def plot(outright_index, fly_index, hedged_index):
date = m["DateTime"].tolist()
outright_prices = m[outright_index].values.tolist()
fly_prices = m[fly_index].values.tolist()
hedged_prices = m[hedged_index].values.tolist()
upper_band = (m[hedged_index+"_mean"]+EntryStd*m[hedged_index+"_std"]).values.tolist()
lower_band = (m[hedged_index+"_mean"]-EntryStd*m[hedged_index+"_std"]).values.tolist()
buy_dates = [x[0] for x in trade_recorder[hedged_index].trades if x[2]>0]
buy_prices = [x[1] for x in trade_recorder[hedged_index].trades if x[2]>0]
sell_dates = [x[0] for x in trade_recorder[hedged_index].trades if x[2]<0]
sell_prices = [x[1] for x in trade_recorder[hedged_index].trades if x[2]<0]
fig, (ax1, ax2, ax3) = plt.subplots(3, sharex = True)
mondays = WeekdayLocator(MONDAY)
mondays.MAXTICKS = 2000
alldays = DayLocator() # minor ticks on the days
alldays.MAXTICKS = 2000
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
ax1.xaxis.set_major_locator(mondays)
ax1.xaxis.set_minor_locator(alldays)
ax1.xaxis.set_major_formatter(weekFormatter)
ax1.plot(date, outright_prices)
ax2.plot(date, fly_prices)
ax3.plot(date, hedged_prices)
ax3.plot(date, upper_band, color='k')
ax3.plot(date, lower_band, color='k')
ax3.plot(buy_dates, buy_prices, "^", markersize = 5, color='m')
ax3.plot(sell_dates, sell_prices, "v", markersize = 5, color='k')
ax1.xaxis_date()
ax1.autoscale_view()
def plot_trade(self, symbol, init_index = None, end_index=None, PnL=False, ATR=False):
if init_index == None:
init_index = 0
init_date = b.bars[symbol][init_index]["D"]
if end_index == None:
end_index = len(b.bars[symbol])
end_date = b.bars[symbol][end_index-1]["D"]
mondays = WeekdayLocator(MONDAY)
mondays.MAXTICKS = 2000
alldays = DayLocator() # minor ticks on the days
alldays.MAXTICKS = 2000
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
history = [(date2num(x["D"]), x["O"], x["H"], x["L"], x["C"]) for x in b.bars[symbol][init_index:end_index]]
if PnL or ATR:
fig, (ax, ax2) = plt.subplots(2, sharex = True)
else:
fig, ax = plt.subplots()
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
candlestick_ohlc(ax, history)
buys = [(x["Date"], x["Price"]) for x in self.trades[symbol] if (x["Lots"] > 0 and x["Date"] > init_date and x["Date"]<end_date) ]
buy_dates, buy_values = zip(*buys)
ax.plot(buy_dates, buy_values, "^", markersize = 5, color='m')
sells = [(x["Date"], x["Price"]) for x in self.trades[symbol] if (x["Lots"] < 0 and x["Date"] > init_date and x["Date"]<end_date)]
sell_dates, sell_values = zip(*sells)
ax.plot(sell_dates, sell_values, "v", markersize = 5, color='k')
ax.xaxis_date()
ax.autoscale_view()
if PnL:
equity_history = [(date, record["equity"], record["drawdown"]) for date, record in self.history.items()]
dates, values, drawdowns = zip(*equity_history)
ax2 = fig.add_subplot(212)
ax2.plot(dates, values, 'b-')
elif ATR:
equity_history = [(x["D"], x["ATR"]) for x in self.bars[symbol][init_index:end_index]]
dates, values = zip(*equity_history)
ax2 = fig.add_subplot(212)
ax2.plot(dates, values, 'b-')
## ax3 = ax2.twinx()
## ax3.plot(dates, drawdowns, 'r-')
plt.show()
def draw(self):
if self.th != None:
self.fig, (self.ax1, self.ax2,
self.ax3) = plt.subplots(3, 1, sharex=True)
else:
self.fig, (self.ax1, self.ax2) = plt.subplots(2, 1, sharex=True)
self.ax1_1 = self.ax1.twinx()
# set figure
self.fig.suptitle(self.title, fontsize=18)
#self.fig.autofmt_xdate(rotation=70)
self.draw_basic(self.ax1, self.ax1_1)
# plot the KD
self.ax2_lines = dict()
self.ax2_lines['k'] = self.ax2.plot(self.df['Date'], self.df['k'])[0]
self.ax2_lines['d'] = self.ax2.plot(self.df['Date'], self.df['d'])[0]
# plot RSI
self.ax2_lines['RSI'] = self.ax2.plot(self.df['Date'],
self.df['RSI'])[0]
self.ax2_lines['RSI'].set_linewidth(0.3)
self.ax2.legend(['K', 'D', 'RSI'], loc='upper left')
plt.connect('motion_notify_event', self.mouse_move)
#plt.connect('scroll_event', cursor.on_scroll)
plt.connect('button_press_event', self.on_scroll)
plt.connect('key_press_event', self.on_key)
ymin = np.min(self.df['k']) if np.min(self.df['k']) < np.min(
self.df['d']) else np.min(self.df['d'])
ymax = np.max(self.df['k']) if np.max(self.df['k']) > np.max(
self.df['d']) else np.max(self.df['d'])
ymin = ymin - 5 if (ymax - 5) > 0 else 0
ymax = ymax + 5 if (ymax + 5) < 100 else 100
self.ax2.set_ylim(ymin, ymax)
# set mouse scroll event
# draw lines for cursor focus
self.lx1 = self.ax1.axhline(color='k',
y=self.y1_df.loc[0],
linewidth=0.5,
linestyle='--') # the horiz line
self.ly1 = self.ax1.axvline(color='k',
x=self.x_df.loc[0],
linewidth=0.5,
linestyle='--') # the vert line
self.lx2_k = self.ax2.axhline(color='k',
y=self.y2_k_df.loc[0],
linewidth=0.5,
linestyle='--') # the horiz line
self.ly2_k = self.ax2.axvline(color='k',
x=self.x_df.loc[0],
linewidth=0.5,
linestyle='--') # the vert line
self.lx2_d = self.ax2.axhline(color='k',
y=self.y2_k_df.loc[0],
linewidth=0.5,
linestyle='--') # the horiz line
self.ly2_d = self.ax2.axvline(color='k',
x=self.x_df.loc[0],
linewidth=0.5,
linestyle='--') # the vert line
# text location in axes coords
self.txt1 = self.ax1.text(0, 1.05, "", transform=self.ax1.transAxes)
self.txt2 = self.ax2.text(0, 1.05, "", transform=self.ax2.transAxes)
if self.th != None:
self.ax3_lines = []
for (th_title, th) in self.th:
marks = th['Date'][th['Buy'] != 0].index.tolist()
#marks = [1, 3, 5, 7, 9]
_line = self.ax3.plot(self.df['Date'],
th['Total'],
label=th_title,
marker='.',
markevery=marks)
self.ax3_lines.append(_line[0])
_line[0].set_linestyle('--')
_line[0].set_linewidth(0.5)
self.ax3_lines[self.focus_th_idx].set_linestyle('-')
self.ax3_lines[self.focus_th_idx].set_linewidth(1.5)
lines, labels = self.ax3.get_legend_handles_labels()
self.ax3.legend(lines, labels, loc='upper left')
#self.ax3.legend(self.focus_th_title, loc='upper right')
self.ax3.grid()
th = self.th[self.focus_th_idx][1]
self.ly3 = self.ax3.axvline(color='k',
x=th['Date'].loc[0],
linewidth=0.5,
linestyle='--') # the vert line
self.txt3 = self.ax3.text(0,
1.05,
"",
transform=self.ax3.transAxes)
self.ax2.grid()
# for x-axis tick label
if len(self.df) < 40:
self.ax1.xaxis.set_major_locator(DayLocator())
elif len(self.df) < 100:
self.ax1.xaxis.set_major_locator(WeekdayLocator())
self.ax1.xaxis.set_minor_locator(DayLocator())
else:
self.ax1.xaxis.set_major_locator(MonthLocator())
self.ax1.xaxis.set_minor_locator(WeekdayLocator())
self.ax1.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d'))
self.ax1.fmt_xdata = DateFormatter('%Y-%m-%d %H:%M:%S')
if self.pngName == None:
plt.show()
else:
plt.savefig(self.pngName)
def pandas_candlestick_ohlc(dat, stick="day", otherseries=None):
"""
:param dat: pandas DataFrame object with datetime64 index, and float columns "Open", "High", "Low", and "Close", likely created via DataReader from "yahoo"
:param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include "day", "week", "month", and "year", ("day" default), and any numeric input indicates the number of trading days included in a period
:param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines
This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.
"""
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
dayFormatter = DateFormatter('%d') # e.g., 12
# Create a new DataFrame which includes OHLC data for each period specified by stick input
transdat = dat.loc[:, ["Open", "High", "Low", "Close"]]
if (type(stick) == str):
if stick == "day":
plotdat = transdat
stick = 1 # Used for plotting
elif stick in ["week", "month", "year"]:
if stick == "week":
transdat["week"] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalendar()[1]) # Identify weeks
elif stick == "month":
transdat["month"] = pd.to_datetime(transdat.index).map(
lambda x: x.month) # Identify months
transdat["year"] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalendar()[0]) # Identify years
grouped = transdat.groupby(list(set(
["year",
stick]))) # Group by year and other appropriate variable
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
}) # Create empty data frame containing what will be plotted
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
"Open": group.iloc[0, 0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1, 3]
},
index=[group.index[0]]))
if stick == "week": stick = 5
elif stick == "month": stick = 30
elif stick == "year": stick = 365
elif (type(stick) == int and stick >= 1):
transdat["stick"] = [
np.floor(i / stick) for i in range(len(transdat.index))
]
grouped = transdat.groupby("stick")
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
}) # Create empty data frame containing what will be plotted
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
"Open": group.iloc[0, 0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1, 3]
},
index=[group.index[0]]))
else:
raise ValueError(
'Valid inputs to argument "stick" include the strings "day", "week", "month", "year", or a positive integer'
)
# Set plot parameters, including the axis object ax used for plotting
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
else:
weekFormatter = DateFormatter('%b %d, %Y')
ax.xaxis.set_major_formatter(weekFormatter)
ax.grid(True)
# Create the candelstick chart
candlestick_ohlc(ax,
list(
zip(list(mdates.date2num(plotdat.index.tolist())),
plotdat["Open"].tolist(),
plotdat["High"].tolist(), plotdat["Low"].tolist(),
plotdat["Close"].tolist())),
colorup="black",
colordown="red",
width=stick * .4)
# Plot other series (such as moving averages) as lines
if otherseries != None:
if type(otherseries) != list:
otherseries = [otherseries]
dat.loc[:, otherseries].plot(ax=ax, lw=1.3, grid=True)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
plt.show()
from pylab import *
from matplotlib.dates import DateFormatter, WeekdayLocator, HourLocator, DayLocator, MONDAY
from matplotlib.finance import quotes_historical_yahoo, candlestick, plot_day_summary, candlestick2
from matplotlib.font_manager import FontProperties
if __name__ == '__main__':
font = FontProperties(fname=r"t:\Windows\Fonts\simsun.ttc",size=18)
# 定义起始、终止日期和股票代码
date1 = ( 2013, 1, 1 )
date2 = ( 2013, 8, 1 )
date3 = ( 2013, 5, 1 )
stock_num = '600036.ss'
# 定义日期格式
mondays = WeekdayLocator(MONDAY) #major ticks on the mondays
alldays = DayLocator() #minor ticks on the days
weekFormatter = DateFormatter('%b %d') # Jan 12
dayFormatter = DateFormatter('%d') # 12
# 获取股票数据
quotes = quotes_historical_yahoo(stock_num, date1, date2)
quotes2 = quotes_historical_yahoo(stock_num, date1, date3)
if len(quotes) == 0:
raise SystemExit
# 绘制蜡烛线或美国线
fig = figure(facecolor='gray')
fig.subplots_adjust(bottom=0.2)
ax = fig.add_subplot(111)
ax2 = fig.add_subplot(111)
#fig, ax = plt.subplots()
#fig.subplots_adjust(bottom=0.2)
import matplotlib.pyplot as plt
from matplotlib.dates import DayLocator, HourLocator, DateFormatter, drange
from numpy import arange
date1 = datetime.datetime(2000, 3, 2)
date2 = datetime.datetime(2000, 3, 6)
delta = datetime.timedelta(hours=6)
dates = drange(date1, date2, delta)
y = arange(len(dates) * 1.0)
fig, ax = plt.subplots()
ax.plot_date(dates, y * y)
# this is superfluous, since the autoscaler should get it right, but
# use date2num and num2date to convert between dates and floats if
# you want; both date2num and num2date convert an instance or sequence
ax.set_xlim(dates[0], dates[-1])
# The hour locator takes the hour or sequence of hours you want to
# tick, not the base multiple
ax.xaxis.set_major_locator(DayLocator())
ax.xaxis.set_minor_locator(HourLocator(arange(0, 25, 6)))
ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d'))
ax.fmt_xdata = DateFormatter('%Y-%m-%d %H:%M:%S')
fig.autofmt_xdate()
plt.show()
# get all those error values and make them into a new series
ye = np.where(y > 400)[0]
xe = x[ye]
ye = np.random.uniform(-1, 0, ye.shape[0])
y[y > 400] = np.nan
fig, ax = plt.subplots()
plt.plot(x, y, 'r-', label='Readings')
# plt.plot(xe, ye, 'bo', label='Errors')
plt.legend(prop={'size': 23})
fig.subplots_adjust(bottom=0.125)
majorFormatter = DateFormatter('%a')
ax.xaxis.set_major_formatter(majorFormatter)
majorLocator = DayLocator()
ax.xaxis.set_major_locator(majorLocator)
minorLocator = HourLocator(interval=3)
minorFormatter = DateFormatter('%-I %p')
ax.xaxis.set_minor_formatter(minorFormatter)
ax.xaxis.set_minor_locator(minorLocator)
# # # # for the minor ticks, use no labels; default NullFormatter
# ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=45)
# print (ax.xaxis.get_majorticklabels())
# ax.set_xticks(ax.get_xticks()[::2])
# plt.setp(ax.xaxis.get_ticklabels(which='both'), rotation=90)
# labels = ax.get_xticklabels()
# plt.setp(ax.get_xticks('both'), rotation=30)
def hallarPatrones(fechaInicio, fechaFin, archivo, procedencia, contador):
terminado = False
suma = contador
vela = False
nombreVela = "no-vela"
fechaInicio = str(fechaInicio)
fechaFin = str(fechaFin)
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
if procedencia == "csv":
quotes = pd.read_csv(archivo, index_col="Date", parse_dates=True)
if procedencia == "menu":
quotes = archivo
quotes = quotes[(quotes.index >= fechaInicio) & (quotes.index <= fechaFin)]
quotes_numpy = {
'Open': np.array(quotes['Open'], dtype='float'),
'High': np.array(quotes['High'], dtype='float'),
'Low': np.array(quotes['Low'], dtype='float'),
'Close': np.array(quotes['Close'], dtype='float'),
'Volume': np.array(quotes['Volume'], dtype='int')
}
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_locator(mondays)
#ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
ax.xaxis.set_minor_formatter(dayFormatter)
candlestick_ohlc(ax,
zip(mdates.date2num(quotes.index.to_pydatetime()),
quotes['Open'], quotes['High'], quotes['Low'],
quotes['Close']),
width=0.9,
colorup='g',
colordown='r')
ax.xaxis_date()
ax.autoscale_view()
plt.rcParams["figure.figsize"] = [16.0, 4.8]
plt.setp(plt.gca().get_xticklabels(),
rotation=90,
horizontalalignment='right')
#Leyenda
l1 = Patch(facecolor='maroon',
label='Formación con Gap Bajista',
alpha=0.8)
l2 = Patch(facecolor='brown', label='Formación con Gap Alcista', alpha=0.8)
l3 = Patch(facecolor='sienna', label='MatHold', alpha=0.8)
l4 = Patch(facecolor='chocolate',
label='Triple Formación Alcista',
alpha=0.8)
l5 = Patch(facecolor='sandybrown',
label='Triple Formación Bajista',
alpha=0.8)
l6 = Patch(facecolor='navy', label='Pequeña Golondrina Oculta', alpha=0.75)
l7 = Patch(facecolor='mediumseagreen',
label='Dos Cuervos en Gap Alcista',
alpha=0.7)
l8 = Patch(facecolor='olive', label='Tres Soldados Blancos', alpha=0.7)
l9 = Patch(facecolor='springgreen', label='Tres Cuervos Negros', alpha=0.7)
l10 = Patch(facecolor='green', label='Bebe Abandonado Alcista', alpha=0.7)
l11 = Patch(facecolor='forestgreen',
label='Bebe Abandonado Bajista',
alpha=0.7)
l12 = Patch(facecolor='lime', label='Estrella Vespertina Doji', alpha=0.7)
l13 = Patch(facecolor='darkseagreen',
label='Estrella de la Mañana Doji',
alpha=0.7)
l14 = Patch(facecolor='greenyellow',
label='Estrella de la Mañana',
alpha=0.7)
l15 = Patch(facecolor='darkcyan', label='Estrella Vespertina', alpha=0.7)
l16 = Patch(facecolor='pink', label='Coz Alcista', alpha=0.6)
l17 = Patch(facecolor='hotpink', label='Coz Bajista', alpha=0.6)
l18 = Patch(facecolor='magenta', label='Ventana Bajista', alpha=0.6)
l19 = Patch(facecolor='purple', label='Ventana Alcista', alpha=0.6)
l20 = Patch(facecolor='indigo', label='Envolvente Alcista', alpha=0.6)
l21 = Patch(facecolor='plum', label='Envolvente Bajista', alpha=0.6)
l22 = Patch(facecolor='g', label='Vela Alcista Grande', alpha=0.5)
l23 = Patch(facecolor='r', label='Vela Bajista Grande', alpha=0.5)
l24 = Patch(facecolor='yellow', label='Hombre Colgado', alpha=0.5)
