"""Version 1.7.4 ------------- # Introduction netcdf4-python is a Python interface to the netCDF C library. [netCDF](http://www.unidata.ucar.edu/software/netcdf/) version 4 has many features not found in earlier versions of the library and is implemented on top of [HDF5](http://www.hdfgroup.org/HDF5). This module can read and write files in both the new netCDF 4 and the old netCDF 3 format, and can create files that are readable by HDF5 clients. The API modelled after [Scientific.IO.NetCDF](http://dirac.cnrs-orleans.fr/ScientificPython/), and should be familiar to users of that module. Most new features of netCDF 4 are implemented, such as multiple unlimited dimensions, groups and data compression. All the new numeric data types (such as 64 bit and unsigned integer types) are implemented. Compound (struct), variable length (vlen) and enumerated (enum) data types are supported, but not the opaque data type. Mixtures of compound, vlen and enum data types (such as compound types containing enums, or vlens containing compound types) are not supported. ## Quick Install - the easiest way to get going is to install via `pip install netCDF4`. (or if you use the [conda](http://conda.io) package manager `conda install -c conda-forge netCDF4`). ## Developer Install - Clone the [github repository](http://github.com/Unidata/netcdf4-python). Make sure you either clone recursively, or run `git submodule update --init` to ensure all the submodules are also checked out. - Make sure the dependencies are satisfied (Python 3.8 or later, [numpy](http://numpy.scipy.org), [Cython](http://cython.org), [cftime](https://github.com/Unidata/cftime), [setuptools](https://pypi.python.org/pypi/setuptools), the [HDF5 C library](https://www.hdfgroup.org/solutions/hdf5/), and the [netCDF C library](https://www.unidata.ucar.edu/software/netcdf/)). For MPI parallel IO support, an MPI-enabled versions of the netcdf library is required, as is [mpi4py](http://mpi4py.scipy.org). Parallel IO further depends on the existence of MPI-enabled HDF5 or the [PnetCDF](https://parallel-netcdf.github.io/) library. - By default, the utility `nc-config` (installed with netcdf-c) will be run used to determine where all the dependencies live. - If `nc-config` is not in your default `PATH`, you can set the `NETCDF4_DIR` environment variable and `setup.py` will look in `$NETCDF4_DIR/bin`. You can also use the file `setup.cfg` to set the path to `nc-config`, or enter the paths to the libraries and include files manually. Just edit the `setup.cfg` file in a text editor and follow the instructions in the comments. To disable the use of `nc-config`, set the env var `USE_NCCONFIG` to 0. To disable the use of `setup.cfg`, set `USE_SETUPCFG` to 0. As a last resort, the library and include paths can be set via environment variables. If you go this route, set `USE_NCCONFIG` and `USE_SETUPCFG` to 0, and specify `NETCDF4_LIBDIR`, `NETCDF4_INCDIR`, `HDF5_LIBDIR` and `HDF5_INCDIR`. If the dependencies are not found in any of the paths specified by environment variables, then standard locations (such as `/usr` and `/usr/local`) are searched. - if the env var `NETCDF_PLUGIN_DIR` is set to point to the location of the netcdf-c compression plugins built by netcdf >= 4.9.0, they will be installed inside the package. In this case `HDF5_PLUGIN_PATH` will be set to the package installation path on import, so the extra compression algorithms available in netcdf-c >= 4.9.0 will automatically be available. Otherwise, the user will have to set `HDF5_PLUGIN_PATH` explicitly to have access to the extra compression plugins. - run `pip install -v .` (as root if necessary) - run the tests in the 'test' directory by running `python run_all.py`. # Tutorial - [Creating/Opening/Closing a netCDF file](#creatingopeningclosing-a-netcdf-file) - [Groups in a netCDF file](#groups-in-a-netcdf-file) - [Dimensions in a netCDF file](#dimensions-in-a-netcdf-file) - [Variables in a netCDF file](#variables-in-a-netcdf-file) - [Attributes in a netCDF file](#attributes-in-a-netcdf-file) - [Dealing with time coordinates](#dealing-with-time-coordinates) - [Writing data to and retrieving data from a netCDF variable](#writing-data-to-and-retrieving-data-from-a-netcdf-variable) - [Reading data from a multi-file netCDF dataset](#reading-data-from-a-multi-file-netcdf-dataset) - [Efficient compression of netCDF variables](#efficient-compression-of-netcdf-variables) - [Beyond homogeneous arrays of a fixed type - compound data types](#beyond-homogeneous-arrays-of-a-fixed-type-compound-data-types) - [Variable-length (vlen) data types](#variable-length-vlen-data-types) - [Enum data type](#enum-data-type) - [Parallel IO](#parallel-io) - [Dealing with strings](#dealing-with-strings) - [In-memory (diskless) Datasets](#in-memory-diskless-datasets) All of the code in this tutorial is available in `examples/tutorial.py`, except the parallel IO example, which is in `examples/mpi_example.py`. Unit tests are in the `test` directory. ## Creating/Opening/Closing a netCDF file To create a netCDF file from python, you simply call the `Dataset` constructor. This is also the method used to open an existing netCDF file. If the file is open for write access (`mode='w', 'r+'` or `'a'`), you may write any type of data including new dimensions, groups, variables and attributes. netCDF files come in five flavors (`NETCDF3_CLASSIC`, `NETCDF3_64BIT_OFFSET`, `NETCDF3_64BIT_DATA`, `NETCDF4_CLASSIC`, and `NETCDF4`). `NETCDF3_CLASSIC` was the original netcdf binary format, and was limited to file sizes less than 2 Gb. `NETCDF3_64BIT_OFFSET` was introduced in version 3.6.0 of the library, and extended the original binary format to allow for file sizes greater than 2 Gb. `NETCDF3_64BIT_DATA` is a new format that requires version 4.4.0 of the C library - it extends the `NETCDF3_64BIT_OFFSET` binary format to allow for unsigned/64 bit integer data types and 64-bit dimension sizes. `NETCDF3_64BIT` is an alias for `NETCDF3_64BIT_OFFSET`. `NETCDF4_CLASSIC` files use the version 4 disk format (HDF5), but omits features not found in the version 3 API. They can be read by netCDF 3 clients only if they have been relinked against the netCDF 4 library. They can also be read by HDF5 clients. `NETCDF4` files use the version 4 disk format (HDF5) and use the new features of the version 4 API. The netCDF4 module can read and write files in any of these formats. When creating a new file, the format may be specified using the `format` keyword in the `Dataset` constructor. The default format is `NETCDF4`. To see how a given file is formatted, you can examine the `data_model` attribute. Closing the netCDF file is accomplished via the `Dataset.close` method of the `Dataset` instance. Here's an example: ```python >>> from netCDF4 import Dataset >>> rootgrp = Dataset("test.nc", "w", format="NETCDF4") >>> print(rootgrp.data_model) NETCDF4 >>> rootgrp.close() ``` Remote [OPeNDAP](http://opendap.org)-hosted datasets can be accessed for reading over http if a URL is provided to the `Dataset` constructor instead of a filename. However, this requires that the netCDF library be built with OPenDAP support, via the `--enable-dap` configure option (added in version 4.0.1). ## Groups in a netCDF file netCDF version 4 added support for organizing data in hierarchical groups, which are analogous to directories in a filesystem. Groups serve as containers for variables, dimensions and attributes, as well as other groups. A `Dataset` creates a special group, called the 'root group', which is similar to the root directory in a unix filesystem. To create `Group` instances, use the `Dataset.createGroup` method of a `Dataset` or `Group` instance. `Dataset.createGroup` takes a single argument, a python string containing the name of the new group. The new `Group` instances contained within the root group can be accessed by name using the `groups` dictionary attribute of the `Dataset` instance. Only `NETCDF4` formatted files support Groups, if you try to create a Group in a netCDF 3 file you will get an error message. ```python >>> rootgrp = Dataset("test.nc", "a") >>> fcstgrp = rootgrp.createGroup("forecasts") >>> analgrp = rootgrp.createGroup("analyses") >>> print(rootgrp.groups) {'forecasts': group /forecasts: dimensions(sizes): variables(dimensions): groups: , 'analyses': group /analyses: dimensions(sizes): variables(dimensions): groups: } >>> ``` Groups can exist within groups in a `Dataset`, just as directories exist within directories in a unix filesystem. Each `Group` instance has a `groups` attribute dictionary containing all of the group instances contained within that group. Each `Group` instance also has a `path` attribute that contains a simulated unix directory path to that group. To simplify the creation of nested groups, you can use a unix-like path as an argument to `Dataset.createGroup`. ```python >>> fcstgrp1 = rootgrp.createGroup("/forecasts/model1") >>> fcstgrp2 = rootgrp.createGroup("/forecasts/model2") ``` If any of the intermediate elements of the path do not exist, they are created, just as with the unix command `'mkdir -p'`. If you try to create a group that already exists, no error will be raised, and the existing group will be returned. Here's an example that shows how to navigate all the groups in a `Dataset`. The function `walktree` is a Python generator that is used to walk the directory tree. Note that printing the `Dataset` or `Group` object yields summary information about it's contents. ```python >>> def walktree(top): ... yield top.groups.values() ... for value in top.groups.values(): ... yield from walktree(value) >>> print(rootgrp) root group (NETCDF4 data model, file format HDF5): dimensions(sizes): variables(dimensions): groups: forecasts, analyses >>> for children in walktree(rootgrp): ... for child in children: ... print(child) group /forecasts: dimensions(sizes): variables(dimensions): groups: model1, model2 group /analyses: dimensions(sizes): variables(dimensions): groups: group /forecasts/model1: dimensions(sizes): variables(dimensions): groups: group /forecasts/model2: dimensions(sizes): variables(dimensions): groups: ``` ## Dimensions in a netCDF file netCDF defines the sizes of all variables in terms of dimensions, so before any variables can be created the dimensions they use must be created first. A special case, not often used in practice, is that of a scalar variable, which has no dimensions. A dimension is created using the `Dataset.createDimension` method of a `Dataset` or `Group` instance. A Python string is used to set the name of the dimension, and an integer value is used to set the size. To create an unlimited dimension (a dimension that can be appended to), the size value is set to `None` or 0. In this example, there both the `time` and `level` dimensions are unlimited. Having more than one unlimited dimension is a new netCDF 4 feature, in netCDF 3 files there may be only one, and it must be the first (leftmost) dimension of the variable. ```python >>> level = rootgrp.createDimension("level", None) >>> time = rootgrp.createDimension("time", None) >>> lat = rootgrp.createDimension("lat", 73) >>> lon = rootgrp.createDimension("lon", 144) ``` All of the `Dimension` instances are stored in a python dictionary. ```python >>> print(rootgrp.dimensions) {'level': (unlimited): name = 'level', size = 0, 'time': (unlimited): name = 'time', size = 0, 'lat': : name = 'lat', size = 73, 'lon': : name = 'lon', size = 144} ``` Using the python `len` function with a `Dimension` instance returns current size of that dimension. `Dimension.isunlimited` method of a `Dimension` instance be used to determine if the dimensions is unlimited, or appendable. ```python >>> print(len(lon)) 144 >>> print(lon.isunlimited()) False >>> print(time.isunlimited()) True ``` Printing the `Dimension` object provides useful summary info, including the name and length of the dimension, and whether it is unlimited. ```python >>> for dimobj in rootgrp.dimensions.values(): ... print(dimobj) (unlimited): name = 'level', size = 0 (unlimited): name = 'time', size = 0 : name = 'lat', size = 73 : name = 'lon', size = 144 ``` `Dimension` names can be changed using the `Dataset.renameDimension` method of a `Dataset` or `Group` instance. ## Variables in a netCDF file netCDF variables behave much like python multidimensional array objects supplied by the [numpy module](http://numpy.scipy.org). However, unlike numpy arrays, netCDF4 variables can be appended to along one or more 'unlimited' dimensions. To create a netCDF variable, use the `Dataset.createVariable` method of a `Dataset` or `Group` instance. The `Dataset.createVariable` method has two mandatory arguments, the variable name (a Python string), and the variable datatype. The variable's dimensions are given by a tuple containing the dimension names (defined previously with `Dataset.createDimension`). To create a scalar variable, simply leave out the dimensions keyword. The variable primitive datatypes correspond to the dtype attribute of a numpy array. You can specify the datatype as a numpy dtype object, or anything that can be converted to a numpy dtype object. Valid datatype specifiers include: | Specifier | Datatype | Old typecodes | |-----------|-------------------------|---------------| | `'f4'` | 32-bit floating point | `'f'` | | `'f8'` | 64-bit floating point | `'d'` | | `'i4'` | 32-bit signed integer | `'i'` `'l'` | | `'i2'` | 16-bit signed integer | `'h'` `'s'` | | `'i8'` | 64-bit signed integer | | | `'i1'` | 8-bit signed integer | `'b'` `'B'` | | `'u1'` | 8-bit unsigned integer | | | `'u2'` | 16-bit unsigned integer | | | `'u4'` | 32-bit unsigned integer | | | `'u8'` | 64-bit unsigned integer | | | `'S1'` | single-character string | `'c'` | The unsigned integer types and the 64-bit integer type can only be used if the file format is `NETCDF4`. The dimensions themselves are usually also defined as variables, called coordinate variables. The `Dataset.createVariable` method returns an instance of the `Variable` class whose methods can be used later to access and set variable data and attributes. ```python >>> times = rootgrp.createVariable("time","f8",("time",)) >>> levels = rootgrp.createVariable("level","i4",("level",)) >>> latitudes = rootgrp.createVariable("lat","f4",("lat",)) >>> longitudes = rootgrp.createVariable("lon","f4",("lon",)) >>> # two dimensions unlimited >>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",)) >>> temp.units = "K" ``` To get summary info on a `Variable` instance in an interactive session, just print it. ```python >>> print(temp) float32 temp(time, level, lat, lon) units: K unlimited dimensions: time, level current shape = (0, 0, 73, 144) filling on, default _FillValue of 9.969209968386869e+36 used ``` You can use a path to create a Variable inside a hierarchy of groups. ```python >>> ftemp = rootgrp.createVariable("/forecasts/model1/temp","f4",("time","level","lat","lon",)) ``` If the intermediate groups do not yet exist, they will be created. You can also query a `Dataset` or `Group` instance directly to obtain `Group` or `Variable` instances using paths. ```python >>> print(rootgrp["/forecasts/model1"]) # a Group instance group /forecasts/model1: dimensions(sizes): variables(dimensions): float32 temp(time,level,lat,lon) groups: >>> print(rootgrp["/forecasts/model1/temp"]) # a Variable instance float32 temp(time, level, lat, lon) path = /forecasts/model1 unlimited dimensions: time, level current shape = (0, 0, 73, 144) filling on, default _FillValue of 9.969209968386869e+36 used ``` All of the variables in the `Dataset` or `Group` are stored in a Python dictionary, in the same way as the dimensions: ```python >>> print(rootgrp.variables) {'time': float64 time(time) unlimited dimensions: time current shape = (0,) filling on, default _FillValue of 9.969209968386869e+36 used, 'level': int32 level(level) unlimited dimensions: level current shape = (0,) filling on, default _FillValue of -2147483647 used, 'lat': float32 lat(lat) unlimited dimensions: current shape = (73,) filling on, default _FillValue of 9.969209968386869e+36 used, 'lon': float32 lon(lon) unlimited dimensions: current shape = (144,) filling on, default _FillValue of 9.969209968386869e+36 used, 'temp': float32 temp(time, level, lat, lon) units: K unlimited dimensions: time, level current shape = (0, 0, 73, 144) filling on, default _FillValue of 9.969209968386869e+36 used} ``` `Variable` names can be changed using the `Dataset.renameVariable` method of a `Dataset` instance. Variables can be sliced similar to numpy arrays, but there are some differences. See [Writing data to and retrieving data from a netCDF variable](#writing-data-to-and-retrieving-data-from-a-netcdf-variable) for more details. ## Attributes in a netCDF file There are two types of attributes in a netCDF file, global and variable. Global attributes provide information about a group, or the entire dataset, as a whole. `Variable` attributes provide information about one of the variables in a group. Global attributes are set by assigning values to `Dataset` or `Group` instance variables. `Variable` attributes are set by assigning values to `Variable` instances variables. Attributes can be strings, numbers or sequences. Returning to our example, ```python >>> import time >>> rootgrp.description = "bogus example script" >>> rootgrp.history = "Created " + time.ctime(time.time()) >>> rootgrp.source = "netCDF4 python module tutorial" >>> latitudes.units = "degrees north" >>> longitudes.units = "degrees east" >>> levels.units = "hPa" >>> temp.units = "K" >>> times.units = "hours since 0001-01-01 00:00:00.0" >>> times.calendar = "gregorian" ``` The `Dataset.ncattrs` method of a `Dataset`, `Group` or `Variable` instance can be used to retrieve the names of all the netCDF attributes. This method is provided as a convenience, since using the built-in `dir` Python function will return a bunch of private methods and attributes that cannot (or should not) be modified by the user. ```python >>> for name in rootgrp.ncattrs(): ... print("Global attr {} = {}".format(name, getattr(rootgrp, name))) Global attr description = bogus example script Global attr history = Created Mon Jul 8 14:19:41 2019 Global attr source = netCDF4 python module tutorial ``` The `__dict__` attribute of a `Dataset`, `Group` or `Variable` instance provides all the netCDF attribute name/value pairs in a python dictionary: ```python >>> print(rootgrp.__dict__) {'description': 'bogus example script', 'history': 'Created Mon Jul 8 14:19:41 2019', 'source': 'netCDF4 python module tutorial'} ``` Attributes can be deleted from a netCDF `Dataset`, `Group` or `Variable` using the python `del` statement (i.e. `del grp.foo` removes the attribute `foo` the the group `grp`). ## Writing data to and retrieving data from a netCDF variable Now that you have a netCDF `Variable` instance, how do you put data into it? You can just treat it like an array and assign data to a slice. ```python >>> import numpy as np >>> lats = np.arange(-90,91,2.5) >>> lons = np.arange(-180,180,2.5) >>> latitudes[:] = lats >>> longitudes[:] = lons >>> print("latitudes =\\n{}".format(latitudes[:])) latitudes = [-90. -87.5 -85. -82.5 -80. -77.5 -75. -72.5 -70. -67.5 -65. -62.5 -60. -57.5 -55. -52.5 -50. -47.5 -45. -42.5 -40. -37.5 -35. -32.5 -30. -27.5 -25. -22.5 -20. -17.5 -15. -12.5 -10. -7.5 -5. -2.5 0. 2.5 5. 7.5 10. 12.5 15. 17.5 20. 22.5 25. 27.5 30. 32.5 35. 37.5 40. 42.5 45. 47.5 50. 52.5 55. 57.5 60. 62.5 65. 67.5 70. 72.5 75. 77.5 80. 82.5 85. 87.5 90. ] ``` Unlike NumPy's array objects, netCDF `Variable` objects with unlimited dimensions will grow along those dimensions if you assign data outside the currently defined range of indices. ```python >>> # append along two unlimited dimensions by assigning to slice. >>> nlats = len(rootgrp.dimensions["lat"]) >>> nlons = len(rootgrp.dimensions["lon"]) >>> print("temp shape before adding data = {}".format(temp.shape)) temp shape before adding data = (0, 0, 73, 144) >>> >>> from numpy.random import uniform >>> temp[0:5, 0:10, :, :] = uniform(size=(5, 10, nlats, nlons)) >>> print("temp shape after adding data = {}".format(temp.shape)) temp shape after adding data = (5, 10, 73, 144) >>> >>> # levels have grown, but no values yet assigned. >>> print("levels shape after adding pressure data = {}".format(levels.shape)) levels shape after adding pressure data = (10,) ``` Note that the size of the levels variable grows when data is appended along the `level` dimension of the variable `temp`, even though no data has yet been assigned to levels. ```python >>> # now, assign data to levels dimension variable. >>> levels[:] = [1000.,850.,700.,500.,300.,250.,200.,150.,100.,50.] ``` However, that there are some differences between NumPy and netCDF variable slicing rules. Slices behave as usual, being specified as a `start:stop:step` triplet. Using a scalar integer index `i` takes the ith element and reduces the rank of the output array by one. Boolean array and integer sequence indexing behaves differently for netCDF variables than for numpy arrays. Only 1-d boolean arrays and integer sequences are allowed, and these indices work independently along each dimension (similar to the way vector subscripts work in fortran). This means that ```python >>> temp[0, 0, [0,1,2,3], [0,1,2,3]].shape (4, 4) ``` returns an array of shape (4,4) when slicing a netCDF variable, but for a numpy array it returns an array of shape (4,). Similarly, a netCDF variable of shape `(2,3,4,5)` indexed with `[0, array([True, False, True]), array([False, True, True, True]), :]` would return a `(2, 3, 5)` array. In NumPy, this would raise an error since it would be equivalent to `[0, [0,1], [1,2,3], :]`. When slicing with integer sequences, the indices ***need not be sorted*** and ***may contain duplicates*** (both of these are new features in version 1.2.1). While this behaviour may cause some confusion for those used to NumPy's 'fancy indexing' rules, it provides a very powerful way to extract data from multidimensional netCDF variables by using logical operations on the dimension arrays to create slices. For example, ```python >>> tempdat = temp[::2, [1,3,6], lats>0, lons>0] ``` will extract time indices 0,2 and 4, pressure levels 850, 500 and 200 hPa, all Northern Hemisphere latitudes and Eastern Hemisphere longitudes, resulting in a numpy array of shape (3, 3, 36, 71). ```python >>> print("shape of fancy temp slice = {}".format(tempdat.shape)) shape of fancy temp slice = (3, 3, 36, 71) ``` ***Special note for scalar variables***: To extract data from a scalar variable `v` with no associated dimensions, use `numpy.asarray(v)` or `v[...]