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Style fixes (trimming long lines) + restored accidentally removed uni…
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…ts check.
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@pineapple-cat
pineapple-cat committed Dec 8, 2023
1 parent 32d4334 commit 0a65375
Showing 1 changed file with 80 additions and 39 deletions.
119 changes: 80 additions & 39 deletions post-processing/post_processing.py
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,8 @@ def __init__(self, debug=False, verbose=False):

def run_post_processing(self, log_path, config):
"""
Return a dataframe containing the information passed to a plotting script and produce relevant graphs.
Return a dataframe containing the information passed to a plotting script
and produce relevant graphs.
Args:
log_path: str, path to a log file or a directory containing log files.
Expand All @@ -42,12 +43,14 @@ def run_post_processing(self, log_path, config):
raise RuntimeError("Perflog file name provided should have a .log extension.")
log_files = [log_path]
elif os.path.isdir(log_path):
log_files_temp = [os.path.join(root, file) for root, _, files in os.walk(log_path) for file in files]
log_files_temp = [os.path.join(root, file) for root, _, files in os.walk(log_path)
for file in files]
for file in log_files_temp:
if os.path.splitext(file)[1] == ".log":
log_files.append(file)
if len(log_files) == 0:
raise RuntimeError("No perflogs found in this path. Perflogs should have a .log extension.")
raise RuntimeError(
"No perflogs found in this path. Perflogs should have a .log extension.")
else:
raise FileNotFoundError(errno.ENOENT, os.strerror(errno.ENOENT), log_path)

Expand All @@ -65,7 +68,8 @@ def run_post_processing(self, log_path, config):
df = pd.concat([df, temp], ignore_index=True)
except KeyError as e:
if self.debug:
print("Discarding %s:" %os.path.basename(file), type(e).__name__ + ":", e.args[0], e.args[1])
print("Discarding %s:" %os.path.basename(file),
type(e).__name__ + ":", e.args[0], e.args[1])
print("")
if df.empty:
raise FileNotFoundError(errno.ENOENT, "Could not find a valid perflog in path", log_path)
Expand All @@ -83,7 +87,8 @@ def run_post_processing(self, log_path, config):
series_filters = [[s[0], "==", s[1]] for s in series]
# check acceptable number of series
if len(set(series_columns)) > 1:
raise RuntimeError("Currently supporting grouping of series by only one column. Please use a single column name in your series configuration.")
raise RuntimeError("Currently supporting grouping of series by only one column. \
Please use a single column name in your series configuration.")
# add series columns to dataframe column list
for c in series_columns:
if c not in columns:
Expand Down Expand Up @@ -130,13 +135,14 @@ def run_post_processing(self, log_path, config):
conversion_type = "float64"
elif pd.api.types.is_integer_dtype(conversion_type):
# all integers treated as Int64 (nullable)
# note: default pandas integer type is int64 (not nullable)
# NOTE: default pandas integer type is int64 (not nullable)
conversion_type = "Int64"
elif pd.api.types.is_datetime64_any_dtype(conversion_type):
# all datetimes treated as datetime64[ns] (nullable)
conversion_type = "datetime64[ns]"
else:
raise RuntimeError("Unsupported user-specified type '{0}' for column '{1}'.".format(conversion_type, col))
raise RuntimeError("Unsupported user-specified type '{0}' for column '{1}'." \
.format(conversion_type, col))

# skip type conversion if column is already the desired type
if conversion_type == df[col].dtype:
Expand Down Expand Up @@ -177,7 +183,9 @@ def run_post_processing(self, log_path, config):
num_x_data_points = series_combinations * len(set(df[config["x_axis"]["value"]][mask]))
# check expected number of rows
if num_filtered_rows > num_x_data_points:
raise RuntimeError("Unexpected number of rows ({0}) does not match number of unique x-axis values per series ({1})".format(num_filtered_rows, num_x_data_points), df[columns][mask])
raise RuntimeError("Unexpected number of rows ({0}) does not match \
number of unique x-axis values per series ({1})" \
.format(num_filtered_rows, num_x_data_points), df[columns][mask])

