Matplotlib 和 Numpy - 创建日历热图

Question

是否可以在不使用 pandas 的情况下创建日历热图？ 如果是这样，有人可以发布一个简单的例子吗？

我有像 8 月 16 日这样的日期和像 16 这样的计数值，我认为这将是一种快速简便的方法，可以在很长一段时间内显示几天之间的计数强度。

谢谢

Answer 1

免责声明：这是我自己的包的插件。虽然我帮助 OP 迟了几年，但我希望其他人会发现它有用。

我对一个相关问题进行了一些挖掘。当我找不到任何其他满足我所有要求的包时，我最终为此编写了一个新包。

这个包还没有完善，它仍然有一个稀疏的文档，但我还是在 PyPi 上发布了它以使其可供其他人使用。任何反馈都表示赞赏，无论是在这里还是在我的GitHub。

七月

包名为july，可以用pip安装：

$ pip install july

以下是直接来自自述文件的一些用例：

导入包并生成数据

import numpy as np
import july
from july.utils import date_range

dates = date_range("2020-01-01", "2020-12-31")
data = np.random.randint(0, 14, len(dates))

GitHub Activity 样图：

july.heatmap(dates, data, title='Github Activity', cmap="github")

连续数据的每日热图（带颜色条）：

july.heatmap(
    osl_df.date, # Here, osl_df is a pandas data frame.
    osl_df.temp, 
    cmap="golden", 
    colorbar=True, 
    title="Average temperatures: Oslo , Norway"
)

用month_grid=True 列出每个月的大纲

july.heatmap(dates=dates, 
             data=data, 
             cmap="Pastel1",
             month_grid=True, 
             horizontal=True,
             value_label=False,
             date_label=False,
             weekday_label=True,
             month_label=True, 
             year_label=True,
             colorbar=False,
             fontfamily="monospace",
             fontsize=12,
             title=None,
             titlesize="large",
             dpi=100)

最后，您还可以创建月份或日历图：

# july.month_plot(dates, data, month=5) # This will plot only May.
july.calendar_plot(dates, data)

类似的包：

• calplot 由 Tom Kwok。
  • GitHub：Link
  • 安装：pip install calplot
  • 比july 积极维护和更好的文档。
  • 以熊猫为中心，采用带有日期和值的熊猫系列。
  • 如果您只寻找热图功能并且不需要month_plot 或calendar_plot，这是非常好的选择。
• calmap Martijn Vermaat。
  • GitHub：Link
  • 安装：pip install calmap
  • calplot 产生的包。
  • 似乎得到了更长时间的积极维护。

Answer 2

我希望创建一个日历热图，其中每个月都单独显示。我还需要用天数（day_of_month）和它的值标签来注释每一天。

我受到了此处发布的答案以及以下网站的启发：

Here, although in R

Heatmap using pcolormesh

但是，我似乎没有找到完全符合我要求的东西，所以我决定在这里发布我的解决方案，也许可以节省其他人想要相同情节的时间。

我的示例使用了一些 Pandas 来生成一些虚拟数据，因此您可以轻松插入自己的数据源。除此之外，它只是 matplotlib。

代码的输出如下所示。为了我的需要，我还想突出显示数据为 0 的日子（见 1 月 1 日）。

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.patches import Polygon

# Settings
years = [2018] # [2018, 2019, 2020]
weeks = [1, 2, 3, 4, 5, 6]
days = ['M', 'T', 'W', 'T', 'F', 'S', 'S']
month_names = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August',
               'September', 'October', 'November', 'December']

def generate_data():
    idx = pd.date_range('2018-01-01', periods=365, freq='D')
    return pd.Series(range(len(idx)), index=idx)


def split_months(df, year):
    """
    Take a df, slice by year, and produce a list of months,
    where each month is a 2D array in the shape of the calendar
    :param df: dataframe or series
    :return: matrix for daily values and numerals
    """
    df = df[df.index.year == year]


    # Empty matrices
    a = np.empty((6, 7))
    a[:] = np.nan

    day_nums = {m:np.copy(a) for m in range(1,13)}  # matrix for day numbers
    day_vals = {m:np.copy(a) for m in range(1,13)}  # matrix for day values