`. The result will be a numpy scalar array. By default, netcdf4-python returns numpy masked arrays with values equal to the `missing_value` or `_FillValue` variable attributes masked for primitive and enum data types. The `Dataset.set_auto_mask` `Dataset` and `Variable` methods can be used to disable this feature so that numpy arrays are always returned, with the missing values included. Prior to version 1.4.0 the default behavior was to only return masked arrays when the requested slice contained missing values. This behavior can be recovered using the `Dataset.set_always_mask` method. If a masked array is written to a netCDF variable, the masked elements are filled with the value specified by the `missing_value` attribute. If the variable has no `missing_value`, the `_FillValue` is used instead. ## Dealing with time coordinates Time coordinate values pose a special challenge to netCDF users. Most metadata standards (such as CF) specify that time should be measure relative to a fixed date using a certain calendar, with units specified like `hours since YY-MM-DD hh:mm:ss`. These units can be awkward to deal with, without a utility to convert the values to and from calendar dates. The functions [num2date](https://unidata.github.io/cftime/api.html) and [date2num](https://unidata.github.io/cftime/api.html) are provided by [cftime](https://unidata.github.io/cftime) to do just that. Here's an example of how they can be used: ```python >>> # fill in times. >>> from datetime import datetime, timedelta >>> from cftime import num2date, date2num >>> dates = [datetime(2001,3,1)+n*timedelta(hours=12) for n in range(temp.shape[0])] >>> times[:] = date2num(dates,units=times.units,calendar=times.calendar) >>> print("time values (in units {}):\\n{}".format(times.units, times[:])) time values (in units hours since 0001-01-01 00:00:00.0): [17533104. 17533116. 17533128. 17533140. 17533152.] >>> dates = num2date(times[:],units=times.units,calendar=times.calendar) >>> print("dates corresponding to time values:\\n{}".format(dates)) [cftime.DatetimeGregorian(2001, 3, 1, 0, 0, 0, 0, has_year_zero=False) cftime.DatetimeGregorian(2001, 3, 1, 12, 0, 0, 0, has_year_zero=False) cftime.DatetimeGregorian(2001, 3, 2, 0, 0, 0, 0, has_year_zero=False) cftime.DatetimeGregorian(2001, 3, 2, 12, 0, 0, 0, has_year_zero=False) cftime.DatetimeGregorian(2001, 3, 3, 0, 0, 0, 0, has_year_zero=False)] ``` `num2date` converts numeric values of time in the specified `units` and `calendar` to datetime objects, and `date2num` does the reverse. All the calendars currently defined in the [CF metadata convention](http://cfconventions.org) are supported. A function called `date2index` is also provided which returns the indices of a netCDF time variable corresponding to a sequence of datetime instances. ## Reading data from a multi-file netCDF dataset If you want to read data from a variable that spans multiple netCDF files, you can use the `MFDataset` class to read the data as if it were contained in a single file. Instead of using a single filename to create a `Dataset` instance, create a `MFDataset` instance with either a list of filenames, or a string with a wildcard (which is then converted to a sorted list of files using the python glob module). Variables in the list of files that share the same unlimited dimension are aggregated together, and can be sliced across multiple files. To illustrate this, let's first create a bunch of netCDF files with the same variable (with the same unlimited dimension). The files must in be in `NETCDF3_64BIT_OFFSET`, `NETCDF3_64BIT_DATA`, `NETCDF3_CLASSIC` or `NETCDF4_CLASSIC` format (`NETCDF4` formatted multi-file datasets are not supported). ```python >>> for nf in range(10): ... with Dataset("mftest%s.nc" % nf, "w", format="NETCDF4_CLASSIC") as f: ... _ = f.createDimension("x",None) ... x = f.createVariable("x","i",("x",)) ... x[0:10] = np.arange(nf*10,10*(nf+1)) ``` Now read all the files back in at once with `MFDataset` ```python >>> from netCDF4 import MFDataset >>> f = MFDataset("mftest*nc") >>> print(f.variables["x"][:]) [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99] ``` Note that `MFDataset` can only be used to read, not write, multi-file datasets. ## Efficient compression of netCDF variables Data stored in netCDF `Variable` objects can be compressed and decompressed on the fly. The compression algorithm used is determined by the `compression` keyword argument to the `Dataset.createVariable` method. `zlib` compression is always available, `szip` is available if the linked HDF5 library supports it, and `zstd`, `bzip2`, `blosc_lz`,`blosc_lz4`,`blosc_lz4hc`, `blosc_zlib` and `blosc_zstd` are available via optional external plugins. The `complevel` keyword regulates the speed and efficiency of the compression for `zlib`, `bzip` and `zstd` (1 being fastest, but lowest compression ratio, 9 being slowest but best compression ratio). The default value of `complevel` is 4. Setting `shuffle=False` will turn off the HDF5 shuffle filter, which de-interlaces a block of data before `zlib` compression by reordering the bytes. The shuffle filter can significantly improve compression ratios, and is on by default if `compression=zlib`. Setting `fletcher32` keyword argument to `Dataset.createVariable` to `True` (it's `False` by default) enables the Fletcher32 checksum algorithm for error detection. It's also possible to set the HDF5 chunking parameters and endian-ness of the binary data stored in the HDF5 file with the `chunksizes` and `endian` keyword arguments to `Dataset.createVariable`. These keyword arguments only are relevant for `NETCDF4` and `NETCDF4_CLASSIC` files (where the underlying file format is HDF5) and are silently ignored if the file format is `NETCDF3_CLASSIC`, `NETCDF3_64BIT_OFFSET` or `NETCDF3_64BIT_DATA`. If the HDF5 library is built with szip support, compression=`szip` can also be used (in conjunction with the `szip_coding` and `szip_pixels_per_block` keyword arguments). If your data only has a certain number of digits of precision (say for example, it is temperature data that was measured with a precision of 0.1 degrees), you can dramatically improve compression by quantizing (or truncating) the data. There are two methods supplied for doing this. You can use the `least_significant_digit` keyword argument to `Dataset.createVariable` to specify the power of ten of the smallest decimal place in the data that is a reliable value. For example if the data has a precision of 0.1, then setting `least_significant_digit=1` will cause data the data to be quantized using `numpy.around(scale*data)/scale`, where scale = 2**bits, and bits is determined so that a precision of 0.1 is retained (in this case bits=4). This is done at the python level and is not a part of the underlying C library. Starting with netcdf-c version 4.9.0, a quantization capability is provided in the library. This can be used via the `significant_digits` `Dataset.createVariable` kwarg (new in version 1.6.0). The interpretation of `significant_digits` is different than `least_signficant_digit` in that it specifies the absolute number of significant digits independent of the magnitude of the variable (the floating point exponent). Either of these approaches makes the compression 'lossy' instead of 'lossless', that is some precision in the data is sacrificed for the sake of disk space. In our example, try replacing the line ```python >>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",)) ``` with ```python >>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",),compression='zlib') ``` and then ```python >>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",),compression='zlib',least_significant_digit=3) ``` or with netcdf-c >= 4.9.0 ```python >>> temp = rootgrp.createVariable("temp","f4",("time","level","lat","lon",),compression='zlib',significant_digits=4) ``` and see how much smaller the resulting files are. ## Beyond homogeneous arrays of a fixed type - compound data types Compound data types map directly to numpy structured (a.k.a 'record') arrays. Structured arrays are akin to C structs, or derived types in Fortran. They allow for the construction of table-like structures composed of combinations of other data types, including other compound types. Compound types might be useful for representing multiple parameter values at each point on a grid, or at each time and space location for scattered (point) data. You can then access all the information for a point by reading one variable, instead of reading different parameters from different variables. Compound data types are created from the corresponding numpy data type using the `Dataset.createCompoundType` method of a `Dataset` or `Group` instance. Since there is no native complex data type in netcdf (but see [Support for complex numbers](#support-for-complex-numbers)), compound types are handy for storing numpy complex arrays. Here's an example: ```python >>> f = Dataset("complex.nc","w") >>> size = 3 # length of 1-d complex array >>> # create sample complex data. >>> datac = np.exp(1j*(1.+np.linspace(0, np.pi, size))) >>> # create complex128 compound data type. >>> complex128 = np.dtype([("real",np.float64),("imag",np.float64)]) >>> complex128_t = f.createCompoundType(complex128,"complex128") >>> # create a variable with this data type, write some data to it. >>> x_dim = f.createDimension("x_dim",None) >>> v = f.createVariable("cmplx_var",complex128_t,"x_dim") >>> data = np.empty(size,complex128) # numpy structured array >>> data["real"] = datac.real; data["imag"] = datac.imag >>> v[:] = data # write numpy structured array to netcdf compound var >>> # close and reopen the file, check the contents. >>> f.close(); f = Dataset("complex.nc") >>> v = f.variables["cmplx_var"] >>> datain = v[:] # read in all the data into a numpy structured array >>> # create an empty numpy complex array >>> datac2 = np.empty(datain.shape,np.complex128) >>> # .. fill it with contents of structured array. >>> datac2.real = datain["real"]; datac2.imag = datain["imag"] >>> print('{}: {}'.format(datac.dtype, datac)) # original data complex128: [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j] >>> >>> print('{}: {}'.format(datac2.dtype, datac2)) # data from file complex128: [ 0.54030231+0.84147098j -0.84147098+0.54030231j -0.54030231-0.84147098j] ``` Compound types can be nested, but you must create the 'inner' ones first. All possible numpy structured arrays cannot be represented as Compound variables - an error message will be raise if you try to create one that is not supported. All of the compound types defined for a `Dataset` or `Group` are stored in a Python dictionary, just like variables and dimensions. As always, printing objects gives useful summary information in an interactive session: ```python >>> print(f) root group (NETCDF4 data model, file format HDF5): dimensions(sizes): x_dim(3) variables(dimensions): {'names':['real','imag'], 'formats':['>> print(f.variables["cmplx_var"]) compound cmplx_var(x_dim) compound data type: {'names':['real','imag'], 'formats':['>> print(f.cmptypes) {'complex128': : name = 'complex128', numpy dtype = {'names':['real','imag'], 'formats':['>> print(f.cmptypes["complex128"]) : name = 'complex128', numpy dtype = {'names':['real','imag'], 'formats':['>> f = Dataset("tst_vlen.nc","w") >>> vlen_t = f.createVLType(np.int32, "phony_vlen") ``` The numpy datatype of the variable-length sequences and the name of the new datatype must be specified. Any of the primitive datatypes can be used (signed and unsigned integers, 32 and 64 bit floats, and characters), but compound data types cannot. A new variable can then be created using this datatype. ```python >>> x = f.createDimension("x",3) >>> y = f.createDimension("y",4) >>> vlvar = f.createVariable("phony_vlen_var", vlen_t, ("y","x")) ``` Since there is no native vlen datatype in numpy, vlen arrays are represented in python as object arrays (arrays of dtype `object`). These are arrays whose elements are Python object pointers, and can contain any type of python object. For this application, they must contain 1-D numpy arrays all of the same type but of varying length. In this case, they contain 1-D numpy `int32` arrays of random length between 1 and 10. ```python >>> import random >>> random.seed(54321) >>> data = np.empty(len(y)*len(x),object) >>> for n in range(len(y)*len(x)): ... data[n] = np.arange(random.randint(1,10),dtype="int32")+1 >>> data = np.reshape(data,(len(y),len(x))) >>> vlvar[:] = data >>> print("vlen variable =\\n{}".format(vlvar[:])) vlen variable = [[array([1, 2, 3, 4, 5, 6, 7, 8], dtype=int32) array([1, 2], dtype=int32) array([1, 2, 3, 4], dtype=int32)] [array([1, 2, 3], dtype=int32) array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32) array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32)] [array([1, 2, 3, 4, 5, 6, 7], dtype=int32) array([1, 2, 3], dtype=int32) array([1, 2, 3, 4, 5, 6], dtype=int32)] [array([1, 2, 3, 4, 5, 6, 7, 8, 9], dtype=int32) array([1, 2, 3, 4, 5], dtype=int32) array([1, 2], dtype=int32)]] >>> print(f) root group (NETCDF4 data model, file format HDF5): dimensions(sizes): x(3), y(4) variables(dimensions): int32 phony_vlen_var(y,x) groups: >>> print(f.variables["phony_vlen_var"]) vlen phony_vlen_var(y, x) vlen data type: int32 unlimited dimensions: current shape = (4, 3) >>> print(f.vltypes["phony_vlen"]) : name = 'phony_vlen', numpy dtype = int32 ``` Numpy object arrays containing python strings can also be written as vlen variables, For vlen strings, you don't need to create a vlen data type. Instead, simply use the python `str` builtin (or a numpy string datatype with fixed length greater than 1) when calling the `Dataset.createVariable` method. ```python >>> z = f.createDimension("z",10) >>> strvar = f.createVariable("strvar", str, "z") ``` In this example, an object array is filled with random python strings with random lengths between 2 and 12 characters, and the data in the object array is assigned to the vlen string variable. ```python >>> chars = "1234567890aabcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ" >>> data = np.empty(10,"O") >>> for n in range(10): ... stringlen = random.randint(2,12) ... data[n] = "".join([random.choice(chars) for i in range(stringlen)]) >>> strvar[:] = data >>> print("variable-length string variable:\\n{}".format(strvar[:])) variable-length string variable: ['Lh' '25F8wBbMI' '53rmM' 'vvjnb3t63ao' 'qjRBQk6w' 'aJh' 'QF' 'jtIJbJACaQk4' '3Z5' 'bftIIq'] >>> print(f) root group (NETCDF4 data model, file format HDF5): dimensions(sizes): x(3), y(4), z(10) variables(dimensions): int32 phony_vlen_var(y,x), strvar(z) groups: >>> print(f.variables["strvar"]) vlen strvar(z) vlen data type: unlimited dimensions: current shape = (10,) ``` It is also possible to set contents of vlen string variables with numpy arrays of any string or unicode data type. Note, however, that accessing the contents of such variables will always return numpy arrays with dtype `object`. ## Enum data type netCDF4 has an enumerated data type, which is an integer datatype that is restricted to certain named values. Since Enums don't map directly to a numpy data type, they are read and written as integer arrays. Here's an example of using an Enum type to hold cloud type data. The base integer data type and a python dictionary describing the allowed values and their names are used to define an Enum data type using `Dataset.createEnumType`. ```python >>> nc = Dataset('clouds.nc','w') >>> # python dict with allowed values and their names. >>> enum_dict = {'Altocumulus': 7, 'Missing': 255, ... 'Stratus': 2, 'Clear': 0, ... 'Nimbostratus': 6, 'Cumulus': 4, 'Altostratus': 5, ... 'Cumulonimbus': 1, 'Stratocumulus': 3} >>> # create the Enum type called 'cloud_t'. >>> cloud_type = nc.createEnumType(np.uint8,'cloud_t',enum_dict) >>> print(cloud_type) : name = 'cloud_t', numpy dtype = uint8, fields/values ={'Altocumulus': 7, 'Missing': 255, 'Stratus': 2, 'Clear': 0, 'Nimbostratus': 6, 'Cumulus': 4, 'Altostratus': 5, 'Cumulonimbus': 1, 'Stratocumulus': 3} ``` A new variable can be created in the usual way using this data type. Integer data is written to the variable that represents the named cloud types in enum_dict. A `ValueError` will be raised if an attempt is made to write an integer value not associated with one of the specified names. ```python >>> time = nc.createDimension('time',None) >>> # create a 1d variable of type 'cloud_type'. >>> # The fill_value is set to the 'Missing' named value. >>> cloud_var = nc.createVariable('primary_cloud',cloud_type,'time', ... fill_value=enum_dict['Missing']) >>> # write some data to the variable. >>> cloud_var[:] = [enum_dict[k] for k in ['Clear', 'Stratus', 'Cumulus', ... 'Missing', 'Cumulonimbus']] >>> nc.close() >>> # reopen the file, read the data. >>> nc = Dataset('clouds.nc') >>> cloud_var = nc.variables['primary_cloud'] >>> print(cloud_var) enum primary_cloud(time) _FillValue: 255 enum data type: uint8 unlimited dimensions: time current shape = (5,) >>> print(cloud_var.datatype.enum_dict) {'Altocumulus': 7, 'Missing': 255, 'Stratus': 2, 'Clear': 0, 'Nimbostratus': 6, 'Cumulus': 4, 'Altostratus': 5, 'Cumulonimbus': 1, 'Stratocumulus': 3} >>> print(cloud_var[:]) [0 2 4 -- 1] >>> nc.close() ``` ## Parallel IO If MPI parallel enabled versions of netcdf and hdf5 or pnetcdf are detected, and [mpi4py](https://mpi4py.scipy.org) is installed, netcdf4-python will be built with parallel IO capabilities enabled. Parallel IO of NETCDF4 or NETCDF4_CLASSIC formatted files is only available if the MPI parallel HDF5 library is available. Parallel IO of classic netcdf-3 file formats is only available if the [PnetCDF](https://parallel-netcdf.github.io/) library is available. To use parallel IO, your program must be running in an MPI environment using [mpi4py](https://mpi4py.scipy.org). ```python >>> from mpi4py import MPI >>> import numpy as np >>> from netCDF4 import Dataset >>> rank = MPI.COMM_WORLD.rank # The process ID (integer 0-3 for 4-process run) ``` To run an MPI-based parallel program like this, you must use `mpiexec` to launch several parallel instances of Python (for example, using `mpiexec -np 4 python mpi_example.py`). The parallel features of netcdf4-python are mostly transparent - when a new dataset is created or an existing dataset is opened, use the `parallel` keyword to enable parallel access. ```python >>> nc = Dataset('parallel_test.nc','w',parallel=True) ``` The optional `comm` keyword may be used to specify a particular MPI communicator (`MPI_COMM_WORLD` is used by default). Each process (or rank) can now write to the file independently. In this example the process rank is written to a different variable index on each task ```python >>> d = nc.createDimension('dim',4) >>> v = nc.createVariable('var', np.int64, 'dim') >>> v[rank] = rank >>> nc.close() % ncdump parallel_test.nc netcdf parallel_test { dimensions: dim = 4 ; variables: int64 var(dim) ; data: var = 0, 1, 2, 3 ; } ``` There are two types of parallel IO, independent (the default) and collective. Independent IO means that each process can do IO independently. It should not depend on or be affected by other processes. Collective IO is a way of doing IO defined in the MPI-IO standard; unlike independent IO, all processes must participate in doing IO. To toggle back and forth between the two types of IO, use the `Variable.set_collective` `Variable` method. All metadata operations (such as creation of groups, types, variables, dimensions, or attributes) are collective. There are a couple of important limitations of parallel IO: - parallel IO for NETCDF4 or NETCDF4_CLASSIC formatted files is only available if the netcdf library was compiled with MPI enabled HDF5. - parallel IO for all classic netcdf-3 file formats is only available if the netcdf library was compiled with [PnetCDF](https://parallel-netcdf.github.io). - If a variable has an unlimited dimension, appending data must be done in collective mode. If the write is done in independent mode, the operation will fail with a a generic "HDF Error". - You can write compressed data in parallel only with netcdf-c >= 4.7.4 and hdf5 >= 1.10.3 (although you can read in parallel with earlier versions). To write compressed data in parallel, the variable must be in 'collective IO mode'. This is done automatically on variable creation if compression is turned on, but if you are appending to a variable in an existing file, you must use `Variable.set_collective(True)` before attempting to write to it. - You cannot use variable-length (VLEN) data types. ***Import warning regarding threads:*** The underlying netcdf-c library is not thread-safe, so netcdf4-python cannot perform parallel IO in a multi-threaded environment. Users should expect segfaults if a netcdf file is opened on multiple threads - care should be taken to restrict netcdf4-python usage to a single thread, even when using free-threaded python. ## Dealing with strings The most flexible way to store arrays of strings is with the [Variable-length (vlen) string data type](#variable-length-vlen-data-type). However, this requires the use of the NETCDF4 data model, and the vlen type does not map very well numpy arrays (you have to use numpy arrays of dtype=`object`, which are arrays of arbitrary python objects). numpy does have a fixed-width string array data type, but unfortunately the netCDF data model does not. Instead fixed-width byte strings are typically stored as [arrays of 8-bit characters](https://www.unidata.ucar.edu/software/netcdf/docs/BestPractices.html#bp_Strings-and-Variables-of-type-char). To perform the conversion to and from character arrays to fixed-width numpy string arrays, the following convention is followed by the python interface. If the `_Encoding` special attribute is set for a character array (dtype `S1`) variable, the `chartostring` utility function is used to convert the array of characters to an array of strings with one less dimension (the last dimension is interpreted as the length of each string) when reading the data. The character set is specified by the `_Encoding` attribute. If `_Encoding` is 'none' or 'bytes', then the character array is converted to a numpy fixed-width byte string array (dtype `S#`), otherwise a numpy unicode (dtype `U#`) array is created. When writing the data, `stringtochar` is used to convert the numpy string array to an array of characters with one more dimension. For example, ```python >>> from netCDF4 import stringtochar >>> nc = Dataset('stringtest.nc','w',format='NETCDF4_CLASSIC') >>> _ = nc.createDimension('nchars',3) >>> _ = nc.createDimension('nstrings',None) >>> v = nc.createVariable('strings','S1',('nstrings','nchars')) >>> datain = np.array(['foo','bar'],dtype='S3') >>> v[:] = stringtochar(datain) # manual conversion to char array >>> print(v[:]) # data returned as char array [[b'f' b'o' b'o'] [b'b' b'a' b'r']] >>> v._Encoding = 'ascii' # this enables automatic conversion >>> v[:] = datain # conversion to char array done internally >>> print(v[:]) # data returned in numpy string array ['foo' 'bar'] >>> nc.close() ``` Even if the `_Encoding` attribute is set, the automatic conversion of char arrays to/from string arrays can be disabled with `Variable.set_auto_chartostring`. A similar situation is often encountered with numpy structured arrays with subdtypes containing fixed-wdith byte strings (dtype=`S#`). Since there is no native fixed-length string netCDF datatype, these numpy structure arrays are mapped onto netCDF compound types with character array elements. In this case the string <-> char array conversion is handled automatically (without the need to set the `_Encoding` attribute) using [numpy views](https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.view.html). The structured array dtype (including the string elements) can even be used to define the compound data type - the string dtype will be converted to character array dtype under the hood when creating the netcdf compound type. Here's an example: ```python >>> nc = Dataset('compoundstring_example.nc','w') >>> dtype = np.dtype([('observation', 'f4'), ... ('station_name','S10')]) >>> station_data_t = nc.createCompoundType(dtype,'station_data') >>> _ = nc.createDimension('station',None) >>> statdat = nc.createVariable('station_obs', station_data_t, ('station',)) >>> data = np.empty(2,dtype) >>> data['observation'][:] = (123.,3.14) >>> data['station_name'][:] = ('Boulder','New