Python matplotlib.pyplot 模块，plot_date() 实例源码

def get_stock(symbol):
    last_year_date = datetime.strftime(datetime.now() - relativedelta(years=1), "%Y-%m-%d")
    date = get_last_trading_date()
    url = requests.get('https://www.quandl.com/api/v3/datasets/WIKI/{}.json?start_date={}&end_date={}'.format(symbol, last_year_date, date))
    json_dataset = url.json()
    json_data = json_dataset['dataset']['data']
    dates = []  
    closing = []
    for day in json_data:
        dates.append(datetime.strptime(day[0], "%Y-%m-%d"))
        closing.append(day[4])
    plt.plot_date(dates, closing, '-')
    plt.title(symbol)
    plt.xlabel('Date')
    plt.ylable('Stock Price')
    plt.savefig('foo.png')

def animate(i):  # updates the graph continuously
    with open('bitcoin_usd.csv') as csv_file:
        readcsv = csv.reader(csv_file, delimiter=',')
        xs = []
        ys = []
        for row in readcsv:
            if len(row) > 1:
                x, y = [float(s) for s in row]
                xs.append(dt.datetime.fromtimestamp(x))
                ys.append(y)
        print(len(xs))
        dates = matplotlib.dates.date2num(xs)
        fig.clear()
        plt.xlabel("timestamp")
        plt.ylabel("price of a bitcoin in usd")
        plt.plot_date(dates, ys, 'r.-')  # this gives a line instead of scatter plot

def vykresli_spojnice(hodnoty, nadpis, jednotky):
    fig, ax = pyplot.subplots()

    pyplot.title(nadpis)
    pyplot.xlabel('datum')
    pyplot.ylabel(jednotky)

    x_hodnoty = [polozka[0] for polozka in hodnoty]
    y_hodnoty = [polozka[1] for polozka in hodnoty]

    pyplot.plot_date(x_hodnoty, y_hodnoty, 'b-', linewidth=0.5)
    pyplot.axhline(0, linewidth=0.2)

    # v jakých intervalech a jak mají vypadat popisky na ose X
    ax.xaxis.set_major_locator(YearLocator())

    pyplot.show()

def plot_by_all( data ):
    x_vals = list()
    y_vals = list()
    for k in sorted(data):
        x_vals.append( datetime.datetime.strptime(k, key_pattern) )
        y_vals.append(data[k])

    x_vals2 = matplotlib.dates.date2num(x_vals)

    plt.clf()
    #plt.plot_date(x_vals2, y_vals)
    plt.plot(x_vals2, y_vals)
    plt.show()

def plot_by_interval(data, zamg_dfs = None):
    p_vals = {}
    # sort by year
    for k in sorted(data):
        v = data[k]
        d_obj = datetime.datetime.strptime(k, key_pattern)

        series_key = datetime.datetime.strftime(d_obj, "%Y")
        #data_key = datetime.datetime.strftime(d_obj, "%m-%d")

        p_vals.setdefault(series_key, dict()).setdefault("x_vals", list()).append(d_obj)
        p_vals.setdefault(series_key, dict()).setdefault("y_vals", list()).append(v)

    plt.clf()

    fig, axis = plt.subplots(nrows=len(p_vals)*2, sharex=False, sharey=False)

    a_iter = iter(axis)

    for k in sorted(p_vals):
        v = p_vals[k]
        ax = next(a_iter)

        y_vals = v["y_vals"]
        x_vals = matplotlib.dates.date2num(v["x_vals"])
        ax.plot_date(x_vals, y_vals)
        # ax.plot(x_vals, y_vals)

        ax = next(a_iter)
        if zamg_dfs is not None and k in zamg_dfs:
            df = zamg_dfs[k]
            df['Wien Hohe Warte']['48,2486']['16,3564']['198.0']['Anhöhe']['Ebene']\
                ['Lufttemperatur']['Lufttemperatur um 14 MEZ (°C)'].plot(ax=ax)

    plt.show()