l25 = Patch(facecolor='orange', label='Doji Sombrilla', alpha=0.5)
l26 = Patch(facecolor='gray', label='Doji Sombrilla Invertida', alpha=0.5)
#Situar la leyenda
plt.legend(loc='upper center',
ncol=8,
handles=[
l1, l2, l3, l4, l5, l6, l7, l8, l9, l10, l11, l12, l13, l14,
l15, l16, l17, l18, l19, l20, l21, l22, l23, l24, l25, l26
],
borderaxespad=-6.0,
shadow=True,
fontsize='x-small')
#Analisis de Patrones
i = 0
delta = float(0.0)
while i < len(quotes['Open']) and vela == False and terminado == False:
encontrado = False
if i > 3:
if patrones.formacionConGapBajista(
aper1=quotes['Open'][i - 4],
cierre1=quotes['Close'][i - 4],
aper2=quotes['Open'][i - 3],
cierre2=quotes['Close'][i - 3],
aper3=quotes['Open'][i - 2],
cierre3=quotes['Close'][i - 2],
aper4=quotes['Open'][i - 1],
cierre4=quotes['Close'][i - 1],
aper5=quotes['Open'][i],
cierre5=quotes['Close'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Formación con Gap Bajista")
print("Tendencia Bajista (Continuación)")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "fgb"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0 or quotes.index[i - 2].weekday(
) == 0 or quotes.index[i - 3].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=4.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='maroon',
alpha=0.8)
if patrones.formacionConGapAlcista(
aper1=quotes['Open'][i - 4],
cierre1=quotes['Close'][i - 4],
aper2=quotes['Open'][i - 3],
cierre2=quotes['Close'][i - 3],
aper3=quotes['Open'][i - 2],
cierre3=quotes['Close'][i - 2],
aper4=quotes['Open'][i - 1],
cierre4=quotes['Close'][i - 1],
aper5=quotes['Open'][i],
cierre5=quotes['Close'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Formación con Gap Alcista")
print("Tendencia Alcista (Continuación)")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "fga"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0 or quotes.index[i - 2].weekday(
) == 0 or quotes.index[i - 3].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=4.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='brown',
alpha=0.8)
if patrones.matHold(aper1=quotes['Open'][i - 4],
cierre1=quotes['Close'][i - 4],
aper2=quotes['Open'][i - 3],
cierre2=quotes['Close'][i - 3],
aper3=quotes['Open'][i - 2],
cierre3=quotes['Close'][i - 2],
aper4=quotes['Open'][i - 1],
cierre4=quotes['Close'][i - 1],
aper5=quotes['Open'][i],
cierre5=quotes['Close'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Mat Hold")
print("Tendencia Alcista (Continuación)")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "mh"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0 or quotes.index[i - 2].weekday(
) == 0 or quotes.index[i - 3].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=4.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='sienna',
alpha=0.8)
if patrones.tripleFormacionAlcista(
aper1=quotes['Open'][i - 4],
cierre1=quotes['Close'][i - 4],
aper2=quotes['Open'][i - 3],
cierre2=quotes['Close'][i - 3],
aper3=quotes['Open'][i - 2],
cierre3=quotes['Close'][i - 2],
aper4=quotes['Open'][i - 1],
cierre4=quotes['Close'][i - 1],
aper5=quotes['Open'][i],
cierre5=quotes['Close'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Triple Formacion Alcista")
print("Tendencia Alcista (Continuación)")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "tfa"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0 or quotes.index[i - 2].weekday(
) == 0 or quotes.index[i - 3].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=4.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='chocolate',
alpha=0.8)
if patrones.tripleFormacionBajista(
aper1=quotes['Open'][i - 4],
cierre1=quotes['Close'][i - 4],
aper2=quotes['Open'][i - 3],
cierre2=quotes['Close'][i - 3],
aper3=quotes['Open'][i - 2],
cierre3=quotes['Close'][i - 2],
aper4=quotes['Open'][i - 1],
cierre4=quotes['Close'][i - 1],
aper5=quotes['Open'][i],
cierre5=quotes['Close'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Triple Formacion Alcista")
print("Tendencia Alcista (Continuación)")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "tfb"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0 or quotes.index[i - 2].weekday(
) == 0 or quotes.index[i - 3].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=4.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='sandybrown',
alpha=0.8)
if i > 2 and encontrado == False:
if patrones.pequeniaGolondrinaOcultada(
aper1=quotes['Open'][i - 3],
cierre1=quotes['Close'][i - 3],
alto1=quotes['High'][i - 3],
bajo1=quotes['Low'][i - 3],
aper2=quotes['Open'][i - 2],
cierre2=quotes['Close'][i - 2],
alto2=quotes['High'][i - 2],
bajo2=quotes['Low'][i - 2],
aper3=quotes['Open'][i - 1],
cierre3=quotes['Close'][i - 1],
alto3=quotes['High'][i - 1],
bajo3=quotes['Low'][i - 1],
aper4=quotes['Open'][i],
cierre4=quotes['Close'][i],
alto4=quotes['High'][i],
bajo4=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Pequeña Golondrina Oculta: ")
print("Tendencia Alcista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "pgo"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i -
1].weekday() == 0 or quotes.index[i -
2].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=3.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='navy',
alpha=0.75)
if i > 1 and encontrado == False:
if patrones.dosCuervosEnGapAlcista(
aper1=quotes['Open'][i - 2],
cierre1=quotes['Close'][i - 2],
aper2=quotes['Open'][i - 1],
cierre2=quotes['Close'][i - 1],
aper3=quotes['Open'][i],
cierre3=quotes['Close'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Dos Cuervos en Gap Alcista: ")
print("Tendencia Bajista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "dcga"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=2.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='mediumseagreen',
alpha=0.7)
if patrones.tresSoldadosBlancos(aper1=quotes['Open'][i - 2],
cierre1=quotes['Close'][i - 2],
alto1=quotes['High'][i - 2],
bajo1=quotes['Low'][i - 2],
aper2=quotes['Open'][i - 1],
cierre2=quotes['Close'][i - 1],
alto2=quotes['High'][i - 1],
bajo2=quotes['Low'][i - 1],
aper3=quotes['Open'][i],
cierre3=quotes['Close'][i],
alto3=quotes['High'][i],
bajo3=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Tres Soldados Blancos: ")
print("Tendencia Alcista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "tsb"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=2.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='olive',
alpha=0.7)
if patrones.tresCuervosNegros(aper1=quotes['Open'][i - 2],
cierre1=quotes['Close'][i - 2],
alto1=quotes['High'][i - 2],
bajo1=quotes['Low'][i - 2],
aper2=quotes['Open'][i - 1],
cierre2=quotes['Close'][i - 1],
alto2=quotes['High'][i - 1],
bajo2=quotes['Low'][i - 1],
aper3=quotes['Open'][i],
cierre3=quotes['Close'][i],
alto3=quotes['High'][i],
bajo3=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Tres Cuervos Negros: ")
print("Tendencia Alcista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "tcn"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=2.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='springgreen',
alpha=0.7)
if patrones.bebeAbandonadoAlcista(aper1=quotes['Open'][i - 2],
cierre1=quotes['Close'][i - 2],
aper2=quotes['Open'][i - 1],
cierre2=quotes['Close'][i - 1],
aper3=quotes['Open'][i],
cierre3=quotes['Close'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Bebe Abandonado Alcista: ")
print("Tendencia Alcista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "baa"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=2.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='green',
alpha=0.7)
if patrones.bebeAbandonadoBajista(aper1=quotes['Open'][i - 2],
cierre1=quotes['Close'][i - 2],
aper2=quotes['Open'][i - 1],
cierre2=quotes['Close'][i - 1],
aper3=quotes['Open'][i],
cierre3=quotes['Close'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Bebe Abandonado Bajista: ")
print("Tendencia Bajista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "bab"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=2.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='forestgreen',
alpha=0.7)
#Cambiar color de la sombra
if patrones.estrellaVespertinaDoji(
aper1=quotes['Open'][i - 2],
cierre1=quotes['Close'][i - 2],
alto1=quotes['High'][i - 2],
bajo1=quotes['Low'][i - 2],
aper2=quotes['Open'][i - 1],
cierre2=quotes['Close'][i - 1],
alto2=quotes['High'][i - 1],
bajo2=quotes['Low'][i - 1],
aper3=quotes['Open'][i],
cierre3=quotes['Close'][i],
alto3=quotes['High'][i],
bajo3=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Estrella Vespertina Doji: ")
print("Tendencia Bajista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "evd"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=2.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='lime',
alpha=0.7)
#Cambiar color de la sombra
if patrones.estrellaDeLaManianaDoji(
aper1=quotes['Open'][i - 2],
cierre1=quotes['Close'][i - 2],
alto1=quotes['High'][i - 2],
bajo1=quotes['Low'][i - 2],
aper2=quotes['Open'][i - 1],
cierre2=quotes['Close'][i - 1],
alto2=quotes['High'][i - 1],
bajo2=quotes['Low'][i - 1],
aper3=quotes['Open'][i],
cierre3=quotes['Close'][i],
alto3=quotes['High'][i],
bajo3=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Estrella de la Mañana Doji: ")
print("Tendencia Alcista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "emd"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=2.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='darkseagreen',
alpha=0.7)
#Cambiar color de la sombra
if patrones.estrellaDeLaManiana(aper1=quotes['Open'][i - 2],
cierre1=quotes['Close'][i - 2],
alto1=quotes['High'][i - 2],
bajo1=quotes['Low'][i - 2],
aper2=quotes['Open'][i - 1],
cierre2=quotes['Close'][i - 1],
alto2=quotes['High'][i - 1],
bajo2=quotes['Low'][i - 1],
aper3=quotes['Open'][i],
cierre3=quotes['Close'][i],
alto3=quotes['High'][i],
bajo3=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Estrella de la Mañana: ")
print("Tendencia Alcista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "em"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=2.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='greenyellow',
alpha=0.7)
#Cambiar color de la sombra
if patrones.estrellaVespertina(aper1=quotes['Open'][i - 2],
cierre1=quotes['Close'][i - 2],
alto1=quotes['High'][i - 2],
bajo1=quotes['Low'][i - 2],
aper2=quotes['Open'][i - 1],
cierre2=quotes['Close'][i - 1],
alto2=quotes['High'][i - 1],
bajo2=quotes['Low'][i - 1],
aper3=quotes['Open'][i],
cierre3=quotes['Close'][i],
alto3=quotes['High'][i],
bajo3=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Estrella Vespertina: ")
print("Tendencia Bajista ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "ev"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0 or quotes.index[
i - 1].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=2.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='darkcyan',
alpha=0.7)
if i > 0 and encontrado == False:
if patrones.cozAlcista(aperturaAct=quotes['Open'][i],
cierreAct=quotes['Close'][i],
aperturaAnt=quotes['Open'][i - 1],
cierreAnt=quotes['Close'][i - 1],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Coz Alcista el día:")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "ca"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=1.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='pink',
alpha=0.6)
if patrones.cozBajista(aperturaAct=quotes['Open'][i],
cierreAct=quotes['Close'][i],
aperturaAnt=quotes['Open'][i - 1],
cierreAnt=quotes['Close'][i - 1],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Coz Bajista el día:")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "cb"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=1.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='hotpink',
alpha=0.6)
if patrones.ventanaBajista(aper1=quotes['Open'][i - 1],
cierre1=quotes['Close'][i - 1],
alto1=quotes['High'][i - 1],
bajo1=quotes['Low'][i - 1],
aper2=quotes['Open'][i],
cierre2=quotes['Close'][i],
alto2=quotes['High'][i],
bajo2=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Ventana Bajista: ")
print("Tendencia Bajista")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "vb"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=1.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='magenta',
alpha=0.6)
if patrones.ventanaAlcita(aper1=quotes['Open'][i - 1],
cierre1=quotes['Close'][i - 1],
alto1=quotes['High'][i - 1],
bajo1=quotes['Low'][i - 1],
aper2=quotes['Open'][i],
cierre2=quotes['Close'][i],
alto2=quotes['High'][i],
bajo2=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Ventana Alcista: ")
print("Tendencia Alcista")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "va"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=1.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='purple',
alpha=0.6)
if patrones.envolventeAlcista(aper1=quotes['Open'][i - 1],
cierre1=quotes['Close'][i - 1],
alto1=quotes['High'][i - 1],
bajo1=quotes['Low'][i - 1],
aper2=quotes['Open'][i],
cierre2=quotes['Close'][i],
alto2=quotes['High'][i],
bajo2=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Envolvente Alcista: ")
print("Tendencia Alcista")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "ea"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=1.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='indigo',
alpha=0.6)
#Cambiar color de la sombra
if patrones.envolventeBajista(aper1=quotes['Open'][i - 1],
cierre1=quotes['Close'][i - 1],
alto1=quotes['High'][i - 1],
bajo1=quotes['Low'][i - 1],
aper2=quotes['Open'][i],
cierre2=quotes['Close'][i],
alto2=quotes['High'][i],
bajo2=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True:
'''
print("Detectada Envolvente Bajista: ")
print("Tendencia Bajista")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "eb"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
if quotes.index[i].weekday() == 0:
delta = float(2.0)
inter = datetime.timedelta(days=0.4)
interNeg = datetime.timedelta(days=1.4 + delta)
plt.axvspan(fechaInt - interNeg,
fechaInt + inter,
facecolor='plum',
alpha=0.6)
if patrones.velaAlcistaGrande(
apertura=quotes['Open'][i],
cierre=quotes['Close'][i],
alto=quotes['High'][i],
bajo=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True and encontrado == False:
'''
print("Detectada Vela Alcista Grande el día: ")
print(quotes.index[i])
'''
fechaInt = quotes.index[i]
encontrado = True
vela = True
nombreVela = "vag"
if i <= contador:
vela = False
inter = datetime.timedelta(days=0.4)
plt.axvspan(fechaInt - inter,
fechaInt + inter,
facecolor='g',
alpha=0.5)
if patrones.velaBajistaGrande(
apertura=quotes['Open'][i],
cierre=quotes['Close'][i],
alto=quotes['High'][i],
bajo=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True and encontrado == False:
'''
print("Detectada Vela Bajista Grande el día: ")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "vbg"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
inter = datetime.timedelta(days=0.4)
plt.axvspan(fechaInt - inter,
fechaInt + inter,
facecolor='r',
alpha=0.5)
if patrones.hombreColgado(
aper1=quotes['Open'][i],
cierre1=quotes['Close'][i],
alto1=quotes['High'][i],
bajo1=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True and encontrado == False:
'''
print("Detectada Vela Hombre Colgado: ")
print("Tendencia Bajista")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "hc"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
inter = datetime.timedelta(days=0.4)
plt.axvspan(fechaInt - inter,
fechaInt + inter,
facecolor='yellow',
alpha=0.5)
#Cambiar color de la sombra
if patrones.dojiSombrilla(
aper1=quotes['Open'][i],
cierre1=quotes['Close'][i],
alto1=quotes['High'][i],
