import matplotlib.pyplot as plt
from matplotlib.finance import quotes_historical_yahoo_ochl
from matplotlib.dates import YearLocator, MonthLocator, DateFormatter
import datetime
date1 = datetime.date(1995, 1, 1)
date2 = datetime.date(2004, 4, 12)
years = YearLocator() # every year
months = MonthLocator() # every month
yearsFmt = DateFormatter('%Y')
quotes = quotes_historical_yahoo_ochl('INTC', date1, date2)
if len(quotes) == 0:
raise SystemExit
dates = [q[0] for q in quotes]
opens = [q[1] for q in quotes]
fig, ax = plt.subplots()
ax.plot_date(dates, opens, '-')
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(yearsFmt)
ax.xaxis.set_minor_locator(months)
ax.autoscale_view()
# format the coords message box
def price(x):
return '$%1.2f' % x
#All devices
# ind = inds[2]
# folder = '/Users/admin/Documents/powernet/powernet_markets_mysql/'+run + '_' + ind
# directory = run + '_' + ind + '_vis'
# df_system = pd.read_csv(directory+'/df_system.csv',index_col=[0], parse_dates=True)
# lns = lns + ax.plot(df_system.index,df_system['measured_real_power'],label='25% PV/Batt/EVs')
ax.plot(df_system_fast.index, [0.0] * len(df_system_fast.index),
'k',
linewidth=1.0)
ax.set_ylabel('Measured system load [MW]')
start = pd.Timestamp(2015, 7, 15) # df_system_fast.index[0]
end = pd.Timestamp(2015, 7, 17) # df_system_fast.index[-1]
ax.set_xlim(xmin=start, xmax=end)
ax.set_ylim(ymin=0.0)
ax.xaxis.set_major_locator(HourLocator(arange(0, 25, 6)))
ax.xaxis.set_minor_locator(HourLocator(arange(0, 25, 3)))
ax.xaxis.set_major_formatter(DateFormatter('%H:%M'))
#Legend
labs = [l.get_label() for l in lns]
L = ax.legend(lns,
labs,
bbox_to_anchor=(0.5, -0.3),
loc='lower center',
ncol=len(labs))
ppt.savefig(run + '/11_measuredload_EV_fastslow.png', bbox_inches='tight')
# Pad the space on the top to allow for an annotation below suptitle
fig.suptitle('Daily Reported Cases and Deaths in the US*', fontsize=22)
fig.subplots_adjust(top=0.9)
plt.annotate(f"*Percentage approximate. Updated on {date_str}", (175, 400),
fontsize=12,
xycoords='axes pixels')
# Annotate with the total number of cases and deaths.
ax.annotate(
'Percent Infected: {:.2f}%\nTotal Cases: {:,}\nTotal Deaths: {:,}'.
format(percent_infected, usa_cases, usa_deaths), (10, 260),
fontsize=14,
xycoords='axes pixels')
# Formate the x-axis dates
date_form = DateFormatter("%b")
ax.xaxis.set_major_formatter(date_form)
# Make axes tick labels the color of the graph
highest_cases, highest_deaths = max(usa_daily.new_cases), max(
usa_daily.new_deaths)
ax2.tick_params(axis='y', labelcolor='purple')
ax.set_ylim([0, highest_cases + highest_cases // 10])
ax2.set_ylim([0, highest_deaths + highest_deaths // 10])
# Format the legend and grid
ax.legend(loc='upper left')
ax2.legend(loc='upper left', bbox_to_anchor=(0, 0.92))
ax2.grid(False)
if __name__ == '__main__':
def view(self, eFreqI: int, **kwargs) -> plt.figure:
"""Plot statistics for evaluation frequency index
Plots a simple scatter of each statistic with datetime on the xaxis (datetime of the window start dates). Number of subplots is equal to numStaStatPerWindow.
Parameters
----------
eFreqI : int
Evaluation frequency index
maskwindows : List, np.ndarray
Global windows to exclude
fig : matplotlib.pyplot.figure, optional
A figure object
plotfonts : Dict, optional
A dictionary of plot fonts
label : str, optional
Label for the plots
clim : List, optional
Limits for colourbar axis
xlim : List, optional
Limits for the x axis
ylim : List, optional
Limits for the y axis
colortitle : str, optional
Title for the colourbar
legened : bool
Boolean flag for adding a legend
Returns
-------
plt.figure
Matplotlib figure object
"""
# get windows to plot and global dates
maskWindows = kwargs["maskwindows"] if "maskwindows" in kwargs else []
plotIndices = self.getMaskedIndices(maskWindows)
globalDates = self.getGlobalDates()
eFreq = self.evalFreq[eFreqI]
# plot params
nrows, ncols = self.getRowsCols(self.maxcols)
plotfonts = kwargs[
"plotfonts"] if "plotfonts" in kwargs else getViewFonts()
fig: plt.figure = (plt.figure(kwargs["fig"].number) if "fig" in kwargs
else plt.figure(figsize=(4 * ncols, 5 * nrows)))
st = fig.suptitle(
"{} data for evaluation frequency: {}".format(
self.statName, eFreq),
fontsize=plotfonts["suptitle"],
)
st.set_y(0.98)
# plot the data
for idx, val in enumerate(self.winStats):
ax = plt.subplot(nrows, ncols, idx + 1)
plt.title("Value {}".format(val), fontsize=plotfonts["title"])
label = kwargs["label"] if "label" in kwargs else eFreq
# limit the data by plotIndices if not False
plotData = np.squeeze(self.stats[:, eFreqI, idx])
plotDates = globalDates
if plotIndices:
plotData = plotData[plotIndices]
plotDates = plotDates[plotIndices]
# the colourdata
colourbool, colourdata, cmap = self.calcColourData(
plotData, val, eFreqI, kwargs)
# scatter plot
if not colourbool:
scat = plt.scatter(
plotDates,
plotData,
edgecolors="none",
marker="o",
s=12,
label=label,
)
else:
scat = plt.scatter(
plotDates,
plotData,
c=colourdata,
edgecolors="none",
marker="o",
s=12,
cmap=cmap,
label=label,
)
clim = (kwargs["clim"] if
("clim" in kwargs and len(kwargs["clim"]) > 0) else
[colourdata.min(), colourdata.max()])
scat.set_clim(clim)
# x axis options
plt.xlabel("Time", fontsize=plotfonts["axisLabel"])
xlim = (kwargs["xlim"] if
("xlim" in kwargs and len(kwargs["xlim"]) > 0) else
[globalDates[0], globalDates[-1]])
plt.xlim(xlim)
ax.format_xdata = DateFormatter("%H-%M-%S")
fig.autofmt_xdate()
# y axis options
if "ylim" in kwargs and len(kwargs["ylim"]) > 0:
plt.ylim(kwargs["ylim"])
plt.ylabel("Value {}".format(val), fontsize=plotfonts["axisLabel"])
# set tick sizes
for label in ax.get_xticklabels() + ax.get_yticklabels():
label.set_fontsize(plotfonts["axisTicks"])
plt.grid(True, ls="--")
# legend
if "legend" in kwargs and kwargs["legend"]:
plt.legend(loc=4)
# show if the figure is not in keywords
if "fig" not in kwargs:
if colourbool:
fig.tight_layout(rect=[0.02, 0.02, 0.85, 0.92])
cax = fig.add_axes([0.88, 0.10, 0.03, 0.80])
colourtitle = (kwargs["colortitle"]
if "colortitle" in kwargs else "Value")
self.addColourbar(scat, cax, colourtitle, plotfonts)
else:
fig.tight_layout(rect=[0.02, 0.02, 0.98, 0.92])
plt.show()
return fig
def TimeBurnDownChart(dots, project):
"""
制作工时“燃尽”图
:param dots: [['日期'，Y-值],...]
:param project: 项目代号
:return: 图文件存放路径
"""
global __test
"""作图"""
rcParams.update({
'font.family': 'sans-serif',
'font.sans-serif': [u'SimHei'],
'axes.unicode_minus': False,
'font.size': 6,
})
autodates = AutoDateLocator()
yearsFmt = DateFormatter('%Y-%m-%d')
fig = figure(figsize=[10, 6], dpi=120)
ax = fig.add_subplot(111)
fig.autofmt_xdate() # 设置x轴时间外观
ax.xaxis.set_major_locator(autodates) # 设置时间间隔
ax.xaxis.set_major_formatter(yearsFmt) # 设置时间显示格式
"""设定显示的时间段"""
_end_date = datetime.date.today() + datetime.timedelta(days=10)
ax.set_xticks(
pd.date_range(start='2017-12-10', end='%s' % _end_date, freq='3D'))
# ax.set_xlim("2017-12-10", "%s" % _end_date)
# ax.set_ylim(0, total+1)
_leg = [None, None, None]
for __dot in dots:
_spent_lines_dots = {'date': [], 'dot': []}
_org_lines_dots = {'date': [], 'dot': []}
for _dot in __dot['dots']:
if _dot[2] == 'spent':
_spent_lines_dots['date'].append(_dot[0])
_spent_lines_dots['dot'].append(_dot[1])
else:
_org_lines_dots['date'].append(_dot[0])
_org_lines_dots['dot'].append(_dot[1])
_leg[0] = ax.fill_between(_spent_lines_dots['date'],
__dot['count'],
_spent_lines_dots['dot'],
facecolor='lightcyan',
alpha=0.6)
_leg[1] = ax.fill_between(_spent_lines_dots['date'],
_spent_lines_dots['dot'],
0,
facecolor='lightpink',
alpha=0.7)
_leg[2] = ax.fill_between(
_spent_lines_dots['date'],
_org_lines_dots['dot'],
0, # _spent_lines_dots['dot'],
facecolor='lightyellow',
alpha=0.3)
# plt.setp(_lines, color='r')
ax.set_xlabel(u'日期', fontsize=11)
ax.set_ylabel(u'工时', fontsize=11)
ax.grid(True)
ax.legend(_leg, [u"规划", u"执行", u"估计"], loc=1, fontsize=12)
"""图示各sprint的区域"""
trans = transforms.blended_transform_factory(ax.transData, ax.transAxes)
for _p in dots:
if _p['sprint'][-1] != 'Active':
_c = 'lightblue'
else:
_c = 'lightgreen'
ax.fill_between(_p['sprint'][:-1],
0,
1,
transform=trans,
alpha=0.1,
color=_c)
ax.plot(_p['sprint'][:-1], [0, _p['count']],
color='r',
linewidth=1,
alpha=0.3)
plt.title(u'工时燃尽图', fontsize=12)
plt.subplots_adjust(left=0.08, right=0.98, bottom=0.06, top=0.96)
_fn = 'D:\\GitHub\\flasky\\app\\static\\images\\%s-time-issue-burndown.png' % project
if not __test:
plt.savefig(_fn, dpi=120)
else:
plt.show()
return _fn
def chartSetup(self, refresh_interval=100):
plt.style.use('dark_background')
fig = plt.figure(num=f"Current Viewer {version}", figsize=(10, 6))
self.ax = plt.axes()
ax = self.ax
ax.set_title(f"Streaming: {connected_device}", color="white")
fig.text(0.2,
0.88,
f"CurrentViewer {version}",
color="yellow",
verticalalignment='bottom',
horizontalalignment='center',
fontsize=9,
alpha=0.7)
fig.text(0.89,
0.0,
f"github.com/MGX3D/CurrentViewer",
color="white",
verticalalignment='bottom',
horizontalalignment='center',
fontsize=9,
alpha=0.5)
ax.set_ylabel("Current draw (Amps)")
ax.set_yscale("log", nonpositive='clip')
ax.set_ylim(1e-10, 1e1)
plt.yticks([
1.0e-9, 1.0e-8, 1.0e-7, 1.0e-6, 1.0e-5, 1.0e-4, 1.0e-3, 1.0e-2,
1.0e-1, 1.0
], [
'1nA', '10nA', '100nA', '1\u00B5A', '10\u00B5A', '100\u00B5A',
'1mA', '10mA', '100mA', '1A'
],
rotation=0)
ax.grid(axis="y", which="both", color="yellow", alpha=.3, linewidth=.5)
ax.set_xlabel("Time")
plt.xticks(rotation=20)
ax.set_xlim(datetime.now(), datetime.now() + timedelta(seconds=10))
ax.grid(axis="x", color="green", alpha=.4, linewidth=2, linestyle=":")
#ax.xaxis.set_major_locator(SecondLocator())
ax.xaxis.set_major_formatter(DateFormatter('%H:%M:%S'))
def on_xlims_change(event_ax):
logging.debug("Interactive zoom: {} .. {}".format(
num2date(event_ax.get_xlim()[0]),
num2date(event_ax.get_xlim()[1])))
chart_len = num2date(event_ax.get_xlim()[1]) - num2date(
event_ax.get_xlim()[0])
if chart_len.total_seconds() < 5:
self.ax.xaxis.set_major_formatter(DateFormatter('%H:%M:%S.%f'))
else:
self.ax.xaxis.set_major_formatter(DateFormatter('%H:%M:%S'))
self.ax.xaxis.set_minor_formatter(DateFormatter('%H:%M:%S.%f'))
ax.callbacks.connect('xlim_changed', on_xlims_change)
lines = ax.plot([], [], label="Current")[0]
lastText = ax.text(0.50, 0.95, '', transform=ax.transAxes)
statusText = ax.text(0.50, 0.50, '', transform=ax.transAxes)
self.anim = animation.FuncAnimation(fig,
self.getSerialData,
fargs=(lines, plt.legend(),
lastText),
interval=refresh_interval)
plt.legend(loc="upper right", framealpha=0.5)
apause = plt.axes([0.91, 0.15, 0.08, 0.07])
self.bpause = Button(apause,
label='Pause',
color='0.2',
hovercolor='0.1')
self.bpause.on_clicked(self.pauseRefresh)
self.bpause.label.set_color('yellow')
aanimation = plt.axes([0.91, 0.25, 0.08, 0.07])
self.bsave = Button(aanimation, 'GIF', color='0.2', hovercolor='0.1')
self.bsave.on_clicked(self.saveAnimation)
self.bsave.label.set_color('yellow')
crs = mplcursors.cursor(ax, hover=True)
@crs.connect("add")
def _(sel):
sel.annotation.arrow_patch.set(arrowstyle="simple",
fc="yellow",
alpha=.4)
sel.annotation.set_text(self.textAmp(sel.target[1]))
self.framerate = 1000 / refresh_interval
plt.gcf().autofmt_xdate()
plt.show()
def plot_iono(df):
"""
Parameters
----------
df : TYPE
DESCRIPTION.
