我编写了一个脚本 (Python 2.6)，以 x、y、z(.csv 格式)对数据进行网格化，并显示数据的等高线图.
I have written a script (Python 2.6) to grid data in x,y,z (.csv format), and display a contour plot of the data.
该脚本适用于某些数据集，但不适用于其他数据集 - 即使两个数据集都是使用相同的脚本创建的.
The script works for some datasets but not others - even though both datasets were created using the same script.
要绘制的数据集是通过从主数据集中进行子采样创建的.
The datasets to be plotted are created by sub-sampling from a master dataset.
这是 every_4.csv 的示例(每 4 行对主数据集进行二次抽样).
Here is an example of every_4.csv (master dataset sub-sampled for every 4 rows).
Easting Northing TMI_nT
526243.0 5254128.9 62278.8
526259.4 5254128.9 62352.3
526275.9 5254128.9 62303.3
526292.3 5254128.9 62361.9
526308.8 5254128.9 62667.8
526325.2 5254128.9 62668.6
526341.7 5254128.9 61700.1
526358.1 5254128.9 62029.6
526374.6 5254128.9 62042.7
此数据使用我的脚本正确绘制.
This data plots correctly using my script.
"every_3.csv" 与every_4.csv 格式相同，导致我的脚本在尝试执行时挂起(没有错误消息):
"every_3.csv" Which is of the same form as every_4.csv causes my script to hang (no error message) when it tries to execute:
zi = griddata(x, y, z, xi, yi)
zi = griddata(x, y, z, xi, yi)
这是我正在使用的脚本:
here is the script I am using:
### program to test gridding and contouring
import numpy as N
import scipy as S
from matplotlib.mlab import griddata
import matplotlib.pyplot as P
import matplotlib.cm as CM
longarray = N.loadtxt('every_3.csv', skiprows=1, delimiter=',')
x = longarray[:,0]
y = longarray[:,1]
z = longarray[:,2]
max_x = max(x)
min_x = min(x)
max_y = max(y)
min_y = min(y)
##print max_y
##print min_x
##print max_x
##print min_y
xi = N.linspace(min_x, max_x)
yi = N.linspace(min_y, max_y)
zi = griddata(x, y, z, xi, yi)
print zi
CS = P.contourf(xi,yi,zi,15,cmap=CM.RdYlBu)
P.show()
我无法弄清楚为什么这个脚本会挂起某些文件而不是其他文件.
I cannot work out why this script hangs for some files and not others.
注意:如果我在 excel 中打开 every_3.csv 并删除一些行(比如文件中 50% 的行)，我可以让脚本运行...
Note: If I open every_3.csv in excel and delete some rows (say 50% of the rows in the file) I can get the script to run...
更新
这是一个更完整的数据集:
Here is a more complete data set:
Easting Northing TMI_nT
526243 5254128.9 62278.8
526247.1 5254128.9 62413.3
526251.2 5254128.9 62398.6
526255.3 5254128.9 62355.7
526259.4 5254128.9 62352.3
526263.6 5254128.9 62327.8
526267.7 5254128.9 62279.6
526271.8 5254128.9 62322.1
526275.9 5254128.9 62303.3
526280 5254128.9 62375.1
526284.1 5254128.9 62561.4
526288.2 5254128.9 62302.4
526292.3 5254128.9 62361.9
526374.6 5254128.9 62042.7
526378.7 5254128.9 62139.6
526382.8 5254128.9 61891.5
526386.9 5254128.9 61937.2
526391 5254128.9 62238.7
526395.1 5254128.9 62344.9
526399.2 5254128.9 62220.4
526403.3 5254128.9 62027
526407.4 5254128.9 62392.2
526411.6 5254128.9 63252.4
526415.7 5254128.9 62951.8
526419.8 5254128.9 62504.5
526423.9 5254128.9 62927.6
526428 5254128.9 62813.9
526432.1 5254128.9 63381.4
526436.2 5254128.9 63458.5
526440.3 5254128.9 63273
526444.4 5254128.9 62945
526448.6 5254128.9 62466.2
526452.7 5254128.9 62300.3
526456.8 5254128.9 62164.3
526460.9 5254128.9 62144.7
526465 5254128.9 62216.7
526469.1 5254128.9 62307.3
526473.2 5254128.9 62437.6
526477.3 5254128.9 62495
526481.4 5254128.9 62256.1
526485.6 5254128.9 61951.2
526489.7 5254128.9 61632
