def plot_coverage(self, sequence_name, coverage, num_bins=100):
try:
import plotext as plt
except:
self.run.warning(
"You don't have the `plotext` library to plot data :/ You can "
"install it by running `pip install plotext` in your anvi'o "
"environment.",
header="NO PLOT FOR YOU :(")
return
plt.clp()
plt.title(f"{sequence_name}")
plt.xlabel("Position")
plt.ylabel("Coverage")
plt.plot(coverage, fillx=True)
plt.plotsize(self.progress.terminal_width, 25)
plt.canvas_color("cloud")
plt.axes_color("cloud")
plt.ticks_color("iron")
try:
plt.show()
except OSError:
self.run.warning(
"Redirecting things into files and working with funny TTYs confuse "
"the plotting services. Is ok tho.",
header="NO PLOT FOR YOU :(")
pass
def print_plot(items,
x,
y=None,
sort=None,
fillx=False,
title=None,
grid=False,
marker=None,
color=None,
background_color=None,
axes_color=None):
df = items_to_dataframe(items, sort=sort)
x_list = df[x].tolist()
y_list = df[y].tolist() if y else None
_x = df['timestamp'].tolist() if x in DATE_FIELDS else df[x]
if y is None:
plt.scatter(_x, marker=marker, color=color, fillx=fillx)
else:
plt.plot(_x, y_list, marker=marker, color=color, fillx=fillx)
plt.xticks(_x, x_list)
if title is not None:
plt.title(title)
if grid:
plt.grid(True, True)
if background_color is not None:
plt.canvas_color(background_color)
plt.axes_color(background_color)
if axes_color is not None:
plt.ticks_color(axes_color)
plt.show()
def make_figure_task_info(extractors, what):
"""
$ oq plot "task_info?kind=classical"
"""
plt = import_plt()
if plt.__name__ == 'plotext':
[ex] = extractors
[(task_name, task_info)] = ex.get(what).to_dict().items()
x = task_info['duration']
mean, std, med = x.mean(), x.std(ddof=1), numpy.median(x)
plt.hist(x, bins=50)
plt.title("mean=%d+-%d seconds, median=%d" % (mean, std, med))
return plt
fig = plt.figure()
[ex] = extractors
[(task_name, task_info)] = ex.get(what).to_dict().items()
x = task_info['duration']
ax = fig.add_subplot(2, 1, 1)
mean, std = x.mean(), x.std(ddof=1)
ax.hist(x, bins=50, rwidth=0.9)
ax.set_title("mean=%d+-%d seconds" % (mean, std))
ax.set_ylabel("tasks=%d" % len(x))
from scipy.stats import linregress
ax = fig.add_subplot(2, 1, 2)
arr = numpy.sort(task_info, order='duration')
x, y = arr['duration'], arr['weight']
reg = linregress(x, y)
ax.plot(x, reg.intercept + reg.slope * x)
ax.plot(x, y)
ax.set_ylabel("weight")
ax.set_xlabel("duration")
return plt
def show_points(
points: t.Sequence[t.Tuple[datetime.datetime, t.Union[int, float]]],
title: str,
y_label: str,
) -> None:
if not points:
print('No data')
return
import plotext as plt
dates, values = zip(*points)
date_time_stamps = [d.timestamp() for d in dates]
plt.plot(date_time_stamps, values)
plt.xticks(date_time_stamps, (d.strftime('%Y/%m/%d') for d in dates))
plt.title(title)
plt.xlabel('Date')
plt.ylabel(y_label)
plt.ylim(0, max(values) * 1.1)
plt.canvas_color('iron')
plt.axes_color('cloud')
plt.grid(False, True)
plt.show()
def plot_terminal(data, title, xtitle):
"""
Plot data to the terminal using plotext
"""
import plotext as plt
x = data.index.tolist()
y = data[title].tolist()
plt.scatter(x, y)
plt.title(title)
plt.xlabel(xtitle)
plt.plot_size(100, 30)
plt.show()
def diff2plot(diff, kind=None, title='Unnamed plot'):
"""Function to plot graph to the terminal. Can be scatter or bar plot (Initial test functionality)
Works only with plotext 4.2, not above"""
# try:
# import plotext as plt
# except ModuleNotFoundError:
# # Error handling
# pass
plx.clear_plot()
diff = diff.round(2)
if kind == 'bar':
# expects a series with index being string names
mask0 = (diff.values != 0) & ~_np.isnan(diff.values)
if mask0.any():
plx.bar(diff.index.values[mask0].tolist(),
diff.values[mask0].tolist(),
orientation="h",
width=0.3)
plx.vertical_line(coordinate=0)
plx.plotsize(100, diff.shape[0] * 2 + 3)
else:
return None
else:
mask0 = ((diff.values != 0) & ~_np.isnan(diff.values)).any(axis=0)
if mask0.any():
cols = diff.columns[mask0]
x1 = plx.datetime.datetime_to_string(
diff.index.astype('timedelta64[ns]') * 1000000000 +
_J2000_ORIGIN)
for i in range(cols.shape[0]):
plx.scatter_date(x1,
diff[cols[i]].to_list(),
color=i,
marker="hd",
label=diff.columns[i])
plx.plotsize(100, 30 + 3)
else:
return None
plx.title(title)
plx.limit_size(limit_xsize=False, limit_ysize=False)
plx.show()
def main(filea, fileb):
if filea is None:
# take the second most recent
profiling_files = parse_and_sort_profiling_files()