    # Logic to shape datetimes to matrices in calendar layout
    for d in df.iteritems():  # use iterrows if you have a DataFrame

        day = d[0].day
        month = d[0].month
        col = d[0].dayofweek

        if d[0].is_month_start:
            row = 0

        day_nums[month][row, col] = day  # day number (0-31)
        day_vals[month][row, col] = d[1] # day value (the heatmap data)

        if col == 6:
            row += 1

    return day_nums, day_vals


def create_year_calendar(day_nums, day_vals):
    fig, ax = plt.subplots(3, 4, figsize=(14.85, 10.5))

    for i, axs in enumerate(ax.flat):

        axs.imshow(day_vals[i+1], cmap='viridis', vmin=1, vmax=365)  # heatmap
        axs.set_title(month_names[i])

        # Labels
        axs.set_xticks(np.arange(len(days)))
        axs.set_xticklabels(days, fontsize=10, fontweight='bold', color='#555555')
        axs.set_yticklabels([])

        # Tick marks
        axs.tick_params(axis=u'both', which=u'both', length=0)  # remove tick marks
        axs.xaxis.tick_top()

        # Modify tick locations for proper grid placement
        axs.set_xticks(np.arange(-.5, 6, 1), minor=True)
        axs.set_yticks(np.arange(-.5, 5, 1), minor=True)
        axs.grid(which='minor', color='w', linestyle='-', linewidth=2.1)

        # Despine
        for edge in ['left', 'right', 'bottom', 'top']:
            axs.spines[edge].set_color('#FFFFFF')

        # Annotate
        for w in range(len(weeks)):
            for d in range(len(days)):
                day_val = day_vals[i+1][w, d]
                day_num = day_nums[i+1][w, d]

                # Value label
                axs.text(d, w+0.3, f"{day_val:0.0f}",
                         ha="center", va="center",
                         fontsize=7, color="w", alpha=0.8)

                # If value is 0, draw a grey patch
                if day_val == 0:
                    patch_coords = ((d - 0.5, w - 0.5),
                                    (d - 0.5, w + 0.5),
                                    (d + 0.5, w + 0.5),
                                    (d + 0.5, w - 0.5))

                    square = Polygon(patch_coords, fc='#DDDDDD')
                    axs.add_artist(square)

                # If day number is a valid calendar day, add an annotation
                if not np.isnan(day_num):
                    axs.text(d+0.45, w-0.31, f"{day_num:0.0f}",
                             ha="right", va="center",
                             fontsize=6, color="#003333", alpha=0.8)  # day

                # Aesthetic background for calendar day number
                patch_coords = ((d-0.1, w-0.5),
                                (d+0.5, w-0.5),
                                (d+0.5, w+0.1))

                triangle = Polygon(patch_coords, fc='w', alpha=0.7)
                axs.add_artist(triangle)

    # Final adjustments
    fig.suptitle('Calendar', fontsize=16)
    plt.subplots_adjust(left=0.04, right=0.96, top=0.88, bottom=0.04)

    # Save to file
    plt.savefig('calendar_example.pdf')


for year in years:
    df = generate_data()
    day_nums, day_vals = split_months(df, year)
    create_year_calendar(day_nums, day_vals)

可能还有很大的优化空间，但这可以满足我的需要。

Answer 3

编辑：我现在看到问题要求没有熊猫的情节。即便如此，这个问题是“python 日历热图”的第一页谷歌结果，所以我将把它留在这里。无论如何，我建议使用熊猫。您可能已经将它作为另一个包的依赖项，而 pandas 拥有迄今为止处理日期时间数据的最佳 API（pandas.Timestamp 和 pandas.DatetimeIndex）。

我能为这些图找到的唯一 Python 包是 calmap，它未维护且与最近的 matplotlib 不兼容。所以我决定自己写。它产生如下图：

这是代码。输入是一个带有日期时间索引的系列，给出了热图的值：

import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt


DAYS = ['Sun.', 'Mon.', 'Tues.', 'Wed.', 'Thurs.', 'Fri.', 'Sat.']
MONTHS = ['Jan.', 'Feb.', 'Mar.', 'Apr.', 'May', 'June', 'July', 'Aug.', 'Sept.', 'Oct.', 'Nov.', 'Dec.']


def date_heatmap(series, start=None, end=None, mean=False, ax=None, **kwargs):
    '''Plot a calendar heatmap given a datetime series.