York') >>> print(statdat.dtype) # strings actually stored as character arrays {'names':['observation','station_name'], 'formats':['>> statdat[:] = data # strings converted to character arrays internally >>> print(statdat[:]) # character arrays converted back to strings [(123. , b'Boulder') ( 3.14, b'New York')] >>> print(statdat[:].dtype) {'names':['observation','station_name'], 'formats':['>> statdat.set_auto_chartostring(False) # turn off auto-conversion >>> statdat[:] = data.view(dtype=[('observation', 'f4'),('station_name','S1',10)]) >>> print(statdat[:]) # now structured array with char array subtype is returned [(123. , [b'B', b'o', b'u', b'l', b'd', b'e', b'r', b'', b'', b'']) ( 3.14, [b'N', b'e', b'w', b' ', b'Y', b'o', b'r', b'k', b'', b''])] >>> nc.close() ``` Note that there is currently no support for mapping numpy structured arrays with unicode elements (dtype `U#`) onto netCDF compound types, nor is there support for netCDF compound types with vlen string components. ## In-memory (diskless) Datasets You can create netCDF Datasets whose content is held in memory instead of in a disk file. There are two ways to do this. If you don't need access to the memory buffer containing the Dataset from within python, the best way is to use the `diskless=True` keyword argument when creating the Dataset. If you want to save the Dataset to disk when you close it, also set `persist=True`. If you want to create a new read-only Dataset from an existing python memory buffer, use the `memory` keyword argument to pass the memory buffer when creating the Dataset. If you want to create a new in-memory Dataset, and then access the memory buffer directly from Python, use the `memory` keyword argument to specify the estimated size of the Dataset in bytes when creating the Dataset with `mode='w'`. Then, the `Dataset.close` method will return a python memoryview object representing the Dataset. Below are examples illustrating both approaches. ```python >>> # create a diskless (in-memory) Dataset, >>> # and persist the file to disk when it is closed. >>> nc = Dataset('diskless_example.nc','w',diskless=True,persist=True) >>> d = nc.createDimension('x',None) >>> v = nc.createVariable('v',np.int32,'x') >>> v[0:5] = np.arange(5) >>> print(nc) root group (NETCDF4 data model, file format HDF5): dimensions(sizes): x(5) variables(dimensions): int32 v(x) groups: >>> print(nc['v'][:]) [0 1 2 3 4] >>> nc.close() # file saved to disk >>> # create an in-memory dataset from an existing python >>> # python memory buffer. >>> # read the newly created netcdf file into a python >>> # bytes object. >>> with open('diskless_example.nc', 'rb') as f: ... nc_bytes = f.read() >>> # create a netCDF in-memory dataset from the bytes object. >>> nc = Dataset('inmemory.nc', memory=nc_bytes) >>> print(nc) root group (NETCDF4 data model, file format HDF5): dimensions(sizes): x(5) variables(dimensions): int32 v(x) groups: >>> print(nc['v'][:]) [0 1 2 3 4] >>> nc.close() >>> # create an in-memory Dataset and retrieve memory buffer >>> # estimated size is 1028 bytes - this is actually only >>> # used if format is NETCDF3 >>> # (ignored for NETCDF4/HDF5 files). >>> nc = Dataset('inmemory.nc', mode='w',memory=1028) >>> d = nc.createDimension('x',None) >>> v = nc.createVariable('v',np.int32,'x') >>> v[0:5] = np.arange(5) >>> nc_buf = nc.close() # close returns memoryview >>> print(type(nc_buf)) >>> # save nc_buf to disk, read it back in and check. >>> with open('inmemory.nc', 'wb') as f: ... f.write(nc_buf) >>> nc = Dataset('inmemory.nc') >>> print(nc) root group (NETCDF4 data model, file format HDF5): dimensions(sizes): x(5) variables(dimensions): int32 v(x) groups: >>> print(nc['v'][:]) [0 1 2 3 4] >>> nc.close() ``` ## Support for complex numbers Although there is no native support for complex numbers in netCDF, there are some common conventions for storing them. Two of the most common are to either use a compound datatype for the real and imaginary components, or a separate dimension. `netCDF4` supports reading several of these conventions, as well as writing using one of two conventions (depending on file format). This support for complex numbers is enabled by setting `auto_complex=True` when opening a `Dataset`: ```python >>> complex_array = np.array([0 + 0j, 1 + 0j, 0 + 1j, 1 + 1j, 0.25 + 0.75j]) >>> with netCDF4.Dataset("complex.nc", "w", auto_complex=True) as nc: ... nc.createDimension("x", size=len(complex_array)) ... var = nc.createVariable("data", "c16", ("x",)) ... var[:] = complex_array ... print(var) compound data(x) compound data type: complex128 unlimited dimensions: current shape = (5,) ``` When reading files using `auto_complex=True`, `netCDF4` will interpret variables stored using the following conventions as complex numbers: - compound datatypes with two `float` or `double` members who names begin with `r` and `i` (case insensitive) - a dimension of length 2 named `complex` or `ri` When writing files using `auto_complex=True`, `netCDF4` will use: - a compound datatype named `_PFNC_DOUBLE_COMPLEX_TYPE` (or `*FLOAT*` as appropriate) with members `r` and `i` for netCDF4 formats; - or a dimension of length 2 named `_pfnc_complex` for netCDF3 or classic formats. Support for complex numbers is handled via the [`nc-complex`](https://github.com/PlasmaFAIR/nc-complex) library. See there for further details. **contact**: Jeffrey Whitaker **copyright**: 2008 by Jeffrey Whitaker. **license**: Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. """ # Make changes to this file, not the c-wrappers that Cython generates. from cpython.mem cimport PyMem_Malloc, PyMem_Free from cpython.buffer cimport PyObject_GetBuffer, PyBuffer_Release, PyBUF_SIMPLE, PyBUF_ANY_CONTIGUOUS from cpython.bytes cimport PyBytes_FromStringAndSize # pure python utilities from .utils import (_StartCountStride, _quantize, _find_dim, _walk_grps, _out_array_shape, _sortbylist, _tostr, _safecast, _is_int) import sys import functools from typing import Union __version__ = "1.7.4" # Initialize numpy import posixpath from cftime import date2num, num2date, date2index import numpy cimport numpy import weakref import warnings import subprocess import pathlib import os from glob import glob from numpy import ma from libc.string cimport memcpy, memset from libc.stdlib cimport malloc, free numpy.import_array() include "membuf.pyx" include "netCDF4.pxi" __has_rename_grp__ = HAS_RENAME_GRP __has_nc_inq_path__ = HAS_NC_INQ_PATH __has_nc_inq_format_extended__ = HAS_NC_INQ_FORMAT_EXTENDED __has_cdf5_format__ = HAS_CDF5_FORMAT __has_nc_open_mem__ = HAS_NC_OPEN_MEM __has_nc_create_mem__ = HAS_NC_CREATE_MEM __has_parallel4_support__ = HAS_PARALLEL4_SUPPORT __has_pnetcdf_support__ = HAS_PNETCDF_SUPPORT __has_parallel_support__ = HAS_PARALLEL_SUPPORT __has_quantization_support__ = HAS_QUANTIZATION_SUPPORT __has_zstandard_support__ = HAS_ZSTANDARD_SUPPORT __has_bzip2_support__ = HAS_BZIP2_SUPPORT __has_blosc_support__ = HAS_BLOSC_SUPPORT __has_szip_support__ = HAS_SZIP_SUPPORT __has_set_alignment__ = HAS_SET_ALIGNMENT __has_ncfilter__ = HAS_NCFILTER __has_nc_rc_set__ = HAS_NCRCSET # set path to SSL certificates (issue #1246) # available starting in version 4.9.1 if __has_nc_rc_set__: import certifi if nc_rc_set("HTTP.SSL.CAINFO", _strencode(certifi.where())) != 0: raise RuntimeError('error setting path to SSL certificates') def rc_get(key): """ **```rc_get(key)```** Returns the internal netcdf-c rc table value corresponding to key. See for more information on rc files and values. """ cdef int ierr cdef char *keyc cdef char *valc if __has_nc_rc_set__: bytestr = _strencode(_tostr(key)) keyc = bytestr valc = nc_rc_get(keyc) if valc is NULL: return None else: return valc.decode('utf-8') else: raise RuntimeError( "This function requires netcdf-c 4.9.0+ to be used at compile time" ) def rc_set(key, value): """ **```rc_set(key, value)```** Sets the internal netcdf-c rc table value corresponding to key. See for more information on rc files and values. """ cdef int ierr cdef char *keyc cdef char *valuec if __has_nc_rc_set__: key_bytestr = _strencode(_tostr(key)) keyc = key_bytestr val_bytestr = _strencode(_tostr(value)) valuec = val_bytestr with nogil: ierr = nc_rc_set(keyc,valuec) _ensure_nc_success(ierr) else: raise RuntimeError( "This function requires netcdf-c 4.9.0+ to be used at compile time" ) # check for required version of netcdf-4 and hdf5. def _gethdf5libversion(): cdef unsigned int majorvers, minorvers, releasevers cdef herr_t ierr with nogil: ierr = H5get_libversion( &majorvers, &minorvers, &releasevers) if ierr != 0: raise RuntimeError('error getting HDF5 library version info') return '%d.%d.%d' % (majorvers,minorvers,releasevers) def getlibversion(): """ **```getlibversion()```** returns a string describing the version of the netcdf library used to build the module, and when it was built. """ return (nc_inq_libvers()).decode('ascii') def get_chunk_cache(): """ **```get_chunk_cache()```** return current netCDF chunk cache information in a tuple (size,nelems,preemption). See netcdf C library documentation for `nc_get_chunk_cache` for details. Values can be reset with `set_chunk_cache`.""" cdef int ierr cdef size_t sizep, nelemsp cdef float preemptionp with nogil: ierr = nc_get_chunk_cache(&sizep, &nelemsp, &preemptionp) _ensure_nc_success(ierr) size = sizep; nelems = nelemsp; preemption = preemptionp return (size,nelems,preemption) def set_chunk_cache(size=None,nelems=None,preemption=None): """ **```set_chunk_cache(size=None,nelems=None,preemption=None)```** change netCDF4 chunk cache settings. See netcdf C library documentation for `nc_set_chunk_cache` for details.""" cdef int ierr cdef size_t sizep, nelemsp cdef float preemptionp # reset chunk cache size, leave other parameters unchanged. size_orig, nelems_orig, preemption_orig = get_chunk_cache() if size is not None: sizep = size else: sizep = size_orig if nelems is not None: nelemsp = nelems else: nelemsp = nelems_orig if preemption is not None: preemptionp = preemption else: preemptionp = preemption_orig with nogil: ierr = nc_set_chunk_cache(sizep,nelemsp, preemptionp) _ensure_nc_success(ierr) def get_alignment(): """**```get_alignment()```** return current netCDF alignment within HDF5 files in a tuple (threshold,alignment). See netcdf C library documentation for `nc_get_alignment` for details. Values can be reset with `set_alignment`. This function was added in netcdf 4.9.0.""" if not __has_set_alignment__: raise RuntimeError( "This function requires netcdf-c 4.9.0+ to be used at compile time" ) cdef int ierr cdef int thresholdp, alignmentp ierr = nc_get_alignment(&thresholdp, &alignmentp) _ensure_nc_success(ierr) threshold = thresholdp alignment = alignmentp return (threshold, alignment) def set_alignment(threshold, alignment): """**```set_alignment(threshold,alignment)```** Change the HDF5 file alignment. See netcdf C library documentation for `nc_set_alignment` for details. This function was added in netcdf 4.9.0.""" if not __has_set_alignment__: raise RuntimeError( "This function requires netcdf-c 4.9.0+ to be used at compile time" ) cdef int ierr cdef int thresholdp, alignmentp thresholdp = threshold alignmentp = alignment ierr = nc_set_alignment(thresholdp, alignmentp) _ensure_nc_success(ierr) __netcdf4libversion__ = getlibversion().split()[0] __hdf5libversion__ = _gethdf5libversion() _needsworkaround_issue485 = __netcdf4libversion__ < "4.4.0" or \ (__netcdf4libversion__.startswith("4.4.0") and \ "-development" in __netcdf4libversion__) class NetCDF4MissingFeatureException(Exception): """Custom exception when trying to use features missing from the linked netCDF library""" def __init__(self, feature: str, version: str): super().__init__( f"{feature} requires netCDF lib >= {version} (using {__netcdf4libversion__}). " f"To enable, rebuild netcdf4-python using netCDF {version} or higher " f"(and possibly enable {feature})" ) # numpy data type <--> netCDF 4 data type mapping. _nptonctype = {'S1' : NC_CHAR, 'i1' : NC_BYTE, 'u1' : NC_UBYTE, 'i2' : NC_SHORT, 'u2' : NC_USHORT, 'i4' : NC_INT, 'u4' : NC_UINT, 'i8' : NC_INT64, 'u8' : NC_UINT64, 'f4' : NC_FLOAT, 'f8' : NC_DOUBLE} # just integer types. _intnptonctype = {'i1' : NC_BYTE, 'u1' : NC_UBYTE, 'i2' : NC_SHORT, 'u2' : NC_USHORT, 'i4' : NC_INT, 'u4' : NC_UINT, 'i8' : NC_INT64, 'u8' : NC_UINT64} _complex_types = { "c16": PFNC_DOUBLE_COMPLEX, "c8": PFNC_FLOAT_COMPLEX, } # create dictionary mapping string identifiers to netcdf format codes _format_dict = {'NETCDF3_CLASSIC' : NC_FORMAT_CLASSIC, 'NETCDF4_CLASSIC' : NC_FORMAT_NETCDF4_CLASSIC, 'NETCDF4' : NC_FORMAT_NETCDF4} # create dictionary mapping string identifiers to netcdf create format codes _cmode_dict = {'NETCDF3_CLASSIC' : NC_CLASSIC_MODEL, 'NETCDF4_CLASSIC' : NC_CLASSIC_MODEL | NC_NETCDF4, 'NETCDF4' : NC_NETCDF4} # dicts for blosc, szip compressors. _blosc_dict={'blosc_lz':0,'blosc_lz4':1,'blosc_lz4hc':2,'blosc_snappy':3,'blosc_zlib':4,'blosc_zstd':5} _blosc_dict_inv = {v: k for k, v in _blosc_dict.items()} _szip_dict = {'ec': 4, 'nn': 32} _szip_dict_inv = {v: k for k, v in _szip_dict.items()} if __has_cdf5_format__: # NETCDF3_64BIT deprecated, saved for compatibility. # use NETCDF3_64BIT_OFFSET instead. _format_dict['NETCDF3_64BIT_OFFSET'] = NC_FORMAT_64BIT_OFFSET _format_dict['NETCDF3_64BIT_DATA'] = NC_FORMAT_64BIT_DATA _cmode_dict['NETCDF3_64BIT_OFFSET'] = NC_64BIT_OFFSET _cmode_dict['NETCDF3_64BIT_DATA'] = NC_64BIT_DATA else: _format_dict['NETCDF3_64BIT'] = NC_FORMAT_64BIT _cmode_dict['NETCDF3_64BIT'] = NC_64BIT_OFFSET # invert dictionary mapping _reverse_format_dict = dict((v, k) for k, v in _format_dict.iteritems()) # add duplicate entry (NETCDF3_64BIT == NETCDF3_64BIT_OFFSET) if __has_cdf5_format__: _format_dict['NETCDF3_64BIT'] = NC_FORMAT_64BIT_OFFSET _cmode_dict['NETCDF3_64BIT'] = NC_64BIT_OFFSET else: _format_dict['NETCDF3_64BIT_OFFSET'] = NC_FORMAT_64BIT _cmode_dict['NETCDF3_64BIT_OFFSET'] = NC_64BIT_OFFSET _parallel_formats = [] if __has_parallel4_support__: _parallel_formats += ['NETCDF4', 'NETCDF4_CLASSIC'] if __has_pnetcdf_support__: _parallel_formats += [ 'NETCDF3_CLASSIC', 'NETCDF3_64BIT_OFFSET', 'NETCDF3_64BIT_DATA', 'NETCDF3_64BIT' ] # Default fill_value to numpy datatype mapping. Last two for complex # numbers only applies to complex dimensions default_fillvals = {#'S1':NC_FILL_CHAR, 'S1':'\0', 'i1':NC_FILL_BYTE, 'u1':NC_FILL_UBYTE, 'i2':NC_FILL_SHORT, 'u2':NC_FILL_USHORT, 'i4':NC_FILL_INT, 'u4':NC_FILL_UINT, 'i8':NC_FILL_INT64, 'u8':NC_FILL_UINT64, 'f4':NC_FILL_FLOAT, 'f8':NC_FILL_DOUBLE, 'c8':NC_FILL_FLOAT, 'c16':NC_FILL_DOUBLE, } # logical for native endian type. is_native_little = numpy.dtype('f4').byteorder == c'=' # hard code these here, instead of importing from netcdf.h # so it will compile with versions <= 4.2. NC_DISKLESS = 0x0008 # introduced in 4.6.2 if __netcdf4libversion__[0:5] >= "4.6.2": NC_PERSIST = 0x4000 else: # prior to 4.6.2 this flag doesn't work, so make the same as NC_DISKLESS NC_PERSIST = NC_DISKLESS # next two lines do nothing, preserved for backwards compatibility. default_encoding = 'utf-8' unicode_error = 'replace' _nctonptype = {} for _key,_value in _nptonctype.items(): _nctonptype[_value] = _key _supportedtypes = _nptonctype.keys() # make sure NC_CHAR points to S1 _nctonptype[NC_CHAR]='S1' # Mapping from numpy dtype endian format to what we expect _dtype_endian_lookup = { "=": "native", ">": "big", "<": "little", "|": None, None: None, } # internal C functions. cdef _get_att_names(int grpid, int varid): # Private function to get all the attribute names in a group cdef int ierr, numatts, n cdef char namstring[NC_MAX_NAME+1] if varid == NC_GLOBAL: with nogil: ierr = nc_inq_natts(grpid, &numatts) else: with nogil: ierr = nc_inq_varnatts(grpid, varid, &numatts) _ensure_nc_success(ierr, err_cls=AttributeError) attslist = [] for n in range(numatts): with nogil: ierr = nc_inq_attname(grpid, varid, n, namstring) _ensure_nc_success(ierr, err_cls=AttributeError) # attribute names are assumed to be utf-8 attslist.append(namstring.decode('utf-8')) return attslist cdef _get_att(grp, int varid, name, encoding='utf-8'): # Private function to get an attribute value given its name cdef int ierr, n, _grpid cdef size_t att_len cdef char *attname cdef nc_type att_type cdef ndarray value_arr # attribute names are assumed to be utf-8 bytestr = _strencode(name,encoding='utf-8') attname = bytestr _grpid = grp._grpid with nogil: ierr = nc_inq_att(_grpid, varid, attname, &att_type, &att_len) _ensure_nc_success(ierr, err_cls=AttributeError) # attribute is a character or string ... if att_type == NC_CHAR: value_arr = numpy.empty(att_len,'S1') with nogil: ierr = nc_get_att_text(_grpid, varid, attname, PyArray_BYTES(value_arr)) _ensure_nc_success(ierr, err_cls=AttributeError) if name == '_FillValue': # make sure _FillValue for character arrays is a byte on python 3 # (issue 271). pstring = value_arr.tobytes() else: pstring =\ value_arr.tobytes().decode(encoding,errors='replace').replace('\x00','') return pstring elif att_type == NC_STRING: values = PyMem_Malloc(sizeof(char*) * att_len) if not values: raise MemoryError() try: with nogil: ierr = nc_get_att_string(_grpid, varid, attname, values) _ensure_nc_success(ierr, err_cls=AttributeError) try: result = [values[j].decode(encoding,errors='replace').replace('\x00','') if values[j] else "" for j in range(att_len)] finally: with nogil: ierr = nc_free_string(att_len, values) # free memory in netcdf C lib finally: PyMem_Free(values) if len(result) == 1: return result[0] else: return result else: # a regular numeric or compound type. if att_type == NC_LONG: att_type = NC_INT try: type_att = _nctonptype[att_type] # see if it is a primitive type value_arr = numpy.empty(att_len,type_att) except KeyError: # check if it's a compound try: type_att = _read_compound(grp, att_type) value_arr = numpy.empty(att_len,type_att.dtype_view) except: # check if it's an enum try: type_att = _read_enum(grp, att_type) value_arr = numpy.empty(att_len,type_att.dtype) except: raise KeyError('attribute %s has unsupported datatype' % attname) with nogil: ierr = nc_get_att(_grpid, varid, attname, PyArray_BYTES(value_arr)) _ensure_nc_success(ierr, err_cls=AttributeError) if value_arr.shape == (): # return a scalar for a scalar array return value_arr.item() elif att_len == 1: # return a scalar for a single element array return value_arr[0] else: return value_arr def _set_default_format(object format='NETCDF4'): # Private function to set the netCDF file format cdef int ierr, formatid if format not in _format_dict: raise ValueError("unrecognized format requested") formatid = _format_dict[format] with nogil: ierr = nc_set_default_format(formatid, NULL) _ensure_nc_success(ierr) cdef _get_format(int grpid): # Private function to get the netCDF file format cdef int ierr, formatp with nogil: ierr = nc_inq_format(grpid, &formatp) _ensure_nc_success(ierr) if formatp not in _reverse_format_dict: raise ValueError('format not supported by python interface') return _reverse_format_dict[formatp] cdef _get_full_format(int grpid): """Private function to get the underlying disk format""" if not __has_nc_inq_format_extended__: return "UNDEFINED" cdef int ierr, formatp, modep with nogil: ierr = nc_inq_format_extended(grpid, &formatp, &modep) _ensure_nc_success(ierr) if formatp == NC_FORMAT_NC3: return 'NETCDF3' if formatp == NC_FORMAT_NC_HDF5: return 'HDF5' if formatp == NC_FORMAT_NC_HDF4: return 'HDF4' if formatp == NC_FORMAT_PNETCDF: return 'PNETCDF' if formatp == NC_FORMAT_DAP2: return 'DAP2' if formatp == NC_FORMAT_DAP4: return 'DAP4' if formatp == NC_FORMAT_UNDEFINED: return 'UNDEFINED' cdef issue485_workaround(int grpid, int varid, char* attname): # check to see if attribute already exists # and is NC_CHAR, if so delete it and re-create it # (workaround for issue #485). Fixed in C library # with commit 473259b7728120bb281c52359b1af50cca2fcb72, # which was included in 4.4.0-RC5. cdef nc_type att_type cdef size_t att_len if not _needsworkaround_issue485: return with nogil: ierr = nc_inq_att(grpid, varid, attname, &att_type, &att_len) if ierr == NC_NOERR and att_type == NC_CHAR: with nogil: ierr = nc_del_att(grpid, varid, attname) _ensure_nc_success(ierr) cdef _set_att(grp, int varid, name, value,\ nc_type xtype=-99, force_ncstring=False): # Private function to set an attribute name/value pair cdef int ierr, lenarr, N, grpid cdef char *attname cdef char *datstring cdef char **string_ptrs cdef ndarray value_arr bytestr = _strencode(name) attname = bytestr grpid = grp._grpid # put attribute value into a numpy array. value_arr = numpy.array(value) if value_arr.ndim > 1: # issue #841 if __version__ > "1.4.2": raise ValueError('multi-dimensional array attributes not supported') else: msg = """ Multi-dimensional array attributes are now deprecated. Instead of silently flattening the array, an error will be raised in the next release.""" warnings.warn(msg,FutureWarning) # if array is 64 bit integers or # if 64-bit datatype not supported, cast to 32 bit integers. fmt = _get_format(grpid) is_netcdf3 = fmt.startswith('NETCDF3') or fmt == 'NETCDF4_CLASSIC' if value_arr.dtype.str[1:] == 'i8' and ('i8' not in _supportedtypes or\ (is_netcdf3 and fmt != 'NETCDF3_64BIT_DATA')): value_arr = value_arr.astype('i4') # if array contains ascii strings, write a text attribute (stored as bytes). # if array contains unicode strings, and data model is NETCDF4, # write as a string. if value_arr.dtype.char in ['S','U']: # force array of strings if array has multiple elements (issue #770) N = value_arr.size if N > 1: force_ncstring=True if not is_netcdf3 and force_ncstring and N > 1: string_ptrs = PyMem_Malloc(N * sizeof(char*)) if not string_ptrs: raise MemoryError() try: strings = [_strencode(s) for s in value_arr.flat] for j in range(N): if len(strings[j]) == 0: strings[j] = _strencode('\x00') string_ptrs[j] = strings[j] issue485_workaround(grpid, varid, attname) with nogil: ierr = nc_put_att_string(grpid, varid, attname, N, string_ptrs) finally: PyMem_Free(string_ptrs) else: # don't allow string array attributes in NETCDF3 files. if is_netcdf3 and N > 1: msg='array string attributes can only be written with NETCDF4' raise OSError(msg) if not value_arr.shape: dats = _strencode(value_arr.item()) else: value_arr1 = value_arr.ravel() dats = _strencode(''.join(value_arr1.tolist())) lenarr = len(dats) datstring = dats if lenarr == 0: # write null byte lenarr=1; datstring = '\x00' if (force_ncstring or value_arr.dtype.char == 'U') and not is_netcdf3: # try to convert to ascii string, write as NC_CHAR # else it's a unicode string, write as NC_STRING (if NETCDF4) try: if force_ncstring: raise UnicodeError dats_ascii = _to_ascii(dats) # try to encode bytes as ascii string with nogil: ierr = nc_put_att_text(grpid, varid, attname, lenarr, datstring) except UnicodeError: issue485_workaround(grpid, varid, attname) with nogil: ierr = nc_put_att_string(grpid, varid, attname, 1, &datstring) else: with nogil: ierr = nc_put_att_text(grpid, varid, attname, lenarr, datstring) _ensure_nc_success(ierr, err_cls=AttributeError) # a 'regular' array type ('f4','i4','f8' etc) else: if value_arr.dtype.kind == 'V': # compound attribute. xtype = _find_cmptype(grp,value_arr.dtype) elif value_arr.dtype.str[1:] not in _supportedtypes: raise TypeError, 'illegal data type for attribute %r, must be one of %s, got %s' % (attname, _supportedtypes, value_arr.dtype.str[1:]) elif xtype == -99: # if xtype is not passed in as kwarg. xtype = _nptonctype[value_arr.dtype.str[1:]] lenarr = PyArray_SIZE(value_arr) with nogil: ierr = nc_put_att(grpid, varid, attname, xtype, lenarr, PyArray_DATA(value_arr)) _ensure_nc_success(ierr, err_cls=AttributeError) cdef _get_types(group): # Private function to create `CompoundType`, # `VLType` or `EnumType` instances for all the # compound, VLEN or Enum types in a `Group` or `Dataset`. cdef int ierr, ntypes, classp, n, _grpid cdef nc_type xtype cdef nc_type *typeids cdef char namstring[NC_MAX_NAME+1] _grpid = group._grpid # get the number of user defined types in this group. with nogil: ierr = nc_inq_typeids(_grpid, &ntypes, NULL) _ensure_nc_success(ierr) if ntypes > 0: typeids = malloc(sizeof(nc_type) * ntypes) with nogil: ierr = nc_inq_typeids(_grpid, &ntypes, typeids) _ensure_nc_success(ierr) # create empty dictionary for CompoundType instances. cmptypes = dict() vltypes = dict() enumtypes = dict() if ntypes > 0: for n in range(ntypes): xtype = typeids[n] with nogil: ierr = nc_inq_user_type(_grpid, xtype, namstring, NULL,NULL,NULL,&classp) _ensure_nc_success(ierr) if classp == NC_COMPOUND: # a compound name = namstring.decode('utf-8') # read the compound type info from the file, # create a CompoundType instance from it. try: cmptype = _read_compound(group, xtype) except KeyError: msg='WARNING: unsupported Compound type, skipping...' warnings.warn(msg) continue cmptypes[name] = cmptype elif classp == NC_VLEN: # a vlen name = namstring.decode('utf-8') # read the VLEN type info from the file, # create a VLType instance from it. try: vltype = _read_vlen(group, xtype) except KeyError: msg='WARNING: unsupported VLEN type, skipping...' warnings.warn(msg) continue vltypes[name] = vltype elif classp == NC_ENUM: # an enum type name = namstring.decode('utf-8') # read the Enum type info from the file, # create a EnumType instance from it. try: enumtype = _read_enum(group, xtype) except KeyError: msg='WARNING: unsupported Enum type, skipping...' warnings.warn(msg) continue enumtypes[name] = enumtype free(typeids) return cmptypes, vltypes, enumtypes cdef _get_dims(group): # Private function to create `Dimension` instances for all the # dimensions in a `Group` or Dataset cdef int ierr, numdims, n, _grpid cdef int *dimids cdef char namstring[NC_MAX_NAME+1] # get number of dimensions in this Group. _grpid = group._grpid with nogil: ierr = nc_inq_ndims(_grpid, &numdims) _ensure_nc_success(ierr) # create empty dictionary for dimensions. dimensions = dict() if numdims > 0: dimids = malloc(sizeof(int) * numdims) if group.data_model == 'NETCDF4': with nogil: ierr = nc_inq_dimids(_grpid, &numdims, dimids, 0) _ensure_nc_success(ierr) else: for n in range(numdims): dimids[n] = n for n in range(numdims): with nogil: ierr = nc_inq_dimname(_grpid, dimids[n], namstring) _ensure_nc_success(ierr) name = namstring.decode('utf-8') dimensions[name] = Dimension(group, name, id=dimids[n]) free(dimids) return dimensions cdef _get_grps(group): # Private function to create `Group` instances for all the # groups in a `Group` or Dataset cdef int ierr, numgrps, n, _grpid cdef int *grpids cdef char namstring[NC_MAX_NAME+1] # get number of groups in this Group. _grpid = group._grpid with nogil: ierr = nc_inq_grps(_grpid, &numgrps, NULL) _ensure_nc_success(ierr) # create dictionary containing `Group` instances for groups in this group groups = dict() if numgrps > 0: grpids = malloc(sizeof(int) * numgrps) with nogil: ierr = nc_inq_grps(_grpid, NULL, grpids) _ensure_nc_success(ierr) for n in range(numgrps): with nogil: ierr = nc_inq_grpname(grpids[n], namstring) _ensure_nc_success(ierr) name = namstring.decode('utf-8') groups[name] = Group(group, name, id=grpids[n]) free(grpids) return groups cdef _get_vars(group, bint auto_complex=False): # Private function to create `Variable` instances for all the # variables in a `Group` or Dataset cdef int ierr, numvars, n, nn, numdims, varid, classp, iendian, _grpid cdef int *varids cdef nc_type xtype cdef char namstring[NC_MAX_NAME+1] cdef char namstring_cmp[NC_MAX_NAME+1] cdef bint is_complex cdef nc_type complex_nc_type # get number of variables in this Group. _grpid = group._grpid with nogil: ierr = nc_inq_nvars(_grpid, &numvars) _ensure_nc_success(ierr, err_cls=AttributeError) # create empty dictionary for variables. variables = dict() if numvars > 0: # get variable ids. varids = malloc(sizeof(int) * numvars) if group.data_model == 'NETCDF4': with nogil: ierr = nc_inq_varids(_grpid, &numvars, varids) _ensure_nc_success(ierr) else: for n in range(numvars): varids[n] = n # loop over variables. for n in range(numvars): varid = varids[n] # get variable name. with nogil: ierr = nc_inq_varname(_grpid, varid, namstring) _ensure_nc_success(ierr) name = namstring.decode('utf-8') # get variable type. with nogil: ierr = nc_inq_vartype(_grpid, varid, &xtype) _ensure_nc_success(ierr) # get endian-ness of variable. endianness = None with nogil: ierr = nc_inq_var_endian(_grpid, varid, &iendian) if ierr == NC_NOERR: if iendian == NC_ENDIAN_LITTLE: endianness = '<' elif iendian == NC_ENDIAN_BIG: endianness = '>' # check to see if it is a supported user-defined type. try: datatype = _nctonptype[xtype] if endianness is not None: datatype = endianness + datatype except KeyError: if xtype == NC_STRING: datatype = str else: with nogil: ierr = nc_inq_user_type(_grpid, xtype, namstring_cmp, NULL, NULL, NULL, &classp) _ensure_nc_success(ierr) if classp == NC_COMPOUND: # a compound type # create CompoundType instance describing this compound type. try: datatype = _read_compound(group, xtype, endian=endianness) except KeyError: msg="WARNING: variable '%s' has unsupported compound datatype, skipping .." % name warnings.warn(msg) continue elif classp == NC_VLEN: # a compound type # create VLType instance describing this compound type. try: datatype = _read_vlen(group, xtype, endian=endianness) except KeyError: msg="WARNING: variable '%s' has unsupported VLEN datatype, skipping .." % name warnings.warn(msg) continue elif classp == NC_ENUM: # create EnumType instance describing this compound type. try: datatype = _read_enum(group, xtype, endian=endianness) except KeyError: msg="WARNING: variable '%s' has unsupported Enum datatype, skipping .." % name warnings.warn(msg) continue else: msg="WARNING: variable '%s' has unsupported datatype, skipping .." % name warnings.warn(msg) continue # get number of dimensions. dimids = _inq_vardimid(_grpid, varid, auto_complex) # loop over dimensions, retrieve names. # if not found in current group, look in parents. # QUESTION: what if grp1 has a dimension named 'foo' # and so does it's parent - can a variable in grp1 # use the 'foo' dimension from the parent? dimensions = [] for dimid in dimids: grp = group found = False while not found: for key, value in grp.dimensions.items(): if value._dimid == dimid: dimensions.append(key) found = True break grp = grp.parent # create new variable instance. dimensions = tuple(_find_dim(group, d) for d in dimensions) if auto_complex and pfnc_var_is_complex(_grpid, varid): with nogil: ierr = pfnc_inq_var_complex_base_type(_grpid, varid, &complex_nc_type) _ensure_nc_success(ierr) # TODO: proper lookup datatype = "c16" if complex_nc_type == NC_DOUBLE else "c8" endian = _dtype_endian_lookup[endianness] or "native" variables[name] = Variable(group, name, datatype, dimensions, id=varid, endian=endian) free(varids) # free pointer holding variable ids. return variables def _ensure_nc_success(ierr, err_cls=RuntimeError, filename=None, extra_msg=None): # print netcdf error message, raise error. if ierr == NC_NOERR: return err_str = (nc_strerror(ierr)).decode('ascii') if issubclass(err_cls, OSError): if isinstance(filename, bytes): filename = filename.decode() raise err_cls(ierr, err_str, filename) if extra_msg: if isinstance(extra_msg, bytes): extra_msg = extra_msg.decode() err_str = f"{err_str}: {extra_msg}" raise err_cls(err_str) def dtype_is_complex(dtype): """Return True if dtype is a complex number""" return dtype in ("c8", "c16") cdef int _inq_varndims(int ncid, int varid, bint auto_complex): """Wrapper around `nc_inq_varndims`/`pfnc_inq_varndims` for complex numbers""" cdef int ierr = NC_NOERR cdef int ndims if auto_complex: with nogil: ierr = pfnc_inq_varndims(ncid, varid, &ndims) else: with nogil: ierr = nc_inq_varndims(ncid, varid, &ndims) _ensure_nc_success(ierr) return ndims cdef _inq_vardimid(int ncid, int varid, bint auto_complex): """Wrapper around `nc_inq_vardimid`/`pfnc_inq_vardimid` for complex numbers""" cdef int ierr = NC_NOERR cdef int ndims = _inq_varndims(ncid, varid, auto_complex) cdef int* dimids = malloc(sizeof(int) * ndims) if auto_complex: with nogil: ierr = pfnc_inq_vardimid(ncid, varid, dimids) else: with nogil: ierr = nc_inq_vardimid(ncid, varid, dimids) _ensure_nc_success(ierr) result = [dimids[n] for n in range(ndims)] free(dimids) return result # these are class attributes that # only exist at the python level (not in the netCDF file). _private_atts = \ ('_grpid','_grp','_varid','groups','dimensions','variables','dtype','data_model','disk_format', '_nunlimdim','path','parent','ndim','mask','scale','cmptypes','vltypes','enumtypes','_isprimitive', 'file_format','_isvlen','_isenum','_iscompound','_cmptype','_vltype','_enumtype','name', '__orthogoral_indexing__','keepweakref','_has_lsd','always_mask', '_buffer','chartostring','_use_get_vars','_ncstring_attrs__', 'auto_complex' ) cdef class Dataset: """ A netCDF `Dataset` is a collection of dimensions, groups, variables and attributes. Together they describe the meaning of data and relations among data fields stored in a netCDF file. See `Dataset.__init__` for more details. A list of attribute names corresponding to global netCDF attributes defined for the `Dataset` can be obtained with the `Dataset.ncattrs` method. These attributes can be created by assigning to an attribute of the `Dataset` instance. A dictionary containing all the netCDF attribute name/value pairs is provided by the `__dict__` attribute of a `Dataset` instance. The following class variables are read-only and should not be modified by the user. **`dimensions`**: The `dimensions` dictionary maps the names of dimensions defined for the `Group` or `Dataset` to instances of the `Dimension` class. **`variables`**: The `variables` dictionary maps the names of variables defined for this `Dataset` or `Group` to instances of the `Variable` class. **`groups`**: The groups dictionary maps the names of groups created for this `Dataset` or `Group` to instances of the `Group` class (the `Dataset` class is simply a special case of the `Group` class which describes the root group in the netCDF4 file). **`cmptypes`**: The `cmptypes` dictionary maps the names of compound types defined for the `Group` or `Dataset` to instances of the `CompoundType` class. **`vltypes`**: The `vltypes` dictionary maps the names of variable-length types defined for the `Group` or `Dataset` to instances of the `VLType` class. **`enumtypes`**: The `enumtypes` dictionary maps the names of Enum types defined for the `Group` or `Dataset` to instances of the `EnumType` class. **`data_model`**: `data_model` describes the netCDF data model version, one of `NETCDF3_CLASSIC`, `NETCDF4`, `NETCDF4_CLASSIC`, `NETCDF3_64BIT_OFFSET` or `NETCDF3_64BIT_DATA`. **`file_format`**: same as `data_model`, retained for backwards compatibility. **`disk_format`**: `disk_format` describes the underlying file format, one of `NETCDF3`, `HDF5`, `HDF4`, `PNETCDF`, `DAP2`, `DAP4` or `UNDEFINED`. Only available if using netcdf C library version >= 4.3.1, otherwise will always return `UNDEFINED`. **`parent`**: `parent` is a reference to the parent `Group` instance. `None` for the root group or `Dataset` instance. **`path`**: `path` shows the location of the `Group` in the `Dataset` in a unix directory format (the names of groups in the hierarchy separated by backslashes). A `Dataset` instance is the root group, so the path is simply `'/'`. **`keepweakref`**: If `True`, child Dimension and Variables objects only keep weak references to the parent Dataset or Group. **`_ncstring_attrs__`**: If `True`, all text attributes will be written as variable-length strings. """ cdef object __weakref__, _inmemory cdef public int _grpid cdef public int _isopen cdef Py_buffer _buffer cdef public groups, dimensions, variables, disk_format, path, parent,\ file_format, data_model, cmptypes, vltypes, enumtypes, __orthogonal_indexing__, \ keepweakref, _ncstring_attrs__, auto_complex def __init__(self, filename, mode='r', clobber=True, format='NETCDF4', diskless=False, persist=False, keepweakref=False, memory=None, encoding=None, parallel=False, comm=None, info=None, auto_complex=False, **kwargs): """ **`__init__(self, filename, mode="r", clobber=True, diskless=False, persist=False, keepweakref=False, memory=None, encoding=None, parallel=False, comm=None, info=None, format='NETCDF4')`** `Dataset` constructor. **`filename`**: Name of netCDF file to hold dataset. Can also be a python 3 pathlib instance or the URL of an OpenDAP dataset. When memory is set this is just used to set the `filepath()`. **`mode`**: access mode. `r` means read-only; no data can be modified. `w` means write; a new file is created, an existing file with the same name is deleted. `x` means write, but fail if an existing file with the same name already exists. `a` and `r+` mean append; an existing file is opened for reading and writing, if file does not exist already, one is created. Appending `s` to modes `r`, `w`, `r+` or `a` will enable unbuffered shared access to `NETCDF3_CLASSIC`, `NETCDF3_64BIT_OFFSET` or `NETCDF3_64BIT_DATA` formatted files. Unbuffered access may be useful even if you don't need shared access, since it may be faster for programs that don't access data sequentially. This option is ignored for `NETCDF4` and `NETCDF4_CLASSIC` formatted files. **`clobber`**: if `True` (default), opening a file with `mode='w'` will clobber an existing file with the same name. if `False`, an exception will be raised if a file with the same name already exists. mode=`x` is identical to mode=`w` with clobber=False. **`format`**: underlying file format (one of `'NETCDF4'`, `'NETCDF4_CLASSIC'`, `'NETCDF3_CLASSIC'`, `'NETCDF3_64BIT_OFFSET'` or `'NETCDF3_64BIT_DATA'`. Only relevant if `mode = 'w'` (if `mode = 'r','a'` or `'r+'` the file format is automatically detected). Default `'NETCDF4'`, which means the data is stored in an HDF5 file, using netCDF 4 API features. Setting `format='NETCDF4_CLASSIC'` will create an HDF5 file, using only netCDF 3 compatible API features. netCDF 3 clients must be recompiled and linked against the netCDF 4 library to read files in `NETCDF4_CLASSIC` format. `'NETCDF3_CLASSIC'` is the classic netCDF 3 file format that does not handle 2+ Gb files. `'NETCDF3_64BIT_OFFSET'` is the 64-bit offset version of the netCDF 3 file format, which fully supports 2+ GB files, but is only compatible with clients linked against netCDF version 3.6.0 or later. `'NETCDF3_64BIT_DATA'` is the 64-bit data version of the netCDF 3 file format, which supports 64-bit dimension sizes plus unsigned and 64 bit integer data types, but is only compatible with clients linked against netCDF version 4.4.0 or later. **`diskless`**: If `True`, create diskless (in-core) file. This is a feature added to the C library after the netcdf-4.2 release. If you need to access the memory buffer directly, use the in-memory feature instead (see `memory` kwarg). **`persist`**: if `diskless=True`, persist file to disk when closed (default `False`). **`keepweakref`**: if `True`, child Dimension and Variable instances will keep weak references to the parent Dataset or Group object. Default is `False`, which means strong references will be kept. Having Dimension and Variable instances keep a strong reference to the parent Dataset instance, which in turn keeps a reference to child Dimension and Variable instances, creates circular references. Circular references complicate garbage collection, which may mean increased memory usage for programs that create may Dataset instances with lots of Variables. It also will result in the Dataset object never being deleted, which means it may keep open files alive as well. Setting `keepweakref=True` allows Dataset instances to be garbage collected as soon as they go out of scope, potentially reducing memory usage and open file handles. However, in many cases this is not desirable, since the associated Variable instances may still be needed, but are rendered unusable when the parent Dataset instance is garbage collected. **`memory`**: if not `None`, create or open an in-memory Dataset. If mode = `r`, the memory kwarg must contain a memory buffer object (an object that supports the python buffer interface). The Dataset will then be created with contents taken from this block of memory. If mode = `w`, the memory kwarg should contain the anticipated size of the Dataset in bytes (used only for NETCDF3 files). A memory buffer containing a copy of the Dataset is returned by the `Dataset.close` method. Requires netcdf-c version 4.4.1 for mode=`r` netcdf-c 4.6.2 for mode=`w`. To persist the file to disk, the raw bytes from the returned buffer can be written into a binary file. The Dataset can also be re-opened using this memory buffer. **`encoding`**: encoding used to encode filename string into bytes. Default is None (`sys.getdefaultfileencoding()` is used). **`parallel`**: open for parallel access using MPI (requires mpi4py and parallel-enabled netcdf-c and hdf5 libraries). Default is `False`. If `True`, `comm` and `info` kwargs may also be specified. **`comm`**: MPI_Comm object for parallel access. Default `None`, which means MPI_COMM_WORLD will be used. Ignored if `parallel=False`. **`info`**: MPI_Info object for parallel access. Default `None`, which means MPI_INFO_NULL will be used. Ignored if `parallel=False`. **`auto_complex`**: if `True`, then automatically convert complex number types """ cdef int grpid, ierr, numgrps, numdims, numvars, cdef size_t initialsize cdef char *path cdef char namstring[NC_MAX_NAME+1] cdef int cmode, parmode cdef MPI_Comm mpicomm cdef MPI_Info mpiinfo memset(&self._buffer, 0, sizeof(self._buffer)) # flag to indicate that Variables in this Dataset support orthogonal indexing. self.__orthogonal_indexing__ = True if diskless and __netcdf4libversion__ < '4.2.1': #diskless = False # don't raise error, instead silently ignore raise ValueError('diskless mode requires netcdf lib >= 4.2.1, you have %s' % __netcdf4libversion__) # convert filename into string (from os.path object for example), # encode into bytes. if encoding is None: encoding = sys.getfilesystemencoding() bytestr = _strencode(_tostr(filename), encoding=encoding) path = bytestr if memory is not None and mode not in ('r', 'w'): raise ValueError("if memory kwarg specified, mode must be 'r' or 'w'") if parallel: if not __has_parallel_support__: raise ValueError("parallel mode requires MPI enabled netcdf-c") if format not in _parallel_formats: raise ValueError( f"parallel mode only works with the following formats: {' '.join(_parallel_formats)}" ) mpicomm = (comm).ob_mpi if comm is not None else MPI_COMM_WORLD mpiinfo = (info).ob_mpi if info is not None else MPI_INFO_NULL parmode = NC_MPIIO | _cmode_dict[format] self._inmemory = False self.auto_complex = auto_complex # mode='x' is the same as mode='w' with clobber=False if mode == "x": mode = "w" clobber = False # r+ is synonym for append if "r+" in mode: mode = mode.replace("r+", "a") # If appending and the file doesn't exist, we need to create it if mode in ("a", "as") and not os.path.exists(filename): mode = mode.replace("a", "w") read_mode = mode in ("r", "rs") write_mode = mode in ("w", "ws") append_mode = mode in ("a", "as") if not (read_mode or write_mode or append_mode): raise ValueError(f"mode must be 'w', 'x', 'r', 'a' or 'r+', got '{mode}'") # Initial value for cmode if write_mode: cmode = NC_CLOBBER if clobber else NC_NOCLOBBER else: cmode = NC_WRITE if append_mode else NC_NOWRITE if mode.endswith("s") and not parallel: cmode |= NC_SHARE if diskless: cmode |= NC_DISKLESS if write_mode and persist: cmode |= NC_WRITE | NC_PERSIST if write_mode: _set_default_format(format=format) if memory is not None: if not __has_nc_create_mem__: raise NetCDF4MissingFeatureException("nc_create_mem", "4.6.2") # if memory is not None and mode='w', memory # kwarg is interpreted as advisory size. initialsize = memory with nogil: ierr = nc_create_mem(path, 0, initialsize, &grpid) self._inmemory = True # checked in close method else: if parallel: with nogil: ierr = nc_create_par(path, cmode | parmode, mpicomm, mpiinfo, &grpid) else: with nogil: ierr = nc_create(path, cmode, &grpid) elif read_mode and memory is not None: if not __has_nc_open_mem__: raise NetCDF4MissingFeatureException("nc_open_mem", "4.4.1") # Store reference to memory result = PyObject_GetBuffer( memory, &self._buffer, PyBUF_SIMPLE | PyBUF_ANY_CONTIGUOUS ) if result != 0: raise ValueError(f"Unable to retrieve Buffer from {memory}") with nogil: ierr = nc_open_mem( path, 0, self._buffer.len, self._buffer.buf, &grpid ) else: # Read or append mode, flags already all set in cmode if parallel: with nogil: ierr = nc_open_par(path, cmode | NC_MPIIO, mpicomm, mpiinfo, &grpid) else: with nogil: ierr = nc_open(path, cmode, &grpid) _ensure_nc_success(ierr, err_cls=OSError, filename=path) # data model and file format attributes self.data_model = _get_format(grpid) # data_model attribute used to be file_format (versions < 1.0.8), retain # file_format for backwards compatibility. self.file_format = self.data_model self.disk_format = _get_full_format(grpid) self._grpid = grpid self._isopen = 1 self.path = '/' self.parent = None self.keepweakref = keepweakref self._ncstring_attrs__ = False # get compound, vlen and enum types in the root Group. self.cmptypes, self.vltypes, self.enumtypes = _get_types(self) # get dimensions in the root group. self.dimensions = _get_dims(self) # get variables in the root Group. self.variables = _get_vars(self, self.auto_complex) # get groups in the root Group. if self.data_model == 'NETCDF4': self.groups = _get_grps(self) else: self.groups = dict() # these allow Dataset objects to be used via a "with" statement. def __enter__(self): return self def __exit__(self,atype,value,traceback): self.close() def __getitem__(self, elem): # return variable or group defined in relative path. # split out group names in unix path. elem = posixpath.normpath(elem) # last name in path, could be a variable or group dirname, lastname = posixpath.split(elem) nestedgroups = dirname.split('/') group = self # iterate over groups in path. for g in nestedgroups: if g: group = group.groups[g] # return last one, either a group or a variable. if lastname in group.groups: return group.groups[lastname] elif lastname in group.variables: return group.variables[lastname] else: raise IndexError('%s not found in %s' % (lastname,group.path)) def __iter__(self): raise TypeError( "Dataset is not iterable. Consider iterating on Dataset.variables." ) def __contains__(self, key): raise TypeError( "Dataset does not support membership operations. Perhaps try 'varname in" " dataset.variables' or 'dimname in dataset.dimensions'." ) def filepath(self,encoding=None): """**`filepath(self,encoding=None)`** Get the file system path (or the opendap URL) which was used to open/create the Dataset. Requires netcdf >= 4.1.2. The path is decoded into a string using `sys.getfilesystemencoding()` by default, this can be changed using the `encoding` kwarg. """ if not __has_nc_inq_path__: raise NetCDF4MissingFeatureException("filepath method", "4.1.2") cdef int ierr cdef size_t pathlen cdef char *c_path if encoding is None: encoding = sys.getfilesystemencoding() with nogil: ierr = nc_inq_path(self._grpid, &pathlen, NULL) _ensure_nc_success(ierr) c_path = malloc(sizeof(char) * (pathlen + 1)) if not c_path: raise MemoryError() try: with nogil: ierr = nc_inq_path(self._grpid, &pathlen, c_path) _ensure_nc_success(ierr) py_path = c_path[:pathlen] # makes a copy of pathlen bytes from c_string finally: free(c_path) return py_path.decode(encoding) def __repr__(self): return self.