async def user_metrics_background_task():
await client.wait_until_ready()
global sentdex_guild
sentdex_guild = client.get_guild(405403391410438165)
while not client.is_closed():
try:
online, idle, offline = community_report(sentdex_guild)
with open(f"{path}/usermetrics.csv", "a") as f:
f.write(f"{int(time.time())},{online},{idle},{offline}\n")
df_msgs = pd.read_csv(f'{path}/msgs.csv',
names=['time', 'uid', 'channel'])
df_msgs = df_msgs[(df_msgs['time'] > time.time() -
(86400 * DAYS_BACK))]
df_msgs['count'] = 1
df_msgs['date'] = pd.to_datetime(df_msgs['time'], unit='s')
df_msgs.drop("time", 1, inplace=True)
df_msgs.set_index("date", inplace=True)
df_no_dup = df_msgs.copy()
df_no_dup['uid2'] = df_no_dup['uid'].shift(-1)
df_no_dup['uid_rm_dups'] = list(
map(df_match, df_no_dup['uid'], df_no_dup['uid2']))
df_no_dup.dropna(inplace=True)
message_volume = df_msgs["count"].resample(RESAMPLE).sum()
user_id_counts_overall = Counter(
df_no_dup[df_no_dup['channel'].isin(COMMUNITY_BASED_CHANNELS)]
['uid'].values).most_common(MOST_COMMON_INT)
#print(user_id_counts_overall)
uids_in_help = Counter(df_no_dup[df_no_dup['channel'].isin(
HELP_CHANNELS)]['uid'].values).most_common(MOST_COMMON_INT)
#print(uids_in_help)
df = pd.read_csv(f"{path}/usermetrics.csv",
names=['time', 'online', 'idle', 'offline'])
df = df[(df['time'] > time.time() - (86400 * DAYS_BACK))]
df['date'] = pd.to_datetime(df['time'], unit='s')
df['total'] = df['online'] + df['offline'] + df['idle']
df.drop("time", 1, inplace=True)
df.set_index("date", inplace=True)
df = df.resample(RESAMPLE).mean()
df = df.join(message_volume)
df.dropna(inplace=True)
#print(df.head())
fig = plt.figure(facecolor=DISCORD_BG_COLOR)
ax1 = plt.subplot2grid((2, 1), (0, 0))
plt.ylabel("Active Users")
plt.title("Community Report")
ax1.set_facecolor(DISCORD_BG_COLOR)
ax1v = ax1.twinx()
plt.ylabel("Message Volume")
#ax1v.set_facecolor(DISCORD_BG_COLOR)
ax2 = plt.subplot2grid((2, 1), (1, 0))
plt.ylabel("Total Users")
ax2.set_facecolor(DISCORD_BG_COLOR)
ax1.plot(df.index, df.online, label="Active Users\n(Not Idle)")
#ax1v.bar(df.index, df["count"], width=0.01)
ax1v.fill_between(df.index,
0,
df["count"],
facecolor="w",
alpha=0.2,
label="Message Volume")
ax1.legend(loc=2)
ax1v.legend(loc=9)
ax2.plot(df.index, df.total, label="Total Users")
ax2.xaxis.set_major_formatter(
mdates.DateFormatter('%Y-%m-%d\n%H:%M'))
#for label in ax2.xaxis.get_ticklabels():
# label.set_rotation(45)
ax2.xaxis.set_major_locator(
mticker.MaxNLocator(nbins=5, prune='lower'))
ax2.legend()
plt.subplots_adjust(left=0.11,
bottom=0.10,
right=0.89,
top=0.95,
wspace=0.2,
hspace=0)
ax1.get_xaxis().set_visible(False)
ax1v.set_ylim(0, 3 * df["count"].values.max())
#plt.show()
plt.savefig(f"{path}/online.png", facecolor=fig.get_facecolor())
plt.clf()
fig = plt.figure(facecolor=DISCORD_BG_COLOR)
ax1 = plt.subplot2grid((2, 1), (0, 0))
plt.xlabel("Message Volume")
plt.title(f"General User Activity (past {DAYS_BACK} days)")
ax1.set_facecolor(DISCORD_BG_COLOR)
users = []
msgs = []
for pair in user_id_counts_overall[::-1]:
try:
users.append(sentdex_guild.get_member(
pair[0]).name) # get member name from here
# Example for trolling people who spam messages for rank. Not the cleanest, but it works :)
if "Dhanos" in sentdex_guild.get_member(pair[0]).name:
msgs.append(pair[1] / 1.0)
else:
msgs.append(pair[1])
except Exception as e:
print(str(e))
y_pos = np.arange(len(users))
ax1.barh(y_pos, msgs, align='center', alpha=0.5)
plt.yticks(y_pos, users)
ax2 = plt.subplot2grid((2, 1), (1, 0))
plt.title(f"Help Channel Activity (past {DAYS_BACK} days)")
plt.xlabel("Help Channel\nMsg Volume")
ax2.set_facecolor(DISCORD_BG_COLOR)
users = []
msgs = []
for pair in uids_in_help[::-1]:
try:
users.append(sentdex_guild.get_member(
pair[0]).name) # get member name from here
if "Dhanos" in sentdex_guild.get_member(pair[0]).name:
msgs.append(pair[1] / 1.0)
else:
msgs.append(pair[1])
#users.append(pair[0])
except Exception as e:
print(str(e))
y_pos = np.arange(len(users))
ax2.barh(y_pos, msgs, align='center', alpha=0.5)
plt.yticks(y_pos, users)
plt.subplots_adjust(left=0.30,
bottom=0.15,
right=0.99,
top=0.95,
wspace=0.2,
hspace=0.55)
plt.savefig(f"{path}/activity.png", facecolor=fig.get_facecolor())
plt.clf()
await asyncio.sleep(300)
except Exception as e:
print(str(e))
await asyncio.sleep(300)
and side == 'long'):
earnings = close / trade_price if side == 'long' else trade_price / close
btc_amount *= earnings * (1 - market_rate)
side = row['signal']
trade_price = row['close']
btc_amount *= (1 - market_rate)
elif (row['signal'] == 'close_long'
and side == 'long') or (row['signal'] == 'close_short'
and side == 'short'):
earnings = close / trade_price if side == 'long' else trade_price / close
btc_amount *= earnings * (1 - market_rate)
side = 'wait'
trade_price = 0
df['assets'] = np.array(btc_amounts)
ax = df[['close', 'assets']].plot(figsize=(20, 10),
grid=True,
xticks=df.index,
rot=90,
subplots=True,
style='b')
interval = int(len(df) / (40 - 1))
# 设置x轴刻度数量
ax[0].xaxis.set_major_locator(ticker.MaxNLocator(40))
# 以date为x轴刻度
ax[0].set_xticklabels(df.index[::interval])
# 美观x轴刻度
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
ax[0].set_title(filename + ' BOLL策略回测')
plt.show()
def __init__(self,
wavelength,
n_radial,
z0=None,
callback=None,
pars={},
parent=None):
super().__init__(parent=parent)
self.log = logging.getLogger(self.__class__.__name__)
self.pars = {**deepcopy(self.def_pars), **deepcopy(pars)}
self.units = 'rad'
self.status = None
self.mul = 1.0
self.figphi = None
self.ax = None
self.im = None
self.cb = None
self.shape = (128, 128)
self.P = 1
self.rzern = RZern(n_radial)
dd = np.linspace(-1, 1, self.shape[0])
xv, yv = np.meshgrid(dd, dd)
self.rzern.make_cart_grid(xv, yv)
self.rad_to_nm = wavelength / (2 * np.pi)
self.callback = callback
self.zernike_rows = []
if z0 is None:
self.z = np.zeros(self.rzern.nk)
else:
self.z = z0.copy()
assert (self.rzern.nk == self.z.size)
group_phase = QGroupBox('phase')
lay_phase = QGridLayout()
group_phase.setLayout(lay_phase)
self.figphi = FigureCanvas(Figure(figsize=(2, 2)))
self.ax = self.figphi.figure.add_subplot(1, 1, 1)
phi = self.rzern.matrix(self.rzern.eval_grid(np.dot(self.P, self.z)))
self.im = self.ax.imshow(phi, origin='lower')
self.cb = self.figphi.figure.colorbar(self.im)
self.cb.locator = ticker.MaxNLocator(nbins=5)
self.cb.update_ticks()
self.ax.axis('off')
self.status = QLabel('')
lay_phase.addWidget(self.figphi, 0, 0)
lay_phase.addWidget(self.status, 1, 0)
def nmodes():
return min(self.pars['shown_modes'], self.rzern.nk)
bot = QGroupBox('Zernike')
lay_zern = QGridLayout()
bot.setLayout(lay_zern)
labzm = QLabel('shown modes')
lezm = QLineEdit(str(nmodes()))
lezm.setMaximumWidth(50)
lezmval = MyQIntValidator(1, self.rzern.nk)
lezmval.setFixup(nmodes())
lezm.setValidator(lezmval)
brad = QCheckBox('rad')
brad.setChecked(True)
breset = QPushButton('reset')
lay_zern.addWidget(labzm, 0, 0)
lay_zern.addWidget(lezm, 0, 1)
lay_zern.addWidget(brad, 0, 2)
lay_zern.addWidget(breset, 0, 3)
scroll = QScrollArea()
lay_zern.addWidget(scroll, 1, 0, 1, 5)
scroll.setWidget(QWidget())
scrollLayout = QGridLayout(scroll.widget())
scroll.setWidgetResizable(True)
def make_hand_slider(ind):
def f(r):
self.z[ind] = r
self.update_phi_plot()
return f
def make_hand_lab(le, i):
def f():
self.pars['zernike_labels'][str(i)] = le.text()
return f
def default_zernike_name(i, n, m):
if i == 1:
return 'piston'
elif i == 2:
return 'tip'
elif i == 3:
return 'tilt'
elif i == 4:
return 'defocus'
elif m == 0:
return 'spherical'
elif abs(m) == 1:
return 'coma'
elif abs(m) == 2:
return 'astigmatism'
elif abs(m) == 3:
return 'trefoil'
elif abs(m) == 4:
return 'quadrafoil'
elif abs(m) == 5:
return 'pentafoil'
else:
return ''
def make_update_zernike_rows():
def f(mynk=None):
if mynk is None:
mynk = len(self.zernike_rows)
ntab = self.rzern.ntab
mtab = self.rzern.mtab
if len(self.zernike_rows) < mynk:
for i in range(len(self.zernike_rows), mynk):
lab = QLabel(
f'Z<sub>{i + 1}</sub> ' +
f'Z<sub>{ntab[i]}</sub><sup>{mtab[i]}</sup>')
slider = RelSlider(self.z[i], make_hand_slider(i))
if str(i) in self.pars['zernike_labels'].keys():
zname = self.pars['zernike_labels'][str(i)]
else:
zname = default_zernike_name(
i + 1, ntab[i], mtab[i])
self.pars['zernike_labels'][str(i)] = zname
lbn = QLineEdit(zname)
lbn.setMaximumWidth(120)
hand_lab = make_hand_lab(lbn, i)
lbn.editingFinished.connect(hand_lab)
scrollLayout.addWidget(lab, i, 0)
scrollLayout.addWidget(lbn, i, 1)
slider.add_to_layout(scrollLayout, i, 2)
self.zernike_rows.append((lab, slider, lbn, hand_lab))
assert (len(self.zernike_rows) == mynk)
elif len(self.zernike_rows) > mynk:
for i in range(len(self.zernike_rows) - 1, mynk - 1, -1):
lab, slider, lbn, hand_lab = self.zernike_rows.pop()
scrollLayout.removeWidget(lab)
slider.remove_from_layout(scrollLayout)
scrollLayout.removeWidget(lbn)
lbn.editingFinished.disconnect(hand_lab)
lab.setParent(None)
lbn.setParent(None)
assert (len(self.zernike_rows) == mynk)
return f
self.update_zernike_rows = make_update_zernike_rows()
def reset_fun():
self.z *= 0.