526493.8 5254128.9 61221.6
526497.9 5254128.9 62414.3
526502 5254128.9 64152.7
526506.1 5254128.9 61964.7
526510.2 5254128.9 61952.5
526514.3 5254128.9 62104.2
526518.4 5254128.9 62190.4
526522.6 5254128.9 62334.8
526526.7 5254128.9 62468.8
526530.8 5254128.9 62437.3
526296.4 5253951.8 62138
526300.6 5253951.8 62283.3
526304.7 5253951.8 62336
526308.8 5253951.8 62367.6
526312.9 5253951.8 62416
526317 5253951.8 62395.3
526321.1 5253951.8 62364.1
526325.2 5253951.8 62363.2
526329.3 5253951.8 62292.6
526333.4 5253951.8 62252
526337.6 5253951.8 62179.1
526341.7 5253951.8 62086.8
526345.8 5253951.8 62069.6
526349.9 5253951.8 62108.4
526354 5253951.8 61994
526358.1 5253951.8 61909.1
526362.2 5253951.8 61943.1
526366.3 5253951.8 61918.6
526370.4 5253951.8 61895.9
526374.6 5253951.8 61871.6
526378.7 5253951.8 61821.4
526382.8 5253951.8 61778.1
526386.9 5253951.8 61738.2
526391 5253951.8 61644.5
526395.1 5253951.8 61555.8
526399.2 5253951.8 61540
526403.3 5253951.8 61549.6
526407.4 5253951.8 61596.4
526411.6 5253951.8 61566.5
526415.7 5253951.8 61565.1
526419.8 5253951.8 61287.6
526423.9 5253951.8 61368
526428 5253951.8 61481.5
526432.1 5253951.8 61228.8
526436.2 5253951.8 61024.6
526440.3 5253951.8 60960.9
526444.4 5253951.8 61146.7
526448.6 5253951.8 61240.8
526452.7 5253951.8 61396.5
526456.8 5253951.8 61536.9
526460.9 5253951.8 61677.1
526465 5253951.8 61715.6
526469.1 5253951.8 61749.8
526473.2 5253951.8 61809.1
526477.3 5253951.8 61939.2
526481.4 5253951.8 61913.3
526485.6 5253951.8 62035.7
526489.7 5253951.8 62108.9
526493.8 5253951.8 62025.4
526497.9 5253951.8 61929.1
526502 5253951.8 61947.2
526506.1 5253951.8 62182.8
526510.2 5253951.8 62239.3
526514.3 5253951.8 62071.7
526518.4 5253951.8 62054.5
526522.6 5253951.8 62001.3
526526.7 5253951.8 61993.5
526530.8 5253951.8 62009.3
526534.9 5253951.8 61966.5
526539 5253951.8 61947.2
526543.1 5253951.8 61936.4
526547.2 5253951.8 61974
526551.3 5253951.8 62023.6
526555.4 5253951.8 62068.7
526559.6 5253951.8 62096.1
526563.7 5253951.8 62113.4
526567.8 5253951.8 62085.1
526571.9 5253951.8 62100.7
526576 5253951.8 62180.1
526580.1 5253951.8 62231.8
526584.2 5253951.8 62142.9
526588.3 5253951.8 62137.1
526592.4 5253951.8 62135.4
526596.6 5253951.8 62118.5
526600.7 5253951.8 62104.9
526604.8 5253951.8 62126.5
526608.9 5253951.8 62129.7
526613 5253951.8 62127
526617.1 5253951.8 62122.8
526621.2 5253951.8 62091.5
526625.3 5253951.8 62143.7
526629.4 5253951.8 62144.2
526633.6 5253951.8 62148.1
526637.7 5253951.8 62145.6
526641.8 5253951.8 62247.1
526645.9 5253951.8 62267.5
526650 5253951.8 62131.8
526243 5253955.9 62073.3
526247.1 5253955.9 62018.5
526251.2 5253955.9 61948.4
526255.3 5253955.9 61965.1
526259.4 5253955.9 61980.3
526263.6 5253955.9 61982.2
526267.7 5253955.9 61985.3
526271.8 5253955.9 61979.6
526275.9 5253955.9 61999.4
526280 5253955.9 62025.7
526284.1 5253955.9 62064.8
526288.2 5253955.9 62068.6
526292.3 5253955.9 62099.2
526296.4 5253955.9 62172.6
526300.6 5253955.9 62283.8
526304.7 5253955.9 62440.8
526308.8 5253955.9 62458.1
526312.9 5253955.9 62588.3
526317 5253955.9 62521.1
526321.1 5253955.9 62390.8
526325.2 5253955.9 62310.4
526329.3 5253955.9 62271
526333.4 5253955.9 62214.3
526337.6 5253955.9 62118.8
