我们从Python开源项目中，提取了以下20个代码示例，用于说明如何使用matplotlib.dates()。
def load_start_end_dates(self): """ function for loading start and end dates of each survey we want to process. File format is yyy/mm/dd hh:mn:ss yyy/mm/dd hh:mn:ss yyy/mm/dd hh:mn:ss yyy/mm/dd hh:mn:ss ... update the Campaign object stored in the program by populating its survey dictionary """ #set the survey selection option required by the survey populating #function: self.survey_selection_option=2 print 'hold on a sec' self.statusBar().showMessage("Hold on a sec") fname = QtGui.QFileDialog.getOpenFileName(self, 'Open file',self.data_path) self.surveydates=read_start_end_dates(fname) #call the next generic step of the survey selection process self.baseStationSelection()
def rel_dt_test(dt, min_rel_dt=(1,1), max_rel_dt=(12,31)): if dt_check(dt): dt_doy = dt2doy(dt) min_doy = dt2doy(datetime(dt.year, *min_rel_dt)) max_doy = dt2doy(datetime(dt.year, *max_rel_dt)) #If both relative dates are in the same year if min_doy < max_doy: min_dt = datetime(dt.year, min_rel_dt[0], min_rel_dt[1]) max_dt = datetime(dt.year, max_rel_dt[0], max_rel_dt[1]) else: #If relative dates span Jan 1 if dt_doy >= min_doy: min_dt = datetime(dt.year, min_rel_dt[0], min_rel_dt[1]) max_dt = datetime(dt.year + 1, max_rel_dt[0], max_rel_dt[1]) else: min_dt = datetime(dt.year - 1, min_rel_dt[0], min_rel_dt[1]) max_dt = datetime(dt.year, max_rel_dt[0], max_rel_dt[1]) out = (dt >= min_dt) & (dt <= max_dt) else: out = False return out
def extractWeekendHighlights(dates): weekendsOut = [] weekendSearch = [5, 6] weekendStart = None for i, date in enumerate(dates): if date.weekday() in weekendSearch: if weekendStart is None: # Mark start of weekend weekendStart = i else: if weekendStart is not None: # Mark end of weekend weekendsOut.append(( weekendStart, i, WEEKEND_HIGHLIGHT_COLOR, HIGHLIGHT_ALPHA )) weekendStart = None # Cap it off if we're still in the middle of a weekend if weekendStart is not None: weekendsOut.append(( weekendStart, len(dates)-1, WEEKEND_HIGHLIGHT_COLOR, HIGHLIGHT_ALPHA )) return weekendsOut
def automaticSurveySelection(self): """ Option for automatic selection of surveys among the raw data set. Algo: A time threshold is asked to the user, and used as a criteria to separate different survey. Time intervals between measurement dates for which station number changes are compared to the threshold. If it is higher, a new survey is considered. The base station is asked to the user Remarks: Only works when the base station (asked to the user) is allways the same. Whether the base station number is the same as the cycling station number is asked to the user. when complicated loop geometries are used, or for specific survey designs, this option is likely to fail. """ #set the survey selection option required by the survey populating #function: self.survey_selection_option=1 text, ok = QtGui.QInputDialog.getText(self, 'Input parameters', 'time threshold (hr)') if ok: self.t_threshold=int(text) text, ok2 = QtGui.QInputDialog.getText(self, 'Input parameters', 'base station=cycling station? (1=y/0=n)') if ok2: self.base_cycling_station=int(text) #call the next generic step of the survey selection process self.baseStationSelection()