Returns
-------
None.
"""
fig = plt.figure(figsize=(8, 4))
fig.suptitle("iono", color="tab:gray")
ax = fig.add_subplot(211)
ax.plot(df.Na, "-o", color="tab:red", ms=10)
# lims = ax.get_xlim()
# ax.hlines(135, *lims, colors='r', alpha=0.5, linestyles='dashed')
# ax.hlines(145, *lims, colors='r', alpha=0.5, linestyles='dashed')
usual = [136, 142]
spread = set(usual) | set(ax.get_ylim())
ax.set_ylim(min(spread), max(spread))
ax.set_ylabel("$Na^+$", color="tab:red")
ax.spines["left"].set_color("tab:red")
ax.tick_params(axis="y", colors="tab:red")
for spine in ["top", "right"]:
ax.spines[spine].set_visible(False)
axT = ax.twinx()
axT.plot(df.Cl, "-o", color="tab:blue", ms=10)
axT.set_ylabel("$Cl^-$", color="tab:blue")
usual = [98, 104]
spread = set(usual) | set(axT.get_ylim())
axT.set_ylim(min(spread), max(spread))
# lims = axT.get_xlim()
# axT.hlines(110, *lims, colors='b', alpha=0.5, linestyles='dashed')
# axT.hlines(95, *lims, colors='b', alpha=0.5, linestyles='dashed')
axT.spines["right"].set_color("tab:blue")
axT.tick_params(axis="y", colors="tab:blue")
for spine in ["top", "left"]:
axT.spines[spine].set_visible(False)
ax2 = fig.add_subplot(212)
ax2.plot(df.K, "-o", color="tab:purple", ms=10)
ax2.set_ylabel("$K^+$", color="tab:purple")
usual = [2.2, 4]
spread = set(usual) | set(ax2.get_ylim())
ax2.set_ylim(min(spread), max(spread))
ax2.spines["left"].set_color("tab:purple")
ax2.tick_params(axis="y", colors="tab:purple")
for spine in ["top", "right"]:
ax2.spines[spine].set_visible(False)
ax2T = ax2.twinx()
ax2T.plot(df.ph, "-o", color="tab:gray", ms=10)
ax2T.set_ylabel("pH", color="tab:gray")
usual = [7.34, 7.48]
spread = set(usual) | set(ax2T.get_ylim())
ax2.set_ylim(min(spread), max(spread))
ax2T.spines["right"].set_color("tab:gray")
ax2T.tick_params(axis="y", colors="tab:gray")
for spine in ["top", "left"]:
ax2T.spines[spine].set_visible(False)
for ax in fig.get_axes():
ax.spines["top"].set_visible(False)
if df.index.dtype != "<M8[ns]":
ax.xaxis.set_ticks(np.arange(len(df)))
# ax.xaxis.set_ticklabels(np.arange(len(df)))
ax.xaxis.set_ticklabels(df.heure)
else:
date_format = DateFormatter("%H:%M")
ax.xaxis.set_major_formatter(date_format)
ax.spines["bottom"].set_color("tab:gray")
ax.tick_params(axis="x", colors="tab:gray")
fig.tight_layout()
return fig
def main():
model_data_path = Path("/RECH2/huziy/BC-MH/bc_mh_044deg/Diagnostics")
# model_data_path = Path("/RECH2/huziy/BC-MH/bc_mh_044deg/Samples")
static_data_file = "/RECH2/huziy/BC-MH/bc_mh_044deg/Samples/bc_mh_044deg_198001/pm1980010100_00000000p"
r = RPN(static_data_file)
fldir = r.get_first_record_for_name("FLDR")
faa = r.get_first_record_for_name("FAA")
lons, lats = r.get_longitudes_and_latitudes_for_the_last_read_rec()
gc = default_domains.bc_mh_044
cell_manager = CellManager(fldir, nx=fldir.shape[0], ny=fldir.shape[1],
lons2d=lons, lats2d=lats, accumulation_area_km2=faa)
selected_station_ids = ["06EA002", ]
stations = cehq_station.load_from_hydat_db(province="SK", selected_ids=selected_station_ids, natural=None)
# (06EA002): CHURCHILL RIVER AT SANDY BAY at (-102.31832885742188,55.52333068847656), accum. area is 212000.0 km**2
# TODO: plot where is this station, compare modelled and observed hydrographs
# for s in stations:
# assert isinstance(s, cehq_station.Station)
# s.latitude += 0.9
# s.longitude -= 0.2
# print(s)
station_to_model_point = cell_manager.get_model_points_for_stations(stations, drainaige_area_reldiff_limit=0.8,
nneighbours=1)
print(station_to_model_point[stations[0]])
station = stations[0]
assert isinstance(station, cehq_station.Station)
obs_not_corrected = pd.Series(index=station.dates, data=station.values).groupby(
by=lambda d: d.replace(day=15)).mean()
obs_corrected = pd.read_csv("mh/obs_data/Churchill Historic Monthly Apportionable Flow_06EA002.csv.bak.original", skiprows=2)
print(obs_corrected.head())
print(obs_corrected.year.iloc[0], obs_corrected.year.iloc[-1])
date_index = pd.date_range(start=datetime(obs_corrected.year.iloc[0] - 1, 12, 15),
end=datetime(obs_corrected.year.iloc[-1], 12, 15),
freq="M")
date_index = date_index.shift(15, freq=pd.datetools.day)
print(date_index)
data = np.concatenate([r for r in obs_corrected.values[:, 1:-1]])
factor = date_index.map(lambda d: 1000 / (calendar.monthrange(d.year, d.month)[1] * 24 * 3600))
print(factor[:10])
obs_corrected = pd.Series(index=date_index, data=data * factor)
station_to_modelled_data = get_model_data(station_to_model_point, output_path=model_data_path,
grid_config=gc, basins_of_interest_shp=default_domains.MH_BASINS_PATH,
cell_manager=cell_manager, vname="STFL")
modelled_data = station_to_modelled_data[station]
fig = plt.figure()
ax = obs_corrected.plot(label="obs corrected")
obs_not_corrected.plot(label="obs not corrected", ax=ax, color="k")
modelled_data.plot(label="CRCM5", ax=ax, color="r")
ax.legend(loc="upper left")
img_file = img_folder.joinpath("{}_validation_monthly.png".format(station.id))
fig.savefig(str(img_file))
plt.close(fig)
# climatology
start_year = 1980
end_year = 2010
date_selector = lambda d: (start_year <= d.year <= end_year) and not ((d.month == 2) and (d.day == 29))
fig = plt.figure()
ax = obs_corrected.select(date_selector).groupby(lambda d: d.replace(year=2001)).mean().plot(label="obs corrected")
obs_not_corrected.select(date_selector).groupby(lambda d: d.replace(year=2001)).mean().plot(
label="obs not corrected", ax=ax, color="k")
modelled_data.select(date_selector).groupby(lambda d: d.replace(year=2001)).mean().plot(label="CRCM5", ax=ax,
color="r")
ax.xaxis.set_major_locator(MonthLocator(bymonthday=15))
ax.xaxis.set_major_formatter(DateFormatter("%b"))
ax.legend(loc="upper left")
img_file = img_folder.joinpath("{}_validation_clim.png".format(station.id))
fig.savefig(str(img_file))
plt.close(fig)
# Interannual variability
fig = plt.figure()
obs_corrected = obs_corrected.select(lambda d: start_year <= d.year <= end_year)
modelled_data = modelled_data.select(lambda d: start_year <= d.year <= end_year)
corr_list = []
for m in range(1, 13):
obs = obs_corrected.select(lambda d: d.month == m)
mod = modelled_data.select(lambda d: d.month == m)
print(obs.head())
obs.index = obs.index.map(lambda d: d.year)
mod.index = mod.index.map(lambda d: d.year)
corr_list.append(obs.corr(mod))
ax = plt.gca()
ax.plot(range(1, 13), corr_list)
ax.set_xlabel("Month")
ax.set_title("Inter-annual variability")
img_file = img_folder.joinpath("{}_interannual.png".format(station.id))
fig.tight_layout()
fig.savefig(str(img_file), bbox_inches="tight")
plt.close(fig)
period = timedelta(hours=24)
start = tellus_data.start_time
end = start + period
fig, ax = plt.subplots()
count = 0
variable = DATA_CO2
for device_id in tellus_data.sensors:
data = tellus_data.sensors[device_id]['data']
date_filter = (data[DATA_TIMESTAMP] >= start) & (data[DATA_TIMESTAMP] <
end)
data = data.loc[date_filter]
x = data[DATA_TIMESTAMP]
y = data[variable]
plt.plot(x, y, label=device_id)
count = count + 1
if count > 8: # limit to 8 plots
break
# beautify the x-labels
plt.gcf().autofmt_xdate()
myFmt = DateFormatter("%H:%M")
ax.xaxis.set_major_formatter(myFmt)
plt.legend()
plt.xlabel('Time of the day')
plt.ylabel(variable)
plt.title(variable + ' readings on ' + start.strftime('%Y-%m-%d') + ' at ' +
room)
plt.show()
fig.savefig(variable + '-' + room + str(start.strftime('-%Y-%m-%d')) + '.png')
# Plot histogram of percent change
fig = stl.plot_histogram(percent_change, location="Singapore")
plt.show()
# Plot full study area time series mean
with plt.style.context("dark_background"):
fig, ax = plt.subplots(figsize=(20, 10))
ax.scatter(timestamps_arr, means_arr, facecolor="#ff7f00")
ax.set_title("Singapore Mean NO2, Full Study Area, Jul 2018 - Jul 2020",
size=24)
ax.set_xlabel("Date", size=20)
ax.set_ylabel(r"Mean NO2 ($\mathrm{mol \cdot m^{-2}}$)", size=24)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
date_form = DateFormatter("%b-%Y")
ax.xaxis.set_major_formatter(date_form)
fig.text(
0.5,
0.025,
"Data Source: European Space Agency",
ha="center",
fontsize=16,
)
plt.show()
# ===========
# DATA EXPORT
# ===========
# Export 2019 to 2020 change
def plot_monthly(series_dates,
series_record,
tbl_name,
start_date,
end_date,
style='fivethirtyeight',
save=True):
series_dates = pd.to_datetime(series_dates, format="%Y-%m-%d %H:%M:%S")
# date_list is require to create x-axis
dates_list = []
for i in series_dates:
dates_list.append(i)
# date1 = dates_list[0]
# date2 = dates_list[len(dates_list)-1]
# print(dates_list[0])
# Best fit line
df_temp = pd.DataFrame()
df_temp['days_since'] = (
series_dates - pd.to_datetime(dates_list[0])).astype('timedelta64[D]')
lr = LinearRegression()
# print(len(series_dates))
# print(len(df_temp['days_since']))
# print(len(series_record))
lr.fit(df_temp[[
'days_since',
]], series_record)
predict = lr.predict(df_temp[[
'days_since',
]])