526341.7 5253955.9 62024.5
526345.8 5253955.9 61989.4
526349.9 5253955.9 61943.5
526354 5253955.9 61835.8
526506.1 5253955.9 61915.8
526510.2 5253955.9 62043
526514.3 5253955.9 62039.2
526518.4 5253955.9 62021.1
526522.6 5253955.9 62014
526526.7 5253955.9 61930.3
526530.8 5253955.9 61880.7
526534.9 5253955.9 61758.4
526539 5253955.9 61733.5
526543.1 5253955.9 61787.5
526547.2 5253955.9 61862.6
526551.3 5253955.9 61934.6
526555.4 5253955.9 61990.5
526559.6 5253955.9 62059.5
526563.7 5253955.9 62092
526567.8 5253955.9 62104.3
526571.9 5253955.9 62146.3
526576 5253955.9 62242.9
526580.1 5253955.9 62420.5
526584.2 5253955.9 62204.1
526588.3 5253955.9 62178.4
526592.4 5253955.9 62159
526596.6 5253955.9 62147.1
526600.7 5253955.9 62159.6
526604.8 5253955.9 62144.3
526608.9 5253955.9 62131.1
526613 5253955.9 62121.7
526617.1 5253955.9 62117.9
526621.2 5253955.9 62084.7
526625.3 5253955.9 62139.2
526629.4 5253955.9 62136.5
526633.6 5253955.9 62142
526637.7 5253955.9 62144.3
526641.8 5253955.9 62250.2
526645.9 5253955.9 62274.5
526650 5253955.9 62119
526243 5253960 62104
526247.1 5253960 62041.8
526251.2 5253960 61978.7
526255.3 5253960 61993.8
526259.4 5253960 61998.6
526263.6 5253960 61981
526267.7 5253960 61982
526271.8 5253960 61995.3
526275.9 5253960 62010.1
526280 5253960 62030.7
526284.1 5253960 62061.3
526288.2 5253960 62089.9
526292.3 5253960 62115.6
526296.4 5253960 62205.4
526300.6 5253960 62319.9
526304.7 5253960 62392.1
526308.8 5253960 62434.8
526312.9 5253960 62466.7
526317 5253960 62479.9
526321.1 5253960 62358.1
526325.2 5253960 62281.7
526329.3 5253960 62214.8
526333.4 5253960 62131.9
526382.8 5253960 61613.3
526386.9 5253960 61591.6
526391 5253960 61406.2
526395.1 5253960 61179.4
526399.2 5253960 61291.8
526403.3 5253960 61307.2
526407.4 5253960 61304.3
526411.6 5253960 61320.5
526415.7 5253960 61376.8
526419.8 5253960 61160.5
526423.9 5253960 61045.8
526428 5253960 61138.2
526432.1 5253960 61113.1
526436.2 5253960 60926.6
526440.3 5253960 60793.8
526444.4 5253960 60775.6
526448.6 5253960 60776.9
526452.7 5253960 60979.6
526456.8 5253960 61309.1
526460.9 5253960 61772.9
526465 5253960 61665.1
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526477.3 5253960 61725.2
526481.4 5253960 61846
526485.6 5253960 61948.4
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526497.9 5253960 61677.3
526502 5253960 61516.1
526506.1 5253960 61634.4
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526539 5253960 61533.9
526543.1 5253960 61670.4
526547.2 5253960 61785.9
526551.3 5253960 61878.6
526555.4 5253960 61963.3
526559.6 5253960 62048.6
526563.7 5253960 62070.7
526567.8 5253960 62099.5
526571.9 5253960 62140.9
526576 5253960 62242.6
526580.1 5253960 62255
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526588.3 5253960 62180.7
526592.4 5253960 62164.1
526596.6 5253960 62158.9
526600.7 5253960 62147.7
526604.8 5253960 62146.6
526608.9 5253960 62129
526613 5253960 62122.5
526617.1 5253960 62114.6
526621.2 5253960 62080.8
526625.3 5253960 62140.4
526629.4 5253960 62142.5
526633.6 5253960 62149.3
526637.7 5253960 62152
526641.8 5253960 62263.4
526645.9 5253960 62291.9
526650 5253960 62286.3
526243 5253964.1 62092
