Python使用ReportLab画图
本文主要介绍如何借助ReportLab画图。首先看一下经典的hello word：#!/usr/bin/env pythonfrom reportlab.graphics.shapes import Drawing, Stringfrom reportlab.graphics import renderPDFd = Drawing(100, 100)s = String(50,
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本文主要介绍如何借助ReportLab画图。
首先看一下经典的hello word：
#!/usr/bin/env python
from reportlab.graphics.shapes import Drawing, String
from reportlab.graphics import renderPDF
d = Drawing(100, 100)
s = String(50, 50, 'Hello, world!', textAnchor='middle')
d.add(s)
renderPDF.drawToFile(d, 'hello.pdf', 'A simple PDF file')运行后可发现当前路径下多了一个hello.pdf，在中间写着hello world
下面用其根据数据画图，代码如下：
#!/usr/bin/env python
from reportlab.lib import colors
from reportlab.graphics.shapes import *
from reportlab.graphics import renderPDF
data = [
# Year Month Predicted High Low
(2007, 8, 113.2, 114.2, 112.2),
(2007, 9, 112.8, 115.8, 109.8),
(2007, 10, 111.0, 116.0, 106.0),
(2007, 11, 109.8, 116.8, 102.8),
(2007, 12, 107.3, 115.3, 99.3),
(2008, 1, 105.2, 114.2, 96.2),
(2008, 2, 104.1, 114.1, 94.1),
(2008, 3, 99.9, 110.9, 88.9),
(2008, 4, 94.8, 106.8, 82.8),
(2008, 5, 91.2, 104.2, 78.2),
]
drawing = Drawing(200, 150)
pred = [row[2]-40 for row in data]
high = [row[3]-40 for row in data]
low = [row[4]-40 for row in data]
times = [200*((row[0] + row[1]/12.0) - 2007)-110 for row in data]
drawing.add(PolyLine(zip(times, pred), strokeColor=colors.blue))
drawing.add(PolyLine(zip(times, high), strokeColor=colors.red))
drawing.add(PolyLine(zip(times, low), strokeColor=colors.green))
drawing.add(String(65, 115, 'Sunspots', fontSize=18, fillColor=colors.red))
renderPDF.drawToFile(drawing, 'report1.pdf', 'Sunspots')运行结果如下：
然后根据网页上的数据进行画图：
代码如下：
from urllib import urlopen
from reportlab.graphics.shapes import *
from reportlab.graphics.charts.lineplots import LinePlot
from reportlab.graphics.charts.textlabels import Label
from reportlab.graphics import renderPDF
URL = 'http://www.swpc.noaa.gov/ftpdir/weekly/Predict.txt'
COMMENT_CHARS = '#:'
drawing = Drawing(400, 200)
data = []
for line in urlopen(URL).readlines():
if not line.isspace() and not line[0] in COMMENT_CHARS:
data.append([float(n) for n in line.split()])
pred = [row[2] for row in data]
high = [row[3] for row in data]
low = [row[4] for row in data]
times = [row[0] + row[1]/12.0 for row in data]
lp = LinePlot()
lp.x = 50
lp.y = 50
lp.height = 125
lp.width = 300
lp.data = [zip(times, pred), zip(times, high), zip(times, low)]
lp.lines[0].strokeColor = colors.blue
lp.lines[1].strokeColor = colors.red
lp.lines[2].strokeColor = colors.green
drawing.add(lp)
drawing.add(String(250, 150, 'Sunspots',
fontSize=14, fillColor=colors.red))
renderPDF.drawToFile(drawing, 'report2.pdf', 'Sunspots')http://www.swpc.noaa.gov/ftpdir/weekly/Predict.txt:Predicted_Sunspot_Numbers_and_Radio_Flux: Predict.txt :Created: 2013 Nov 04 0600 UTC # Prepared by the U.S. Dept. of Commerce, NOAA, Space Weather Prediction Center (SWPC). # Please send comments and suggestions to swpc.webmaster@noaa.gov # # Sunspot Number: S.I.D.C. Brussels International Sunspot Number. # 10.7cm Radio Flux value: Penticton, B.C. Canada. # Predicted values are based on the consensus of the Solar Cycle 24 Prediction Panel. # # See the README3 file for further information. # # Missing or not applicable data: -1 # # Predicted Sunspot Number And Radio Flux Values # With Expected