bajo1=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True and encontrado == False:
'''
print("Detectada Vela Doji Sombrilla: ")
print("Tendencia Alcista")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "ds"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
inter = datetime.timedelta(days=0.4)
plt.axvspan(fechaInt - inter,
fechaInt + inter,
facecolor='orange',
alpha=0.5)
#Cambiar color de la sombra
if patrones.dojiSombrillaInvertida(
aper1=quotes['Open'][i],
cierre1=quotes['Close'][i],
alto1=quotes['High'][i],
bajo1=quotes['Low'][i],
fechaInicio=fechaInicio,
fechaFin=fechaFin,
archivo=archivo,
procedencia=procedencia) == True and encontrado == False:
'''
print("Detectada Vela Doji Sombrilla Invertida: ")
print("Tendencia Bajista")
print(quotes.index[i])
'''
encontrado = True
vela = True
nombreVela = "dsi"
if i <= contador:
vela = False
fechaInt = quotes.index[i]
inter = datetime.timedelta(days=0.4)
plt.axvspan(fechaInt - inter,
fechaInt + inter,
facecolor='gray',
alpha=0.5)
suma = i
i = i + 1
if i == len(quotes['Open']):
terminado = True
contador = suma
if len(quotes['Open']) == suma:
terminado = True
if procedencia == "csv":
archivo = archivo.replace(".csv", "")
archivo = archivo.replace(dir, "")
archivo = archivo.replace("/", "")
imagen = "grafica"
imagen += archivo
imagen += "analisis"
imagen += ".png"
else:
imagen = "grafica"
imagen += "analisis"
imagen += ".png"
plt.savefig(imagen)
return imagen, terminado, contador, nombreVela
ford_stock['Volume'].plot(label='Ford', ax=axes[0, 1])
gm_stock['Volume'].plot(label='GM', ax=axes[0, 1])
plt2.set_ylabel('Number of stocks')
plt2.legend()
plt3 = tesla_stock['Total Traded'].plot(label='Tesla',
figsize=(12, 8),
title='Total Traded',
ax=axes[1, 0])
ford_stock['Total Traded'].plot(label='Ford', ax=axes[1, 0])
gm_stock['Total Traded'].plot(label='GM', ax=axes[1, 0])
plt3.set_ylabel('US Dollars, in billions')
plt3.legend()
mondays = WeekdayLocator(MONDAY) # major ticks on Monday
alldays = DayLocator() # minor ticks on every other day beside Mondays
week_formatter = DateFormatter('%b %d') # format e.g., Jan 12
day_formatter = DateFormatter('%b %d') # e.g., 12
'''Set candle chart as plot #4'''
plt4_axes = axes[1, 1]
plt4_axes.xaxis.set_major_locator(mondays)
plt4_axes.xaxis.set_minor_locator(alldays)
plt4_axes.xaxis.set_major_formatter(week_formatter)
plt4_axes.set_xlabel('Date')
plt4_axes.set_ylabel('US Dollars/share')
candlestick_ohlc(plt4_axes,
tesla_values,
width=0.6,
colorup='g',
colordown='r')
# print(bars)
# xtick_pos = np.arange(0, bars+1)
# # print(bars)
# fig, ax = plt.subplots()
# print(var1.size)
delta = dt.timedelta(days=1)
dates = pd.date_range(begin_date1, end_date1, freq='1D')
print(dates)
fig, ax = plt.subplots()
ax.bar(dates, var1)
ax.set_xlim(dates[0], dates[-1] + delta)
ax.xaxis.set_major_locator(DayLocator(interval=7))
Axis.set_minor_locator(ax.xaxis, DayLocator())
ax.xaxis.set_major_formatter(DateFormatter('%d.%m'))
ax.fmt_xdata = DateFormatter('%Y-%m-%d %H:%M:%S')
fig.autofmt_xdate()
fig.suptitle(
"Диаграмма изменчивости суточного гидротермического коэффициента Селянинова\n"
+ r"%s год" % (begin_date1.strftime("%Y")) + "\n" +
r"вегетационный период с %s по %s" %
(begin_date1.strftime("%d.%m"), end_date1.strftime("%d.%m")),
fontsize=12,
fontweight='bold')
plt.show()
fecha1 = (2014, 10, 13)
fecha2 = (2014, 11, 13)
# Vamos a la web y nos traemos la información de la cotización
valores = quotes_historical_yahoo_ohlc('AAPL', fecha1, fecha2)
if len(valores) == 0:
raise SystemExit
# Definimos el aspecto de la gráfica
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
# Ponemos marcas mayores para los lunes
lunes = WeekdayLocator(MONDAY)
ax.xaxis.set_major_locator(lunes)
# Ponemos marcas menores para el resto de días
resto_dias = DayLocator()
ax.xaxis.set_minor_locator(resto_dias)
# Damos formato a los días
formato_semana = DateFormatter('%b %d') # e.g., Ene 12
ax.xaxis.set_major_formatter(formato_semana)
ax.xaxis_date()
candlestick_ohlc(ax, valores, width=0.6)
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
plt.show()
def candleLinePlots(candleData, candleTitle='a', **kwargs):
Date = [date2num(date) for date in candleData.index]
candleData.loc[:, 'Date'] = Date
listData = []
for i in range(len(candleData)):
a = [candleData.Date[i],\
candleData.Open[i],candleData.High[i],\
candleData.Low[i],candleData.Close[i]]
listData.append(a)
# 如 果 不 定 長 參 數 無 取 值 ， 只 畫 蠟 燭 圖
ax = plt.subplot()
# 如 果 不 定 長 參 數 有 值 ， 則 分 成 兩 個 子 圖
flag = 0
if kwargs:
if kwargs['splitFigures']:
ax = plt.subplot(211)
ax2 = plt.subplot(212)
flag = 1
for key in kwargs:
if key == 'title':
ax2.set_title(kwargs[key])
if key == 'ylabel':
ax2.set_ylabel(kwargs[key])
if key == 'grid':
ax2.grid(kwargs[key])
if key == 'Data':
plt.sca(ax)
if flag:
plt.sca(ax2)
#一維數據
if kwargs[key].ndim == 1:
plt.plot(kwargs[key],\
color='k',\
label=kwargs[key].name)
plt.legend(loc='best')
#二維數據有兩個columns
elif all([kwargs[key].ndim==2,\
len(kwargs[key].columns)==2]):
plt.plot(kwargs[key].iloc[:, 0],
color='k',
label=kwargs[key].iloc[:, 0].name)
plt.plot(kwargs[key].iloc[:,1],\
linestyle='dashed',\
label=kwargs[key].iloc[:,1].name)
plt.legend(loc='best')
#二維數據有3個columns
elif all([kwargs[key].ndim==2,\
len(kwargs[key].columns)==3]):
plt.plot(kwargs[key].iloc[:, 0],
color='k',
label=kwargs[key].iloc[:, 0].name)
plt.plot(kwargs[key].iloc[:, 1],
linestyle='dashed',
label=kwargs[key].iloc[:, 1].name)
plt.bar(left=kwargs[key].iloc[:, 2].index,
height=kwargs[key].iloc[:, 2],
color='r',
label=kwargs[key].iloc[:, 2].name)
plt.legend(loc='best')
mondays = WeekdayLocator(MONDAY)
weekFormatter = DateFormatter('%y %b %d')
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(DayLocator())
ax.xaxis.set_major_formatter(weekFormatter)
plt.sca(ax)
candlestick_ohlc(ax,listData, width=0.7,\
colorup='r',colordown='g')
ax.set_title(candleTitle)
plt.setp(ax.get_xticklabels(),\
rotation=20,\
horizontalalignment='center')
ax.autoscale_view()
return (plt.show())
def plot(
x,
y,
x2=None,
y2=None,
subplots=1,
title='Plot',
title2=None,
xlabel=None,
ylabel=None,
xlabel2=None,
ylabel2=None,
xlim_low=None,
ylim_low=None,
xlim2_low=None,
ylim2_low=None,
xlim_high=None,
ylim_high=None,
xlim2_high=None,
ylim2_high=None,
color='#ce5f5f',
color2='#66A7C5',
sharex=False,
sharey=False,
extra=False,
extray=False,
show=False,
save=None,
dates=False,
major='days',
minor=None,
interval=1,
):
"""
Wrapper of matplotlib.pyplot. Makes a plot.
Only two inputs are required: x and y.
However, some inputs require other inputs.
:param: x (list/np.array) - main x array
:param: y (list/np.array) - main y array
:param: x2 (list/np.array) - secondary x array
:param: y2 (list/np.array) - secondary y array
:param: subplots (int) - number of subplots (max 2)
:param: title (str) - main title
:param: title2 (str) - secondary title
:param: xlabel (str) - main x axis label
:param: ylabel (str) - main y axis label
:param: xlabel2 (str) - secondary x axis label
:param: ylabel2 (str) - secondary y axis label
:param: xlim_low (int/float) - main x axis lower limit
:param: ylim_low (int/float) - main y axis lower limit
:param: xlim2_low (int/float) - secondary x axis lower limit
:param: ylim2_low (int/float) - secondary y axis lower limit
:param: xlim_high (int/float) - main x axis upper limit
:param: ylim_high (int/float) - main y axis upper limit
:param: xlim2_high (int/float) - secondary x axis upper limit
:param: ylim2_high (int/float) - secondary y axis upper limit
:param: color (str) - main line color, hexcode or matplotlib color
:param: color2 (str) - secondary line color, hexcode or matplotlib color
:param: sharex (boolean) - share x axis trigger
:param: sharey (boolean) - share y axis trigger
:param: extra (boolean) - plot y2 on same plot
:param: extray (boolean) - plot y2 on secondary axis
:param: show (boolean) - shows plot
:param: save (str) - name of output plot
:param: dates (boolean) - indicates whether x axis are datetime objects
:param: major (str) - time scale to put tick marks and label
:param: minor (str) - time scale to put tick marks and label (buggy)
:param: interval (int) - intervals between tick marks
:return: fig (matplotlib.figure) - plot figure
Optional inputs requirements -
subplots: x2 (if not sharex), y2 (if not sharey)
ylim_high: ylim_low (vice-versa)
xlim_high: xlim_low (vice-versa)
extra: y2
extray: extra=True
dates: x=arr. of [datetime.datetime] or x2=arr of [datetime.datetime]
"""
if subplots == 2 and x2 is None and sharex is False:
print('\nMissing input "x2" to create second plot!\n')
raise (MissingInput)
if subplots == 2 and y2 is None and sharey is False:
print('\nMissing input "y2" to create second plot!\n')
raise (MissingInput)
if ylim_high is not None and ylim_low is None:
print('\nMissing input "ylim_low" to set upper y limit!\n')
raise (MissingInput)
if ylim_low is not None and ylim_high is None:
print('\nMissing input "ylim_high" to set lower y limit!\n')
raise (MissingInput)
if ylim2_high is not None and ylim2_low is None:
print('\nMissing input "ylim2_low" to set secondary upper y limit!\n')
raise (MissingInput)
if ylim2_low is not None and ylim2_high is None:
print('\nMissing input "ylim2_high" to set secondary lower y limit!\n')
raise (MissingInput)
if extra and y2 is None:
print('\nMissing a "y2" to use "extra" input!\n')
raise (MissingInput)
if extray and extra is False:
print('Input "extra" automatically set to True because "of extray"')
extra = True
if subplots > 1:
if sharey:
fig, ax = plt.subplots(1,
subplots,
sharex=sharex,
sharey=sharey,
figsize=(20, 15))
else:
fig, ax = plt.subplots(subplots,
sharex=sharex,
sharey=sharey,
figsize=(20, 15))
if dates:
ax[0].plot_date(x, y, color)
else:
ax[0].plot(x, y, color)
# Here's some logic to reduce redundancy
if sharex:
if xlabel is None:
if xlabel2 is not None:
xlabel = xlabel2
else:
print('No xlabel found, defaulting to "x"')
xlabel = 'x'
elif xlabel2 is None:
if xlabel is not None:
xlabel2 = xlabel
else:
print('No xlabel2 found, defaulting to "x2"')
xlabel2 = 'x2'
ax[0].set_ylabel(ylabel, fontsize=16.5)
if dates:
ax[1].plot_date(x, y2, color2)
else:
ax[1].plot(x, y2, color2)
ax[1].set_ylabel(ylabel2, fontsize=16.5)
elif sharey:
if ylabel is None:
if ylabel2 is not None:
ylabel = ylabel2
else:
print('No ylabel found, defaulting to "y"')
ylabel = 'y'
elif ylabel2 is None:
if ylabel is not None:
ylabel2 = ylabel
else:
print('No ylabel2 found, defaulting to "y2"')
ylabel2 = 'y2'
ax[0].set_xlabel(xlabel, fontsize=16.5)
ax[0].set_ylabel(ylabel, fontsize=16.5)
if dates:
ax[1].plot_date(x2, y, color2)
else:
ax[1].plot(x2, y, color2)
else:
if xlabel is None:
print('No xlabel found, defaulting to "x"')
xlabel = 'x'
if xlabel2 is None:
print('No xlabel2 found, defaulting to "x2"')
xlabel2 = 'x2'
if ylabel is None:
print('No ylabel found, defaulting to "y"')
ylabel = 'y'
if ylabel2 is None:
print('No ylabel2 found, defaulting to "y2"')
ylabel2 = 'y2'
ax[0].set_xlabel(xlabel, fontsize=16.5)
ax[0].set_ylabel(ylabel, fontsize=16.5)
if dates:
ax[1].plot_date(x2, y2, color2)
else:
ax[1].plot(x2, y2, color2)
ax[1].set_ylabel(ylabel2, fontsize=16.5)
ax[1].set_xlabel(xlabel2, fontsize=16.5)
ax[0].set_title(title, fontsize=23, y=1.03, color='.50')
if title2 is not None:
ax[1].set_title(title2, fontsize=23, y=1.03, color='.50')
if dates:
if major == 'years':
major = YearLocator(interval=interval)
majorFmt = DateFormatter('%Y')
elif major == 'months':
major = MonthLocator(interval=interval)
majorFmt = DateFormatter('%m%Y')
elif major == 'days':
major = DayLocator(interval=interval)
majorFmt = DateFormatter('%m/%d')
elif major == 'hours':
major = HourLocator(interval=interval)
majorFmt = DateFormatter('\n%H')
if minor is not None:
if minor == 'years':
minor = YearLocator(interval=interval)
minorFmt = DateFormatter('\n%Y')
elif minor == 'months':
minor = MonthLocator(interval=interval)
minorFmt = DateFormatter('\n%m')
elif minor == 'days':
minor = DayLocator(interval=interval)
minorFmt = DateFormatter('\n%d')
elif minor == 'hours':
minor = HourLocator(interval=interval)
minorFmt = DateFormatter('\n%H')
for i in range(subplots):
if major is not None:
ax[i].xaxis.set_major_locator(major)
ax[i].xaxis.set_major_formatter(majorFmt)
if minor is not None:
ax[i].xaxis.set_minor_locator(minor)
ax[i].xaxis.set_minor_formatter(minorFmt)
plt.gcf()
fig.autofmt_xdate()
if ylim_high is not None and ylim_low is not None:
ax[0].set_ylim(ylim_low, ylim_high)
if xlim_high is not None and xlim_low is not None:
ax[0].set_xlim(xlim_low, xlim_high)
if ylim2_high is not None and ylim2_low is not None:
ax[1].set_ylim(ylim2_low, ylim2_high)
if xlim2_high is not None and xlim2_low is not None:
ax[1].set_xlim(xlim2_low, xlim2_high)
# Apply some prettifying
for i in range(subplots):
prettify_plot(ax[i])
elif subplots == 1:
fig, ax = plt.subplots(figsize=(21, 13))
if dates:
ax.plot_date(x, y, color)
if major == 'years':
major = YearLocator()
majorFmt = DateFormatter('%Y')
elif major == 'months':
major = MonthLocator()
majorFmt = DateFormatter('%m')
elif major == 'days':
major = DayLocator()
majorFmt = DateFormatter('%d')
elif major == 'hours':
major = HourLocator()
majorFmt = DateFormatter('%H')
if minor is not None:
if minor == 'years':
minor = YearLocator()
minorFmt = DateFormatter('\n%Y')
elif minor == 'months':
minor = MonthLocator()
minorFmt = DateFormatter('\n%m')
elif minor == 'days':
minor = DayLocator()
minorFmt = DateFormatter('\n%d')
elif minor == 'hours':
minor = HourLocator()
minorFmt = DateFormatter('\n%H')
ax.xaxis.set_major_locator(major)
ax.xaxis.set_major_formatter(majorFmt)
if minor is not None:
ax.xaxis.set_minor_locator(minor)
ax.xaxis.set_minor_formatter(minorFmt)
fig.autofmt_xdate()
else:
ax.plot(x, y, color)
if ylim_high is not None and ylim_low is not None:
ax.set_ylim(ylim_low, ylim_high)
if xlim_high is not None and xlim_low is not None:
ax.set_xlim(xlim_low, xlim_high)
if xlabel is None:
print('No xlabel found, defaulting to "x"')
xlabel = 'x'
if ylabel is None:
print('No ylabel found, defaulting to "y"')
ylabel = 'y'
ax.set_title(title, fontsize=21, y=1.03, color='.50')
ax.set_xlabel(xlabel, fontsize=16.5)
ax.set_ylabel(ylabel, fontsize=16.5)
if extra:
if extray:
ax2 = ax.twinx()
ax2.plot(x, y2, color2)
if ylabel2 is None:
print('No ylabel2 found, defaulting to "y2"')
ylabel2 = 'y2'
ax2.set_ylabel(ylabel2, fontsize=16.5)
if ylim2_high is not None and ylim2_low is not None:
ax2.set_ylim(ylim2_low, ylim2_high)
if xlim_high is not None and xlim_low is not None:
ax2.set_xlim(xlim_low, xlim_high)
ax2.spines["top"].set_visible(False)
ax2.spines["bottom"].set_visible(False)
ax2.spines["right"].set_visible(False)
ax2.spines["left"].set_visible(False)
ax2.get_yaxis().tick_right()
ax2.yaxis.label.set_color(color2)
ax2.tick_params(axis='y', colors=color2)
ax2.grid(b=True, which='major', color=color2, linestyle='--')
else:
ax.plot(x, y2, color2)
# Prettify it!
prettify_plot(ax)
if extray:
ax.yaxis.label.set_color(color)
ax.tick_params(axis='y', colors=color)
ax.yaxis.grid(b=True, which='major', color=color, linestyle='--')
else:
ax.yaxis.label.set_color('.4')
ax.tick_params(axis='y', colors='.5')
ax.yaxis.grid(b=True, which='major', color='.55', linestyle='--')
else:
print('Sorry, this function only supports two subplots\n!')
raise (Unsupported)
if save is not None:
fig.savefig(save)
if show:
fig.show()
return fig
def plot_station_data(date,site,sitetitle,df):
'''Given site station ID, the title of that site, and a dataframe of ASOS observation data from the last 3 days,
returns a plot of the last 3-days of weather at that site.