526247.1 5253964.1 62041.8
526251.2 5253964.1 61978.3
526255.3 5253964.1 62000.1
526259.4 5253964.1 62012.2
526263.6 5253964.1 62027.1
526267.7 5253964.1 62001.6
526271.8 5253964.1 62003.2
526275.9 5253964.1 61988.3
526280 5253964.1 62028.8
526284.1 5253964.1 62056.9
526288.2 5253964.1 62075.6
526292.3 5253964.1 62149.4
526296.4 5253964.1 62248
526300.6 5253964.1 62365.5
526555.4 5253964.1 61980.9
526559.6 5253964.1 62035.4
526563.7 5253964.1 62055.7
526567.8 5253964.1 62100.7
526571.9 5253964.1 62146.3
526576 5253964.1 62199
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526600.7 5253964.1 62114
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526645.9 5253964.1 62265.8
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526243 5253968.3 62086
526247.1 5253968.3 62035.9
526251.2 5253968.3 61980
526255.3 5253968.3 61995.1
526259.4 5253968.3 62021.7
526263.6 5253968.3 62012.6
526267.7 5253968.3 61992.3
526271.8 5253968.3 62024.4
526275.9 5253968.3 61968.1
526280 5253968.3 62030.8
526284.1 5253968.3 62045.5
526288.2 5253968.3 62092.9
526292.3 5253968.3 62189.2
526296.4 5253968.3 62316.8
526300.6 5253968.3 62476.9
526304.7 5253968.3 62513.2
526308.8 5253968.3 62437.8
526312.9 5253968.3 62356.8
526317 5253968.3 62305.8
526321.1 5253968.3 62248.8
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526329.3 5253968.3 62115.2
526333.4 5253968.3 62031
526337.6 5253968.3 62028.7
526341.7 5253968.3 61980.8
526345.8 5253968.3 61979.9
526349.9 5253968.3 61825.2
526354 5253968.3 61721.9
526358.1 5253968.3 61733.8
526362.2 5253968.3 61654.4
526366.3 5253968.3 61493.2
526370.4 5253968.3 61363.9
526374.6 5253968.3 61270.6
526378.7 5253968.3 61302.3
526382.8 5253968.3 61352.9
526386.9 5253968.3 61309
526391 5253968.3 61151.5
526395.1 5253968.3 60971.2
526399.2 5253968.3 60867.7
526403.3 5253968.3 60888.3
526407.4 5253968.3 60935
526411.6 5253968.3 60938.5
526415.7 5253968.3 61003.7
526419.8 5253968.3 61103.4
526423.9 5253968.3 60876.9
526428 5253968.3 60551.8
526432.1 5253968.3 60572.6
526436.2 5253968.3 60717
526440.3 5253968.3 60755.9
526444.4 5253968.3 60572.4
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526563.7 5253968.3 62039.3
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526592.4 5253968.3 62210.8
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526613 5253968.3 62100.4
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526641.8 5253968.3 62244.9
526645.9 5253968.3 62267.5
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526243 5253972.4 62069.2
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526271.8 5253972.4 62052.7
526275.9 5253972.4 62073.4
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526284.1 5253972.4 62089.7
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526292.3 5253972.4 62244.1
526296.4 5253972.4 62372
526300.6 5253972.4 62451.7
526304.7 5253972.4 62476
526308.8 5253972.4 62392.1
526312.9 5253972.4 62354.4
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526321.1 5253972.4 62209.1
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526329.3 5253972.4 62089.1
526333.4 5253972.4 62038.1
526337.6 5253972.4 61983.1
526341.7 5253972.4 61910.2
526345.8 5253972.4 61794.9
526349.9 5253972.4 61654