def setSingleSurvey(self,swin): """ When user chooses to enter manually start and end dates of a single survey. Fill in the caimpagndata property. """ self.surveydates=[(datetime(int(swin.yrEdit.text()),int(swin.monthEdit.text()), int(swin.dayEdit.text()),int(swin.hrEdit.text()), int(swin.mnEdit.text()),int(swin.ssEdit.text())), datetime(int(swin.yrEditend.text()),int(swin.monthEditend.text()), int(swin.dayEditend.text()),int(swin.hrEditend.text()), int(swin.mnEditend.text()),int(swin.ssEditend.text())))] #call the next generic step of the survey selection process self.baseStationSelection()
def mpl_dates_to_datestrings(dates, mpl_formatter): """Convert matplotlib dates to iso-formatted-like time strings. Plotly's accepted format: "YYYY-MM-DD HH:MM:SS" (e.g., 2001-01-01 00:00:00) Info on mpl dates: http://matplotlib.org/api/dates_api.html """ _dates = dates # this is a pandas datetime formatter, times show up in floating point days # since the epoch (1970-01-01T00:00:00+00:00) if mpl_formatter == "TimeSeries_DateFormatter": try: dates = matplotlib.dates.epoch2num( [date*24*60*60 for date in dates] ) dates = matplotlib.dates.num2date(dates, tz=pytz.utc) except: return _dates # the rest of mpl dates are in floating point days since # (0001-01-01T00:00:00+00:00) + 1. I.e., (0001-01-01T00:00:00+00:00) == 1.0 # according to mpl --> try num2date(1) else: try: dates = matplotlib.dates.num2date(dates, tz=pytz.utc) except: return _dates time_stings = [' '.join(date.isoformat().split('+')[0].split('T')) for date in dates] return time_stings
def dt2o(dt): """Convert datetime to Python ordinal """ #return datetime.toordinal(dt) #This works for arrays of dt #return np.array(matplotlib.dates.date2num(dt)) return matplotlib.dates.date2num(dt) #Need to split ordinal into integer and decimal parts
def o2dt(o): """Convert Python ordinal to datetime """ #omod = np.modf(o) #return datetime.fromordinal(int(omod[1])) + timedelta(days=omod[0]) #Note: num2date returns dt or list of dt #This funciton should always return a list #return np.array(matplotlib.dates.num2date(o)) return matplotlib.dates.num2date(o) #Return integer DOY (julian)
def weekday_candlestick(ohlc_data, ax, fmt='%b %d', freq=7, **kwargs): """ Wrapper function for matplotlib.finance.candlestick_ohlc that artificially spaces data to avoid gaps from weekends ?????????? fmt: ???? freq: ??????? """ # Convert data to numpy array ohlc_data_arr = np.array(ohlc_data) ohlc_data_arr2 = np.hstack( [np.arange(ohlc_data_arr[:,0].size)[:,np.newaxis], ohlc_data_arr[:,1:]]) ndays = ohlc_data_arr2[:,0] # array([0, 1, 2, ... n-2, n-1, n]) # Convert matplotlib date numbers to strings based on `fmt` dates = mdates.num2date(ohlc_data_arr[:,0]) date_strings = [] for date in dates: date_strings.append(date.strftime(fmt)) # Plot candlestick chart mpf.candlestick_ohlc(ax, ohlc_data_arr2, **kwargs) # Format x axis ax.set_xticks(ndays[::freq]) ax.set_xticklabels(date_strings[::freq], rotation=45, ha='right') ax.set_xlim(ndays.min(), ndays.max())
