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_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 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)
def plot_score_trace(self, t, R_scores):
r = R_scores.shape[0]
plt.clear_plot()
plt.set_output_file(self._logfile)
for i in range(self.g.p["mcmc"]["n_indep"]):
if t < r:
plt.scatter(R_scores[:t, i], label=str(i), line_color="red")
else:
plt.scatter(R_scores[np.r_[(t % r):r, 0:(t % r)], i],
label=str(i),
line_color="red")
plt.figsize(80, 20)
plt.ticks(4, 4)
if t < 1000:
xticks = [int(w * t) for w in np.arange(0, 1 + 1 / 3, 1 / 3)]
xlabels = [str(round(x / 1000, 1)) + "k" for x in xticks]
else:
xticks = [0, 333, 666, 999]
xlabels = [str(round((x + t) / 1000, 1)) + "k" for x in xticks]
plt.xticks(xticks, xlabels)
plt.canvas_color("none")
plt.axes_color("none")
plt.ticks_color("none")
plt.show()
print(file=self._logfile)
self._logfile.flush()
def set_plot(self, earnings, times):
y = earnings
x = times
listofzeros = [0] * len(earnings)
ptt.plot(y, line_color='red')
ptt.plot(listofzeros, line_color='green')
ptt.ylim(-500, -250)
ptt.grid(True)
ptt.canvas_color("black")
ptt.axes_color("black")
ptt.ticks_color("cloud")
ptt.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("")
#For formating
plt.clear_plot()
plt.clear_terminal()
plt.scatter(y,
point_color=color,
fill=True,
label=ticker + "- Open:" + str(opening) + " Current:" +
str(current) + "1d High:" +
str(current_high)) # Labled as Open: , Current: , High:
#Most recent hours of trades will be highlighted blue
plt.scatter(list(range(540, 600)),
y[-60:],
fill=True,
label="Current Hour",
point_color="blue")
#More formating
plt.canvas_color('none')
plt.axes_color("none")
plt.grid(False, False)
plt.axes(False, False)
# Adds padding to right side
plt.xlim([0, 625])
plt.ticks(0, 10)
# Render graph every 1m (API only updates at 1m intervals)
plt.show()
sleep(60)
import plotext as plt
# format: <<ID:default_value>>
scatter_size = <<SCATTER_SIZE:1>>
labels = <<LABELS:None>>
h = <<HEIGHT:None>>
w = <<WIDTH:None>>
vv = <<DATA:[]>>
if w is not None:
plt.figure(figsize=(w*cm, h*cm))
# scatter
for ii in range(len(vv)):
v = vv[ii]
x = [v[2*i] for i in range(len(v)//2)]
y = [v[2*i+1] for i in range(len(v)//2) if 2*i + 1 < len(v)]
if labels and ii < len(labels):
label = labels[ii]
plt.scatter(x, y, label=label)
else:
plt.scatter(x, y)
plt.canvas_color('black')
plt.axes_color('black')
plt.ticks_color('white')
if w is not None:
plt.plot_size(w, h)
plt.show()