def plot_pvals_filtered_dfs(p_vals, filtered_dfs, years = None):
    year_count = len(p_vals)

    if years is not None:
        i = 0
        for k in p_vals:
            if not k in years:
                continue
            i += 1

        year_count = i

    plt.clf()
    fig, axis = plt.subplots(nrows=year_count * 2, sharex=False, sharey=False)

    a_iter = iter(axis)

    for k in sorted(p_vals):
        if years is not None and not k in years:
            continue

        v = p_vals[k]
        ax = next(a_iter)

        y_vals = v["y_vals"]
        x_vals = matplotlib.dates.date2num(v["x_vals"])
        ax.plot_date(x_vals, y_vals, marker='x')
        # ax.plot(x_vals, y_vals)

        ax = next(a_iter)
        if filtered_dfs is not None and k in filtered_dfs:
            df = filtered_dfs[k]
            df.plot(ax=ax, marker='+', linestyle='' )

    plt.show()

def plot_tick_range_normalised(tick_path, range_start, range_end):

    if os.path.exists(tick_path) == False:
        print(tick_path + ' file doesnt exist')

        quit()

    date_cols = ['RateDateTime']

    df = pd.read_csv(tick_path, usecols=['RateDateTime','RateBid','RateAsk'])

    start_index = tfh.find_index_closest_date(range_start, tick_path)
    end_index = tfh.find_index_closest_date(range_end, tick_path)

    # dont proceed if we didnt find indices
    if (start_index is None or end_index is None):
        print('start_index or end_index was None')
        quit()

    ticks_s = df.iloc[start_index:end_index]

    ticks = ((ticks_s['RateAsk'] + ticks_s['RateBid']) / 2.0)

    ticks_norm = (ticks - ticks.min()) / (ticks.max() - ticks.min())

    dates_dt = [dt.datetime.strptime(str.split(x, '.')[0], '%Y-%m-%d %H:%M:%S') for x in ticks_s['RateDateTime'].values]

    dates = mdates.date2num(dates_dt)

    plt.plot_date(dates, ticks_norm, 'b-')

def plot_tick_range(tick_path, range_start, range_end):

    if os.path.exists(tick_path) == False:
        print(tick_path + ' file doesnt exist')

        quit()

    date_cols = ['RateDateTime']

    df = pd.read_csv(tick_path, usecols=['RateDateTime','RateBid','RateAsk'])

    start_index = tfh.find_index_closest_date(range_start, tick_path)
    end_index = tfh.find_index_closest_date(range_end, tick_path)

    # dont proceed if we didnt find indices
    if (start_index is None or end_index is None):
        print('start_index or end_index was None')
        quit()

    ticks_s = df.iloc[start_index:end_index]

    ticks = (ticks_s['RateAsk'] + ticks_s['RateBid']) / 2.0

    dates_dt = [dt.datetime.strptime(str.split(x, '.')[0], '%Y-%m-%d %H:%M:%S') for x in ticks_s['RateDateTime'].values]

    dates = mdates.date2num(dates_dt)

    #fig = plt.figure()
    #ax1 = plt.subplot2grid((1,1), (0,0))

    plt.plot_date(dates, ticks, 'b-')

def main():
    array_metrics=get_array_kpi()
    perfdatalist=array_metrics.get('perf_data')
    hostiolist = []
    dtstimelist = []
    readresponselist =[]
    print (perfdatalist)
    for perf_host in perfdatalist:
        hostiolist.append(perf_host.get('HostIOs'))
        readresponselist.append(perf_host.get('ReadResponseTime'))
        epochtime=(perf_host.get ('timestamp'))
        dtstime = round(epochtime/1000)
        dtstimelist.append(dtstime)