# Styling needs to be at top.
plt.style.use(style)
plt.rcParams['font.family'] = 'serif'
plt.rcParams['font.serif'] = 'Ubuntu'
plt.rcParams['font.monospace'] = 'Ubuntu Mono'
plt.rcParams['font.size'] = 10
plt.rcParams['axes.labelsize'] = 10
plt.rcParams['axes.labelweight'] = 'bold'
plt.rcParams['axes.titlesize'] = 18
plt.rcParams['axes.titleweight'] = 'bold'
plt.rcParams['xtick.labelsize'] = 8
plt.rcParams['ytick.labelsize'] = 8
plt.rcParams['legend.fontsize'] = 10
plt.rcParams['figure.titlesize'] = 12
# Main part
fig = plt.figure()
ax = fig.add_subplot(111)
print(dates_list)
ax.plot(dates_list, series_record, c='#DCD6F7', alpha=0.3
) #dashes=(1,10), dash_capstyle='round', dash_joinstyle='round'
ax.scatter(dates_list, series_record, c='#4E4C67', s=20, alpha=0.8)
ax.plot(dates_list, predict, c='#A6B1E1', solid_capstyle='round')
#print(plt.style.available)
# sns.lmplot(dates_list, series_record, data=series_record, fit_reg=True)
# plt.gcf().autofmt_xdate()
# Intelligently scaling x-axis labels
# If it's under 3 month
if len(series_dates) < 110:
years = YearLocator() # every year
months = MonthLocator(interval=1) # every month
days = DayLocator(bymonthday=range(1, 31, 7)) #, interval=5
loc = WeekdayLocator(byweekday=MO)
dateFmt_Maj = DateFormatter('%d-%m-%Y %a')
dateFmt_Min = DateFormatter('%d')
# format the ticks
ax.xaxis.set_major_locator(loc)
ax.xaxis.set_major_formatter(dateFmt_Maj)
ax.xaxis.set_minor_locator(days)
ax.xaxis.set_minor_formatter(dateFmt_Min)
# ax.autoscale_view()
#if it's over 3 month and under year (ish)
elif len(series_dates) >= 110 and len(series_dates) < 350:
years = YearLocator() # every year
months = MonthLocator(interval=1) # every month
days = DayLocator(bymonthday=range(7, 31, 7)) #, interval=5
dateFmt_Maj = DateFormatter('%d-%m')
dateFmt_Min = DateFormatter('%d')
# format the ticks
ax.xaxis.set_major_locator(months)
ax.xaxis.set_major_formatter(dateFmt_Maj)
ax.xaxis.set_minor_locator(days)
ax.xaxis.set_minor_formatter(dateFmt_Min)
else:
years = YearLocator() # every year
months = MonthLocator(interval=3) # every month
#days = DayLocator(bymonthday=range(1,31,7)) #, interval=5
loc = WeekdayLocator(byweekday=MO)
dateFmt_Maj = DateFormatter('%Y')
dateFmt_Min = DateFormatter('%m')
# format the ticks
ax.xaxis.set_major_locator(years)
ax.xaxis.set_major_formatter(dateFmt_Maj)
ax.xaxis.set_minor_locator(months)
ax.xaxis.set_minor_formatter(dateFmt_Min)
# Intelligently plot y-axis range
lim_list = set_min_max_record(tbl_name, series_record.name, start_date,
end_date)
# print('lim_list'.format(lim_list))
# print(start_date)
# print(type(start_date))
# print(end_date)
ax.set_ylim(lim_list)
# Plot x-range so it shows whole of x-axis instead of streching from where data is available.
days = 1 # So edge values of x-axis doesn't get cut.
start_date = datetime.datetime.strptime(start_date, "%Y-%m-%d")
start_date = start_date + relativedelta.relativedelta(days=-days)
# print(start_date)
# print(type(start_date))
end_date = datetime.datetime.strptime(end_date, "%Y-%m-%d")
end_date = end_date + relativedelta.relativedelta(days=+days)
ax.set_xlim([start_date, end_date])
# name axis
ax.set_xlabel(removeUnderLine(series_dates.name), color='#5c5f6d')
ax.set_ylabel(removeUnderLine(series_record.name), color='#5c5f6d')
ax.set_title(removeUnderLine(tbl_name), color='#5c5f6d') #'#B4869F'
# def price(x):
# return '$%1.2f' % x
# ax.fmt_xdata = DateFormatter('%Y-%m-%d')
# ax.fmt_ydata = price
# ax.grid(True)
fig.autofmt_xdate()
if save:
# Format file name to save
file_date_start = datetime.datetime.strftime(series_dates.iloc[0],
'%Y-%m-%d')
file_date_end = datetime.datetime.strftime(
series_dates.iloc[len(series_dates) - 1], '%Y-%m-%d')
fil_name = '{}_{}_to_{}_a01.png'.format(tbl_name, file_date_start,
file_date_end)
print(fil_name)
plt.savefig(fil_name)
plt.show()
def plotcsv(tipo,
columna,
csvPath,
csvName,
pngPath,
pngName,
pathLogo,
estacion,
extraDhi=0,
extraDni=0):
# Read data from 'file.dat'
tabla = numpy.genfromtxt(
csvPath + csvName, # Data to be read
delimiter=',',
names=True,
dtype=None)
tabla = numpy.unique(tabla)
tabla = numpy.sort(tabla)
# imprimo el cabezal de las tablas para tenerlas como referencia en caso de error
print tabla.dtype.names
# guardo las distintas columnas en listas independientes
col1 = tabla['Timestamp']
col2 = tabla[columna]
if tipo == 'uv':
coliuv = tabla['IUV_AV']
if extraDhi:
extraColDhi = tabla[extraDhi]
if extraDni:
extraColDni = tabla[extraDni]
# casteo los timestamps a strings
fecha = [
dt.datetime.strptime(date, '%Y/%m/%d %H:%M:%S.%f') for date in col1
]
# en las tablas pueden haber literales
dato = []
bag = []
error_list = []
j = 0
for n in col2:
if n == 'UnderRange' or n == 'OverRange' or n == 'NotYetSet':
dato.append(-2.)
error_list.append(j)
else:
dato.append(float(n))
bag.append(float(n))
j += 1
# if
fig = plt.figure()
ax = fig.add_subplot(111)
plt.xticks(fontsize=7)
plt.yticks(fontsize=5)
# hours = HourLocator(byhour=range(2,23), interval=6)
hours = HourLocator(byhour=[6, 12, 18])
ax.xaxis.set_major_locator(DayLocator(bymonthday=range(1, 32)))
ax.xaxis.set_major_formatter(FuncFormatter(my_format_function))
ax.xaxis.set_minor_locator(hours)
ax.xaxis.set_minor_formatter(DateFormatter("%Hhs"))
ax.tick_params(which='minor', labelsize=4.5)
# ----------------------
# seteo fin para el dia de ayer con ultima hora minuto y segundo
fin = dt.datetime.now() - dt.timedelta(days=1)
fin = fin.replace(hour=23)
fin = fin.replace(minute=59)
fin = fin.replace(second=59)
# el comienzo del rango es el dia fin menos 7199 minutos
ini = fin - dt.timedelta(minutes=7199)
# parche por las diferencias de los timestamps de las medidas
fin = fin - dt.timedelta(hours=1)
print fin
new_fecha = []
new_dato = []
err_dato = []
print estacion
print columna
if (len(col2) != len(error_list)):
print numpy.floor(numpy.amin(bag))
print numpy.ceil(numpy.amax(bag))
if tipo == 'temp':
# tmin = numpy.floor(numpy.amin(bag))
# tmax = numpy.ceil(numpy.amax(bag))
# min_dato = tmin - 1 # <--
# err = min_dato - (tmax - min_dato) / 44
err = -6.
min_dato = -5.1
elif tipo == 'ghi':
err = -32.
min_dato = -2.
elif tipo == 'dhi':
err = -18.
min_dato = -2.
elif tipo == 'dni':
err = -32.
min_dato = -2.
elif tipo == 'uv':
err = -2.
min_dato = 0.