self.update_gui_controls()
self.update_phi_plot()
def change_nmodes():
try:
ival = int(lezm.text())
assert (ival > 0)
assert (ival <= self.rzern.nk)
except Exception:
lezm.setText(str(len(self.zernike_rows)))
return
if ival != len(self.zernike_rows):
self.update_zernike_rows(ival)
self.update_phi_plot()
lezm.setText(str(len(self.zernike_rows)))
def f2():
def f(b):
if b:
self.units = 'rad'
self.mul = 1.0
else:
self.units = 'nm'
self.mul = self.rad_to_nm
self.update_phi_plot()
return f
self.update_zernike_rows(nmodes())
brad.stateChanged.connect(f2())
breset.clicked.connect(reset_fun)
lezm.editingFinished.connect(change_nmodes)
splitv = QSplitter(Qt.Vertical)
top = QSplitter(Qt.Horizontal)
top.addWidget(group_phase)
splitv.addWidget(top)
splitv.addWidget(bot)
self.top = top
self.bot = bot
l1 = QGridLayout()
l1.addWidget(splitv)
self.setLayout(l1)
self.lezm = lezm
def animate(i):
global refreshRate
global counter
if chartLoad:
if paneCount == 1:
if dataPace == "tick":
try:
if exchange == "BTC-e":
a = plt.subplot2grid((6, 4), (0, 0),
rowspan=5,
colspan=4)
a2 = plt.subplot2grid((6, 4), (5, 0),
rowspan=1,
colspan=4,
sharex=a)
dataLink = 'https://wex.nz/api/3/trades/btc_usd?limit=2000'
data = urllib.request.urlopen(dataLink)
data = data.read().decode("utf-8")
data = json.loads(data)
#Takes all the data under 'btc_usd' in the api
data = data["btc_usd"]
#Makes data a panda dataset
data = pd.DataFrame(data)
data["datestamp"] = np.array(
data["timestamp"]).astype("datetime64[s]")
allDates = data["datestamp"].tolist()
buys = data[(data['type'] == 'bid')]
#buys["datestamp"] = np.array(buys["timestamp"]).astype("datetime64[s]")
buyDates = (buys["datestamp"]).tolist()
sells = data[(data['type'] == 'ask')]
#sells["datestamp"] = np.array(sells["timestamp"]).astype("datetime64[s]")
sellDates = (sells["datestamp"]).tolist()
volume = data["amount"]
a.clear()
a.plot_date(buyDates,
buys["price"],
lightColour,
label="buys")
a.plot_date(sellDates,
sells["price"],
darkColour,
label="sells")
a2.fill_between(allDates,
0,
volume,
facecolor=darkColour)
#Makes it easier to see dates on the graph
a.xaxis.set_major_locator(mticker.MaxNLocator(5))
a.xaxis.set_major_formatter(
mdates.DateFormatter("%Y-%m-%d %H:%M:%S"))
plt.setp(a.get_xticklabels(), visible=False)
#Creates legend
a.legend(bbox_to_anchor=(0, 1.02, 1, .102),
loc=3,
ncol=2,
borderaxespad=0)
a.set_title("BTC Prices (USD) \nLast Price: " +
str(data["price"][1999]))
priceData = data['price'].apply(float).tolist()
if exchange == "Bitstamp":
a = plt.subplot2grid((6, 4), (0, 0),
rowspan=5,
colspan=4)
a2 = plt.subplot2grid((6, 4), (5, 0),
rowspan=1,
colspan=4,
sharex=a)
dataLink = 'https://www.bitstamp.net/api/transactions/'
data = urllib.request.urlopen(dataLink)
data = data.read().decode("utf-8")
data = json.loads(data)
# Makes data a panda dataset
data = pd.DataFrame(data)
data["datestamp"] = np.array(
data["date"].apply(int)).astype("datetime64[s]")
dateStamps = data["datestamp"].tolist()
#allDates = data["datestamp"].tolist()
# buys = data[(data['type'] == 'bid')]
# # buys["datestamp"] = np.array(buys["timestamp"]).astype("datetime64[s]")
# buyDates = (buys["datestamp"]).tolist()
#
# sells = data[(data['type'] == 'ask')]
# # sells["datestamp"] = np.array(sells["timestamp"]).astype("datetime64[s]")
# sellDates = (sells["datestamp"]).tolist()
volume = data["amount"].apply(float).tolist()
a.clear()
a.plot_date(dateStamps,
data["price"],
lightColour,
label="buys")
a2.fill_between(dateStamps,
0,
volume,
facecolor=darkColour)
# Makes it easier to see dates on the graph
a.xaxis.set_major_locator(mticker.MaxNLocator(5))
a.xaxis.set_major_formatter(
mdates.DateFormatter("%Y-%m-%d %H:%M:%S"))
plt.setp(a.get_xticklabels(), visible=False)
# Creates legend
a.legend(bbox_to_anchor=(0, 1.02, 1, .102),
loc=3,
ncol=2,
borderaxespad=0)
a.set_title(
"Bitstamp BTC Prices (USD) \nLast Price: " +
str(data["price"][0]))
priceData = data['price'].apply(float).tolist()
if exchange == "Bitfinex":
a = plt.subplot2grid((6, 4), (0, 0),
rowspan=5,
colspan=4)
a2 = plt.subplot2grid((6, 4), (5, 0),
rowspan=1,
colspan=4,
sharex=a)
dataLink = 'https://api.bitfinex.com/v1/trades/btcusd?limit=2000'
data = urllib.request.urlopen(dataLink)
data = data.read().decode("utf-8")
data = json.loads(data)
#Makes data a panda dataset
data = pd.DataFrame(data)
data["datestamp"] = np.array(
data["timestamp"]).astype("datetime64[s]")
allDates = data["datestamp"].tolist()
buys = data[(data['type'] == 'buy')]
#buys["datestamp"] = np.array(buys["timestamp"]).astype("datetime64[s]")
buyDates = (buys["datestamp"]).tolist()
sells = data[(data['type'] == 'sell')]
#sells["datestamp"] = np.array(sells["timestamp"]).astype("datetime64[s]")
sellDates = (sells["datestamp"]).tolist()
volume = data["amount"].apply(float).tolist()
a.clear()
a.plot_date(buyDates,
buys["price"],
lightColour,
label="buys")
a.plot_date(sellDates,
sells["price"],
darkColour,
label="sells")
a2.fill_between(allDates,
0,
volume,
facecolor=darkColour)
#Makes it easier to see dates on the graph
a.xaxis.set_major_locator(mticker.MaxNLocator(5))
a.xaxis.set_major_formatter(
mdates.DateFormatter("%Y-%m-%d %H:%M:%S"))
plt.setp(a.get_xticklabels(), visible=False)
#Creates legend
a.legend(bbox_to_anchor=(0, 1.02, 1, .102),
loc=3,
ncol=2,
borderaxespad=0)
a.set_title(
"Bitfinex BTC Prices (USD) \nLast Price: " +
str(data["price"][0]))
priceData = data['price'].apply(float).tolist()
if exchange == "Huobi":
a = plt.subplot2grid((6, 4), (0, 0),
rowspan=6,
colspan=4)
data = urllib.request.urlopen(
"http://seaofbtc.com/api/basic/price?key=1&tf=1d&exchange="
+ programName).read()
data = data.decode()
data = json.loads(data)
dateStamp = np.array(data[0]).astype("datetime64[s]")
dateStamp = dateStamp.tolist()
df = pd.DataFrame({'Datetime': dateStamp})
df['Price'] = data[1]
df['Volume'] = data[2]
df['Symbol'] = "BTCUSD"
df['MPLDate'] = df['Datetime'].apply(
lambda date: mdates.date2num(date.to_pydatetime()))
df = df.set_index("Datetime")
lastPrice = df["Price"][-1]
a.plot_date(df["MPLDate"][-4500:],
df['Price'][-4500:],
lightColour,
label="price")
a.xaxis.set_major_locator(mticker.MaxNLocator(5))
a.xaxis.set_major_formatter(
mdates.DateFormatter("%Y-%m-%d %H:%M:%S"))
a.set_title(
"Huobi BTCUSD PRICES BTC Prices (USD) \nLast Price: "
+ str(lastPrice))
priceData = df['price'].apply(float).tolist()
except Exception as e:
print("Failed because of:", e)
else:
if counter > 12:
try:
if exchange == 'Huobi':
if topIndicator != "none":
a = plt.subplot2grid((6, 4), (1, 0),
rowspan=5,
colspan=4)
a0 = plt.subplot2grid((6, 4), (0, 0),
sharex=a,
rowspan=1,
colspan=4)
else:
a = plt.subplot2grid((6, 4), (0, 0),
rowspan=6,
colspan=4)
else:
if topIndicator != "none" and bottomIndicator != "none":
# actual price chart.
a = plt.subplot2grid((6, 4), (1, 0),
rowspan=3,
colspan=4)
# volume!
a2 = plt.subplot2grid((6, 4), (4, 0),
sharex=a,
rowspan=1,
colspan=4)
# top indicator
a0 = plt.subplot2grid((6, 4), (0, 0),
sharex=a,
rowspan=1,
colspan=4)
# bottom indicator
a3 = plt.subplot2grid((6, 4), (5, 0),
sharex=a,
rowspan=1,
colspan=4)
elif topIndicator != "none":
a = plt.subplot2grid((6, 4), (1, 0),
rowspan=4,
colspan=4)
a2 = plt.subplot2grid((6, 4), (5, 0),
sharex=a,
rowspan=1,
colspan=4)
a0 = plt.subplot2grid((6, 4), (0, 0),
sharex=a,
rowspan=1,
colspan=4)
elif bottomIndicator != "none":
a = plt.subplot2grid((6, 4), (0, 0),
rowspan=4,
colspan=4)
a2 = plt.subplot2grid((6, 4), (4, 0),
sharex=a,
rowspan=1,
colspan=4)
# a0 = plt.subplot2grid((6,4), (0,0), sharex=a, rowspan=1, colspan=4)
a3 = plt.subplot2grid((6, 4), (5, 0),
sharex=a,
rowspan=1,
colspan=4)
else:
a = plt.subplot2grid((6, 4), (0, 0),
rowspan=5,
colspan=4)
a2 = plt.subplot2grid((6, 4), (5, 0),
sharex=a,
rowspan=1,
colspan=4)
print('http://seaofbtc.com/api/basic/price?key=1&tf=' +
dataPace + '&exchange=' + programName)
data = urllib.request.urlopen(
'http://seaofbtc.com/api/basic/price?key=1&tf=' +
dataPace + '&exchange=' + programName).read()
data = str(data).replace('b', '').replace("'", '')
data = json.loads(data)
dateStamp = np.array(data[0]).astype('datetime64[s]')
dateStamp = dateStamp.tolist()
df = pd.DataFrame({'Datetime': dateStamp})
df['Price'] = data[1]
df['Volume'] = data[2]
df['Symbol'] = "BTCUSD"
df['MPLDate'] = df['Datetime'].apply(
lambda date: mdates.date2num(date.to_pydatetime()))
df = df.set_index('Datetime')
OHLC = df['Price'].resample(resampleSize).ohlc()
OHLC = OHLC.dropna()
volumeData = df['Volume'].resample(resampleSize).sum()
OHLC['dateCopy'] = OHLC.index
OHLC['MPLDates'] = OHLC['dateCopy'].apply(
lambda date: mdates.date2num(date.to_pydatetime()))
del OHLC['dateCopy']
# volumeData['dateCopy'] = volumeData.index
# volumeData['MPLDates'] = volumeData['dateCopy'].apply(lambda date: mdates.date2num(date.to_pydatetime()))
# del volumeData['dateCopy']
priceData = OHLC['close'].apply(float).tolist()
a.clear()
if middleIndicators != "none":
for eachMA in middleIndicators:
ewma = pd.stats.moments.ewma
# print("type:",eachMA[0],"periods:",eachMA[1])
if eachMA[0] == "sma":
sma = pd.rolling_mean(
OHLC["close"], eachMA[1])
label = str(eachMA[1]) + " SMA"
a.plot(OHLC['MPLDates'], sma, label=label)
if eachMA[0] == "ema":
ewma = pd.stats.moments.ewma
label = str(eachMA[1]) + " EMA"
a.plot(OHLC['MPLDates'],
ewma(OHLC["close"], eachMA[1]),
label=label)
# a.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
# ncol=2, borderaxespad=0.)
a.legend(loc=0)
if topIndicator[0] == "rsi":
rsiIndicator(priceData, "top")
elif topIndicator == "macd":
try:
computeMACD(priceData, location="top")
except:
print("failed macd")
if bottomIndicator[0] == "rsi":
rsiIndicator(priceData, "bottom")
elif bottomIndicator == "macd":
try:
computeMACD(priceData, location="bottom")
except:
print("failed macd")
csticks = candlestick_ohlc(
a,
OHLC[['MPLDates', 'open', 'high', 'low',
'close']].values,
width=candleWidth,
colorup=lightColour,
colordown=darkColour)
a.set_ylabel("price")
if exchange != 'Huobi':
a2.fill_between(volumeData['MPLDates'],
0,
volumeData['volume'],
facecolor='#183A54') # , alpha=.4)
a2.set_ylabel("volume")
a.xaxis.set_major_locator(mticker.MaxNLocator(3))
a.xaxis.set_major_formatter(
mdates.DateFormatter('%Y-%m-%d %H:%M'))
plt.setp(a.get_xticklabels(), visible=False)
if topIndicator != "none":
plt.setp(a0.get_xticklabels(), visible=False)
if bottomIndicator != "none":
plt.setp(a2.get_xticklabels(), visible=False)
x = (len(OHLC['close'])) - 1
if dataPace == '1d':
title = exchange + ' 1 Day Data with ' + resampleSize + ' Bars\nLast Price: ' + str(
OHLC['close'][x])
if dataPace == '3d':
title = exchange + ' 3 Day Data with ' + resampleSize + ' Bars\nLast Price: ' + str(
OHLC['close'][x])
if dataPace == '7d':
title = exchange + ' 7 Day Data with ' + resampleSize + ' Bars\nLast Price: ' + str(
OHLC['close'][x])
if topIndicator != "none":
a0.set_title(title)
else:
a.set_title(title)
print('NewGraph!')
counter = 0
except Exception as e:
counter = 9000
else:
counter += 1
def animate(i):
global refreshRate
global DatCounter
##################################
##################################
def computeMACD(x, slow=26, fast=12,location="bottom"):
"""
compute the MACD (Moving Average Convergence/Divergence) using a fast and slow exponential moving avg'
return value is emaslow, emafast, macd which are len(x) arrays
"""
values = {'key': 1,'prices':x}
url = "http://seaofbtc.com/api/indicator/macd"
data = urllib.parse.urlencode(values)
data = data.encode('utf-8')
req = urllib.request.Request(url, data)
resp = urllib.request.urlopen(req)
respData = resp.read()
newData = str(respData).replace("b","").replace('[','').replace(']','').replace("'",'')
#print(newData)
split = newData.split('::')
macd = split[0]
ema9 = split[1]
hist = split[2]
macd = macd.split(", ")
ema9 = ema9.split(", ")
hist = hist.split(", ")
try:
macd = [float(i) for i in macd]
except Exception as e:
print(str(e)+" macd")
try:
ema9 = [float(i) for i in ema9]
except Exception as e:
print(str(e)+" ema9")
try:
hist = [float(i) for i in hist]
except Exception as e:
print(str(e)+" hist")
print("call!!!")
if location == "top":
try:
a0.plot(OHLC['MPLDates'][fast:], macd[fast:], color=darkColor, lw=2)
a0.plot(OHLC['MPLDates'][fast:], ema9[fast:], color=lightColor, lw=1)
a0.fill_between(OHLC['MPLDates'][fast:], hist[fast:], 0, alpha=0.5, facecolor=darkColor, edgecolor=darkColor)
datLabel = "MACD"
a0.set_ylabel(datLabel)
except Exception as e:
print(str(e))
topIndicator = "none"
elif location == "bottom":
try:
a3.plot(OHLC['MPLDates'][fast:], macd[fast:], color=darkColor, lw=2)
a3.plot(OHLC['MPLDates'][fast:], ema9[fast:], color=lightColor, lw=1)
a3.fill_between(OHLC['MPLDates'][fast:], hist[fast:], 0, alpha=0.5, facecolor=darkColor, edgecolor=darkColor)
datLabel = "MACD"
a3.set_ylabel(datLabel)
except Exception as e:
print(str(e))
bottomIndicator = "none"
##################################
##################################
def rsiIndicator(priceData,location="top"):
if location == "top":
values = {'key': 1,'prices':priceData,'periods':topIndicator[1]}
elif location == "bottom":
values = {'key': 1,'prices':priceData,'periods':bottomIndicator[1]}
url = "http://seaofbtc.com/api/indicator/rsi"
data = urllib.parse.urlencode(values)
data = data.encode('utf-8')
req = urllib.request.Request(url, data)
resp = urllib.request.urlopen(req)
respData = resp.read()
newData = str(respData).replace("b","").replace('[','').replace(']','').replace("'",'')
priceList = newData.split(', ')
rsiData = [float(i) for i in priceList]
print("call!!!")
if location == "top":
a0.plot_date(OHLC['MPLDates'], rsiData,lightColor, label ="RSI")
datLabel = "RSI("+str(topIndicator[1])+")"
a0.set_ylabel(datLabel)
elif location == "bottom":
a3.plot_date(OHLC['MPLDates'], rsiData,lightColor, label ="RSI")
datLabel = "RSI("+str(bottomIndicator[1])+")"
a3.set_ylabel(datLabel)
def moving_average(x, n, type='simple'):
x = np.asarray(x)
if type=='simple':
weights = np.ones(n)
else:
weights = np.exp(np.linspace(-1, 0, n))
weights /= weights.sum()
a = np.convolve(x, weights, mode='full')[:len(x)]
return a
if chartLoad:
if paneCount == 1:
if DataPace == "tick":
try:
if exchange == "BTC-e":
a = plt.subplot2grid((6,4), (0,0), rowspan=5, colspan=4)
a2 = plt.subplot2grid((6,4), (5,0), rowspan=1, colspan=4, sharex = a)
dataLink = 'https://btc-e.com/api/3/trades/btc_usd?limit=2000'
data = urllib.request.urlopen(dataLink)
data = data.readall().decode('utf-8')
data = json.loads(data)
data = data["btc_usd"]
data = pd.DataFrame(data)
data["datestamp"] = np.array(data['timestamp']).astype('datetime64[s]')
allDates = data["datestamp"].tolist()
buys = data[(data['type']=='bid')]
buyDates = (buys["datestamp"]).tolist()
sells = data[(data['type']=='ask')]
sellDates = (sells["datestamp"]).tolist()
volume = data["amount"]
a.clear()
a.plot_date(buyDates,buys["price"], lightColor, label ="buys")
a.plot_date(sellDates,sells["price"], darkColor, label = "sells")
a2.fill_between(allDates,0, volume, facecolor='#183A54')
a.xaxis.set_major_locator(mticker.MaxNLocator(5))
a.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))
plt.setp(a.get_xticklabels(), visible=False)
a.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
ncol=2, borderaxespad=0.)
title = 'Last Price: '+str(data["price"][1999])
a.set_title(title)
if exchange == 'Bitstamp':
a = plt.subplot2grid((6,4), (0,0), rowspan=5, colspan=4)
a2 = plt.subplot2grid((6,4), (5,0), rowspan=1, colspan=4, sharex = a)
dataLink = 'https://www.bitstamp.net/api/transactions/'
data = urllib.request.urlopen(dataLink)
data = data.readall().decode('utf-8')
data = json.loads(data)
data = pd.DataFrame(data)
data["datestamp"] = np.array(data['date'].apply(int)).astype('datetime64[s]')
datestamps = data["datestamp"].tolist()
volume = data["amount"].apply(float).tolist()
a.clear()
a.plot_date(datestamps,data["price"], '#183A54')
a2.fill_between(datestamps,0, volume, facecolor='#183A54')
a.xaxis.set_major_locator(mticker.MaxNLocator(5))
a.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))
plt.setp(a.get_xticklabels(), visible=False)
title = exchange+' Tick Data\nLast Price: '+str(data["price"][0])
a.set_title(title)
priceData = data["price"].apply(float).tolist()
if exchange == 'Bitfinex':
a = plt.subplot2grid((6,4), (0,0), rowspan=5, colspan=4)
a2 = plt.subplot2grid((6,4), (5,0), rowspan=1, colspan=4, sharex = a)
dataLink = 'https://api.bitfinex.com/v1/trades/btcusd?limit=2000'
data = urllib.request.urlopen(dataLink)
data = data.readall().decode('utf-8')
data = json.loads(data)
data = pd.DataFrame(data)
volume = data["amount"].apply(float).tolist()
data["datestamp"] = np.array(data['timestamp']).astype('datetime64[s]')
allDates = data["datestamp"].tolist()
buys = data[(data['type']=='buy')]
buyDates = (buys["datestamp"]).tolist()
sells = data[(data['type']=='sell')]
sellDates = (sells["datestamp"]).tolist()
a.clear()
a.plot_date(buyDates,buys["price"], lightColor, label ="buys")
a.plot_date(sellDates,sells["price"], darkColor, label = "sells")
a2.fill_between(allDates,0, volume, facecolor='#183A54')
a.xaxis.set_major_locator(mticker.MaxNLocator(5))
a.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))
plt.setp(a.get_xticklabels(), visible=False)
a.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=3,
ncol=2, borderaxespad=0.)