def plotDatePrice(productID, productTitle, data): # Data setup x, y = [], [] for datapoint in data: date = datapoint.split('|')[0] price = float(datapoint.split('|')[1]) x.append(dt.datetime.strptime(date, '%Y-%m-%d')) y.append(price) x = matplotlib.dates.date2num(x) x_np, y_np = np.array(x), np.array(y) # Plot setup ax = plt.figure(figsize=(6, 3)).add_subplot(111) ax.spines['top'].set_visible(False) ax.spines['right'].set_visible(False) ax.get_xaxis().tick_bottom() ax.get_yaxis().tick_left() ax.plot(x_np, y_np, color='lightblue', lw=2) ax.margins(0.05) ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: ('$%i' % (x)))) ax.xaxis.set_major_formatter(DateFormatter('%Y-%m-%d')) plt.yticks(fontsize=8) plt.ylim(ymin=min(y)*0.7, ymax=max(y)*1.3) plt.title('Recent Price History\n'+productTitle, weight ='light', fontsize=12, y=1.08) plt.xticks(rotation=40, fontsize=7) plt.tight_layout() plt.savefig(productID+'.png') return productID+'.png' # ----- Email Configuration ----------------------------------------------------
def default(general, task, scope): #Read and clean data data = pd.read_csv(os.path.join(general["folder"], general["zite_data"])) data = clean_additional_header(data) data['time'] = pd.to_datetime(data['time'], format="%Y-%m-%d_%H-%M-%S") data['peers_total'] = data['peers_total'].astype('int') #Peers by time plot_series = [] for el in scope: zite_name = el['zite'] temp_ = data[data.address == zite_name][['time','peers_total']].sort_values(by='time') plot_series.append({'time':temp_['time'], 'peers_total':temp_['peers_total'], 'label':el['label']}) #Plots fig = plt.figure() ax = plt.subplot(111) for p in plot_series: ax.plot(p['time'], p['peers_total'], label=p['label']) plt.xticks(rotation=15) majorFormatter = matplotlib.dates.DateFormatter('%m-%d %H:%M:%S') ax.xaxis.set_major_formatter(majorFormatter) ax.autoscale_view() ax.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1), ncol=3, fancybox=True, shadow=True) plt.show()
def __init__(self, *args, **kwargs): super(NuPICPlotOutput, self).__init__(*args, **kwargs) # Turn matplotlib interactive mode on. plt.ion() self.dates = [] self.convertedDates = [] self.value = [] self.rawValue = [] self.allValues = [] self.allRawValues = [] self.predicted = [] self.anomalyScore = [] self.anomalyLikelihood = [] self.actualLine = None self.rawLine = None self.predictedLine = None self.anomalyScoreLine = None self.anomalyLikelihoodLine = None self.linesInitialized = False self._chartHighlights = [] fig = plt.figure(figsize=(16, 10)) gs = gridspec.GridSpec(2, 1, height_ratios=[3, 1]) self._mainGraph = fig.add_subplot(gs[0, 0]) plt.title(self.name) plt.ylabel('Value') plt.xlabel('Date') self._anomalyGraph = fig.add_subplot(gs[1]) plt.ylabel('Percentage') plt.xlabel('Date') # Maximizes window mng = plt.get_current_fig_manager() mng.resize(800, 600) plt.tight_layout()
def plot_resid(d,savename='resfig1.png'): """ Plots the residual frequency after the first wipe using the TLE velocity. """ flim = [-2.e3, 2.e3] t = d['tvec'] dates = [dt.datetime.fromtimestamp(ts) for ts in t] datenums = md.date2num(dates) xfmt = md.DateFormatter('%Y-%m-%d %H:%M:%S') fig1 = plt.figure(figsize=(7, 9)) doppler_residual = sp.interpolate.interp1d(d['tvec'],d['dopfit']) fvec = d["fvec"] res0 = d["res0"] res1 = d["res1"] plt.subplot(211) mesh = plt.pcolormesh(datenums, fvec, sp.transpose(10.*sp.log10(res0+1e-12)), vmin=-5, vmax=25) plt.plot(datenums, (150.0/400.0)*doppler_residual(t), "r--", label="doppler resid") ax = plt.gca() ax.xaxis.set_major_formatter(xfmt) plt.ylim(flim) plt.subplots_adjust(bottom=0.2) plt.xticks(rotation=25) plt.xlabel("UTC") plt.ylabel("Frequency (Hz)") plt.title("Power ch0 (dB) %1.2f MHz"%(150.012)) plt.legend() plt.colorbar(mesh, ax=ax) # quicklook spectra of residuals spectra along with measured Doppler residual from second channel. plt.subplot(212) mesh = plt.pcolormesh(datenums, fvec, sp.transpose(10.