hdf

问题 Everything that is available online is too complicated. My database is large to I exported it in parts. I now have three .h5 file and I would like to combine them into one .h5 file for further work. How can I do it? 回答1: There are at least 3 ways to combine data from individual HDF5 files into a single file: Use external links to create a new file that points to the data in your other files (requires pytables/tables module) Copy the data with the HDF Group utility: h5copy.exe Copy the data

问题 A colleague of mine used labview to write an ASCII string as an attribute in an HDF5 file. I can see that the attribute exist, and read it, but I can't print it. The attribute is, as shown in HDF Viewer: Date = 2015\07\09 So "Date" is its name. I'm trying to read the attribute with this code hsize_t sz = H5Aget_storage_size(dateAttribHandler); std::cout<<sz<<std::endl; //prints 16 hid_t atype = H5Aget_type(dateAttribHandler); std::cout<<atype<<std::endl; //prints 50331867 std::cout<<H5Aread

问题 I have two tables one in mysql test.employee and other in hive default.dept I want to pass empid of test.employee table as a parameter to query in hive table and store data into HDFS ExecuteSQL -> select empid from test.employee (gives 10 records) SelectHiveQL -> SELECT * FROM default.dept where empid = ${empid} (should retrieve 10 records) image description here 回答1: You could do the following: ExecuteSQL - to retrieve the employee records ConvertAvroToJson - for later processing of empid

问题 Does anyone know how to make the modification of the following code so that I can read the specific z component slice of 3D hdf data in Python? As you can see from the attached image, z value spans from 0 to 160 and I want to plot the '80' only. And the dimension is 400x160x160. Here is my code. import h5handler as h5h h5h.manager.setPath('E:\data\Data5', False) for i in np.arange(0,1,5000): cycleFile = h5h.CycleFile(h5h.manager.cycleFiles['cycle_'+str(i)+'.hdf'], 'r') fig = plt.figure() fig

问题 I am dealing with 28 HDF4 files on ocean primary productivity (annual .tar files can be found here: http://orca.science.oregonstate.edu/1080.by.2160.monthly.hdf.cbpm2.v.php) My goal is to do some calculations (I need to calculate concentrations per area and obtain the mean over several years, i.e. combine all files spatially) and then convert them to a georeferenced file I can work with in ArcGIS (preferably shapefile, or geotiff). I have tried several ways to convert to ASCII or raster files

问题 I created a hd5 file by hdf=pandas.HDFStore(pfad) hdf.append('df', df, data_columns=True) I have a list that contains numpy.datetime64 values called expirations and try to read the portion of the hd5 table into a dataframe, that has values between expirations[1] and expirations[0] in column "expiration". Column expiration entries have the format Timestamp('2002-05-18 00:00:00'). I use the following command: df=hdf.select('df', where=('expiration<expiration[1] & expiration>=expirations[0]'))

问题 There are more than 1,000 MODIS HDF images in a folder: M:\join Their names show us which files must be mosaiced together. For example, in the below files, 2009090 means these three images must be mosaiced together: MOD05_L2.A2009090.0420.051.2010336084010 MOD05_L2.A2009090.0555.051.2010336100338 MOD05_L2.A2009090.0600.051.2010336100514 Or these two, are for the same date, 2009091 : MOD05_L2.A2009091.0555.051.2010336162871 MOD05_L2.A2009091.0600.051.2010336842395 I am going to mosaic them

问题 Pandas to_hdf succeeds but then read_hdf fails when I use custom objects as column headers (I use custom objects because I need to store other info in them). Is there some way to make this work? Or is this just a Pandas bug or PyTables bug? As an example, below, I will show first making a DataFrame foo that uses string column headers, and everything works fine with to_hdf / read_hdf , but then changing foo to use a custom Col class for column headers, to_hdf still works fine but then read_hdf

问题 I often have to compare hdf files. How I do it is either with a binary diff (which tells me files are different even though the actual numbers inside are the same) or by dumping the content into a txt file with h5dump and the comparing the content of the two files (which is also quite annoying). I was wondering if there is a more clever way to do this, perhaps a feature of h5 or of softwares like HDFView or Panoply . 回答1: Perhaps hdiff is what you require ? Some examples here 来源： https:/

问题 I am trying to read a HDF4 file (https://www.dropbox.com/s/5d40ukfsu0yupwl/MOD13A2.A2016001.h23v05.006.2016029070140.hdf?dl=0). import os import numpy as np from pyhdf.SD import SD, SDC # Open file. FILE_NAME = 'MOD13A2.A2016001.h23v05.006.2016029070140.hdf' hdf = SD(FILE_NAME, SDC.READ) # List available SDS datasets. print (hdf.datasets()) # Read dataset. DATAFIELD_NAME="1_km_16_days_NDVI" data2D = hdf.select(DATAFIELD_NAME) data = data2D[:,:] When I executed this script, I am getting