scaling_column = None
scaling_series_mask = None
Expand All @@ -191,29 +199,37 @@ def run_post_processing(self, log_path, config):
scaling_column = df[config["y_axis"]["scaling"]["column"]["name"]].copy()
# get mask of scaling series
if config["y_axis"]["scaling"]["column"].get("series") is not None:
scaling_series_mask = self.row_filter(series_filters[config["y_axis"]["scaling"]["column"]["series"]], df)
scaling_series_mask = self.row_filter(
series_filters[config["y_axis"]["scaling"]["column"]["series"]], df)
# get mask of scaling x-value
if config["y_axis"]["scaling"]["column"].get("x_value"):
scaling_x_value_mask = df[config["x_axis"]["value"]] == config["y_axis"]["scaling"]["column"]["x_value"]
scaling_x_value_mask = (
df[config["x_axis"]["value"]] == config["y_axis"]["scaling"]["column"]["x_value"])

# check custom value is not zero
elif not config["y_axis"]["scaling"].get("custom"):
raise RuntimeError("Invalid custom scaling value (cannot divide by {0}).".format(config["y_axis"]["scaling"].get("custom")))
raise RuntimeError("Invalid custom scaling value (cannot divide by {0})." \
.format(config["y_axis"]["scaling"].get("custom")))

# apply data transformation per series
if series_filters:
for f in series_filters:
m = self.row_filter(f, df)
df[mask & m] = self.transform_axis(df[mask & m], mask & m, config["y_axis"], scaling_column, scaling_series_mask, scaling_x_value_mask)
df[mask & m] = self.transform_axis(
df[mask & m], mask & m, config["y_axis"], scaling_column,
scaling_series_mask, scaling_x_value_mask)
# apply data transformation to all data
else:
df[mask] = self.transform_axis(df[mask], mask, config["y_axis"], scaling_column, scaling_series_mask, scaling_x_value_mask)
df[mask] = self.transform_axis(
df[mask], mask, config["y_axis"], scaling_column,
scaling_series_mask, scaling_x_value_mask)

print("Selected dataframe:")
print(df[columns][mask])

# call a plotting script
self.plot_generic(config["title"], df[columns][mask], config["x_axis"], config["y_axis"], series_filters)
self.plot_generic(
config["title"], df[columns][mask], config["x_axis"], config["y_axis"], series_filters)

if self.debug & self.verbose:
print("")
Expand Down Expand Up @@ -252,7 +268,8 @@ def plot_generic(self, title, df: pd.DataFrame, x_axis, y_axis, series_filters):
# combine group names for later plotting with groupby
index_group_col = "_".join(groups)
# group by group names (or just x-axis if no other groups are present)
grouped_df = df.groupby(x_column, sort=False) if len(groups) == 1 else df.groupby(groups, sort=False)
grouped_df = df.groupby(x_column, sort=False) if len(groups) == 1 \
else df.groupby(groups, sort=False)

if self.debug:
print("")
Expand All @@ -267,13 +284,17 @@ def plot_generic(self, title, df: pd.DataFrame, x_axis, y_axis, series_filters):
else math.ceil(np.nanmax(df[y_column])*1.2)

# create html file to store plot in
output_file(filename=os.path.join(Path(__file__).parent, "{0}.html".format(title.replace(" ", "_"))), title=title)
output_file(filename=os.path.join(
Path(__file__).parent, "{0}.html".format(title.replace(" ", "_"))), title=title)

# create plot
plot = figure(x_range=grouped_df, y_range=(min_y, max_y), title=title, width=800, toolbar_location="above")
plot = figure(x_range=grouped_df, y_range=(min_y, max_y), title=title,
width=800, toolbar_location="above")
# configure tooltip
plot.add_tools(HoverTool(tooltips=[(y_label, "@{0}_mean".format(y_column)
+ ("{%0.2f}" if pd.api.types.is_float_dtype(df[y_column].dtype) else ""))],
plot.add_tools(HoverTool(tooltips=
[(y_label, "@{0}_mean".format(y_column)
+ ("{%0.2f}" if pd.api.types.is_float_dtype(df[y_column].dtype)
else ""))],
formatters={"@{0}_mean".format(y_column) : "printf"}))