title = exchange+' Tick Data\nLast Price: '+str(data["price"][0])
a.set_title(title)
priceData = data["price"].apply(float).tolist()
if exchange == 'Huobi':
try:
a = plt.subplot2grid((6,4), (0,0), rowspan=6, colspan=4)
data = urllib.request.urlopen('http://seaofbtc.com/api/basic/price?key=1&tf=1d&exchange='+programName).read()
data = str(data).replace('b','').replace("'",'')
data = json.loads(data)
dateStamp = np.array(data[0]).astype('datetime64[s]')
dateStamp = dateStamp.tolist()
print('here')
df = pd.DataFrame({'Datetime':dateStamp})
df['Price'] = data[1]
df['Volume'] = data[2]
df['Symbol'] = "BTCUSD"
df['MPLDate'] = df['Datetime'].apply(lambda date: mdates.date2num(date.to_pydatetime()))
df = df.set_index('Datetime')
lastPrice = df['Price'][-1]
a.plot_date(df['MPLDate'][-4500:],df['Price'][-4500:], lightColor, label ="price")
a.xaxis.set_major_locator(mticker.MaxNLocator(5))
a.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))
title = exchange+' Tick Data\nLast Price: '+str(lastPrice)
a.set_title(title)
priceData = df['Price'].apply(float).tolist()
except Exception as e:
print(str(e))
except Exception as e:
print("failed",str(e))
DatCounter = 9000
##### ALL OTHER, NON-TICK, DATA. ##################################
else:
if DatCounter > 12:
try:
if exchange == 'Huobi':
if topIndicator != "none":
a = plt.subplot2grid((6,4), (1,0), rowspan=5, colspan=4)
a0 = plt.subplot2grid((6,4), (0,0), sharex=a, rowspan=1, colspan=4)
else:
a = plt.subplot2grid((6,4), (0,0), rowspan=6, colspan=4)
else:
if topIndicator != "none" and bottomIndicator != "none":
# actual price chart.
a = plt.subplot2grid((6,4), (1,0), rowspan=3, colspan=4)
# volume!
a2 = plt.subplot2grid((6,4), (4,0), sharex=a, rowspan=1, colspan=4)
# top indicator
a0 = plt.subplot2grid((6,4), (0,0), sharex=a, rowspan=1, colspan=4)
# bottom indicator
a3 = plt.subplot2grid((6,4), (5,0), sharex=a, rowspan=1, colspan=4)
elif topIndicator != "none":
a = plt.subplot2grid((6,4), (1,0), rowspan=4, colspan=4)
a2 = plt.subplot2grid((6,4), (5,0), sharex=a, rowspan=1, colspan=4)
a0 = plt.subplot2grid((6,4), (0,0), sharex=a, rowspan=1, colspan=4)
elif bottomIndicator != "none":
a = plt.subplot2grid((6,4), (0,0), rowspan=4, colspan=4)
a2 = plt.subplot2grid((6,4), (4,0), sharex=a, rowspan=1, colspan=4)
#a0 = plt.subplot2grid((6,4), (0,0), sharex=a, rowspan=1, colspan=4)
a3 = plt.subplot2grid((6,4), (5,0), sharex=a, rowspan=1, colspan=4)
else:
a = plt.subplot2grid((6,4), (0,0), rowspan=5, colspan=4)
a2 = plt.subplot2grid((6,4), (5,0), sharex=a, rowspan=1, colspan=4)
print('http://seaofbtc.com/api/basic/price?key=1&tf='+DataPace+'&exchange='+programName)
data = urllib.request.urlopen('http://seaofbtc.com/api/basic/price?key=1&tf='+DataPace+'&exchange='+programName).read()
data = str(data).replace('b','').replace("'",'')
data = json.loads(data)
dateStamp = np.array(data[0]).astype('datetime64[s]')
dateStamp = dateStamp.tolist()
df = pd.DataFrame({'Datetime':dateStamp})
df['Price'] = data[1]
df['Volume'] = data[2]
df['Symbol'] = "BTCUSD"
df['MPLDate'] = df['Datetime'].apply(lambda date: mdates.date2num(date.to_pydatetime()))
df = df.set_index('Datetime')
OHLC = df['Price'].resample(resampleSize, how='ohlc')
OHLC = OHLC.dropna()
volumeData = df['Volume'].resample(resampleSize, how={'volume':'sum'})
OHLC['dateCopy'] = OHLC.index
OHLC['MPLDates'] = OHLC['dateCopy'].apply(lambda date: mdates.date2num(date.to_pydatetime()))
del OHLC['dateCopy']
volumeData['dateCopy'] = volumeData.index
volumeData['MPLDates'] = volumeData['dateCopy'].apply(lambda date: mdates.date2num(date.to_pydatetime()))
del volumeData['dateCopy']
priceData = OHLC['close'].apply(float).tolist()
a.clear()
if middleIndicator != "none":
for eachMA in middleIndicator:
ewma = pd.stats.moments.ewma
if eachMA[0] == "sma":
sma = pd.rolling_mean(OHLC["close"],eachMA[1])
label = str(eachMA[1])+" SMA"
a.plot(OHLC['MPLDates'],sma, label=label)
if eachMA[0] == "ema":
ewma = pd.stats.moments.ewma
label = str(eachMA[1])+" EMA"
a.plot(OHLC['MPLDates'],ewma(OHLC["close"], eachMA[1]), label=label)
a.legend(loc=0)
if topIndicator[0] == "rsi":
rsiIndicator(priceData,"top")
elif topIndicator == "macd":
try:
computeMACD(priceData,location="top")
except:
print("failed macd")
if bottomIndicator[0] == "rsi":
rsiIndicator(priceData,"bottom")
elif bottomIndicator == "macd":
try:
computeMACD(priceData,location="bottom")
except:
print("failed macd")
csticks = candlestick_ohlc(a, OHLC[['MPLDates', 'open', 'high', 'low', 'close']].values, width=candleWidth, colorup=lightColor, colordown=darkColor)
a.set_ylabel("price")
if exchange != 'Huobi':
a2.fill_between(volumeData['MPLDates'],0, volumeData['volume'], facecolor='#183A54')#, alpha=.4)
a2.set_ylabel("volume")
a.xaxis.set_major_locator(mticker.MaxNLocator(3))
a.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d %H:%M'))
plt.setp(a.get_xticklabels(), visible=False)
if topIndicator != "none":
plt.setp(a0.get_xticklabels(), visible=False)
if bottomIndicator != "none":
plt.setp(a2.get_xticklabels(), visible=False)
x = (len(OHLC['close']))-1
if DataPace == '1d':
title = exchange+' 1 Day Data with '+resampleSize+' Bars\nLast Price: '+str(OHLC['close'][x])
if DataPace == '3d':
title = exchange+' 3 Day Data with '+resampleSize+' Bars\nLast Price: '+str(OHLC['close'][x])
if DataPace == '7d':
title = exchange+' 7 Day Data with '+resampleSize+' Bars\nLast Price: '+str(OHLC['close'][x])
if topIndicator != "none":
a0.set_title(title)
else:
a.set_title(title)
print('NewGraph!')
DatCounter = 0
except Exception as e:
print(str(e),"main animate non tick")
DatCounter = 9000
else:
DatCounter += 1
def plotheatmaper(mymatrix, outFileName,
colorMapDict={'colorMap': ['binary'], 'missingDataColor': 'black', 'alpha': 1.0},
plotTitle='',
xAxisLabel='', yAxisLabel='', regionsLabel='',
zMin=None, zMax=None,
yMin=None, yMax=None,
averageType='median',
reference_point_label=None,
startLabel='TSS', endLabel="TES",
heatmapHeight=25,
heatmapWidth=7.5,
perGroup=False, whatToShow='plot, heatmap and colorbar',
plot_type='lines',
linesAtTickMarks=False,
image_format=None,
legend_location='upper-left',
box_around_heatmaps=True,
label_rotation=0.0,
dpi=200,
interpolation_method='auto',
num_samples=1):
if reference_point_label is not None:
reference_point_label = [reference_point_label] * num_samples
startLabel = startLabel
endLabel = endLabel
matrix_flatten = None
if zMin is None:
matrix_flatten = mymatrix.flatten()
# try to avoid outliers by using np.percentile
zMin = np.percentile(matrix_flatten, 1.0)
if np.isnan(zMin):
zMin = [None]
else:
zMin = [zMin] # convert to list to support multiple entries
elif 'auto' in zMin:
matrix_flatten = mymatrix.flatten()
auto_min = np.percentile(matrix_flatten, 1.0)
if np.isnan(auto_min):
auto_min = None
new_mins = [float(x) if x != 'auto' else auto_min for x in zMin]
zMin = new_mins
else:
new_mins = [float(x) for x in zMin]
zMin = new_mins
if zMax is None:
if matrix_flatten is None:
matrix_flatten = mymatrix.flatten()
# try to avoid outliers by using np.percentile
zMax = np.percentile(matrix_flatten, 98.0)
if np.isnan(zMax) or zMax <= zMin[0]:
zMax = [None]
else:
zMax = [zMax]
elif 'auto' in zMax:
matrix_flatten = mymatrix.flatten()
auto_max = np.percentile(matrix_flatten, 98.0)
if np.isnan(auto_max):
auto_max = None
new_maxs = [float(x) if x != 'auto' else auto_max for x in zMax]
zMax = new_maxs
else:
new_maxs = [float(x) for x in zMax]
zMax = new_maxs
if (len(zMin) > 1) & (len(zMax) > 1):
for index, value in enumerate(zMax):
if value <= zMin[index]:
sys.stderr.write("Warnirng: In bigwig {}, the given zmin ({}) is larger than "
"or equal to the given zmax ({}). Thus, it has been set "
"to None. \n".format(index + 1, zMin[index], value))
zMin[index] = None
if yMin is None:
yMin = [None]
if yMax is None:
yMax = [None]
if not isinstance(yMin, list):
yMin = [yMin]
if not isinstance(yMax, list):
yMax = [yMax]
plt.rcParams['font.size'] = 8.0
fontP = FontProperties()
showSummaryPlot = False
showColorbar = False
if whatToShow == 'plot and heatmap':
showSummaryPlot = True
elif whatToShow == 'heatmap and colorbar':
showColorbar = True
elif whatToShow == 'plot, heatmap and colorbar':
showSummaryPlot = True
showColorbar = True
# colormap for the heatmap
if colorMapDict['colorMap']:
cmap = []
for color_map in colorMapDict['colorMap']:
cmap.append(plt.get_cmap(color_map))
cmap[-1].set_bad(colorMapDict['missingDataColor']) # nans are printed using this color
if colorMapDict['colorList'] and len(colorMapDict['colorList']) > 0:
# make a cmap for each color list given
cmap = []
for color_list in colorMapDict['colorList']:
cmap.append(matplotlib.colors.LinearSegmentedColormap.from_list(
'my_cmap', color_list.replace(' ', '').split(","), N=colorMapDict['colorNumber']))
cmap[-1].set_bad(colorMapDict['missingDataColor']) # nans are printed using this color
if len(cmap) > 1 or len(zMin) > 1 or len(zMax) > 1:
# position color bar below heatmap when more than one
# heatmap color is given
colorbar_position = 'below'
else:
colorbar_position = 'side'
grids = prepare_layout(mymatrix, (heatmapWidth, heatmapHeight),
showSummaryPlot, showColorbar, perGroup, colorbar_position)
# figsize: w,h tuple in inches
figwidth = heatmapWidth / 2.54
figheight = heatmapHeight / 2.54
if showSummaryPlot:
# the summary plot ocupies a height
# equal to the fig width
figheight += figwidth
numsamples = num_samples
if perGroup:
num_cols = num_groups
else:
num_cols = numsamples
total_figwidth = figwidth * num_cols
if showColorbar:
if colorbar_position == 'below':
figheight += 1 / 2.54
else:
total_figwidth += 1 / 2.54
fig = plt.figure(figsize=(total_figwidth, figheight))
fig.suptitle(plotTitle, y=1 - (0.06 / figheight))
# color map for the summary plot (profile) on top of the heatmap
cmap_plot = plt.get_cmap('jet')
numgroups = num_groups
if perGroup:
color_list = cmap_plot(np.arange(num_samples) / num_samples)
else:
color_list = cmap_plot(np.arange(numgroups) / numgroups)
alpha = colorMapDict['alpha']
first_group = 0 # helper variable to place the title per sample/group
for sample in range(num_samples):
sample_idx = sample
for group in range(numgroups):
group_idx = group
# add the respective profile to the
# summary plot
sub_matrix = get_matrix(mymatrix, group, sample)
if showSummaryPlot:
if perGroup:
sample_idx = sample + 2 # plot + spacer
else:
group += 2 # plot + spacer
first_group = 1
if perGroup:
ax = fig.add_subplot(grids[sample_idx, group])
# the remainder (%) is used to iterate
# over the available color maps (cmap).
# if the user only provided, lets say two
# and there are 10 groups, colormaps they are reused every
# two groups.
cmap_idx = group_idx % len(cmap)
zmin_idx = group_idx % len(zMin)
zmax_idx = group_idx % len(zMax)
else:
ax = fig.add_subplot(grids[group, sample])
# see above for the use of '%'
cmap_idx = sample % len(cmap)
zmin_idx = sample % len(zMin)
zmax_idx = sample % len(zMax)
if group == first_group and not showSummaryPlot and not perGroup:
title = sample_labels[sample]
ax.set_title(title)
if box_around_heatmaps is False:
# Turn off the boxes around the individual heatmaps
ax.spines['top'].set_visible(False)
ax.spines['right'].set_visible(False)
ax.spines['bottom'].set_visible(False)
ax.spines['left'].set_visible(False)
rows, cols = sub_matrix['matrix'].shape
# if the number of rows is too large, then the 'nearest' method simply
# drops rows. A better solution is to relate the threshold to the DPI of the image
if interpolation_method == 'auto':
if rows >= 1000:
interpolation_method = 'bilinear'
else:
interpolation_method = 'nearest'