Ranges # # -----Sunspot Number------ ----10.7 cm Radio Flux---- # YR MO PREDICTED HIGH LOW PREDICTED HIGH LOW #-------------------------------------------------------------- 2013 05 60.3 61.3 59.3 117.8 118.8 116.8 2013 06 63.3 65.3 61.3 119.8 120.8 118.8 2013 07 66.2 69.2 63.2 121.5 123.5 119.5 2013 08 69.2 74.2 64.2 123.3 126.3 120.3 2013 09 72.3 77.3 67.3 125.8 129.8 121.8 2013 10 73.9 79.9 67.9 127.3 131.3 123.3 2013 11 74.5 81.5 67.5 127.8 132.8 122.8 2013 12 75.9 82.9 68.9 128.9 134.9 122.9 2014 01 78.1 86.1 70.1 130.6 137.6 123.6 2014 02 79.6 88.6 70.6 132.0 140.0 124.0 2014 03 81.8 90.8 72.8 133.8 141.8 125.8 2014 04 83.1 93.1 73.1 134.8 143.8 125.8 2014 05 82.2 92.2 72.2 134.1 143.1 125.1 2014 06 81.4 91.4 71.4 133.5 142.5 124.5 2014 07 80.2 90.2 70.2 132.3 141.3 123.3 2014 08 78.9 88.9 68.9 131.2 140.2 122.2 2014 09 77.6 87.6 67.6 129.9 138.9 120.9 2014 10 76.2 86.2 66.2 128.7 137.7 119.7 2014 11 74.8 84.8 64.8 127.4 136.4 118.4 2014 12 73.3 83.3 63.3 126.0 135.0 117.0 2015 01 71.8 81.8 61.8 124.6 133.6 115.6 2015 02 70.2 80.2 60.2 123.2 132.2 114.2 2015 03 68.7 78.7 58.7 121.7 130.7 112.7 2015 04 67.0 77.0 57.0 120.2 129.2 111.2 2015 05 65.4 75.4 55.4 118.7 127.7 109.7 2015 06 63.8 73.8 53.8 117.2 126.2 108.2 2015 07 62.1 72.1 52.1 115.7 124.7 106.7 2015 08 60.4 70.4 50.4 114.1 123.1 105.1 2015 09 58.7 68.7 48.7 112.6 121.6 103.6 2015 10 57.0 67.0 47.0 111.0 120.0 102.0 2015 11 55.3 65.3 45.3 109.5 118.5 100.5 2015 12 53.6 63.6 43.6 107.9 116.9 98.9 2016 01 51.9 61.9 41.9 106.3 115.3 97.3 2016 02 50.2 60.2 40.2 104.8 113.8 95.8 2016 03 48.5 58.5 38.5 103.2 112.2 94.2 2016 04 46.9 56.9 36.9 101.7 110.7 92.7 2016 05 45.2 55.2 35.2 100.2 109.2 91.2 2016 06 43.6 53.6 33.6 98.7 107.7 89.7 2016 07 42.0 52.0 32.0 97.2 106.2 88.2 2016 08 40.4 50.4 30.4 95.8 104.8 86.8 2016 09 38.8 48.8 28.8 94.3 103.3 85.3 2016 10 37.3 47.3 27.3 92.9 101.9 83.9 2016 11 35.7 45.7 25.7 91.5 100.5 82.5 2016 12 34.3 44.3 24.3 90.2 99.2 81.2 2017 01 32.8 42.8 22.8 88.8 97.8 79.8 2017 02 31.4 41.4 21.4 87.5 96.5 78.5 2017 03 30.0 40.0 20.0 86.3 95.3 77.3 2017 04 28.7 38.7 18.7 85.0 94.0 76.0 2017 05 27.4 37.4 17.4 83.8 92.8 74.8 2017 06 26.1 36.1 16.1 82.6 91.6 73.6 2017 07 24.9 34.9 14.9 81.5 90.5 72.5 2017 08 23.7 33.7 13.7 80.4 89.4 71.4 2017 09 22.5 32.5 12.5 79.3 88.3 70.3 2017 10 21.4 31.4 11.4 78.3 87.3 69.3 2017 11 20.3 30.3 10.3 77.3 86.3 68.3 2017 12 19.2 29.2 9.2 76.4 85.4 67.4 2018 01 18.2 28.2 8.2 75.4 84.4 66.4 2018 02 17.2 27.2 7.2 74.5 83.5 65.5 2018 03 16.3 26.3 6.3 73.7 82.7 64.7 2018 04 15.4 25.4 5.4 72.8 81.8 63.8 2018 05 14.5 24.5 4.5 72.1 81.1 63.1 2018 06 13.7 23.7 3.7 71.3 80.3 62.3 2018 07 12.9 22.9 2.9 70.6 79.6 61.6 2018 08 12.2 22.2 2.2 69.9 78.9 60.9 2018 09 11.5 21.5 1.5 69.2 78.2 60.2 2018 10 10.8 20.8 0.8 68.6 77.6 60.0 2018 11 10.1 20.1 0.1 68.0 77.0 60.0 2018 12 9.5 19.5 0.0 67.4 76.4 60.0 2019 01 8.9 18.9 0.0 66.9 75.9 60.0 2019 02 8.3 18.3 0.0 66.3 75.3 60.0 2019 03 7.8 17.8 0.0 65.9 74.9 60.0 2019 04 7.3 17.3 0.0 65.4 74.4 60.0 2019 05 6.8 16.8 0.0 65.0 74.0 60.0 2019 06 6.4 16.4 0.0 64.5 73.5 60.0 2019 07 5.9 15.9 0.0 64.1 73.1 60.0 2019 08 5.5 15.5 0.0 63.8 72.8 60.0 2019 09 5.1 15.1 0.0 63.4 72.4 60.0 2019 10 4.8 14.8 0.0 63.1 72.1 60.0 2019 11 4.4 14.4 0.0 62.8 71.8 60.0 2019 12 4.1 14.1 0.0 62.5 71.5 60.0运行结果如下：
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