def chart_2d_log_data(settings, dataset):
#
# Raw data must be processed into series data + enriched
#
data = supporting.process_dataset(settings, dataset)
datatypes = data['datatypes']
#
# Create matplotlib figure and first axis. The 'host' axis is used for
# x-axis and as a basis for the second and third y-axis
#
fig, host = plt.subplots()
fig.set_size_inches(9, 5)
plt.margins(0)
#
# Generates the axis for the graph with a maximum of 3 axis (per type of
# iops,lat,bw)
#
axes = supporting.generate_axes(host, datatypes)
#
# Create title and subtitle
#
supporting.create_title_and_sub(settings, plt)
#
# The extra offsets are requred depending on the size of the legend, which
# in turn depends on the number of legend items.
#
extra_offset = len(datatypes) * len(settings['iodepth']) * len(
settings['numjobs']) * len(settings['filter'])
bottom_offset = 0.18 + (extra_offset / 120)
if 'bw' in datatypes and (len(datatypes) > 2):
#
# If the third y-axis is enabled, the graph is ajusted to make room for
# this third y-axis.
#
fig.subplots_adjust(left=0.21)
fig.subplots_adjust(bottom=bottom_offset)
else:
fig.subplots_adjust(bottom=bottom_offset)
lines = []
labels = []
colors = supporting.get_colors()
marker_list = list(markers.MarkerStyle.markers.keys())
fontP = FontProperties(family='monospace')
fontP.set_size('xx-small')
maximum = dict.fromkeys(settings['type'], 0)
for item in data['dataset']:
for rw in settings['filter']:
if rw in item.keys():
if settings['enable_markers']:
marker_value = marker_list.pop(0)
else:
marker_value = None
xvalues = item[rw]['xvalues']
yvalues = item[rw]['yvalues']
#
# Use a moving average as configured by the commandline option
# to smooth out the graph for better readability.
#
if settings['moving_average']:
yvalues = supporting.running_mean(
yvalues, settings['moving_average'])
#
# PLOT
#
dataplot = f"{item['type']}_plot"
axes[dataplot] = axes[item['type']].plot(
xvalues,
yvalues,
marker=marker_value,
markevery=(len(yvalues) / (len(yvalues) * 10)),
color=colors.pop(0),
label=item[rw]['ylabel'])[0]
host.set_xlabel(item['xlabel'])
#
# Assure axes are scaled correctly, starting from zero.
#
factor = 1.1
if item['type'] == 'bw':
factor = 1.2
if settings['max']:
max_yvalue = settings['max']
else:
max_yvalue = max(yvalues)
if max_yvalue > maximum[item['type']]:
maximum[item['type']] = max_yvalue
axes[item['type']].set_ylim(0, maximum[item['type']] * factor)
#
# Label Axis
#
padding = axes[f"{item['type']}_pos"]
axes[item['type']].set_ylabel(item[rw]['ylabel'],
labelpad=padding)
#
# Add line to legend
#
lines.append(axes[dataplot])
labels.append(
f"|{item['type']:>4}|{rw:>5}|qd: {item['iodepth']:>2}|nj: {item['numjobs']:>2}|mean: {item[rw]['mean']:>6}|std%: {item[rw]['stdv']:>6} |P{settings['percentile']}: {item[rw]['percentile']:>6}"
)
host.legend(lines,
labels,
prop=fontP,
bbox_to_anchor=(0.5, -0.15),
loc='upper center',
ncol=2)
#
# Save graph to file (png)
#
if settings['source']:
axis = list(axes.keys())[0]
ax = axes[axis]
plt.text(1,
-0.10,
str(settings['source']),
ha='right',
va='top',
transform=ax.transAxes,
fontsize=8,
fontfamily='monospace')
now = datetime.now().strftime('%Y-%m-%d_%H%M%S')
title = settings['title'].replace(" ", '-')
title = title.replace("/", '-')
plt.tight_layout(rect=[0, 0.00, 0.95, 0.95])
fig.savefig(f"{title}-{now}.png", dpi=settings['dpi'])
def chart_2d_log_data(settings, dataset):
#
# Raw data must be processed into series data + enriched
#
data = supporting.process_dataset(settings, dataset)
datatypes = data["datatypes"]
directories = logdata.get_unique_directories(dataset)
# pprint.pprint(data)
#
# Create matplotlib figure and first axis. The 'host' axis is used for
# x-axis and as a basis for the second and third y-axis
#
fig, host = plt.subplots()
fig.set_size_inches(9, 5)
plt.margins(0)
#
# Generates the axis for the graph with a maximum of 3 axis (per type of
# iops,lat,bw)
#
axes = supporting.generate_axes(host, datatypes)
#
# Create title and subtitle
#
supporting.create_title_and_sub(settings, plt)