# if np.clip is not used, then values of the matrix that exceed the zmax limit are
# highlighted. Usually, a significant amount of pixels are equal or above the zmax and
# the default behaviour produces images full of large highlighted dots.
# If interpolation='nearest' is used, this has no effect
sub_matrix['matrix'] = np.clip(sub_matrix['matrix'], zMin[zmin_idx], zMax[zmax_idx])
img = ax.imshow(sub_matrix['matrix'],
aspect='auto',
interpolation=interpolation_method,
origin='upper',
vmin=zMin[zmin_idx],
vmax=zMax[zmax_idx],
cmap=cmap[cmap_idx],
alpha=alpha,
extent=[0, cols, rows, 0])
img.set_rasterized(True)
# plot border at the end of the regions
if perGroup:
ax.axes.set_xlabel(sub_matrix['group'])
if sample < hm.matrix.get_num_samples() - 1:
ax.axes.get_xaxis().set_visible(False)
else:
ax.axes.get_xaxis().set_visible(False)
ax.axes.set_xlabel(xAxisLabel)
ax.axes.set_yticks([])
if perGroup and group == 0:
ax.axes.set_ylabel(sub_matrix['sample'])
elif not perGroup and sample == 0:
ax.axes.set_ylabel(sub_matrix['group'])
# Plot vertical lines at tick marks if desired
if linesAtTickMarks:
xticks_heat, xtickslabel_heat = getTicks(reference_point_label, sample , startLabel, endLabel)
xticks_heat = [x + 0.5 for x in xticks_heat] # There's an offset of 0.5 compared to the profile plot
if np.ceil(max(xticks_heat)) != float(sub_matrix['matrix'].shape[1]):
tickscale = float(sub_matrix['matrix'].shape[1]) / max(xticks_heat)
xticks_heat_use = [x * tickscale for x in xticks_heat]
else:
xticks_heat_use = xticks_heat
for x in xticks_heat_use:
ax.axvline(x=x, color='black', linewidth=0.5, dashes=(3, 2))
# add labels to last block in a column
if (perGroup and sample == numsamples - 1) or \
(not perGroup and group_idx == numgroups - 1):
# add xticks to the bottom heatmap (last group)
ax.axes.get_xaxis().set_visible(True)
xticks_heat, xtickslabel_heat = getTicks(reference_point_label, sample , startLabel, endLabel)
xticks_heat = [x + 0.5 for x in xticks_heat] # There's an offset of 0.5 compared to the profile plot
if np.ceil(max(xticks_heat)) != float(sub_matrix['matrix'].shape[1]):
tickscale = float(sub_matrix['matrix'].shape[1]) / max(xticks_heat)
xticks_heat_use = [x * tickscale for x in xticks_heat]
ax.axes.set_xticks(xticks_heat_use)
else:
ax.axes.set_xticks(xticks_heat)
ax.axes.set_xticklabels(xtickslabel_heat, size=8)
# align the first and last label
# such that they don't fall off
# the heatmap sides
ticks = ax.xaxis.get_major_ticks()
ticks[0].label1.set_horizontalalignment('left')
ticks[-1].label1.set_horizontalalignment('right')
ax.get_xaxis().set_tick_params(
which='both',
top=False,
direction='out')
if showColorbar and colorbar_position == 'below':
# draw a colormap per each heatmap below the last block
if perGroup:
col = group_idx
else:
col = sample
ax = fig.add_subplot(grids[-1, col])
tick_locator = ticker.MaxNLocator(nbins=3)
cbar = fig.colorbar(img, cax=ax, alpha=alpha, orientation='horizontal', ticks=tick_locator)
labels = cbar.ax.get_xticklabels()
ticks = cbar.ax.get_xticks()
if ticks[0] == 0:
# if the label is at the start of the colobar
# move it a bit inside to avoid overlapping
# with other labels
labels[0].set_horizontalalignment('left')
if ticks[-1] == 1:
# if the label is at the end of the colobar
# move it a bit inside to avoid overlapping
# with other labels
labels[-1].set_horizontalalignment('right')
# cbar.ax.set_xticklabels(labels, rotation=90)
if showColorbar and colorbar_position != 'below':
if showSummaryPlot:
# we don't want to colorbar to extend
# over the profiles and spacer top rows
grid_start = 2
else:
grid_start = 0
ax = fig.add_subplot(grids[grid_start:, -1])
fig.colorbar(img, cax=ax, alpha=alpha)
if box_around_heatmaps:
plt.subplots_adjust(wspace=0.10, hspace=0.025, top=0.85, bottom=0, left=0.04, right=0.96)
else:
# When no box is plotted the space between heatmaps is reduced
plt.subplots_adjust(wspace=0.05, hspace=0.01, top=0.85, bottom=0, left=0.04, right=0.96)
plt.savefig(outFileName, bbox_inches='tight', pdd_inches=0, dpi=dpi, format=image_format)
plt.close()
def VSO_avg(table, VSE=False):
#grab table range for plot title
table_start = str(np.array(table.index.values[0], dtype='datetime64[s]'))
table_end = str(np.array(table.index.values[-1], dtype='datetime64[s]'))
time_range_str = table_start + ' TO ' + table_end
time = table.index.values
#num_ti = len(time)-1
mag_line_i = []
#n = 100 # vectors
r = 6051.8
scale = 500 / r # venus radii/nT
#scale location data by venus radius
table['XSC'] = table['XSC'] / r
table['YSC'] = table['YSC'] / r
table['ZSC'] = table['ZSC'] / r
table['RSC'] = table['RSC'] / r
#plot location data
fig, (ax1, ax2, ax3) = plt.subplots(nrows=1,
ncols=3,
sharex=True,
sharey=True)
ax1.plot(table['XSC'], table['YSC'], color=(139 / 255, 0, 0))
ax2.plot(table['XSC'], table['ZSC'], color=(139 / 255, 0, 0))
ax3.plot(table['YSC'], table['ZSC'], color=(139 / 255, 0, 0))
#set axis labels
ax1.set(xlabel='VSO X', ylabel='VSO Y')
ax2.set(xlabel='VSO X', ylabel='VSO Z')
ax3.set(xlabel='VSO Y', ylabel='VSO Z')
if VSE == True:
ax1.set(xlabel='VSE X', ylabel='VSE Y')
ax2.set(xlabel='VSE X', ylabel='VSE Z')
ax3.set(xlabel='VSE Y', ylabel='VSE Z')
cax = plt.axes([0.93, 0.1, 0.020, 0.8])
plt.subplots_adjust(bottom=0.1, left=0.07, right=0.91, top=0.9)
#downsample table to minute cadence
table = table.resample('T').mean()
time = table.index.values
num_ti = len(time) - 1
print(num_ti)
#linspace the # of vectors wanted
for i in np.linspace(0, num_ti, num=num_ti):
mag_line_i = mag_line_i + [int(round(i))]
# define 3rd-axis values
#o_mags_1 = table['Bz'].values # X vs Y
#o_mags_2 = table['By'].values # X vs Z
#o_mags_3 = table['Bx'].values # Y vs Z
# define the colormap
cmap = plt.get_cmap('viridis')
# define the bins and normalize
# bounds1 = np.linspace(np.nanmin(o_mags_1), np.nanmax(o_mags_1), 60)
# norm1 = matplotlib.colors.BoundaryNorm(bounds1, cmap.N)
#
# bounds2 = np.linspace(np.nanmin(o_mags_2), np.nanmax(o_mags_2), 60)
# norm2 = matplotlib.colors.BoundaryNorm(bounds2, cmap.N)
#
# bounds3 = np.linspace(np.nanmin(o_mags_3), np.nanmax(o_mags_3), 60)
# norm3 = matplotlib.colors.BoundaryNorm(bounds3, cmap.N)
#o_mags = list(o_mags_1) + list(o_mags_2) + list(o_mags_3)
o_mags = table['|B|'].values
bounds = np.linspace(np.nanmin(o_mags), np.nanmax(o_mags), 60)
norm = matplotlib.colors.BoundaryNorm(bounds, cmap.N)
# plot each MAG 3D vector individually
for i, item in enumerate(mag_line_i):
# Getting the color for the Bz line
# r = cmap(norm(o_mags_1[i]), bytes=True)[0]
# g = cmap(norm(o_mags_1[i]), bytes=True)[1]
# b = cmap(norm(o_mags_1[i]), bytes=True)[2]
r = cmap(norm(o_mags[i]), bytes=True)[0]
g = cmap(norm(o_mags[i]), bytes=True)[1]
b = cmap(norm(o_mags[i]), bytes=True)[2]
color1 = rgb2hex(r, g, b)
# Plot
bplot_1 = ax1.plot([
table['XSC'][item], table['XSC'][item] + scale * table['Bx'][item]
], [
table['YSC'][item], table['YSC'][item] + scale * table['By'][item]
],
color=color1)
# Getting the color for the By line
# r = cmap(norm(o_mags_2[i]), bytes=True)[0]
# g = cmap(norm(o_mags_2[i]), bytes=True)[1]
# b = cmap(norm(o_mags_2[i]), bytes=True)[2]
r = cmap(norm(o_mags[i]), bytes=True)[0]
g = cmap(norm(o_mags[i]), bytes=True)[1]
b = cmap(norm(o_mags[i]), bytes=True)[2]
color2 = rgb2hex(r, g, b)
# Plot
bplot_2 = ax2.plot([
table['XSC'][item], table['XSC'][item] + scale * table['Bx'][item]
], [
table['ZSC'][item], table['ZSC'][item] + scale * table['Bz'][item]
],
color=color2)
# Getting the color for the Bx line
# r = cmap(norm(o_mags_3[i]), bytes=True)[0]
# g = cmap(norm(o_mags_3[i]), bytes=True)[1]
# b = cmap(norm(o_mags_3[i]), bytes=True)[2]
r = cmap(norm(o_mags[i]), bytes=True)[0]
g = cmap(norm(o_mags[i]), bytes=True)[1]
b = cmap(norm(o_mags[i]), bytes=True)[2]
color3 = rgb2hex(r, g, b)
# Plot
bplot_3 = ax3.plot([
table['YSC'][item], table['YSC'][item] + scale * table['By'][item]
], [
table['ZSC'][item], table['ZSC'][item] + scale * table['Bz'][item]
],
color=color3)
cb = matplotlib.colorbar.ColorbarBase(cax,
cmap=cmap,
norm=norm,
ticks=bounds,
orientation='vertical')
cb.set_label(r'$B_{\perp}$ Strength (nT)')
tick_locator = ticker.MaxNLocator(nbins=11)
cb.locator = tick_locator
cb.update_ticks()
#add circle to represent venus
add_venus_2D((0, 0), radius=1, angle=90, ax=ax1, colors=('k', 'w'))
add_venus_2D((0, 0), radius=1, angle=90, ax=ax2, colors=('k', 'w'))
add_venus_2D((0, 0), radius=1, angle=90, ax=ax3, colors=('w', 'w'))
#darken plot background
ax1.set_facecolor('xkcd:slate grey')
ax2.set_facecolor('xkcd:slate grey')
ax3.set_facecolor('xkcd:slate grey')
#set title above middle plot
ax2.set(title='VEX Orbit MAG Data: ' + time_range_str)
#standardize plot limits
#ax1.set_xlim([-5.5,3.5])
#ax1.set_ylim([-12,2.5])
#ax2.set_xlim([-5.5,3.5])
#ax2.set_ylim([-12,2.5])
#ax3.set_xlim([-5.5,3.5])
#ax3.set_ylim([-12,2.5])
#model bow shock
L = 1.303
epsilon = 1.056
x0 = 0.788
x = table['XSC'].values + [1.2]
y = table['YSC'].values
#print(table.iloc[11653])
rho = np.sqrt((table['YSC'].values)**2 + (table['ZSC'].values)**2)
BS = np.sqrt(L**2 - 2 * epsilon * (x - x0) * L - (epsilon**2 - 1) *
(x - x0)**2)
BS_yz = np.sqrt(L**2 - 2 * epsilon * (0 - x0) * L - (epsilon**2 - 1) *
(0 - x0)**2)
ax1.plot(x, BS, 'r')
ax1.plot(x, -BS, 'r')
ax2.plot(x, BS, 'r')
ax2.plot(x, -BS, 'r')
BSplt = plt.Circle((0, 0), BS_yz * 1.2, color='r', fill=False)
ax3.add_artist(BSplt)
#ax1.axis('equal')
#ax2.axis('equal')
# X = table['XSC']
# Y = table['YSC']
# Z = table['ZSC']
#
# max_range = np.array([X.max()-X.min(), Y.max()-Y.min(), Z.max()-Z.min()]).max() / 2.0
#
# mid_x = (X.max()+X.min()) * 0.5
# mid_y = (Y.max()+Y.min()) * 0.5
# mid_z = (Z.max()+Z.min()) * 0.5
# ax1.set_xlim(mid_x - max_range, mid_x + max_range)
# ax1.set_ylim(mid_y - max_range, mid_y + max_range)
# ax1.set_zlim(mid_z - max_range, mid_z + max_range)
# ax2.set_xlim(mid_x - max_range, mid_x + max_range)
# ax2.set_ylim(mid_y - max_range, mid_y + max_range)
# ax2.set_zlim(mid_z - max_range, mid_z + max_range)
plt.show()
##############
# left panel #
##############
ax = plt.subplot(131, projection=imap.wcs)
ax = plotstyle.setup_axis(ax, nticks=[5, 5], fmt=None)
opts = {
'cmap': 'planck_half',
'interpolation': 'nearest',
'vmin': 0,
'vmax': 20,
# 'norm': colors.LogNorm(vmin=5, vmax=50),
}
# background: total intensity
im = ax.imshow(imap_sm[0], **opts)
cax = plotstyle.add_colorbar_hpad(ax, pad="1%", hpad="50%")
locator = ticker.MaxNLocator(nbins=4)
fig.colorbar(im, cax=cax, orientation='horizontal',
ticks=locator).set_label(texify("I [MJy/sr]"), fontsize=10)
cax.xaxis.set_label_position('top')
cax.xaxis.set_ticks_position('top')
ax.text(0.12, 1.03, texify("f090"), transform=ax.transAxes, fontsize=12)
ax.set_xlabel(r"$l$")
ax.set_ylabel(r"$b$")
# foreground: magnetic field orientation
theta = lib.Bangle(imap_sm[1], imap_sm[2], toIAU=True)
theta += np.pi / 2 # this gets the B-field angle corrected
# x- and y-components of magnetic field
Bx = np.cos(theta)
By = np.sin(theta)
# calculate polarization error as masking
def plot_map(fill=True,
zorder=None,
labelstopright: bool = True,
labelsbottomleft: bool = True,
borders: bool = False,
rivers: bool = False,
lakes: bool = False,
outline: bool = False,
ax=None,
lw=0.5):
"""Plots map into existing axes.
Parameters
----------
fill : bool, optional
fills the continents in light gray, by default True
zorder : int, optional
zorder of the map, by default -10
projection : cartopy.crs.projection, optional
projection to be used for the map.
labelstopright : bool, optional
flag to turn on or off the ticks
labelsbottomleft : bool, optional
flag to turn on or off the ticks
borders : bool
plot borders. Default True
rivers : bool
plot rivers. Default False
lakes : bool
plot lakes. Default True
lw : float
outline width
Returns
-------
matplotlib.pyplot.Axes
Axes in which the map was plotted
Notes
-----
:Author:
Lucas Sawade ([email protected])
:Last Modified:
2021.09.22 11.45
"""
if ax is None:
ax = plt.gca()
# Put lables all around
if isinstance(ax.projection, cartopy.crs.PlateCarree):
# Set xticks Should be automated, but I just don't know how rn
# ax.set_xticks([-180, -135, -90, -45, 0, 45,
# 90, 135, 180], crs=ax.projection)
# ax.set_yticks([-90, -45, 0, 45, 90], crs=ax.projection)
# Set label formatter
degree_locator = mticker.MaxNLocator(nbins=9, steps=steps)
ax.xaxis.set_major_locator(degree_locator)
ax.yaxis.set_major_locator(degree_locator)
ax.xaxis.set_major_formatter(LongitudeFormatter())
ax.yaxis.set_major_formatter(LatitudeFormatter())
ax.tick_params(labeltop=labelstopright,
labelright=labelstopright,
labelbottom=labelsbottomleft,
labelleft=labelsbottomleft)
ax.grid(linewidth=2, color='black', alpha=0.5, linestyle='--')
ax.spines['geo'].set_linewidth(lw)
# Set gridlines
# gl = ax.gridlines(draw_labels=False, linewidth=1, color='lightgray',
#
# alpha=0.5, linestyle='-', zorder=-1.5)
if outline:
edgecolor = 'black'
else:
edgecolor = 'none'
# Add land
if fill:
ax.add_feature(cartopy.feature.LAND,
zorder=zorder,
edgecolor=edgecolor,
linewidth=0.5,
facecolor=(0.8, 0.8, 0.8))
else:
ax.add_feature(cartopy.feature.LAND,
zorder=zorder,
edgecolor=edgecolor,
linewidth=0.5,
facecolor=(0, 0, 0, 0))
if borders:
ax.add_feature(cartopy.feature.BORDERS,
zorder=None if zorder is None else zorder + 1,
facecolor='none',
edgecolor=(0.5, 0.5, 0.5),
linewidth=0.25)
if rivers:
ax.add_feature(
cartopy.feature.RIVERS,
zorder=zorder,
edgecolor=(0.3, 0.3, 0.7),
)
# edgecolor=(0.5, 0.5, 0.7) )
if lakes:
ax.add_feature(cartopy.feature.LAKES,
zorder=None if zorder is None else zorder + 1,
edgecolor='black',
linewidth=0.5,
facecolor=(1.0, 1.0, 1.0))
return ax
def comp2huybers(plname, dir='.', xrange=False, show=True):
"""
Creates plots of insolation, temperature, albedo, ice mass,
and bed rock height over the length of the simulation
Parameters
----------
plname : string
The name of the planet with .Climate data
Keyword Arguments
-----------------
dir : string
Directory of vplanet simulation (default = '.')