*sp.log10(res1+1e-12)), vmin=-5, vmax=25) plt.plot(datenums, doppler_residual(t), "r--", label="doppler resid") ax = plt.gca() ax.xaxis.set_major_formatter(xfmt) plt.ylim(flim) plt.xlabel("UTC") plt.ylabel("Frequency (Hz)") plt.title("Power ch1 (dB), %1.2f MHz"%(400.032)) plt.subplots_adjust(bottom=0.2) plt.xticks(rotation=25) plt.legend() plt.colorbar(mesh, ax=ax) plt.tight_layout() print('Saving residual plots: '+savename) plt.savefig(savename, dpi=300) plt.close(fig1)
def openRawdata(self): """ - Display a file opening window - Populate a Campaign object: read all raw data - Set a new window for survey selection options - link selection options to apropriate functions: 3 options are currently available: an automatic selection, a a selection with a user input file containing start-end dates for each survey, and a single srvey selection with a single start-end date Each option calls the appropriate function: automaticSurveySelection() load_start_end_dates() askUserSingleSurvey() which then calls the generic self.baseStationSelection() function which eventually calls the data populating methods within the data_objects module with the appropriate options. """ # open file fname = QtGui.QFileDialog.getOpenFileName(self, 'Open file',self.data_path) campaigndata=Campaign() #populate a Campaign object campaigndata.readRawDataFile(fname) self.campaigndata=campaigndata if fname: #create new window and set as central Widget: surveySelectionWin=QtGui.QWidget() self.setCentralWidget(surveySelectionWin) self.statusBar().showMessage("Please choose survey selection method") # create buttons and actions surveySelectionWin.btn1 = QtGui.QPushButton('automatic survey selection', self) surveySelectionWin.btn1.clicked.connect(self.automaticSurveySelection) surveySelectionWin.btn2 = QtGui.QPushButton('Load survey dates file', self) surveySelectionWin.btn2.clicked.connect(self.load_start_end_dates) surveySelectionWin.btn3 = QtGui.QPushButton('Single survey selection', self) surveySelectionWin.btn3.clicked.connect(self.askUserSingleSurvey) #locations grid = QtGui.QGridLayout() grid.addWidget(surveySelectionWin.btn1,0,0,1,1) grid.addWidget(surveySelectionWin.btn2,1,0,1,1) grid.addWidget(surveySelectionWin.btn3,2,0,1,1) surveySelectionWin.setLayout(grid) surveySelectionWin.setWindowTitle('Survey selections') surveySelectionWin.show()
def askUserSingleSurvey(self): """ ask the user for start and end dates of a single survey """ self.survey_selection_option=3 chooseSingleSurvey=QtGui.QWidget() self.setCentralWidget(chooseSingleSurvey) self.statusBar().showMessage("Please enter start/end dates of a survey") chooseSingleSurvey.yr=QtGui.QLabel('year') chooseSingleSurvey.month=QtGui.QLabel('month') chooseSingleSurvey.day=QtGui.QLabel('day') chooseSingleSurvey.hr=QtGui.QLabel('hr') chooseSingleSurvey.mn=QtGui.QLabel('mn') chooseSingleSurvey.ss=QtGui.QLabel('ss') chooseSingleSurvey.yrEdit=QtGui.QLineEdit() chooseSingleSurvey.monthEdit=QtGui.QLineEdit() chooseSingleSurvey.dayEdit=QtGui.QLineEdit() chooseSingleSurvey.hrEdit=QtGui.QLineEdit() chooseSingleSurvey.mnEdit=QtGui.QLineEdit() chooseSingleSurvey.ssEdit=QtGui.QLineEdit() chooseSingleSurvey.yrEditend=QtGui.QLineEdit() chooseSingleSurvey.monthEditend=QtGui.QLineEdit() chooseSingleSurvey.dayEditend=QtGui.QLineEdit() chooseSingleSurvey.hrEditend=QtGui.QLineEdit() chooseSingleSurvey.mnEditend=QtGui.QLineEdit() chooseSingleSurvey.ssEditend=QtGui.QLineEdit() # create