def plot_curve(dates, y, title=None):
"""
绘制曲线
:param dates:
:param y:
:return:
"""
fig, ax = plt.subplots()
# ax.plot(y, 'o-')
# ax.plot(range(len(y)), y, 'o-')
ax.plot_date(dates,
y,
marker="*",
linewidth=1,
linestyle="-",
color="blue")
plt.title(title, fontproperties='SimHei', fontsize=12)
plt.xlabel("time")
plt.ylabel("rate")
### 设置X轴 ###
## 设置x轴范围
# datemin = datetime.date(dates.min().year - 5, 1, 1)
# datemax = datetime.date(dates.max().year + 5, 1, 1)
# ax.set_xlim(datemin, datemax)
## 设置x轴tick和ticklabel
# plt.xticks(np.arange(0, 50, 10), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue'), rotation=90)
# ax.set_xticks([])
# ax.set_xticks(list(y.index))
# ax.set_xticklabels(dates)
# ax.set_xticklabels(["start","medium","end"])
# ax.xaxis.set_major_formatter(NullFormatter()) # 设置x轴标签文本的格式
# ax.xaxis.set_major_formatter(mdate.DateFormatter('%Y-%m-%d')) # 设置x轴时间标签显示格式
# 设置主刻度标签的位置,有标签文本的格式
ax.xaxis.set_major_locator(YearLocator(
base=1, month=1, day=1, tz=None)) # tick every year on Feb 1st
ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d')) # 设置x轴标签文本的格式
# rule = rrulewrapper(YEARLY, byeaster=1, interval=5) # tick every 5th easter
# ax.xaxis.set_major_locator(RRuleLocator(rule))
# ax.xaxis.set_major_formatter(DateFormatter('%m/%d/%y')) # 设置x轴标签文本的格式
# 设置次刻度标签的位置,没有标签文本格式
ax.xaxis.set_minor_locator(YearLocator()) # 将x轴次刻度标签设置为年
### 设置Y轴 ###
ax.yaxis.set_major_locator(MultipleLocator(1)) # 将y轴主刻度标签设置为2的倍数
ax.yaxis.set_major_formatter(FormatStrFormatter('%1.1f')) # 设置y轴标签文本的格式
ax.yaxis.set_minor_locator(MultipleLocator(0.25)) # 将此y轴次刻度标签设置为0.1的倍数
# 打开网格
# ax.xaxis.grid(True, which='major') # x坐标轴的网格使用主刻度
# ax.yaxis.grid(True, which='major') # y坐标轴的网格使用次刻度
ax.grid(True)
fig.autofmt_xdate()
plt.show()
def locator(span):
if span < 400:
return MonthLocator()
elif 400 <= span < 800:
return MonthLocator(bymonth=[1, 4, 7, 10])
elif 800 <= span < 3700:
return YearLocator()
else:
return YearLocator(5)
def plotter(cities, citytotxt):
dates = {}
temps = {}
for city in cities:
d, t = citydata(city, citytotxt)
dates[city] = d
temps[city] = t
fig, ax = plt.subplots()
legends = []
for city in temps:
ax.plot_date(dates[city], temps[city], '-', label=city)
legends.append(city)
ax.legend(legends, loc='lower left')
ax.set_ylabel('Temperature [F]')
ax.set_xlabel('Date')
# Format the ticks
years = YearLocator(2) # major ticks every 2 years
months = MonthLocator(
bymonth=(1, 7)) # minor ticks every 6 months, for months 1&7
yearsfmt = DateFormatter('%Y')
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsfmt)
ax.xaxis.set_minor_locator(months)
ax.autoscale_view()
fig.autofmt_xdate()
plt.savefig("ej6.14.png")
def fmt_major_minor(axe):
axe.fmt_xdata = DateFormatter('%d/%m/%y %H:%M')
x_lim = axe.get_xlim()
if (x_lim[-1] - x_lim[0]) == 0:
axe.xaxis.set_major_locator(HourLocator())
axe.xaxis.set_major_formatter(DateFormatter('%d/%m/%y\n%H:%M'))
elif 0 < (x_lim[-1] - x_lim[0]) < 5:
axe.xaxis.set_major_locator(DayLocator())
axe.xaxis.set_major_formatter(DateFormatter('%d/%m/%y'))
axe.xaxis.set_minor_locator(HourLocator(byhour=(3, 6, 9, 12,
15, 18, 21)))
axe.xaxis.set_minor_formatter(DateFormatter('%H:%M'))
elif 5 <= (x_lim[-1] - x_lim[0]) < 11:
axe.xaxis.set_major_locator(DayLocator())
axe.xaxis.set_major_formatter(DateFormatter('%d/%m/%y'))
axe.xaxis.set_minor_locator(HourLocator(byhour=(6, 12, 18)))
axe.xaxis.set_minor_formatter(DateFormatter('%H:%M'))
elif 11 <= (x_lim[-1] - x_lim[0]) < 45:
axe.xaxis.set_major_locator(DayLocator(interval=3))
axe.xaxis.set_major_formatter(DateFormatter('%d/%m/%y'))
axe.xaxis.set_minor_locator(HourLocator(byhour=(12, 00)))
else:
axe.xaxis.set_minor_locator(MonthLocator())
axe.xaxis.set_major_locator(YearLocator())