Parameters:
site (str): string of ASOS station ID
sitetitle (str): string of ASOS station full name
df (dataframe): dataframe containing last 72 hours (3 days) of ASOS station data
Returns:
None
*saves plots to plot_dir listed near top of script*
'''
if isinstance(df, int): #Returns if the station is not reporting
return
lower_site = site.lower()
timestamp_end=str(df.index[-1].strftime('%Y%m%d%H%M'))
dt = df.index[:]
dt_array = np.array(dt.values)
graphtimestamp_start=dt[0].strftime("%m/%d/%y")
graphtimestamp=dt[-1].strftime("%m/%d/%y")
#now = datetime.datetime.utcnow()
now = datetime.strptime(date,'%Y%m%d')
today_date = dt[-1].strftime('%Y%m%d')
markersize = 1.5
linewidth = 1.0
#make figure and axes
fig = plt.figure()
fig.set_size_inches(18,10)
if 'snow_depth_set_1' in df.keys(): #six axes if snow depth
ax1 = fig.add_subplot(6,1,1)
ax2 = fig.add_subplot(6,1,2,sharex=ax1)
ax3 = fig.add_subplot(6,1,3,sharex=ax1)
ax4 = fig.add_subplot(6,1,4,sharex=ax1)
ax5 = fig.add_subplot(6,1,5,sharex=ax1)
ax6 = fig.add_subplot(6,1,6,sharex=ax1)
ax6.set_xlabel('Time (UTC)')
else:
ax1 = fig.add_subplot(5,1,1) #five axes if no snow depth
ax2 = fig.add_subplot(5,1,2,sharex=ax1)
ax3 = fig.add_subplot(5,1,3,sharex=ax1)
ax4 = fig.add_subplot(5,1,4,sharex=ax1)
ax5 = fig.add_subplot(5,1,5,sharex=ax1)
ax5.set_xlabel('Time (UTC)')
#ax1.set_title(site+' '+sitetitle+' '+graphtimestamp_start+' - '+graphtimestamp+' '+now.strftime("%H:%MZ"))
ax1.set_title(site+' '+sitetitle+' '+graphtimestamp_start+' - '+graphtimestamp)
#------------------
#plot airT and dewT
#------------------
if 'tmpc' in df.keys():
airT = df['tmpc']
airT_new = airT.dropna()
airT_list = list(airT_new.values)
airT_dt_list = []
for i in range(0,len(airT)):
if pd.isnull(airT[i]) == False:
airT_dt_list.append(dt[i])
ax1.plot_date(airT_dt_list,airT_list,'o-',label="Temp",color="blue",linewidth=linewidth,markersize=markersize)
#ax1.plot_date(dt,airT,'-',label="Temp",color="blue",linewidth=linewidth)
if 'dwpc' in df.keys():
dewT = df['dwpc']
dewT_new = dewT.dropna()
dewT_list = list(dewT_new.values)
dewT_dt_list = []
for i in range(0,len(dewT)):
if pd.isnull(dewT[i]) == False:
dewT_dt_list.append(dt[i])
ax1.plot_date(dewT_dt_list,dewT_list,'o-',label="Dew Point",color="black",linewidth=linewidth,markersize=markersize)
if ax1.get_ylim()[0] < 0 < ax1.get_ylim()[1]:
ax1.axhline(0, linestyle='-', linewidth = 1.0, color='deepskyblue')
trans = transforms.blended_transform_factory(ax1.get_yticklabels()[0].get_transform(), ax1.transData)
ax1.text(0,0,'0C', color="deepskyblue", transform=trans, ha="right", va="center") #light blue line at 0 degrees C
ax1.set_ylabel('Temp ($^\circ$C)')
ax1.legend(loc='best',ncol=2)
axes = [ax1] #begin axes
#----------------------------
#plotting wind speed and gust
#----------------------------
if 'sknt' in df.keys():
wnd_spd = df['sknt']
ax2.plot_date(dt,wnd_spd,'o-',label='Speed',color="forestgreen",linewidth=linewidth,markersize=markersize)
if 'gust' in df.keys():
wnd_gst = df['gust']
max_wnd_gst = wnd_gst.max(skipna=True)
ax2.plot_date(dt,wnd_gst,'o-',label='Gust (Max ' + str(round(max_wnd_gst,1)) + 'kt)',color="red",linewidth=0.0,markersize=markersize)
ax2.set_ylabel('Wind (kt)')
ax2.legend(loc='best',ncol=2)
axes.append(ax2)
#-----------------------
#plotting wind direction
#-----------------------
if 'drct' in df.keys():
wnd_dir = df['drct']
ax3.plot_date(dt,wnd_dir,'o-',label='Direction',color="purple",linewidth=0.2, markersize=markersize)
ax3.set_ylim(-10,370)
ax3.set_ylabel('Wind Direction')
ax3.set_yticks([0,90,180,270,360])
axes.append(ax3)
#-------------
#plotting MSLP
#-------------
if 'mslp' in df.keys():
mslp = df['mslp']
mslp_new = mslp.dropna()
mslp_list = list(mslp_new.values)
mslp_dt_list = []
for i in range(0,len(mslp)):
if pd.isnull(mslp[i]) == False:
mslp_dt_list.append(dt[i])
max_mslp = mslp.max(skipna=True)
min_mslp = mslp.min(skipna=True)
labelname = 'Min ' + str(round(min_mslp,1)) + 'hPa, Max ' + str(round(max_mslp,2)) + 'hPa'
ax4.plot_date(mslp_dt_list,mslp_list,'o-',label=labelname,color='darkorange',linewidth=linewidth,markersize=markersize)
ax4.legend(loc='best')
ax4.set_ylabel('MSLP (hPa)')
ax4.set_xlabel('Time (UTC)')
axes.append(ax4)
#-------------------------------------------
#plotting precip accumulation & precip types
#-------------------------------------------
# Move date_time from index to column
df = df.reset_index()
# MODIFY THIS SO THAT WHEN READINGS COME IN WE KEEP THEM INSTEAD OF JUST ACCEPTING HOURLY ACCUMS
# WILL MAKE PLOT LESS CHOPPY
# Plot precip time series (use only values at minute 53)
if 'p01m' in df.keys():
df['p01m'] = df['p01m'].fillna(0)
precip_inc = list(df['p01m'].values)
precip_inc_dt = list(df['date_time'].values)
precip_accum = 0.0
precip_accum_list = []
precip_dt_list = []
precip_accum_indices = []
for i in range(0,len(precip_inc)):
time_obj = pd.to_datetime(precip_inc_dt[i])
minutes = time_obj.strftime('%M')
if minutes == '53':
precip_accum = precip_accum + precip_inc[i]
precip_accum_list.append(precip_accum)
precip_dt_list.append(precip_inc_dt[i])
precip_accum_indices.append(i)
times = precip_dt_list
precip_accums = precip_accum_list
"""
precip_accum = 0.0
precip_accum_list = []
last_increment = df.loc[0].p01m
precip_accum_indices = []
for index in range(1,len(df)):
if df.loc[index].p01m < last_increment:
precip_accum = precip_accum + last_increment
precip_accum_list.append(precip_accum)
precip_accum_indices.append(index)
else:
precip_accum_list.append(precip_accum)
last_increment = df.loc[index].p01m
# append last element in dataframe in case there's some precip there
precip_accum = precip_accum + last_increment
precip_accum_list.append(precip_accum)
# values to use for the plot
times = [precip_dt_list[i] for i in precip_accum_indices]
precip_accums = [precip_accum_list[i] for i in precip_accum_indices]
"""
max_precip = max(precip_accum_list)
labelname = 'Precip (' + str(round(max_precip,2)) + 'mm)'
ax5.plot_date(precip_dt_list,precip_accum_list,'o-',label=labelname,color='navy',linewidth=linewidth,markersize=markersize)
if max_precip > 0:
ax5.set_ylim(-0.1*max_precip,max_precip+max_precip*0.2)
else:
ax5.set_ylim(-0.5,5)
# Add weather_code info to plot
if 'wxcodes' in df.keys():
df['wxcodes'] = df['wxcodes'].fillna('')
wxcodes_list = list(df['wxcodes'].values)
#wxcodes_dt_list = list(df['date_time'].values)
wxcodes_num_list = []
for i in range(0,len(wxcodes_list)):
wxcodes = wxcodes_list[i]
if len(wxcodes) > 0:
wxcode_wto = wx_code_map[wxcodes.split()[0]]
else:
wxcode_wto = 0
wxcodes_num_list.append(wxcode_wto)
wxcodes = [wxcodes_num_list[i] for i in precip_accum_indices]
# Set y values for weather symbols on plot
dummy_y_vals = np.ones(len(wxcodes)) * (0.10*max_precip)
sp = StationPlot(ax5, times, dummy_y_vals)
#ax.plot(dates, temps)
#sp.plot_symbol('C', wxcodes, current_weather, fontsize=16, color='red')
sp.plot_symbol('C', wxcodes, current_weather, fontsize=14, color='red')
"""
wxcodes_num_list_hrly = []
wxcodes_dt_list_hrly = []
for i in range(0,len(wxcodes_num_list)):
time_obj = pd.to_datetime(wxcodes_dt_list[i])
minutes = time_obj.strftime('%M')
if minutes == '00':
wxcodes_num_list_hrly.append(wxcodes_num_list[i])
wxcodes_dt_list_hrly.append(wxcodes_dt_list[i])
dummy_y_vals = np.zeros(len(wxcodes_num_list_hrly)) # only to place on plot
sp = StationPlot(ax5, wxcodes_dt_list_hrly, dummy_y_vals)
#ax5.plot(precip_dt_list,precip_accum_list)
sp.plot_symbol('C', wxcodes_num_list_hrly, current_weather, fontsize=16, color='red')
"""
ax5.legend(loc='best')
ax5.set_ylabel('Precip (mm)')
axes.append(ax5)
"""
#-------------------
#plotting snow depth
#-------------------
if 'snow_depth_set_1' in df.keys():
snow_depth = df['snow_depth_set_1']
snow_depth_new = snow_depth.dropna()
snow_depth_dt_list = []
for i in range(0,len(snow_depth)):
if pd.isnull(snow_depth[i]) == False:
snow_depth_dt_list.append(dt[i])
max_snow_depth = snow_depth.max(skipna=True)
min_snow_depth = snow_depth.min(skipna=True)
labelname = 'Min Depth ' + str(round(min_snow_depth,2)) + 'mm, Max Depth ' + str(round(max_snow_depth,2)) + 'mm'
ax6.plot_date(snow_depth_dt_list,snow_depth_new,'o-',label=labelname,color='deepskyblue',linewidth=linewidth,markersize=markersize)
if max_snow_depth > 0:
ax6.set_ylim(-0.1*max_snow_depth,max_snow_depth+max_snow_depth*0.2)
else:
ax6.set_ylim(-0.5,5)
ax6.legend(loc='best')
ax6.set_ylabel('Snow Depth (mm)')
axes.append(ax6)
"""
# Axes formatting
for ax in axes:
ax.spines["top"].set_visible(False) #darker borders on the grids of each subplot
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.tick_params(axis='x',which='both',bottom='on',top='off') #add ticks at labeled times
ax.tick_params(axis='y',which='both',left='on',right='off')
ax.xaxis.set_major_locator( DayLocator() )
ax.xaxis.set_major_formatter( DateFormatter('%b-%d') )
ax.xaxis.set_minor_locator( HourLocator(np.linspace(6,18,3)) )
ax.xaxis.set_minor_formatter( DateFormatter('%H') )
ax.fmt_xdata = DateFormatter('Y%m%d%H%M%S')
ax.yaxis.grid(linestyle = '--')
ax.get_yaxis().set_label_coords(-0.06,0.5)
# Write plot to file
plot_path = plot_dir+'/'+today_date
if not os.path.exists(plot_path):
os.makedirs(plot_path)
try:
plt.savefig(plot_path+'/ops.asos.'+timestamp_end+'.'+lower_site+'.png',bbox_inches='tight')
except:
print("Problem saving figure for %s. Usually a maxticks problem" %site)
plt.close()
def pandas_candlestick_ohlc(self,
share,
fechasNum,
window,
stick="day",
otherseries=["20d", "50d"],
adj=False):
"""
:param share: pandas DataFrame object with datetime64 index, and float columns "Open", "High", "Low", and "Close", likely created via DataReader from "yahoo"
:param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include "day", "week", "month", and "year", ("day" default), and any numeric input indicates the number of trading days included in a period
:param adj: A boolean indicating whether to use adjusted prices
:param otherseries: An iterable that will be coerced into a list, containing the columns of share that hold other series to be plotted as lines
This will show a Japanese candlestick plot for stock data stored in share, also plotting other series if passed.
"""
share["20d"] = np.round(
share["Adj. Close"].rolling(window=20, center=False).mean(), 2)
share["50d"] = np.round(
share["Adj. Close"].rolling(window=50, center=False).mean(), 2)
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
dayFormatter = DateFormatter('%d') # e.g., 12
# Create a new DataFrame which includes OHLC data for each period specified by stick input
fields = ["Open", "High", "Low", "Close"]
if adj:
fields = ["Adj. " + s for s in fields]
transdat = share.loc[:, fields]
transdat.columns = pd.Index(["Open", "High", "Low", "Close"])
if (type(stick) == str):
if stick == "day":
plotdat = transdat
stick = 1 # Used for plotting
elif stick in ["week", "month", "year"]:
if stick == "week":
transdat["week"] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalendar()[1]) # Identify weeks
elif stick == "month":
transdat["month"] = pd.to_datetime(transdat.index).map(
lambda x: x.month) # Identify months
transdat["year"] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalendar()[0]) # Identify years
grouped = transdat.groupby(list(set(
["year",
stick]))) # Group by year and other appropriate variable
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
}) # Create empty data frame containing what will be plotted
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
"Open": group.iloc[0, 0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1, 3]
},
index=[group.index[0]]))
if stick == "week": stick = 5
elif stick == "month": stick = 30
elif stick == "year": stick = 365
elif (type(stick) == int and stick >= 1):
transdat["stick"] = [
np.floor(i / stick) for i in range(len(transdat.index))
]
grouped = transdat.groupby("stick")
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
}) # Create empty data frame containing what will be plotted
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
"Open": group.iloc[0, 0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1, 3]
},
index=[group.index[0]]))
else:
raise ValueError(
'Valid inputs to argument "stick" include the strings "day", "week", "month", "year", or a positive integer'
)
# Set plot parameters, including the axis object ax used for plotting
fig = Figure(figsize=(7.5, 4.5), dpi=100)
self.ax = fig.add_subplot(111)
self.ax.clear()
self.ax.grid(True)
# Create the candelstick chart
candlestick_ohlc(self.ax,
list(
zip(list(date2num(plotdat.index.tolist())),
plotdat["Open"].tolist(),
plotdat["High"].tolist(),
plotdat["Low"].tolist(),
plotdat["Close"].tolist())),
colorup="black",
colordown="red",
width=stick * .4)
# Plot other series (such as moving averages) as lines
if otherseries != None:
if type(otherseries) != list:
otherseries = [otherseries]
share.loc[:, otherseries].plot(ax=self.ax, lw=1.3, grid=True)
self.ax.xaxis_date()
self.ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
# estadis = Estadisticas()
# desviEstandar = estadis.desviacionEstadar(share["Adj. Close"].data.obj)
# media = estadis.regresionLineal(share,fechasNum)
# mediaMasDE = estadis.cambiarValorPrecios(media.copy(),desviEstandar*2)
# mediaMenosDE = estadis.cambiarValorPrecios(media.copy(),-desviEstandar*2)
# ax.plot(plotdat.index.tolist(),media,"r",label = "regression")
# ax.plot(plotdat.index.tolist(),mediaMasDE,"b",label = "+DE")
# ax.plot(plotdat.index.tolist(),mediaMenosDE,"b",label = "-DE")
#navigation tool
if self.grafico:
self.canvas.get_tk_widget().destroy()
if ~self.grafico:
self.canvas = FigureCanvasTkAgg(fig, master=window)
self.canvas.get_tk_widget().pack()
self.toolbar = NavigationToolbar2Tk(self.canvas, window)
self.canvas._tkcanvas.pack(side=TOP, fill=BOTH, expand=True)
self.canvas.draw()
self.grafico = True
def pandas_candlestick_ohlc(dat, days='day', adj=False, otherseries=None):
mondays = WeekdayLocator(MONDAY)
all_days = DayLocator()
fields = ['Open', 'High', 'Low', 'Close']
transdat = dat.loc[:,
fields] # Selects single column or subset of columns by label
transdat.columns = pd.Index(['Open', 'High', 'Low', 'Close'])
if (type(days) == str):
if days == "day":
plotdat = transdat
days = 1
elif days in ['week', 'month', 'year']:
if days == 'week':
transdat['week'] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalender()[1]
) #Return a 3-tuple, (ISO year, ISO week number, ISO weekday).
elif days == 'month':
transdat['month'] = pd.to_datetime(
transdat.index).map(lambda x: x.month)
transdat['year'] = pd.to_datetime(
transdat.index).map(lambda x: x.isocalendar()[0])
grouped = transdat.groupby(list({'year', days}))
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
})
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
'Open': group.iloc[0, 0],
'High': max(group.High),
'Low': min(group.Low),
'Close': group.iloc[-1, 3]
},
index=[group.index[0]]))
if days == 'week':
days = 5
elif days == 'month':
days = 30
elif days == 'year':
days = 365
elif (type(days) == int and days >= 1):
transdat['stick'] = [
pd.np.floor(i / days) for i in range(len(transdat.index))
]
grouped = transdat.groupby('stick')
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
})
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
'Open': group.iloc[0, 0],
'High': max(group.High),
'Low': min(group.Low),
'Close': group.iloc[-1, 3]
},
index=[group.index[0]]))
else:
raise ValueError(
'Valid inputs - "days" include the strings or a positive integer')
# Set plot parameters, including the axis object ax used for plotting
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):
weekFormatter = DateFormatter('%b %d') # for example Jan 12
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(all_days)
else:
weekFormatter = DateFormatter('%b %d, %Y')
ax.xaxis.set_major_formatter(weekFormatter)
ax.grid(True)
# Create the candelstick chart
candlestick_ohlc(ax,
list(
zip(list(date2num(plotdat.index.tolist())),
plotdat["Open"].tolist(),
plotdat["High"].tolist(), plotdat["Low"].tolist(),
plotdat["Close"].tolist())),
colorup="black",
colordown="red",
width=days * .4)
# Plot other series (such as moving averages) as lines
if otherseries != None:
if type(otherseries) != list:
otherseries = [otherseries]
dat.loc[:, otherseries].plot(ax=ax, lw=1.3, grid=True)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
plt.show()
def plot_station_data(site_data):
'''Given a pandas dataframe containing all weather data for a specific station, this function saves a plot with
the last 3 days worth of weather data for that station (or as much data as available if not yet 3-days).
