axis: column name of the dataframe we want to plot
:type y_axis: str
:param xlabel: label that appears on the plot's x-axis
:type xlabel: str
:param ylabel: label that appears on the plot's y-axis
:type ylabel: str
"""
df_sorted = df.sort_values(by=y_axis, ascending=False)
df_top = df_sorted[df_sorted[y_axis] > threshold]\
.groupby(x_axis).head(1)
df_top.plot(kind="bar", y=y_axis, x=x_axis)
ax = plt.gca()
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
plt.show()
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