axe.xaxis.set_major_formatter(DateFormatter('%Y'))
for label in axe.xaxis.get_majorticklabels():
label.set_fontsize(8)
label.set_ha('right')
label.set_rotation(45)
for label in axe.xaxis.get_minorticklabels():
label.set_fontsize(7)
label.set_ha('right')
label.set_rotation(45)
for label in axe.get_yticklabels():
label.set_fontsize(8)
def plotequity_trades(trades, res=None, title="Title", dataformat="%Y-%m-%d"):
mydpi = 96
plt.close("all")
eq = np.cumsum(trades)
fig = plt.figure(figsize=(1800 / mydpi, 1000 / mydpi), dpi=mydpi)
ax = fig.add_subplot(111)
ax.autoscale_view()
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y')
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
datemin = datetime.date(
datetime.datetime.strptime(res.index.min(), dataformat).year, 1, 1)
datemax = datetime.date(
datetime.datetime.strptime(res.index.max(), dataformat).year + 1, 1, 1)
ax.set_xlim(datemin, datemax)
ax.fmt_xdata = DateFormatter('%Y-%m-%d')
ax.plot(trades.index, eq.values, color="green")
if res is not None:
ax.plot(res.index, np.cumsum(res).values, color="black")
fig.autofmt_xdate()
plt.title(title)
plt.show()
def show_plot_actual(model, dates, close_v, predicted):
###############################################################################
# print trained parameters and plot
# print("Transition matrix")
# print(model.transmat_)
# print()
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y')
fig = plt.figure()
ax = fig.add_subplot(111)
# for i in range(model.n_components):
# # use fancy indexing to plot data in each state
# idx = (hidden_states == i)
# ax.plot_date(dates[idx], close_v[idx], '--', label="%dth hidden state" % i)
ax.plot_date(dates, close_v, '--')
ax.legend()
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.autoscale_view()
# format the coords message box
ax.fmt_xdata = DateFormatter('%Y-%m-%d')
ax.fmt_ydata = lambda x: '$%1.2f' % x
ax.grid(True)
fig.autofmt_xdate()
plt.show()
return 0
def plot_monthly(series_dates, series_record):
series_dates = pd.to_datetime(series_dates, format="%Y-%m-%d %H:%M:%S")
dates_list = []
for i in series_dates:
dates_list.append(i)
date1 = dates_list[0]
date2 = dates_list[len(dates_list) - 1]
years = YearLocator() # every year
months = MonthLocator() # every month
days = DayLocator()
dateFmt = DateFormatter('%d-%m-%Y')
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(dates_list, series_record)
# plt.gcf().autofmt_xdate()
# format the ticks
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(dateFmt)
# ax.xaxis.set_minor_locator(days)
ax.autoscale_view()
# def price(x):
# return '$%1.2f' % x
# ax.fmt_xdata = DateFormatter('%Y-%m-%d')
# ax.fmt_ydata = price
# ax.grid(True)
fig.autofmt_xdate()
plt.show()
def plotPairs(df, intercept, coef):
fig = plt.figure(figsize=(10, 5))
gs = gridspec.GridSpec(1, 2)
ax1 = plt.subplot(gs[0, 0])
ax2 = plt.subplot(gs[0, 1])
# 绘制拆线图
ax1.plot(df.index, df.ttmpea, color='blue', label='ts_codea')
ax1.plot(df.index, df.ttmpeb, color='yellow', label='ts_codeb')
ax1.legend()
# 设置X轴的刻度间隔
# 可选:YearLocator,年刻度; MonthLocator,月刻度; DayLocator,日刻度
ax1.xaxis.set_major_locator(YearLocator())
# 设置X轴主刻度的显示格式
ax1.xaxis.set_major_formatter(DateFormatter('%Y'))
# 设置鼠标悬停时，在左下显示的日期格式
ax1.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')
# 自动调整X轴标签角度
fig.autofmt_xdate()
# 绘制散点图
ax2.scatter(df.ttmpea, df.ttmpeb)
plt.grid(True)
x1 = int(min(df.ttmpea))
x2 = int(max(df.ttmpea) + 1)
X = [x1, x2]
y1 = intercept + x1 * coef
y2 = intercept + x2 * coef
Y = [y1, y2]
ax2.plot(X, Y, color='r')
# print('X:', X)
# print('Y:', Y)
plt.show()
def plot_paper_citations(paperbibcode=None, **plotkws):
from astropy.time import Time
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
if paperbibcode is None:
