Add a "markers" page to the performance-log viewer, which lists
the event markers contained in the log, and allows navigating
between them.
Update docs accordingly.
In the perofmance-log viewer, add header-bar buttons to clear and
invert the selection, and allow inverting the selection by ctrl-
right-clicking on the sample-selection area.
Update the docs.
In sample-search predicates, remove the "exclusive" parameter of
the "function()" function, and replace it with optional "id" and
"state" parameters, which limit the match to the call-stacks of
matching threads, as per the "thread()" function.
Sort the backtrace thread-list by thread ID.
In the performance-log viewer's profile view, allow sorting the
call-graph tree-views by function name, in addition to the
inclusive/exclusive frequencies.
In the performance-log viewer's profile view, displasy in-line bar-
chart-like visualization of function and source-line sample
percentages, as part of the corresponding tree-view cells.
In performance-log-viewer.py, fix thread-state toggling in the
profile-view thread-filter popover, when not all threads are
included in the current selection.
Add an annotated source view to the performance-log viewer's
profile view. When selecting the [Self] entry of a function's
profile, for which source information is available and whose source
is found locally, a new column opens, showing the function's
source, annotated with sample statistics. Header-bar buttons allow
navigation through the annotated lines, selection of all the
samples corresponding to a given line, and opening the text editor
at the current line.
... when selecting a function's samples
Since we now preserve the call-graph path across state changes,
there's no need to explictly set the path after selecting a
function's samples in the profile view.
... in backtraces
In the performance-log viewer's backtrace viewer, show a document
icon next to stack frames with source-location information, whose
source file is found locally. Clicking the icon opens the source
file in a text editor at the relevant line.
Two environment variables control this feature:
- PERFORMANCE_LOG_VIEWER_PATH is a list of colon-separated
directories in which to look for source files. If this
variable is undefined, the current directory is used.
- PERFORMANCE_LOG_VIEWER_EDITOR is the command to use to launch
the text editor, for editing a specific file at a specific
line. The special strings "{file}" and "{line}" are replaced
with the filename and line-number, respectively. If this
variable is undefined, "xdg-open {file}" is used.
Don't take infinite values into account when calculating the
vertical scale of sample graphs, and rather display infinite values
as dashed lines at the top of the graph.
Fix int-ratio variable formatting when the input is NaN, which can
happen when calculating the standard deviation, if all the values
are infinite.
Fix keyboard sample-range selection.
Deselect all samples when right-clicking a sample graph.
In the performance-log viewer, add an option to filter which
threads, and which states of each thread, are included in the
profile. By default, all threads in the RUNNING state are
included.
In the performance-log viewer, defer updates to the various UI
elements when the selection changes until they're actually shown.
This improves responsiveness when changing the selection.
Use the less-ambiguous, if just as clumsy,
"format-indent-more/less" icons, to stand for the
caller -> callee, and callee -> caller, directions, respectively.
Fix searching for samples by thread name, in particular, when there
are unnamed threads.
Use GtkMenuButton, instead of GtkButton, for the find-samples
header button.
Add mnemonics to the find-samples popover.
performance-log-viewer.py is a viewer for GIMP performance logs.
The viewer is made up of two parts: a sample-selection area at the
top, and an information area at the bottom.
The sample-selection area visualizes the sampled variables and
markers using a simultaneous set of plots, and displays the
currently selected samples. Samples can be selected directly
through the sample-selection area, or by other means, such as
searching for all samples satisfying a certain condition, or
containing a certain function.
The information area shows global information stored in the log, as
well as information specific to the currently selected samples,
including variable listing and statistics, full backtrace, and
profile/call-graph information.
Note that performance-log-viewer.py takes its input from STDIN,
like the rest of the performance-log tools, and is therefore
suitable for use as part of a pipeline. For standalone use, the
performance-log-viewer driver is also included, which takes the log
file as a command-line argument, and processes it through an
appropriate pipeline before feeding it to the viewer.
Ell