__str__() def __str__(self): ncdump = [repr(type(self)).replace("._netCDF4", "")] dimnames = tuple(_tostr(dimname)+'(%s)'%len(self.dimensions[dimname])\ for dimname in self.dimensions.keys()) varnames = tuple(\ [_tostr(self.variables[varname].dtype)+' '+_tostr(varname)+ ((_tostr(self.variables[varname].dimensions)).replace(",)",")")).replace("'","") for varname in self.variables.keys()]) grpnames = tuple(_tostr(grpname) for grpname in self.groups.keys()) if self.path == '/': ncdump.append('root group (%s data model, file format %s):' % (self.data_model, self.disk_format)) else: ncdump.append('group %s:' % self.path) for name in self.ncattrs(): ncdump.append(' %s: %s' % (name, self.getncattr(name))) ncdump.append(' dimensions(sizes): %s' % ', '.join(dimnames)) ncdump.append(' variables(dimensions): %s' % ', '.join(varnames)) ncdump.append(' groups: %s' % ', '.join(grpnames)) return '\n'.join(ncdump) def _close(self, check_err): cdef int ierr with nogil: ierr = nc_close(self._grpid) if check_err: _ensure_nc_success(ierr) self._isopen = 0 # indicates file already closed, checked by __dealloc__ # Only release buffer if close succeeded # per impl of PyBuffer_Release: https://github.com/python/cpython/blob/master/Objects/abstract.c#L667 # view.obj is checked, ref on obj is decremented and obj will be null'd out PyBuffer_Release(&self._buffer) def _close_mem(self, check_err): cdef int ierr cdef NC_memio memio with nogil: ierr = nc_close_memio(self._grpid, &memio) if check_err: _ensure_nc_success(ierr) self._isopen = 0 PyBuffer_Release(&self._buffer) # membuf_fromptr from membuf.pyx - creates a python memoryview # from a raw pointer without making a copy. return memview_fromptr(memio.memory, memio.size) def close(self): """**`close(self)`** Close the Dataset. """ if __has_nc_create_mem__ and self._inmemory: return self._close_mem(True) self._close(True) def isopen(self): """ **`isopen(self)`** Is the Dataset open or closed? """ return bool(self._isopen) def __dealloc__(self): # close file when there are no references to object left and clear the cache. if self.get_variables_by_attributes: self.get_variables_by_attributes.cache_clear() if self._isopen: self._close(False) def __reduce__(self): # raise error is user tries to pickle a Dataset object. raise NotImplementedError('Dataset is not picklable') def sync(self): """ **`sync(self)`** Writes all buffered data in the `Dataset` to the disk file.""" cdef int ierr with nogil: ierr = nc_sync(self._grpid) _ensure_nc_success(ierr) def _redef(self): cdef int ierr with nogil: ierr = nc_redef(self._grpid) def _enddef(self): cdef int ierr with nogil: ierr = nc_enddef(self._grpid) def set_fill_on(self): """ **`set_fill_on(self)`** Sets the fill mode for a `Dataset` open for writing to `on`. This causes data to be pre-filled with fill values. The fill values can be controlled by the variable's `_Fill_Value` attribute, but is usually sufficient to the use the netCDF default `_Fill_Value` (defined separately for each variable type). The default behavior of the netCDF library corresponds to `set_fill_on`. Data which are equal to the `_Fill_Value` indicate that the variable was created, but never written to.""" cdef int oldmode, ierr with nogil: ierr = nc_set_fill(self._grpid, NC_FILL, &oldmode) _ensure_nc_success(ierr) def set_fill_off(self): """ **`set_fill_off(self)`** Sets the fill mode for a `Dataset` open for writing to `off`. This will prevent the data from being pre-filled with fill values, which may result in some performance improvements. However, you must then make sure the data is actually written before being read.""" cdef int oldmode, ierr with nogil: ierr = nc_set_fill(self._grpid, NC_NOFILL, &oldmode) _ensure_nc_success(ierr) def createDimension(self, dimname, size=None): """ **`createDimension(self, dimname, size=None)`** Creates a new dimension with the given `dimname` and `size`. `size` must be a positive integer or `None`, which stands for "unlimited" (default is `None`). Specifying a size of 0 also results in an unlimited dimension. The return value is the `Dimension` class instance describing the new dimension. To determine the current maximum size of the dimension, use the `len` function on the `Dimension` instance. To determine if a dimension is 'unlimited', use the `Dimension.isunlimited` method of the `Dimension` instance.""" self.dimensions[dimname] = Dimension(self, dimname, size=size) return self.dimensions[dimname] def renameDimension(self, oldname, newname): """ **`renameDimension(self, oldname, newname)`** rename a `Dimension` named `oldname` to `newname`.""" cdef char *namstring cdef Dimension dim bytestr = _strencode(newname) namstring = bytestr if self.data_model != 'NETCDF4': self._redef() try: dim = self.dimensions[oldname] except KeyError: raise KeyError('%s not a valid dimension name' % oldname) with nogil: ierr = nc_rename_dim(self._grpid, dim._dimid, namstring) if self.data_model != 'NETCDF4': self._enddef() _ensure_nc_success(ierr) # remove old key from dimensions dict. self.dimensions.pop(oldname) # add new key. self.dimensions[newname] = dim # Variable.dimensions is determined by a method that # looks in the file, so no need to manually update. def createCompoundType(self, datatype, datatype_name): """ **`createCompoundType(self, datatype, datatype_name)`** Creates a new compound data type named `datatype_name` from the numpy dtype object `datatype`. ***Note***: If the new compound data type contains other compound data types (i.e. it is a 'nested' compound type, where not all of the elements are homogeneous numeric data types), then the 'inner' compound types **must** be created first. The return value is the `CompoundType` class instance describing the new datatype.""" self.cmptypes[datatype_name] = CompoundType(self, datatype,\ datatype_name) return self.cmptypes[datatype_name] def createVLType(self, datatype, datatype_name): """ **`createVLType(self, datatype, datatype_name)`** Creates a new VLEN data type named `datatype_name` from a numpy dtype object `datatype`. The return value is the `VLType` class instance describing the new datatype.""" self.vltypes[datatype_name] = VLType(self, datatype, datatype_name) return self.vltypes[datatype_name] def createEnumType(self, datatype, datatype_name, enum_dict): """ **`createEnumType(self, datatype, datatype_name, enum_dict)`** Creates a new Enum data type named `datatype_name` from a numpy integer dtype object `datatype`, and a python dictionary defining the enum fields and values. The return value is the `EnumType` class instance describing the new datatype.""" self.enumtypes[datatype_name] = EnumType(self, datatype, datatype_name, enum_dict) return self.enumtypes[datatype_name] def createVariable(self, varname, datatype, dimensions=(), compression=None, zlib=False, complevel=4, shuffle=True, szip_coding='nn',szip_pixels_per_block=8, blosc_shuffle=1,fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, significant_digits=None,quantize_mode='BitGroom',fill_value=None, chunk_cache=None): """ **`createVariable(self, varname, datatype, dimensions=(), compression=None, zlib=False, complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, szip_coding='nn', szip_pixels_per_block=8, blosc_shuffle=1, endian='native', least_significant_digit=None, significant_digits=None, quantize_mode='BitGroom', fill_value=None, chunk_cache=None)`** Creates a new variable with the given `varname`, `datatype`, and `dimensions`. If dimensions are not given, the variable is assumed to be a scalar. If `varname` is specified as a path, using forward slashes as in unix to separate components, then intermediate groups will be created as necessary For example, `createVariable('/GroupA/GroupB/VarC', float, ('x','y'))` will create groups `GroupA` and `GroupA/GroupB`, plus the variable `GroupA/GroupB/VarC`, if the preceding groups don't already exist. The `datatype` can be a numpy datatype object, or a string that describes a numpy dtype object (like the `dtype.str` attribute of a numpy array). Supported specifiers include: `'S1' or 'c' (NC_CHAR), 'i1' or 'b' or 'B' (NC_BYTE), 'u1' (NC_UBYTE), 'i2' or 'h' or 's' (NC_SHORT), 'u2' (NC_USHORT), 'i4' or 'i' or 'l' (NC_INT), 'u4' (NC_UINT), 'i8' (NC_INT64), 'u8' (NC_UINT64), 'f4' or 'f' (NC_FLOAT), 'f8' or 'd' (NC_DOUBLE)`. `datatype` can also be a `CompoundType` instance (for a structured, or compound array), a `VLType` instance (for a variable-length array), or the python `str` builtin (for a variable-length string array). Numpy string and unicode datatypes with length greater than one are aliases for `str`. Data from netCDF variables is presented to python as numpy arrays with the corresponding data type. `dimensions` must be a tuple containing `Dimension` instances and/or dimension names (strings) that have been defined previously using `Dataset.createDimension`. The default value is an empty tuple, which means the variable is a scalar. If the optional keyword argument `compression` is set, the data will be compressed in the netCDF file using the specified compression algorithm. Currently `zlib`,`szip`,`zstd`,`bzip2`,`blosc_lz`,`blosc_lz4`,`blosc_lz4hc`, `blosc_zlib` and `blosc_zstd` are supported. Default is `None` (no compression). All of the compressors except `zlib` and `szip` use the HDF5 plugin architecture. If the optional keyword `zlib` is `True`, the data will be compressed in the netCDF file using zlib compression (default `False`). The use of this option is deprecated in favor of `compression='zlib'`. The optional keyword `complevel` is an integer between 0 and 9 describing the level of compression desired (default 4). Ignored if `compression=None`. A value of zero disables compression. If the optional keyword `shuffle` is `True`, the HDF5 shuffle filter will be applied before compressing the data with zlib (default `True`). This significantly improves compression. Default is `True`. Ignored if `zlib=False`. The optional kwarg `blosc_shuffle`is ignored unless the blosc compressor is used. `blosc_shuffle` can be 0 (no shuffle), 1 (byte-wise shuffle) or 2 (bit-wise shuffle). Default is 1. The optional kwargs `szip_coding` and `szip_pixels_per_block` are ignored unless the szip compressor is used. `szip_coding` can be `ec` (entropy coding) or `nn` (nearest neighbor coding). Default is `nn`. `szip_pixels_per_block` can be 4, 8, 16 or 32 (default 8). If the optional keyword `fletcher32` is `True`, the Fletcher32 HDF5 checksum algorithm is activated to detect errors. Default `False`. If the optional keyword `contiguous` is `True`, the variable data is stored contiguously on disk. Default `False`. Setting to `True` for a variable with an unlimited dimension will trigger an error. Fixed size variables (with no unlimited dimension) with no compression filters are contiguous by default. The optional keyword `chunksizes` can be used to manually specify the HDF5 chunksizes for each dimension of the variable. A detailed discussion of HDF chunking and I/O performance is available [here](https://support.hdfgroup.org/HDF5/doc/Advanced/Chunking). The default chunking scheme in the netcdf-c library is discussed [here](https://www.unidata.ucar.edu/software/netcdf/documentation/NUG/netcdf_perf_chunking.html). Basically, you want the chunk size for each dimension to match as closely as possible the size of the data block that users will read from the file. `chunksizes` cannot be set if `contiguous=True`. The optional keyword `endian` can be used to control whether the data is stored in little or big endian format on disk. Possible values are `little, big` or `native` (default). The library will automatically handle endian conversions when the data is read, but if the data is always going to be read on a computer with the opposite format as the one used to create the file, there may be some performance advantage to be gained by setting the endian-ness. The optional keyword `fill_value` can be used to override the default netCDF `_FillValue` (the value that the variable gets filled with before any data is written to it, defaults given in the dict `netCDF4.default_fillvals`). If fill_value is set to `False`, then the variable is not pre-filled. If the optional keyword parameters `least_significant_digit` or `significant_digits` are specified, variable data will be truncated (quantized). In conjunction with `compression='zlib'` this produces 'lossy', but significantly more efficient compression. For example, if `least_significant_digit=1`, data will be quantized using `numpy.around(scale*data)/scale`, where scale = 2**bits, and bits is determined so that a precision of 0.1 is retained (in this case bits=4). From the [PSL metadata conventions](http://www.esrl.noaa.gov/psl/data/gridded/conventions/cdc_netcdf_standard.shtml): "least_significant_digit -- power of ten of the smallest decimal place in unpacked data that is a reliable value." Default is `None`, or no quantization, or 'lossless' compression. If `significant_digits=3` then the data will be quantized so that three significant digits are retained, independent of the floating point exponent. The keyword argument `quantize_mode` controls the quantization algorithm (default 'BitGroom', 'BitRound' and 'GranularBitRound' also available). The 'GranularBitRound' algorithm may result in better compression for typical geophysical datasets. This `significant_digits` kwarg is only available with netcdf-c >= 4.9.0, and only works with `NETCDF4` or `NETCDF4_CLASSIC` formatted files. When creating variables in a `NETCDF4` or `NETCDF4_CLASSIC` formatted file, HDF5 creates something called a 'chunk cache' for each variable. The default size of the chunk cache may be large enough to completely fill available memory when creating thousands of variables. The optional keyword `chunk_cache` allows you to reduce (or increase) the size of the default chunk cache when creating a variable. The setting only persists as long as the Dataset is open - you can use the set_var_chunk_cache method to change it the next time the Dataset is opened. Warning - messing with this parameter can seriously degrade performance. The return value is the `Variable` class instance describing the new variable. A list of names corresponding to netCDF variable attributes can be obtained with the `Variable` method `Variable.ncattrs`. A dictionary containing all the netCDF attribute name/value pairs is provided by the `__dict__` attribute of a `Variable` instance. `Variable` instances behave much like array objects. Data can be assigned to or retrieved from a variable with indexing and slicing operations on the `Variable` instance. A `Variable` instance has six Dataset standard attributes: `dimensions, dtype, shape, ndim, name` and `least_significant_digit`. Application programs should never modify these attributes. The `dimensions` attribute is a tuple containing the names of the dimensions associated with this variable. The `dtype` attribute is a string describing the variable's data type (`i4`, `f8`, `S1`, etc). The `shape` attribute is a tuple describing the current sizes of all the variable's dimensions. The `name` attribute is a string containing the name of the Variable instance. The `least_significant_digit` attributes describes the power of ten of the smallest decimal place in the data the contains a reliable value. assigned to the `Variable` instance. The `ndim` attribute is the number of variable dimensions.""" # if varname specified as a path, split out group names. varname = posixpath.normpath(varname) dirname, varname = posixpath.split(varname) # varname is last. # create parent groups (like mkdir -p). if not dirname: group = self else: group = self.createGroup(dirname) # if dimensions is a single string or Dimension instance, # convert to a tuple. # This prevents a common error that occurs when # dimensions = 'lat' instead of ('lat',) if isinstance(dimensions, (str, bytes, Dimension)): dimensions = dimensions, # convert elements of dimensions tuple to Dimension # instances if they are strings. # _find_dim looks for dimension in this group, and if not # found there, looks in parent (and it's parent, etc, back to root). dimensions =\ tuple(_find_dim(group,d) if isinstance(d,(str,bytes)) else d for d in dimensions) # create variable. group.variables[varname] = Variable(group, varname, datatype, dimensions=dimensions, compression=compression, zlib=zlib, complevel=complevel, shuffle=shuffle, szip_coding=szip_coding, szip_pixels_per_block=szip_pixels_per_block, blosc_shuffle=blosc_shuffle, fletcher32=fletcher32, contiguous=contiguous, chunksizes=chunksizes, endian=endian, least_significant_digit=least_significant_digit, significant_digits=significant_digits,quantize_mode=quantize_mode,fill_value=fill_value, chunk_cache=chunk_cache) return group.variables[varname] def renameVariable(self, oldname, newname): """ **`renameVariable(self, oldname, newname)`** rename a `Variable` named `oldname` to `newname`""" cdef char *namstring cdef Variable var try: var = self.variables[oldname] except KeyError: raise KeyError('%s not a valid variable name' % oldname) bytestr = _strencode(newname) namstring = bytestr if self.data_model != 'NETCDF4': self._redef() with nogil: ierr = nc_rename_var(self._grpid, var._varid, namstring) if self.data_model != 'NETCDF4': self._enddef() _ensure_nc_success(ierr) # remove old key from dimensions dict. self.variables.pop(oldname) # add new key. self.variables[newname] = var def createGroup(self, groupname): """ **`createGroup(self, groupname)`** Creates a new `Group` with the given `groupname`. If `groupname` is specified as a path, using forward slashes as in unix to separate components, then intermediate groups will be created as necessary (analogous to `mkdir -p` in unix). For example, `createGroup('/GroupA/GroupB/GroupC')` will create `GroupA`, `GroupA/GroupB`, and `GroupA/GroupB/GroupC`, if they don't already exist. If the specified path describes a group that already exists, no error is raised. The return value is a `Group` class instance.""" # if group specified as a path, split out group names groupname = posixpath.normpath(groupname) nestedgroups = groupname.split('/') group = self # loop over group names, create parent groups if they do not already # exist. for g in nestedgroups: if not g: continue if g not in group.groups: group.groups[g] = Group(group, g) group = group.groups[g] # if group already exists, just return the group # (prior to 1.1.8, this would have raised an error) return group def ncattrs(self): """ **`ncattrs(self)`** return netCDF global attribute names for this `Dataset` or `Group` in a list.""" return _get_att_names(self._grpid, NC_GLOBAL) def setncattr(self,name,value): """ **`setncattr(self,name,value)`** set a netCDF dataset or group attribute using name,value pair. Use if you need to set a netCDF attribute with the with the same name as one of the reserved python attributes.""" cdef nc_type xtype xtype=-99 if self.data_model != 'NETCDF4': self._redef() _set_att(self, NC_GLOBAL, name, value, xtype=xtype, force_ncstring=self._ncstring_attrs__) if self.data_model != 'NETCDF4': self._enddef() def setncattr_string(self,name,value): """ **`setncattr_string(self,name,value)`** set a netCDF dataset or group string attribute using name,value pair. Use if you need to ensure that a netCDF attribute is created with type `NC_STRING` if the file format is `NETCDF4`.""" cdef nc_type xtype xtype=-99 if self.data_model != 'NETCDF4': msg='file format does not support NC_STRING attributes' raise OSError(msg) _set_att(self, NC_GLOBAL, name, value, xtype=xtype, force_ncstring=True) def setncatts(self,attdict): """ **`setncatts(self,attdict)`** set a bunch of netCDF dataset or group attributes at once using a python dictionary. This may be faster when setting a lot of attributes for a `NETCDF3` formatted file, since nc_redef/nc_enddef is not called in between setting each attribute""" if self.data_model != 'NETCDF4': self._redef() for name, value in attdict.items(): _set_att(self, NC_GLOBAL, name, value) if self.data_model != 'NETCDF4': self._enddef() def getncattr(self,name,encoding='utf-8'): """ **`getncattr(self,name)`** retrieve a netCDF dataset or group attribute. Use if you need to get a netCDF attribute with the same name as one of the reserved python attributes. option kwarg `encoding` can be used to specify the character encoding of a string attribute (default is `utf-8`).""" return _get_att(self, NC_GLOBAL, name, encoding=encoding) def __delattr__(self,name): # if it's a netCDF attribute, remove it if name not in _private_atts: self.delncattr(name) else: raise AttributeError( "'%s' is one of the reserved attributes %s, cannot delete. Use delncattr instead." % (name, tuple(_private_atts))) def delncattr(self, name): """ **`delncattr(self,name,value)`** delete a netCDF dataset or group attribute. Use if you need to delete a netCDF attribute with the same name as one of the reserved python attributes.""" cdef char *attname cdef int ierr bytestr = _strencode(name) attname = bytestr if self.data_model != 'NETCDF4': self._redef() with nogil: ierr = nc_del_att(self._grpid, NC_GLOBAL, attname) if self.data_model != 'NETCDF4': self._enddef() _ensure_nc_success(ierr) def __setattr__(self,name,value): # if name in _private_atts, it is stored at the python # level and not in the netCDF file. if name not in _private_atts: self.setncattr(name, value) elif not name.endswith('__'): if hasattr(self,name): raise AttributeError( "'%s' is one of the reserved attributes %s, cannot rebind. Use setncattr instead." % (name, tuple(_private_atts))) else: self.__dict__[name]=value def __getattr__(self,name): # if name in _private_atts, it is stored at the python # level and not in the netCDF file. if name.startswith('__') and name.endswith('__'): # if __dict__ requested, return a dict with netCDF attributes. if name == '__dict__': names = self.ncattrs() values = [] for name in names: values.append(_get_att(self, NC_GLOBAL, name)) return dict(zip(names, values)) else: raise AttributeError elif name in _private_atts: return self.