526354 5253972.4 61547
526358.1 5253972.4 61532.1
526362.2 5253972.4 61464.3
526366.3 5253972.4 61343.5
526370.4 5253972.4 61173
526374.6 5253972.4 61061.3
526378.7 5253972.4 61041.7
526382.8 5253972.4 61234.2
526386.9 5253972.4 61196.5
526391 5253972.4 61047.1
526395.1 5253972.4 60856.4
526399.2 5253972.4 60764.9
526403.3 5253972.4 60778.6
526407.4 5253972.4 60836.6
526411.6 5253972.4 60887.4
526415.7 5253972.4 61012
526419.8 5253972.4 61134.9
526423.9 5253972.4 60972.7
526428 5253972.4 60599.3
526432.1 5253972.4 60154.9
526436.2 5253972.4 60802.8
526440.3 5253972.4 60958.2
526444.4 5253972.4 60530.1
526448.6 5253972.4 60229.9
526452.7 5253972.4 60197.6
526456.8 5253972.4 60425.8
526460.9 5253972.4 60450.8
526465 5253972.4 59787.6
526469.1 5253972.4 59404.4
526473.2 5253972.4 60447.7
526477.3 5253972.4 61011.9
526481.4 5253972.4 61304
526485.6 5253972.4 61404
526489.7 5253972.4 61431.8
526493.8 5253972.4 61455.5
526497.9 5253972.4 61489.8
526502 5253972.4 61445.9
526506.1 5253972.4 61581.3
526510.2 5253972.4 61723.1
526514.3 5253972.4 61744.5
526518.4 5253972.4 61759.3
526522.6 5253972.4 61651.6
526526.7 5253972.4 61357.4
526530.8 5253972.4 61088.1
526534.9 5253972.4 61505.6
526539 5253972.4 61793.5
526543.1 5253972.4 61846.8
526547.2 5253972.4 61890.7
526551.3 5253972.4 61929
526555.4 5253972.4 61953.5
526559.6 5253972.4 62004
526563.7 5253972.4 62017.1
526567.8 5253972.4 62069.5
526571.9 5253972.4 62119.6
526576 5253972.4 62142.3
526580.1 5253972.4 62190.1
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526588.3 5253972.4 62268.3
526592.4 5253972.4 62257.5
526596.6 5253972.4 62212.8
526600.7 5253972.4 62193.3
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526608.9 5253972.4 62084.3
526613 5253972.4 62075.9
526617.1 5253972.4 62072.8
526621.2 5253972.4 62030.6
526625.3 5253972.4 62100.9
526629.4 5253972.4 62107.4
526633.6 5253972.4 62124.6
526637.7 5253972.4 62140.9
526641.8 5253972.4 62249.8
526645.9 5253972.4 62275.2
526650 5253972.4 62325
526243 5253976.5 62069.2
526247.1 5253976.5 62037.5
526251.2 5253976.5 61984.2
526255.3 5253976.5 62000.4
526259.4 5253976.5 62020.3
526263.6 5253976.5 62019.6
526267.7 5253976.5 62028.1
526271.8 5253976.5 62079.9
526275.9 5253976.5 62095.9
526280 5253976.5 62084.5
526284.1 5253976.5 62129.4
526288.2 5253976.5 62203.6
526292.3 5253976.5 62304.3
526296.4 5253976.5 62380.6
526300.6 5253976.5 62398
526304.7 5253976.5 62358.6
526308.8 5253976.5 62323.8
526312.9 5253976.5 62283
526317 5253976.5 62240.6
526321.1 5253976.5 62175.5
526325.2 5253976.5 62113.9
526329.3 5253976.5 62021.2
526333.4 5253976.5 61958.1
526337.6 5253976.5 61902.3
526341.7 5253976.5 61846.7
526345.8 5253976.5 61647.2
526349.9 5253976.5 61442.7
526354 5253976.5 61346.2
526358.1 5253976.5 61387.1
526362.2 5253976.5 61324.2
526366.3 5253976.5 61168.7
526370.4 5253976.5 61043.5
526374.6 5253976.5 60854.9
526378.7 5253976.5 60797.1
526382.8 5253976.5 60889.2
526386.9 5253976.5 60915.6
526391 5253976.5 60822.2
526395.1 5253976.5 60760.4
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526403.3 5253976.5 60540.3
526407.4 5253976.5 60525.5
526411.6 5253976.5 60693.8
526415.7 5253976.5 60905.6
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526423.9 5253976.5 60992.9