# if
hay_error = False
# float('nan')
i = 0
if fecha[i] < ini:
# adelantar i hasta inicio
while i < len(fecha) and fecha[i].strftime(
"%Y/%m/%d %H") < ini.strftime("%Y/%m/%d %H"):
i += 1
# while
# if
for minutal in rrule(MINUTELY, dtstart=ini, until=fin):
# si existe la entrada de fecha en el csv
new_fecha.append(minutal)
if i < len(fecha) and minutal.strftime(
"%Y/%m/%d %H:%M") == fecha[i].strftime("%Y/%m/%d %H:%M"):
if i in error_list:
new_dato.append(float('nan'))
err_dato.append(err)
hay_error = True
else:
new_dato.append(dato[i])
err_dato.append(min_dato)
i += 1
else: # si no existe esa fecha en el csv
new_dato.append(float('nan'))
err_dato.append(err)
hay_error = True
# #######################
i = 0
if fecha[i] < ini:
# adelantar i hasta inicio
while i < len(fecha) and fecha[i].strftime(
"%Y/%m/%d %H") < ini.strftime("%Y/%m/%d %H"):
i += 1
# while
# if
j = i
if tipo == 'uv':
new_iuv = []
for minutal in rrule(MINUTELY, dtstart=ini, until=fin):
# si existe la entrada de fecha en el csv
if i < len(fecha) and minutal.strftime(
"%Y/%m/%d %H:%M") == fecha[i].strftime("%Y/%m/%d %H:%M"):
if i in error_list:
new_iuv.append(min_dato)
else:
new_iuv.append(coliuv[i])
i += 1
else: # si no existe esa fecha en el csv
new_iuv.append(min_dato)
if extraDhi:
newExtraDhi = []
for minutal in rrule(MINUTELY, dtstart=ini, until=fin):
# si existe la entrada de fecha en el csv
if i < len(fecha) and minutal.strftime(
"%Y/%m/%d %H:%M") == fecha[i].strftime("%Y/%m/%d %H:%M"):
if i in error_list:
newExtraDhi.append(min_dato)
else:
newExtraDhi.append(extraColDhi[i])
i += 1
else: # si no existe esa fecha en el csv
newExtraDhi.append(min_dato)
if extraDni:
newExtraDni = []
for minutal in rrule(MINUTELY, dtstart=ini, until=fin):
# si existe la entrada de fecha en el csv
if j < len(fecha) and minutal.strftime(
"%Y/%m/%d %H:%M") == fecha[j].strftime("%Y/%m/%d %H:%M"):
if j in error_list:
newExtraDni.append(min_dato)
else:
newExtraDni.append(extraColDni[j])
j += 1
else: # si no existe esa fecha en el csv
newExtraDni.append(min_dato)
# ----------------------
if tipo == 'temp':
# if hay_error:
# vmin = numpy.floor(numpy.amin(new_dato))
# else:
# vmin = numpy.floor(numpy.amin(new_dato) - 1)
# # if
# vmax = numpy.ceil(numpy.amax(new_dato) + 1)
vmin = -5
vmax = 50
elif tipo == 'ghi':
vmin = 0
vmax = 1400
elif tipo == 'dhi':
vmin = 0
vmax = 800
elif tipo == 'dni':
vmin = 0
vmax = 1400
elif tipo == 'uv':
vmin = 0
vmax = 100
# if
if tipo == 'temp':
ylabel = u'Temperatura (ºC)'
ax.plot_date(new_fecha,
new_dato,
ls='-',
marker="",
linewidth=1,
color='red',
clip_on=False)
if hay_error:
# ax.plot_date(new_fecha, err_dato, ls='-', marker="", linewidth=1, color='red', clip_on=False)
ax.fill_between(new_fecha,
err_dato,
min_dato,
linewidth=0,
facecolor='red',
alpha=1,
clip_on=False,
zorder=-100)
elif tipo == 'uv':
ylabel = u'Irradiancia UVA (W/m²)'
ax.plot_date(new_fecha,
new_dato,
ls='-',
marker="",
linewidth=0.7,
color='#7F00FF',
clip_on=False)
ax.fill_between(new_fecha,
0,
new_dato,
linewidth=0,
facecolor='#b571fa',
alpha=0.7,
clip_on=False,
zorder=-100)
if hay_error:
# ax.plot_date(new_fecha, err_dato, ls='-', marker="", linewidth=1, color='red', clip_on=False)
ax.fill_between(new_fecha,
err_dato,
0,
linewidth=0,
facecolor='red',
alpha=1,
clip_on=False,
zorder=-100)
ax_iuv = ax.twinx()
max = numpy.ceil(numpy.amax(new_iuv) + 1)
ax_iuv.set_ylim([0, max])
ax_iuv.plot_date(new_fecha,
new_iuv,
ls='-',
marker="",
linewidth=0.5,
color='red',
clip_on=True,
zorder=-100)
ax_iuv.set_ylabel(u"Índice UV", fontsize=7)
for label in ax_iuv.yaxis.get_majorticklabels():
label.set_fontsize(5)
elif extraDhi or extraDni:
ylabel = u'Irradiancia (W/m²)'
ax.plot_date(new_fecha,
new_dato,
ls='-',
marker="",
linewidth=0.7,
color='#3737FF',
clip_on=False)
# ax.fill_between(new_fecha, 0, new_dato, linewidth=0, facecolor='#b571fa', alpha=0.4, clip_on=False, zorder=-100)
if hay_error:
# ax.plot_date(new_fecha, err_dato, ls='-', marker="", linewidth=1, color='red', clip_on=False)
ax.fill_between(new_fecha,
err_dato,
0,
linewidth=0,
facecolor='red',
alpha=1,
clip_on=False,
zorder=-100)
else:
ylabel = u'Irradiancia (W/m²)'
ax.plot_date(new_fecha,
new_dato,
ls='-',
marker="",
linewidth=0.7,
color='#3737ff',
clip_on=False)
ax.fill_between(new_fecha,
0,
new_dato,
linewidth=0,
facecolor='#bfbfff',
alpha=0.7,
clip_on=False,
zorder=-100)
if hay_error:
# ax.plot_date(new_fecha, err_dato, ls='-', marker="", linewidth=1, color='red', clip_on=False)
ax.fill_between(new_fecha,
err_dato,
0,
linewidth=0,
facecolor='red',
alpha=1,
clip_on=False,
zorder=-100)
# if
if extraDni:
ax_dni = ax.twinx()
ax_dni.set_ylim([0, vmax])
ax_dni.plot_date(new_fecha,
newExtraDni,
ls='-',
marker="",
linewidth=0.7,
color='green',
alpha=0.5,
clip_on=True,
zorder=-100)
plt.setp(ax_dni.get_yticklabels(), visible=False)
if extraDhi:
ax_dhi = ax.twinx()
ax_dhi.set_ylim([0, vmax])
ax_dhi.plot_date(new_fecha,
newExtraDhi,
ls='-',
marker="",
linewidth=0.7,
color='red',
alpha=0.5,
clip_on=True,
zorder=-100)
plt.setp(ax_dhi.get_yticklabels(), visible=False)
ax.set_title(estacion.decode('utf-8'), fontsize=8)
ax.set_xlabel('Fecha', fontsize=7)
ax.set_ylabel(ylabel, fontsize=7)
ax.grid(True)
ax.set_axisbelow(True)
ax.set_ylim([vmin, vmax])
# agrego el logo en el documento
logo = plt.imread(pathLogo)
plt.figimage(logo, 0, 370)
if tipo == 'ghi':
pie = u"Irradiancia global horizontal"
elif tipo == 'dhi':
pie = u"Irradiancia difusa horizontal"
elif tipo == 'dni':
pie = u"Irradiancia directa normal"
elif tipo == 'temp':
pie = u"Temperatura de aire ambiente"
elif tipo == 'uv':
pie = u"Irradiancia global horizontal UVA e índice UV"
# if
if extraDhi or extraDni:
fileName = "priv_" + pngName
else:
fileName = pngName
# genero el pie de la imagen, con el logo y la info del archivo
if extraDhi or extraDni:
plt.annotate("GHI", (0, 0), (-30, -15),
color='#3737FF',
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=7,
family='monospace')
if extraDhi:
plt.annotate("DHI", (0, 0), (-10, -15),
color="red",
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=7,
family='monospace')
if extraDni:
plt.annotate("DNI", (0, 0), (10, -15),
color="green",
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=7,
family='monospace')
else:
plt.annotate(pie, (0, 0), (-30, -15),
xycoords='axes fraction',
textcoords='offset points',
va='top',
fontsize=7,
family='monospace')
# guardo la imagen en la ruta destino
fig.set_figheight(1.8)
fig.set_figwidth(8)
plt.savefig(pngPath + fileName,
bbox_inches='tight',
dpi=200,
transparent=True)
plt.close() # cierro el archivo
Author : Yuxing Yan Date : 12/26/2013 email : [email protected] [email protected] """ import datetime import matplotlib.pyplot as plt from matplotlib.finance import quotes_historical_yahoo from matplotlib.dates import MonthLocator, DateFormatter ticker = 'AAPL' begdate = datetime.date(2012, 1, 2) enddate = datetime.date(2013, 12, 4) months = MonthLocator(range(1, 13), bymonthday=1, interval=3) # every 3rd month monthsFmt = DateFormatter("%b '%Y") x = quotes_historical_yahoo(ticker, begdate, enddate) if len(x) == 0: print('Found no quotes') raise SystemExit dates = [q[0] for q in x] closes = [q[4] for q in x] fig, ax = plt.subplots() ax.plot_date(dates, closes, '-') ax.xaxis.set_major_locator(months) ax.xaxis.set_major_formatter(monthsFmt) ax.xaxis.set_minor_locator(mondays) ax.autoscale_view() ax.grid(True) fig.autofmt_xdate() show()
#RC_7_3: 上網抓資料並畫K線圖
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter
from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc
#下載資料起迄日, 日期格式與股票代號
start = (2016, 4, 1)
end = (2016, 4, 25)
weekFormatter = DateFormatter('%b %d') # 例如, Jan 03 2016
quotes = quotes_historical_yahoo_ohlc('AAPL', start, end)
#若抓取的資料是空字串則離開系統
if len(quotes) == 0:
raise SystemExit
#設定繪圖區域的格式化
fig, ax = plt.subplots()
ax.xaxis_date()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
#畫K線圖並顯示
candlestick_ohlc(ax, quotes, width=0.6)
plt.show()
def pandas_candlestick_ohlc(dat, stick="day", otherseries=None):
"""
:param dat: pandas DataFrame object with datetime64 index, and float columns "Open", "High", "Low", and "Close", likely created via DataReader from "yahoo"
:param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include "day", "week", "month", and "year", ("day" default), and any numeric input indicates the number of trading days included in a period
:param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines
This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.
"""
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
dayFormatter = DateFormatter('%d') # e.g., 12
# Create a new DataFrame which includes OHLC data for each period specified by stick input
transdat = dat.loc[:, ["Open", "High", "Low", "Close"]]
if (type(stick) == str):
if stick == "day":
plotdat = transdat
stick = 1 # Used for plotting
elif stick in ["week", "month", "year"]:
if stick == "week":
transdat["week"] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalendar()[1]) # Identify weeks
elif stick == "month":
transdat["month"] = pd.to_datetime(transdat.index).map(
lambda x: x.month) # Identify months
transdat["year"] = pd.to_datetime(transdat.index).map(
lambda x: x.isocalendar()[0]) # Identify years
grouped = transdat.groupby(list(set(
["year",
stick]))) # Group by year and other appropriate variable
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
}) # Create empty data frame containing what will be plotted
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
"Open": group.iloc[0, 0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1, 3]
},
index=[group.index[0]]))
if stick == "week": stick = 5
elif stick == "month": stick = 30
elif stick == "year": stick = 365
elif (type(stick) == int and stick >= 1):
transdat["stick"] = [
np.floor(i / stick) for i in range(len(transdat.index))
]
grouped = transdat.groupby("stick")
plotdat = pd.DataFrame({
"Open": [],
"High": [],
"Low": [],
"Close": []
}) # Create empty data frame containing what will be plotted
for name, group in grouped:
plotdat = plotdat.append(
pd.DataFrame(
{
"Open": group.iloc[0, 0],
"High": max(group.High),
"Low": min(group.Low),
"Close": group.iloc[-1, 3]
},
index=[group.index[0]]))
else:
raise ValueError(
'Valid inputs to argument "stick" include the strings "day", "week", "month", "year", or a positive integer'
)
# Set plot parameters, including the axis object ax used for plotting
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
else:
weekFormatter = DateFormatter('%b %d, %Y')