__dict__[name] else: return self.getncattr(name) def renameAttribute(self, oldname, newname): """ **`renameAttribute(self, oldname, newname)`** rename a `Dataset` or `Group` attribute named `oldname` to `newname`.""" cdef char *oldnamec cdef char *newnamec cdef int ierr bytestr = _strencode(oldname) oldnamec = bytestr bytestr = _strencode(newname) newnamec = bytestr with nogil: ierr = nc_rename_att(self._grpid, NC_GLOBAL, oldnamec, newnamec) _ensure_nc_success(ierr) def renameGroup(self, oldname, newname): """ **`renameGroup(self, oldname, newname)`** rename a `Group` named `oldname` to `newname` (requires netcdf >= 4.3.1).""" cdef char *newnamec cdef int grpid cdef int ierr if not __has_rename_grp__: raise ValueError( "renameGroup method not enabled. To enable, install Cython, make sure you have" "version 4.3.1 or higher of the netcdf C lib, and rebuild netcdf4-python." ) bytestr = _strencode(newname) newnamec = bytestr try: grp = self.groups[oldname] grpid = grp._grpid except KeyError: raise KeyError('%s not a valid group name' % oldname) with nogil: ierr = nc_rename_grp(grpid, newnamec) _ensure_nc_success(ierr) # remove old key from groups dict. self.groups.pop(oldname) # add new key. self.groups[newname] = grp def set_auto_chartostring(self, value): """ **`set_auto_chartostring(self, True_or_False)`** Call `Variable.set_auto_chartostring` for all variables contained in this `Dataset` or `Group`, as well as for all variables in all its subgroups. **`True_or_False`**: Boolean determining if automatic conversion of all character arrays <--> string arrays should be performed for character variables (variables of type `NC_CHAR` or `S1`) with the `_Encoding` attribute set. ***Note***: Calling this function only affects existing variables. Variables created after calling this function will follow the default behaviour. """ # this is a hack to make inheritance work in MFDataset # (which stores variables in _vars) _vars = self.variables if _vars is None: _vars = self._vars for var in _vars.values(): var.set_auto_chartostring(value) for groups in _walk_grps(self): for group in groups: for var in group.variables.values(): var.set_auto_chartostring(value) def set_auto_maskandscale(self, value): """ **`set_auto_maskandscale(self, True_or_False)`** Call `Variable.set_auto_maskandscale` for all variables contained in this `Dataset` or `Group`, as well as for all variables in all its subgroups. **`True_or_False`**: Boolean determining if automatic conversion to masked arrays and variable scaling shall be applied for all variables. ***Note***: Calling this function only affects existing variables. Variables created after calling this function will follow the default behaviour. """ # this is a hack to make inheritance work in MFDataset # (which stores variables in _vars) _vars = self.variables if _vars is None: _vars = self._vars for var in _vars.values(): var.set_auto_maskandscale(value) for groups in _walk_grps(self): for group in groups: for var in group.variables.values(): var.set_auto_maskandscale(value) def set_auto_mask(self, value): """ **`set_auto_mask(self, True_or_False)`** Call `Variable.set_auto_mask` for all variables contained in this `Dataset` or `Group`, as well as for all variables in all its subgroups. Only affects Variables with primitive or enum types (not compound or vlen Variables). **`True_or_False`**: Boolean determining if automatic conversion to masked arrays shall be applied for all variables. ***Note***: Calling this function only affects existing variables. Variables created after calling this function will follow the default behaviour. """ # this is a hack to make inheritance work in MFDataset # (which stores variables in _vars) _vars = self.variables if _vars is None: _vars = self._vars for var in _vars.values(): var.set_auto_mask(value) for groups in _walk_grps(self): for group in groups: for var in group.variables.values(): var.set_auto_mask(value) def set_auto_scale(self, value): """ **`set_auto_scale(self, True_or_False)`** Call `Variable.set_auto_scale` for all variables contained in this `Dataset` or `Group`, as well as for all variables in all its subgroups. **`True_or_False`**: Boolean determining if automatic variable scaling shall be applied for all variables. ***Note***: Calling this function only affects existing variables. Variables created after calling this function will follow the default behaviour. """ # this is a hack to make inheritance work in MFDataset # (which stores variables in _vars) _vars = self.variables if _vars is None: _vars = self._vars for var in _vars.values(): var.set_auto_scale(value) for groups in _walk_grps(self): for group in groups: for var in group.variables.values(): var.set_auto_scale(value) def set_always_mask(self, value): """ **`set_always_mask(self, True_or_False)`** Call `Variable.set_always_mask` for all variables contained in this `Dataset` or `Group`, as well as for all variables in all its subgroups. **`True_or_False`**: Boolean determining if automatic conversion of masked arrays with no missing values to regular numpy arrays shall be applied for all variables. Default True. Set to False to restore the default behaviour in versions prior to 1.4.1 (numpy array returned unless missing values are present, otherwise masked array returned). ***Note***: Calling this function only affects existing variables. Variables created after calling this function will follow the default behaviour. """ # this is a hack to make inheritance work in MFDataset # (which stores variables in _vars) _vars = self.variables if _vars is None: _vars = self._vars for var in _vars.values(): var.set_always_mask(value) for groups in _walk_grps(self): for group in groups: for var in group.variables.values(): var.set_always_mask(value) def set_ncstring_attrs(self, value): """ **`set_ncstring_attrs(self, True_or_False)`** Call `Variable.set_ncstring_attrs` for all variables contained in this `Dataset` or `Group`, as well as for all its subgroups and their variables. **`True_or_False`**: Boolean determining if all string attributes are created as variable-length NC_STRINGs, (if True), or if ascii text attributes are stored as NC_CHARs (if False; default) ***Note***: Calling this function only affects newly created attributes of existing (sub-) groups and their variables. """ self._ncstring_attrs__ = bool(value) # this is a hack to make inheritance work in MFDataset # (which stores variables in _vars) _vars = self.variables if _vars is None: _vars = self._vars for var in _vars.values(): var.set_ncstring_attrs(value) for groups in _walk_grps(self): for group in groups: group.set_ncstring_attrs(value) # recurse into subgroups... @functools.lru_cache(maxsize=128) def get_variables_by_attributes(self, **kwargs): """ **`get_variables_by_attributes(self, **kwargs)`** Returns a list of variables that match specific conditions. Can pass in key=value parameters and variables are returned that contain all of the matches. For example, ```python >>> # Get variables with x-axis attribute. >>> vs = nc.get_variables_by_attributes(axis='X') >>> # Get variables with matching "standard_name" attribute >>> vs = nc.get_variables_by_attributes(standard_name='northward_sea_water_velocity') ``` Can pass in key=callable parameter and variables are returned if the callable returns True. The callable should accept a single parameter, the attribute value. None is given as the attribute value when the attribute does not exist on the variable. For example, ```python >>> # Get Axis variables >>> vs = nc.get_variables_by_attributes(axis=lambda v: v in ['X', 'Y', 'Z', 'T']) >>> # Get variables that don't have an "axis" attribute >>> vs = nc.get_variables_by_attributes(axis=lambda v: v is None) >>> # Get variables that have a "grid_mapping" attribute >>> vs = nc.get_variables_by_attributes(grid_mapping=lambda v: v is not None) ``` """ vs = [] has_value_flag = False # this is a hack to make inheritance work in MFDataset # (which stores variables in _vars) _vars = self.variables if _vars is None: _vars = self._vars for vname in _vars: var = _vars[vname] for k, v in kwargs.items(): if callable(v): has_value_flag = v(getattr(var, k, None)) if has_value_flag is False: break elif hasattr(var, k) and getattr(var, k) == v: has_value_flag = True else: has_value_flag = False break if has_value_flag is True: vs.append(_vars[vname]) return vs def _getname(self): # private method to get name associated with instance. cdef int ierr cdef char namstring[NC_MAX_NAME+1] with nogil: ierr = nc_inq_grpname(self._grpid, namstring) _ensure_nc_success(ierr) return namstring.decode('utf-8') property name: """string name of Group instance""" def __get__(self): return self._getname() def __set__(self,value): raise AttributeError("name cannot be altered") @staticmethod def fromcdl(cdlfilename,ncfilename=None,mode='a',format='NETCDF4'): """ **`fromcdl(cdlfilename, ncfilename=None, mode='a',format='NETCDF4')`** call [ncgen][ncgen] via subprocess to create Dataset from [CDL][cdl] text representation. Requires [ncgen][ncgen] to be installed and in `$PATH`. **`cdlfilename`**: CDL file. **`ncfilename`**: netCDF file to create. If not given, CDL filename with suffix replaced by `.nc` is used.. **`mode`**: Access mode to open Dataset (Default `'a'`). **`format`**: underlying file format to use (one of `'NETCDF4'`, `'NETCDF4_CLASSIC'`, `'NETCDF3_CLASSIC'`, `'NETCDF3_64BIT_OFFSET'` or `'NETCDF3_64BIT_DATA'`. Default `'NETCDF4'`. Dataset instance for `ncfilename` is returned. [ncgen]: https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_utilities_guide.html#ncgen_guide [cdl]: https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_utilities_guide.html#cdl_guide """ filepath = pathlib.Path(cdlfilename) if ncfilename is None: ncfilename = filepath.with_suffix('.nc') else: ncfilename = pathlib.Path(ncfilename) formatcodes = {'NETCDF4': 4, 'NETCDF4_CLASSIC': 7, 'NETCDF3_CLASSIC': 3, 'NETCDF3_64BIT': 6, # legacy 'NETCDF3_64BIT_OFFSET': 6, 'NETCDF3_64BIT_DATA': 5} if format not in formatcodes: raise ValueError('illegal format requested') if not filepath.exists(): raise FileNotFoundError(filepath) if ncfilename.exists(): raise FileExistsError(ncfilename) ncgenargs="-knc%s" % formatcodes[format] subprocess.run(["ncgen", ncgenargs, "-o", str(ncfilename), str(filepath)], check=True) return Dataset(ncfilename, mode=mode) def tocdl(self,coordvars=False,data=False,outfile=None): """ **`tocdl(self, coordvars=False, data=False, outfile=None)`** call [ncdump][ncdump] via subprocess to create [CDL][cdl] text representation of Dataset. Requires [ncdump][ncdump] to be installed and in `$PATH`. **`coordvars`**: include coordinate variable data (via `ncdump -c`). Default False **`data`**: if True, write out variable data (Default False). **`outfile`**: If not None, file to output ncdump to. Default is to return a string. [ncdump]: https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_utilities_guide.html#ncdump_guide [cdl]: https://www.unidata.ucar.edu/software/netcdf/docs/netcdf_utilities_guide.html#cdl_guide """ self.sync() if coordvars: ncdumpargs = "-cs" else: ncdumpargs = "-s" if not data: ncdumpargs += "h" result=subprocess.run(["ncdump", ncdumpargs, self.filepath()], check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE, encoding='utf-8') if outfile is None: return result.stdout else: f = open(outfile,'w') f.write(result.stdout) f.close() def has_blosc_filter(self): """**`has_blosc_filter(self)`** returns True if blosc compression filter is available """ #if __has_blosc_support__: # return True #else: # return False cdef int ierr with nogil: ierr = nc_inq_filter_avail(self._grpid, H5Z_FILTER_BLOSC) return ierr == 0 def has_zstd_filter(self): """**`has_zstd_filter(self)`** returns True if zstd compression filter is available """ #if __has_zstandard_support__: # return True #else: # return False cdef int ierr with nogil: ierr = nc_inq_filter_avail(self._grpid, H5Z_FILTER_ZSTD) return ierr == 0 def has_bzip2_filter(self): """**`has_bzip2_filter(self)`** returns True if bzip2 compression filter is available """ #if __has_bzip2_support__: # return True #else: # return False cdef int ierr with nogil: ierr = nc_inq_filter_avail(self._grpid, H5Z_FILTER_BZIP2) return ierr == 0 def has_szip_filter(self): """**`has_szip_filter(self)`** returns True if szip compression filter is available """ #if not __has_ncfilter__: # return __has_szip_support__ #if __has_szip_support__: # return True #else: # return False cdef int ierr with nogil: ierr = nc_inq_filter_avail(self._grpid, H5Z_FILTER_SZIP) return ierr == 0 cdef class Group(Dataset): """ Groups define a hierarchical namespace within a netCDF file. They are analogous to directories in a unix filesystem. Each `Group` behaves like a `Dataset` within a Dataset, and can contain it's own variables, dimensions and attributes (and other Groups). See `Group.__init__` for more details. `Group` inherits from `Dataset`, so all the `Dataset` class methods and variables are available to a `Group` instance (except the `close` method). Additional read-only class variables: **`name`**: String describing the group name. """ def __init__(self, parent, name, **kwargs): """ **`__init__(self, parent, name)`** `Group` constructor. **`parent`**: `Group` instance for the parent group. If being created in the root group, use a `Dataset` instance. **`name`**: - Name of the group. ***Note***: `Group` instances should be created using the `Dataset.createGroup` method of a `Dataset` instance, or another `Group` instance, not using this class directly. """ cdef char *groupname cdef int ierr, grpid # flag to indicate that Variables in this Group support orthogonal indexing. self.__orthogonal_indexing__ = True # set data_model and file_format attributes. self.data_model = parent.data_model self.file_format = parent.file_format # full path to Group. self.path = posixpath.join(parent.path, name) # parent group. self.parent = parent # propagate weak reference setting from parent. self.keepweakref = parent.keepweakref # propagate _ncstring_attrs__ setting from parent. self._ncstring_attrs__ = parent._ncstring_attrs__ self.auto_complex = parent.auto_complex if 'id' in kwargs: self._grpid = kwargs['id'] # get compound, vlen and enum types in this Group. self.cmptypes, self.vltypes, self.enumtypes = _get_types(self) # get dimensions in this Group. self.dimensions = _get_dims(self) # get variables in this Group. self.variables = _get_vars(self, self.auto_complex) # get groups in this Group. self.groups = _get_grps(self) else: bytestr = _strencode(name) groupname = bytestr grpid = parent._grpid with nogil: ierr = nc_def_grp(grpid, groupname, &self._grpid) _ensure_nc_success(ierr) self.cmptypes = dict() self.vltypes = dict() self.enumtypes = dict() self.dimensions = dict() self.variables = dict() self.groups = dict() def close(self): """ **`close(self)`** overrides `Dataset` close method which does not apply to `Group` instances, raises OSError.""" raise OSError('cannot close a `Group` (only applies to Dataset)') cdef class Dimension: """ A netCDF `Dimension` is used to describe the coordinates of a `Variable`. See `Dimension.__init__` for more details. The current maximum size of a `Dimension` instance can be obtained by calling the python `len` function on the `Dimension` instance. The `Dimension.isunlimited` method of a `Dimension` instance can be used to determine if the dimension is unlimited. Read-only class variables: **`name`**: String name, used when creating a `Variable` with `Dataset.createVariable`. **`size`**: Current `Dimension` size (same as `len(d)`, where `d` is a `Dimension` instance). """ cdef public int _dimid, _grpid cdef public _data_model, _name, _grp def __init__(self, grp, name, size=None, **kwargs): """ **`__init__(self, group, name, size=None)`** `Dimension` constructor. **`group`**: `Group` instance to associate with dimension. **`name`**: Name of the dimension. **`size`**: Size of the dimension. `None` or 0 means unlimited. (Default `None`). ***Note***: `Dimension` instances should be created using the `Dataset.createDimension` method of a `Group` or `Dataset` instance, not using `Dimension.__init__` directly. """ cdef int ierr cdef char *dimname cdef size_t lendim self._grpid = grp._grpid # make a weakref to group to avoid circular ref (issue 218) # keep strong reference the default behaviour (issue 251) if grp.keepweakref: self._grp = weakref.proxy(grp) else: self._grp = grp self._data_model = grp.data_model self._name = name if 'id' in kwargs: self._dimid = kwargs['id'] else: bytestr = _strencode(name) dimname = bytestr if size is not None: lendim = size else: lendim = NC_UNLIMITED if grp.data_model != 'NETCDF4': grp._redef() with nogil: ierr = nc_def_dim(self._grpid, dimname, lendim, &self._dimid) if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr) def _getname(self): # private method to get name associated with instance. cdef int err, _grpid cdef char namstring[NC_MAX_NAME+1] _grpid = self._grp._grpid with nogil: ierr = nc_inq_dimname(_grpid, self._dimid, namstring) _ensure_nc_success(ierr) return namstring.decode('utf-8') property name: """string name of Dimension instance""" def __get__(self): return self._getname() def __set__(self,value): raise AttributeError("name cannot be altered") property size: """current size of Dimension (calls `len` on Dimension instance)""" def __get__(self): return len(self) def __set__(self,value): raise AttributeError("size cannot be altered") def __repr__(self): return self.__str__() def __str__(self): if not dir(self._grp): return 'Dimension object no longer valid' typ = repr(type(self)).replace("._netCDF4", "") if self.isunlimited(): return "%r (unlimited): name = '%s', size = %s" %\ (typ, self._name, len(self)) else: return "%r: name = '%s', size = %s" %\ (typ, self._name, len(self)) def __len__(self): # len(`Dimension` instance) returns current size of dimension cdef int ierr cdef size_t lengthp with nogil: ierr = nc_inq_dimlen(self._grpid, self._dimid, &lengthp) _ensure_nc_success(ierr) return lengthp def group(self): """ **`group(self)`** return the group that this `Dimension` is a member of.""" return self._grp def isunlimited(self): """ **`isunlimited(self)`** returns `True` if the `Dimension` instance is unlimited, `False` otherwise.""" cdef int ierr, n, numunlimdims, ndims, nvars, ngatts, xdimid cdef int *unlimdimids if self._data_model == 'NETCDF4': with nogil: ierr = nc_inq_unlimdims(self._grpid, &numunlimdims, NULL) _ensure_nc_success(ierr) if numunlimdims == 0: return False else: unlimdimids = malloc(sizeof(int) * numunlimdims) dimid = self._dimid with nogil: ierr = nc_inq_unlimdims(self._grpid, &numunlimdims, unlimdimids) _ensure_nc_success(ierr) unlimdim_ids = [] for n in range(numunlimdims): unlimdim_ids.append(unlimdimids[n]) free(unlimdimids) if dimid in unlimdim_ids: return True else: return False else: # if not NETCDF4, there is only one unlimited dimension. # nc_inq_unlimdims only works for NETCDF4. with nogil: ierr = nc_inq(self._grpid, &ndims, &nvars, &ngatts, &xdimid) if self._dimid == xdimid: return True else: return False cdef class Variable: """ A netCDF `Variable` is used to read and write netCDF data. They are analogous to numpy array objects. See `Variable.__init__` for more details. A list of attribute names corresponding to netCDF attributes defined for the variable can be obtained with the `Variable.ncattrs` method. These attributes can be created by assigning to an attribute of the `Variable` instance. A dictionary containing all the netCDF attribute name/value pairs is provided by the `__dict__` attribute of a `Variable` instance. The following class variables are read-only: **`dimensions`**: A tuple containing the names of the dimensions associated with this variable. **`dtype`**: A numpy dtype object describing the variable's data type. **`ndim`**: The number of variable dimensions. **`shape`**: A tuple with the current shape (length of all dimensions). **`scale`**: If True, `scale_factor` and `add_offset` are applied, and signed integer data is automatically converted to unsigned integer data if the `_Unsigned` attribute is set to "true" or "True". Default is `True`, can be reset using `Variable.set_auto_scale` and `Variable.set_auto_maskandscale` methods. **`mask`**: If True, data is automatically converted to/from masked arrays when missing values or fill values are present. Default is `True`, can be reset using `Variable.set_auto_mask` and `Variable.set_auto_maskandscale` methods. Only relevant for Variables with primitive or enum types (ignored for compound and vlen Variables). **`chartostring`**: If True, data is automatically converted to/from character arrays to string arrays when the `_Encoding` variable attribute is set. Default is `True`, can be reset using `Variable.set_auto_chartostring` method. **`least_significant_digit`**: Describes the power of ten of the smallest decimal place in the data the contains a reliable value. Data is truncated to this decimal place when it is assigned to the `Variable` instance. If `None`, the data is not truncated. **`significant_digits`**: New in version 1.6.0. Describes the number of significant digits in the data the contains a reliable value. Data is truncated to retain this number of significant digits when it is assigned to the `Variable` instance. If `None`, the data is not truncated. Only available with netcdf-c >= 4.9.0, and only works with `NETCDF4` or `NETCDF4_CLASSIC` formatted files. The number of significant digits used in the quantization of variable data can be obtained using the `Variable.significant_digits` method. Default `None` - no quantization done. **`quantize_mode`**: New in version 1.6.0. Controls the quantization algorithm (default 'BitGroom', 'BitRound' and 'GranularBitRound' also available). The 'GranularBitRound' algorithm may result in better compression for typical geophysical datasets. Ignored if `significant_digits` not specified. If 'BitRound' is used, then `significant_digits` is interpreted as binary (not decimal) digits. **`__orthogonal_indexing__`**: Always `True`. Indicates to client code that the object supports 'orthogonal indexing', which means that slices that are 1d arrays or lists slice along each dimension independently. This behavior is similar to Fortran or Matlab, but different than numpy. **`datatype`**: numpy data type (for primitive data types) or VLType/CompoundType instance (for compound or vlen data types). **`name`**: String name. **`size`**: The number of stored elements. """ cdef public int _varid, _grpid, _nunlimdim cdef public _name, ndim, dtype, mask, scale, always_mask, chartostring, _isprimitive, \ _iscompound, _isvlen, _isenum, _grp, _cmptype, _vltype, _enumtype,\ __orthogonal_indexing__, _has_lsd, _use_get_vars, _ncstring_attrs__, auto_complex def __init__(self, grp, name, datatype, dimensions=(), compression=None, zlib=False, complevel=4, shuffle=True, szip_coding='nn', szip_pixels_per_block=8, blosc_shuffle=1, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, significant_digits=None,quantize_mode='BitGroom',fill_value=None, chunk_cache=None, **kwargs): """ **`__init__(self, group, name, datatype, dimensions=(), compression=None, zlib=False, complevel=4, shuffle=True, szip_coding='nn', szip_pixels_per_block=8, blosc_shuffle=1, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None,fill_value=None,chunk_cache=None)`** `Variable` constructor. **`group`**: `Group` or `Dataset` instance to associate with variable. **`name`**: Name of the variable. **`datatype`**: `Variable` data type. Can be specified by providing a numpy dtype object, or a string that describes a numpy dtype object. Supported values, corresponding to `str` attribute of numpy dtype objects, include `'f4'` (32-bit floating point), `'f8'` (64-bit floating point), `'i4'` (32-bit signed integer), `'i2'` (16-bit signed integer), `'i8'` (64-bit signed integer), `'i4'` (8-bit signed integer), `'i1'` (8-bit signed integer), `'u1'` (8-bit unsigned integer), `'u2'` (16-bit unsigned integer), `'u4'` (32-bit unsigned integer), `'u8'` (64-bit unsigned integer), or `'S1'` (single-character string). From compatibility with Scientific.IO.NetCDF, the old Numeric single character typecodes can also be used (`'f'` instead of `'f4'`, `'d'` instead of `'f8'`, `'h'` or `'s'` instead of `'i2'`, `'b'` or `'B'` instead of `'i1'`, `'c'` instead of `'S1'`, and `'i'` or `'l'` instead of `'i4'`). `datatype` can also be a `CompoundType` instance (for