citations = get_paper_citations()
else:
citations = get_paper_citations(paperbibcode)
datenums = np.sort([
Time(date.replace('-00', '-01')).plot_date
for date in citations['pubdate']
])
fig = plt.gcf()
ax = plt.gca()
plotkws.setdefault('lw', 2)
plotkws.setdefault('ls', '-')
plotkws.setdefault('ms', 0)
ax.plot_date(datenums, np.arange(len(datenums)), **plotkws)
ax.xaxis.set_major_locator(YearLocator())
ax.xaxis.set_major_formatter(DateFormatter('%Y'))
ax.xaxis.set_minor_locator(MonthLocator())
ax.autoscale_view()
fig.autofmt_xdate()
plt.ylabel('# of Citations', fontsize=30)
plt.xticks(fontsize=20)
plt.tight_layout()
return datenums
def plot_worth_vs_time(self, names=None):
if names is None:
names = [
'Investor ({:0.2f},{:0.2f})'.format(inv.buy_at, inv.sell_at)
for inv in self.investors
]
dates = [x[0] for x in self.pe_array]
year = YearLocator()
date_fmt = DateFormatter('%Y')
plt.xkcd()
# investor worth plots
fig = plt.figure()
ax = fig.gca()
lines = []
for i in range(len(self.investors)):
result = ax.plot_date(dates, self.worth_matrix[i], '-')
lines.append(result[0])
ax.xaxis.set_major_locator(year)
ax.xaxis.set_major_formatter(date_fmt)
# ax.xaxis.set_minor_formatter(MonthLocator())
ax.autoscale_view()
ax.legend(lines, names, 'upper left')
fig.autofmt_xdate()
fig_pe = plt.figure()
ax_pe = fig_pe.gca()
ax_pe.plot_date(dates, [x[1] for x in self.pe_array], '-')
ax_pe.xaxis.set_major_locator(year)
ax_pe.xaxis.set_major_formatter(date_fmt)
ax_pe.autoscale_view()
ax_pe.set_title('PE Ratio vs. Time')
fig_pe.autofmt_xdate()
plt.show()
def traffic_offense_per_hour_plot(offense_per_hour):
traffic_by_hour()
years = YearLocator()
month = MonthLocator()
day_loc = DayLocator()
hours = HourLocator()
day_format = DateFormatter('%d%m%Y%H')
offense_number = []
year_of_offense = []
fig, ax = plt.subplots()
for dates in offense_per_hour:
print(dates)
offense_number.append(dates[0])
d = datetime.datetime.strftime(dates[1], "%d%m%Y%H")
year_of_offense.append(d)
ax.plot_date(year_of_offense, offense_number)
ax.set_title('Rate of Traffic Offense by Day')
ax.xaxis.set_major_locator(day_loc)
ax.xaxis.set_major_formatter(day_format)
ax.xaxis.set_minor_locator(hours)
ax.autoscale_view()
ax.fmt_xdate = DateFormatter('%Y%m%d%H')
ax.fmt_ydate = offense_number
ax.grid(True)
fig.autofmt_xdate()
plt.show()
def plot_nino_adjusted():
y = df['Inflation Adjusted Price']
x = df['Date'].map(lambda x: datetime.strptime(x, '%Y-%m-%d'))
x = np.array([x.to_pydatetime() for x in x])
y_avg = movingaverage(y, 504)
nino_mask = (df['ONI Index'] > 0).values
fig = plt.figure(figsize=(14, 8))
ax = fig.add_subplot(1, 1, 1)
loc = YearLocator(5)
plt.tick_params(axis='both', which='major', labelsize=11)
ax.plot(x, y, color='gray', alpha=.6, label='Inflation Adjusted Price')
ax.plot(x[252:-252],
y_avg[252:-252],
color='r',
alpha=.7,
label='2-Year Moving Average')
ax.fill_between(x,
np.zeros(len(x)),
y,
where=nino_mask,
alpha=.6,
label='El Nino Years')
ax.xaxis.set_major_locator(loc)
plt.suptitle('Corn Prices Over Time:\n1959 - 2016', size=20, alpha=.8)
plt.ylabel('Inflation Adjusted\nPrice of Nearest Futures Contract ($)',
size=14,
labelpad=30)
plt.legend()
# plt.show()
plt.savefig('Figures/inflation_adjusted_nino.png')
def main(sta1, sta2, filterid, components, mov_stack=1, show=True, outfile=None):
db = connect()
components_to_compute = get_components_to_compute(db)
maxlag = float(get_config(db,'maxlag'))
cc_sampling_rate = float(get_config(db,'cc_sampling_rate'))
start, end, datelist = build_movstack_datelist(db)
# mov_stack = get_config(db,"mov_stack")
plt.figure(figsize=(16,16))
sta1 = sta1.replace('.','_')
sta2 = sta2.replace('.','_')
if sta2 > sta1: # alphabetical order filtering!