    dateconv=np.vectorize(dt.datetime.fromtimestamp)
    convtimelist =(dateconv(dtstimelist))
    # print(convtimelist)
    fig, ax = plt.subplots(1)
    fig.autofmt_xdate()
    xfmt = md.DateFormatter('%Y-%m-%d %H:%M:%S')
    ax.xaxis.set_major_formatter(xfmt)
    plt.plot_date(convtimelist,hostiolist,'-')
    plt.plot_date(convtimelist, readresponselist, '-')
    plt.legend(['HostIOs', 'ReadResponseTime'], loc='upper left')
    plt.subplots_adjust(bottom=0.1)
    plt.xticks(rotation=25)
    plt.ylabel('Host IOs')
    plt.xlabel('Time')
    plt.title('Host IOs and Read Response times over the last Hour')
    plt.show()

def UMDmodelVcss(modelfile,cssfile,plotdir):
    ''' Plot comparison of model run to observed data '''
    import matplotlib
    matplotlib.use('Agg')
    import numpy as np
    import sys
    from netCDF4 import Dataset
    import matplotlib.pyplot as plt
    from datetime import datetime
    # read in file and vars
    f = Dataset(modelfile,'r')
    wrfeta = f.variables['ZNU'][0][:]
    times = f.variables['Times'][:]
    wrflats = f.variables['XLAT'][0][:]
    wrflons = f.variables['XLONG'][0][:]
    var = f.variables['CO2_ANT'][:,0,:,:]
    z = 0 # assuming lowest level of model
    # College Park lat/lon
    UMD = [38.99,-76.94]
    UMDx,UMDy = findpoint(wrflats,wrflons,UMD[0],UMD[1])

    # read in CO2 data from SENSE format
    date,time,co2css,ch4css,h2ocss = np.genfromtxt(cssfile,missing_values=-9999.00,filling_values=np.nan,usecols=(0,1,2,3,4),unpack=True)
    co2css[co2css==-9999]=np.nan                 # above doesn't work; explicitly define here
    tim = ["%8d%06d" % (date[t],time[t]) for t in range(len(date))]
    tim = [datetime.strptime(tim[t], "%Y%m%d%H%M%S") for t in range(len(tim))]

    # convert wrf times to datetime objects
    times = [''.join(times[t,:]) for t in range(len(times))]
    times = [datetime.strptime(times[t],"%Y-%m-%d_%H:%M:%S") for t in range(len(times))]

    # plot data
    plt.plot_date(times,var[:,UMDy,UMDx],color='red',label='WRF')
    plt.plot_date(tim,co2css,color='black',label='LGR')
    plt.ylim([380,430])
    plt.legend()
    plt.savefig(plotdir+'/UMDLGRvWRF.png')

def generateActivityInfoForGroup(self, groupName):
        timestampNow = int(time())
        timestampYesterday = timestampNow - self.timestampSubtract
        records = list(self.coll.find({ 'to': groupName, 'timestamp': { '$gt': timestampYesterday } }).sort([ ('timestamp', DESCENDING) ]))
        fn = self.generateTmpFileName()
        # Get histogram for activity
        hist, bins = np.histogram([ x['timestamp'] for x in records ], bins=24)
        center = (bins[:-1] + bins[1:]) / 2
        datex = [ datetime.fromtimestamp(x) for x in center ]
        pp.figure(figsize=(6,14))
        ax = pp.subplot(2, 1, 1)
        pp.plot_date(datex, hist, '.-')
        pp.gcf().autofmt_xdate()
        pp.xlabel(u'??????', fontproperties=self.prop)
        pp.ylabel(u'??????', fontproperties=self.prop)
        ax.xaxis.set_major_formatter(DateFormatter('%m-%d %H:%M'))
        # Get bar chart for active users
        pieDat = Counter([ x['from'] for x in records ])
        pieDatSorted = sorted([ (k, pieDat[k]) for k in pieDat ],key=lambda x: x[1], reverse=True)
        if len(pieDatSorted) > self.maxActivityInfoCount:
            pieDatSorted = pieDatSorted[:self.maxActivityInfoCount]
        ax = pp.subplot(2, 1, 2)
        width = 0.7
        x = np.arange(len(pieDatSorted)) + width
        xText = [ xx[0] for xx in pieDatSorted ]
        y = [ xx[1] for xx in pieDatSorted ]
        pp.bar(x, y, width)
        a = pp.gca()
        a.set_xticklabels(a.get_xticks(), { 'fontProperties': self.prop })
        pp.xticks(x, xText, rotation='vertical')
        pp.xlabel(u'??', fontproperties=self.prop)
        pp.ylabel(u'24?????', fontproperties=self.prop)
        ax.set_xlim([ 0, len(xText) + 1 - width ])
        pp.margins(0.2)
        pp.savefig(fn)
        return fn

