@@ -34,7 +34,7 @@ use std::{
Arc,
},
};
-use util::ResultExt;
+use util::{measure, ResultExt};
mod element_cx;
pub use element_cx::*;
@@ -310,7 +310,9 @@ impl Window {
platform_window.on_request_frame(Box::new({
let mut cx = cx.to_async();
move || {
- handle.update(&mut cx, |_, cx| cx.draw()).log_err();
+ measure("frame duration", || {
+ handle.update(&mut cx, |_, cx| cx.draw()).log_err();
+ })
}
}));
platform_window.on_resize(Box::new({
@@ -7,19 +7,21 @@ pub mod paths;
#[cfg(any(test, feature = "test-support"))]
pub mod test;
+pub use backtrace::Backtrace;
+use futures::Future;
+use lazy_static::lazy_static;
+use rand::{seq::SliceRandom, Rng};
use std::{
borrow::Cow,
cmp::{self, Ordering},
+ env,
ops::{AddAssign, Range, RangeInclusive},
panic::Location,
pin::Pin,
task::{Context, Poll},
+ time::Instant,
};
-pub use backtrace::Backtrace;
-use futures::Future;
-use rand::{seq::SliceRandom, Rng};
-
pub use take_until::*;
#[macro_export]
@@ -133,6 +135,24 @@ pub fn merge_non_null_json_value_into(source: serde_json::Value, target: &mut se
}
}
+pub fn measure<R>(label: &str, f: impl FnOnce() -> R) -> R {
+ lazy_static! {
+ pub static ref ZED_MEASUREMENTS: bool = env::var("ZED_MEASUREMENTS")
+ .map(|measurements| measurements == "1" || measurements == "true")
+ .unwrap_or(false);
+ }
+
+ if *ZED_MEASUREMENTS {
+ let start = Instant::now();
+ let result = f();
+ let elapsed = start.elapsed();
+ eprintln!("{}: {:?}", label, elapsed);
+ result
+ } else {
+ f()
+ }
+}
+
pub trait ResultExt<E> {
type Ok;
@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+
+# Required dependencies for this script:
+#
+# pandas: For data manipulation and analysis.
+# matplotlib: For creating static, interactive, and animated visualizations in Python.
+# seaborn: For making statistical graphics in Python, based on matplotlib.
+
+# To install these dependencies, use the following pip command:
+# pip install pandas matplotlib seaborn
+
+# This script is designed to parse log files for performance measurements and create histograms of these measurements.
+# It expects log files to contain lines with measurements in the format "measurement: timeunit" where timeunit can be in milliseconds (ms) or microseconds (µs).
+# Lines that do not contain a colon ':' are skipped.
+# The script takes one or more file paths as command-line arguments, parses each log file, and then combines the data into a single DataFrame.
+# It then converts all time measurements into milliseconds, discards the original time and unit columns, and creates histograms for each unique measurement type.
+# The histograms display the distribution of times for each measurement, separated by log file, and normalized to show density rather than count.
+# To use this script, run it from the command line with the log file paths as arguments, like so:
+# python this_script.py log1.txt log2.txt ...
+# The script will then parse the provided log files and display the histograms for each type of measurement found.
+
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+import sys
+
+def parse_log_file(file_path):
+ data = {'measurement': [], 'time': [], 'unit': [], 'log_file': []}
+ with open(file_path, 'r') as file:
+ for line in file:
+ if ':' not in line:
+ continue
+
+ parts = line.strip().split(': ')
+ if len(parts) != 2:
+ continue
+
+ measurement, time_with_unit = parts[0], parts[1]
+ if 'ms' in time_with_unit:
+ time, unit = time_with_unit[:-2], 'ms'
+ elif 'µs' in time_with_unit:
+ time, unit = time_with_unit[:-2], 'µs'
+ else:
+ raise ValueError(f"Invalid time unit in line: {line.strip()}")
+ continue
+
+ data['measurement'].append(measurement)
+ data['time'].append(float(time))
+ data['unit'].append(unit)
+ data['log_file'].append(file_path.split('/')[-1])
+ return pd.DataFrame(data)
+
+def create_histograms(df, measurement):
+ filtered_df = df[df['measurement'] == measurement]
+ plt.figure(figsize=(12, 6))
+ sns.histplot(data=filtered_df, x='time_ms', hue='log_file', element='step', stat='density', common_norm=False, palette='bright')
+ plt.title(f'Histogram of {measurement}')
+ plt.xlabel('Time (ms)')
+ plt.ylabel('Density')
+ plt.grid(True)
+ plt.xlim(filtered_df['time_ms'].quantile(0.01), filtered_df['time_ms'].quantile(0.99))
+ plt.show()
+
+
+file_paths = sys.argv[1:]
+dfs = [parse_log_file(path) for path in file_paths]
+combined_df = pd.concat(dfs, ignore_index=True)
+combined_df['time_ms'] = combined_df.apply(lambda row: row['time'] if row['unit'] == 'ms' else row['time'] / 1000, axis=1)
+combined_df.drop(['time', 'unit'], axis=1, inplace=True)
+
+measurement_types = combined_df['measurement'].unique()
+for measurement in measurement_types:
+ create_histograms(combined_df, measurement)