def gauge_plot(arrow_index, labels):
list_colors = np.linspace(0, 1, int(len(labels) / 2))
size_of_groups = np.ones(len(labels))
white_half = np.ones(len(list_colors)) * .5
color_half = list_colors
cs1 = cm.RdYlGn_r(color_half)
cs2 = cm.seismic(white_half)
cs = np.concatenate([cs1, cs2])
fig, ax = plt.subplots()
ax.pie(size_of_groups, colors=cs, labels=labels)
my_circle = plt.Circle((0, 0), 0.6, color='white')
ax.add_artist(my_circle)
arrow_angle = (arrow_index / float(len(list_colors))) * 3.14159
arrow_x = 0.8 * math.cos(arrow_angle)
arrow_y = 0.8 * math.sin(arrow_angle)
arr = plt.arrow(0,0,-arrow_x,arrow_y, width=.02, head_width=.05, \
head_length=.1, fc='k', ec='k')
ax.add_artist(arr)
ax.add_artist(plt.Circle((0, 0), radius=0.04, facecolor='k'))
ax.add_artist(plt.Circle((0, 0), radius=0.03, facecolor='w',
zorder=11))
ax.set_aspect('equal')
st.pyplot(fig)
return True
def ledMetars(strip, df, stat, i):
strip.setPixelColorRGB(0, int(cm.RdYlGn_r(stat)[0] * 255),
int(colorMapStat(stat)[1] * 255),
int(colorMapStat(stat)[2] * 255))
print('UPDATE: ' + str(i) + ', LED: ' + '0' + ', SID: ' + str(stat) +
', DATA: ' + 'status' + ', COLOR: ' + str([
int(colorMapStat(stat)[1] * 255),
int(colorMapStat(stat)[0] * 255),
int(colorMapStat(stat)[2] * 255)
]))
if stat == float(1):
for led in df.index:
r = df['R'][led]
g = df['G'][led]
b = df['B'][led]
station = df['station_id'][led]
data = df[dataUse][led]
try:
strip.setPixelColorRGB(int(led), int(r * 255), int(g * 255),
int(b * 255))
except:
strip.setPixelColorRGB(int(led), 0, 0, 0)
print('UPDATE: ' + str(i) + ', LED: ' + str(led) + ', SID: ' +
str(station) + ', DATA: ' + str(data) + ', COLOR: [' +
str(r * 255) + ',' + str(g * 255) + ',' + str(b * 255) + ']')
strip.show()
def ledMetars(strip, f, stat, i, update):
strip.setPixelColor(
0,
Color(int(cm.RdYlGn_r(stat)[1] * 255),
int(colorMapStat(stat)[0] * 255),
int(colorMapStat(stat)[2] * 255)))
strip.show()
print('UPDATE: ' + str(i) + ', LED: ' + '0' + ', SID: ' + 'status' +
', DATA: ' + 'status' + ', COLOR: ' + str([
int(colorMapStat(stat)[1] * 255),
int(colorMapStat(stat)[0] * 255),
int(colorMapStat(stat)[2] * 255)
]))
if update == 'true':
for id in df.index:
if df['led'][id] > 0:
if df['R'][id] > -1:
strip.setPixelColor(
int(df['led'][id]),
Color(int(df['G'][id] * 255), int(df['R'][id] * 255),
int(df['B'][id] * 255)))
strip.show()
if ledVerbose:
print('UPDATE: ' + str(i) + ', LED: ' +
str(df['led'][id]) + ', SID: ' +
str(df['station_id'][id]) + ', DATA: ' +
str(df[dataUse][id]) + ', COLOR: ' +
str(df['clr255'][id]))
def plot(data, fname='lifecycle-carbon-emissions-nolabel.png'):
labels, values = zip(*reversed(sorted(data['val'].items(),
key=lambda kv: kv[1][1])))