526428 5253976.5 60873.2
526432.1 5253976.5 60135.2
526436.2 5253976.5 60421.8
526440.3 5253976.5 60699.9
526444.4 5253976.5 60422.8
526448.6 5253976.5 60075.5
526452.7 5253976.5 59948.4
526456.8 5253976.5 60170.3
526460.9 5253976.5 60534
526465 5253976.5 59849.7
526469.1 5253976.5 59221.9
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526481.4 5253976.5 60958.8
526485.6 5253976.5 61166
526489.7 5253976.5 61296.1
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526497.9 5253976.5 61448
526502 5253976.5 61423.1
526506.1 5253976.5 61574.7
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526518.4 5253976.5 61847.8
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526526.7 5253976.5 61915.7
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526539 5253976.5 61954.5
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526563.7 5253976.5 61991.2
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526629.4 5253976.5 62110.7
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526637.7 5253976.5 62139
526641.8 5253976.5 62251.8
526645.9 5253976.5 62277.1
526650 5253976.5 62337.3
526243 5253980.6 62066.3
526247.1 5253980.6 62048
526251.2 5253980.6 62002.1
526255.3 5253980.6 62005.9
526259.4 5253980.6 62016.9
526263.6 5253980.6 62024
526267.7 5253980.6 62054.9
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526275.9 5253980.6 62090.7
526280 5253980.6 62121.7
526284.1 5253980.6 62169.8
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526292.3 5253980.6 62311.2
526296.4 5253980.6 62336.4
526300.6 5253980.6 62305.7
526304.7 5253980.6 62294.3
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526312.9 5253980.6 62203
526317 5253980.6 62172.3
526321.1 5253980.6 62097.8
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526329.3 5253980.6 61944.4
526333.4 5253980.6 61889.2
526337.6 5253980.6 61820.7
526341.7 5253980.6 61742.6
526345.8 5253980.6 61514
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526370.4 5253980.6 60946.3
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526444.4 5253980.6 60293.5
526448.6 5253980.6 60012
526452.7 5253980.6 59445.5
526456.8 5253980.6 59617.3
526460.9 5253980.6 60258.1
526465 5253980.6 60222.1
526469.1 5253980.6 59939.5
526473.2 5253980.6 60081.4
526477.3 5253980.6 60348.9
526481.4 5253980.6 60695.2
526485.6 5253980.6 60930.1
526489.7 5253980.6 61133.6
526493.8 5253980.6 61264.2
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526502 5253980.6 61355.8
526506.1 5253980.6 61398.3
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526530.8 5253980.6 62759.8
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526539 5253980.6 62091.4
526543.1 5253980.6 61996.8
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526551.3 5253980.6 61935.6
526555.4 5253980.6 61932.6
526559.6 5253980.6 61922.5
526563.7 5253980.6 61940.6
526567.8 5253980.6 61991.2
526571.9 5253980.6 62040.7
526576 5253980.6 62075.9
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526592.4 5253980.6 62166.6
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526613 5253980.6 62061.9
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526641.8 5253980.6 62251.3
526645.9 5253980.6 62280.6