xrange : float tuple, list, or numpy array
Range of x-values (time) to restrict plot
(default = False (no restriction))
orbit : bool
Plot orbital data (obliquity, eccentricity, COPP)
(default = False)
show : bool
Show plot in Python (default = True)
Output
------
PDF format plot with name 'evol_<dir>.pdf'
"""
if not isinstance(dir, (list, tuple)):
dir = [dir]
nfiles = len(dir)
if nfiles > 1 and orbit == True:
raise Exception("Error: cannot plot multiple files when orbit = True")
fig = plt.figure(figsize=(8, 12))
fig.subplots_adjust(wspace=0.3, top=0.9, hspace=0.2)
for ii in np.arange(nfiles):
out = vplot.GetOutput(dir[ii])
#pdb.set_trace()
ctmp = 0
for p in range(len(out.bodies)):
if out.bodies[p].name == plname:
body = out.bodies[p]
ctmp = 1
else:
if p == len(out.bodies) - 1 and ctmp == 0:
raise Exception("Planet %s not found in folder %s" %
(plname, dir[ii]))
try:
ecc = body.Eccentricity
except:
ecc = np.zeros_like(body.Time) + getattr(out.log.initial,
plname).Eccentricity
try:
inc = body.Inc
except:
inc = np.zeros_like(body.Time)
try:
obl = body.Obliquity
except:
obltmp = getattr(out.log.initial, plname).Obliquity
if obltmp.unit == 'rad':
obltmp *= 180 / np.pi
obl = np.zeros_like(body.Time) + obltmp
f = open(dir[ii] + '/' + plname + '.in', 'r')
lines = f.readlines()
f.close()
pco2 = 0
#pdb.set_trace()
for i in range(len(lines)):
if lines[i].split() != []:
if lines[i].split()[0] == 'dRotPeriod':
P = -1 * np.float(lines[i].split()[1])
if lines[i].split()[0] == 'dSemi':
semi = np.float(lines[i].split()[1])
if semi < 0:
semi *= -1
if lines[i].split()[0] == 'dpCO2':
pco2 = np.float(lines[i].split()[1])
try:
longp = (body.ArgP + body.LongA + body.PrecA + 180) * np.pi / 180.0
except:
longp = body.PrecA * np.pi / 180.0
esinv = ecc * np.sin(longp)
lats = np.unique(body.Latitude)
nlats = len(lats)
ntimes = len(body.Time)
# plot temperature
temp = np.reshape(body.TempLandLat, (ntimes, nlats))
ax1 = plt.subplot(7, 1, 5)
pos = ax1.figbox.get_points()
c = plt.contourf(body.Time / 1e6, lats[lats > 58], temp.T[lats > 58],
20)
plt.ylabel('Latitude')
plt.ylim(60, 83)
plt.yticks([60, 70, 80])
if xrange == False:
left = 0
else:
left = xrange[0]
# plt.text(left,140,'\n'.join(titlestr),fontsize=20)
if xrange:
plt.xlim(xrange)
# plt.contour(body.Time/1e6,lats[lats>60],temp.T[lats>60],levels=[0],colors='w')
plt.xticks(visible=False)
clb = plt.colorbar(c,
cax=plt.axes([
pos[1, 0] + 0.01, pos[0, 1], 0.01,
pos[1, 1] - pos[0, 1]
]),
ticks=[-17, -15, -13, -11, -9, -7, -5])
clb.set_label('Surface Temp.\n($^{\circ}$C)', fontsize=12)
# clb.ax.set_yticklabels([-17,-15,-13,-11,-9,-7])
# plot ice height
ice = np.reshape(body.IceHeight + body.BedrockH, (ntimes, nlats))
ax3 = plt.subplot(7, 1, 4)
pos = ax3.figbox.get_points()
# pdb.set_trace()
c = plt.contourf(body.Time / 1e6, lats[lats > 58], ice.T[lats > 58, :],
20)
plt.ylabel('Latitude')
plt.ylim(60, 83)
# plt.xlim(0,2e6)
plt.yticks([60, 70, 80])
if xrange:
plt.xlim(xrange)
# plt.contour(body.Time,lats[lats>60],ice.T[lats>60],levels=[0],colors='w')
plt.xticks(visible=False)
clb = plt.colorbar(c,
cax=plt.axes([
pos[1, 0] + 0.01, pos[0, 1], 0.01,
pos[1, 1] - pos[0, 1]
]))
#clb.set_label('Ice height (m)')
clb.set_label('Ice sheet\nheight (m)', fontsize=12)
# ax3p = ax3.twinx()
# plt.plot(body.Time,esinv,linestyle = 'solid',marker='None',color='salmon',linewidth=2)
ax4 = plt.subplot(7, 1, 6)
pos = ax4.figbox.get_points()
acc = body.IceAccum
c = plt.contourf(body.Time / 1e6, lats[lats > 58], acc.T[lats > 58],
20)
plt.ylabel('Latitude')
plt.ylim(60, 83)
plt.yticks([60, 70, 80])
plt.xticks(visible=False)
if xrange == False:
left = 0
else:
left = xrange[0]
# plt.text(left,140,'\n'.join(titlestr),fontsize=20)
if xrange:
plt.xlim(xrange)
clb = plt.colorbar(c,
cax=plt.axes([
pos[1, 0] + 0.01, pos[0, 1], 0.01,
pos[1, 1] - pos[0, 1]
]))
tloc = ticker.MaxNLocator(nbins=5)
clb.locator = tloc
clb.update_ticks()
clb.set_label('Ice Accum.\n(m year$^{-1}$)', fontsize=12)
ax5 = plt.subplot(7, 1, 7)
pos = ax5.figbox.get_points()
abl = body.IceAblate
c = plt.contourf(body.Time / 1e6, lats[lats > 58], -abl.T[lats > 58],
20)
plt.ylabel('Latitude')
# plt.title(r'Ice Ablation (m year$^{-1}$)',fontsize=12)
plt.ylim(60, 83)
plt.yticks([60, 70, 80])
plt.xlabel('Time (Myr)')
if xrange == False:
left = 0
else:
left = xrange[0]
# plt.text(left,140,'\n'.join(titlestr),fontsize=20)
if xrange:
plt.xlim(xrange)
clb = plt.colorbar(c,
cax=plt.axes([
pos[1, 0] + 0.01, pos[0, 1], 0.01,
pos[1, 1] - pos[0, 1]
]))
clb.set_label('Ice Ablation\n(m year$^{-1}$)', fontsize=12)
plt.subplot(7, 1, 1)
plt.plot(body.Time / 1e6,
obl,
linestyle='solid',
marker='None',
color=vplot.colors.dark_blue,
linewidth=2)
plt.ylabel('Obliquity')
plt.xticks(visible=False)
if xrange:
plt.xlim(xrange)
plt.subplot(7, 1, 2)
plt.plot(body.Time / 1e6,
ecc,
linestyle='solid',
marker='None',
color=vplot.colors.purple,
linewidth=2)
plt.ylabel('Eccenticity')
plt.xticks(visible=False)
if xrange:
plt.xlim(xrange)
plt.subplot(7, 1, 3)
plt.plot(body.Time / 1e6,
esinv,
linestyle='solid',
marker='None',
color=vplot.colors.red,
linewidth=2)
plt.ylabel('CPP')
plt.xticks(visible=False)
if xrange:
plt.xlim(xrange)
if dir[ii] == '.':
dir[ii] = 'cwd'
if (sys.argv[1] == 'pdf'):
plt.savefig('EarthClimateMilankovitch.pdf')
if (sys.argv[1] == 'png'):
plt.savefig('EarthClimateMilankovitch.png')
if show:
plt.show()
else:
plt.close()
plot_kwargs=dict(edgecolor='w', orientation='h'))
ax1.set_ylabel('counts')
ax1.set_xlabel('x')
ax2.set_xlabel('counts')
ax2.set_ylabel('x')
# work with labeled data
fig, (ax1, ax2) = plt.subplots(1,
2,
figsize=(9, 4.5),
tight_layout=True,
sharey=True)
arts = stack_hist(ax1,
dict_data,
color_cycle + hatch_cycle,
hist_func=hist_func)
arts = stack_hist(ax2,
dict_data,
color_cycle + hatch_cycle,
hist_func=hist_func,
labels=['set 0', 'set 3'])
ax1.xaxis.set_major_locator(mticker.MaxNLocator(5))
ax1.set_xlabel('counts')
ax1.set_ylabel('x')
ax2.set_ylabel('x')
plt.show()
def plot_pnl(self):
style.use("ggplot")
plt.figure(figsize=(10, 7))
#get data
total_pnl = self.get_total() / 1000000
real_gains = self.get_real() / 1000000
unreal_gains = self.get_unreal() / 1000000
date = total_pnl.index.values
ax1 = plt.subplot2grid(
(1, 1),
(0, 0),
rowspan=1,
colspan=1,
)
#create subplots
ax1.plot_date(date,
total_pnl,
'-',
label="Total",
color="#0715FD",
linewidth=.7) #totla pnl
ax1.plot_date(date,
real_gains,
'--',
label="Realized",
color='#0715FD',
linewidth=.7) #realized pnl
ax1.plot_date(date,
unreal_gains,
'-',
label="Unrealized",
color='#aeabab',
linewidth=.7) #unrealized pnl
plt.title("Portfolio PnL", loc='left', color='#8b8b8b', fontsize=20)
plt.ylabel("PnL (in millions of US$)", color='#636363', labelpad=10)
#changes made to pnl plot(ax1)
ax1.legend(loc=2, frameon=False, ncol=3)
ax1.set_facecolor('w') #change background of chart
ax1.spines["left"].set_color('#8b8b8b')
#formats y axis ticklabels to include commas
ax1.get_yaxis().set_major_formatter(
mticks.FuncFormatter(lambda x, p: format(int(x), ',')))
#max number of tickers
ax1.yaxis.set_major_locator(mticks.MaxNLocator(nbins=7, prune='both'))
ax1.tick_params(axis='both', labelcolor='#636363', length=0)
ax1.axhline(y=0, color='#636363', linewidth=.7)
ax1.grid(False)
#x axis tick labels for ax1
ax1.xaxis.set_major_locator(mticks.MaxNLocator(nbins=10))
ax1.tick_params(axis='both', labelcolor='#636363', length=0)
ax1.set_xlim(xmin=date[0]) #set x-axis minimum
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()
return
def test_integer(self, vmin, vmax, steps, expected):
loc = mticker.MaxNLocator(nbins=5, integer=True, steps=steps)
assert_almost_equal(loc.tick_values(vmin, vmax), expected)
def test_basic(self, vmin, vmax, expected):
loc = mticker.MaxNLocator(nbins=5)
assert_almost_equal(loc.tick_values(vmin, vmax), expected)
def produce_figure_row(fig,axes,nrows,ncols,Delta_list, title_list,colorbar_unit_list,y_title_list,datapath, file_extension,cscale, row_num, \
difference_on,pole,field_name,field_type_list,axes_direction='xy'):
title = None
letter_labels = np.array(
['(a)', '(b)', '(c)', '(d)', '(e)', '(f)', '(g)', '(h)', '(i)'])
#for k in range(ncols):
for k in range(ncols):
print 'Plotting ' + field_name + ' fields for ' + Delta_list[k]
ax = subplot(nrows, ncols, (row_num - 1) * ncols + (k + 1))
filename = datapath + Delta_list[k] + file_extension
#print filename
(lat, lon, data, p_values, field, layer_interface,
lat_lon_bounds) = load_data_from_mat_file(filename)
if axes_direction == 'xy':
(cscale, datamap) = plot_polar_field(lat,
lon,
data,
pole,
difference_on,
title,
p_values,
cscale,
field,
colorbar_on=False,
return_data_map=True)
else:
file_type = 'ocean_month_z'
datamap=plot_vertical_section(lon,lat,data,difference_on,title,p_values,cscale,field,layer_interface,axes_direction,lat_lon_bounds,file_type,\
colorbar_on=False,return_data_map=True)
if row_num == 1:
#ax.set_title(title_list[k], fontsize=18, y=1.13)
ax.set_title(title_list[Delta_list[k]], fontsize=20, y=1.13)
#if k==0:
# plt.ylabel(y_title_list[row_num-1], fontsize=18, labelpad=25)
if k > 0:
ax.set_yticks([])
if k == 0 and row_num < 3:
#ax.annotate(y_title_list[row_num-1], xy=(-0.4, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
#ax.annotate(field_type_list[row_num-1], xy=(-0.25, 0.5), xycoords='axes fraction', fontsize=21,rotation=90,verticalalignment='center')
plt.ylabel(field_type_list[row_num - 1], fontsize=20, labelpad=25)
text(1,
1,
letter_labels[(row_num - 1) * ncols + (k)],
ha='right',
va='bottom',
transform=ax.transAxes,
fontsize=15)
#i_ row_num!=nrows:
# ax.set_xticks([])
#else:
# plt.xlabel('latitude (deg)', fontsize=14)
# ax.set_xticks([-40 ,-60, -80])
#Creating colorbar
fig.subplots_adjust(right=0.8)
tick_locator = ticker.MaxNLocator(nbins=5)
cbar_ax = fig.add_axes(
[0.825, 0.115 + ((nrows - row_num) * 0.435), 0.03, 0.33])
if row_num == 1:
ticks = np.array([-0.2, -0.1, 0, 0.1, 0.2])
if row_num == 2:
ticks = np.array([-0.1, -0.05, 0, 0.05, 0.1])
#cbar=fig.colorbar(datamap, cax=cbar_ax)
cbar = fig.colorbar(datamap, cax=cbar_ax, ticks=ticks)
#cbar=fig.colorbar(datamap, cax=cbar_ax,ticks=tick_locator)
cbar.set_label(colorbar_unit_list[row_num - 1], rotation=90, fontsize=20)
cbar.ax.tick_params(labelsize=20)
def plotind(self,
ind,
subinds=None,
upinds=None,
downinds=None,
masterax=None):
ind._plotinit()
sch = self.p.scheme
# check subind
subinds = subinds or []
upinds = upinds or []
downinds = downinds or []
# plot subindicators on self with independent axis above
for upind in upinds:
self.plotind(upind)
# Get an axis for this plot
ax = masterax or self.newaxis(ind, rowspan=self.pinf.sch.rowsminor)
indlabel = ind.plotlabel()
for lineidx in range(ind.size()):
line = ind.lines[lineidx]
linealias = ind.lines._getlinealias(lineidx)
lineplotinfo = getattr(ind.plotlines, '_%d' % lineidx, None)
if not lineplotinfo:
lineplotinfo = getattr(ind.plotlines, linealias, None)
if not lineplotinfo:
lineplotinfo = AutoInfoClass()
if lineplotinfo._get('_plotskip', False):
continue
# Legend label only when plotting 1st line
if masterax and not ind.plotinfo.plotlinelabels:
label = indlabel * (lineidx == 0) or '_nolegend'
else:
label = lineplotinfo._get('_name', '') or linealias
# plot data
lplot = line.plotrange(self.pinf.xstart, self.pinf.xend)
if not math.isnan(lplot[-1]):
label += ' %.2f' % lplot[-1]
plotkwargs = dict()
linekwargs = lineplotinfo._getkwargs(skip_=True)
if linekwargs.get('color', None) is None:
if not lineplotinfo._get('_samecolor', False):