pair = "%s:%s"%(sta1,sta2)
print "New Data for %s-%s-%i-%i"%(pair,components,filterid, mov_stack)
format = "matrix"
nstack, stack_total = get_results(db,sta1,sta2,filterid,components,datelist,mov_stack, format=format)
# vmax = scoreatpercentile(np.abs(stack_total[np.isnan(stack_total)==False]) , 98)
# for i in range(stack_total.shape[0]):
# if not np.all( np.isnan(stack_total[i,:])):
# print np.max(stack_total[i,:])
# stack_total[i,:] /= np.max(stack_total[i,:])
# stack_total /= np.max(stack_total, axis=0)
xextent = (date2num(start), date2num(end),-maxlag,maxlag)
ax = plt.subplot(111)
plt.imshow(stack_total.T, extent=xextent, aspect="auto",interpolation='none',origin='lower',cmap='seismic',
vmin=-1e-2,vmax=1e-2)
plt.ylabel("Lag Time (s)")
plt.axhline(0,lw=0.5,c='k')
plt.grid()
ax.xaxis.set_major_locator( YearLocator() )
ax.xaxis.set_major_formatter( DateFormatter('%Y-%m') )
# ax.xaxis.set_minor_locator( MonthLocator(interval=2) )
# ax.xaxis.set_minor_formatter( DateFormatter('%Y-%m-%d') )
lag = 120
plt.ylim(-lag,lag)
plt.title('%s : %s'%(sta1,sta2))
name = '%i.%s_%s.png'%(filterid,sta1,sta2)
#~ plt.savefig('interfero_publi.png',dpi=300)
# plt.figure()
# maxx = np.argmax(stack_total, axis=0)
# plt.plot(maxx)
if outfile:
if outfile.startswith("?"):
pair = pair.replace(':','-')
outfile = outfile.replace('?', '%s-%s-f%i-m%i' % (pair,
components,
filterid,
mov_stack))
outfile = "interferogram " + outfile
print "output to:", outfile
plt.savefig(outfile)
if show:
plt.show()
def plot_all_states(self):
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y')
fig = plt.figure(figsize=(20,10))
ax = fig.add_subplot(111)
ax.plot(self.dates, self.fracChange, 'g-', alpha=.4)
for i in range(self.model.n_components):
# use fancy indexing to plot data in each state
idx = (self.hidden_states == i)
ax.plot_date(self.dates[idx], self.fracChange[idx], 'o', label="hidden state $\# %d$" % i)
ax.legend()
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.autoscale_view()
# format the coords message box
ax.fmt_xdata = DateFormatter('%Y-%m-%d')
ax.fmt_ydata = lambda x: '$%1.2f' % x
ax.grid(True)
fig.autofmt_xdate()
plt.title('fracChange by GHMM Hidden States predicted on stock %s\
\n from %s to %s' % (self.symbol.upper(), self.fr_date, self.to_date))
plt.show()
def plot_in_sample_hidden_states(hmm_model, df, series):
"""
Plot the adjusted closing prices masked by
the in-sample hidden states as a mechanism
to understand the market regimes.
"""
# Predict the hidden states array
hidden_states = hmm_model.predict(df)
# Create the correctly formatted plot
fig, axs = plt.subplots(hmm_model.n_components,
sharex=True,
sharey=True,
figsize=[20, 20])
colours = cm.rainbow(np.linspace(0, 1, hmm_model.n_components))
for i, (ax, colour) in enumerate(zip(axs, colours)):
mask = hidden_states == i
ax.plot_date(df.index[mask],
series[mask],
".",
linestyle='none',
c=colour)
ax.set_title("Hidden State #%s" % i)
ax.xaxis.set_major_locator(YearLocator())
ax.xaxis.set_minor_locator(MonthLocator())
ax.grid(True)
plt.show()
def applyTicks(axs, days, stop):
for ax in axs:
month = MonthLocator()
year = YearLocator()
day = DayLocator()
day2 = DayLocator(bymonthday=1)
# rrule = RRuleLocator()
if days < 100:
fmt1 = DateFormatter('week %W')
week = WeekdayLocator()
ax.xaxis.set_major_locator(week)
ax.xaxis.set_major_formatter(fmt1)
else:
fmt2 = DateFormatterEx()
ax.xaxis.set_major_locator(day2)
ax.xaxis.set_major_formatter(fmt2)
ax.xaxis.set_minor_locator(day)
# ax.xaxis.set_minor_formatter(fmt2)
for ax in axs:
ax.axhline(y=0, lw=1.0)
# ax.axvline(x=pd.to_datetime(stop+STARTUNIXTIME, unit="s"), lw=1.0)
ax.axvline(x=pd.to_datetime(STARTUNIXTIME, unit="s"), lw=1.0)
ax.set_xlim(pd.to_datetime(STARTUNIXTIME, unit="s"), pd.to_datetime(stop+STARTUNIXTIME-3600*24*7, unit="s"))
for item in ([ax.xaxis.label] + ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(32)
def make_line_plot(
ax,
df,
x_col,
y_col,
title='',
ylabel='',
xlabel='',
):
ax.set_title(title)
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
if x_col == 'Date':
dates = [