526650 5253980.6 62348
526243 5253984.7 62070.5
526247.1 5253984.7 62032.9
526251.2 5253984.7 61995.8
526255.3 5253984.7 62013.9
526259.4 5253984.7 62021.1
526263.6 5253984.7 62031.5
526267.7 5253984.7 62062.8
526271.8 5253984.7 62111.6
526275.9 5253984.7 62114.9
526280 5253984.7 62139
526284.1 5253984.7 62200.9
526288.2 5253984.7 62248.8
526292.3 5253984.7 62278.6
526296.4 5253984.7 62268.5
526300.6 5253984.7 62259.9
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526312.9 5253984.7 62159.2
526317 5253984.7 62085
526321.1 5253984.7 62005
526325.2 5253984.7 61934.3
526329.3 5253984.7 61855.4
526333.4 5253984.7 61811.4
526337.6 5253984.7 61785.8
526341.7 5253984.7 61567.1
526345.8 5253984.7 61412.7
526349.9 5253984.7 61300.2
526354 5253984.7 61168
526358.1 5253984.7 61129.6
526362.2 5253984.7 61029.5
526366.3 5253984.7 60792.3
526370.4 5253984.7 60739
526374.6 5253984.7 60601.2
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526386.9 5253984.7 60371.7
526391 5253984.7 60576.7
526395.1 5253984.7 60709.4
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526403.3 5253984.7 60407.7
526407.4 5253984.7 60270.3
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526436.2 5253984.7 60831.1
526440.3 5253984.7 60244.9
526444.4 5253984.7 60060.9
526448.6 5253984.7 60139.4
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526460.9 5253984.7 60277.9
526465 5253984.7 60327.2
526469.1 5253984.7 60454.4
526473.2 5253984.7 60387.6
526477.3 5253984.7 60202.1
526481.4 5253984.7 60292.2
526485.6 5253984.7 60657.1
526489.7 5253984.7 60909
526493.8 5253984.7 61070.8
526497.9 5253984.7 61128.9
526502 5253984.7 61233.6
526506.1 5253984.7 61286.2
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526514.3 5253984.7 61691.4
526518.4 5253984.7 61822.6
526522.6 5253984.7 62016.1
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526530.8 5253984.7 62299.4
526534.9 5253984.7 62263.7
526539 5253984.7 62161.4
526543.1 5253984.7 62070.3
526547.2 5253984.7 61992.3
526551.3 5253984.7 61919.4
526555.4 5253984.7 61842.4
526559.6 5253984.7 61810.8
526563.7 5253984.7 61862.3
526567.8 5253984.7 61926.4
526571.9 5253984.7 61919
526576 5253984.7 61948.4
526580.1 5253984.7 61989.3
526584.2 5253984.7 62017
526588.3 5253984.7 62065.8
526592.4 5253984.7 62058.4
526596.6 5253984.7 62053.2
526600.7 5253984.7 62041.8
526604.8 5253984.7 62066.9
526608.9 5253984.7 62072.8
526613 5253984.7 62072.4
526617.1 5253984.7 62055.3
抱歉，条目未对齐，这是从 MS Excel 中复制出来的...
Sorry about the miss-aligned entries, this comes from copying out of MS Excel...
数据被排列成一系列的线，当组合起来时，这些线组成了一个坐标列表，该坐标列表来自一个由最高点和最低点的x"和y"值界定的区域上的网格.
The data is arranged as a series of lines which when combined constitute a list of co-ordinates from a grid over an area bounded by the upper and lower most points' 'x' and 'y' values.
导致我的脚本挂起的问题是，为了将它的计数图像与主数据集进行比较，我试图对其进行网格化的子采样数据集具有未对齐的线起点和终点.对于使用奇数子区间创建的所有子采样数据集都是如此.
The problem which caused my script to hang was that the sub-sampled data set, which I was trying to grid in order to compare a countour image of it with the master dataset, had mis-aligned line start and end points. This was true for all sub-sampled datasets which were created using a sub interval of an odd number.
