Matplotlib和Numpy-创建日历热图 [英] Matplotlib and Numpy - Create a calendar heatmap

问题描述

是否可以在不使用熊猫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示例吗?

Is it possible to create a calendar heatmap without using pandas? If so, can someone post a simple example?

我有像8月16日这样的日期，还有一个像16这样的计数值，我认为这将是一种显示长时间内几天之间计数强度的快速简便的方法.

I have dates like Aug-16 and a count value like 16 and I thought this would be a quick and easy way to show intensity of counts between days for a long period of time.

谢谢

推荐答案

当然有可能，但是您需要跳过几圈.

It's certainly possible, but you'll need to jump through a few hoops.

首先，我假设您的意思是日历显示看起来像日历，而不是线性的格式(线性格式的热图"要容易得多).

First off, I'm going to assume you mean a calendar display that looks like a calendar, as opposed to a more linear format (a linear formatted "heatmap" is much easier than this).

关键是将任意长度的1D系列重塑为Nx7 2D阵列，其中每行是一周，列是几天.这很容易，但是您还需要正确标记月份和日期，这样可以使操作变得冗长.

The key is reshaping your arbitrary-length 1D series into an Nx7 2D array where each row is a week and columns are days. That's easy enough, but you also need to properly label months and days, which can get a touch verbose.

这是一个例子.它甚至没有远程尝试跨年边界(例如，2014年12月至2015年1月，等等).但是，希望它能帮助您入门:

Here's an example. It doesn't even remotely try to handle crossing across year boundaries (e.g. Dec 2014 to Jan 2015, etc). However, hopefully it gets you started:

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()

这篇关于Matplotlib和Numpy-创建日历热图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！