a structured, or compound array), a `VLType` instance (for a variable-length array), or the python `str` builtin (for a variable-length string array). Numpy string and unicode datatypes with length greater than one are aliases for `str`. **`dimensions`**: a tuple containing the variable's Dimension instances (defined previously with `createDimension`). Default is an empty tuple which means the variable is a scalar (and therefore has no dimensions). **`compression`**: compression algorithm to use. Currently `zlib`,`szip`,`zstd`,`bzip2`,`blosc_lz`,`blosc_lz4`,`blosc_lz4hc`, `blosc_zlib` and `blosc_zstd` are supported. Default is `None` (no compression). All of the compressors except `zlib` and `szip` use the HDF5 plugin architecture. **`zlib`**: if `True`, data assigned to the `Variable` instance is compressed on disk. Default `False`. Deprecated - use `compression='zlib'` instead. **`complevel`**: the level of compression to use (1 is the fastest, but poorest compression, 9 is the slowest but best compression). Default 4. Ignored if `compression=None` or `szip`. A value of 0 disables compression. **`shuffle`**: if `True`, the HDF5 shuffle filter is applied to improve zlib compression. Default `True`. Ignored unless `compression = 'zlib'`. **`blosc_shuffle`**: shuffle filter inside blosc compressor (only relevant if compression kwarg set to one of the blosc compressors). Can be 0 (no blosc shuffle), 1 (bytewise shuffle) or 2 (bitwise shuffle)). Default is 1. Ignored if blosc compressor not used. **`szip_coding`**: szip coding method. Can be `ec` (entropy coding) or `nn` (nearest neighbor coding). Default is `nn`. Ignored if szip compressor not used. **`szip_pixels_per_block`**: Can be 4,8,16 or 32 (Default 8). Ignored if szip compressor not used. **`fletcher32`**: if `True` (default `False`), the Fletcher32 checksum algorithm is used for error detection. **`contiguous`**: if `True` (default `False`), the variable data is stored contiguously on disk. Default `False`. Setting to `True` for a variable with an unlimited dimension will trigger an error. Fixed size variables (with no unlimited dimension) with no compression filters are contiguous by default. **`chunksizes`**: Can be used to specify the HDF5 chunksizes for each dimension of the variable. A detailed discussion of HDF chunking and I/O performance is available [here](https://support.hdfgroup.org/HDF5/doc/Advanced/Chunking). The default chunking scheme in the netcdf-c library is discussed [here](https://www.unidata.ucar.edu/software/netcdf/documentation/NUG/netcdf_perf_chunking.html). Basically, you want the chunk size for each dimension to match as closely as possible the size of the data block that users will read from the file. `chunksizes` cannot be set if `contiguous=True`. **`endian`**: Can be used to control whether the data is stored in little or big endian format on disk. Possible values are `little, big` or `native` (default). The library will automatically handle endian conversions when the data is read, but if the data is always going to be read on a computer with the opposite format as the one used to create the file, there may be some performance advantage to be gained by setting the endian-ness. For netCDF 3 files (that don't use HDF5), only `endian='native'` is allowed. The `compression, zlib, complevel, shuffle, fletcher32, contiguous` and `chunksizes` keywords are silently ignored for netCDF 3 files that do not use HDF5. **`least_significant_digit`**: If this or `significant_digits` are specified, variable data will be truncated (quantized). In conjunction with `compression='zlib'` this produces 'lossy', but significantly more efficient compression. For example, if `least_significant_digit=1`, data will be quantized using around(scale*data)/scale, where scale = 2**bits, and bits is determined so that a precision of 0.1 is retained (in this case bits=4). Default is `None`, or no quantization. **`significant_digits`**: New in version 1.6.0. As described for `least_significant_digit` except the number of significant digits retained is prescribed independent of the floating point exponent. Default `None` - no quantization done. **`quantize_mode`**: New in version 1.6.0. Controls the quantization algorithm (default 'BitGroom', 'BitRound' and 'GranularBitRound' also available). The 'GranularBitRound' algorithm may result in better compression for typical geophysical datasets. Ignored if `significant_digts` not specified. If 'BitRound' is used, then `significant_digits` is interpreted as binary (not decimal) digits. **`fill_value`**: If specified, the default netCDF fill value (the value that the variable gets filled with before any data is written to it) is replaced with this value, and the `_FillValue` attribute is set. If fill_value is set to `False`, then the variable is not pre-filled. The default netCDF fill values can be found in the dictionary `netCDF4.default_fillvals`. If not set, the default fill value will be used but no `_FillValue` attribute will be created (this is the default behavior of the netcdf-c library). If you want to use the default fill value, but have the `_FillValue` attribute set, use `fill_value='default'` (note - this only works for primitive data types). `Variable.get_fill_value` can be used to retrieve the fill value, even if the `_FillValue` attribute is not set. **`chunk_cache`**: If specified, sets the chunk cache size for this variable. Persists as long as Dataset is open. Use `set_var_chunk_cache` to change it when Dataset is re-opened. ***Note***: `Variable` instances should be created using the `Dataset.createVariable` method of a `Dataset` or `Group` instance, not using this class directly. """ cdef int ierr, ndims, icontiguous, icomplevel, numdims, _grpid, nsd, cdef unsigned int iblosc_complevel,iblosc_blocksize,iblosc_compressor,iblosc_shuffle cdef int iszip_coding, iszip_pixels_per_block cdef char namstring[NC_MAX_NAME+1] cdef char *varname cdef nc_type xtype cdef int *dimids = NULL cdef size_t sizep, nelemsp cdef size_t *chunksizesp cdef float preemptionp cdef int nc_complex_typeid, complex_base_type_id, complex_dim_id cdef int _nc_endian # Extra information for more helpful error messages error_info = f"(variable '{name}', group '{grp.name}')" # flag to indicate that orthogonal indexing is supported self.__orthogonal_indexing__ = True # For backwards compatibility, deprecated zlib kwarg takes # precedence if compression kwarg not set. if zlib and not compression: compression = 'zlib' # if complevel is set to zero, turn off compression if not complevel: compression = None zlib = False szip = False zstd = False bzip2 = False blosc_lz = False blosc_lz4 = False blosc_lz4hc = False #blosc_snappy = False blosc_zlib = False blosc_zstd = False if compression == 'zlib': zlib = True elif compression == 'szip': szip = True elif compression == 'zstd': zstd = True elif compression == 'bzip2': bzip2 = True elif compression == 'blosc_lz': blosc_lz = True elif compression == 'blosc_lz4': blosc_lz4 = True elif compression == 'blosc_lz4hc': blosc_lz4hc = True #elif compression == 'blosc_snappy': # blosc_snappy = True elif compression == 'blosc_zlib': blosc_zlib = True elif compression == 'blosc_zstd': blosc_zstd = True elif not compression: compression = None # if compression evaluates to False, set to None. pass else: raise ValueError(f"Unsupported value for compression kwarg {error_info}") if grp.data_model.startswith("NETCDF3") and endian != 'native': raise RuntimeError( f"only endian='native' allowed for NETCDF3 files, got '{endian}' {error_info}" ) if endian not in ("little", "big", "native"): raise ValueError( f"'endian' keyword argument must be 'little','big' or 'native', got '{endian}' " f"{error_info}" ) self._grpid = grp._grpid # make a weakref to group to avoid circular ref (issue 218) # keep strong reference the default behaviour (issue 251) if grp.keepweakref: self._grp = weakref.proxy(grp) else: self._grp = grp self._iscompound = isinstance(datatype, CompoundType) self._isvlen = isinstance(datatype, VLType) or datatype==str self._isenum = isinstance(datatype, EnumType) user_type = self._iscompound or self._isvlen or self._isenum # convert to a real numpy datatype object if necessary. if not user_type and type(datatype) != numpy.dtype: datatype = numpy.dtype(datatype) # convert numpy string dtype with length > 1 # or any numpy unicode dtype into str if (isinstance(datatype, numpy.dtype) and ((datatype.kind == 'S' and datatype.itemsize > 1) or datatype.kind == 'U')): datatype = str user_type = True self._isvlen = True # If datatype is complex, convert to compoundtype is_complex = dtype_is_complex(datatype) if is_complex and not self._grp.auto_complex: raise ValueError( f"complex datatypes ({datatype}) are only supported with `auto_complex=True`" ) # check if endian keyword consistent with datatype specification. dtype_endian = _dtype_endian_lookup[getattr(datatype, "byteorder", None)] if dtype_endian is not None and dtype_endian != endian: if not (dtype_endian == 'native' and endian == sys.byteorder): warnings.warn('endian-ness of dtype and endian kwarg do not match, using endian kwarg') # check validity of datatype. self._isprimitive = not user_type if user_type: if self._iscompound: self._cmptype = datatype if self._isvlen: self._vltype = datatype if self._isenum: self._enumtype = datatype if datatype==str: if grp.data_model != 'NETCDF4': raise ValueError( 'Variable length strings are only supported for the ' 'NETCDF4 format. For other formats, consider using ' 'netCDF4.stringtochar to convert string arrays into ' 'character arrays with an additional dimension.' f' {error_info}') datatype = VLType(self._grp, str, None) self._vltype = datatype xtype = datatype._nc_type # make sure this a valid user defined datatype defined in this Group with nogil: ierr = nc_inq_type(self._grpid, xtype, namstring, NULL) _ensure_nc_success(ierr, extra_msg=error_info) # dtype variable attribute is a numpy datatype object. self.dtype = datatype.dtype elif datatype.str[1:] in _supportedtypes: # find netCDF primitive data type corresponding to # specified numpy data type. xtype = _nptonctype[datatype.str[1:]] # dtype variable attribute is a numpy datatype object. self.dtype = datatype elif is_complex: xtype = _complex_types[datatype.str[1:]] self.dtype = datatype else: raise TypeError(f'Illegal primitive data type, must be one of {_supportedtypes}, got {datatype} {error_info}') if 'id' in kwargs: self._varid = kwargs['id'] else: bytestr = _strencode(name) varname = bytestr ndims = len(dimensions) # find dimension ids. if ndims: dimids = malloc(sizeof(int) * ndims) for n in range(ndims): dimids[n] = dimensions[n]._dimid # go into define mode if it's a netCDF 3 compatible # file format. Be careful to exit define mode before # any exceptions are raised. if grp.data_model != 'NETCDF4': grp._redef() # define variable. with nogil: ierr = pfnc_def_var(self._grpid, varname, xtype, ndims, dimids, &self._varid) if ndims: free(dimids) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) # set chunk cache size if desired # default is 1mb per var, can cause problems when many (1000's) # of vars are created. This change only lasts as long as file is # open. if grp.data_model.startswith('NETCDF4') and chunk_cache is not None: with nogil: ierr = nc_get_var_chunk_cache(self._grpid, self._varid, &sizep, &nelemsp, &preemptionp) _ensure_nc_success(ierr, extra_msg=error_info) # reset chunk cache size, leave other parameters unchanged. sizep = chunk_cache with nogil: ierr = nc_set_var_chunk_cache(self._grpid, self._varid, sizep, nelemsp, preemptionp) _ensure_nc_success(ierr, extra_msg=error_info) # set compression, shuffle, chunking, fletcher32 and endian # variable settings. # don't bother for NETCDF3* formats. # for NETCDF3* formats, the comopression,zlib,shuffle,chunking, # and fletcher32 flags are silently ignored. Only # endian='native' allowed for NETCDF3. if grp.data_model in ['NETCDF4','NETCDF4_CLASSIC']: # set compression and shuffle parameters. if compression is not None and ndims: # don't bother for scalar variable if zlib: icomplevel = complevel if shuffle: with nogil: ierr = nc_def_var_deflate(self._grpid, self._varid, 1, 1, icomplevel) else: with nogil: ierr = nc_def_var_deflate(self._grpid, self._varid, 0, 1, icomplevel) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) if szip: if not __has_szip_support__: raise ValueError("compression='szip' only works if linked version of hdf5 has szip functionality enabled") try: iszip_coding = _szip_dict[szip_coding] except KeyError: raise ValueError("unknown szip coding ('ec' or 'nn' supported)") iszip_pixels_per_block = szip_pixels_per_block with nogil: ierr = nc_def_var_szip(self._grpid, self._varid, iszip_coding, iszip_pixels_per_block) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) if zstd: if not __has_zstandard_support__: raise NetCDF4MissingFeatureException("compression='zstd'", "4.9.0") icomplevel = complevel with nogil: ierr = nc_def_var_zstandard(self._grpid, self._varid, icomplevel) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) if bzip2: if not __has_bzip2_support__: raise NetCDF4MissingFeatureException("compression='bzip2'", "4.9.0") icomplevel = complevel with nogil: ierr = nc_def_var_bzip2(self._grpid, self._varid, icomplevel) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) if blosc_zstd or blosc_lz or blosc_lz4 or blosc_lz4hc or blosc_zlib: if not __has_blosc_support__: raise NetCDF4MissingFeatureException("compression='blosc_*'", "4.9.0") iblosc_compressor = _blosc_dict[compression] iblosc_shuffle = blosc_shuffle iblosc_blocksize = 0 # not currently used by c lib iblosc_complevel = complevel with nogil: ierr = nc_def_var_blosc(self._grpid, self._varid, iblosc_compressor, iblosc_complevel,iblosc_blocksize, iblosc_shuffle) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) # set checksum. if fletcher32 and ndims: # don't bother for scalar variable with nogil: ierr = nc_def_var_fletcher32(self._grpid, self._varid, 1) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) # set chunking stuff. if ndims: # don't bother for scalar variable. if contiguous: icontiguous = NC_CONTIGUOUS if chunksizes is not None: raise ValueError('cannot specify chunksizes for a contiguous dataset') else: icontiguous = NC_CHUNKED if chunksizes is None: chunksizesp = NULL else: if len(chunksizes) != len(dimensions): if grp.data_model != 'NETCDF4': grp._enddef() raise ValueError('chunksizes must be a sequence with the same length as dimensions') chunksizesp = malloc(sizeof(size_t) * ndims) for n in range(ndims): if not dimensions[n].isunlimited() and \ chunksizes[n] > dimensions[n].size: msg = 'chunksize cannot exceed dimension size' raise ValueError(msg) chunksizesp[n] = chunksizes[n] if chunksizes is not None or contiguous: with nogil: ierr = nc_def_var_chunking(self._grpid, self._varid, icontiguous, chunksizesp) free(chunksizesp) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) # set endian-ness of variable if endian != 'native': _nc_endian = NC_ENDIAN_LITTLE if endian == "little" else NC_ENDIAN_BIG with nogil: ierr = nc_def_var_endian(self._grpid, self._varid, _nc_endian) _ensure_nc_success(ierr, extra_msg=error_info) # set quantization if significant_digits is not None: if not __has_quantization_support__: raise ValueError( "significant_digits kwarg only works with netcdf-c >= 4.9.0. " "To enable, install Cython, make sure you have version 4.9.0 " "or higher netcdf-c, and rebuild netcdf4-python. Otherwise, " f"use least_significant_digit kwarg for quantization. {error_info}" ) nsd = significant_digits if quantize_mode == 'BitGroom': with nogil: ierr = nc_def_var_quantize(self._grpid, self._varid, NC_QUANTIZE_BITGROOM, nsd) elif quantize_mode == 'GranularBitRound': with nogil: ierr = nc_def_var_quantize(self._grpid, self._varid, NC_QUANTIZE_GRANULARBR, nsd) elif quantize_mode == 'BitRound': ierr = nc_def_var_quantize(self._grpid, self._varid, NC_QUANTIZE_BITROUND, nsd) else: raise ValueError("'quantize_mode' keyword argument must be 'BitGroom','GranularBitRound' or 'BitRound', got '%s'" % quantize_mode) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) # set a fill value for this variable if fill_value keyword # given. This avoids the HDF5 overhead of deleting and # recreating the dataset if it is set later (after the enddef). if fill_value is not None: if fill_value is False: # no filling for this variable if fill_value==False. if self._isprimitive: with nogil: ierr = nc_def_var_fill(self._grpid, self._varid, 1, NULL) if ierr != NC_NOERR: if grp.data_model != 'NETCDF4': grp._enddef() _ensure_nc_success(ierr, extra_msg=error_info) elif fill_value == 'default': if self._isprimitive: fillval = numpy.array(default_fillvals[self.dtype.str[1:]]) if not fillval.dtype.isnative: fillval.byteswap(True) _set_att(self._grp, self._varid, '_FillValue',\ fillval, xtype=xtype) else: msg = """ WARNING: there is no default fill value for this data type, so fill_value='default' does not do anything.""" warnings.warn(msg) else: if self._isprimitive or self._isenum or \ (self._isvlen and self.dtype == str): if self._isvlen and self.dtype == str: _set_att(self._grp, self._varid, '_FillValue',\ _tostr(fill_value), xtype=xtype, force_ncstring=True) else: fillval = numpy.array(fill_value, self.dtype) if not fillval.dtype.isnative: fillval.byteswap(True) _set_att(self._grp, self._varid, '_FillValue',\ fillval, xtype=xtype) else: raise AttributeError("cannot set _FillValue attribute for VLEN or compound variable") if least_significant_digit is not None: self.least_significant_digit = least_significant_digit # leave define mode if not a NETCDF4 format file. if grp.data_model != 'NETCDF4': grp._enddef() # If the variable is a complex number, we need to check if # it was created using a compound type or a complex # dimension, and then make the equivalent class in Python if is_complex: self._fix_complex_numbers() # count how many unlimited dimensions there are. self._nunlimdim = 0 for dim in dimensions: if dim.isunlimited(): self._nunlimdim = self._nunlimdim + 1 # set ndim attribute (number of dimensions). self.ndim = _inq_varndims(self._grpid, self._varid, self._grp.auto_complex) self._name = name # default for automatically applying scale_factor and # add_offset, and converting to/from masked arrays is True. self.scale = True self.mask = True # issue 809: default for converting arrays with no missing values to # regular numpy arrays self.always_mask = True # default is to automatically convert to/from character # to string arrays when _Encoding variable attribute is set. self.chartostring = True # propagate _ncstring_attrs__ setting from parent group. self._ncstring_attrs__ = grp._ncstring_attrs__ if 'least_significant_digit' in self.ncattrs(): self._has_lsd = True # avoid calling nc_get_vars for strided slices by default. # a fix for strided slice access using HDF5 was added # in 4.6.2. # always use nc_get_vars for strided access with OpenDAP (issue #838). if __netcdf4libversion__ >= "4.6.2" or\ self._grp.filepath().startswith('http'): self._use_get_vars = True else: self._use_get_vars = False def _fix_complex_numbers(self): cdef char name[NC_MAX_NAME + 1] cdef int complex_typeid, complex_dim_id error_info = f"(variable '{name}', group '{self._grp.name}')" if pfnc_var_is_complex_type(self._grpid, self._varid): self._isprimitive = False self._iscompound = True with nogil: ierr = pfnc_inq_var_complex_base_type(self._grpid, self._varid, &complex_typeid) _ensure_nc_success(ierr, extra_msg=error_info) np_complex_type = _nctonptype[complex_typeid] compound_complex_type = f"{np_complex_type}, {np_complex_type}" self._cmptype = CompoundType( self._grp, compound_complex_type, "complex", typeid=complex_typeid ) else: with nogil: ierr = pfnc_get_complex_dim(self._grpid, &complex_dim_id) _ensure_nc_success(ierr, extra_msg=error_info) with nogil: ierr = nc_inq_dimname(self._grpid, complex_dim_id, name) _ensure_nc_success(ierr, extra_msg=error_info) self._grp.dimensions[name.decode("utf-8")] = Dimension( self._grp, name, size=2, id=complex_dim_id ) def __array__(self): # numpy special method that returns a numpy array. # allows numpy ufuncs to work faster on Variable objects # (issue 216). return self[...] def __repr__(self): return self.