# sort x-axis values in descending order (otherwise default sort is ascending)
Expand All @@ -289,7 +310,8 @@ def plot_generic(self, title, df: pd.DataFrame, x_axis, y_axis, series_filters):
colour_factors = [str(x) for x in sorted(pd.Series(df[groups[-1]].unique(),
dtype=last_group_dtype))]
# divide and assign colours
index_cmap = factor_cmap(index_group_col, palette=viridis(len(colour_factors)), factors=colour_factors, start=len(groups)-1, end=len(groups))
index_cmap = factor_cmap(index_group_col, palette=viridis(len(colour_factors)),
factors=colour_factors, start=len(groups)-1, end=len(groups))
# add legend labels to data source
data_source = ColumnDataSource(grouped_df).data
legend_labels = ["{0} = {1}".format(groups[-1].replace("_", " "),
Expand All @@ -300,7 +322,8 @@ def plot_generic(self, title, df: pd.DataFrame, x_axis, y_axis, series_filters):
# create legend outside plot
plot.add_layout(Legend(), "right")
# add bars
plot.vbar(x=index_group_col, top="{0}_mean".format(y_column), width=0.9, source=data_source, line_color="white", fill_color=index_cmap, legend_group="legend_labels", hover_alpha=0.9)
plot.vbar(x=index_group_col, top="{0}_mean".format(y_column), width=0.9, source=data_source,
line_color="white", fill_color=index_cmap, legend_group="legend_labels", hover_alpha=0.9)
# add labels
plot.xaxis.axis_label = x_label
plot.yaxis.axis_label = y_label
Expand Down Expand Up @@ -333,7 +356,8 @@ def plot_generic(self, title, df: pd.DataFrame, x_axis, y_axis, series_filters):

def row_filter(self, filter, df: pd.DataFrame):
"""
Return a dataframe mask based on a filter condition. The filter is a list that contains a column name, an operator, and a value (e.g. ["flops_value", ">=", 1.0]).
Return a dataframe mask based on a filter condition. The filter is a list that
contains a column name, an operator, and a value (e.g. ["flops_value", ">=", 1.0]).
Args:
filter: list, a condition based on which a dataframe is filtered.
Expand Down Expand Up @@ -368,7 +392,8 @@ def row_filter(self, filter, df: pd.DataFrame):

return mask

def transform_axis(self, df: pd.DataFrame, df_mask, axis, scaling_column, scaling_series_mask, scaling_x_value_mask):
def transform_axis(self, df: pd.DataFrame, df_mask, axis, scaling_column,
scaling_series_mask, scaling_x_value_mask):
"""
Divide axis values by specified values and reflect this change in the dataframe.
Expand Down Expand Up @@ -427,18 +452,25 @@ def read_args():
Return parsed command line arguments.
"""

parser = argparse.ArgumentParser(description="Plot benchmark data. At least one perflog must be supplied.")
parser = argparse.ArgumentParser(description="Plot benchmark data. \
At least one perflog must be supplied.")

# required positional arguments (log path, config path)
parser.add_argument("log_path", type=str, help="path to a perflog file or a directory containing perflog files")
parser.add_argument("config_path", type=str, help="path to a configuration file specifying what to plot")
parser.add_argument("log_path", type=str,
help="path to a perflog file or a directory containing perflog files")
parser.add_argument("config_path", type=str,
help="path to a configuration file specifying what to plot")

# optional argument (plot type)
parser.add_argument("-p", "--plot_type", type=str, default="generic", help="type of plot to be generated (default: 'generic')")
parser.add_argument("-p", "--plot_type", type=str, default="generic",
help="type of plot to be generated (default: 'generic')")