ax.xaxis.set_major_formatter(weekFormatter)
ax.grid(True)
# Create the candelstick chart
candlestick_ohlc(ax,
list(
zip(list(date2num(plotdat.index.tolist())),
plotdat["Open"].tolist(),
plotdat["High"].tolist(), plotdat["Low"].tolist(),
plotdat["Close"].tolist())),
colorup="black",
colordown="red",
width=stick * .4)
# Plot other series (such as moving averages) as lines
if otherseries != None:
if type(otherseries) != list:
otherseries = [otherseries]
dat.loc[:, otherseries].plot(ax=ax, lw=1.3, grid=True)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(),
rotation=45,
horizontalalignment='right')
plt.show()
import sqlite3
from matplotlib import pyplot as plt
from matplotlib.dates import datetime as dt
from matplotlib.dates import DateFormatter
import time
formatter = DateFormatter('%H-%M')
conn = sqlite3.connect('25_10_18.2.db')
cur=conn.cursor()
data=[]
symbols=[]
symbols2=['CRUDEOIL18NOVFUT']
#symbols2=['USDINR18OCTFUT','CRUDEOIL18NOVFUT','BANKNIFTY18OCTFUT','NIFTY18OCTFUT']
ltp_nifty50=10443.95#kite.ltp(256265)['256265']['last_price']
ltp_niftybank=25177#kite.ltp(260105)['260105']['last_price']
strike_nifty50=100*int(ltp_nifty50/100)-400
strike_niftybank=100*int(ltp_niftybank/100)-400
for i in range(8):
symbols.append("NIFTY18OCT"+str(strike_nifty50+(100*i))+"CE")
symbols.append("NIFTY18OCT"+str(strike_nifty50+(100*i))+"PE")
symbols.append("BANKNIFTY18OCT"+str(strike_niftybank+(100*i))+"CE")
symbols.append("BANKNIFTY18OCT"+str(strike_niftybank+(100*i))+"PE")
symbols.append("NIFTY18OCTFUT")
symbols.append("BANKNIFTY18OCTFUT")
symbols.append("BANKNIFTY18OCT25400PE")
symbols.append("BANKNIFTY18OCT25200CE")
if __name__ == "__main__":
register_matplotlib_converters()
# 读取数据
ssec2015 = pd.read_csv("ssec2015.csv")
ssec2015 = ssec2015.iloc[:, 1:]
print(ssec2015.head(n = 3))
print(ssec2015.tail())
ssec2015.Date = [date2num(datetime.strptime(date, "%Y-%m-%d")) for date in ssec2015.Date]
ssec2015list = list()
for i in range(len(ssec2015)):
ssec2015list.append(ssec2015.iloc[i, :])
print(ssec2015list[0:10])
ax = plt.subplot()
mondays = WeekdayLocator(MONDAY)
weekFormatter = DateFormatter("%y %b %d")
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_major_locator(DayLocator())
ax.xaxis.set_major_formatter(weekFormatter)
ax.set_title("SH2015")
mf.candlestick_ohlc(ax, ssec2015list, width = 0.7, colorup = "r", colordown = "g")
# plt.setp(plt.gca().get_xticklabels(), rotation = 50, horizontalalignment "center")
plt.savefig("SH2015-candle.png")
# 动量交易
print("动量交易策略")
Wanke = pd.read_csv("Vanke.csv")
Wanke.index = Wanke.iloc[:, 1]
Wanke.index = pd.to_datetime(Wanke.index, format = "%Y-%m-%d")
Wanke = Wanke.iloc[:, 2:]
print(Wanke.head())
import sys
from datetime import date
import matplotlib.pyplot as plt
import numpy as np
today = date.today()
start = (today.year - 1, today.month, today.day)
symbol = 'DISH'
if len(sys.argv) == 2:
symbol = sys.argv[1]
quotes = quotes_historical_yahoo(symbol, start, today)
quotes = np.array(quotes)
dates = quotes.T[0]
volume = quotes.T[5]
alldays = DayLocator()
months = MonthLocator()
month_formatter = DateFormatter("%b %Y")
fig = plt.figure()
ax = fig.add_subplot(111)
plt.semilogy(dates, volume)
ax.xaxis.set_major_locator(months)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(month_formatter)
fig.autofmt_xdate()
plt.show()
# Observed mesh (thin subplot on bottom)
mw = ax2.pcolormesh(a['DATETIME'],
range(3), [a[S['MW var']], a[S['MW var']]],
cmap=S['cmap'],
vmax=hmax,
vmin=hmin)
ax2.axes.yaxis.set_ticklabels([])
ax2.set_yticks([])
ax2.set_ylabel('Observed')
ax2.set_xlim(hovmoller['valid_1d+'][0], hovmoller['valid_1d+'][-1])
#
ax1.grid()
ax2.grid()
#
fig.subplots_adjust(hspace=0, right=0.8)
cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.7])
cb = fig.colorbar(hv, cax=cbar_ax)
cb.ax.set_ylabel('%s (%s)' % (s, S['units']))
#
ax1.xaxis.set_major_locator(
HourLocator(byhour=[0, 3, 6, 9, 12, 15, 18, 21]))
ax2.xaxis.set_major_locator(
HourLocator(byhour=[0, 3, 6, 9, 12, 15, 18, 21]))
dateFmt = DateFormatter('%b %d\n%H:%M')
ax2.xaxis.set_major_formatter(dateFmt)
#
ax2.set_xlabel(r'Contour shows difference between maximum value in %s km$\mathregular{^{2}}$ box centered at %s %s and the value at the center point.' \
% (half_box*6, locations[stn]['latitude'], locations[stn]['longitude']), fontsize=8)
#
plt.savefig(SAVE + '%s_%s.png' % (stn, S['MW var']))
coviddaily.sample(2).T
# calculate new cases and deaths by day
coviddailytotals = coviddaily.loc[coviddaily.casedate.between('2020-02-01','2020-07-12')].\
groupby(['casedate'])[['new_cases','new_deaths']].\
sum().\
reset_index()
coviddailytotals.sample(7)
# show line charts for new cases and new deaths by day
fig = plt.figure()
plt.suptitle("New Covid Cases and Deaths By Day Worldwide in 2020")
ax1 = plt.subplot(2, 1, 1)
ax1.plot(coviddailytotals.casedate, coviddailytotals.new_cases)
ax1.xaxis.set_major_formatter(DateFormatter("%b"))
ax1.set_xlabel("New Cases")
ax2 = plt.subplot(2, 1, 2)
ax2.plot(coviddailytotals.casedate, coviddailytotals.new_deaths)
ax2.xaxis.set_major_formatter(DateFormatter("%b"))
ax2.set_xlabel("New Deaths")
plt.tight_layout()
fig.subplots_adjust(top=0.88)
plt.show()
# calculate new cases and new deaths by region and day
regiontotals = coviddaily.loc[coviddaily.casedate.between('2020-02-01','2020-07-12')].\
groupby(['casedate','region'])[['new_cases','new_deaths']].\
sum().\
reset_index()
regiontotals.sample(7)
def pollyxt_ift_displayLTLCali(tmpFile, saveFolder):
'''
Description
-----------
Display the housekeeping data from laserlogbook file.
Parameters
----------
tmpFile: str
the .mat file which stores the housekeeping data.
saveFolder: str
Usage
-----
pollyxt_ift_displayLTLCali(tmpFile)
History
-------
2019-01-10. First edition by Zhenping
'''
if not os.path.exists(tmpFile):
print('{filename} does not exists.'.format(filename=tmpFile))
return
# read matlab .mat data
try:
mat = spio.loadmat(tmpFile, struct_as_record=True)
figDPI = mat['figDPI'][0][0]
flagWatermarkOn = mat['flagWatermarkOn'][0][0]
if mat['partnerLabel'].size:
partnerLabel = mat['partnerLabel'][0]
else:
partnerLabel = ''
if mat['LCTime355'].size:
thisLCTime355 = mat['LCTime355'][0][:]
else:
thisLCTime355 = np.array([])
if mat['LCTime532'].size:
thisLCTime532 = mat['LCTime532'][0][:]
else:
thisLCTime532 = np.array([])
if mat['LCTime1064'].size:
thisLCTime1064 = mat['LCTime1064'][0][:]
else:
thisLCTime1064 = np.array([])
if mat['LCTime387'].size:
thisLCTime387 = mat['LCTime387'][0][:]
else:
thisLCTime387 = np.array([])
if mat['LCTime607'].size:
thisLCTime607 = mat['LCTime607'][0][:]
else:
thisLCTime607 = np.array([])
if mat['LC355Status'].size:
LC355Status = mat['LC355Status'][0][:]
else:
LC355Status = np.array([])
if mat['LC532Status'].size:
LC532Status = mat['LC532Status'][0][:]
else:
LC532Status = np.array([])
if mat['LC1064Status'].size:
LC1064Status = mat['LC1064Status'][0][:]
else:
LC1064Status = np.array([])
if mat['LC387Status'].size:
LC387Status = mat['LC387Status'][0][:]
else:
LC387Status = np.array([])
if mat['LC607Status'].size:
LC607Status = mat['LC607Status'][0][:]
else:
LC607Status = np.array([])
if mat['LC355History'].size:
LC355History = mat['LC355History'][0][:]
else:
LC355History = np.array([])
if mat['LC532History'].size:
LC532History = mat['LC532History'][0][:]
else:
LC532History = np.array([])
if mat['LC1064History'].size:
LC1064History = mat['LC1064History'][0][:]
else:
LC1064History = np.array([])
if mat['LC387History'].size:
LC387History = mat['LC387History'][0][:]
else:
LC387History = np.array([])
if mat['LC607History'].size:
LC607History = mat['LC607History'][0][:]
else:
LC607History = np.array([])
if mat['logbookTime'].size:
thisLogbookTime = mat['logbookTime'][0][:]
else:
thisLogbookTime = np.array([])
if mat['flagOverlap'].size:
flagOverlap = mat['flagOverlap'][0][:]
else:
flagOverlap = np.array([])
if mat['flagWindowwipe'].size:
flagWindowwipe = mat['flagWindowwipe'][0][:]
else:
flagWindowwipe = np.array([])
if mat['flagFlashlamps'].size:
flagFlashlamps = mat['flagFlashlamps'][0][:]
else:
flagFlashlamps = np.array([])
if mat['flagPulsepower'].size:
flagPulsepower = mat['flagPulsepower'][0][:]
else:
flagPulsepower = np.array([])
if mat['flagRestart'].size:
flagRestart = mat['flagRestart'][0][:]
else:
flagRestart = np.array([])
if mat['flag_CH_NDChange'].size:
flag_CH_NDChange = mat['flag_CH_NDChange'][:]
else:
flag_CH_NDChange = np.array([])
if mat['flagCH355FR'].size:
flagCH355FR = mat['flagCH355FR'][0][:]
else:
flagCH355FR = np.array([])
if mat['flagCH532FR'].size:
flagCH532FR = mat['flagCH532FR'][0][:]
else:
flagCH532FR = np.array([])
if mat['flagCH1064FR'].size:
flagCH1064FR = mat['flagCH1064FR'][0][:]
else:
flagCH1064FR = np.array([])
if mat['flagCH387FR'].size:
flagCH387FR = mat['flagCH387FR'][0][:]
else:
flagCH387FR = np.array([])
if mat['flagCH607FR'].size:
flagCH607FR = mat['flagCH607FR'][0][:]
else:
flagCH607FR = np.array([])
if mat['flagCH407FR'].size:
flagCH407FR = mat['flagCH407FR'][0][:]
else:
flagCH407FR = np.array([])
if mat['flagCH532FR_X'].size:
flagCH532FR_X = mat['flagCH532FR_X'][0][:]
else:
flagCH532FR_X = np.array([])
else_time = mat['else_time'][:]
else_label = mat['else_label']
if mat['WVCaliTime'].size:
thisWVCaliTime = mat['WVCaliTime'][0][:]
else:
thisWVCaliTime = np.array([])
if mat['WVConst'].size:
WVConst = mat['WVConst'][0][:]
else:
WVConst = np.array([])
if mat['depolCaliTime532'].size:
thisDepolCaliTime532 = mat['depolCaliTime532'][0][:]
else:
thisDepolCaliTime532 = np.array([])
if mat['depolCaliConst532'].size:
depolCaliConst532 = mat['depolCaliConst532'][0][:]
else:
depolCaliConst532 = np.array([])
if mat['yLim355'].size:
yLim355 = mat['yLim355'][0][:]
else:
yLim355 = np.array([])
if mat['yLim532'].size:
yLim532 = mat['yLim532'][0][:]
else:
yLim532 = np.array([])
if mat['yLim1064'].size:
yLim1064 = mat['yLim1064'][0][:]
else:
yLim1064 = np.array([])
if mat['yLim_LC_ratio_355_387'].size:
yLim_LC_ratio_355_387 = mat['yLim_LC_ratio_355_387'][0][:]
else:
yLim_LC_ratio_355_387 = np.array([])
if mat['yLim_LC_ratio_532_607'].size:
yLim_LC_ratio_532_607 = mat['yLim_LC_ratio_532_607'][0][:]
else:
yLim_LC_ratio_532_607 = np.array([])
if mat['wvLim'].size:
wvLim = mat['wvLim'][0][:]
else:
wvLim = np.array([])
if mat['depolConstLim532'].size:
depolConstLim532 = mat['depolConstLim532'][0][:]
else:
depolConstLim532 = np.array([])
imgFormat = mat['imgFormat'][:][0]
pollyVersion = mat['CampaignConfig']['name'][0][0][0]
dataTime = mat['PollyDataInfo']['dataTime'][0][0][0]