    Arguments:
        series (pd.Series):
            A series of numeric values with a datetime index. Values occurring
            on the same day are combined by sum.
        start (Any):
            The first day to be considered in the plot. The value can be
            anything accepted by :func:`pandas.to_datetime`. The default is the
            earliest date in the data.
        end (Any):
            The last day to be considered in the plot. The value can be
            anything accepted by :func:`pandas.to_datetime`. The default is the
            latest date in the data.
        mean (bool):
            Combine values occurring on the same day by mean instead of sum.
        ax (matplotlib.Axes or None):
            The axes on which to draw the heatmap. The default is the current
            axes in the :module:`~matplotlib.pyplot` API.
        **kwargs:
            Forwarded to :meth:`~matplotlib.Axes.pcolormesh` for drawing the
            heatmap.

    Returns:
        matplotlib.collections.Axes:
            The axes on which the heatmap was drawn. This is set as the current
            axes in the `~matplotlib.pyplot` API.
    '''
    # Combine values occurring on the same day.
    dates = series.index.floor('D')
    group = series.groupby(dates)
    series = group.mean() if mean else group.sum()

    # Parse start/end, defaulting to the min/max of the index.
    start = pd.to_datetime(start or series.index.min())
    end = pd.to_datetime(end or series.index.max())

    # We use [start, end) as a half-open interval below.
    end += np.timedelta64(1, 'D')

    # Get the previous/following Sunday to start/end.
    # Pandas and numpy day-of-week conventions are Monday=0 and Sunday=6.
    start_sun = start - np.timedelta64((start.dayofweek + 1) % 7, 'D')
    end_sun = end + np.timedelta64(7 - end.dayofweek - 1, 'D')

    # Create the heatmap and track ticks.
    num_weeks = (end_sun - start_sun).days // 7
    heatmap = np.zeros((7, num_weeks))
    ticks = {}  # week number -> month name
    for week in range(num_weeks):
        for day in range(7):
            date = start_sun + np.timedelta64(7 * week + day, 'D')
            if date.day == 1:
                ticks[week] = MONTHS[date.month - 1]
            if date.dayofyear == 1:
                ticks[week] += f'\n{date.year}'
            if start <= date < end:
                heatmap[day, week] = series.get(date, 0)

    # Get the coordinates, offset by 0.5 to align the ticks.
    y = np.arange(8) - 0.5
    x = np.arange(num_weeks + 1) - 0.5

    # Plot the heatmap. Prefer pcolormesh over imshow so that the figure can be
    # vectorized when saved to a compatible format. We must invert the axis for
    # pcolormesh, but not for imshow, so that it reads top-bottom, left-right.
    ax = ax or plt.gca()
    mesh = ax.pcolormesh(x, y, heatmap, **kwargs)
    ax.invert_yaxis()

    # Set the ticks.
    ax.set_xticks(list(ticks.keys()))
    ax.set_xticklabels(list(ticks.values()))
    ax.set_yticks(np.arange(7))
    ax.set_yticklabels(DAYS)

    # Set the current image and axes in the pyplot API.
    plt.sca(ax)
    plt.sci(mesh)

    return ax


def date_heatmap_demo():
    '''An example for `date_heatmap`.

    Most of the sizes here are chosen arbitrarily to look nice with 1yr of
    data. You may need to fiddle with the numbers to look right on other data.
    '''
    # Get some data, a series of values with datetime index.
    data = np.random.randint(5, size=365)
    data = pd.Series(data)
    data.index = pd.date_range(start='2017-01-01', end='2017-12-31', freq='1D')

    # Create the figure. For the aspect ratio, one year is 7 days by 53 weeks.
    # We widen it further to account for the tick labels and color bar.
    figsize = plt.figaspect(7 / 56)
    fig = plt.figure(figsize=figsize)

    # Plot the heatmap with a color bar.
    ax = date_heatmap(data, edgecolor='black')
    plt.colorbar(ticks=range(5), pad=0.02)

    # Use a discrete color map with 5 colors (the data ranges from 0 to 4).
    # Extending the color limits by 0.5 aligns the ticks in the color bar.
    cmap = mpl.cm.get_cmap('Blues', 5)
    plt.set_cmap(cmap)
    plt.clim(-0.5, 4.5)

    # Force the cells to be square. If this is set, the size of the color bar
    # may look weird compared to the size of the heatmap. That can be corrected
    # by the aspect ratio of the figure or scale of the color bar.
    ax.set_aspect('equal')

    # Save to a file. For embedding in a LaTeX doc, consider the PDF backend.
    # http://sbillaudelle.de/2015/02/23/seamlessly-embedding-matplotlib-output-into-latex.html
    fig.savefig('heatmap.pdf', bbox_inches='tight')