labels = [label.capitalize() for label in labels]
values = np.array(values)[:,1]
width = 0.35
index = np.arange(len(labels))
fig, ax = plt.subplots(dpi=300)
# _r is for reversed colormap :)
colors = cm.RdYlGn_r(values/100.0)
bars = ax.bar(index, values, width, color=colors, edgecolor="k")
#bars = ax.bar(index, values, width)
plt.title(data['title'])
ax.set_ylabel('Lifecycle emissions ({})'.format(data['units']))
ax.set_xticks(index)
ax.set_xticklabels(labels)
ax.grid(alpha=0.7, linestyle='--', axis='y')
# ha needed or else labels rotate on center
plt.xticks(rotation=50, ha="right")
ax.set_ylim([0,900]) # make room for data label
# Add data labels on each bar
for bar in bars:
height = bar.get_height()
ax.annotate('{:.0f}'.format(height),
xy=(bar.get_x() + bar.get_width() / 2, height),
xytext=(0, 3), # 3 points vertical offset
textcoords="offset points",
ha='center', va='bottom', size=6)
# Point out nuclear
ann = ax.annotate("Nuclear is among\nthe lowest-carbon forms\nof energy we know",
xy=(11, 70), xycoords='data',
xytext=(8, 500), textcoords='data',
size=12, va="center", ha="center",
bbox=dict(boxstyle="round4", fc="w"),
arrowprops=dict(arrowstyle="-|>",
connectionstyle="arc3,rad=-0.2",
fc="w"),
)
#plt.tight_layout()
# Manually squish the subplot to make room for labels
#fig.subplots_adjust(bottom=0.4,top=0.90)
fig.subplots_adjust(bottom=0.3,top=0.90)
#ann = ax.text(0.1, 0.1, '\n'.join(textwrap.wrap(data['ref'] + ". Plot by whatisnuclear.com.",130)),
# size=6, va="center", ha="left", transform=fig.transFigure
# )
if fname:
plt.savefig(fname)
else:
plt.show()
def plot_cumulative_returns_by_quantile(quantile_returns, period=1, ax=None):
"""
Plots the cumulative returns of various factor quantiles.
Parameters
----------
quantile_returns : pd.DataFrame
Cumulative returns by factor quantile.
period: int, optional
Period over which the daily returns are calculated
ax : matplotlib.Axes, optional
Axes upon which to plot.
Returns
-------
ax : matplotlib.Axes
"""
if ax is None:
f, ax = plt.subplots(1, 1, figsize=(18, 6))
ret_wide = quantile_returns.reset_index()\
.pivot(index='date', columns='quantile', values=period)
if period > 1:
daily_returns = lambda ret, period: (np.nanmean(ret)**
(1. / period)) - 1
period_ret_wide = ret_wide.add(1)**period
ret_wide = pd.rolling_apply(period_ret_wide,
period,
daily_returns,
min_periods=1,
args=(period, ))
cum_ret = ret_wide.add(1).cumprod()
cum_ret = cum_ret.loc[:, ::-1]
num_quant = len(cum_ret.columns)
colors = cm.RdYlGn_r(np.linspace(0, 1, num_quant))
cum_ret.plot(lw=2, ax=ax, color=colors)
ax.legend()
ymin, ymax = cum_ret.min().min(), cum_ret.max().max()
ax.set(ylabel='Log Cumulative Returns',
title='Cumulative Return by Quantile ({} Period Forward Return)'.