Parameters:
site_data (dataframe): pandas dataframe containing all data, both directly observed and calculated,
for a specific station
Returns:
None
*saves plots to plot_dir listed near top of script*
'''
latest = site_data.index[-1]
site = site_data['station'][0]
lower_site = site.lower()
site_slice = site_data.loc[site_data.index >= (
latest - timedelta(hours=72))] #slice data to last 72hrs
timestamp_end = site_slice.index[-1].strftime(
'%Y%m%d%H%M') #timestamp end for saving .csv files
dt = site_slice.index[:] #define dt for making subplots
sitetitle = site_slice['name'][0] #define sitetitle for fig title
graphtimestamp_start = dt[0].strftime(
"%m/%d/%y") #start date, for fig title
graphtimestamp = dt[-1].strftime("%m/%d/%y") #end date, for fig title
markersize = 1.5 #markersize, for subplots
linewidth = 1.0 #linewidth, for subplots
fig = plt.figure() #create figure
fig.set_size_inches(10, 10) #size figure
if max(site_slice['snow_depth [cm]']
) > 0: #six axes if there is snow depth
ax1 = fig.add_subplot(6, 1, 1)
ax2 = fig.add_subplot(6, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(6, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(6, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(6, 1, 5, sharex=ax1)
ax6 = fig.add_subplot(6, 1, 6, sharex=ax1)
ax6.set_xlabel('Time (UTC)')
else: #five axes if no snow depth
ax1 = fig.add_subplot(5, 1, 1)
ax2 = fig.add_subplot(5, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(5, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(5, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(5, 1, 5, sharex=ax1)
ax5.set_xlabel('Time (UTC)')
ax1.set_title(site + ' ' + sitetitle + ', NY ' + graphtimestamp_start +
' - ' + graphtimestamp) #title figure
#plot airT and dewT
if 'temp_2m [degC]' in site_slice.keys():
airT = site_slice['temp_2m [degC]']
ax1.plot_date(dt,
airT,
'o-',
label="Temp",
color="blue",
linewidth=linewidth,
markersize=markersize)
if 'dew_point_temp_2m [degC]' in site_slice.keys():
Td = site_slice['dew_point_temp_2m [degC]']
ax1.plot_date(dt,
Td,
'o-',
label="Dew Point",
color="black",
linewidth=linewidth,
markersize=markersize)
if ax1.get_ylim()[0] < 0 < ax1.get_ylim()[1]:
ax1.axhline(0, linestyle='-', linewidth=1.0, color='deepskyblue')
trans = transforms.blended_transform_factory(
ax1.get_yticklabels()[0].get_transform(), ax1.transData)
ax1.text(0,
0,
'0C',
color="deepskyblue",
transform=trans,
ha="right",
va="center") #light blue line at 0 degrees C
ax1.set_ylabel('2m Temp ($^\circ$C)')
ax1.legend(loc='best', ncol=2)
axes = [ax1] #begin axes list
#plot wind speed and gust
if 'avg_wind_speed_merge [m/s]' in site_slice.keys():
wnd_spd = site_slice[
'avg_wind_speed_merge [m/s]'] * 1.94384 #convert to knots
ax2.plot_date(dt,
wnd_spd,
'o-',
label='Speed',
color="forestgreen",
linewidth=linewidth,
markersize=markersize)
if 'max_wind_speed_merge [m/s]' in site_slice.keys():
wnd_gst = site_slice[
'max_wind_speed_merge [m/s]'] * 1.94384 #convert to knots
max_wnd_gst = wnd_gst.max(skipna=True)
ax2.plot_date(dt,
wnd_gst,
'o-',
label='Gust (Max ' + str(round(max_wnd_gst, 1)) + 'kt)',
color="red",
linewidth=linewidth,
markersize=markersize)
ax2.set_ylabel('Wind (kt)')
ax2.legend(loc='best', ncol=2)
axes.append(ax2)
#plot wind direction
if 'wind_direction_merge [degree]' in site_slice.keys():
wnd_dir = site_slice['wind_direction_merge [degree]']
ax3.plot_date(dt,
wnd_dir,
'o-',
label='Direction',
color="purple",
linewidth=0.2,
markersize=markersize)
ax3.set_ylim(-10, 370)
ax3.set_ylabel('Wind Direction')
ax3.set_yticks([0, 90, 180, 270, 360]) #locking y-ticks for wind direction
axes.append(ax3)
#plot MSLP (or station pressure, if MSLP unavailable)
if 'mean_sea_level_pressure [mbar]' in site_slice.keys():
mslp = site_slice['mean_sea_level_pressure [mbar]']
min_mslp = mslp.min(skipna=True) #min 3-day mslp value
max_mslp = mslp.max(skipna=True) #max 3-day mslp value
labelname = 'Min ' + str(round(min_mslp, 2)) + 'hPa, Max ' + str(
round(max_mslp, 2)) + 'hPa'
ax4.plot_date(dt,
mslp,
'o-',
label=labelname,
color='darkorange',
linewidth=linewidth,
markersize=markersize)
ax4.set_ylabel('MSLP (hPa)')
elif 'station_pressure [mbar]' in site_slice.keys():
sp = site_slice['station_pressure [mbar]']
min_sp = sp.min(skipna=True) #min 3-day station pressure value
max_sp = sp.max(skipna=True) #max 3-day station pressure value
labelname = 'Min ' + str(round(min_sp, 2)) + 'hPa, Max ' + str(
round(max_sp, 2)) + 'hPa'
ax4.plot_date(dt,
sp,
'o-',
label=labelname,
color='darkorange',
linewidth=linewidth,
markersize=markersize)
ax4.set_ylabel('STATION Pressure (hPa)')
print('unable to get mslp, used station pressure instead')
ax4.legend(loc='best')
axes.append(ax4)
#plot precip accum
if 'precip_incremental [mm]' in site_slice.keys():
precip_inc = site_slice['precip_incremental [mm]']
precip_accum = 0.0
precip_accum_list = []
for increment in precip_inc: #calculate precip accumulation
precip_accum = precip_accum + increment
precip_accum_list.append(precip_accum)
max_precip = max(precip_accum_list)
labelname = 'Precip (' + str(round(max_precip, 2)) + 'mm)'
ax5.plot_date(dt,
precip_accum_list,
'o-',
label=labelname,
color='navy',
linewidth=linewidth,
markersize=markersize)
if max_precip > 0:
ax5.set_ylim(-0.1 * max_precip, max_precip + max_precip * 0.2)
else:
ax5.set_ylim(-0.5, 5)
ax5.legend(loc='best')
ax5.set_ylabel('Precip (mm)')
axes.append(ax5)
#plot snow depth
if 'snow_depth [cm]' in site_slice.keys() and max(
site_slice['snow_depth [cm]']) > 0:
snow_depth = site_slice['snow_depth [cm]'] * 10 #convert to mm
max_snow_depth = snow_depth.max(skipna=True)
min_snow_depth = snow_depth.min(skipna=True)
labelname = 'Min Depth ' + str(round(
min_snow_depth, 2)) + 'mm, Max Depth ' + str(
round(max_snow_depth, 2)) + 'mm'
ax6.plot_date(dt,
snow_depth,
'o-',
label=labelname,
color='deepskyblue',
linewidth=linewidth,
markersize=markersize)
ax6.set_ylim(-0.1 * max_snow_depth,
max_snow_depth + max_snow_depth * 0.2)
if max_snow_depth > 0:
ax5.set_ylim(-0.1 * max_snow_depth,
max_snow_depth + max_snow_depth * 0.2)
ax6.legend(loc='best')
ax6.set_ylabel('Snow Depth (mm)')
axes.append(ax6)
for ax in axes:
ax.spines["top"].set_visible(False) #remove dark borders on subplots
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.tick_params(axis='x', which='both', bottom='on',
top='off') #add ticks at labeled times
ax.tick_params(axis='y', which='both', left='on', right='off')
ax.xaxis.set_major_locator(DayLocator()) #one date written per day
ax.xaxis.set_major_formatter(
DateFormatter('%b-%d')) #show date, written as 'Jul-12'
ax.xaxis.set_minor_locator(HourLocator(np.linspace(
6, 18, 3))) #hour labels every 6 hours
ax.xaxis.set_minor_formatter(DateFormatter('%H')) #show hour labels
ax.fmt_xdata = DateFormatter('Y%m%d%H%M%S') #fixes labels
ax.yaxis.grid(linestyle='--') #adds y-axis grid lines
ax.get_yaxis().set_label_coords(
-0.06, 0.5) #properly places y-labels away from figure
#define dates in YYYYmmdd format (for saving and finding files)
three_days_ago_date = (latest - timedelta(hours=72)).strftime('%Y%m%d')
two_days_ago_date = (latest - timedelta(hours=48)).strftime('%Y%m%d')
yesterday_date = (latest - timedelta(hours=24)).strftime('%Y%m%d')
today_date = latest.strftime('%Y%m%d')
plot_path = plot_dir + '/' + today_date
if not os.path.exists(plot_path):
os.mkdir(plot_path)
plt.savefig(plot_path + '/ops.nys_ground.' + timestamp_end + '.' +
lower_site + '.png',
bbox_inches='tight')
plt.close()
print('plotted ' + site)
def plot_station_data(site, site_long, dt, time_start, time_end, site_data,
logo_path):
'''Given a pandas dataframe containing all weather data for a specific station, this function saves a plot with
the last 3 days worth of weather data for that station (or as much data as available if not yet 3-days).
Parameters:
site: 4 letter site identifier
site_long: long name of site
dt: dataframe contain all datetimes being plotted
time_start: first datetime in dt; redundant
time_end: last datetime in dt; redundant
site_data (dataframe): pandas df containing all site data for 3 days to be plotted
logo_path: path to png file containing NYS Mesonet logo
Returns:
Saves plots to plot_dir listed near top of MAIN CODE*
plot_dir = '/home/disk/funnel/impacts/archive/ops/nys_ground'
plot_dir = '/home/disk/bob/impacts/images/NYSM_standard'+'/'+site
'''
time_start_string = datetime.strftime(time_start, '%H UTC %m/%d/%y')
time_end_string = datetime.strftime(time_end, '%H UTC %m/%d/%y')
markersize = 1.5 #markersize, for subplots
linewidth = 1.5 #linewidth, for subplots
fig = plt.figure() #create figure
fig.set_size_inches(10, 10) #size figure
ax1 = fig.add_subplot(6, 1, 1)
ax2 = fig.add_subplot(6, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(6, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(6, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(6, 1, 5, sharex=ax1)
ax6 = fig.add_subplot(6, 1, 6, sharex=ax1)
ax1.set_title('{}, NY ({}) Meteogram\n{} - {}'.format(
site_long, site, time_start_string, time_end_string),
fontsize=16)
#ax1.set_title('{}, NY ({}) Meteogram\n{} - {}'.format(site_data['name'][0], site,
# time_start_string, time_end_string), fontsize=16)
#plot airT and dewT
#if 'temp_2m [degC]' in site_data.keys():
if 'tair' in site_data.keys():
#airT = site_data['temp_2m [degC]']
airT = site_data['tair']
ax1.plot_date(dt,
airT,
'o-',
label="Temp",
color="red",
linewidth=linewidth,
markersize=markersize)
ax1.set_xlim(time_start, time_end)
#if 'dew_point_temp_2m [degC]' in site_data.keys():
#if 'dew_point_temp_2m [degC]' in site_data.keys():
if 'dewpt_temp' in site_data.keys():
#Td = site_data['dew_point_temp_2m [degC]']
Td = site_data['dewpt_temp']
ax1.plot_date(dt,
Td,
'o-',
label="Dew Point",
color="forestgreen",
linewidth=linewidth,
markersize=markersize)
# REDUNDANT??
#ax1.set_xlim(time_start, time_end)
# draw 0 degC line if y limits go from negative to positive values
if ax1.get_ylim()[0] < 0 < ax1.get_ylim()[1]:
ax1.axhline(0, linestyle='-', linewidth=1.0, color='deepskyblue')
# USED FOR PLACING TEXT THAT WE DON'T NEED - drawing '0 degC' in light blue on y axis
##trans = transforms.blended_transform_factory(ax1.get_yticklabels()[0].get_transform(), ax1.transData)
##ax1.text(0, 0, '0$^\circ$C', color="deepskyblue", transform=trans, ha="right", va="center") #light blue line at 0 degrees C
ax1.set_ylabel('2-m Temp ($^\circ$C)')
ax1.legend(loc='best', ncol=2)
axes = [ax1] #begin axes list
#plot wind speed and gust
#if 'avg_wind_speed_merge [m/s]' in site_data.keys():
if 'wspd_merge' in site_data.keys():
#wnd_spd = site_data['avg_wind_speed_merge [m/s]'] * 1.94384 #convert to knots
wnd_spd = mps_to_kts(site_data['wspd_merge']) #convert to knots
ax2.plot_date(dt,
wnd_spd,
'o-',
label='Speed',
color="black",
linewidth=linewidth,
markersize=markersize)
#if 'max_wind_speed_merge [m/s]' in site_data.keys():
if 'wmax_merge' in site_data.keys():
#wnd_gst = site_data['max_wind_speed_merge [m/s]'] * 1.94384 #convert to knots
wnd_gst = mps_to_kts(site_data['wmax_merge']) #convert to knots
max_wnd_gst = wnd_gst.max(skipna=True)
ax2.plot_date(dt,
wnd_gst,
'o-',
label='Gust [Max=' + str(round(max_wnd_gst, 1)) + ' kt]',
color="blue",
linewidth=linewidth,
markersize=markersize)
ax2.set_ylabel('Wind (kt)')
ax2.legend(loc='best', ncol=2)
axes.append(ax2)
#plot wind direction
#if 'wind_direction_merge [degree]' in site_data.keys():
if 'wdir_merge' in site_data.keys():
#wnd_dir = site_data['wind_direction_merge [degree]']
wnd_dir = site_data['wdir_merge']
ax3.plot_date(dt,
wnd_dir,
'o-',
label='Direction',
color="purple",
linewidth=0.2,
markersize=markersize)
ax3.set_ylim(-10, 370)
ax3.set_ylabel('Wind Direction')
ax3.set_yticks([0, 90, 180, 270, 360]) #locking y-ticks for wind direction
axes.append(ax3)
#plot MSLP (or station pressure, if MSLP unavailable)
#if 'mean_sea_level_pressure [mbar]' in site_data.keys():
if 'mslp' in site_data.keys():
#mslp = site_data['mean_sea_level_pressure [mbar]']
mslp = site_data['mslp']
min_mslp = mslp.min(skipna=True) #min 3-day mslp value
max_mslp = mslp.max(skipna=True) #max 3-day mslp value
labelname = 'Min=' + str(round(min_mslp, 1)) + ' | Max=' + str(
round(max_mslp, 1)) + ' hPa'
ax4.plot_date(dt,
mslp,
'o-',
label=labelname,
color='darkorange',
linewidth=linewidth,
markersize=markersize)
ax4.set_ylabel('MSLP (hPa)')
#elif 'station_pressure [mbar]' in site_data.keys():
elif 'pres' in site_data.keys():
#sp = site_data['station_pressure [mbar]']
sp = site_data['pres']
min_sp = sp.min(skipna=True) #min 3-day station pressure value
max_sp = sp.max(skipna=True) #max 3-day station pressure value
labelname = 'Min=' + str(round(min_sp, 1)) + ' | Max=' + str(
round(max_sp, 1)) + ' hPa'
ax4.plot_date(dt,
sp,
'o-',
label=labelname,
color='darkorange',
linewidth=linewidth,
markersize=markersize)
ax4.set_ylabel('Station Pressure (hPa)')
print('unable to get mslp, used station pressure instead')
ax4.legend(loc='best')
axes.append(ax4)
#plot precip accum
#if 'precip_incremental [mm]' in site_data.keys():
if 'precip' in site_data.keys() and 'precip_total' in site_data.keys():
#precip_inc = site_data['precip_incremental [mm]']
#precip_accum = np.nancumsum(precip_inc)
precip_offset = site_data['precip_total'][0]
precip_accum = site_data['precip_total'] - precip_offset
labelname = 'Max=' + str(round(precip_accum[-1], 2)) + ' mm'
ax5.plot_date(dt,
precip_accum,
'o-',
label=labelname,
color='navy',
linewidth=linewidth,
markersize=markersize)
if precip_accum[-1] > 0:
ax5.set_ylim(0, precip_accum[-1] * 1.2)
else:
ax5.set_ylim(0, 1)
ax5.legend(loc='best')
ax5.set_ylabel('Precip (mm)')
axes.append(ax5)
#plot snow depth
#if 'snow_depth [cm]' in site_data.keys():
if 'snow_depth' in site_data.keys():
#snow_depth_mm = site_data['snow_depth [cm]'] * 10 # input data in cm; convert to mm
snow_depth_mm = site_data[
'snow_depth'] * 1000 # input data in m; convert to mm
max_snow_depth_mm = snow_depth_mm.max(skipna=True)
min_snow_depth_mm = snow_depth_mm.min(skipna=True)
labelname = 'Min=' + str(round(min_snow_depth_mm,
2)) + ' | Max=' + str(
round(max_snow_depth_mm, 2)) + ' mm'
ax6.plot_date(dt,
snow_depth_mm,
'o-',
label=labelname,
color='deepskyblue',
linewidth=linewidth,
markersize=markersize)
if max_snow_depth_mm > 0:
ax6.set_ylim(-0.1 * max_snow_depth_mm, max_snow_depth_mm * 1.2)
else:
ax6.set_ylim(0, 1)
ax6.legend(loc='best')
ax6.set_ylabel('Snow Depth (mm)')
axes.append(ax6)
for item, ax in enumerate(axes):
# remove dark borders on all subplots except bottom border
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_visible(True)
# set x & y axis params
ax.tick_params(axis='both', which='major', length=8)
ax.tick_params(axis='both', which='minor', length=4)
ax.xaxis.set_major_locator(DayLocator()) #one date written per day
ax.xaxis.set_major_formatter(
DateFormatter('%b-%d')) #show date, written as 'Jul-12'
ax.xaxis.set_minor_locator(HourLocator(np.linspace(
3, 21, 7))) #hour labels every 3 hours
ax.xaxis.set_minor_formatter(DateFormatter('%H')) #show hour labels
ax.yaxis.grid(linestyle='--') #adds y-axis grid lines
ax.get_yaxis().set_label_coords(
-0.06, 0.5) #properly places y-labels away from figure
# Add mesonet logo
fig.subplots_adjust(bottom=0.1, left=.05, right=1.1)
im = plt.imread(get_sample_data(logo_path))
new_ax = fig.add_axes([1, 0, 0.1, 0.1])
new_ax.imshow(im)
new_ax.axis('off')