location = mat['CampaignConfig']['location'][0][0][0]
startTime = mat['CampaignConfig']['startTime'][0][0][0]
version = mat['PicassoConfig']['PicassoVersion'][0][0][0]
fontname = mat['PicassoConfig']['fontname'][0][0][0]
except Exception as e:
print(e)
print('Failed reading %s' % (tmpFile))
return
# set the default font
matplotlib.rcParams['font.sans-serif'] = fontname
matplotlib.rcParams['font.family'] = "sans-serif"
# convert matlab datenum tp datetime
startTime = datenum_to_datetime(float(startTime[0]))
dataTime = datenum_to_datetime(float(dataTime[0]))
LCTime355 = [datenum_to_datetime(thisTime) for thisTime in thisLCTime355]
LCTime532 = [datenum_to_datetime(thisTime) for thisTime in thisLCTime532]
LCTime1064 = [datenum_to_datetime(thisTime) for thisTime in thisLCTime1064]
LCTime387 = [datenum_to_datetime(thisTime) for thisTime in thisLCTime387]
LCTime607 = [datenum_to_datetime(thisTime) for thisTime in thisLCTime607]
logbookTime = [
datenum_to_datetime(thisTime) for thisTime in thisLogbookTime
]
elseTime = [
datenum_to_datetime(thisElseTime) for thisElseTime in else_time
]
WVCaliTime = [datenum_to_datetime(thisTime) for thisTime in thisWVCaliTime]
depolCaliTime532 = [
datenum_to_datetime(thisTime) for thisTime in thisDepolCaliTime532
]
lineColor = {
'overlap': '#f48f42',
'windowwipe': '#ff66ff',
'flashlamps': '#993333',
'pulsepower': '#990099',
'restart': '#ffff00',
'NDChange': '#333300',
'else': '#00ff00'
}
# display lidar constants at 355mn
fig, (ax1, ax2, ax3, ax4, ax5, ax6,
ax7) = plt.subplots(7,
figsize=(10, 15),
sharex=True,
gridspec_kw={
'height_ratios': [1, 1, 1, 1, 1, 1, 1],
'hspace': 0.1
})
plt.subplots_adjust(top=0.96, bottom=0.05, left=0.07, right=0.98)
# lidar constants at 355 nm
LCTime355 = [
LCTime355[indx] for indx in np.arange(0, len(LCTime355))
if LC355Status[indx] == 2
]
p1 = ax1.scatter(LCTime355,
LC355History[LC355Status == 2],
s=7,
c='#0000ff',
marker='o',
label='lidar constant')
# default line for create legend
l1 = ax1.axvline(x=0,
linestyle='--',
color=lineColor['overlap'],
label='overlap')
l2 = ax1.axvline(x=0,
linestyle='--',
color=lineColor['pulsepower'],
label='pulsepower')
l3 = ax1.axvline(x=0,
linestyle='--',
color=lineColor['windowwipe'],
label='windowwipe')
l4 = ax1.axvline(x=0,
linestyle='--',
color=lineColor['restart'],
label='restart')
l5 = ax1.axvline(x=0,
linestyle='--',
color=lineColor['flashlamps'],
label='flashlamps')
l6 = ax1.axvline(x=0,
linestyle='--',
color=lineColor['NDChange'],
label='NDChange')
l7 = ax1.axvline(x=0,
linestyle='--',
color=lineColor['else'],
label=else_label[0])
ax1.legend(handles=[p1, l1, l2, l3, l4, l5, l6, l7],
loc='upper left',
fontsize=11)
for iLogbookInfo in np.arange(0, len(logbookTime)):
if flagOverlap[iLogbookInfo]:
ax1.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['overlap'])
if flagPulsepower[iLogbookInfo]:
ax1.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['pulsepower'])
if flagWindowwipe[iLogbookInfo]:
ax1.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['windowwipe'])
if flagRestart[iLogbookInfo]:
ax1.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['restart'])
if flagFlashlamps[iLogbookInfo]:
ax1.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['flashlamps'])
if flag_CH_NDChange[iLogbookInfo, flagCH355FR == 1]:
ax1.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['NDChange'])
for elseTime in else_time:
ax1.axvline(x=elseTime, linestyle='--', color=lineColor['else'])
ax1.set_ylabel('LC @ 355nm')
ax1.grid(False)
ax1.set_title('Lidar constants for {instrument} at {location}'.format(
instrument=pollyVersion, location=location),
fontsize=20)
ax1.set_ylim(yLim355.tolist())
ax1.set_xlim([startTime - timedelta(days=2), dataTime + timedelta(days=2)])
# lidar constant at 532 nm
LCTime532 = [
LCTime532[indx] for indx in np.arange(0, len(LCTime532))
if LC532Status[indx] == 2
]
p1 = ax2.scatter(LCTime532,
LC532History[LC532Status == 2],
s=7,
c='#0000ff',
marker='o')
for iLogbookInfo in np.arange(0, len(logbookTime)):
if flagOverlap[iLogbookInfo]:
ax2.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['overlap'])
if flagPulsepower[iLogbookInfo]:
ax2.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['pulsepower'])
if flagWindowwipe[iLogbookInfo]:
ax2.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['windowwipe'])
if flagRestart[iLogbookInfo]:
ax2.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['restart'])
if flagFlashlamps[iLogbookInfo]:
ax2.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['flashlamps'])
if flag_CH_NDChange[iLogbookInfo, flagCH532FR == 1]:
ax2.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['NDChange'])
for elseTime in else_time:
ax2.axvline(x=elseTime, linestyle='--', color=lineColor['else'])
ax2.set_ylabel('LC @ 532nm')
ax2.grid(False)
ax2.set_ylim(yLim532.tolist())
ax2.set_xlim([startTime - timedelta(days=2), dataTime + timedelta(days=2)])
# lidar constant at 1064 nm
LCTime1064 = [
LCTime1064[indx] for indx in np.arange(0, len(LCTime1064))
if LC1064Status[indx] == 2
]
p1 = ax3.scatter(LCTime1064,
LC1064History[LC1064Status == 2],
s=7,
c='#0000ff',
marker='o')
for iLogbookInfo in np.arange(0, len(logbookTime)):
if flagOverlap[iLogbookInfo]:
ax3.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['overlap'])
if flagPulsepower[iLogbookInfo]:
ax3.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['pulsepower'])
if flagWindowwipe[iLogbookInfo]:
ax3.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['windowwipe'])
if flagRestart[iLogbookInfo]:
ax3.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['restart'])
if flagFlashlamps[iLogbookInfo]:
ax3.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['flashlamps'])
if flag_CH_NDChange[iLogbookInfo, flagCH1064FR == 1]:
ax3.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['NDChange'])
for elseTime in else_time:
ax3.axvline(x=elseTime, linestyle='--', color=lineColor['else'])
ax3.set_ylabel('LC @ 1064nm')
ax3.grid(False)
ax3.set_ylim(yLim1064.tolist())
ax3.set_xlim([startTime - timedelta(days=2), dataTime + timedelta(days=2)])
# transmission ratio at 355/387 nm
flagRamanLC = np.logical_and(LC355Status == 2, LC387Status == 2)
LCTimeRaman = [
LCTime387[indx] for indx in np.arange(0, len(LCTime387))
if flagRamanLC[indx]
]
p1 = ax4.scatter(LCTimeRaman,
LC355History[flagRamanLC] / LC387History[flagRamanLC],
s=7,
c='#0000ff',
marker='o')
for iLogbookInfo in np.arange(0, len(logbookTime)):
if flagOverlap[iLogbookInfo]:
ax4.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['overlap'])
if flagPulsepower[iLogbookInfo]:
ax4.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['pulsepower'])
if flagWindowwipe[iLogbookInfo]:
ax4.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['windowwipe'])
if flagRestart[iLogbookInfo]:
ax4.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['restart'])
if flagFlashlamps[iLogbookInfo]:
ax4.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['flashlamps'])
if flag_CH_NDChange[iLogbookInfo, flagCH355FR == 1] or \
flag_CH_NDChange[iLogbookInfo, flagCH387FR == 1]:
ax4.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['NDChange'])
for elseTime in else_time:
ax4.axvline(x=elseTime, linestyle='--', color=lineColor['else'])
ax4.set_ylabel('Ratio 355/387')
ax4.grid(False)
ax4.set_ylim(yLim_LC_ratio_355_387.tolist())
ax4.set_xlim([startTime - timedelta(days=2), dataTime + timedelta(days=2)])
# transmission ratio at 532/607 nm
flagRamanLC = np.logical_and(LC532Status == 2, LC607Status == 2)
LCTimeRaman = [
LCTime607[indx] for indx in np.arange(0, len(LCTime607))
if flagRamanLC[indx]
]
p1 = ax5.scatter(LCTimeRaman,
LC532History[flagRamanLC] / LC607History[flagRamanLC],
s=7,
c='#0000ff',
marker='o')
for iLogbookInfo in np.arange(0, len(logbookTime)):
if flagOverlap[iLogbookInfo]:
ax5.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['overlap'])
if flagPulsepower[iLogbookInfo]:
ax5.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['pulsepower'])
if flagWindowwipe[iLogbookInfo]:
ax5.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['windowwipe'])
if flagRestart[iLogbookInfo]:
ax5.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['restart'])
if flagFlashlamps[iLogbookInfo]:
ax5.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['flashlamps'])
if flag_CH_NDChange[iLogbookInfo, flagCH532FR == 1] or \
flag_CH_NDChange[iLogbookInfo, flagCH607FR == 1]:
ax5.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['NDChange'])
for elseTime in else_time:
ax5.axvline(x=elseTime, linestyle='--', color=lineColor['else'])
ax5.set_ylabel('Ratio 532/607')
ax5.grid(False)
ax5.set_ylim(yLim_LC_ratio_532_607.tolist())
ax5.set_xlim([startTime - timedelta(days=2), dataTime + timedelta(days=2)])
# wv calibration constant
p1 = ax6.scatter(WVCaliTime, WVConst, s=7, c='#0000ff', marker='o')
for iLogbookInfo in np.arange(0, len(logbookTime)):
if flagOverlap[iLogbookInfo]:
ax6.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['overlap'])
if flagPulsepower[iLogbookInfo]:
ax6.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['pulsepower'])
if flagWindowwipe[iLogbookInfo]:
ax6.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['windowwipe'])
if flagRestart[iLogbookInfo]:
ax6.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['restart'])
if flagFlashlamps[iLogbookInfo]:
ax6.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['flashlamps'])
if flag_CH_NDChange[iLogbookInfo, flagCH407FR == 1] or \
flag_CH_NDChange[iLogbookInfo, flagCH387FR == 1]:
ax6.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['NDChange'])
for elseTime in else_time:
ax6.axvline(x=elseTime, linestyle='--', color=lineColor['else'])
ax6.set_ylabel('WV const [g*kg^{-1}]')
ax6.grid(False)
ax6.set_ylim(wvLim.tolist())
ax6.set_xlim([startTime - timedelta(days=2), dataTime + timedelta(days=2)])
# depolarization calibration constant at 532 nm
p1 = ax7.scatter(depolCaliTime532,
depolCaliConst532,
s=7,
c='#0000ff',
marker='o')
for iLogbookInfo in np.arange(0, len(logbookTime)):
if flagOverlap[iLogbookInfo]:
ax7.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['overlap'])
if flagPulsepower[iLogbookInfo]:
ax7.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['pulsepower'])
if flagWindowwipe[iLogbookInfo]:
ax7.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['windowwipe'])
if flagRestart[iLogbookInfo]:
ax7.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['restart'])
if flagFlashlamps[iLogbookInfo]:
ax7.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['flashlamps'])
