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In the following data, the first column is longitude, the second column is latitude, and from the third column to the last column, there is monthly data from January to December in that particular lat and long grid.

  123.00   52.50 0.8808496E+03 0.7983786E+03 0.8826313E+03 0.8459452E+03 0.8619072E+03 0.8350419E+03 0.8608338E+03 0.8610720E+03 0.8383484E+03 0.8741709E+03 0.8564337E+03 0.8842023E+03
  123.25   52.50 0.1667402E+03 0.1520321E+03 0.1676579E+03 0.1580190E+03 0.1569843E+03 0.1524035E+03 0.1564315E+03 0.1565542E+03 0.1541065E+03 0.1633005E+03 0.1634209E+03 0.1684670E+03
  123.50   52.50 0.6747873E+03 0.6210926E+03 0.6822463E+03 0.6258512E+03 0.5954875E+03 0.5802058E+03 0.5909939E+03 0.5919910E+03 0.5940483E+03 0.6468277E+03 0.6697598E+03 0.6888232E+03
  123.75   52.50 0.2467868E+04 0.2271493E+04 0.2495147E+04 0.2288896E+04 0.2177849E+04 0.2121960E+04 0.2161414E+04 0.2165062E+04 0.2172585E+04 0.2365612E+04 0.2449481E+04 0.2519201E+04
  124.00   52.50 0.1109248E+04 0.1020982E+04 0.1121510E+04 0.1028805E+04 0.9788915E+03 0.9537708E+03 0.9715045E+03 0.9731437E+03 0.9765255E+03 0.1063287E+04 0.1100984E+04 0.1132321E+04
  124.25   52.50 0.3159128E+03 0.2907748E+03 0.3194048E+03 0.2930025E+03 0.2787873E+03 0.2716329E+03 0.2766835E+03 0.2771504E+03 0.2781134E+03 0.3028230E+03 0.3135591E+03 0.3224839E+03
  124.50   52.50 0.1338592E+04 0.1232077E+04 0.1353389E+04 0.1241516E+04 0.1181283E+04 0.1150969E+04 0.1172369E+04 0.1174347E+04 0.1178428E+04 0.1283128E+04 0.1328619E+04 0.1366436E+04
  124.75   52.50 0.7045669E+03 0.6388000E+03 0.7061202E+03 0.6761815E+03 0.6880540E+03 0.6666766E+03 0.6871183E+03 0.6873259E+03 0.6695590E+03 0.6987448E+03 0.6853247E+03 0.7074897E+03
  125.00   52.50 0.3381786E+04 0.3059350E+04 0.3384892E+04 0.3261380E+04 0.3348760E+04 0.3242371E+04 0.3346889E+04 0.3347304E+04 0.3248136E+04 0.3370142E+04 0.3279667E+04 0.3387631E+04
  125.25   52.50 0.1961657E+04 0.1781027E+04 0.1967574E+04 0.1876828E+04 0.1898756E+04 0.1840622E+04 0.1895192E+04 0.1895983E+04 0.1851602E+04 0.1939479E+04 0.1911656E+04 0.1972791E+04
  125.50   52.50 0.1072950E+03 0.9878714E+02 0.1084769E+03 0.9711996E+02 0.9447763E+02 0.9202362E+02 0.9307782E+02 0.9390406E+02 0.9427554E+02 0.1027549E+03 0.1064984E+03 0.1095190E+03
  125.75   52.50 0.1514417E+03 0.1394381E+03 0.1531092E+03 0.1370604E+03 0.1333164E+03 0.1293685E+03 0.1313361E+03 0.1325040E+03 0.1330356E+03 0.1450181E+03 0.1503177E+03 0.1545795E+03
  126.00   52.50 0.4479888E+03 0.4084135E+03 0.4529629E+03 0.4214777E+03 0.4226213E+03 0.4140549E+03 0.4204238E+03 0.4224188E+03 0.4175742E+03 0.4413225E+03 0.4433801E+03 0.4573489E+03
  126.25   52.50 0.1038578E+02 0.9562582E+01 0.1050013E+02 0.9399520E+01 0.9142758E+01 0.8872013E+01 0.9006948E+01 0.9087042E+01 0.9123500E+01 0.9945253E+01 0.1030870E+02 0.1060096E+02
   98.50   52.25 0.2600510E+01 0.2401866E+01 0.2600510E+01 0.2534295E+01 0.2317481E+01 0.2166105E+01 0.2232320E+01 0.2270205E+01 0.2251266E+01 0.2440210E+01 0.2534295E+01 0.2600510E+01
   99.25   52.25 0.2019357E+02 0.1865105E+02 0.2019357E+02 0.1967939E+02 0.1799578E+02 0.1682032E+02 0.1733449E+02 0.1762867E+02 0.1748161E+02 0.1894880E+02 0.1967939E+02 0.2019357E+02
  120.75   52.25 0.2512760E+02 0.2285111E+02 0.2528853E+02 0.2298190E+02 0.2178992E+02 0.2072507E+02 0.2038844E+02 0.2111930E+02 0.2130790E+02 0.2376282E+02 0.2485684E+02 0.2504733E+02