buttons and actions chooseSingleSurvey.btn1 = QtGui.QPushButton('ok', self) chooseSingleSurvey.btn1.clicked.connect(lambda : self.setSingleSurvey(chooseSingleSurvey)) #locations grid = QtGui.QGridLayout() grid.addWidget(QtGui.QLabel('Start date'),1,1) grid.addWidget(QtGui.QLabel('End date'),1,2) grid.addWidget(chooseSingleSurvey.yr,2,0) grid.addWidget(chooseSingleSurvey.yrEdit,2,1) grid.addWidget(chooseSingleSurvey.yrEditend,2,2) grid.addWidget(chooseSingleSurvey.month,3,0) grid.addWidget(chooseSingleSurvey.monthEdit,3,1) grid.addWidget(chooseSingleSurvey.monthEditend,3,2) grid.addWidget(chooseSingleSurvey.day,4,0) grid.addWidget(chooseSingleSurvey.dayEdit,4,1) grid.addWidget(chooseSingleSurvey.dayEditend,4,2) grid.addWidget(chooseSingleSurvey.hr,5,0) grid.addWidget(chooseSingleSurvey.hrEdit,5,1) grid.addWidget(chooseSingleSurvey.hrEditend,5,2) grid.addWidget(chooseSingleSurvey.mn,6,0) grid.addWidget(chooseSingleSurvey.mnEdit,6,1) grid.addWidget(chooseSingleSurvey.mnEditend,6,2) grid.addWidget(chooseSingleSurvey.ss,7,0) grid.addWidget(chooseSingleSurvey.ssEdit,7,1) grid.addWidget(chooseSingleSurvey.ssEditend,7,2) grid.addWidget(chooseSingleSurvey.btn1,8,0) chooseSingleSurvey.setLayout(grid) chooseSingleSurvey.setWindowTitle('Survey selection') chooseSingleSurvey.show()
def fn_getdatetime_list(fn): """Extract all datetime strings from input filename """ #Want to split last component fn = os.path.split(os.path.splitext(fn)[0])[-1] import re #WV01_12JUN152223255-P1BS_R1C1-102001001B3B9800__WV01_12JUN152224050-P1BS_R1C1-102001001C555C00-DEM_4x.tif #Need to parse above with month name #Note: made this more restrictive to avoid false matches: #'20130304_1510_1030010020770600_1030010020CEAB00-DEM_4x' #This is a problem, b/c 2015/17/00: #WV02_20130315_10300100207D5600_1030010020151700 #This code should be obsolete before 2019 #Assume new filenames #fn = fn[0:13] #Use cascading re find to pull out timestamps #Note: Want to be less restrictive here - could have a mix of YYYYMMDD_HHMM, YYYYMMDD and YYYY in filename #Should probably search for all possibilities, then prune #NOTE: these don't include seconds in the time #NOTE: could have 20130304_1510__20130304__whatever in filename #The current approach will only catch the first datetime dstr = None dstr = re.findall(r'(?:^|_|-)(?:19|20)[0-9][0-9](?:0[1-9]|1[012])(?:0[1-9]|[12][0-9]|3[01])[_T](?:0[0-9]|1[0-9]|2[0-3])[0-5][0-9]', fn) if not dstr: dstr = re.findall(r'(?:^|_|-)(?:19|20)[0-9][0-9](?:0[1-9]|1[012])(?:0[1-9]|[12][0-9]|3[01])(?:0[0-9]|1[0-9]|2[0-3])[0-5][0-9]', fn) if not dstr: dstr = re.findall(r'(?:^|_|-)(?:19|20)[0-9][0-9](?:0[1-9]|1[012])(?:0[1-9]|[12][0-9]|3[01])(?:_|-)', fn) #This should pick up dates separated by a dash #dstr = re.findall(r'(?:^|_|-)(?:19|20)[0-9][0-9](?:0[1-9]|1[012])(?:0[1-9]|[12][0-9]|3[01])', fn) if not dstr: dstr = re.findall(r'(?:^|_|-)(?:19|20)[0-9][0-9](?:_|-)', fn) #This is for USGS archive filenames if not dstr: dstr = re.findall(r'[0-3][0-9][a-z][a-z][a-z][0-9][0-9]', fn) #if not dstr: # dstr = re.findall(r'(?:^|_)(?:19|20)[0-9][0-9]', fn) #This is a hack to remove peripheral underscores and dashes dstr = [d.lstrip('_').rstrip('_') for d in dstr] dstr = [d.lstrip('-').rstrip('-') for d in dstr] #This returns an empty list of nothing is found out = [strptime_fuzzy(s) for s in dstr] #This is USGS archive format #out = [datetime.strptime(s, '%d%b%y') for s in dstr][0] return out