datetime.datetime.strptime(x, "%Y-%m-%d").date() for x in df[x_col]
]
for col in y_col:
ax.plot(dates, df[col], label=col)
years = YearLocator()
months = MonthLocator()
yearsFmt = DateFormatter('%Y')
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.fmt_xdata = DateFormatter(yearsFmt)
else:
for col in y_col:
ax.plot(df[x_col], df[col], label=col)
if len(y_col) > 1:
ax.legend(bbox_to_anchor=(1.05, 1))
def plot_date_format(ax, time_range=None, locator=None, time_format=None):
''' This function formats plots by plt.plot_date
Input:
ax: plotting axis
time_range: a tuple of two datetime objects indicating xlim. e.g., (dt.date(1991,1,1), dt.date(1992,12,31))
locator: time locator on the plot; 'year' for year; 'month' for month. e.g., ('month', 3) for plot one tick every 3 months
time_format: a string of time format, e.g. '%Y/%m'
'''
import matplotlib.pyplot as plt
import datetime as dt
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
# Plot time range
if time_range != None:
plt.xlim(time_range[0], time_range[1])
# Set time locator (interval)
if locator != None:
if locator[0] == 'year':
ax.xaxis.set_major_locator(YearLocator(locator[1]))
elif locator[0] == 'month':
ax.xaxis.set_major_locator(MonthLocator(interval=locator[1]))
# Set time ticks format
if time_format != None:
ax.xaxis.set_major_formatter(DateFormatter(time_format))
return ax
def _show_plot(self, dates, close_v, hidden_states, title):
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y/%m')
fig = plt.figure()
ax = fig.add_subplot(111)
fig.canvas.set_window_title(title)
value = hidden_states
ax.plot_date(dates, close_v, '-', label="Actual value")
ax.plot_date(dates, value, '-', label="Predicted value")
ax.legend()
# format the ticks
ax.xaxis.set_major_formatter(yearsFmt)
ax.autoscale_view()
# format the coords message box
ax.fmt_xdata = DateFormatter('%Y-%m-%d')
# ax.fmt_ydata = lambda x: '$%1f' % x
ax.grid(True)
fig.autofmt_xdate()
plt.savefig('plot.png')
plt.show()
error = np.sum(np.abs((close_v - hidden_states) * 1.0 /
close_v)) * 100.0 / close_v.shape[0]
return error
def plotequity_pred(pred, res, title, dataformat="%Y-%m-%d"):
mydpi = 96
plt.close("all")
pos = np.where(pred > 0, 1, np.where(pred < -0, -1, 0))
eq = np.cumsum(pos * res)
fig = plt.figure(figsize=(1800 / mydpi, 1000 / mydpi), dpi=mydpi)
ax = fig.add_subplot(111)
ax.autoscale_view()
print(res.head())
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y')
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
datemin = datetime.date(
datetime.datetime.strptime(res.index.min(), dataformat).year, 1, 1)
datemax = datetime.date(
datetime.datetime.strptime(res.index.max(), dataformat).year + 1, 1, 1)
ax.set_xlim(datemin, datemax)
ax.fmt_xdata = DateFormatter('%Y-%m-%d')
ax.plot(res.index, eq.values, color="green")
ax.plot(res.index, np.cumsum(res).values, color="black")
fig.autofmt_xdate()
plt.title(title)
plt.show()
def plot_corn_prices_adjusted():
y = df['Inflation Adjusted Price']
x = df['Date'].map(lambda x: datetime.strptime(x, '%Y-%m-%d'))
corn_syrup = datetime(1970, 1, 1)
e85 = datetime(2000, 1, 1)
y_avg = movingaverage(y, 504)
fig = plt.figure(figsize=(14, 8))
ax = fig.add_subplot(1, 1, 1)
loc = YearLocator(5)
ax.plot(x, y, color='gray', alpha=.6, label='Inflation Adjusted Price')
ax.plot(x[252:-252],
y_avg[252:-252],
color='r',
alpha=.7,
label='2-Year Moving Average')
ax.plot((corn_syrup, corn_syrup), (0, 1500), color='k')
ax.plot((e85, e85), (0, 1500), color='k')
ax.xaxis.set_major_locator(loc)
plt.tick_params(axis='both', which='major', labelsize=11)
plt.text(corn_syrup + timedelta(days=100), 50,
'Corn Syrup\'s\nCommerical Introduction')
plt.text(e85 + timedelta(days=100), 50, 'E85 Becomes\nWidely Used')
plt.suptitle('Corn Prices Over Time:\n1959 - 2016', size=20, alpha=.8)
plt.ylabel('Inflation Adjusted\nPrice of Nearest Futures Contract ($)',
size=14,
labelpad=30)
plt.legend()
# plt.show()
plt.savefig('Figures/inflation_adjusted_demand_shocks.png')
def plot_timeline_sentinel(gf):
"""Plot dinosar inventory acquisitions as a timeline.