def plot_lamptemp_ts(ax, data):
    """
    Plots the lamp temperature

    """
    plt.setp(ax.get_xticklabels(), visible=False)

    ax.set_ylabel('temp (degC)')
    ax.set_ylim(34, 38)
    ax.plot_date(data['mpl_timestamp'][:, 0].ravel(),
                 data['AL52CO_lamptemp'][:].ravel(),
                 '-', color='#ff4d4d')
    ax.text(0.05, 0.98, 'Lamp Temp', axes_title_style, transform=ax.transAxes)
    return ax

def make_efficiency_date(
            total_data,
            avg_data,
            f_name,
            title=None,
            x_label=None,
            y_label=None,
            x_ticks=None,
            y_ticks=None):

        fig = plt.figure()

        if title is not None:
            plt.title(title, fontsize=16)
        if x_label is not None:
            plt.ylabel(x_label)
        if y_label is not None:
            plt.xlabel(y_label)

        v_date = []
        v_val = []

        for data in total_data:
            dates = dt.date2num(datetime.datetime.strptime(data[0], '%H:%M'))
            to_int = round(float(data[1]))
            plt.plot_date(dates, data[1], color=plt.cm.brg(to_int))
        for data in avg_data:
            dates = dt.date2num(datetime.datetime.strptime(data[0], '%H:%M'))
            v_date.append(dates)
            v_val.append(data[1])

        plt.plot_date(v_date, v_val, "^y-", label='Average')
        plt.legend()
        plt.savefig(f_name)
        plt.close(fig)

def plot_tick_range(tick_path, range_start, range_end):

    if os.path.exists(tick_path) == False:
        print(tick_path + ' file doesnt exist')

        quit()

    date_cols = ['RateDateTime']

    df = pd.read_csv(tick_path, usecols=['RateDateTime','RateBid','RateAsk'])

    start_index = tfh.find_index_closest_date(range_start, tick_path)
    end_index = tfh.find_index_closest_date(range_end, tick_path)

    # dont proceed if we didnt find indices
    if (start_index is None or end_index is None):
        print('start_index or end_index was None')
        quit()

    ticks_s = df.iloc[start_index:end_index]

    ticks = (ticks_s['RateAsk'] + ticks_s['RateBid']) / 2.0

    dates_dt = [dt.datetime.strptime(str.split(x, '.')[0], '%Y-%m-%d %H:%M:%S') for x in ticks_s['RateDateTime'].values]

    dates = mdates.date2num(dates_dt)

    #fig = plt.figure()
    #ax1 = plt.subplot2grid((1,1), (0,0))

    plt.plot_date(dates, ticks, 'b-')

    # candlestick_ohlc(ax1, ohlc, width=0.0004, colorup='#77d879', colordown='#db3f3f')

    # for label in ax1.xaxis.get_ticklabels():
    #     label.set_rotation(45)

    # ax1.xaxis.set_major_formatter(mdates.DateFormatter('%m-%d %H:%M'))
    # ax1.xaxis.set_major_locator(mticker.MaxNLocator(10))
    # ax1.grid(True)


    # plt.xlabel('Date')
    # plt.ylabel('Price')
    # plt.title(ohlc_path)
    # plt.legend()
    # plt.subplots_adjust(left=0.09, bottom=0.20, right=0.94, top=0.90, wspace=0.2, hspace=0)
    #plt.show()

    # plot_ohlc_range