def factory(jds, mags, magerrs=None, telescopename="Unknown", object="Unknown", verbose=False): """Returns a valid lightcurve object from the provided arrays. The numpy arrays jds and mags are mandatory. If you do not specify a third array containing the magerrs, we will calculate them "automatically" (all the same value), to avoid having 0.0 errors. @type jds: 1D numpy array @param jds: julian dates @type mags: 1D numpy array @param mags: magnitudes @type magerrs: 1D numpy array @param magerrs: optional magnitude errors @todo: improve it and use this in file importing functions """ # Make a brand new lightcurve object : newlc = lightcurve() # Of couse we can/should check a lot of things, but let's be naive : newlc.jds = np.asarray(jds) newlc.mags = np.asarray(mags) if magerrs is None: newlc.magerrs = np.zeros(len(newlc.jds)) + 0.1 else: newlc.magerrs = np.asarray(magerrs) if len(newlc.jds) != len(newlc.mags) or len(newlc.jds) != len(newlc.magerrs): raise RuntimeError, "lightcurve factory called with arrays of incoherent lengths" newlc.mask = newlc.magerrs >= 0.0 # This should be true for all ! newlc.properties = [{}] * len(newlc.jds) newlc.telescopename = telescopename newlc.object = object newlc.setindexlabels() newlc.commentlist = [] newlc.sort() # not sure if this is needed / should be there newlc.validate() if verbose: print "New lightcurve %s with %i points" % (str(newlc), len(newlc.jds)) return newlc
def plot_volume_inensity_analysis_stacked_bar_graph(mus_names, wo_dates_list, mus_vol, mus_int): x = wo_dates_list c_1 = np.array(mus_vol) c_1_colors = colors c_1_labels = mus_names c_2 = np.array(mus_int) c_2_colors = colors c_2_labels = c_1_labels ind = np.arange(len(c_1[0])) width = .8 f, ax = plt.subplots(2, 1, sharex=True, figsize=(17,11)) f.subplots_adjust(bottom=0.5) #make room for the legend locs, labels = plt.xticks() plt.setp(labels, rotation=25) p = [] # list of bar properties def create_subplot(matrix, colors, axis, title, dates): bar_renderers = [] #ind = np.arange(matrix.shape[1]) ind = dates bottoms = np.cumsum(np.vstack((np.zeros(matrix.shape[1]), matrix)), axis=0)[:-1] for i, row in enumerate(matrix): r = axis.bar(ind, row, width=0.5, color=colors[i], bottom=bottoms[i]) bar_renderers.append(r) axis.set_title(title) axis.set_xlim((x[0],x[-1])) axis.xaxis_date() return bar_renderers p.extend(create_subplot(c_1, c_1_colors, ax[0], 'Volume', x)) p.extend(create_subplot(c_2, c_2_colors, ax[1], 'Intensity', x)) plt.suptitle('Volume and Intensity by Muscle Group') ax[0].set_ylabel('Total Volume') # add left y label ax[1].set_ylabel('Total Intensity') # add left y label ax[0].grid(True, color='w') ax[0].set_axis_bgcolor('black') ax[1].grid(True, color='w') ax[1].set_axis_bgcolor('black') f.legend(((x[0] for x in p)), # bar properties (c_1_labels), bbox_to_anchor=(0.5, 0), loc='lower center', ncol=4) f.canvas.set_window_title('WorkoutAnalyzer - Volume and Intensity by Muscle Group') f.set_size_inches(20,20) plt.subplots_adjust(left=0.08, bottom=0.20, right=0.9, top=0.93, wspace=0.20, hspace=0.10) plt.show()