Parameters
----------
gf : GeoDataFrame
A geopandas GeoDataFrame
"""
dfA = gf.query('platform == "Sentinel-1A"')
dfAa = dfA.query(' flightDirection == "ASCENDING" ')
dfAd = dfA.query(' flightDirection == "DESCENDING" ')
dfB = gf.query('platform == "Sentinel-1B"')
dfBa = dfB.query(' flightDirection == "ASCENDING" ')
dfBd = dfB.query(' flightDirection == "DESCENDING" ')
# Same colors as map
orbits = gf.relativeOrbit.unique()
colors = plt.cm.jet(np.linspace(0, 1, orbits.size))
fig, ax = plt.subplots(figsize=(11, 8.5))
plt.scatter(dfAa.timeStamp.values,
dfAa.orbitCode.values,
edgecolors=colors[dfAa.orbitCode.values],
facecolors='None',
cmap='jet',
s=60,
label='Asc S1A')
plt.scatter(dfBa.timeStamp.values,
dfBa.orbitCode.values,
edgecolors=colors[dfBa.orbitCode.values],
facecolors='None',
cmap='jet',
s=60,
marker='d',
label='Asc S1B')
plt.scatter(dfAd.timeStamp.values,
dfAd.orbitCode.values,
c=colors[dfAd.orbitCode.values],
cmap='jet',
s=60,
label='Dsc S1A')
plt.scatter(dfBd.timeStamp.values,
dfBd.orbitCode.values,
c=colors[dfBd.orbitCode.values],
cmap='jet',
s=60,
marker='d',
label='Dsc S1B')
plt.yticks(gf.orbitCode.unique(), gf.relativeOrbit.unique())
ax.xaxis.set_minor_locator(MonthLocator())
ax.xaxis.set_major_locator(YearLocator())
plt.legend(loc='lower right')
plt.ylim(-1, orbits.size)
plt.ylabel('Orbit Number')
fig.autofmt_xdate()
plt.title('Acquisition Timeline')
plt.savefig('timeline.pdf', bbox_inches='tight')
def test_date_demo1():
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y')
# make file paths OS independent
infile = os.path.join('examples','data','yahoofinance-INTC-19950101-20040412.csv')
quotes = pd.read_csv(infile,index_col=0,parse_dates=True,infer_datetime_format=True)
dates = quotes.index
opens = quotes['Open']
fig, ax = plt.subplots()
ax.plot_date(dates, opens, '-')
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.autoscale_view()
# format the coords message box
def price(x):
return '$%1.2f' % x
ax.fmt_xdata = DateFormatter('%Y-%m-%d')
ax.fmt_ydata = price
ax.grid(True)
fig.autofmt_xdate()
buf = io.BytesIO()
plt.savefig(buf)
def _create_xaxis_date(self, ax, date_index):
""" Uses buest guess for x axis labels """
window = (date_index[-1] - date_index[0]).astype(int)
interval = 5
xlabel = ""
if window <= 3600:
minor_locator = MinuteLocator(interval=interval)
minor_formatter = DateFormatter("%M")
major_locator = HourLocator()
major_formatter = DateFormatter("\n%Y-%m-%d %H:%M")
xlabel = "Minute"
elif window <= 86400:
minor_locator = HourLocator(interval=interval)
minor_formatter = DateFormatter("%H:%M")
major_locator = DayLocator()
major_formatter = DateFormatter("\n%Y-%m-%d")
xlabel = "Hour"
elif window <= (7 * 86400):
minor_locator = HourLocator(interval=6)
minor_formatter = DateFormatter("%H:%M")
major_locator = DayLocator()
major_formatter = DateFormatter("\n%Y-%m-%d")
xlabel = "Hour"
elif window <= (60 * 86400):
#if len(date_index) > 30:
# interval = 2
minor_locator = DayLocator(interval=interval)
minor_formatter = DateFormatter("%m-%d")
major_locator = YearLocator()
major_formatter = DateFormatter("\n%Y")
xlabel = "Day"
else:
minor_locator = MonthLocator(interval=interval)
minor_formatter = DateFormatter("%B")
major_locator = YearLocator()
major_formatter = DateFormatter("\n%Y")
xlabel = "Month"
ax.xaxis.set_minor_locator(minor_locator)
ax.xaxis.set_minor_formatter(minor_formatter)
ax.xaxis.set_major_locator(major_locator)
ax.xaxis.set_major_formatter(major_formatter)
ax.fmt_xdata = DateFormatter("%Y-%m-%d %H:%M:%S")
ax.set_xlabel(xlabel)
def plot_timeline_table(gf):
"""Plot dinosar inventory acquisitions as a timeline with a table.