if flag_CH_NDChange[iLogbookInfo, flagCH532FR == 1] or \
flag_CH_NDChange[iLogbookInfo, flagCH532FR_X == 1]:
ax7.axvline(x=logbookTime[iLogbookInfo],
linestyle='--',
color=lineColor['NDChange'])
for elseTime in else_time:
ax7.axvline(x=elseTime, linestyle='--', color=lineColor['else'])
ax7.set_ylabel('$\eta_{532}$')
ax7.set_xlabel('Date (mm-dd)')
ax7.set_ylim(depolConstLim532.tolist())
ax7.xaxis.set_major_formatter(DateFormatter('%m-%d'))
ax7.grid(False)
ax7.set_xlim([startTime - timedelta(days=2), dataTime + timedelta(days=2)])
# add watermark
if flagWatermarkOn:
rootDir = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
im_license = matplotlib.image.imread(
os.path.join(rootDir, 'img', 'by-sa.png'))
newax_license = fig.add_axes([0.60, 0.01, 0.10, 0.02], zorder=10)
newax_license.imshow(im_license, alpha=0.8, aspect='auto')
newax_license.axis('off')
fig.text(0.71,
0.01,
'Preliminary\nResults.',
fontweight='bold',
fontsize=12,
color='red',
ha='left',
va='bottom',
alpha=0.8,
zorder=10)
fig.text(0.84,
0.01,
u"\u00A9 {1} {0}.\nCC BY SA 4.0 License.".format(
datetime.now().strftime('%Y'), partnerLabel),
fontweight='bold',
fontsize=7,
color='black',
ha='left',
va='bottom',
alpha=1,
zorder=10)
fig.text(0.03, 0.03, startTime.strftime("%Y"), fontsize=12)
fig.text(0.03,
0.02,
'Version: {version}'.format(version=version),
fontsize=12)
fig.savefig(os.path.join(
saveFolder,
'{pollyType}_{date}_long_term_cali_results.{imgFormat}'.format(
pollyType=pollyVersion,
date=dataTime.strftime('%Y%m%d'),
imgFormat=imgFormat)),
dpi=figDPI)
plt.close()
import matplotlib.pyplot as plt
from matplotlib.dates import DateFormatter, WeekdayLocator,\
DayLocator, MONDAY
from matplotlib.finance import quotes_historical_yahoo_ohlc, candlestick_ohlc
# (Year, month, day) tuples suffice as args for quotes_historical_yahoo
date1 = (2004, 2, 1)
date2 = (2004, 4, 12)
mondays = WeekdayLocator(MONDAY) # major ticks on the mondays
alldays = DayLocator() # minor ticks on the days
weekFormatter = DateFormatter('%b %d') # e.g., Jan 12
dayFormatter = DateFormatter('%d') # e.g., 12
quotes = quotes_historical_yahoo_ohlc('INTC', date1, date2)
if len(quotes) == 0:
raise SystemExit
fig, ax = plt.subplots()
fig.subplots_adjust(bottom=0.2)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#ax.xaxis.set_minor_formatter(dayFormatter)
#plot_day_summary(ax, quotes, ticksize=3)
candlestick_ohlc(ax, quotes, width=0.6)
ax.xaxis_date()
ax.autoscale_view()
plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
def doDotBase(title,
y_label,
x_label,
datas,
limit=None,
label_pos=None,
lines=None,
ylines=None,
dots=None):
rcParams.update({
'font.family': 'sans-serif',
'font.sans-serif': [u'SimHei'],
'axes.unicode_minus': False,
'font.size': 8,
})
_show_date = False
if _show_date:
autodates = AutoDateLocator()
yearsFmt = DateFormatter('%Y-%m-%d %H:%M:%S')
fig = plt.figure(figsize=[10, 12], dpi=120)
fig.autofmt_xdate()
plt.xticks(rotation=45)
ax = fig.add_subplot(111)
ax_twiny = ax.twiny()
ax_twiny.grid(False)
ax_twiny.set_xticks([])
ax.xaxis.set_major_locator(autodates) # 设置时间间隔
ax.xaxis.set_major_formatter(yearsFmt) # 设置时间显示格式
ax.set_xticks(
pd.date_range(start='2017-9-27 00:00:00',
end='2018-03-31 23:59:59',
freq='3D'))
"""设定显示的时间段"""
_day = datetime.date.today().day
_month = datetime.date.today().month
if _day < 27:
_day += 3
else:
_month += 1
_day = 1
_end_date = datetime.date.today().replace(day=_day, month=_month)
ax.set_xlim("2017-9-1 00:00:00", "%s 00:00:00" % _end_date)
else:
plt.figure()
for _data in datas:
_color = _data[1]
_dot = _data[2]
_label = _data[3]
plt.plot(range(len(_data[0])),
_data[0],
_dot,
label=_label,
color=_color)
if limit is not None:
ylim(limit[0], limit[1])
if dots is not None:
for _dot in dots:
plt.scatter(_dot[0],
_dot[1],
marker=_dot[2],
color=_dot[3],
label=_dot[4],
alpha=0.5)
if lines is not None:
for _line in lines:
plt.axvline(x=_line[0],
linestyle=_line[1],
linewidth=2,
color=_line[2],
label=_line[3])
if ylines is not None:
for _line in ylines:
plt.axhline(y=_line[0],
linestyle=_line[1],
linewidth=1,
color=_line[2],
label=_line[3])
plt.ylabel(y_label)
plt.xlabel(x_label)
plt.title(title)
if label_pos is None:
plt.legend()
else:
plt.legend(loc=label_pos)
_fn = 'pic/%s-bar.png' % time.time()
if not __test:
plt.savefig(_fn, dpi=120)
else:
plt.show()
return _fn
print(yearlyR.head(n=2))
# Print total hours studied programming
allTimeProgHours = progPomYear[0] + progPomYear[1] + progPomYear[2]
print('\n\nTotal time spent studying programming: ', str(allTimeProgHours))
# Print total pom hours for 2016
totalYearPomHours = progPomYear[1] + otherPomYear[1]
print('\n\nTotal Pom hours for 2016: ', str(totalYearPomHours))
# Print total pom hours for 2017
totalYearPomHours = progPomYear[2] + otherPomYear[2]
print('\n\nTotal Pom hours for 2017: ', str(totalYearPomHours))
# Format the dates for plots
DWformatter = DateFormatter('%-m-%d')
Mformatter = DateFormatter('%b')
Yformatter = DateFormatter('%Y')
# Plot the daily data in a stacked bar plot
mpl_fig2 = plt.figure()
ax2 = mpl_fig2.add_subplot(111)
p1 = ax2.bar(forDailyPlot.index,
forDailyPlot[' Prog'],
width=.75,
label='Programming')
p2 = ax2.bar(forDailyPlot.index,
forDailyPlot[' Misc'],
width=.75,
label='Other',
color=(1.0, 0.5, 0.62),
def doIssueAction(issues, dots, figsize=[10, 12]):
"""
制作“活动分布”图
:param issues: 任务
:param dots: 活动
:param figsize: 图大小
:return:
"""
global __test
_issue_point_marker = {
'timeoriginalestimate': 'v',
'timeestimate': 'o',
'timespent': '^',
'WorklogTimeSpent': 's',
'status': '+',
u"bug产生人": '*',
'resolution': '>'
}
_issue_point_color = {
'timeoriginalestimate': 0,
'timeestimate': 1,
'timespent': 2,
'WorklogTimeSpent': 3,
'status': 4,
u"bug产生人": 5,
'resolution': 6
}
"""
_issue_point_marker = {"agg_time": 'v',
"org_time": 'o',
"spent_time": '^',
"status": 's',
"updated": '+',
"landmark": '>',
"sprint": '<',
"users": 'x',
"epic_link": 'D',
"lastViewed": 'p',
}
_issue_point_color = {"agg_time": 1,
"org_time": 2,
"spent_time": 3,
"status": 4,
"updated": 5,
"landmark": 6,
"sprint": 7,
"users": 8,
"epic_link": 9,
"lastViewed": 10,
}
"""
# _colors = plt.cm.BuPu(np.linspace(1, 255, len(_issue_point_marker)+1)); hsv; jet
_colors = plt.cm.hsv(np.linspace(0.5, 1., len(_issue_point_marker)))
"""作图"""
rcParams.update({
'font.family': 'sans-serif',
'font.sans-serif': [u'SimHei'],
'axes.unicode_minus': False,
'font.size': 6,
})
autodates = AutoDateLocator()
yearsFmt = DateFormatter('%Y-%m-%d')
fig = figure(figsize=figsize, dpi=120)
ax = fig.add_subplot(111)
fig.autofmt_xdate() # 设置x轴时间外观
ax.xaxis.set_major_locator(autodates) # 设置时间间隔
ax.xaxis.set_major_formatter(yearsFmt) # 设置时间显示格式
"""设定显示的时间段"""
_day = datetime.date.today().day
_month = datetime.date.today().month
if _day < 27:
_day += 3
else:
_month += 1
_day = 1
_end_date = datetime.date.today().replace(day=_day, month=_month)
ax.set_xticks(
pd.date_range(start='2018-02-01', end='%s' % _end_date, freq='3D'))
ax.set_xlim("2018-02-01", "%s" % _end_date)
ax.set_yticks(range(1, len(issues) + 1))
ax.set_yticklabels(issues, )
ax.set_ylim(0, len(issues) + 1)
_leg = []
for __i in range(len(_issue_point_marker) + 1):
_leg.append(None)
for _dot in dots:
if _dot[2] in _issue_point_marker:
_marker = _issue_point_marker[_dot[2]]
_index = _issue_point_color[_dot[2]]
_c = _colors[_index]
# _c = 'k'
_leg[_index] = ax.scatter(_dot[0],
_dot[1],
color=_c,
marker=_marker,
s=30,
alpha=0.7)
ax.set_xlabel(u'日期', fontsize=11)
ax.set_ylabel(u'任务', fontsize=11)
ax.grid(True)
ax.legend(
_leg,
[u"预估工时", u"设置工时", u"花费工时", u"记工时日志", u"状态修改", u"bug报告", u"解决问题"],
loc=2,
fontsize=12)
plt.title(u'任务活动分布图', fontsize=12)
plt.subplots_adjust(left=0.10, right=0.98, bottom=0.06, top=0.96)
dt = datetime.datetime.now()
_fn = 'pic/%s-issue-action.png' % dt.strftime('%Y%m%d%H%M%S%f')
if not __test:
plt.savefig(_fn, dpi=120)
else:
plt.show()
return _fn
def plot_station_data(site_data):
'''Given a pandas dataframe containing all weather data for a specific station, this function saves a plot with
the last 3 days worth of weather data for that station (or as much data as available if not yet 3-days).
Parameters:
site_data (dataframe): pandas dataframe containing all data, both directly observed and calculated,
for a specific station
Returns:
None
*saves plots to plot_dir listed near top of script*
'''
latest = site_data.index[-1]
site = site_data['station'][0]
lower_site = site.lower()
site_slice = site_data.loc[site_data.index >= (
latest - timedelta(hours=72))] #slice data to last 72hrs
timestamp_end = site_slice.index[-1].strftime(
'%Y%m%d%H%M') #timestamp end for saving .csv files
dt = site_slice.index[:] #define dt for making subplots
sitetitle = site_slice['name'][0] #define sitetitle for fig title
graphtimestamp_start = dt[0].strftime(
"%m/%d/%y") #start date, for fig title
graphtimestamp = dt[-1].strftime("%m/%d/%y") #end date, for fig title
markersize = 1.5 #markersize, for subplots
linewidth = 1.0 #linewidth, for subplots
fig = plt.figure() #create figure
fig.set_size_inches(10, 10) #size figure
if max(site_slice['snow_depth [cm]']
) > 0: #six axes if there is snow depth
ax1 = fig.add_subplot(6, 1, 1)
ax2 = fig.add_subplot(6, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(6, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(6, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(6, 1, 5, sharex=ax1)
ax6 = fig.add_subplot(6, 1, 6, sharex=ax1)
ax6.set_xlabel('Time (UTC)')
else: #five axes if no snow depth
ax1 = fig.add_subplot(5, 1, 1)
ax2 = fig.add_subplot(5, 1, 2, sharex=ax1)
ax3 = fig.add_subplot(5, 1, 3, sharex=ax1)
ax4 = fig.add_subplot(5, 1, 4, sharex=ax1)
ax5 = fig.add_subplot(5, 1, 5, sharex=ax1)
ax5.set_xlabel('Time (UTC)')
ax1.set_title(site + ' ' + sitetitle + ', NY ' + graphtimestamp_start +
' - ' + graphtimestamp) #title figure
#plot airT and dewT
if 'temp_2m [degC]' in site_slice.keys():
airT = site_slice['temp_2m [degC]']
ax1.plot_date(dt,
airT,
'o-',
label="Temp",
color="blue",
linewidth=linewidth,
markersize=markersize)
if 'dew_point_temp_2m [degC]' in site_slice.keys():
Td = site_slice['dew_point_temp_2m [degC]']
ax1.plot_date(dt,
Td,
'o-',
label="Dew Point",
color="black",
linewidth=linewidth,
markersize=markersize)
if ax1.get_ylim()[0] < 0 < ax1.get_ylim()[1]:
ax1.axhline(0, linestyle='-', linewidth=1.0, color='deepskyblue')
trans = transforms.blended_transform_factory(
ax1.get_yticklabels()[0].get_transform(), ax1.transData)
ax1.text(0,
0,
'0C',
color="deepskyblue",
transform=trans,
ha="right",
va="center") #light blue line at 0 degrees C