def initializeLines(self, timestamp): print "initializing %s" % self.name anomalyRange = (0.0, 1.0) self.dates = deque([timestamp] * WINDOW, maxlen=WINDOW) self.convertedDates = deque( [date2num(date) for date in self.dates], maxlen=WINDOW ) self.value = deque([0.0] * WINDOW, maxlen=WINDOW) self.rawValue = deque([0.0] * WINDOW, maxlen=WINDOW) self.predicted = deque([0.0] * WINDOW, maxlen=WINDOW) self.anomalyScore = deque([0.0] * WINDOW, maxlen=WINDOW) self.anomalyLikelihood = deque([0.0] * WINDOW, maxlen=WINDOW) actualPlot, = self._mainGraph.plot(self.dates, self.value) self.actualLine = actualPlot rawPlot, = self._mainGraph.plot(self.dates, self.rawValue) self.rawLine = rawPlot predictedPlot, = self._mainGraph.plot(self.dates, self.predicted) self.predictedLine = predictedPlot self._mainGraph.legend(tuple(['actual', 'raw', 'predicted']), loc=3) anomalyScorePlot, = self._anomalyGraph.plot( self.dates, self.anomalyScore, 'm' ) anomalyScorePlot.axes.set_ylim(anomalyRange) self.anomalyScoreLine = anomalyScorePlot anomalyLikelihoodPlot, = self._anomalyGraph.plot( self.dates, self.anomalyScore, 'r' ) anomalyLikelihoodPlot.axes.set_ylim(anomalyRange) self.anomalyLikelihoodLine = anomalyLikelihoodPlot self._anomalyGraph.legend( tuple(['anomaly score', 'anomaly likelihood']), loc=3 ) dateFormatter = DateFormatter('%H:%M:%S.%f') self._mainGraph.xaxis.set_major_formatter(dateFormatter) self._anomalyGraph.xaxis.set_major_formatter(dateFormatter) self._mainGraph.relim() self._mainGraph.autoscale_view(True, True, True) self.linesInitialized = True
def write(self, timestamp, value, predicted, anomalyScore, rawValue): # We need the first timestamp to initialize the lines at the right X value, # so do that check first. if not self.linesInitialized: self.initializeLines(timestamp) anomalyLikelihood = self.anomalyLikelihoodHelper.anomalyProbability( value, anomalyScore, timestamp ) self.dates.append(timestamp) self.convertedDates.append(date2num(timestamp)) self.value.append(value) self.rawValue.append(rawValue) self.allValues.append(value) self.allRawValues.append(rawValue) self.predicted.append(predicted) self.anomalyScore.append(anomalyScore) self.anomalyLikelihood.append(anomalyLikelihood) # Update main chart data self.actualLine.set_xdata(self.convertedDates) self.actualLine.set_ydata(self.value) self.rawLine.set_xdata(self.convertedDates) self.rawLine.set_ydata(self.rawValue) self.predictedLine.set_xdata(self.convertedDates) self.predictedLine.set_ydata(self.predicted) # Update anomaly chart data self.anomalyScoreLine.set_xdata(self.convertedDates) self.anomalyScoreLine.set_ydata(self.anomalyScore) self.anomalyLikelihoodLine.set_xdata(self.convertedDates) self.anomalyLikelihoodLine.set_ydata(self.anomalyLikelihood) # Remove previous highlighted regions for poly in self._chartHighlights: poly.remove() self._chartHighlights = [] # weekends = extractWeekendHighlights(self.dates) anomalies = extractAnomalyIndices(self.anomalyLikelihood) # Highlight weekends in main chart # self.highlightChart(weekends, self._mainGraph) # Highlight anomalies in anomaly chart self.highlightChart(anomalies, self._anomalyGraph) maxValue = max(max(self.allValues), max(self.allRawValues)) self._mainGraph.relim() self._mainGraph.axes.set_ylim(0, maxValue + (maxValue * 0.02)) self._mainGraph.relim() self._mainGraph.autoscale_view(True, scaley=False) self._anomalyGraph.relim() self._anomalyGraph.autoscale_view(True, True, True) plt.draw() plt.pause(0.00000000001)