self.pinf.nextcolor(ax)
plotkwargs['color'] = self.pinf.color(ax)
plotkwargs.update(dict(aa=True, label=label))
plotkwargs.update(**linekwargs)
if ax in self.pinf.zorder:
plotkwargs['zorder'] = self.pinf.zordernext(ax)
pltmethod = getattr(ax, lineplotinfo._get('_method', 'plot'))
plottedline = pltmethod(self.pinf.x, lplot, **plotkwargs)
try:
plottedline = plottedline[0]
except:
# Possibly a container of artists (when plotting bars)
pass
self.pinf.zorder[ax] = plottedline.get_zorder()
if not math.isnan(lplot[-1]):
# line has valid values, plot a tag for the last value
self.drawtag(ax,
len(self.pinf.xreal),
lplot[-1],
facecolor='white',
edgecolor=self.pinf.color(ax))
# plot subindicators that were created on self
for subind in subinds:
self.plotind(subind, subinds=self.dplotsover[subind], masterax=ax)
if not masterax:
# adjust margin if requested ... general of particular
ymargin = ind.plotinfo._get('plotymargin', 0.0)
ymargin = max(ymargin, self.pinf.sch.yadjust)
if ymargin:
ax.margins(y=ymargin)
# Set specific or generic ticks
yticks = ind.plotinfo._get('plotyticks', [])
if not yticks:
yticks = ind.plotinfo._get('plotyhlines', [])
if yticks:
ax.set_yticks(yticks)
else:
locator = mticker.MaxNLocator(nbins=4, prune='both')
ax.yaxis.set_major_locator(locator)
# Set specific hlines if asked to
hlines = ind.plotinfo._get('plothlines', [])
if not hlines:
hlines = ind.plotinfo._get('plotyhlines', [])
for hline in hlines:
ax.axhline(hline,
color=self.pinf.sch.hlinescolor,
ls=self.pinf.sch.hlinesstyle,
lw=self.pinf.sch.hlineswidth)
if self.pinf.sch.legendind and \
ind.plotinfo._get('plotlegend', True):
handles, labels = ax.get_legend_handles_labels()
# Ensure that we have something to show
if labels:
# Legend done here to ensure it includes all plots
legend = ax.legend(loc=self.pinf.sch.legendindloc,
numpoints=1,
frameon=False,
shadow=False,
fancybox=False,
prop=self.pinf.prop)
legend.set_title(indlabel, prop=self.pinf.prop)
# hack: if title is set. legend has a Vbox for the labels
# which has a default "center" set
legend._legend_box.align = 'left'
# plot subindicators on self with independent axis below
for downind in downinds:
self.plotind(downind)
def run(opt='twse', debug=False, limit=0):
maxlen = 30
starttime = datetime.utcnow() - timedelta(days=300)
endtime = datetime.utcnow()
report = Report('bbands',
sort=[('buys', False), ('sells', False),
('portfolio_value', False)],
limit=20)
kwargs = {'debug': debug, 'limit': limit, 'opt': opt}
idhandler = TwseIdDBHandler(
**kwargs) if kwargs['opt'] == 'twse' else OtcIdDBHandler(**kwargs)
for stockid in idhandler.stock.get_ids():
try:
kwargs = {
'opt': opt,
'targets': ['stock', 'future', 'credit'],
'starttime': starttime,
'endtime': endtime,
'stockids': [stockid],
'traderids': [],
'base': 'stock',
'callback': None,
'limit': 1,
'debug': debug
}
panel, dbhandler = collect_hisframe(**kwargs)
if len(panel[stockid].index) < maxlen:
continue
sim_params = SimulationParameters(
period_start=panel[stockid].index[0],
period_end=panel[stockid].index[-1],
data_frequency='daily',
emission_rate='daily')
bbands = BBands(dbhandler=dbhandler,
debug=debug,
sim_params=sim_params)
results = bbands.run(panel).fillna(0)
risks = bbands.perf_tracker.handle_simulation_end()
report.collect(stockid, results, risks)
print "%s pass" % (stockid)
except:
print traceback.format_exc()
continue
if report.report.empty:
return
# report summary
stream = report.summary(dtype='html')
report.write(stream, 'bbands.html')
for stockid in report.iter_symbol():
stream = report.iter_report(stockid, dtype='html')
report.write(stream, "bbands_%s.html" % (stockid))
for stockid in report.iter_symbol():
perf = report.pool[stockid]
dates = [date2num(i) for i in perf.index[maxlen:]]
quotes = [
perf[label][maxlen:].values
for label in ['open', 'high', 'low', 'close']
]
quotes = zip(*([dates] + quotes))
fig = plt.figure(facecolor='#07000d')
ax1 = plt.subplot2grid((6, 4), (1, 0),
rowspan=4,
colspan=4,
axisbg='#07000d')
candlestick_ohlc(ax1,
quotes,
width=.6,
colorup='#53c156',
colordown='#ff1717')
ax1.plot(dates,
perf['upper'][maxlen:].values,
'#e1edf9',
label='upper',
linewidth=1.5)
ax1.plot(dates,
perf['middle'][maxlen:].values,
'#e1edf9',
label='middle',
linewidth=1.5)
ax1.plot(dates,
perf['lower'][maxlen:].values,
'#e1edf9',
label='lower',
linewidth=1.5)
ax1.grid(True, color='w')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', colors='w')
plt.ylabel('Stock price and Volume')
bbLeg = plt.legend(loc=9,
ncol=2,
prop={'size': 7},
fancybox=True,
borderaxespad=0.)
bbLeg.get_frame().set_alpha(0.4)
textEd = pylab.gca().get_legend().get_texts()
pylab.setp(textEd[0:6], color='w')
ax1v = ax1.twinx()
ax1v.fill_between(dates,
0,
perf['volume'][maxlen:].values,
facecolor='#00ffe8',
alpha=.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.grid(False)
###Edit this to 3, so it's a bit larger
ax1v.set_ylim(0, 3 * perf['volume'][maxlen:].values.max())
ax1v.spines['bottom'].set_color("#5998ff")
ax1v.spines['top'].set_color("#5998ff")
ax1v.spines['left'].set_color("#5998ff")
ax1v.spines['right'].set_color("#5998ff")
ax1v.tick_params(axis='x', colors='w')
ax1v.tick_params(axis='y', colors='w')
plt.setp(ax1.get_xticklabels(), visible=False)
plt.subplots_adjust(left=.09,
bottom=.14,
right=.94,
top=.95,
wspace=.20,
hspace=0)
plt.gcf().set_size_inches(18, 8)
plt.savefig("bbands_%s.png" % (stockid), facecolor=fig.get_facecolor())
def plotdata(self, data, indicators):
for ind in indicators:
upinds = self.dplotsup[ind]
for upind in upinds:
self.plotind(upind,
subinds=self.dplotsover[upind],
upinds=self.dplotsup[upind],
downinds=self.dplotsdown[upind])
# set the x axis data (if needed)
self.setxdata(data)
opens = data.open.plotrange(self.pinf.xstart, self.pinf.xend)
highs = data.high.plotrange(self.pinf.xstart, self.pinf.xend)
lows = data.low.plotrange(self.pinf.xstart, self.pinf.xend)
closes = data.close.plotrange(self.pinf.xstart, self.pinf.xend)
volumes = data.volume.plotrange(self.pinf.xstart, self.pinf.xend)
vollabel = 'Volume'
if self.pinf.sch.volume and self.pinf.sch.voloverlay:
volplot = self.plotvolume(data, opens, highs, lows, closes,
volumes, vollabel)
axvol = self.pinf.daxis[data.volume]
ax = axvol.twinx()
self.pinf.daxis[data] = ax
else:
ax = self.newaxis(data, rowspan=self.pinf.sch.rowsmajor)
datalabel = ''
dataname = ''
if hasattr(data, '_name') and data._name:
datalabel += data._name
if hasattr(data, '_compression') and \
hasattr(data, '_timeframe'):
tfname = TimeFrame.getname(data._timeframe, data._compression)
datalabel += ' (%d %s)' % (data._compression, tfname)
datalabel += ' O:%.2f H:%2.f L:%.2f C:%.2f' % \
(opens[-1], highs[-1], lows[-1], closes[-1])
if self.pinf.sch.style.startswith('line'):
plotted = plot_lineonclose(ax,
self.pinf.x,
closes,
color=self.pinf.sch.loc,
label=datalabel)
else:
if self.pinf.sch.style.startswith('candle'):
plotted = plot_candlestick(ax,
self.pinf.x,
opens,
highs,
lows,
closes,
colorup=self.pinf.sch.barup,
colordown=self.pinf.sch.bardown,
label=datalabel)
elif self.pinf.sch.style.startswith('bar') or True:
# final default option -- should be "else"
plotted = plot_ohlc(ax,
self.pinf.x,
opens,
highs,
lows,
closes,
colorup=self.pinf.sch.barup,
colordown=self.pinf.sch.bardown,
label=datalabel)
self.pinf.zorder[ax] = plotted[0].get_zorder()
# Code to place a label at the right hand side with the last value
self.drawtag(ax,
len(self.pinf.xreal),
closes[-1],
facecolor='white',
edgecolor=self.pinf.sch.loc)
ax.yaxis.set_major_locator(mticker.MaxNLocator(prune='both'))
# make sure "over" indicators do not change our scale
ax.set_ylim(ax.get_ylim())
if self.pinf.sch.volume:
if not self.pinf.sch.voloverlay:
self.plotvolume(data, opens, highs, lows, closes, volumes,
vollabel)
else:
# Prepare overlay scaling/pushup or manage own axis
if self.pinf.sch.volpushup:
# push up overlaid axis by lowering the bottom limit
axbot, axtop = ax.get_ylim()
axbot *= (1.0 - self.pinf.sch.volpushup)
ax.set_ylim(axbot, axtop)
for ind in indicators:
self.plotind(ind, subinds=self.dplotsover[ind], masterax=ax)
handles, labels = ax.get_legend_handles_labels()
if handles:
# put data and volume legend entries in the 1st positions
# because they are "collections" they are considered after Line2D
# for the legend entries, which is not our desire
if self.pinf.sch.volume and self.pinf.sch.voloverlay:
if volplot:
# even if volume plot was requested, there may be no volume
labels.insert(0, vollabel)
handles.insert(0, volplot)
didx = labels.index(datalabel)
labels.insert(0, labels.pop(didx))
handles.insert(0, handles.pop(didx))
# feed handles/labels to legend to get right order
legend = ax.legend(handles,
labels,
loc='upper left',
frameon=False,
shadow=False,
fancybox=False,
prop=self.pinf.prop,
numpoints=1,
ncol=1)
# hack: if title is set. legend has a Vbox for the labels
# which has a default "center" set
legend._legend_box.align = 'left'
for ind in indicators:
downinds = self.dplotsdown[ind]
for downind in downinds:
self.plotind(downind,
subinds=self.dplotsover[downind],
upinds=self.dplotsup[downind],
downinds=self.dplotsdown[downind])
def gen_eq_movie(allfiles,
my_rank,
ncpu,
quantity_code=1,
dpi=300,
xypix=512,
minval=-150,
maxval=150,
title_text='',
cbar_text='',
remove_mean=True):
from rayleigh_diagnostics import Equatorial_Slices
import numpy
import matplotlib.pyplot as plt
from matplotlib import ticker, font_manager
nfiles = len(allfiles)
dfiles = nfiles // ncpu
my_start = my_rank * dfiles
fmod = nfiles % ncpu
if (my_rank < fmod):
my_start = my_start + my_rank
dfiles = dfiles + 1
else:
my_start = my_start + fmod
my_end = my_start + dfiles
#Before making the plots, set up the grid
es = Equatorial_Slices(allfiles[0], path='')
nr = es.nr
nphi = es.nphi
r = es.radius / numpy.max(es.radius)
phi = numpy.zeros(nphi + 1, dtype='float64')
phi[0:nphi] = es.phi
phi[nphi] = numpy.pi * 2 # For display purposes, it is best to have a redunant data point at 0,2pi
#We need to generate a cartesian grid of x-y coordinates (both X & Y are 2-D)
radius_matrix, phi_matrix = numpy.meshgrid(r, phi)
X = radius_matrix * numpy.cos(phi_matrix)
Y = radius_matrix * numpy.sin(phi_matrix)
qind = es.lut[quantity_code]
field = numpy.zeros((nphi + 1, nr), dtype='float64')
colormap = 'jet'
colormap = 'RdYlBu_r'
for i in range(my_start, my_end):
if (my_rank == 0):
print(' Processing file ', i - my_start + 1, 'of',
my_end - my_start)
f = allfiles[i]
es = Equatorial_Slices(f, path='')
niter = es.niter
for j in range(niter):
jstring = "{:0>8d}".format(es.iters[j])
savefile = 'eq_pngs/' + jstring + '.png'
field[0:nphi, :] = es.vals[:, :, qind, j]
field[nphi, :] = field[0, :] #replicate phi=0 values at phi=2pi
#remove the m=0 mean if desired (usually a good idea, but not always)
if (remove_mean):
for i in range(nr):
the_mean = numpy.mean(field[:, i])
field[:, i] = field[:, i] - the_mean
#Plot
xyin = xypix / dpi
fig, ax = plt.subplots(figsize=(xyin, xyin), dpi=dpi)
tsize = 10 # title font size
cbfsize = 6 # colorbar font size
img = ax.pcolormesh(X,
Y,
field,
cmap=colormap,
vmin=minval,
vmax=maxval)
ax.axis('equal'
) # Ensure that x & y axis ranges have a 1:1 aspect ratio
ax.axis('off') # Do not plot x & y axes
# Plot bounding circles
ax.plot(r[nr - 1] * numpy.cos(phi),
r[nr - 1] * numpy.sin(phi),
color='black') # Inner circle
ax.plot(r[0] * numpy.cos(phi),
r[0] * numpy.sin(phi),
color='black') # Outer circle
ax.set_title(title_text, fontsize=tsize)
#colorbar ...