# Get warning on next line:
# "This figure includes Axes that are not compatible with tight_layout, so results might be incorrect."
plt.tight_layout()
# Save the plot
plot_path = plot_dir + '/' + current_date_string
if not os.path.exists(plot_path):
os.mkdir(plot_path)
catName = 'surface.Meteogram.' + current_dt_filestring + '.NYSM_' + site_long + '_NY' + '.png'
plt.savefig(plot_path + '/' + catName, bbox_inches='tight')
plt.clf(), plt.cla(), plt.close()
# Open ftp connection
if test:
catalogFTP = FTP(ftpCatalogServer, ftpCatalogUser, ftpCatalogPassword)
catalogFTP.cwd(catalogDestDir)
else:
catalogFTP = FTP(ftpCatalogServer, ftpCatalogUser)
catalogFTP.cwd(catalogDestDir)
# ftp plot to catalog
ftpFile = open(plot_path + '/' + catName, 'rb')
catalogFTP.storbinary('STOR ' + catName, ftpFile)
ftpFile.close()
# Close ftp connection
catalogFTP.quit()
def pandas_candlestick_ohlc(dat, stick = "day", otherseries = None):
mondays = WeekdayLocator(MONDAY)
alldays = DayLocator()
dayFormatter = DateFormatter('%d')
transdat = dat.loc[:,["Open", "High", "Low", "Close"]]
if (type(stick) == str):
if stick == "day":
plotdat = transdat
stick = 1
elif stick in ["week", "month", "year"]:
if stick == "week":
transdat["week"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[1])
elif stick == "month":
transdat["month"] = pd.to_datetime(transdat.index).map(lambda x: x.month)
transdat["year"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[0])
grouped = transdat.groupby(list(set(["year",stick])))
plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []})
for name, group in grouped:
plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1,3]},
index = [group.index[0]]))
if stick == "week": stick = 5
elif stick == "month": stick = 30
elif stick == "year": stick = 365
elif (type(stick) == int and stick >= 1):
transdat["stick"] = [np.floor(i / stick) for i in range(len(transdat.index))]
grouped = transdat.groupby("stick")
plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []})
for name, group in grouped:
plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1,3]},
index = [group.index[0]]))
else:
raise ValueError('Valid inputs to argument "stick" include the strings "day", "week", "month", "year", or a positive integer')
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):
weekFormatter = DateFormatter('%b %d')
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
else:
weekFormatter = DateFormatter('%b %d, %Y')
ax.xaxis.set_major_formatter(weekFormatter)
ax.grid(True)
candlestick_ohlc(ax, list(zip(list(date2num(plotdat.index.tolist())), plotdat["Open"].tolist(), plotdat["High"].tolist(),
plotdat["Low"].tolist(), plotdat["Close"].tolist())),
colorup = "black", colordown = "red", width = stick * .4)
if otherseries != None:
if type(otherseries) != list:
otherseries = [otherseries]
dat.loc[:,otherseries].plot(ax = ax, lw = 1.3, grid = True)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
plt.show()
corrPrecip[exptNum,periodNum] = float(line[42])
#print rmsePrecip
#------------------------------------------------------------------------------
# Start plotting the data
#------------------------------------------------------------------------------
#Create doc to host several plots (now defined near top)
#pp = PdfPages('Precip-RMSE_Microphys.pdf')
#Precip plots
#fig = plt.figure()
for exptNum in range(numExpts):
plt.gca().xaxis.set_major_locator(DayLocator())
plt.gca().xaxis.set_major_formatter(DateFormatter('%m/%d-%HZ'))
plt.plot_date(allDates, rmsePrecip[exptNum,:], 'o-', label=exptLabels[exptNum], color=myColors[exptNum], linewidth=2)
plt.gcf().autofmt_xdate()
#Use this to get min/max of plot and then reset y-range to include 0
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,0,y2))
plt.ylabel('24-hr Precipitation RMSE (mm)')
plt.title('24-hr Precip RMSE Time Series ')
plt.legend(loc='best', shadow=True, fontsize='x-small', fancybox=True)
#plt.show()
pp.savefig()
pylab.savefig('Precip_RMSE.png')
def plot_station_data(site, sitetitle, df):
'''Given site station ID, the title of that site, and a dataframe of ASOS observation data from the last 3 days,
returns a plot of the last 3-days of weather at that site.
Parameters:
site (str): string of ASOS station ID
sitetitle (str): string of ASOS station full name
df (dataframe): dataframe containing last 72 hours (3 days) of ASOS station data
Returns:
None
*saves plots to plot_dir listed near top of script*
'''
if isinstance(df, int): #Returns if the station is not reporting
return
lower_site = site.lower()
timestamp_end = str(df.index[-1].strftime('%Y%m%d%H%M'))
dt = df.index[:]
graphtimestamp_start = dt[0].strftime("%m/%d/%y")
graphtimestamp = dt[-1].strftime("%m/%d/%y")
now = datetime.datetime.utcnow()
today_date = dt[-1].strftime('%Y%m%d')
markersize = 1.5
linewidth = 1.0
#make figure and axes
fig = plt.figure()
fig.set_size_inches(18, 10)
if 'snow_depth_set_1' in df.keys(): #six axes if snow depth
ax1 = fig.add_subplot(6, 1, 1)
ax2 = fig.add_subplot(6, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(6, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(6, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(6, 1, 5, sharex=ax1)
ax6 = fig.add_subplot(6, 1, 6, sharex=ax1)
ax6.set_xlabel('Time (UTC)')
else:
ax1 = fig.add_subplot(5, 1, 1) #five axes if no snow depth
ax2 = fig.add_subplot(5, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(5, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(5, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(5, 1, 5, sharex=ax1)
ax5.set_xlabel('Time (UTC)')
#ax1.set_title(site+' '+sitetitle+' '+graphtimestamp_start+' - '+graphtimestamp+' '+now.strftime("%H:%MZ"))
ax1.set_title(site + ' ' + sitetitle + ' ' + graphtimestamp_start + ' - ' +
graphtimestamp)
#plot airT and dewT
if 'air_temp_set_1' in df.keys():
airT = df['air_temp_set_1']
ax1.plot_date(dt,
airT,
'o-',
label="Temp",
color="blue",
linewidth=linewidth,
markersize=markersize)
if 'dew_point_temperature_set_1d' in df.keys():
dewT = df['dew_point_temperature_set_1d']
ax1.plot_date(dt,
dewT,
'o-',
label="Dew Point",
color="black",
linewidth=linewidth,
markersize=markersize)
elif 'dew_point_temperature_set_1' in df.keys():
dewT = df['dew_point_temperature_set_1']
dewT_new = dewT.dropna()
dewT_dt_list = []
for i in range(0, len(dewT)):
if pd.isnull(dewT[i]) == False:
dewT_dt_list.append(dt[i])
ax1.plot_date(dewT_dt_list,
dewT_new,
'o-',
label="Dew Point",
color="black",
linewidth=linewidth,
markersize=markersize)
if ax1.get_ylim()[0] < 0 < ax1.get_ylim()[1]:
ax1.axhline(0, linestyle='-', linewidth=1.0, color='deepskyblue')
trans = transforms.blended_transform_factory(
ax1.get_yticklabels()[0].get_transform(), ax1.transData)
ax1.text(0,
0,
'0C',
color="deepskyblue",
transform=trans,
ha="right",
va="center") #light blue line at 0 degrees C
ax1.set_ylabel('Temp ($^\circ$C)')
ax1.legend(loc='best', ncol=2)
axes = [ax1] #begin axes
#plotting wind speed and gust
if 'wind_speed_set_1' in df.keys():
wnd_spd = df['wind_speed_set_1']
ax2.plot_date(dt,
wnd_spd,
'o-',
label='Speed',
color="forestgreen",
linewidth=linewidth,
markersize=markersize)
if 'wind_gust_set_1' in df.keys():
wnd_gst = df['wind_gust_set_1']
max_wnd_gst = wnd_gst.max(skipna=True)
ax2.plot_date(dt,
wnd_gst,
'o-',
label='Gust (Max ' + str(round(max_wnd_gst, 1)) + 'kt)',
color="red",
linewidth=0.0,
markersize=markersize)
ax2.set_ylabel('Wind (kt)')
ax2.legend(loc='best', ncol=2)
axes.append(ax2)
#plotting wind direction
if 'wind_direction_set_1' in df.keys():
wnd_dir = df['wind_direction_set_1']
ax3.plot_date(dt,
wnd_dir,
'o-',
label='Direction',
color="purple",
linewidth=0.2,
markersize=markersize)
ax3.set_ylim(-10, 370)
ax3.set_ylabel('Wind Direction')
ax3.set_yticks([0, 90, 180, 270, 360])
axes.append(ax3)
#plotting MSLP
if 'sea_level_pressure_set_1d' in df.keys():
mslp = df['sea_level_pressure_set_1d']
max_mslp = mslp.max(skipna=True)
min_mslp = mslp.min(skipna=True)
labelname = 'Min ' + str(round(min_mslp, 1)) + 'hPa, Max ' + str(
round(max_mslp, 2)) + 'hPa'
ax4.plot_date(dt,
mslp,
'o-',
label=labelname,
color='darkorange',
linewidth=linewidth,
markersize=markersize)
elif 'sea_level_pressure_set_1' in df.keys():
mslp = df['sea_level_pressure_set_1']
mslp_new = mslp.dropna()
mslp_dt_list = []
for i in range(0, len(mslp)):
if pd.isnull(mslp[i]) == False:
mslp_dt_list.append(dt[i])
max_mslp = mslp.max(skipna=True)
min_mslp = mslp.min(skipna=True)
labelname = 'Min ' + str(round(min_mslp, 2)) + 'hPa, Max ' + str(
round(max_mslp, 2)) + 'hPa'
ax4.plot_date(mslp_dt_list,
mslp_new,
'o-',
label=labelname,
color='darkorange',
linewidth=linewidth,
markersize=markersize)
ax4.legend(loc='best')
ax4.set_ylabel('MSLP (hPa)')
ax4.set_xlabel('Time (UTC)')
axes.append(ax4)
#plotting precip accumulation
if 'precip_intervals_set_1d' in df.keys():
precip_inc = df['precip_intervals_set_1d']
precip_inc_new = precip_inc.dropna()
precip_inc_new = list(precip_inc_new.values)
precip_accum = 0.0
precip_accum_list = []
for increment in precip_inc_new:
precip_accum = precip_accum + increment
precip_accum_list.append(precip_accum)
precip_dt_list = []
for i in range(0, len(precip_inc)):
if pd.isnull(precip_inc[i]) == False:
precip_dt_list.append(dt[i])
max_precip = max(precip_accum_list)
labelname = 'Precip (' + str(round(max_precip, 2)) + 'mm)'
ax5.plot_date(precip_dt_list,
precip_accum_list,
'o-',
label=labelname,
color='navy',
linewidth=linewidth,
markersize=markersize)
if max_precip > 0:
ax5.set_ylim(-0.1 * max_precip, max_precip + max_precip * 0.2)
else:
ax5.set_ylim(-0.5, 5)
ax5.legend(loc='best')
ax5.set_ylabel('Precip (mm)')
axes.append(ax5)
#plotting snow depth
if 'snow_depth_set_1' in df.keys():
snow_depth = df['snow_depth_set_1']
snow_depth_new = snow_depth.dropna()
snow_depth_dt_list = []
for i in range(0, len(snow_depth)):
if pd.isnull(snow_depth[i]) == False:
snow_depth_dt_list.append(dt[i])
max_snow_depth = snow_depth.max(skipna=True)
min_snow_depth = snow_depth.min(skipna=True)
labelname = 'Min Depth ' + str(round(
min_snow_depth, 2)) + 'mm, Max Depth ' + str(
round(max_snow_depth, 2)) + 'mm'
ax6.plot_date(snow_depth_dt_list,
snow_depth_new,
'o-',
label=labelname,
color='deepskyblue',
linewidth=linewidth,
markersize=markersize)
if max_snow_depth > 0:
ax6.set_ylim(-0.1 * max_snow_depth,
max_snow_depth + max_snow_depth * 0.2)
else:
ax6.set_ylim(-0.5, 5)
ax6.legend(loc='best')
ax6.set_ylabel('Snow Depth (mm)')
axes.append(ax6)
for ax in axes:
ax.spines["top"].set_visible(
False) #darker borders on the grids of each subplot
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.tick_params(axis='x', which='both', bottom='on',
top='off') #add ticks at labeled times
ax.tick_params(axis='y', which='both', left='on', right='off')
ax.xaxis.set_major_locator(DayLocator())
ax.xaxis.set_major_formatter(DateFormatter('%b-%d'))
ax.xaxis.set_minor_locator(HourLocator(np.linspace(6, 18, 3)))
ax.xaxis.set_minor_formatter(DateFormatter('%H'))
ax.fmt_xdata = DateFormatter('Y%m%d%H%M%S')
ax.yaxis.grid(linestyle='--')
ax.get_yaxis().set_label_coords(-0.06, 0.5)
plot_path = plot_dir + '/' + today_date
if not os.path.exists(plot_path):
os.makedirs(plot_path)
try:
plt.savefig(plot_path + '/ops.asos.' + timestamp_end + '.' +
lower_site + '.png',
bbox_inches='tight')
except:
print("Problem saving figure for %s. Usually a maxticks problem" %
site)
plt.close()
def plot_station_data(date, site, sitetitle, df):
'''Given site station ID, the title of that site, and a dataframe of ASOS observation data from the last 3 days,
returns a plot of the last 3-days of weather at that site.