#
# The extra offsets are requred depending on the size of the legend, which
# in turn depends on the number of legend items.
#
if settings["colors"]:
support2d.validate_colors(settings["colors"])
extra_offset = (
len(datatypes)
* len(settings["iodepth"])
* len(settings["numjobs"])
* len(settings["filter"])
)
bottom_offset = 0.18 + (extra_offset / 120)
if "bw" in datatypes and (len(datatypes) > 2):
#
# If the third y-axis is enabled, the graph is ajusted to make room for
# this third y-axis.
#
fig.subplots_adjust(left=0.21)
fig.subplots_adjust(bottom=bottom_offset)
else:
fig.subplots_adjust(bottom=bottom_offset)
supportdata = {
"lines": [],
"labels": [],
"colors": support2d.get_colors(settings),
"marker_list": list(markers.MarkerStyle.markers.keys()),
"fontP": FontProperties(family="monospace"),
"maximum": supporting.get_highest_maximum(settings, data),
"axes": axes,
"host": host,
"maxlabelsize": support2d.get_max_label_size(settings, data, directories),
"directories": directories,
}
supportdata["fontP"].set_size("xx-small")
#
# Converting the data and drawing the lines
#
for item in data["dataset"]:
for rw in settings["filter"]:
if rw in item.keys():
support2d.drawline(settings, item, rw, supportdata)
#
# Generating the legend
#
values, ncol = support2d.generate_labelset(settings, supportdata)
host.legend(
supportdata["lines"],
values,
prop=supportdata["fontP"],
bbox_to_anchor=(0.5, -0.18),
loc="upper center",
ncol=ncol,
frameon=False,
)
#
# Save graph to file (png)
#
if settings["source"]:
axis = list(axes.keys())[0]
ax = axes[axis]
plt.text(
1,
-0.10,
str(settings["source"]),
ha="right",
va="top",
transform=ax.transAxes,
fontsize=8,
fontfamily="monospace",
)
#
# Save graph to PNG file
#
supporting.save_png(settings, plt, fig)
def chart_2d_log_data(settings, dataset):
#
# Raw data must be processed into series data + enriched
#
data = supporting.process_dataset(settings, dataset)
datatypes = data['datatypes']
directories = logdata.get_unique_directories(dataset)
#
# Create matplotlib figure and first axis. The 'host' axis is used for
# x-axis and as a basis for the second and third y-axis
#
fig, host = plt.subplots()
fig.set_size_inches(9, 5)
plt.margins(0)
#
# Generates the axis for the graph with a maximum of 3 axis (per type of
# iops,lat,bw)
#
axes = supporting.generate_axes(host, datatypes)
#
# Create title and subtitle
#
supporting.create_title_and_sub(settings, plt)
#
# The extra offsets are requred depending on the size of the legend, which
# in turn depends on the number of legend items.
#
extra_offset = len(datatypes) * len(settings['iodepth']) * len(
settings['numjobs']) * len(settings['filter'])
bottom_offset = 0.18 + (extra_offset / 120)
if 'bw' in datatypes and (len(datatypes) > 2):
#
# If the third y-axis is enabled, the graph is ajusted to make room for
# this third y-axis.
#
fig.subplots_adjust(left=0.21)
fig.subplots_adjust(bottom=bottom_offset)
else:
fig.subplots_adjust(bottom=bottom_offset)
lines = []
labels = []
colors = supporting.get_colors()
marker_list = list(markers.MarkerStyle.markers.keys())
fontP = FontProperties(family='monospace')
fontP.set_size('xx-small')
maximum = dict.fromkeys(settings['type'], 0)
for item in data['dataset']:
for rw in settings['filter']:
if rw in item.keys():
if settings['enable_markers']:
marker_value = marker_list.pop(0)
else:
marker_value = None
xvalues = item[rw]['xvalues']
yvalues = item[rw]['yvalues']
#
# Use a moving average as configured by the commandline option
# to smooth out the graph for better readability.
#
if settings['moving_average']:
yvalues = supporting.running_mean(
yvalues, settings['moving_average'])
#
# PLOT
#
dataplot = f"{item['type']}_plot"
axes[dataplot] = axes[item['type']].plot(
xvalues,
yvalues,
marker=marker_value,
markevery=(len(yvalues) / (len(yvalues) * 10)),
color=colors.pop(0),
label=item[rw]['ylabel'],
linewidth=settings['line_width'])[0]
host.set_xlabel(item['xlabel'])
#
# Assure axes are scaled correctly, starting from zero.
#
factordict = {'iops': 1.05, 'lat': 1.25, 'bw': 1.5}
if settings['max']:
maximum[item['type']] = settings['max']
else:
max_yvalue = max(yvalues)
if max_yvalue > maximum[item['type']]:
maximum[item['type']] = max_yvalue
min_y = 0
if settings['min_y'] == "None":
min_y = None
else:
try:
min_y = int(settings['min_y'])
except ValueError:
print(f"Min_y value is invalid (not None or integer).")