cbar = plt.colorbar(img,
orientation='horizontal',
shrink=0.5,
aspect=10,
ax=ax)
cbar.set_label(cbar_text)
tick_locator = ticker.MaxNLocator(nbins=5)
cbar.locator = tick_locator
cbar.update_ticks()
cbar.ax.tick_params(labelsize=cbfsize) #font size for the ticks
t = cbar.ax.xaxis.label
t.set_fontsize(cbfsize) # font size for the axis title
plt.savefig(savefile)
plt.close()
def data(self):
#120 candlov max za zapros
#BTC,ETH,XRP,EOS,ETH,OMG,BCH,IOTA,LTC,NEO,XRP,ELF,EO
#url = "https://api.bitfinex.com/v2/candles/trade:5m:tBTCUSD/hist?start=1497887100000&end=1497922800000&limit=1000"
url = "https://api.bitfinex.com/v2/candles/trade:1D:tETCUSD/hist?start=1502496000000&limit=1000"
#start = datetime.utcnow()
response = requests.request("GET", url)
time.sleep(1)
with open('myRest.ts', 'w') as file:
#for i in range(len(qq[0][0])):
#stri = (str(qq[0][0][i]))
file.write(
(response.text.replace('],[',
'\n').replace('[[',
'').replace(']]', '')))
#file.write("\n")
time.sleep(1)
loaded = pd.read_csv(
'myRest.ts',
delimiter=',',
names=['Timestamp', 'Open', 'Close', 'High', 'Low', 'Volume'])
loaded = loaded[::-1]
#loaded = indicators.randomwalk(loaded)
#loaded.reindex(indeex=loaded.index[::-1])
loaded = indicators.HA(loaded)
#loaded = indicators.chaikin_oscillator(loaded)
#loaded = indicators.chaikin_oscillator2(loaded)
#loaded = indicators.moving_average(loaded,10)
#loaded = indicators.exponential_moving_average(loaded,10)
#loaded = indicators.acc_dist(loaded)
#loaded = indicators.chaikin_oscillator(loaded)
chaikin = tb.ADOSC(high=loaded['HA_High'].values,
low=loaded['HA_Low'].values,
close=loaded['HA_Close'].values,
volume=loaded['Volume'].values,
fastperiod=3,
slowperiod=10)
#loaded = indicators.chaikin_oscillator2(loaded)
loaded = loaded.join(pd.Series(chaikin, name='TALib_Chaikin'))
#upperband, middleband, lowerband = tb.BBANDS(close=loaded['Close'].values, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
#loaded = loaded.join(pd.Series(upperband,name='BOLLINGER_upper'))
#loaded = loaded.join(pd.Series(middleband,name='BOLLINGER_middle'))
#loaded = loaded.join(pd.Series(lowerband,name='BOLLINGER_lower'))
#loaded = indicators.accumulation_distribution(loaded,0)
loaded = indicators.nulltest(loaded)
#loaded = indicators.backtest(loaded)#backtest(loaded)
#loaded = indicators.backtest_bull(loaded)
#loaded=np.loadtxt('myRest.ts',delimiter=',')#,ndmin=2)
#logging.info(type(loaded))
#logging.info(loaded['Graph'].values)
#logging.info(loaded.get_value(999, 'Balance'))
with open('backtest', 'w') as file:
file.write(loaded.to_string())
########################################################### PLOT NUMBER ONE - GRAPH
#fig = plt.figure()
ax1 = plt.subplot2grid((1, 1), (0, 0))
timestamps = loaded['Timestamp'].values
timestamps = np.multiply(timestamps, 0.001)
balancep = loaded['Balance'].values
openp = loaded['HA_Open'].values
highp = loaded['HA_High'].values
lowp = loaded['HA_Low'].values
closep = loaded['HA_Close'].values
volumep = loaded['Volume'].values
x = 0
ohlc = []
datep = [
mdates.date2num(dt.datetime.fromtimestamp(ts)) for ts in timestamps
]
y = len(datep)
sizemultiplier = 3 * 8
while x < y:
append_me = datep[x], openp[x], highp[x], lowp[x], closep[
x], volumep[x]
ohlc.append(append_me)
if loaded.at[x, 'Graph'] == 1:
plt.text(datep[x],
closep[x] + 0.4 * sizemultiplier,
"o",
fontsize=12,
color='k')
plt.plot([datep[x], datep[x]],
[closep[x], closep[x] + 0.35 * sizemultiplier],
color='k',
linestyle='-',
linewidth=2)
else:
if loaded.at[x, 'Graph'] == -1:
if balancep[x] - balancep[x - 1] > 0:
coloris = 'g'
else:
coloris = 'r'
plt.text(datep[x],
closep[x] + 0.4 * sizemultiplier,
"c" +
str(round(balancep[x] - balancep[x - 1], 3)),
fontsize=12,
color=coloris)
plt.plot([datep[x], datep[x]],
[closep[x], closep[x] + 0.35 * sizemultiplier],
color='k',
linestyle='-',
linewidth=2)
else:
if loaded.at[x, 'Graph'] == 2:
plt.text(datep[x],
closep[x] + 0.4 * sizemultiplier,
"oS",
fontsize=12,
color='k')
plt.plot(
[datep[x], datep[x]],
[closep[x], closep[x] + 0.35 * sizemultiplier],
color='k',
linestyle='-',
linewidth=2)
else:
if loaded.at[x, 'Graph'] == -2:
if balancep[x] - balancep[x - 1] < 0:
coloris = 'r'
else:
coloris = 'g'
plt.text(
datep[x],
closep[x] + 0.4 * sizemultiplier,
"cS" +
str(round(balancep[x] - balancep[x - 1], 3)),
fontsize=12,
color=coloris)
plt.plot(
[datep[x], datep[x]],
[closep[x], closep[x] + 0.35 * sizemultiplier],
color='k',
linestyle='-',
linewidth=2)
x += 1
candlestick_ohlc(ax1,
ohlc,
width=0.02 * sizemultiplier,
colorup='#77d879',
colordown='#db3f3f')
plt.plot_date(datep, balancep, fmt='-')
for label in ax1.xaxis.get_ticklabels():
label.set_rotation(45)
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.grid(True)
plt.xlabel('Date')
plt.ylabel('Price')
plt.title('ETC/USD, 3h')
plt.legend()
plt.subplots_adjust(left=0.09,
bottom=0.20,
right=0.94,
top=0.90,
wspace=0.2,
hspace=0)
plt.show()
########################################################### PLOT NUMBER TWO - REVENUE
'''
#3 plot the SAR data
# Let's plot the SAR data image nicely...
from matplotlib import ticker
import cartopy.crs as ccrs
SAR_C = xr.open_rasterio('/content/sentinel 1_data.tif')
array_input = SAR_C
plt.figure(figsize=(9, 6))
ax1 = plt.axes(projection=ccrs.Robinson())
ax1.coastlines()
ax1.add_feature(cartopy.feature.OCEAN)
ax1.gridlines(draw_labels=True)
p1 = array_input.plot(ax=ax1,
vmin=-24,
vmax=10,
cmap='RdBu_r',
transform=ccrs.PlateCarree(),
add_colorbar=False)
plt.title('sar for 15/Mar/2017 (Spain)')
ax_cb = plt.axes([0.25, 0.05, 0.525, 0.02])
tick_locator = ticker.MaxNLocator(nbins=10)
cb = plt.colorbar(p1, cax=ax_cb, orientation='horizontal')
cb.locator = tick_locator
cb.update_ticks()
cb.ax.set_xlabel(r'$\overline{\gamma^o}$ [dB]')
colspan=4,
axisbg='#07000d')
candlestick(ax1,
newAr[-SP:],
width=.6,
colorup='#53c156',
colordown='#ff1717')
Label1 = str(MA1) + ' SMA'
Label2 = str(MA2) + ' SMA'
ax1.plot(date[-SP:], Av1[-SP:], '#e1edf9', label=Label1, linewidth=1.5)
ax1.plot(date[-SP:], Av2[-SP:], '#4ee6fd', label=Label2, linewidth=1.5)
ax1.grid(True, color='w')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', colors='w')
plt.ylabel('Stock price and Volume')
maLeg = plt.legend(loc=9,
ncol=2,
prop={'size': 7},
fancybox=True,
#plot results
if exudynTestGlobals.useGraphics:
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
symStr = ['r-', 'g-', 'b-', 'k-']
symStr2 = ['r--', 'g--', 'b--', 'k--']
for i in range(4):
s = str(i)
data = np.loadtxt('solution/rollingDiscTrail' + s + '.txt',
comments='#',
delimiter=',')
plt.plot(data[:, 1], data[:, 2], symStr[i],
label='trail wheel' + s) #x/y coordinates of trail
data = np.loadtxt('solution/rollingDiscForce' + s + '.txt',
comments='#',
delimiter=',')
#plt.plot(data[:,0], data[:,2], symStr[i],label='wheel force y'+s)
#data = np.loadtxt('solution/rollingDiscAngVelLocal'+s+'.txt', comments='#', delimiter=',')
#plt.plot(data[:,0], data[:,1], symStr2[i],label='wheel ang vel'+s)
ax = plt.gca() # get current axes
ax.grid(True, 'major', 'both')
ax.xaxis.set_major_locator(
ticker.MaxNLocator(10)) #use maximum of 8 ticks on y-axis
ax.yaxis.set_major_locator(
ticker.MaxNLocator(10)) #use maximum of 8 ticks on y-axis
plt.tight_layout()
plt.legend()
plt.show()
colspan=4,
facecolor='#07000d') # 第6行第1列起，占1行4列
ax1.plot(plot_mat.time[100:160].values,
plot_mat.macd[100:160].values,
color='#4ee6fd',
linewidth=2) # MACD线
ax1.plot(plot_mat.time[100:160].values,
plot_mat.dea9[100:160].values,
color='#e1edf9',
linewidth=1) # DEA线
ax1.fill_between(plot_mat.time[100:160].values,
plot_mat.macd[100:160].values - plot_mat.dea9[100:160].values,
0,
alpha=0.5,
facecolors='#00ffe8') # 填充差值
ax1.yaxis.set_major_locator(mticker.MaxNLocator()) # 设置纵坐标
ax1.spines['bottom'].set_color('#5998ff')
ax1.spines['top'].set_color('#5998ff')
ax1.spines['left'].set_color('#5998ff')
ax1.spines['right'].set_color('#5998ff')
ax1.tick_params(axis='y', colors='w')
ax1.tick_params(axis='x', colors='w')
plt.ylabel('MACD', color='w')
plt.plot()
plt.show()
#绘制一个汇总图
fig = plt.figure(facecolor='#07000d', figsize=(15, 10))
ax = plt.subplot2grid((6, 4), (1, 0),
rowspan=4,
def graph_data(self, istock, MA1, MA2):
"""
Use this to dynamically pull a stock.
"""
try:
print('Currently Pulling', stock)
urlToVisit = 'http://chartapi.finance.yahoo.com/instrument/1.0/'+stock+'/chartdata;type=quote;range=10y/csv'
stockFile =[]
try:
sourceCode = urllib.request.urlopen(urlToVisit).read().decode()
splitSource = sourceCode.split('\n')
for eachLine in splitSource:
splitLine = eachLine.split(',')
if len(splitLine)==6:
if 'values' not in eachLine:
stockFile.append(eachLine)
except Exception as e:
print(str(e), 'failed to organize pulled data.')
except Exception as e:
print(str(e), 'failed to pull pricing data')
try:
date, closep, highp, lowp, openp, volume = np.loadtxt(
stockFile,
delimiter=',',
unpack=True,
converters={ 0: self.bytes_per_date_to_num('%Y%m%d')}
)
x = 0
y = len(date)
newAr = []
while x < y:
appendLine = date[x],openp[x],highp[x],lowp[x],closep[x],volume[x]
newAr.append(appendLine)
x+=1
Av1 = self.moving_average(closep, MA1)
Av2 = self.moving_average(closep, MA2)
SP = len(date[MA2-1:])
fig = plt.figure(facecolor='#07000d')
ax1 = plt.subplot2grid((6,4), (1,0), rowspan=4, colspan=4, axisbg='#07000d')
candlestick_ohlc(ax1, newAr[-SP:], width=.6, colorup='#53c156', colordown='#ff1717')
Label1 = str(MA1)+' SMA'
Label2 = str(MA2)+' SMA'
ax1.plot(date[-SP:],Av1[-SP:],'#e1edf9',label=Label1, linewidth=1.5)
ax1.plot(date[-SP:],Av2[-SP:],'#4ee6fd',label=Label2, linewidth=1.5)
ax1.grid(True, color='w')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', colors='w')
plt.ylabel('Stock price and Volume')
maLeg = plt.legend(
loc=9,
ncol=2,
prop={'size':7},
fancybox=True,
borderaxespad=0.0
)
maLeg.get_frame().set_alpha(0.4)
textEd = pylab.gca().get_legend().get_texts()
pylab.setp(textEd[0:5], color = 'w')
volumeMin = 0
ax0 = plt.subplot2grid((6,4), (0,0), sharex=ax1, rowspan=1, colspan=4, axisbg='#07000d')
rsi = self.rsi(closep)
rsiCol = '#c1f9f7'
posCol = '#386d13'
negCol = '#8f2020'
ax0.plot(date[-SP:], rsi[-SP:], rsiCol, linewidth=1.5)
ax0.axhline(70, color=negCol)
ax0.axhline(30, color=posCol)
ax0.fill_between(date[-SP:], rsi[-SP:], 70, where=(rsi[-SP:]>=70), facecolor=negCol, edgecolor=negCol, alpha=0.5)
ax0.fill_between(date[-SP:], rsi[-SP:], 30, where=(rsi[-SP:]<=30), facecolor=posCol, edgecolor=posCol, alpha=0.5)
ax0.set_yticks([30,70])
ax0.yaxis.label.set_color("w")
ax0.spines['bottom'].set_color("#5998ff")
ax0.spines['top'].set_color("#5998ff")
ax0.spines['left'].set_color("#5998ff")
ax0.spines['right'].set_color("#5998ff")
ax0.tick_params(axis='y', colors='w')
ax0.tick_params(axis='x', colors='w')
plt.ylabel('RSI')
ax1v = ax1.twinx()
ax1v.fill_between(date[-SP:],volumeMin, volume[-SP:], facecolor='#00ffe8', alpha=.4)
ax1v.axes.yaxis.set_ticklabels([])
ax1v.grid(False)
ax1v.set_ylim(0, 3*volume.max())
ax1v.spines['bottom'].set_color("#5998ff")
ax1v.spines['top'].set_color("#5998ff")
ax1v.spines['left'].set_color("#5998ff")
ax1v.spines['right'].set_color("#5998ff")
ax1v.tick_params(axis='x', colors='w')
ax1v.tick_params(axis='y', colors='w')
ax2 = plt.subplot2grid(
(6, 4),
(5, 0),
sharex=ax1,
rowspan=1,
colspan=4,
axisbg='#07000d'
)
fillcolor = '#00ffe8'
# nslow = 26
# nfast = 12
nema = 9
_, _, macd = self.compute_macd(closep)
ema9 = self.exp_moving_average(macd, nema)
ax2.plot(date[-SP:], macd[-SP:], color='#4ee6fd', lw=2)
ax2.plot(date[-SP:], ema9[-SP:], color='#e1edf9', lw=1)
ax2.fill_between(
date[-SP:],
macd[-SP:]-ema9[-SP:],
0,
alpha=0.5,
facecolor=fillcolor,
edgecolor=fillcolor
)
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax2.spines['bottom'].set_color("#5998ff")
ax2.spines['top'].set_color("#5998ff")
ax2.spines['left'].set_color("#5998ff")
ax2.spines['right'].set_color("#5998ff")
ax2.tick_params(axis='x', colors='w')
ax2.tick_params(axis='y', colors='w')
plt.ylabel('MACD', color='w')
ax2.yaxis.set_major_locator(mticker.MaxNLocator(nbins=5, prune='upper'))
for label in ax2.xaxis.get_ticklabels():
label.set_rotation(45)
plt.suptitle(stock.upper(),color='w')
plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), visible=False)
ax1.annotate(
'Big news!',
(date[510], Av1[510]),
xytext=(0.8, 0.9),
textcoords='axes fraction',
arrowprops=dict(facecolor='white', shrink=0.05),
fontsize=14,
color = 'w',
horizontalalignment='right',
verticalalignment='bottom'
)
plt.subplots_adjust(
left=.09,
bottom=.14,
right=.94,
top=.95,
wspace=.20,
hspace=0
)
plt.show()
fig.savefig('example.png', facecolor=fig.get_facecolor())
except Exception as e:
print('main loop', str(e))
def createPage(self):
ds=g.sday
de=g.eday
stockn=g.stock
matplotlib.use('TkAgg')
# df1 = jq.get_price(stockn,start_date=ds,end_date=de, frequency='daily') # 聚宽获取股票数据
# df1 = ts.get_hist_data(stockn,start=ds,end=de) # tushare获取股票 数据
# df1 = Getdailyfromtscode(stockn,ds,de)
df1 = get_data(stockn, ds, de)
df1['trade_date'] = mdates.date2num(df1['trade_date'])
df2=df1.copy()
# df2.dropna(inplace=True)
# df2.insert(0,'date',df2.index)
df2 = df2.reset_index()
# df2 = data_clean(df2)
print(df1)
print(df2)
days=df2
g.df=df2
MA1 = g.MA1
MA2 = g.MA2
Av1=mylib.G_MA(days['close'],MA1)
Av2=mylib.G_MA(days['close'],MA2)
SP = len(days.trade_date.values[MA2-1:])
SP1 = len(days.trade_date.values[MA1-1:])
fig = plt.figure(facecolor='#07000d',figsize=(7,4))
ax1 = plt.subplot2grid((7,4), (0,0), rowspan=4, colspan=4, facecolor='#07000d')
daysreshape = days.reset_index()
print(daysreshape)
# daysreshape['trade_date']=mdates.date2num(daysreshape['trade_date'].astype(dt.date))
daysreshape = daysreshape.reindex(columns=['date','open','high','low','close'])
candlestick_ohlc(ax1, daysreshape.values, width=.6, colorup='#ff1717', colordown='#53c156')
# candlestick_ohlc(ax1, daysreshape.values, width=.6, colorup='#ff1717', colordown='#53c156')
Label1 = str(MA1)+' MA'
Label2 = str(MA2)+' MA'
ax1.plot(days.trade_date.values,Av1,'#e1edf9',label=Label1, linewidth=1.5)
ax1.plot(days.trade_date.values[-SP:],Av2[-SP:],'#4ee6fd',label=Label2, linewidth=1.5)
ax1.grid(True, color='r')
ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
ax1.xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m-%d'))
ax1.yaxis.label.set_color("w")
ax1.spines['bottom'].set_color("#5998ff")
ax1.spines['top'].set_color("#5998ff")
ax1.spines['left'].set_color("#5998ff")
ax1.spines['right'].set_color("#5998ff")
ax1.tick_params(axis='y', colors='w')
plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(prune='upper'))
ax1.tick_params(axis='x', colors='w')
plt.ylabel('Stock price')
ax1v = ax1.twinx()
ax1v.spines['bottom'].set_color("#5998ff")
ax1v.spines['top'].set_color("#5998ff")
ax1v.spines['left'].set_color("#5998ff")
ax1v.spines['right'].set_color("#5998ff")
ax1v.tick_params(axis='x', colors='w')
ax1v.tick_params(axis='y', colors='w')
ax0 = plt.subplot2grid((7,4), (4,0),sharex=ax1,rowspan=1, colspan=4, facecolor='#07000d')
v1=mylib.G_MA(days['vol'],g.MA1)
v2=mylib.G_MA(days['vol'],g.MA2)
v3=mylib.G_MA(days['vol'],g.MA3)
rsiCol = '#c1f9f7'
posCol = '#386d13'
negCol = '#8f2020'
ax0.plot(days.trade_date.values, v1, rsiCol, linewidth=1)
ax0.plot(days.trade_date.values, v2, posCol, linewidth=1)
ax0.bar(days.trade_date.values,days.vol.values, facecolor='yellow', alpha=.4)
ax0.yaxis.label.set_color("w")
ax0.spines['bottom'].set_color("#5998ff")
ax0.spines['top'].set_color("#5998ff")
ax0.spines['left'].set_color("#5998ff")
ax0.spines['right'].set_color("#5998ff")
ax0.tick_params(axis='y', colors='w')
ax0.tick_params(axis='x', colors='w')
ax0.yaxis.set_major_locator(mticker.MaxNLocator(nbins=4, prune='upper'))#plt.gca().yaxis.set_major_locator(mticker.MaxNLocator(nbins=4,prune='upper'))
ax0.tick_params(axis='x', colors='w')
plt.ylabel('volume')
if g.index=='KDJ' :
mydraw.draw_KDJ(ax1,days,9,3,3)
if g.index=='MACD' :
mydraw.draw_MACD(ax1,days,12,26,9)
if g.index=='RSI' :
mydraw.draw_RSI(ax1,days,6,12,24)
if g.index=='OBV' :
mydraw.draw_OBV(ax1,days,6,12)
plt.suptitle(stockn,color='w')
plt.setp(ax0.get_xticklabels(), visible=False)
plt.setp(ax1.get_xticklabels(), visible=False)
plt.subplots_adjust(left=.04, bottom=.04, right=.96, top=.96, wspace=.15, hspace=0)
self.canvas =FigureCanvasTkAgg(fig, master=self.root)
toolbar =NavigationToolbar2Tk(self.canvas, self.root)
toolbar.update()
self.canvas.get_tk_widget().pack(side=tk.TOP, fill=tk.BOTH, expand=1)
def plot_error_map(backend, figsize=(12, 9), show_title=True):
"""Plots the error map of a given backend.