Parameters
----------
gf : GeoDataFrame
A geopandas GeoDataFrame
"""
dfA = gf.query('platform == "Sentinel-1A"')
dfAa = dfA.query(' flightDirection == "ASCENDING" ')
dfAd = dfA.query(' flightDirection == "DESCENDING" ')
dfB = gf.query('platform == "Sentinel-1B"')
dfBa = dfB.query(' flightDirection == "ASCENDING" ')
dfBd = dfB.query(' flightDirection == "DESCENDING" ')
# summary table
dfS = gpd.pd.DataFrame(index=gf.relativeOrbit.unique())
dfS['Start'] = gf.groupby('relativeOrbit').sceneDateString.min()
dfS['Stop'] = gf.groupby('relativeOrbit').sceneDateString.max()
dfS['Dates'] = gf.groupby('relativeOrbit').sceneDateString.nunique()
dfS['Frames'] = gf.groupby('relativeOrbit').sceneDateString.count()
dfS['Direction'] = gf.groupby('relativeOrbit').flightDirection.first()
dfS['UTC'] = gf.groupby('relativeOrbit').utc.first()
dfS.sort_index(inplace=True, ascending=False)
dfS.index.name = 'Orbit'
# Same colors as map
orbits = gf.relativeOrbit.unique()
colors = plt.cm.jet(np.linspace(0, 1, orbits.size))
fig, ax = plt.subplots(figsize=(11, 8.5))
plt.scatter(dfAa.timeStamp.values, dfAa.orbitCode.values,
c=colors[dfAa.orbitCode.values], cmap='jet',
s=60, facecolor='none', label='S1A')
plt.scatter(dfBa.timeStamp.values, dfBa.orbitCode.values,
c=colors[dfBa.orbitCode.values], cmap='jet',
s=60, facecolor='none', marker='d', label='S1B')
plt.scatter(dfAd.timeStamp.values, dfAd.orbitCode.values,
c=colors[dfAd.orbitCode.values], cmap='jet',
s=60, label='S1A')
plt.scatter(dfBd.timeStamp.values, dfBd.orbitCode.values,
c=colors[dfBd.orbitCode.values], cmap='jet',
s=60, marker='d', label='S1B')
plt.yticks(gf.orbitCode.unique(), gf.relativeOrbit.unique())
table(ax, dfS, loc='top', zorder=10, fontsize=12,
cellLoc='center', rowLoc='center',
bbox=[0.1, 0.7, 0.6, 0.3]) # [left, bottom, width, height])
ax.xaxis.set_minor_locator(MonthLocator())
ax.xaxis.set_major_locator(YearLocator())
plt.legend(loc='upper right')
plt.ylim(-1, orbits.size+3)
plt.ylabel('Orbit Number')
fig.autofmt_xdate()
plt.title('Sentinel-1 timeline')
plt.savefig('timeline_with_table.pdf', bbox_inches='tight')
def prettify_ax(ax, title="", center=False, percentage=False, start_date=config.start_date, end_date=config.end_date):
"""Makes matplotlib.pyplot.Axes look pretty
Parameters:
ax : Axes
title : str
center : bool
x axis in center of graph instead of bottom
start_date : date, optional
end_date : date, optional
"""
if title != "":
ax.set_title(title)
ax.set_xlabel("Date")
ax.set_ylabel("Price")
ax.legend(loc="upper left", fontsize="small", labelspacing=0.2)
'''
matplotlib.rcParams["legend.loc"] = "upper left" # matplotlib.rc("legend", loc="upper left")
matplotlib.pyplot.rcParams["legend.fontsize"] = "small" # matplotlib.pyplot.rc("legend", fontsize="small")
matplotlib.pyplot.rcParams["legend.labelspacing"] = 0.2 # matplotlib.pyplot.rc("legend", labelspacing=0.2)
'''
# ax.spines["top"].set_visible(False)
# ax.spines["right"].set_visible(False)
ax.xaxis.set_major_locator(YearLocator())
ax.xaxis.set_minor_locator(MonthLocator((1, 7)))
ax.xaxis.set_major_formatter(DateFormatter("\n%Y"))
ax.xaxis.set_minor_formatter(DateFormatter("%b"))
ax.get_xaxis().tick_bottom()
ax.get_yaxis().tick_left()
# ax.tick_params(axis="both", which="both", bottom="off", top="off", labelbottom="on", left="off", right="off", labelleft="on") # Remove the tick marks
# TODO: what causes smooshing
# ax.set_xlim(start_date, end_date) # this stops smooshing
ax.margins(x=0)
min_x = ax.get_xlim()[0].astype(int)
max_x = ax.get_xlim()[1].astype(int)
if not center and ax.get_ylim()[0] < 0:
ax.set_ylim(ymin=0)
if center:
min_y = ax.get_ylim()[0].astype(float)
max_y = ax.get_ylim()[1].astype(float)
if abs(min_y) != abs(max_y):
ax.set_ylim(ymin=-max(abs(min_y), abs(max_y)), ymax=max(abs(min_y), abs(max_y)))
ax.plot(range(min_x, max_x), [0] * len(range(min_x, max_x)), "-", linewidth=0.5, color="black")
if percentage:
ax.set_ylim(ymin=0, ymax=100)
# ax.yaxis.set_ticks(np.arange(0, 100, 10))
# ax.set_yticks([0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100])
# TODO: do this for the dates too. Have to get locators, since there are too many ticks
# for y in range(ax.get_ylim()[0], ax.get_ylim()[1], 10):
for i, y in enumerate(ax.get_yticks().astype(float)[1:-1]):