axes[item['type']].set_ylim(
min_y, maximum[item['type']] * factordict[item['type']])
#
# Label Axis
#
padding = axes[f"{item['type']}_pos"]
axes[item['type']].set_ylabel(item[rw]['ylabel'],
labelpad=padding)
#
# Add line to legend
#
lines.append(axes[dataplot])
maxlabelsize = get_max_label_size(settings, data, directories)
mylabel = create_label(settings, item, directories)
mylabel = get_padding(mylabel, maxlabelsize)
labels.append(
f"|{mylabel:>4}|{rw:>5}|qd: {item['iodepth']:>2}|nj: {item['numjobs']:>2}|mean: {item[rw]['mean']:>6}|std%: {item[rw]['stdv']:>6} |P{settings['percentile']}: {item[rw]['percentile']:>6}"
)
host.legend(lines,
labels,
prop=fontP,
bbox_to_anchor=(0.5, -0.15),
loc='upper center',
ncol=2)
#
# Save graph to file (png)
#
if settings['source']:
axis = list(axes.keys())[0]
ax = axes[axis]
plt.text(1,
-0.10,
str(settings['source']),
ha='right',
va='top',
transform=ax.transAxes,
fontsize=8,
fontfamily='monospace')
#
# Save graph to PNG file
#
supporting.save_png(settings, plt, fig)
def chart_2d_log_data(settings, dataset):
#
# Raw data must be processed into series data + enriched
#
data = supporting.process_dataset(settings, dataset)
datatypes = data["datatypes"]
directories = logdata.get_unique_directories(dataset)
# pprint.pprint(data)
#
# Create matplotlib figure and first axis. The 'host' axis is used for
# x-axis and as a basis for the second and third y-axis
#
fig, host = plt.subplots()
fig.set_size_inches(9, 5)
plt.margins(0)
#
# Generates the axis for the graph with a maximum of 3 axis (per type of
# iops,lat,bw)
#
axes = supporting.generate_axes(host, datatypes)
#
# Create title and subtitle
#
supporting.create_title_and_sub(settings, plt)
#
# The extra offsets are requred depending on the size of the legend, which
# in turn depends on the number of legend items.
#
if settings["colors"]:
support2d.validate_colors(settings["colors"])
extra_offset = (len(datatypes) * len(settings["iodepth"]) *
len(settings["numjobs"]) * len(settings["filter"]))
bottom_offset = 0.18 + (extra_offset / 120)
if "bw" in datatypes and (len(datatypes) > 2):
#
# If the third y-axis is enabled, the graph is ajusted to make room for
# this third y-axis.
#
fig.subplots_adjust(left=0.21)
try:
fig.subplots_adjust(bottom=bottom_offset)
except ValueError as v:
print(f"\nError: {v} - probably too many lines in the graph.\n")
sys.exit(1)
supportdata = {
"lines": [],
"labels": [],
"colors": support2d.get_colors(settings),
"marker_list": list(markers.MarkerStyle.markers.keys()),
"fontP": FontProperties(family="monospace"),
"maximum": supporting.get_highest_maximum(settings, data),
"axes": axes,
"host": host,
"maxlabelsize": support2d.get_max_label_size(settings, data,
directories),
"directories": directories,
}
supportdata["fontP"].set_size("xx-small")
#
# Converting the data and drawing the lines
#
for item in data["dataset"]:
for rw in settings["filter"]:
if rw in item.keys():
support2d.drawline(settings, item, rw, supportdata)
#
# Generating the legend
#
values, ncol = support2d.generate_labelset(settings, supportdata)
host.legend(
supportdata["lines"],
values,
prop=supportdata["fontP"],
bbox_to_anchor=(0.5, -0.18),
loc="upper center",
ncol=ncol,
frameon=False,
)
def get_axis_for_label(axes):
axis = list(axes.keys())[0]
ax = axes[axis]
return ax
#
# A ton of work to get the Fio-version from .json output if it exists.
#
jsondata = support2d.get_json_data(settings)
ax = get_axis_for_label(axes)
if jsondata[0]["data"] and not settings["disable_fio_version"]:
fio_version = jsondata[0]["data"][0]["fio_version"]
supporting.plot_fio_version(settings, fio_version, plt, ax, -0.12)
else:
supporting.plot_fio_version(settings, None, plt, ax, -0.12)
#
# Print source
#
ax = get_axis_for_label(axes)
supporting.plot_source(settings, plt, ax, -0.12)
#
# Save graph to PNG file
#
supporting.save_png(settings, plt, fig)