filea = profiling_files[-2]
if fileb is None:
# take the most recent
profiling_files = parse_and_sort_profiling_files()
fileb = profiling_files[-1]
profile_a = pd.read_pickle(filea)
profile_b = pd.read_pickle(fileb)
merged = pd.merge(
profile_a,
profile_b,
how='left',
on=['document_length','document_depth', 'working_depth', 'item_length']
)
plt.clp()
bins = 30
plt.hist(merged['tottime_x'], bins, label="profile a")
plt.hist(merged['tottime_y'], bins, label="profile b")
plt.title("tottime distribution")
plt.xlabel("time bins")
plt.ylabel("frequency")
plt.canvas_color("none")
plt.axes_color("none")
plt.ticks_color("cloud")
plt.figsize(50, 15)
plt.show()
merged['diff'] = merged['tottime_x'] - merged['tottime_y']
print("")
print(f"## avg improvement: {merged['diff'].mean()} seconds ##")
print("")
return len(where(output.split("\n"), "stshoot"))
def generate_counts(*args):
while True:
yield count_lines(govc(*args))
if __name__ == '__main__':
counts = 200 # number of data points
interval = 0 # seconds per count
xs = range(1, counts + 1)
ys = []
plt.title("GOVC Session Count")
plt.clc()
plt.xlim(xs[0], xs[-1])
plt.xticks(ticks=[1, 20, 40, 60, 80, 100, 120, 140, 160, 180, 200])
for y in generate_counts("session.ls"):
ys.append(y)
while len(ys) > counts:
ys.pop(0)
y_min, y_max = min(ys), max(ys) + 1
plt.yticks(ticks=range(y_min, y_max))
plt.ylim(y_min, y_max)
#plt.ylim(150,165)
plt.cld()
plt.clt()
def build_axes(time_series):
count_by_month = Counter(
[note.strftime("%b %Y") for note in sorted(time_series)])
x_axis = []
y_axis = []
for date, count in count_by_month.items():
x_axis.append(date)
y_axis.append(count)
return x_axis, y_axis
def plotter(x, y, foreground_color, background_color):
plt.datetime.set_datetime_form(date_form="%b %Y")
plt.plot_date(x, y, marker="dot", color=foreground_color)
plt.ticks_color(foreground_color)
plt.plotsize(88, 30)
plt.canvas_color(background_color)
plt.axes_color(background_color)
if __name__ == "__main__":
notes = parse_notes()
x_axis, y_axis = build_axes(notes)
print(f"\nYour Zettelkasten contains [b]{len(notes)}[/b] notes.\n\n")
plt.clp()
plotter(x_axis, y_axis, "black", "yellow")
plt.title("Notes Written Per Month")
plt.xlabel("Date")
plt.show()
def show_statistics(rule_file=None, rule_set=None, indx=None):
interface = None
if rule_set == "-e":
interface = "external"
elif rule_set == "-i":
interface = "internal"
logs = utils.load_logs('logs.json')
print("")
print("-" * 13, "OVERALL FIREWALL STATISTICS", "-" * 13)
print("TOTAL PACkETS RECEIVED BY THE FIREWALL: ", logs["total_packets"])
print("TOTAL PACkETS DROPPED BY THE FIREWALL: ", logs["total_dropped"])
if rule_file != None:
if interface != None:
print("")
print("-" * 12, interface.upper(), "INTERFACE STATISTICS",
"-" * 12)
print("TOTAL PACKETS RECEIVED ON", interface.upper(),
"INTERFACE: ", logs[rule_file][interface]["total_packets"])
print("TOTAL PACKETS DROPPED ON", interface.upper(), "INTERFACE: ",
logs[rule_file][interface]["total_dropped"])
if indx != None:
if indx in logs[rule_file][interface]:
print("\nTOTAL PACKETS DROPPED DUE TO RULE", indx, "ON",
interface.upper(), "INTERFACE :",
logs[rule_file][interface][indx])
else:
print("Invalid rule index!")
else:
print("")
print("-" * 12, "EXTERNAL INTERFACE STATISTICS", "-" * 12)
print("TOTAL PACKETS RECEIVED ON EXTERNAL INTERFACE: ",
logs[rule_file]["external"]["total_packets"])
print("TOTAL PACKETS DROPPED ON EXTERNAL INTERFACE: ",
logs[rule_file]["external"]["total_dropped"])
print("")
print("-" * 12, "INTERNAL INTERFACE STATISTICS", "-" * 12)
print("TOTAL PACKETS RECEIVED ON INTERNAL INTERFACE: ",
logs[rule_file]["internal"]["total_packets"])
print("TOTAL PACKETS DROPPED ON INTERNAL INTERFACE: ",
logs[rule_file]["internal"]["total_dropped"])
else:
print("")
print("-" * 16, "ICMP FLOOD STATISTICS", "-" * 16)
print("FLOODS ENCOUNTERED SO FAR: ", logs["icmp_floods"])
print("PACKETS DROPPED DURING ICMP BLOCK:", logs["icmp_dropped"])
print("")
print("-" * 22, "PPS INFO", "-" * 22)
print("MAXIMUM PPS SO FAR: ", logs["max_pps"])
print("")
title = "-" * 18 + "NETWORK TRAFFIC" + "-" * 18
net_traffic = logs["traffic"]
max_sec = max(np.array(list(net_traffic.keys())).astype(int))
packets_num = np.zeros(max_sec + 1)
for i in net_traffic:
packets_num[int(i)] = net_traffic[i]
packets_num = packets_num.astype(int)
plt.scatter(np.arange(max_sec + 1), packets_num, fillx=True)
plt.figsize(80, 20)
plt.title(title)
plt.xlabel("Time")
plt.ylabel("Packets")
plt.show()