ax1.set_ylabel('2m Temp ($^\circ$C)')
ax1.legend(loc='best', ncol=2)
axes = [ax1] #begin axes list
#plot wind speed and gust
if 'avg_wind_speed_merge [m/s]' in site_slice.keys():
wnd_spd = site_slice[
'avg_wind_speed_merge [m/s]'] * 1.94384 #convert to knots
ax2.plot_date(dt,
wnd_spd,
'o-',
label='Speed',
color="forestgreen",
linewidth=linewidth,
markersize=markersize)
if 'max_wind_speed_merge [m/s]' in site_slice.keys():
wnd_gst = site_slice[
'max_wind_speed_merge [m/s]'] * 1.94384 #convert to knots
max_wnd_gst = wnd_gst.max(skipna=True)
ax2.plot_date(dt,
wnd_gst,
'o-',
label='Gust (Max ' + str(round(max_wnd_gst, 1)) + 'kt)',
color="red",
linewidth=linewidth,
markersize=markersize)
ax2.set_ylabel('Wind (kt)')
ax2.legend(loc='best', ncol=2)
axes.append(ax2)
#plot wind direction
if 'wind_direction_merge [degree]' in site_slice.keys():
wnd_dir = site_slice['wind_direction_merge [degree]']
ax3.plot_date(dt,
wnd_dir,
'o-',
label='Direction',
color="purple",
linewidth=0.2,
markersize=markersize)
ax3.set_ylim(-10, 370)
ax3.set_ylabel('Wind Direction')
ax3.set_yticks([0, 90, 180, 270, 360]) #locking y-ticks for wind direction
axes.append(ax3)
#plot MSLP (or station pressure, if MSLP unavailable)
if 'mean_sea_level_pressure [mbar]' in site_slice.keys():
mslp = site_slice['mean_sea_level_pressure [mbar]']
min_mslp = mslp.min(skipna=True) #min 3-day mslp value
max_mslp = mslp.max(skipna=True) #max 3-day mslp value
labelname = 'Min ' + str(round(min_mslp, 2)) + 'hPa, Max ' + str(
round(max_mslp, 2)) + 'hPa'
ax4.plot_date(dt,
mslp,
'o-',
label=labelname,
color='darkorange',
linewidth=linewidth,
markersize=markersize)
ax4.set_ylabel('MSLP (hPa)')
elif 'station_pressure [mbar]' in site_slice.keys():
sp = site_slice['station_pressure [mbar]']
min_sp = sp.min(skipna=True) #min 3-day station pressure value
max_sp = sp.max(skipna=True) #max 3-day station pressure value
labelname = 'Min ' + str(round(min_sp, 2)) + 'hPa, Max ' + str(
round(max_sp, 2)) + 'hPa'
ax4.plot_date(dt,
sp,
'o-',
label=labelname,
color='darkorange',
linewidth=linewidth,
markersize=markersize)
ax4.set_ylabel('STATION Pressure (hPa)')
print('unable to get mslp, used station pressure instead')
ax4.legend(loc='best')
axes.append(ax4)
#plot precip accum
if 'precip_incremental [mm]' in site_slice.keys():
precip_inc = site_slice['precip_incremental [mm]']
precip_accum = 0.0
precip_accum_list = []
for increment in precip_inc: #calculate precip accumulation
precip_accum = precip_accum + increment
precip_accum_list.append(precip_accum)
max_precip = max(precip_accum_list)
labelname = 'Precip (' + str(round(max_precip, 2)) + 'mm)'
ax5.plot_date(dt,
precip_accum_list,
'o-',
label=labelname,
color='navy',
linewidth=linewidth,
markersize=markersize)
if max_precip > 0:
ax5.set_ylim(-0.1 * max_precip, max_precip + max_precip * 0.2)
else:
ax5.set_ylim(-0.5, 5)
ax5.legend(loc='best')
ax5.set_ylabel('Precip (mm)')
axes.append(ax5)
#plot snow depth
if 'snow_depth [cm]' in site_slice.keys() and max(
site_slice['snow_depth [cm]']) > 0:
snow_depth = site_slice['snow_depth [cm]'] * 10 #convert to mm
max_snow_depth = snow_depth.max(skipna=True)
min_snow_depth = snow_depth.min(skipna=True)
labelname = 'Min Depth ' + str(round(
min_snow_depth, 2)) + 'mm, Max Depth ' + str(
round(max_snow_depth, 2)) + 'mm'
ax6.plot_date(dt,
snow_depth,
'o-',
label=labelname,
color='deepskyblue',
linewidth=linewidth,
markersize=markersize)
ax6.set_ylim(-0.1 * max_snow_depth,
max_snow_depth + max_snow_depth * 0.2)
if max_snow_depth > 0:
ax5.set_ylim(-0.1 * max_snow_depth,
max_snow_depth + max_snow_depth * 0.2)
ax6.legend(loc='best')
ax6.set_ylabel('Snow Depth (mm)')
axes.append(ax6)
for ax in axes:
ax.spines["top"].set_visible(False) #remove dark borders on subplots
ax.spines["right"].set_visible(False)
ax.spines["left"].set_visible(False)
ax.spines["bottom"].set_visible(False)
ax.tick_params(axis='x', which='both', bottom='on',
top='off') #add ticks at labeled times
ax.tick_params(axis='y', which='both', left='on', right='off')
ax.xaxis.set_major_locator(DayLocator()) #one date written per day
ax.xaxis.set_major_formatter(
DateFormatter('%b-%d')) #show date, written as 'Jul-12'
ax.xaxis.set_minor_locator(HourLocator(np.linspace(
6, 18, 3))) #hour labels every 6 hours
ax.xaxis.set_minor_formatter(DateFormatter('%H')) #show hour labels
ax.fmt_xdata = DateFormatter('Y%m%d%H%M%S') #fixes labels
ax.yaxis.grid(linestyle='--') #adds y-axis grid lines
ax.get_yaxis().set_label_coords(
-0.06, 0.5) #properly places y-labels away from figure
#define dates in YYYYmmdd format (for saving and finding files)
three_days_ago_date = (latest - timedelta(hours=72)).strftime('%Y%m%d')
two_days_ago_date = (latest - timedelta(hours=48)).strftime('%Y%m%d')
yesterday_date = (latest - timedelta(hours=24)).strftime('%Y%m%d')
today_date = latest.strftime('%Y%m%d')
plot_path = plot_dir + '/' + today_date
if not os.path.exists(plot_path):
os.mkdir(plot_path)
plt.savefig(plot_path + '/ops.nys_ground.' + timestamp_end + '.' +
lower_site + '.png',
bbox_inches='tight')
plt.close()
print('plotted ' + site)
def main():
if regenerate_data:
created = {}
seen_times = {}
seen_ups = {}
seen_scores = {}
with open(InputFilename, 'r') as f:
for line in f:
parts = [x for x in line.strip().split("\t")]
if len(parts) != 5:
raise Exception(f"Unexpected data here: {line}")
# this_time = int(parts[2][0:3])
this_time = datetime.datetime.strptime(
parts[0].split(".")[0],
"%Y-%m-%d %H:%M:%S") - datetime.timedelta(hours=7)
this_id = parts[1]
this_created = datetime.datetime.fromtimestamp(float(parts[2]))
this_ups = int(parts[3])
this_score = int(parts[4])
if this_id not in created:
created[this_id] = this_created
assert (this_id not in seen_times)
seen_times[this_id] = []
seen_ups[this_id] = []
seen_scores[this_id] = []
assert (created[this_id] == this_created)
if this_time < this_created + datetime.timedelta(hours=4):
seen_times[this_id].append(this_time)
seen_ups[this_id].append(this_ups)
seen_scores[this_id].append(this_score)
with open(CreatedPickle, 'wb') as f:
pickle.dump(created, f)
with open(SeenTimesPickle, 'wb') as f:
pickle.dump(seen_times, f)
with open(SeenUpsPickle, 'wb') as f:
pickle.dump(seen_ups, f)
with open(SeenScoresPickle, 'wb') as f:
pickle.dump(seen_scores, f)
with open(CreatedPickle, 'rb') as f:
created = pickle.load(f)
with open(SeenTimesPickle, 'rb') as f:
seen_times = pickle.load(f)
with open(SeenUpsPickle, 'rb') as f:
seen_ups = pickle.load(f)
with open(SeenScoresPickle, 'rb') as f:
seen_scores = pickle.load(f)
print(f"Found {len(created)} unique submissions")
ax = plt.subplot()
# Only show submissions created since we started logging
beginning_of_log = min([x[0] for x in seen_times.values() if len(x) > 0])
created = [x for x in created if (created[x] > beginning_of_log)]
# Only show submissions that reached thresh
# thresh = 50
# created = [x for x in created if len(seen_scores[x]) > 0 and max(seen_scores[x]) >= thresh]
# Only show a random sample of submissions
# proportion = 0.1
# created = [x for x in created if random.uniform(0, 1) < proportion]
print(f"After filtering, {len(created)} will be displayed")
for id in created:
plt.plot_date(seen_times[id],
seen_ups[id],
xdate=True,
markersize=1,
marker=".",
linestyle="solid")
rule = rrulewrapper(DAILY, interval=1)
loc = RRuleLocator(rule)
ax.xaxis.set_major_locator(loc)
formatter = DateFormatter('%m/%d')
ax.xaxis.set_major_formatter(formatter)
ax.set_ylim([0, 500])
plt.tight_layout()
plt.show()
from pathlib import Path
import epimargin.plots as plt
import numpy as np
import pandas as pd
from epimargin.smoothing import convolution
from matplotlib.dates import DateFormatter
root = Path.home() / "Dropbox" / "Covid"
results = root / "results"
data = Path("./data")
plt.set_theme("substack")
formatter = DateFormatter("%b\n%Y")
# fig 1
meta_ifrs = pd.read_stata(data / "meta_ifrs.dta")
all_location_comparison = pd.read_stata(data / "all_location_comparison.dta")
## male
levin_male = meta_ifrs[(meta_ifrs.location == "levin") & (
meta_ifrs.male == 1.0)].query('age <= 70').query('age >= 10')
od_male = meta_ifrs[(meta_ifrs.location == "od") & (
meta_ifrs.male == 1.0)].query('age <= 70').query('age >= 10')
cai_bihar_male = all_location_comparison[
(all_location_comparison.location == "Bihar")
& (all_location_comparison.male == 1.0)].query('ifr > 0')
cai_mumbai_male = all_location_comparison[
(all_location_comparison.location == "Mumbai")
& (all_location_comparison.male == 1.0)].sort_values("age_bin_pooled")
infile = "/scratch/dknapp4/Western_Hawaii/Moorea/moorea_sample_coral_output_sr_20190716.csv"
tdata = pd.read_csv(infile)
header = list(tdata)
header.pop(0)
thedates = np.array(tdata['Date'], dtype='S8')
data = np.asarray(tdata[header])
np.save("quick1.npy", data)
np.save("quick2.npy", thedates)
## data = np.load("quick1.npy")
## thedates = np.load("quick2.npy")
thedates = np.asarray(
[date(int(day[0:4]), int(day[4:6]), int(day[6:8])) for day in thedates])
rule = rrulewrapper(MONTHLY, interval=1)
loc = RRuleLocator(rule)
formatter = DateFormatter('%m/%d/%y')
with PdfPages('coral_change_moorea_sr_mean_rev20190716.pdf') as pdf:
## Page 1, Red
fig = plt.figure(figsize=(8, 10))
ax = plt.subplot(2, 1, 1)
ax.set_title('')
good = np.not_equal(data[:, 0], -9.)
tmean = np.mean(data[good, 0:8], axis=1)
tsdev = np.std(data[good, 0:8], axis=1)
plt.errorbar(thedates[good], tmean, yerr=tsdev, fmt='-bo', capsize=3)
ax.xaxis.set_major_locator(loc)
ax.xaxis.set_major_formatter(formatter)
ax.xaxis.set_tick_params(rotation=30, labelsize=10)
## plt.plot_date(thedates[good], tmean, 'b')
from pylab import *
from matplotlib.dates import DateFormatter, WeekdayLocator, HourLocator, DayLocator, MONDAY
from matplotlib.finance import quotes_historical_yahoo, candlestick, plot_day_summary, candlestick2
from matplotlib.font_manager import FontProperties
font = FontProperties(fname=r"c:\windows\fonts\simsun.ttc",size=18)
# 定义起始、终止日期和股票代码
date1 = ( 2012, 12, 25 )
date2 = ( 2013, 6, 1 )
stock_num = '000002.sz'
# 定义日期格式
mondays = WeekdayLocator(MONDAY)
alldays = DayLocator()
weekFormatter = DateFormatter('%b %d')
dayFormatter = DateFormatter('%d')
# 获取股票数据
quotes = quotes_historical_yahoo(stock_num, date1, date2)
if len(quotes) == 0:
raise SystemExit
# 绘制蜡烛线或美国线
fig = figure()
fig.subplots_adjust(bottom=0.2)
ax = fig.add_subplot(111)
ax.xaxis.set_major_locator(mondays)
ax.xaxis.set_minor_locator(alldays)
ax.xaxis.set_major_formatter(weekFormatter)
#注释掉下面的其中一行，可以得到蜡烛线或美国线