format(period),
xlabel='',
yscale='symlog',
yticks=np.linspace(ymin, ymax, 5),
ylim=(ymin, ymax))
ax.yaxis.set_major_formatter(ScalarFormatter())
ax.axhline(1.0, linestyle='-', color='black', lw=1)
return ax
def plot_bar(seq=None, shape_ls=None, colormap='RdYlGn_r', savefn=None):
if shape_ls is None:
shape_ls = [random.random() for i in range(len(seq))]
shape_ls = np.array(shape_ls)
if colormap == 'RdYlGn_r':
colors = cm.RdYlGn_r(shape_ls, )
plot = plt.scatter(shape_ls, shape_ls, c = shape_ls, cmap = colormap)
plt.clf()
plt.colorbar(plot)
plt.bar(range(len(shape_ls)), shape_ls, color = colors)
plt.xticks(range(len(seq)), list(seq))
plt.tight_layout()
plt.savefig(savefn)
plt.close()
def plot_quantile_average_cumulative_return(avg_cumulative_returns,
by_quantile=False,
std_bar=False,
title=None,
ax=None):
"""
Plots sector-wise mean daily returns for factor quantiles
across provided forward price movement columns.
Parameters
----------
avg_cumulative_returns: pd.Dataframe
The format is the one returned by
performance.average_cumulative_return_by_quantile
by_quantile : boolean, optional
Disaggregated figures by quantile (useful to clearly see std dev bars)
std_bar : boolean, optional
Plot standard deviation plot
title: string, optional
Custom title
ax : matplotlib.Axes, optional
Axes upon which to plot.
Returns
-------
ax : matplotlib.Axes
"""
avg_cumulative_returns = avg_cumulative_returns.multiply(DECIMAL_TO_BPS)
quantiles = len(avg_cumulative_returns.index.levels[0].unique())
palette = [cm.RdYlGn_r(i) for i in np.linspace(0, 1, quantiles)]
if by_quantile:
if ax is None:
v_spaces = ((quantiles - 1) // 2) + 1
f, ax = plt.subplots(v_spaces,
2,
sharex=False,
sharey=False,
figsize=(18, 6 * v_spaces))
ax = ax.flatten()
for i, (quantile, q_ret) in enumerate(
avg_cumulative_returns.groupby(level='factor_quantile')):
mean = q_ret.loc[(quantile, 'mean')]
mean.name = 'Quantile ' + str(quantile)
mean.plot(ax=ax[i], color=palette[i])
ax[i].set_ylabel('Mean Return (bps)')
if std_bar:
std = q_ret.loc[(quantile, 'std')]
ax[i].errorbar(std.index,
mean,
yerr=std,
fmt=None,
ecolor=palette[i],
label=None)
ax[i].axvline(x=0, color='k', linestyle='--')
ax[i].legend()
i += 1
else:
if ax is None:
f, ax = plt.subplots(1, 1, figsize=(18, 6))
for i, (quantile, q_ret) in enumerate(
avg_cumulative_returns.groupby(level='factor_quantile')):
mean = q_ret.loc[(quantile, 'mean')]
mean.name = 'Quantile ' + str(quantile)
mean.plot(ax=ax, color=palette[i])
if std_bar:
std = q_ret.loc[(quantile, 'std')]
ax.errorbar(std.index,
mean,
yerr=std,
fmt=None,
ecolor=palette[i],
label='none')
i += 1
ax.axvline(x=0, color='k', linestyle='--')
ax.legend()
ax.set(ylabel='Mean Return (bps)',
title=("Average Cumulative Returns by Quantile"
if title is None else title),
xlabel='Periods')
return ax