# info dump flags
parser.add_argument("-d", "--debug", action="store_true", help="debug flag for printing additional information")
parser.add_argument("-v", "--verbose", action="store_true", help="verbose flag for printing more debug information (must be used in conjunction with the debug flag)")
parser.add_argument("-d", "--debug", action="store_true",
help="debug flag for printing additional information")
parser.add_argument("-v", "--verbose", action="store_true",
help="verbose flag for printing more debug information \
(must be used in conjunction with the debug flag)")

return parser.parse_args()

Expand Down Expand Up @@ -483,9 +515,11 @@ def read_config(path):

# check series length
if config.get("series") is None:
raise KeyError("Missing series information (specify an empty list [] if there is only one series).")
raise KeyError(
"Missing series information (specify an empty list [] if there is only one series).")
if len(config["series"]) == 1:
raise KeyError("Number of series must be >= 2 (specify an empty list [] if there is only one series).")
raise KeyError(
"Number of series must be >= 2 (specify an empty list [] if there is only one series).")

# check filters are present
if not config.get("filters"):
Expand All @@ -509,7 +543,8 @@ def read_perflog(path):
Args:
path: str, path to log file.
NB: This currently depends on having a non-default handlers_perflog.filelog.format in reframe's configuration. See code.
NB: This currently depends on having a non-default handlers_perflog.filelog.format
in reframe's configuration. See code.
The returned dataframe will have columns for all fields in a performance log record
except display name, extra resources, and env vars. Display name will be broken up
Expand All @@ -522,7 +557,8 @@ def read_perflog(path):
REQUIRED_LOG_FIELDS = ["job_completion_time", r"\w+_value$", r"\w+_unit$", "display_name"]

# look for required column matches
required_field_matches = [len(list(filter(re.compile(rexpr).match, df.columns))) > 0 for rexpr in REQUIRED_LOG_FIELDS]
required_field_matches = [len(list(filter(re.compile(rexpr).match, df.columns))) > 0
for rexpr in REQUIRED_LOG_FIELDS]
# check all required columns are present
if False in required_field_matches:
raise KeyError("Perflog missing one or more required fields", REQUIRED_LOG_FIELDS)
Expand All @@ -549,10 +585,13 @@ def read_perflog(path):

def get_display_name_info(display_name):
"""
Return a tuple containing the test name and a dictionary of parameter names and their values from the given input string. The parameter dictionary may be empty if no parameters are present.
Return a tuple containing the test name and a dictionary of parameter names
and their values from the given input string. The parameter dictionary may be empty
if no parameters are present.
Args:
display_name: str, expecting a format of <test_name> followed by zero or more %<param>=<value> pairs.
display_name: str, expecting a format of <test_name> followed by zero or more
%<param>=<value> pairs.
"""

split_display_name = display_name.split(" %")
Expand All @@ -578,7 +617,8 @@ def insert_key_cols(df: pd.DataFrame, index, results):

def get_axis_info(df: pd.DataFrame, axis, series_filters):
"""
Return the column name and label for a given axis. If a column name is supplied as units information, the actual units will be extracted from a dataframe.
Return the column name and label for a given axis. If a column name is supplied as
units information, the actual units will be extracted from a dataframe.
Args:
df: dataframe, data to plot.
Expand All @@ -592,8 +632,8 @@ def get_axis_info(df: pd.DataFrame, axis, series_filters):
if axis.get("units").get("column"):
unit_set = set(df[axis["units"]["column"]].dropna())
# check all rows have the same units
#if len(unit_set) != 1:
# raise RuntimeError("Unexpected number of axis unit entries {0}".format(unit_set))
if len(unit_set) != 1:
raise RuntimeError("Unexpected number of axis unit entries {0}".format(unit_set))
units = next(iter(unit_set))

# get scaling information
Expand All @@ -612,7 +652,8 @@ def get_axis_info(df: pd.DataFrame, axis, series_filters):

# determine axis label
label = "{0}{1}{2}".format(titlecase(col_name.replace("_", " ")),
titlecase(" Scaled by {0}".format(scaling.replace("_", " "))) if scaling else "",
titlecase(" Scaled by {0}".format(scaling.replace("_", " ")))
if scaling else "",
" ({0})".format(units) if units else "")

return col_name, label
Expand Down

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