我通过在对数据集进行二次采样的for"循环中使用附加检查解决了这个问题.
I fixed the problem by using an additional check in a 'for' loop which sub-sampled the dataset.
这是固定的子样本"代码.
Here is the fixed 'sub-sample' code.
- Note: The plotting code works seamlessly.
def sub_sample():
print ""
print " - This program will sub-sample a dataset for every <n> datapoint."
print " - <n> should realistically should be between 1 and 10."
print " - If <n> > 10, interpretation confidence is reduced significantly."
print ""
n = raw_input(" - What value should <n> take?: ")
if int(n) > 0 and int(n) < 2001:
print ""
print " - You have chosen to subsample the data every "+str(n)+" datapoints."
perc = (float(1)/float(n)*int(100))
print " - This corresponds to "+str(perc)+" percent of the data."
indataarray = numpy.loadtxt("out.csv", skiprows=1, delimiter=',')
out_name = "every_"+str(n)+".csv"
t = indataarray.shape
totalrows = t[0]
totalcolumns = t[1]
holding_array = numpy.zeros( [totalrows, totalcolumns] )
for i in range(totalrows):
if ((i % 100) % int(n) == 0):
for j in range(totalcolumns):
holding_array[i,j] = indataarray[i,j]
no_zeros = (holding_array == 0).sum(1)
new_holding = holding_array[no_zeros == 0, :]
numpy.savetxt(out_name, new_holding, delimiter=',', fmt='%f')
hdr_c1 = 'Easting,'
hdr_c2 = 'Northing,'
hdr_c3 = 'TMI_nT'
f = open(out_name)
text = f.read()
f.close()
f = open(out_name, 'w')
f.write(hdr_c1+hdr_c2+hdr_c3+'
')
f.write(text)
f.close
print " - The file "+out_name+" has been created."
else:
print ""
print " - You have entered an unrealistic value for <n>."
print " - Please try again."
sub_sample()
它失败了，因为你试图在一个 非常 奇怪的区域上进行插值.
It's failing because you're trying to interpolate over a very strange region.
查看您的输入坐标，您的数据都在一条线上，但您正试图在 2D 中进行插值.你的yi坐标都是一样的！
Look at your input coordinates, your data is all in a line, but you're trying to interpolate in 2D. Your yi coordinates are all the same!
如果你想要一维插值，那么只需使用众多一维插值例程之一...(numpy.interp 如果你只想要线性插值或 scipy.interp1d如果您需要某种样条曲线.还有其他几个选项...).
If you want 1D interpolation, then just use one of the many 1D interpolation routines... (numpy.interp if you just want linear interpolation or scipy.interp1d if you need splines of some sort. There are several other options as well...).
完整的数据集是什么样的?
What does the full dataset look like?
griddata 默认使用 delauney 三角剖分.每当您在输入点之间制作出非常薄、面积接近零的三角形时，该算法都会出现问题.
griddata uses delauney triangulation by default. Anytime you wind up making very thin, near-zero-area triangles between your input points, this algorithm will have problems.
如果您真的想要在面积为零的区域上进行 2D 插值(请参阅此处的问题！！)尝试另一种(最好是 1D)插值算法.如果您需要帮助，请告诉我.
If you really want to interpolate in 2D over a region with an area of zero (See the problems here!!) try another (preferably 1D) interpolation algorithm. If you want help let me know.
如果您确实想要伪二维"插值，使用线性距离函数的 scipy.interpolate.Rbf 将是一种选择.请注意，(2D)结果可能不是您所期望的，因为您在 y 方向上绝对没有变化......
If you really do want a "pseudo-2D" interpolation, scipy.interpolate.Rbf using a linear distance function would be one choice. Just be aware that the (2D) results may not be what you expect as you have absolutely no variation in the y-direction...
如果您希望所有内容在 y 方向上都相同，您也可以在 y 方向上平铺一维插值结果.
Alternately you could just tile the 1D interpolation results in the y-direction if you want everything to be the same in the y-direction.
这篇关于使用“matplotlib.mlab.griddata"时程序挂起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跟版网！