  121.00   52.25 0.8857477E+03 0.8055014E+03 0.8914200E+03 0.8101117E+03 0.7680945E+03 0.7305585E+03 0.7186925E+03 0.7444552E+03 0.7511031E+03 0.8376392E+03 0.8762033E+03 0.8829180E+03
  121.25   52.25 0.2084019E+04 0.1895213E+04 0.2097365E+04 0.1906060E+04 0.1807200E+04 0.1718884E+04 0.1690965E+04 0.1751581E+04 0.1767222E+04 0.1970827E+04 0.2061562E+04 0.2077361E+04
  121.50   52.25 0.7002205E+03 0.6367824E+03 0.7047048E+03 0.6404270E+03 0.6072106E+03 0.5775369E+03 0.5681562E+03 0.5885227E+03 0.5937784E+03 0.6621887E+03 0.6926752E+03 0.6979835E+03
  121.75   52.25 0.1565343E+04 0.1423671E+04 0.1575358E+04 0.1431346E+04 0.1356564E+04 0.1277269E+04 0.1269264E+04 0.1314830E+04 0.1326676E+04 0.1480163E+04 0.1548493E+04 0.1560347E+04
  122.00   52.25 0.8609155E+03 0.7802265E+03 0.8635948E+03 0.8108029E+03 0.8050565E+03 0.7695794E+03 0.7816993E+03 0.7938904E+03 0.7827984E+03 0.8381255E+03 0.8421456E+03 0.8595789E+03
  122.25   52.25 0.1371758E+04 0.1241285E+04 0.1374083E+04 0.1308128E+04 0.1323301E+04 0.1272367E+04 0.1303039E+04 0.1313615E+04 0.1283834E+04 0.1351988E+04 0.1335317E+04 0.1370599E+04
  122.50   52.25 0.8593264E+03 0.7785834E+03 0.8617936E+03 0.8110353E+03 0.8078933E+03 0.7730782E+03 0.7863868E+03 0.7976119E+03 0.7852496E+03 0.8383423E+03 0.8398946E+03 0.8580957E+03
  122.75   52.25 0.5374568E+02 0.4888142E+02 0.5408952E+02 0.4914493E+02 0.4657730E+02 0.4385473E+02 0.4357987E+02 0.4514437E+02 0.4555110E+02 0.5082106E+02 0.5316713E+02 0.5357415E+02
  123.00   52.25 0.4366411E+03 0.3952134E+03 0.4371731E+03 0.4206180E+03 0.4309849E+03 0.4173623E+03 0.4306644E+03 0.4307356E+03 0.4183496E+03 0.4346468E+03 0.4237498E+03 0.4376422E+03
  123.25   52.25 0.5455102E+03 0.5015980E+03 0.5512159E+03 0.5071299E+03 0.4848490E+03 0.4722130E+03 0.4814114E+03 0.4821741E+03 0.4828020E+03 0.5241220E+03 0.5407183E+03 0.5562471E+03
  123.50   52.25 0.3113906E+03 0.2866125E+03 0.3148327E+03 0.2888084E+03 0.2747966E+03 0.2677446E+03 0.2727229E+03 0.2731830E+03 0.2741324E+03 0.2984882E+03 0.3090706E+03 0.3178677E+03

From this data, I want to make a separate 2-D array of latitude, longitude and data of a individual month. I am able to extract the column of latitude and longitude values and make their 2-D grid using the following code:

xx,yy = np.meshgrid(np.unique(longitude),np.unique(latitude))

But, I am having a hard time trying to make a 2-D grid of a particular month that corresponds to these 2-D lat and long values. I did try the following link Python numpy: create 2d array of values based on coordinates. But, I couldn't plot the values since the coordinates are in floats rather than integer and the data aren't properly arranged and there are only values that are available in particular lat and long grid with a lot of missing data in between.

Mala Pokhrel
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1 Answers1

1

I assume the following about your data:

  1. Your data is a matrix of n rows and 14 columns where columns 1 and 2 are latitude and longitude data.
  2. Next I assume each month is follows in order i.e. column 3 is Jan, 4 is Feb, e.t.c.

With this, we can extract the requisite data via fancy indexing. The general format is:

month_data = data[:, [0, 1, month_column]]

Some concrete examples are:

january_data = data[:, [0, 1, 2]]
february_data = data[:, [0, 1, 3]]
july_data = data[:, [0, 1, 8]]
Xero Smith
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