Args:
backend (IBMQBackend): Given backend.
figsize (tuple): Figure size in inches.
show_title (bool): Show the title or not.
Returns:
Figure: A matplotlib figure showing error map.
Raises:
VisualizationError: Input is not IBMQ backend.
Example:
.. jupyter-execute::
:hide-code:
:hide-output:
from qiskit.test.ibmq_mock import mock_get_backend
mock_get_backend('FakeVigo')
.. jupyter-execute::
from qiskit import QuantumCircuit, execute, IBMQ
from qiskit.visualization import plot_error_map
%matplotlib inline
IBMQ.load_account()
provider = IBMQ.get_provider(hub='ibm-q')
backend = provider.get_backend('ibmq_vigo')
plot_error_map(backend)
"""
color_map = cm.viridis
props = backend.properties().to_dict()
config = backend.configuration().to_dict()
n_qubits = config['n_qubits']
# U2 error rates
single_gate_errors = [0] * n_qubits
for gate in props['gates']:
if gate['gate'] == 'u2':
_qubit = gate['qubits'][0]
single_gate_errors[_qubit] = gate['parameters'][0]['value']
# Convert to percent
single_gate_errors = 100 * np.asarray(single_gate_errors)
avg_1q_err = np.mean(single_gate_errors)
single_norm = matplotlib.colors.Normalize(vmin=min(single_gate_errors),
vmax=max(single_gate_errors))
q_colors = [color_map(single_norm(err)) for err in single_gate_errors]
cmap = config['coupling_map']
directed = False
if n_qubits < 20:
for edge in cmap:
if not [edge[1], edge[0]] in cmap:
directed = True
break
cx_errors = []
for line in cmap:
for item in props['gates']:
if item['qubits'] == line:
cx_errors.append(item['parameters'][0]['value'])
break
else:
continue
# Convert to percent
cx_errors = 100 * np.asarray(cx_errors)
avg_cx_err = np.mean(cx_errors)
cx_norm = matplotlib.colors.Normalize(vmin=min(cx_errors),
vmax=max(cx_errors))
line_colors = [color_map(cx_norm(err)) for err in cx_errors]
# Measurement errors
read_err = []
for qubit in range(n_qubits):
for item in props['qubits'][qubit]:
if item['name'] == 'readout_error':
read_err.append(item['value'])
read_err = 100 * np.asarray(read_err)
avg_read_err = np.mean(read_err)
max_read_err = np.max(read_err)
fig = plt.figure(figsize=figsize)
gridspec.GridSpec(nrows=2, ncols=3)
grid_spec = gridspec.GridSpec(
12,
12,
height_ratios=[1] * 11 + [0.5],
width_ratios=[2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2])
left_ax = plt.subplot(grid_spec[2:10, :1])
main_ax = plt.subplot(grid_spec[:11, 1:11])
right_ax = plt.subplot(grid_spec[2:10, 11:])
bleft_ax = plt.subplot(grid_spec[-1, :5])
bright_ax = plt.subplot(grid_spec[-1, 7:])
plot_gate_map(backend,
qubit_color=q_colors,
line_color=line_colors,
qubit_size=28,
line_width=5,
plot_directed=directed,
ax=main_ax)
main_ax.axis('off')
main_ax.set_aspect(1)
single_cb = matplotlib.colorbar.ColorbarBase(bleft_ax,
cmap=color_map,
norm=single_norm,
orientation='horizontal')
tick_locator = ticker.MaxNLocator(nbins=5)
single_cb.locator = tick_locator
single_cb.update_ticks()
single_cb.update_ticks()
bleft_ax.set_title('H error rate (%) [Avg. = {}]'.format(
round(avg_1q_err, 3)))
cx_cb = matplotlib.colorbar.ColorbarBase(bright_ax,
cmap=color_map,
norm=cx_norm,
orientation='horizontal')
tick_locator = ticker.MaxNLocator(nbins=5)
cx_cb.locator = tick_locator
cx_cb.update_ticks()
bright_ax.set_title('CNOT error rate (%) [Avg. = {}]'.format(
round(avg_cx_err, 3)))
if n_qubits < 10:
num_left = n_qubits
num_right = 0
else:
num_left = math.ceil(n_qubits / 2)
num_right = n_qubits - num_left
left_ax.barh(range(num_left),
read_err[:num_left],
align='center',
color='#007d79')
left_ax.axvline(avg_read_err, linestyle='--', color='#212121')
left_ax.set_yticks(range(num_left))
left_ax.set_xticks([0, round(avg_read_err, 2), round(max_read_err, 2)])
left_ax.set_yticklabels([str(kk) for kk in range(num_left)], fontsize=12)
left_ax.invert_yaxis()
left_ax.set_title('Readout Error (%)', fontsize=12)
for spine in left_ax.spines.values():
spine.set_visible(False)
if num_right:
right_ax.barh(range(num_left, n_qubits),
read_err[num_left:],
align='center',
color='#007d79')
right_ax.axvline(avg_read_err, linestyle='--', color='#212121')
right_ax.set_yticks(range(num_left, n_qubits))
right_ax.set_xticks(
[0, round(avg_read_err, 2),
round(max_read_err, 2)])
right_ax.set_yticklabels([str(kk) for kk in range(num_left, n_qubits)],
fontsize=12)
right_ax.invert_yaxis()
right_ax.invert_xaxis()
right_ax.yaxis.set_label_position("right")
right_ax.yaxis.tick_right()
right_ax.set_title('Readout Error (%)', fontsize=12)
else:
right_ax.axis('off')
for spine in right_ax.spines.values():
spine.set_visible(False)
if show_title:
fig.suptitle('{name} Error Map'.format(name=backend.name()),
fontsize=24,
y=0.9)
if get_backend() in ['module://ipykernel.pylab.backend_inline', 'nbAgg']:
plt.close(fig)
return fig
def template_ratemap(module, cmap='YlGnBu_r', length_unit='cm', box=True,
window_type=None, palette=None):
"""
Convenience function to plot nice template cell firing rate maps
Parameters
----------
module : Module
Module for which to plot the template cell firing rate.
cmap : Colormap or registered colormap name, optional
Colormap to use for the plot.
length_unit : string, optional
The length unit to add to the axis labels. If None, no length unit is
printed.
box : bool, optional
If True, a box representning the environment where the true recordings
were done is added to the plot.
window_type : None or string, optional
If not None, a polygon representning the window of possible phases is
added to the plot. The value of this variable is passed to
`Module.window` as the keyword `window_type`. See this method for
possible values.
palette : sequence, optional
Color palette to use for the box and window: the first color is used
for the box, the second for the window.
Returns
-------
See `TemplateGridCell.plot_ratemap`
"""
cell = module.template()
axes, cbar = cell.plot_ratemap(cmap=cmap,
#edgecolor='face'
)
#cbar.solids.set(edgecolor='face')
cbar.set_label(r"$f \ / \ \bar{f}$")
cbar.locator = ticker.MaxNLocator(nbins=2)
cbar.update_ticks()
range_ = next(iter(module)).params['range_']
axes.set(xticks=range_[0], yticks=range_[1])
if length_unit is None:
axes.set(xlabel=r"$x$",
ylabel=r"$y$")
else:
axes.set(xlabel=r"$x \ / \ \mathrm{{{}}}$".format(length_unit),
ylabel=r"$y \ / \ \mathrm{{{}}}$".format(length_unit))
if palette is None:
palette = COLOR_CYCLE
if box:
xy = (range_[0][0], range_[1][0])
width = range_[0][1] - range_[0][0]
height = range_[1][1] - range_[1][0]
rect = patches.Rectangle(xy=xy, width=width, height=height,
fill=False, color=palette[0],
linewidth=4.0)
axes.add_patch(rect)
if window_type is not None:
try:
windowpatch = module.window(window_type=window_type).patch(
fill=False, color=palette[1], linewidth=4.0)
axes.add_patch(windowpatch)
except ValueError:
for c, wt in zip(palette[1:], window_type):
windowpatch = module.window(window_type=wt).patch(
fill=False, color=c, linewidth=4.0)
axes.add_patch(windowpatch)
return axes, cbar
import numpy as np
import datetime as dt
import os
from matplotlib import pylab as plt
import matplotlib.ticker as ticker
os.chdir(
"c:\\Users\\nishitsuji\\Documents\\myfile\\python_tensorflow\\9_12_DGIM_validation\\"
)
folder_name = "9_12_data_save"
if folder_name not in os.listdir():
os.chdir("./" + folder_name)
os.chdir("./" + folder_name)
accuracy_load = np.load(file="./" + "2019_09_14accuracy.npy")
# accuracy_load = np.load(file="./"+"2019_09_14accuracy.npy")
print("accuracy_load:", accuracy_load)
x = np.linspace(0, 20, 20)
y = accuracy_load
fig = plt.figure(figsize=(12, 8))
ax = fig.add_subplot(111)
# ax = fig.add_axes([0,0,1,1])
ax.plot(x, y * 100, label="Accuracy", color="red")
# plt.legend()
# plt.title("Accuracy")
plt.xlabel("Epoch")
plt.gca().get_xaxis().set_major_locator(ticker.MaxNLocator(integer=True))
plt.ylabel("accuracy")
plt.show()
def plot_impact_summary(df, fname):
fig, axes = plt.subplots(figsize=(7, 4),
nrows=2,
ncols=2,
sharex=True,
sharey='row')
plt.subplots_adjust(hspace=0.05, wspace=0.065)
axes = axes.flat
mods = [
'tuz', 'sox1', 'wue', 'cmax', 'pmax', 'cgn', 'sox2', 'pmax2', 'lcst',
'cap', 'mes'
]
# exclude unnecessary data
df = df[df['Unnamed: 0'] == 'actual']
df.drop(df.filter(like='WUE(').columns.to_list(), axis=1, inplace=True)
GPP = df.filter(like='A(').columns.to_list()
E = df.filter(like='E(').columns.to_list()
twet = [e for e in df.index.to_list() if e.split('_')[-1] == 'wet']
tinter = [e for e in df.index.to_list() if e.split('_')[-1] == 'inter']
for i, ax in enumerate(axes):
if i < 2:
sub = df[GPP]
else:
sub = df[E]
if i % 2 == 0:
sub = sub.loc[twet]
ax.axvspan(-0.1,
0.9,
hatch='.' * 6,
facecolor='none',
edgecolor='#2e7d9b',
alpha=0.1) # color calib.
else:
sub = sub.loc[tinter]
ax.axvspan(0.9,
1.9,
hatch='.' * 6,
facecolor='none',
edgecolor='#fc8635',
alpha=0.1) # color calib.
# we're plotting in reverse order
sub = sub.iloc[::-1]
sub.reset_index(inplace=True)
pos = np.arange(float(len(sub)) + 1)
# ref model
ax.hlines(sub[sub.filter(like='std').columns],
pos[:-1] - 0.1,
pos[1:] - 0.1,
linewidth=0.75,
alpha=0.75,
zorder=20,
label=which_model('std'))
pos += 0.0275 # necessary alignment when plotting
for j, p in enumerate(pos[:-1]):
next(ax._get_lines.prop_cycler) # skip black used for Medlyn
for mod in mods:
ax.scatter(p,
sub[sub.filter(like=mod).columns].values[j][0],
marker=r'$\diamondsuit$',
s=45.,
c=next(ax._get_lines.prop_cycler)['color'],
label=which_model(mod))
p += 0.075
ax.set_xlim(-0.1, 4.9)
if i > 1:
ax.set_xticks(np.arange(0.4, 4.5))
ax.set_xticklabels(
['Wet', 'Inter.', 'Dry', '2 x $D_a$', '2 x $C_a$'])
if i < 3:
render_ylabels(ax, r'$E$', r'mm w$^{-1}$')
elif i < 1:
render_ylabels(ax, 'GPP', r'gC m$^{-2}$ w$^{-1}$')
ax.set_title('Wet calibration')
else:
ax.set_title('Intermediate calibration')
ax.set_xticks(np.arange(0.9, 5.), minor=True)
ax.yaxis.set_major_locator(ticker.MaxNLocator(3))
ax.set_yticklabels(ax.get_yticks()) # force LaTex
ax.yaxis.set_major_formatter(ticker.FormatStrFormatter('%.d'))
# subplot labelling
ax.text(0.025,
0.95,
r'\textbf{(%s)}' % (string.ascii_lowercase[i]),
transform=ax.transAxes,
weight='bold')
# remove spines and add grid
ax.spines['right'].set_visible(False)
ax.spines['top'].set_visible(False)
ax.tick_params(which='major', length=0)
ax.tick_params(which='minor', length=0)
#ax.grid(which='major', axis='y')
ax.xaxis.grid(which='minor')
ax.yaxis.grid(which='major')
__, top = ax.get_ylim()
ax.set_ylim(0., top)
# legend
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles[:len(mods) + 1],
labels[:len(mods) + 1],
bbox_to_anchor=(1.025, 1. / 3.),
loc=3)
fig.savefig(fname)
plt.close()