    # The firgure must be explicitly closed if it was not shown.
    plt.close(fig)

Answer 4

下面是一个代码，可用于为某个值的每日配置文件生成日历图。

"""
Created on Tue Sep  4 11:17:25 2018

@author: woldekidank
"""

import numpy as np
from datetime import date
import datetime
import matplotlib.pyplot as plt
import random


D = date(2016,1,1)
Dord = date.toordinal(D)
Dweekday = date.weekday(D)

Dsnday = Dord - Dweekday + 1 #find sunday
square = np.array([[0, 0],[ 0, 1], [1, 1], [1, 0], [0, 0]])#x and y to draw a square
row = 1
count = 0
while row != 0:
    for column in range(1,7+1):    #one week per row
        prof = np.ones([24, 1])
        hourly = np.zeros([24, 1])
        for i in range(1,24+1):
            prof[i-1, 0] = prof[i-1, 0] * random.uniform(0, 1)
            hourly[i-1, 0] = i / 24
        plt.title('Temperature Profile')
        plt.plot(square[:, 0] + column - 1, square[:, 1] - row + 1,color='r')    #go right each column, go down each row
        if date.fromordinal(Dsnday).month == D.month:
            if count == 0:
                plt.plot(hourly, prof)
            else:
                plt.plot(hourly + min(square[:, 0] + column - 1), prof + min(square[:, 1] - row + 1))

            plt.text(column - 0.5, 1.8 - row, datetime.datetime.strptime(str(date.fromordinal(Dsnday)),'%Y-%m-%d').strftime('%a'))
            plt.text(column - 0.5, 1.5 - row, date.fromordinal(Dsnday).day)

        Dsnday = Dsnday + 1
        count = count + 1

    if date.fromordinal(Dsnday).month == D.month:
        row = row + 1    #new row
    else:
        row = 0    #stop the while loop

下面是这段代码的输出

Answer 5

这当然是可能的，但你需要跳过几个圈子。

首先，我假设您的意思是看起来像日历的日历显示，而不是更线性的格式（线性格式的“热图”比这更容易）。

关键是将任意长度的 1D 系列重塑为 Nx7 2D 数组，其中每行是一周，列是天。这很容易，但您还需要正确标记月份和日期，这可能会有点冗长。

这是一个例子。它甚至不会远程尝试处理跨年边界（例如 2014 年 12 月至 2015 年 1 月等）。但是，希望它能让您入门：

import datetime as dt
import matplotlib.pyplot as plt
import numpy as np

def main():
    dates, data = generate_data()
    fig, ax = plt.subplots(figsize=(6, 10))
    calendar_heatmap(ax, dates, data)
    plt.show()

def generate_data():
    num = 100
    data = np.random.randint(0, 20, num)
    start = dt.datetime(2015, 3, 13)
    dates = [start + dt.timedelta(days=i) for i in range(num)]
    return dates, data

def calendar_array(dates, data):
    i, j = zip(*[d.isocalendar()[1:] for d in dates])
    i = np.array(i) - min(i)
    j = np.array(j) - 1
    ni = max(i) + 1

    calendar = np.nan * np.zeros((ni, 7))
    calendar[i, j] = data
    return i, j, calendar


def calendar_heatmap(ax, dates, data):
    i, j, calendar = calendar_array(dates, data)
    im = ax.imshow(calendar, interpolation='none', cmap='summer')
    label_days(ax, dates, i, j, calendar)
    label_months(ax, dates, i, j, calendar)
    ax.figure.colorbar(im)

def label_days(ax, dates, i, j, calendar):
    ni, nj = calendar.shape
    day_of_month = np.nan * np.zeros((ni, 7))
    day_of_month[i, j] = [d.day for d in dates]

    for (i, j), day in np.ndenumerate(day_of_month):
        if np.isfinite(day):
            ax.text(j, i, int(day), ha='center', va='center')

    ax.set(xticks=np.arange(7), 
           xticklabels=['M', 'T', 'W', 'R', 'F', 'S', 'S'])
    ax.xaxis.tick_top()

def label_months(ax, dates, i, j, calendar):
    month_labels = np.array(['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul',
                             'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])
    months = np.array([d.month for d in dates])
    uniq_months = sorted(set(months))
    yticks = [i[months == m].mean() for m in uniq_months]
    labels = [month_labels[m - 1] for m in uniq_months]
    ax.set(yticks=yticks)
    ax.set_yticklabels(labels, rotation=90)

main()