ax.plot(range(min_x, max_x), [y] * len(range(min_x, max_x)), "--", linewidth=0.5, color="black", alpha=config.alpha)
def _plot_balance(self, _):
ax = pl.gca()
formatter = DateFormatter("%Y-%m-%d")
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_major_locator(MonthLocator())
ax.xaxis.set_minor_locator(YearLocator())
pl.plot(*MODEL.balance_data())
pl.gcf().autofmt_xdate()
pl.show()
def Draw_Z_Index_Graph(outfile, strdate, data):
fig = plt.figure(figsize=(8,5))
ax = fig.add_subplot(111)
# plt.plot(strdate, data, '*', color = 'black', lw = 0.8)
plt.plot(strdate, data[53519], '.', marker ='.',color='k', lw = 0.8, label = u"惠农")
plt.plot(strdate, data[53612], '.', marker ='^',color='r', lw = 0.8, label = u"吴忠")
plt.plot(strdate, data[53614], '.', marker ='D',color='g', lw = 0.8, label = u"银川")
plt.plot(strdate, data[53615], '.', marker ='+',color='b', lw = 0.8, label = u"陶乐")
plt.plot(strdate, data[53704], '.', marker ='x',color='c', lw = 0.8, label = u"中卫")
plt.plot(strdate, data[53705], '.', marker ='*',color='y', lw = 0.8, label = u"中宁")
plt.plot(strdate, data[53723], '.', marker ='s',color='m', lw = 0.8, label = u"盐池")
plt.plot(strdate, data[53806], '.', marker ='8',color='gold', lw = 0.8, label = u"海源")
plt.plot(strdate, data[53810], '.', marker ='p',color='peru', lw = 0.8, label = u"同心")
plt.plot(strdate, data[53817], '.', marker ='h',color='brown', lw = 0.8, label = u"固原")
plt.plot(strdate, data[53903], '.', marker ='v',color='wheat', lw = 0.8, label = u"西吉")
plt.plot(strdate, data[53910], '.', marker ='.',color='tan', lw = 0.8, label = u"六盘山")
##获取时间轴的起始、结束时间
s = np.int(strdate[0].strftime('%Y'))
e = np.int(strdate[-1].strftime('%Y'))
if (s % 10) > 5:
syear = s / 10 * 10 + 5
else:
syear = s / 10 * 10
if (e % 10) > 5:
eyear = e / 10 * 10 + 10
else:
eyear = e / 10 * 10 + 5
xstart = datetime.datetime.strptime(str(syear), '%Y')
xend = datetime.datetime.strptime(str(eyear), '%Y')
# 绘制一次拟合线性趋势线
ax.set_xlim(xstart, xend)
# ax.set_ylim(150, 450)
formatter = DateFormatter('%Y')
ax.xaxis.set_major_locator(formatter)
ax.xaxis.set_major_locator(YearLocator(base=5))
# ax.set_xlabel(str, fontsize=14)
# ax.xaxis.set_label_coords(0.75, -2.5)
plt.title(u'Z指数时间序列图', fontproperties=font, fontsize=12)
ax.set_xlabel(u'年 份', fontproperties=font, fontsize=10)
# ax.set_ylabel(u'降水量/mm', fontproperties=font, fontsize=10)
ax.legend(loc='upper right', prop=font, fontsize=6, edgecolor ='white')
# plt.savefig(outfile, dpi=200, bbox_to_anchor='tight')
plt.show()
def drawData(ax, _data):
"""
使用柱状图表示股市数据
"""
candlestick_ochl(ax,
_data[["date2num", "open_price", "close_price", "high_price", "low_price"]].values,
colorup="r", colordown="g", width=0.5)
ax.xaxis.set_major_locator(YearLocator())
ax.xaxis.set_major_formatter(DateFormatter('%Y'))
return ax
def plot_graph(people, overall, output, individuals):
# i dunno what this stuff is but it makes the graph happen
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y-%m')
fig, ax = plt.subplots(figsize=(16,12), dpi=80)
fig.suptitle('#PLACEHOLDER activity per day', fontsize=20)
legend = []
if individuals:
top_talkers = sorted(people, reverse=True, key=lambda k: people[k]['msgcount'])[:individuals]
for person in top_talkers:
(p_dates, p_activity) = build_date_arrays(people[person]['dates'])
print("plotting {}'s activity: {} lines".format(person, people[person]['msgcount']))
ax.plot_date(p_dates, p_activity, '-')
legend.append(person)
#print(str(people[person]['dates']))
#print(str(p_activity))
#i = 0
#while i < len(p_dates):
# print("{}: {}".format(str(p_dates[i]), p_activity[i]))
# i += 1
#print(str(p_dates))
#break
else:
(o_dates,o_activity) = build_date_arrays(overall)
ax.plot_date(o_dates, o_activity, '-')
legend.append('Overall')
# TODO figure out how to show individual months along the x axis, right now it's kinda bare
plt.legend(legend, loc='upper right')
#ax.set_yscale('log') # doesn't work as well as i'd hoped :(
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.autoscale_view()
ax.fmt_xdata = DateFormatter("%Y-%m-%d")
ax.grid(True)
fig.autofmt_xdate()
if output == 'x11':
plt.show()
else:
filename = 'ircstatter_{}.png'.format(time.strftime('%Y-%m-%d_%H:%M:%S'))
plt.savefig(filename)
print('graph saved as {}'.format(filename))