#----------------------- # RMSD w.r.t 1ake vs RMSD w.r.t 4ake plot #----------------------- print "\t2D CV" #-- create figure ------ fig = plt.figure(figsize=(10, 10)) ax = fig.add_subplot(111) ax.set_aspect(1) #-- plot data ---------- ax.scatter(ref_rmsd, 0, marker="*", s=100, label="4ake", c='cyan') ax.scatter(0, ref_rmsd, marker="x", s=80, label="1ake", c='magenta') if multicolor: colors = cm.RdYlGn_r( 1.0*time/np.max(time) ) ax.scatter(trj_rmsd[:,0], trj_rmsd[:,1], c=colors, s=ss, \ edgecolor='black', linewidth=0.2) else: ax.scatter(trj_rmsd[:,0], trj_rmsd[:,1], c='blue', s=ss, \ edgecolor='none') #-- plot settings ------ ax.tick_params(axis='both', which='major', labelsize=16) ax.grid(True) #-- labels/legend ------ ax.set_ylabel(r'$\Delta$RMSD from 4ake ($\AA$)', fontsize=20) ax.set_xlabel(r'$\Delta$RMSD from 1ake ($\AA$)', fontsize=20) ax.set_title(r'$\Delta$RMSD from End Structures (1ake vs 4ake) -- '+adk+' trj'+postfix)
def plot_quantile_average_cumulative_return(avg_cumulative_returns,
by_quantile=False,
std_bar=False,
ax=None,
periods_before='',
periods_after=''):
avg_cumulative_returns = avg_cumulative_returns.multiply(DECIMAL_TO_BPS)
quantiles = len(avg_cumulative_returns.index.levels[0].unique())
palette = [cm.RdYlGn_r(i) for i in np.linspace(0, 1, quantiles)]
if by_quantile:
if ax is None:
v_spaces = ((quantiles - 1) // 2) + 1
f, ax = plt.subplots(v_spaces,
2,
sharex=False,
sharey=False,
figsize=(18, 6 * v_spaces))
ax = ax.flatten()
for i, (quantile, q_ret) in enumerate(
avg_cumulative_returns.groupby(level='factor_quantile')):
mean = q_ret.loc[(quantile, 'mean')]
mean.name = AVGCUMRET.get("COLUMN").format(quantile)
mean.plot(ax=ax[i], color=palette[i])
ax[i].set_ylabel(AVGCUMRET.get("YLABEL"))
if std_bar:
std = q_ret.loc[(quantile, 'std')]
ax[i].errorbar(std.index,
mean,
yerr=std,
fmt='none',
ecolor=palette[i],
label=None)
ax[i].axvline(x=0, color='k', linestyle='--')
ax[i].legend()
i += 1
else:
if ax is None:
f, ax = plt.subplots(1, 1, figsize=(18, 6))
for i, (quantile, q_ret) in enumerate(
avg_cumulative_returns.groupby(level='factor_quantile')):
mean = q_ret.loc[(quantile, 'mean')]
mean.name = AVGCUMRET.get("COLUMN").format(quantile)
mean.plot(ax=ax, color=palette[i])
if std_bar:
std = q_ret.loc[(quantile, 'std')]
ax.errorbar(std.index,
mean,
yerr=std,
fmt='none',
ecolor=palette[i],
label=None)
i += 1
ax.axvline(x=0, color='k', linestyle='--')
ax.legend()
ax.set(
title=AVGCUMRET.get("YLABEL").format(periods_before,
periods_after),
xlabel=AVGCUMRET.get("XLABEL"),
ylabel=AVGCUMRET.get("YLABEL"),
)
return ax
values = np.mat(values)
ax = plt.subplot(projection='3d')
#mns, avr, tcx, met, _ = prepare_averages(simulations)
top = np.ravel(values)
X = len(scales)
Y = len(methods)
xx = [[i] * X for i in range(Y)]
yy = [i for i in range(X)] * Y
xx = np.ravel(xx)
#top = np.log10(top)*10
mx = np.max(values)
mn = np.min(values)
alpha = 0.7
color_mapping = [cm.RdYlGn_r((i - mn) / (mx - mn)) for i in top]
color_mapping = [(r, g, b, alpha) for r, g, b, a in color_mapping]
ax.bar3d(xx, yy, np.zeros_like(top), 0.9, 0.9, top, color=color_mapping)
for i in range(len(top)):
text = to_si(top[i] * 1e-9)
ax.text3D(xx[i] + 0.5,
yy[i] + 0.5,
top[i] + 1,
text,
horizontalalignment='center')
# ax.set_zscale('log')
ax.set_xlabel("")
#ax.set_xlim(0,max(len(methods), len(scales)))
ax.set_xticks([i + 0.5 for i in range(len(methods))])