Parameters:
site (str): string of ASOS station ID
sitetitle (str): string of ASOS station full name
df (dataframe): dataframe containing last 72 hours (3 days) of ASOS station data
Returns:
None
*saves plots to plot_dir listed near top of script*
'''
#if isinstance(df, int): #Returns if the station is not reporting
# return
if df.empty:
print('dataframe is empty -- go to next plot')
return
lower_site = site.lower()
timestamp_end = str(df.index[-1].strftime('%Y%m%d%H%M'))
dt = df.index[:]
dt_array = np.array(dt.values)
graphtimestamp_start = dt[0].strftime("%m/%d/%y")
graphtimestamp = dt[-1].strftime("%m/%d/%y")
#now = datetime.datetime.utcnow()
now = datetime.strptime(date, '%Y%m%d')
today_date = dt[-1].strftime('%Y%m%d')
markersize = 1.5
linewidth = 1.0
#make figure and axes
fig = plt.figure()
fig.set_size_inches(18, 10)
if 'snow_depth_set_1' in df.keys(): #six axes if snow depth
ax1 = fig.add_subplot(6, 1, 1)
ax2 = fig.add_subplot(6, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(6, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(6, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(6, 1, 5, sharex=ax1)
ax6 = fig.add_subplot(6, 1, 6, sharex=ax1)
ax6.set_xlabel('Time (UTC)')
else:
ax1 = fig.add_subplot(5, 1, 1) #five axes if no snow depth
ax2 = fig.add_subplot(5, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(5, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(5, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(5, 1, 5, sharex=ax1)
ax5.set_xlabel('Time (UTC)')
#ax1.set_title(site+' '+sitetitle+' '+graphtimestamp_start+' - '+graphtimestamp+' '+now.strftime("%H:%MZ"))
ax1.set_title(site + ' ' + sitetitle + ' ' + graphtimestamp_start + ' - ' +
graphtimestamp)
#------------------
#plot airT and dewT
#------------------
if 'tmpc' in df.keys():
airT = df['tmpc']
airT_new = airT.dropna()
airT_list = list(airT_new.values)
airT_dt_list = []
for i in range(0, len(airT)):
if pd.isnull(airT[i]) == False:
airT_dt_list.append(dt[i])
#ax1.plot_date(airT_dt_list,airT_list,'o-',label="Temp",color="blue",linewidth=linewidth,markersize=markersize)
ax1.plot_date(airT_dt_list,
airT_list,
linestyle='solid',
label="Temp",
color="blue",
linewidth=linewidth,
marker='None')
#ax1.plot_date(dt,airT,'-',label="Temp",color="blue",linewidth=linewidth)
if 'dwpc' in df.keys():
dewT = df['dwpc']
dewT_new = dewT.dropna()
dewT_list = list(dewT_new.values)
dewT_dt_list = []
for i in range(0, len(dewT)):
if pd.isnull(dewT[i]) == False:
dewT_dt_list.append(dt[i])
#ax1.plot_date(dewT_dt_list,dewT_list,'o-',label="Dew Point",color="black",linewidth=linewidth,markersize=markersize)
ax1.plot_date(dewT_dt_list,
dewT_list,
linestyle='solid',
label="Dew Point",
color="black",
linewidth=linewidth,
marker='None')
if ax1.get_ylim()[0] < 0 < ax1.get_ylim()[1]:
ax1.axhline(0, linestyle='-', linewidth=1.0, color='deepskyblue')
trans = transforms.blended_transform_factory(
ax1.get_yticklabels()[0].get_transform(), ax1.transData)
ax1.text(0,
0,
'0C',
color="deepskyblue",
transform=trans,
ha="right",
va="center") #light blue line at 0 degrees C
ax1.set_ylabel('Temp ($^\circ$C)')
ax1.legend(loc='best', ncol=2)
axes = [ax1] #begin axes
#----------------------------
#plotting wind speed and gust
#----------------------------
if 'sknt' in df.keys():
wnd_spd = df['sknt']
#ax2.plot_date(dt,wnd_spd,'o-',label='Speed',color="forestgreen",linewidth=linewidth,markersize=markersize)
ax2.plot_date(dt,
wnd_spd,
linestyle='solid',
label='Speed',
color="forestgreen",
linewidth=linewidth,
marker='None')
if 'gust' in df.keys():
wnd_gst = df['gust']
max_wnd_gst = wnd_gst.max(skipna=True)
ax2.plot_date(dt,
wnd_gst,
'o-',
label='Gust (Max ' + str(round(max_wnd_gst, 1)) + 'kt)',
color="red",
linewidth=0.0,
markersize=markersize)
ax2.set_ylabel('Wind (kt)')
ax2.legend(loc='best', ncol=2)
axes.append(ax2)
#-----------------------
#plotting wind direction
#-----------------------
if 'drct' in df.keys():
wnd_dir = df['drct']
wnd_dir_new = wnd_dir.dropna()
wnd_dir_list = list(wnd_dir_new.values)
wnd_dir_dt_list = []
for i in range(0, len(wnd_dir)):
if pd.isnull(wnd_dir[i]) == False:
wnd_dir_dt_list.append(dt[i])
#ax3.plot_date(dt,wnd_dir,'o-',label='Direction',color="purple",linewidth=0.2, markersize=markersize)
#ax3.plot_date(wnd_dir_dt_list,wnd_dir_list,'o-',label='Direction',color="purple",linewidth=0.2, markersize=markersize)
ax3.plot_date(wnd_dir_dt_list,
wnd_dir_list,
linestyle='solid',
label='Direction',
color="purple",
linewidth=linewidth,
marker='None')
ax3.set_ylim(-10, 370)
ax3.set_ylabel('Wind Direction')
ax3.set_yticks([0, 90, 180, 270, 360])
axes.append(ax3)
#-------------
#plotting MSLP
#-------------
if 'mslp' in df.keys():
mslp = df['mslp']
mslp_new = mslp.dropna()
mslp_list = list(mslp_new.values)
mslp_dt_list = []
for i in range(0, len(mslp)):
if pd.isnull(mslp[i]) == False:
mslp_dt_list.append(dt[i])
max_mslp = mslp.max(skipna=True)
min_mslp = mslp.min(skipna=True)
labelname = 'Min ' + str(round(min_mslp, 1)) + 'hPa, Max ' + str(
round(max_mslp, 2)) + 'hPa'
#ax4.plot_date(mslp_dt_list,mslp_list,'o-',label=labelname,color='darkorange',linewidth=linewidth,markersize=markersize)
ax4.plot_date(mslp_dt_list,
mslp_list,
linestyle='solid',
label=labelname,
color='darkorange',
linewidth=linewidth,
marker='None')
ax4.legend(loc='best')
ax4.set_ylabel('MSLP (hPa)')
ax4.set_xlabel('Time (UTC)')
axes.append(ax4)
#-------------------------------------------
#plotting precip accumulation & precip types
#-------------------------------------------
# Move date_time from index to column
df = df.reset_index()
if 'p01m' in df.keys():
df['p01m'] = df['p01m'].fillna(0)
last_val = df.iloc[0]['p01m']
last_time = df.iloc[0]['time']
last_hour = last_time.strftime('%H')
last_minute = last_time.strftime('%M')
precip_inc = [last_val]
precip_accum = 0.0
precip_accum_list = [last_val]
num_ge_55_for_curr_hour = 0
for index in range(1, len(df)):
#for index in range(1,12):
val = df.iloc[index]['p01m']
time = df.iloc[index]['time']
hour = time.strftime('%H')
minute = time.strftime('%M')
#print('LAST: val=',last_val,' hour=',last_hour,' minute=',last_minute)
#print('CURR: val=',val,' hour=',hour,' minute=',minute)
if hour != last_hour:
num_ge_55_for_curr_hour = 0
#if val == last_val:
# increment = 0.0
#else:
# #if last_minute == '53':
# if last_minute > '50' and last_minute < '55':
# increment = val
# else:
# increment = val-last_val
if minute >= '55':
if num_ge_55_for_curr_hour == 0:
increment = val
else:
if val > last_val:
increment = val - last_val
else:
increment = 0
num_ge_55_for_curr_hour = num_ge_55_for_curr_hour + 1
else:
if val == last_val:
increment = 0
else:
if val > last_val:
increment = val - last_val
else:
increment = 0
precip_accum = precip_accum + increment
precip_accum_list.append(precip_accum)
precip_inc.append(increment)
last_val = val
last_hour = hour
last_minute = minute
#df['p01m_mod'] = precip_inc
max_precip = sum(precip_inc)
# max_precip is also precip_accum_list[-1]
#p01m_mod = list(df['p01m_mod'].values)
p01m_mod_dt = list(df['time'].values)
#max_precip = max(precip_accum_list)
labelname = 'Precip (' + str(round(max_precip, 2)) + 'mm)'
#ax5.plot_date(p01m_mod_dt,precip_accum_list,'o-',label=labelname,color='navy',linewidth=linewidth,markersize=markersize)
ax5.plot_date(p01m_mod_dt,
precip_accum_list,
linestyle='solid',
label=labelname,
color='navy',
linewidth=linewidth,
marker='None')
if max_precip > 0:
ax5.set_ylim(-0.1 * max_precip, max_precip + max_precip * 0.2)
else:
ax5.set_ylim(-0.1, 0.5)
# Add weather_code info to plot
if 'wxcodes' in df.keys():
df['wxcodes'] = df['wxcodes'].fillna('')
wxcodes_wto = []
for index in range(0, len(df)):
time = df.iloc[index]['time']
minute = time.strftime('%M')
#if minute == '53':
if minute > '50' and minute < '55':
# convert alphanumeric code to wto code number
wxcodes = df.iloc[index]['wxcodes']
# Added in a check in case of unexpected weather codes
# Used to resolve case when code set to -FZRAGR instead of -FZRA GR
# Unidata says this will work as well:
# wxcode_num = wx_code_map.get(wxcodes.split()[0], 0)
# Here is reference: https://docs.python.org/3/library/stdtypes.html#dict.get
if len(wxcodes) > 0 and wxcodes.split()[0] in wx_codes.keys():
wxcode_num = wx_code_map[wxcodes.split()[0]]
else:
wxcode_num = 0
else:
wxcode_num = 0
wxcodes_wto.append(wxcode_num)
#df['wxcodes_wto'] = wxcode_wto
#wxcodes_wto = list(df['wxcodes_wto'].values)
wxcodes_wto_dt = list(df['time'].values)
if max_precip > 0:
dummy_y_vals = np.ones(len(wxcodes_wto)) * (0.10 * max_precip)
else:
dummy_y_vals = np.ones(len(wxcodes_wto)) * (0.10 * 0.5)
sp = StationPlot(ax5, wxcodes_wto_dt, dummy_y_vals)
#ax.plot(dates, temps)
sp.plot_symbol('C',
wxcodes_wto,
current_weather,
fontsize=16,
color='red')
#sp.plot_symbol('C', wxcodes_wto, current_weather, fontsize=14, color='red')
ax5.legend(loc='best')
ax5.set_ylabel('Precip (mm)')
axes.append(ax5)
# Axes formatting
for ax in axes:
ax.spines["top"].set_visible(
False) #darker borders on the grids of each subplot
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.tick_params(axis='x', which='both', bottom='on',
top='off') #add ticks at labeled times
ax.tick_params(axis='y', which='both', left='on', right='off')
ax.xaxis.set_major_locator(DayLocator())
ax.xaxis.set_major_formatter(DateFormatter('%b-%d'))
ax.xaxis.set_minor_locator(HourLocator(np.linspace(6, 18, 3)))
ax.xaxis.set_minor_formatter(DateFormatter('%H'))
ax.fmt_xdata = DateFormatter('Y%m%d%H%M%S')
ax.yaxis.grid(linestyle='--')
ax.get_yaxis().set_label_coords(-0.06, 0.5)
# Write plot to file
plot_path = plot_dir + '/' + today_date
if not os.path.exists(plot_path):
os.makedirs(plot_path)
try:
catalogName = 'surface.Meteogram.' + timestamp_end + '.ASOS_' + asos_sites[
lower_site] + '.png'
#plt.savefig(plot_path+'/ops.asos.'+timestamp_end+'.'+lower_site+'.png',bbox_inches='tight')
plt.savefig(plot_path + '/' + catalogName, bbox_inches='tight')
except:
print("Problem saving figure for %s. Usually a maxticks problem" %
site)
plt.close()
# DON'T NEED THIS CHECK FOR RT PROCESSING
# ftp plot if in asos_for_cat list
#if lower_site in asos_for_cat:
# Open ftp connection
if test:
catalogFTP = FTP(ftpCatalogServer, ftpCatalogUser, ftpCatalogPassword)
catalogFTP.cwd(catalogDestDir)
else:
catalogFTP = FTP(ftpCatalogServer, ftpCatalogUser)
catalogFTP.cwd(catalogDestDir)
catalogFTP.set_pasv(False)
# ftp image
ftpFile = open(os.path.join(plot_path, catalogName), 'rb')
catalogFTP.storbinary('STOR ' + catalogName, ftpFile)
ftpFile.close()
# Close ftp connection
catalogFTP.quit()
def new_plot(timezone,
plot_title,
figname,
times_utc,
height,
bs_final,
ML_height=0,
tick_every=12,
save=False,
show=True):
## Creates a plot of backscatter
# convert DateTime array to matplotlib.dates format
plot_dates = mdates.date2num(times_utc)
# set plotting grids
ptt = np.zeros(bs_final.shape)
phh = np.zeros(bs_final.shape)
for i in range(ptt.shape[0]):
ptt[i, :] = plot_dates[i]
for j in range(phh.shape[1]):
phh[:, j] = height[j]
# Set plotting range
minv = -7.4
maxv = -6.8
#fig = plt.figure(1, figsize=(20,8)) # Long plot
fig = plt.figure(1, figsize=(12, 6)) # Short Plot
ax = fig.add_subplot(111)
qmesh = ax.pcolormesh(ptt,
phh,
bs_final,
cmap=plt.cm.jet,
vmin=minv,
vmax=maxv)
# Colorbar
cb = fig.colorbar(qmesh, ax=ax, shrink=0.8, pad=0.02)
cb.ax.set_ylabel('Backscatter [log10 m-1 sr-1]', fontsize=14)
tv = np.linspace(minv, maxv, 7)
cb.ax.set_yticklabels(tv, fontsize=14)
cb.set_ticks(tv)
# Labels
plt.ylabel('Height (m)', fontsize=16)
plt.xlabel('Hour:Minute (' + timezone + ')', fontsize=16)
#plt.xlabel('Time ('+timezone+')')
plt.title(plot_title, fontsize=21)
# Format Ticks
if tick_every == 12:
# Place a major tick and label every 12 hours, place minor tick every hour.
days = DayLocator(interval=2)
days_each = DayLocator()
hours = HourLocator(byhour=[0, 12])
hours_each = HourLocator()
mins = MinuteLocator(byminute=[10, 20, 30, 40, 50])
#mins = MinuteLocator(byminute=30)
mins_each = MinuteLocator()
dateFmt = DateFormatter('%H:%M')
ax.xaxis.set_major_locator(hours_each)
ax.xaxis.set_major_formatter(dateFmt)
ax.xaxis.set_minor_locator(mins)
#increase size of tick marks
ax.tick_params('both', length=8, width=1.2, which='major')
ax.tick_params('both', length=4, width=1, which='minor')
plt.xticks(rotation=30, fontsize=14)
plt.yticks(fontsize=14)
## Plot mixed layer height if provided:
#if ML_height!=0:
if len(ML_height) > 0:
plt.plot(times_utc, ML_height, 'k', linewidth=2)
plt.tight_layout()
if show == True:
plt.show()
if save == True:
save_plot(figname, fig)
return fig
def plot_factor_info(factor_name, the_dates, signature, signature_avg, signature_std, \
signature_th04, signature_th04_avg, signature_th04_std,\
signature_th06, signature_th06_avg, signature_th06_std,\
signature_th08, signature_th08_avg, signature_th08_std,\
scores, scores_avg, scores_std, \
basepath, TIME_SLOTS_PER_DAY, plot_scores=True, \
whole_series_std_dev=None, \
whole_series_std_dev_th04=None, \
whole_series_std_dev_th06=None, \
whole_series_std_dev_th08=None):
''' Plot all the cluster signatures in a single plot '''
max_figs_per_line = 4
if plot_scores:
max_rows_per_page = 7
else:
max_rows_per_page = 5
f = plt.figure()
f.set_size_inches(10 * max_figs_per_line, 4 * max_rows_per_page)
ax = plt.subplot(max_rows_per_page, 1, 1)
base_line, = ax.plot_date(x=the_dates, y=signature, fmt="-", linewidth=2)
ax.xaxis.grid(True, which='major', color='gray', linestyle='-', alpha=1.0)
ax.xaxis.grid(True, which='minor', color='gray', linestyle='--', alpha=0.4)
ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d (%a)'))
ax.xaxis.set_major_locator(WeekdayLocator(byweekday=MONDAY))
ax.xaxis.set_minor_locator(DayLocator())
ax.set_xlim([the_dates[0], the_dates[-1]])
ax.set_xlabel('Signature')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
if whole_series_std_dev is not None:
base_c = base_line.get_color()
ax.fill_between(the_dates,
np.array(signature) - np.array(whole_series_std_dev),
np.array(signature) + np.array(whole_series_std_dev),
alpha=0.4,
edgecolor=base_c,
facecolor=base_c)
ax = plt.subplot(max_rows_per_page, 1, 2)
base_line, = ax.plot_date(x=the_dates,
y=signature_th04,
fmt="-",
linewidth=2)
ax.xaxis.grid(True, which='major', color='gray', linestyle='-', alpha=1.0)
ax.xaxis.grid(True, which='minor', color='gray', linestyle='--', alpha=0.4)
ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d (%a)'))
ax.xaxis.set_major_locator(WeekdayLocator(byweekday=MONDAY))
ax.xaxis.set_minor_locator(DayLocator())
ax.set_xlim([the_dates[0], the_dates[-1]])
ax.set_xlabel('Signature with th. 0.4')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
if whole_series_std_dev is not None:
base_c = base_line.get_color()
ax.fill_between(
the_dates,
np.array(signature_th04) - np.array(whole_series_std_dev_th04),
np.array(signature_th04) + np.array(whole_series_std_dev_th04),
alpha=0.4,
edgecolor=base_c,
facecolor=base_c)
ax = plt.subplot(max_rows_per_page, 1, 3)
ax.plot_date(x=the_dates, y=signature_th06, fmt="-", linewidth=2)
ax.xaxis.grid(True, which='major', color='gray', linestyle='-', alpha=1.0)
ax.xaxis.grid(True, which='minor', color='gray', linestyle='--', alpha=0.4)
ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d (%a)'))
ax.xaxis.set_major_locator(WeekdayLocator(byweekday=MONDAY))
ax.xaxis.set_minor_locator(DayLocator())
ax.set_xlim([the_dates[0], the_dates[-1]])
ax.set_xlabel('Signature with th. 0.6')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
if whole_series_std_dev is not None:
base_c = base_line.get_color()
ax.fill_between(
the_dates,
np.array(signature_th06) - np.array(whole_series_std_dev_th06),
np.array(signature_th06) + np.array(whole_series_std_dev_th06),
alpha=0.4,
edgecolor=base_c,
facecolor=base_c)
ax = plt.subplot(max_rows_per_page, 1, 4)
ax.plot_date(x=the_dates, y=signature_th08, fmt="-", linewidth=2)
ax.xaxis.grid(True, which='major', color='gray', linestyle='-', alpha=1.0)
ax.xaxis.grid(True, which='minor', color='gray', linestyle='--', alpha=0.4)
ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d (%a)'))
ax.xaxis.set_major_locator(WeekdayLocator(byweekday=MONDAY))
ax.xaxis.set_minor_locator(DayLocator())
ax.set_xlim([the_dates[0], the_dates[-1]])
ax.set_xlabel('Signature with th. 0.8')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
if whole_series_std_dev is not None:
base_c = base_line.get_color()
ax.fill_between(
the_dates,
np.array(signature_th08) - np.array(whole_series_std_dev_th08),
np.array(signature_th08) + np.array(whole_series_std_dev_th08),
alpha=0.4,
edgecolor=base_c,
facecolor=base_c)
ax = plt.subplot(max_rows_per_page, max_figs_per_line, 17)
plotAverageSeries(signature_avg, signature_std, TIME_SLOTS_PER_DAY, ax=ax)
ax.set_xlabel('Average Signature')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
ax = plt.subplot(max_rows_per_page, max_figs_per_line, 18)
plotAverageSeries(signature_th04_avg,
signature_th04_std,
TIME_SLOTS_PER_DAY,
ax=ax)
ax.set_xlabel('Average Signature with th. 0.4')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
ax = plt.subplot(max_rows_per_page, max_figs_per_line, 19)
plotAverageSeries(signature_th06_avg,
signature_th06_std,
TIME_SLOTS_PER_DAY,
ax=ax)
ax.set_xlabel('Average Signature with th. 0.6')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
ax = plt.subplot(max_rows_per_page, max_figs_per_line, 20)
plotAverageSeries(signature_th08_avg,
signature_th08_std,
TIME_SLOTS_PER_DAY,
ax=ax)
ax.set_xlabel('Average Signature with th. 0.8')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
if plot_scores:
ax = plt.subplot(max_rows_per_page, 1, 6)
ax.plot_date(x=the_dates, y=scores, fmt="-", linewidth=2)
ax.xaxis.grid(True,
which='major',
color='gray',
linestyle='-',
alpha=1.0)
ax.xaxis.grid(True,
which='minor',
color='gray',
linestyle='--',
alpha=0.4)
ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d (%a)'))
ax.xaxis.set_major_locator(WeekdayLocator(byweekday=MONDAY))
ax.xaxis.set_minor_locator(DayLocator())
ax.set_xlim([the_dates[0], the_dates[-1]])
ax.set_xlabel('Scores')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
ax = plt.subplot(max_rows_per_page, max_figs_per_line, 25)
plotAverageSeries(scores_avg, scores_std, TIME_SLOTS_PER_DAY, ax=ax)
ax.set_xlabel('Average Scores')
ax.xaxis.set_label_coords(0.5, 0.96)
for item in ([ax.title, ax.xaxis.label, ax.yaxis.label] +
ax.get_xticklabels() + ax.get_yticklabels()):
item.set_fontsize(18)
plt.tight_layout()
if plot_scores:
plt.savefig('%s%s_signatures_and_scores.pdf' % (basepath, factor_name))
else:
plt.savefig('%s%s_signatures.pdf' % (basepath, factor_name))
plt.close()
except Exception as e:
print(e)
#蜡烛图
from matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY, date2num
from matplotlib.finance import candlestick_ohlc, candlestick2_ochl
import datetime
from datetime import date
import pylab
#怎么使用candlestick_ohlc(candlestick2_ochl)
#matplotlib.finance.candlestick_ohlc(ax, quotes, width=0.2, colorup='k', colordown='r', alpha=1.0)
#https://matplotlib.org/api/finance_api.html
mondays = WeekdayLocator(MONDAY) #用于横坐标设定，每个显示的横坐标都是周一
alldays = DayLocator() #横坐标的最小单位为一天
%pylab inline
pylab.rcParams['figure.figsize'] = (15, 9) #设置图片的大小
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
weekFormatter = DateFormatter('%b %d, %Y') #设置坐标显示的日期格式Jun 5, 2018
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
ax.grid(True)
candlestick_ohlc(ax, list(zip(list(date2num(apple.index.tolist())), apple["Open"].tolist(), apple["High"].tolist(),
apple["Low"].tolist(), apple["Close"].tolist())),
colorup = "red", colordown = "green", width = 1 * .4)
# candlestick2_ochl(ax, apple["Open"].tolist(), apple["Close"].tolist(), apple["High"].tolist(),
# apple["Low"].tolist(),
yen_data = pd.read_csv(
'http://research.stlouisfed.org/fred2/data/EXJPUS.txt',
skiprows=28,
index_col=0,
delim_whitespace=True,
converters={0: lambda x: datetime.strptime(x, "%Y-%m-%d")})
############## Creation of Top Chart ###########
fig, axes = plt.subplots(
2, 1) # Create a grid of 2 rows and 1 column of charts (2 charts total)
# Let's define x-axis date interval sizes: year, month, week, or day
year = YearLocator()
month = MonthLocator(bymonth=range(1, 13), bymonthday=1, interval=1)
week = WeekdayLocator(byweekday=dateutil.rrule.MO) # Every MOnday
day = DayLocator(bymonthday=range(1, 32), interval=1)
axes[0] = fig.add_axes([0, 1.3, 1.5, 1]) # left, bottom, width, height
# Let's define x-axis formatting
axes[0].xaxis.set_major_locator(year)
axes[0].xaxis.grid(which='major')
# Let's define y-axis formatting
y_major_ticks = MultipleLocator(50)
axes[0].yaxis.set_major_locator(y_major_ticks)
axes[0].yaxis.grid(which='major')
# Now plot the data...
axes[0].plot_date(yen_data.index, yen_data.values, 'r')
axes[0].set_title("Yen-to-US$ Exchange Rate Since 1971", weight="bold")
# Checking if there is a relationship between these stocks using
# a scatter matrix plot. Rearranging the Open columns into a new dataframe.
car_comp = pd.concat([tesla['Open'], gm['Open'], ford['Open']], axis=1)
car_comp.columns = ['Tesla Open', 'GM Open', 'Ford Open']
scatter_matrix(car_comp, figsize=(8, 8), alpha=0.2, hist_kwds={'bins': 50})
# Creating a candlestick chart for Ford in January 2012
ford_reset = ford.loc['2012-01'].reset_index()
ford_reset['date_ax'] = ford_reset['Date'].apply(lambda date: date2num(date))
list_of_cols = ['date_ax', 'Open', 'High', 'Low', 'Close']
ford_values = [tuple(vals) for vals in ford_reset[list_of_cols].values]
mondays = WeekdayLocator(MONDAY)
alldays = DayLocator()
weekFormatter = DateFormatter('%b %d')
dayFormatter = DateFormatter('%d')
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)
candlestick_ohlc(ax, ford_values, width=0.6, colorup='g', colordown='r')
# Calculating daily percentage change
tesla['returns'] = (tesla['Close'] / tesla['Close'].shift(1)) - 1
def drawPic(df, code, name):
mondays = WeekdayLocator(MONDAY) # 主要刻度
alldays = DayLocator() # 次要刻度
# weekFormatter = DateFormatter('%b %d') # 如：Jan 12
mondayFormatter = DateFormatter('%m-%d-%Y') # 如：2-29-2015
dayFormatter = DateFormatter('%d') # 如：12
xx = []
t = 0
for DAT, DD in df.iterrows():
t = t + 10
xx.append(t)
# --------------------------------------------------------------------
plt.figure(4) # 创建图表2
ax1 = plt.subplot(411) # 在图表2中创建子图1
ax2 = plt.subplot(412) # 在图表2中创建子图2
ax3 = plt.subplot(413) # 在图表2中创建子图2
ax4 = plt.subplot(414) # 在图表2中创建子图2
# --------------------------------------------(子图表1)
plt.sca(ax1)
ax1.xaxis.set_major_locator(mondays)
ax1.xaxis.set_minor_locator(alldays)
ax1.xaxis.set_major_formatter(mondayFormatter)
_candlestick(ax1, df, width=0.6, colorup='r', colordown='g')
ax1.xaxis_date()
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
"""
plt.plot( xx, df['ma5'], linewidth = 0.5 )
plt.plot( xx, df['ma10'], linewidth = 0.5 )
plt.plot( xx, df['ma20'], linewidth = 0.5 )
plt.plot( xx, df['ma30'], linewidth = 0.5 )
plt.plot( xx, df['ma40'], linewidth = 0.5 )
plt.plot( xx, df['ma60'], linewidth = 0.5 )
plt.plot( xx, df['ma89'], linewidth = 0.5 )
plt.plot( xx, df['ma144'], linewidth = 0.5 )
plt.plot( xx, df['ma233'], linewidth = 0.5 )
plt.plot( xx, df['ma377'], linewidth = 0.5 )
plt.plot( xx, df['ma610'], linewidth = 0.5 )
"""
# ax.grid(True)
# --------------------------------------------(子图表2)
plt.sca(ax2)
# plt.plot( xx[len(xx)-500:len(xx)], df['j1'].tail(500), linewidth = 0.5 )
plt.plot(xx, df['j1'], linewidth=0.5)
plt.plot(xx, df['e1'], linewidth=0.5)
plt.plot(xx, df['j2'], linewidth=0.5)
plt.plot(xx, df['e2'], linewidth=0.5)
plt.plot(xx, df['j3'], linewidth=0.5)
plt.plot(xx, df['e3'], linewidth=0.5)
plt.plot(xx, df['j4'], linewidth=0.5)
plt.plot(xx, df['e4'], linewidth=0.5)
plt.sca(ax3)
plt.plot(xx, df['x1'], linewidth=0.5)
plt.plot(xx, df['x2'] * 7, linewidth=0.5)
plt.plot(xx, df['x3'] * 30, linewidth=0.5)
plt.plot(xx, df['x4'] * 80, linewidth=0.5)
plt.sca(ax4)
plt.plot(xx, df['gain'], linewidth=0.5)
plt.show()
def plot_incidence(self, X, Y):
# Create plot
fig, ax = plt.subplots()
# Rotate datetimes
plt.setp(ax.get_xticklabels(), rotation=90, ha='right')
# plt.rc('xtick', labelsize=SMALL_SIZE)
ax.xaxis.set_major_locator(DayLocator(interval=1))
ax.xaxis.set_major_formatter(DateFormatter('%d/%m'))
ax.set_xlim([X[0], X[-1]])
ax.set_ylim(XLIMIT, YLIMIT)
# Set risk masks
Yl = np.ma.masked_inside(Y, 50, 5000)
Ym = np.ma.masked_inside(Y, 0, 50)
Yh = np.ma.masked_inside(Y, 0, 150)
Ye = np.ma.masked_inside(Y, 0, 250)
# Add data
ax.bar(X,
Yl,
color=mcolors.CSS4_COLORS['green'],
linewidth=1.8,
label='Risc baix (IA14 inferior a 50)',
align='center')
ax.bar(X,
Ym,
color=mcolors.CSS4_COLORS['darkorange'],
linewidth=1.8,
label='Risc mitjà (IA14 entre 50 i 150)',
align='center')
ax.bar(X,
Yh,
color=mcolors.CSS4_COLORS['red'],
linewidth=1.8,
label='Risc alt (IA14 entre 150 i 250)',
align='center')
ax.bar(X,
Ye,
color=mcolors.CSS4_COLORS['darkred'],
linewidth=1.8,
label='Risc extrem (IA14 superior a 250)',
align='center')
# Turn region name into bold string
bold = self.bold(self.region)
# Labels
ax.set_title(bold + ': Incidència acumulada en els últims 14 dies')
ax.set_xlabel('Data')
ax.set_ylabel('IA14')
ax.legend(loc='upper left', framealpha=0.5)
plt.figtext(0.03,
0,
"Font: Dades Obertes de la Generalitat de Catalunya",
ha="left",
fontsize=7)
plt.figtext(0.72,
0,
"Bot de Telegram: t.me/CatalunyaCOVID19bot",
ha="left",
fontsize=7)
# Use tight layout
fig.tight_layout()
# Save
self.file_path = INCIDENCE_PATH + self.region + '.png'
plt.savefig(self.file_path, dpi=DPI, bbox_inches='tight')
plt.close()
for legobj in leg.legendHandles:
legobj.set_linewidth(2.0)
plt.ylabel(Ylabel)
if LocatorInterval == 'multi_year':
ax2.xaxis.set_major_locator(YearLocator())
ax2.xaxis.set_minor_locator(MonthLocator(bymonth=6))
ax2.xaxis.set_major_formatter(ticker.NullFormatter())
ax2.xaxis.set_minor_formatter(DateFormatter('%Y'))
ax2.tick_params(axis='both', which='minor', labelsize=12)
elif LocatorInterval == 'multi_day':
ax2.xaxis.set_major_locator(
MonthLocator(bymonth=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
bymonthday=15))
ax2.xaxis.set_minor_locator(
DayLocator(bymonthday=[0, 5, 10, 15, 20, 25, 30]))
ax2.xaxis.set_major_formatter(DateFormatter('%b'))
ax2.xaxis.set_minor_formatter(DateFormatter('%d'))
ax2.yaxis.set_minor_locator(ticker.MultipleLocator(1))
ax2.tick_params(axis='both', which='minor', labelsize=12)
ax2.grid(which='both', color='k', linestyle='--', linewidth=0.25)
elif LocatorInterval == 'multi_month':
ax2.xaxis.set_major_locator(MonthLocator())
ax2.xaxis.set_minor_locator(
MonthLocator(bymonth=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
bymonthday=15))
ax2.xaxis.set_major_formatter(ticker.NullFormatter())
ax2.xaxis.set_minor_formatter(DateFormatter('%b'))
ax2.tick_params(axis='both', which='minor', labelsize=12)
else:
ax2.xaxis.set_major_locator(MonthLocator())
ssec2015 = ssec2015.iloc[:, 1:]
ssec2015.head(n=3)
ssec2015.iloc[-3:, :]
ssec2015.Date=[date2num(datetime.strptime(date,"%Y-%m-%d"))\
for date in ssec2015.Date]
type(ssec2015)
ssec15list = list()
for i in range(len(ssec2015)):
ssec15list.append(ssec2015.iloc[i, :])
ax = plt.subplot()
mondays = WeekdayLocator(MONDAY)
weekFormatter = DateFormatter('%y %b %d')
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(DayLocator())
ax.xaxis.set_major_formatter(weekFormatter)
plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus'] = False
ax.set_title("上证综指2015年3月份K线图")
candlestick_ohlc(ax, ssec15list, width=0.7, colorup='r', colordown='g')
plt.setp(plt.gca().get_xticklabels(),
rotation=50,
horizontalalignment='center')
plt.show()
#morning star
ssec2012 = pd.read_csv('ssec2012.csv')
ssec2012.index = ssec2012.iloc[:, 1]
ssec2012.index = pd.to_datetime(ssec2012.index, format='%Y-%m-%d')
ssec2012 = ssec2012.iloc[:, 2:]
def pandas_candlestick_ohlc(dat, stick="day", otherseries=None):
"""
:param dat: pandas DataFrame object with datetime64 index,
and float columns "open", "high", "low", and "close"
:param stick: A string or number indicating the period of time covered by a single candlestick.
Valid string inputs include "day", "week", "month", and "year", ("day" default)
:param otherseries: An iterable that will be coerced into a list,
containing the columns of dat that hold other series to be plotted as lines
"""
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator(
) # Make ticks on occurrences of each day of the month,minor ticks
dayFormatter = DateFormatter('%d') # e.g., 12
transdat = dat.loc[:, ["open", "high", "low", "close"]]
if (type(stick) == str):
if stick == "day":
plotdat = transdat
stick = 1 # Used for plotting, defaul to 1 for day-plot
elif stick in ["week", "month", "year"]:
if stick == "week":
transdat["week"] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalendar()[1]) # Identify weeks
elif stick == "month":
transdat["month"] = pd.to_datetime(transdat.index).map(
lambda x: x.month) # Identify months
transdat["year"] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalendar()[0]) # Identify years
grouped = transdat.groupby(list(set(
["year",
stick]))) # Group by year and other appropriate variable
plotdat = pd.DataFrame({
"open": [],
"high": [],
"low": [],
"close": []
}) # Create empty data frame containing what will be plotted
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
"open": group.iloc[0, 0],
"high": max(group.high),
"low": min(group.low),
"close": group.iloc[-1, 3]
},
index=[group.index[0]]))
if stick == "week": stick = 5
elif stick == "month": stick = 30
elif stick == "year": stick = 365
else:
raise ValueError(
'Valid inputs to argument "stick" include the strings "day", "week", "month", "year", or a positive integer'
)
# Set plot parameters, including the axis object ax used for plotting
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('365 days'):
weekFormatter = DateFormatter('%b %d, %Y') # e.g., Jan 12,2007
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
else:
weekFormatter = DateFormatter('%b %d, %Y')
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xasix.set_major_formatter(weekFormatter)
ax.grid(True)
# Create the candelstick chart
candlestick_ohlc(ax,
list(
zip(list(date2num(plotdat.index.tolist())),
plotdat["open"].tolist(),
plotdat["high"].tolist(), plotdat["low"].tolist(),
plotdat["close"].tolist())),
colorup="red",
colordown="green",
width=stick * .4)
# Plot other series (such as moving averages) as lines
if otherseries != None:
if type(otherseries) != list:
otherseries = [otherseries]
dat.loc[:, otherseries].plot(ax=ax, lw=1.3, grid=True)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
plt.show()
return fig