__str__() def __str__(self): cdef int ierr, no_fill if not dir(self._grp): return 'Variable object no longer valid' ncdump = [repr(type(self)).replace("._netCDF4", "")] show_more_dtype = True if self._iscompound: kind = 'compound' elif self._isvlen: kind = 'vlen' elif self._isenum: kind = 'enum' else: show_more_dtype = False kind = str(self.dtype) dimnames = tuple(_tostr(dimname) for dimname in self.dimensions) ncdump.append('%s %s(%s)' %\ (kind, self._name, ', '.join(dimnames))) for name in self.ncattrs(): ncdump.append(' %s: %s' % (name, self.getncattr(name))) if show_more_dtype: ncdump.append('%s data type: %s' % (kind, self.dtype)) unlimdims = [] for dimname in self.dimensions: dim = _find_dim(self._grp, dimname) if dim.isunlimited(): unlimdims.append(dimname) if (self._grp.path != '/'): ncdump.append('path = %s' % self._grp.path) ncdump.append('unlimited dimensions: %s' % ', '.join(unlimdims)) ncdump.append('current shape = %r' % (self.shape,)) if __netcdf4libversion__ < '4.5.1' and\ self._grp.file_format.startswith('NETCDF3'): # issue #908: no_fill not correct for NETCDF3 files before 4.5.1 # before 4.5.1 there was no way to turn off filling on a # per-variable basis for classic files. no_fill=0 else: with nogil: ierr = nc_inq_var_fill(self._grpid,self._varid,&no_fill,NULL) _ensure_nc_success(ierr) if self._isprimitive: if no_fill != 1: try: fillval = self._FillValue msg = 'filling on' except AttributeError: fillval = default_fillvals[self.dtype.str[1:]] if self.dtype.str[1:] in ['u1','i1']: msg = 'filling on, default _FillValue of %s ignored' % fillval else: msg = 'filling on, default _FillValue of %s used' % fillval ncdump.append(msg) else: ncdump.append('filling off') return '\n'.join(ncdump) def _getdims(self): # Private method to get variables's dimension names cdef int ierr, dimid cdef char namstring[NC_MAX_NAME+1] dimids = _inq_vardimid(self._grpid, self._varid, self._grp.auto_complex) # loop over dimensions, retrieve names. dimensions = () for dimid in dimids: with nogil: ierr = nc_inq_dimname(self._grpid, dimid, namstring) _ensure_nc_success(ierr) name = namstring.decode('utf-8') dimensions = dimensions + (name,) return dimensions def _getname(self): # Private method to get name associated with instance cdef int err, _grpid cdef char namstring[NC_MAX_NAME+1] _grpid = self._grp._grpid with nogil: ierr = nc_inq_varname(_grpid, self._varid, namstring) _ensure_nc_success(ierr) return namstring.decode('utf-8') property name: """string name of Variable instance""" def __get__(self): return self._getname() def __set__(self,value): raise AttributeError("name cannot be altered") property datatype: """numpy data type (for primitive data types) or VLType/CompoundType/EnumType instance (for compound, vlen or enum data types)""" def __get__(self): if self._iscompound: return self._cmptype elif self._isvlen: return self._vltype elif self._isenum: return self._enumtype elif self._isprimitive: return self.dtype property shape: """find current sizes of all variable dimensions""" def __get__(self): shape = () for dimname in self._getdims(): # look in current group, and parents for dim. dim = _find_dim(self._grp,dimname) shape = shape + (len(dim),) return shape def __set__(self,value): raise AttributeError("shape cannot be altered") property size: """Return the number of stored elements.""" def __get__(self): # issue #957: add int since prod(())=1.0 return int(numpy.prod(self.shape)) property dimensions: """get variables's dimension names""" def __get__(self): return self._getdims() def __set__(self,value): raise AttributeError("dimensions cannot be altered") def group(self): """ **`group(self)`** return the group that this `Variable` is a member of.""" return self._grp def get_fill_value(self): """ **`get_fill_value(self)`** return the fill value associated with this `Variable` (returns `None` if data is not pre-filled). Works even if default fill value was used, and `_FillValue` attribute does not exist.""" cdef int ierr, no_fill with nogil: ierr = nc_inq_var_fill(self._grpid,self._varid,&no_fill,NULL) _ensure_nc_success(ierr) if no_fill == 1: # no filling for this variable return None else: try: fillval = self._FillValue return fillval except AttributeError: # _FillValue attribute not set, see if we can retrieve _FillValue. # for primitive data types. if self._isprimitive: #return numpy.array(default_fillvals[self.dtype.str[1:]],self.dtype) fillval = numpy.empty((),self.dtype) ierr=nc_inq_var_fill(self._grpid,self._varid,&no_fill,PyArray_DATA(fillval)) _ensure_nc_success(ierr) return fillval else: # no default filling for non-primitive data types. return None def ncattrs(self): """ **`ncattrs(self)`** return netCDF attribute names for this `Variable` in a list.""" return _get_att_names(self._grpid, self._varid) def setncattr(self,name,value): """ **`setncattr(self,name,value)`** set a netCDF variable attribute using name,value pair. Use if you need to set a netCDF attribute with the same name as one of the reserved python attributes.""" cdef nc_type xtype xtype=-99 # issue #959 - trying to set _FillValue results in mysterious # error when close method is called so catch it here. It is # already caught in __setattr__. if name == '_FillValue': msg='_FillValue attribute must be set when variable is '+\ 'created (using fill_value keyword to createVariable)' raise AttributeError(msg) if self._grp.data_model != 'NETCDF4': self._grp._redef() _set_att(self._grp, self._varid, name, value, xtype=xtype, force_ncstring=self._ncstring_attrs__) if self._grp.data_model != 'NETCDF4': self._grp._enddef() def setncattr_string(self,name,value): """ **`setncattr_string(self,name,value)`** set a netCDF variable string attribute using name,value pair. Use if you need to ensure that a netCDF attribute is created with type `NC_STRING` if the file format is `NETCDF4`. Use if you need to set an attribute to an array of variable-length strings.""" cdef nc_type xtype xtype=-99 if self._grp.data_model != 'NETCDF4': msg='file format does not support NC_STRING attributes' raise OSError(msg) _set_att(self._grp, self._varid, name, value, xtype=xtype, force_ncstring=True) def setncatts(self,attdict): """ **`setncatts(self,attdict)`** set a bunch of netCDF variable attributes at once using a python dictionary. This may be faster when setting a lot of attributes for a `NETCDF3` formatted file, since nc_redef/nc_enddef is not called in between setting each attribute""" if self._grp.data_model != 'NETCDF4': self._grp._redef() for name, value in attdict.items(): _set_att(self._grp, self._varid, name, value) if self._grp.data_model != 'NETCDF4': self._grp._enddef() def getncattr(self,name,encoding='utf-8'): """ **`getncattr(self,name)`** retrieve a netCDF variable attribute. Use if you need to set a netCDF attribute with the same name as one of the reserved python attributes. option kwarg `encoding` can be used to specify the character encoding of a string attribute (default is `utf-8`).""" return _get_att(self._grp, self._varid, name, encoding=encoding) def delncattr(self, name): """ **`delncattr(self,name,value)`** delete a netCDF variable attribute. Use if you need to delete a netCDF attribute with the same name as one of the reserved python attributes.""" cdef char *attname bytestr = _strencode(name) attname = bytestr if self._grp.data_model != 'NETCDF4': self._grp._redef() with nogil: ierr = nc_del_att(self._grpid, self._varid, attname) if self._grp.data_model != 'NETCDF4': self._grp._enddef() _ensure_nc_success(ierr) def filters(self): """ **`filters(self)`** return dictionary containing HDF5 filter parameters.""" cdef int ierr,ideflate,ishuffle,icomplevel,ifletcher32 cdef int izstd=0 cdef int ibzip2=0 cdef int iblosc=0 cdef int iszip=0 cdef int iszip_coding=0 cdef int iszip_pixels_per_block=0 cdef int icomplevel_zstd=0 cdef int icomplevel_bzip2=0 cdef unsigned int iblosc_shuffle=0 cdef unsigned int iblosc_compressor=0 cdef unsigned int iblosc_blocksize=0 cdef unsigned int iblosc_complevel=0 filtdict = {'zlib':False,'szip':False,'zstd':False,'bzip2':False,'blosc':False,'shuffle':False,'complevel':0,'fletcher32':False} if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']: return with nogil: ierr = nc_inq_var_deflate(self._grpid, self._varid, &ishuffle, &ideflate, &icomplevel) _ensure_nc_success(ierr) with nogil: ierr = nc_inq_var_fletcher32(self._grpid, self._varid, &ifletcher32) _ensure_nc_success(ierr) if __has_zstandard_support__: with nogil: ierr = nc_inq_var_zstandard(self._grpid, self._varid, &izstd,\ &icomplevel_zstd) if ierr != 0: izstd=0 # _ensure_nc_success(ierr) if __has_bzip2_support__: with nogil: ierr = nc_inq_var_bzip2(self._grpid, self._varid, &ibzip2,\ &icomplevel_bzip2) if ierr != 0: ibzip2=0 #_ensure_nc_success(ierr) if __has_blosc_support__: with nogil: ierr = nc_inq_var_blosc(self._grpid, self._varid, &iblosc,\ &iblosc_compressor,&iblosc_complevel,&iblosc_blocksize,&iblosc_shuffle) if ierr != 0: iblosc=0 #_ensure_nc_success(ierr) if __has_szip_support__: with nogil: ierr = nc_inq_var_szip(self._grpid, self._varid, &iszip_coding,\ &iszip_pixels_per_block) if ierr != 0: iszip=0 else: if iszip_coding: iszip=1 else: iszip=0 #_ensure_nc_success(ierr) if ideflate: filtdict['zlib']=True filtdict['complevel']=icomplevel if izstd: filtdict['zstd']=True filtdict['complevel']=icomplevel_zstd if ibzip2: filtdict['bzip2']=True filtdict['complevel']=icomplevel_bzip2 if iblosc: blosc_compressor = iblosc_compressor filtdict['blosc']={'compressor':_blosc_dict_inv[blosc_compressor],'shuffle':iblosc_shuffle} filtdict['complevel']=iblosc_complevel if iszip: szip_coding = iszip_coding filtdict['szip']={'coding':_szip_dict_inv[szip_coding],'pixels_per_block':iszip_pixels_per_block} if ishuffle: filtdict['shuffle']=True if ifletcher32: filtdict['fletcher32']=True return filtdict def quantization(self): """ **`quantization(self)`** return number of significant digits and the algorithm used in quantization. Returns None if quantization not active. """ if not __has_quantization_support__: return None cdef int ierr, nsd, quantize_mode if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']: return None with nogil: ierr = nc_inq_var_quantize(self._grpid, self._varid, &quantize_mode, &nsd) _ensure_nc_success(ierr) if quantize_mode == NC_NOQUANTIZE: return None if quantize_mode == NC_QUANTIZE_GRANULARBR: sig_digits = nsd quant_mode = 'GranularBitRound' elif quantize_mode == NC_QUANTIZE_BITROUND: sig_digits = nsd # interpreted as bits, not decimal quant_mode = 'BitRound' else: sig_digits = nsd quant_mode = 'BitGroom' return sig_digits, quant_mode def endian(self): """ **`endian(self)`** return endian-ness (`little,big,native`) of variable (as stored in HDF5 file).""" cdef int ierr, iendian if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']: return 'native' with nogil: ierr = nc_inq_var_endian(self._grpid, self._varid, &iendian) _ensure_nc_success(ierr) if iendian == NC_ENDIAN_LITTLE: return 'little' elif iendian == NC_ENDIAN_BIG: return 'big' else: return 'native' def chunking(self): """ **`chunking(self)`** return variable chunking information. If the dataset is defined to be contiguous (and hence there is no chunking) the word 'contiguous' is returned. Otherwise, a sequence with the chunksize for each dimension is returned.""" cdef int ierr, icontiguous, ndims cdef size_t *chunksizesp if self._grp.data_model not in ['NETCDF4_CLASSIC','NETCDF4']: return None ndims = self.ndim chunksizesp = malloc(sizeof(size_t) * ndims) with nogil: ierr = nc_inq_var_chunking(self._grpid, self._varid, &icontiguous, chunksizesp) _ensure_nc_success(ierr) chunksizes=[] for n in range(ndims): chunksizes.append(chunksizesp[n]) free(chunksizesp) if icontiguous: return 'contiguous' else: return chunksizes def get_var_chunk_cache(self): """ **`get_var_chunk_cache(self)`** return variable chunk cache information in a tuple (size,nelems,preemption). See netcdf C library documentation for `nc_get_var_chunk_cache` for details.""" cdef int ierr cdef size_t sizep, nelemsp cdef float preemptionp with nogil: ierr = nc_get_var_chunk_cache(self._grpid, self._varid, &sizep, &nelemsp, &preemptionp) _ensure_nc_success(ierr) size = sizep; nelems = nelemsp; preemption = preemptionp return (size,nelems,preemption) def set_var_chunk_cache(self,size=None,nelems=None,preemption=None): """ **`set_var_chunk_cache(self,size=None,nelems=None,preemption=None)`** change variable chunk cache settings. See netcdf C library documentation for `nc_set_var_chunk_cache` for details.""" cdef int ierr cdef size_t sizep, nelemsp cdef float preemptionp # reset chunk cache size, leave other parameters unchanged. size_orig, nelems_orig, preemption_orig = self.get_var_chunk_cache() if size is not None: sizep = size else: sizep = size_orig if nelems is not None: nelemsp = nelems else: nelemsp = nelems_orig if preemption is not None: preemptionp = preemption else: preemptionp = preemption_orig with nogil: ierr = nc_set_var_chunk_cache(self._grpid, self._varid, sizep, nelemsp, preemptionp) _ensure_nc_success(ierr) def __delattr__(self,name): # if it's a netCDF attribute, remove it if name not in _private_atts: self.delncattr(name) else: raise AttributeError( "'%s' is one of the reserved attributes %s, cannot delete. Use delncattr instead." % (name, tuple(_private_atts))) def __setattr__(self,name,value): # if name in _private_atts, it is stored at the python # level and not in the netCDF file. if name not in _private_atts: # if setting _FillValue or missing_value, make sure value # has same type and byte order as variable. if name == '_FillValue': msg='_FillValue attribute must be set when variable is '+\ 'created (using fill_value keyword to createVariable)' raise AttributeError(msg) #if self._isprimitive: # value = numpy.array(value, self.dtype) #else: # msg="cannot set _FillValue attribute for "+\ # "VLEN or compound variable" # raise AttributeError(msg) elif name in ['valid_min','valid_max','valid_range','missing_value'] and self._isprimitive: # make sure these attributes written in same data type as variable. # also make sure it is written in native byte order # (the same as the data) valuea = numpy.array(value, self.dtype) # check to see if array cast is safe if _safecast(numpy.array(value),valuea): value = valuea if not value.dtype.isnative: value.byteswap(True) else: # otherwise don't do it, but issue a warning msg="WARNING: %s cannot be safely cast to variable dtype" \ % name warnings.warn(msg) self.setncattr(name, value) elif not name.endswith('__'): if hasattr(self,name): raise AttributeError( "'%s' is one of the reserved attributes %s, cannot rebind. Use setncattr instead." % (name, tuple(_private_atts))) else: self.__dict__[name]=value def __getattr__(self,name): # if name in _private_atts, it is stored at the python # level and not in the netCDF file. if name.startswith('__') and name.endswith('__'): # if __dict__ requested, return a dict with netCDF attributes. if name == '__dict__': names = self.ncattrs() values = [] for name in names: values.append(_get_att(self._grp, self._varid, name)) return dict(zip(names, values)) else: raise AttributeError elif name in _private_atts: return self.__dict__[name] else: return self.getncattr(name) def renameAttribute(self, oldname, newname): """ **`renameAttribute(self, oldname, newname)`** rename a `Variable` attribute named `oldname` to `newname`.""" cdef int ierr cdef char *oldnamec cdef char *newnamec bytestr = _strencode(oldname) oldnamec = bytestr bytestr = _strencode(newname) newnamec = bytestr with nogil: ierr = nc_rename_att(self._grpid, self._varid, oldnamec, newnamec) _ensure_nc_success(ierr) def __getitem__(self, elem): # This special method is used to index the netCDF variable # using the "extended slice syntax". The extended slice syntax # is a perfect match for the "start", "count" and "stride" # arguments to the nc_get_var() function, and is much more easy # to use. start, count, stride, put_ind =\ _StartCountStride(elem,self.shape,dimensions=self.dimensions,grp=self._grp,use_get_vars=self._use_get_vars) datashape = _out_array_shape(count) if self._isvlen: data = numpy.empty(datashape, dtype='O') else: data = numpy.empty(datashape, dtype=self.dtype) # Determine which dimensions need to be # squeezed (those for which elem is an integer scalar). # The convention used is that for those cases, # put_ind for this dimension is set to -1 by _StartCountStride. squeeze = data.ndim * [slice(None),] for i,n in enumerate(put_ind.shape[:-1]): if n == 1 and put_ind.size > 0 and put_ind[...,i].ravel()[0] == -1: squeeze[i] = 0 # Reshape the arrays so we can iterate over them. start = start.reshape((-1, self.ndim or 1)) count = count.reshape((-1, self.ndim or 1)) stride = stride.reshape((-1, self.ndim or 1)) put_ind = put_ind.reshape((-1, self.ndim or 1)) # Fill output array with data chunks. for (a,b,c,i) in zip(start, count, stride, put_ind): datout = self._get(a,b,c) if not hasattr(datout,'shape') or data.shape == datout.shape: data = datout else: shape = getattr(data[tuple(i)], 'shape', ()) if self._isvlen and not len(self.dimensions): # special case of scalar VLEN data[0] = datout else: if self._isvlen and not shape: # issue #1306 - convert length 1 object array to string data[tuple(i)] = datout.item() else: data[tuple(i)] = datout.reshape(shape) # Remove extra singleton dimensions. if hasattr(data,'shape'): data = data[tuple(squeeze)] if hasattr(data,'ndim') and self.ndim == 0: # Make sure a numpy scalar array is returned instead of a 1-d array of # length 1. if data.ndim != 0: data = numpy.asarray(data[0]) # if auto_scale mode set to True, (through # a call to set_auto_scale or set_auto_maskandscale), # perform automatic unpacking using scale_factor/add_offset. # if auto_mask mode is set to True (through a call to # set_auto_mask or set_auto_maskandscale), perform # automatic conversion to masked array using # missing_value/_Fill_Value. # applied for primitive and (non-string) vlen, # ignored for compound and enum datatypes. try: # check to see if scale_factor and add_offset is valid (issue 176). if hasattr(self,'scale_factor'): float(self.scale_factor) if hasattr(self,'add_offset'): float(self.add_offset) valid_scaleoffset = True except: valid_scaleoffset = False if self.scale: msg = 'invalid scale_factor or add_offset attribute, no unpacking done...' warnings.warn(msg) if self.mask and (self._isprimitive or self._isenum):\ data = self._toma(data) else: # if attribute _Unsigned is "true", and variable has signed integer # dtype, return view with corresponding unsigned dtype (issue #656) if self.scale: # only do this if autoscale option is on. is_unsigned = getattr(self, '_Unsigned', False) in ["true","True"] if is_unsigned and data.dtype.kind == 'i': data=data.view('%su%s'%(data.dtype.byteorder,data.dtype.itemsize)) if self.scale and\ (self._isprimitive or (self._isvlen and self.dtype != str)) and\ valid_scaleoffset: # if variable has scale_factor and add_offset attributes, apply # them. if hasattr(self, 'scale_factor') and hasattr(self, 'add_offset'): if self.add_offset != 0.0 or self.scale_factor != 1.0: data = data*self.scale_factor + self.add_offset else: data = data.astype(self.scale_factor.dtype) # issue 913 # else if variable has only scale_factor attribute, rescale. elif hasattr(self, 'scale_factor') and self.scale_factor != 1.0: data = data*self.scale_factor # else if variable has only add_offset attribute, add offset. elif hasattr(self, 'add_offset') and self.add_offset != 0.0: data = data + self.add_offset # if _Encoding is specified for a character variable, return # a numpy array of strings with one less dimension. if self.chartostring and getattr(self.dtype,'kind',None) == 'S' and\ getattr(self.dtype,'itemsize',None) == 1: encoding = getattr(self,'_Encoding',None) # should this only be done if self.scale = True? # should there be some other way to disable this? if encoding is not None: # only try to return a string array if rightmost dimension of # sliced data matches rightmost dimension of char variable if len(data.shape) > 0 and data.shape[-1] == self.shape[-1]: # also make sure slice is along last dimension matchdim = True for cnt in count: if cnt[-1] != self.shape[-1]: matchdim = False break if matchdim: data = chartostring(data, encoding=encoding) # if structure array contains char arrays, return view as strings # if _Encoding att set (issue #773) if self._iscompound and \ self._cmptype.dtype != self._cmptype.dtype_view and \ self.chartostring: # self.chartostring and getattr(self,'_Encoding',None) is not None: data = data.view(self._cmptype.dtype_view) return data def _toma(self,data): cdef int ierr, no_fill # if attribute _Unsigned is "true", and variable has signed integer # dtype, return view with corresponding unsigned dtype (issues #656, # #794) # _Unsigned attribute must be "true" or "True" (string). Issue #1232. is_unsigned = getattr(self, '_Unsigned', False) in ["True","true"] is_unsigned_int = is_unsigned and data.dtype.kind == 'i' if self.scale and is_unsigned_int: # only do this if autoscale option is on. dtype_unsigned_int='%su%s' % (data.dtype.byteorder,data.dtype.itemsize) data = data.view(dtype_unsigned_int) # private function for creating a masked array, masking missing_values # and/or _FillValues. totalmask = numpy.zeros(data.shape, numpy.bool_) fill_value = None safe_missval = self._check_safecast('missing_value') if safe_missval: mval = numpy.array(self.missing_value, self.dtype) if self.scale and is_unsigned_int: mval = mval.view(dtype_unsigned_int) # create mask from missing values. mvalmask = numpy.zeros(data.shape, numpy.bool_) if mval.shape == (): # mval a scalar. mval = [mval] # make into iterable. for m in mval: # is scalar missing value a NaN? try: mvalisnan = numpy.isnan(m) except TypeError: # isnan fails on some dtypes (issue 206) mvalisnan = False if mvalisnan: mvalmask += numpy.isnan(data) else: mvalmask += data==m if mvalmask.any(): # set fill_value for masked array # to missing_value (or 1st element # if missing_value is a vector). fill_value = mval[0] totalmask += mvalmask # set mask=True for data == fill value safe_fillval = self._check_safecast('_FillValue') if safe_fillval: fval = numpy.array(self._FillValue, self.dtype) if self.scale and is_unsigned_int: fval = fval.view(dtype_unsigned_int) # is _FillValue a NaN? try: fvalisnan = numpy.isnan(fval) except: # isnan fails on some dtypes (issue 202) fvalisnan = False if fvalisnan: mask = numpy.isnan(data) elif (data == fval).any(): mask = data==fval else: mask = None if mask is not None: if fill_value is None: fill_value = fval totalmask += mask # issue 209: don't return masked array if variable filling # is disabled. else: if __netcdf4libversion__ < '4.5.1' and\ self._grp.file_format.startswith('NETCDF3'): # issue #908: no_fill not correct for NETCDF3 files before 4.5.1 # before 4.5.1 there was no way to turn off filling on a # per-variable basis for classic files. no_fill=0 else: with nogil: ierr = nc_inq_var_fill(self._grpid,self._varid,&no_fill,NULL) _ensure_nc_success(ierr) # if no_fill is not 1, and not a byte variable, then use default fill value. # from http://www.unidata.ucar.edu/software/netcdf/docs/netcdf-c/Fill-Values.html#Fill-Values # "If you need a fill value for a byte variable, it is recommended # that you explicitly define an appropriate _FillValue attribute, as # generic utilities such as ncdump will not assume a default fill # value for byte variables." # Explained here too: # http://www.unidata.ucar.edu/software/netcdf/docs/known_problems.html#ncdump_ubyte_fill # "There should be no default fill values when reading any byte # type, signed or unsigned, because the byte ranges are too # small to assume one of the values should appear as a missing # value unless a _FillValue attribute is set explicitly." # (do this only for non-vlens, since vlens don't have a default _FillValue) if not self._isvlen and (no_fill != 1 or self.dtype.str[1:] not in ['u1','i1']): fillval = numpy.array(default_fillvals[self.dtype.str[1:]],self.dtype) has_fillval = data == fillval # if data is an array scalar, has_fillval will be a boolean. # in that case convert to an array. if type(has_fillval) == bool: has_fillval=numpy.asarray(has_fillval) if has_fillval.any(): if fill_value is None: fill_value = fillval mask=data==fillval totalmask += mask # set mask=True for data outside valid_min,valid_max. # (issue #576) validmin = None; validmax = None # if valid_range exists use that, otherwise # look for valid_min, valid_max. No special # treatment of byte data as described at # http://www.unidata.ucar.edu/software/netcdf/docs/attribute_conventions.html). safe_validrange = self._check_safecast('valid_range') safe_validmin = self._check_safecast('valid_min') safe_validmax = self._check_safecast('valid_max') if safe_validrange and self.valid_range.size == 2: validmin = numpy.array(self.valid_range[0], self.dtype) validmax = numpy.array(self.valid_range[1], self.dtype) else: if safe_validmin: validmin = numpy.array(self.valid_min, self.dtype) if safe_validmax: validmax = numpy.array(self.valid_max, self.dtype) if validmin is not None and self.scale and is_unsigned_int: validmin = validmin.view(dtype_unsigned_int) if validmax is not None and self.scale and is_unsigned_int: validmax = validmax.view(dtype_unsigned_int) # http://www.unidata.ucar.edu/software/netcdf/docs/attribute_conventions.html). # "If the data type is byte and _FillValue # is not explicitly defined, # then the valid range should include all possible values. # Otherwise, the valid range should exclude the _FillValue # (whether defined explicitly or by default) as follows. # If the _FillValue is positive then it defines a valid maximum, # otherwise it defines a valid minimum." byte_type = self.dtype.str[1:] in ['u1','i1'] if safe_fillval: fval = numpy.array(self._FillValue, self.dtype) else: fval = numpy.array(default_fillvals[self.dtype.str[1:]],self.dtype) if byte_type: fval = None if self.dtype.kind != 'S': # don't set mask for character data # issues #761 and #748: setting valid_min/valid_max to the # _FillVaue is too surprising for many users (despite the # netcdf docs attribute best practices suggesting clients # should do this). #if validmin is None and (fval is not None and fval <= 0): # validmin = fval #if validmax is None and (fval is not None and fval > 0): # validmax = fval if validmin is not None: totalmask += data < validmin if validmax is not None: totalmask += data > validmax if fill_value is None and fval is not None: fill_value = fval # if all else fails, use default _FillValue as fill_value # for masked array. if fill_value is None: fill_value = default_fillvals[self.dtype.str[1:]] # create masked array with computed mask masked_values = bool(totalmask.any()) if masked_values: data = ma.masked_array(data,mask=totalmask,fill_value=fill_value) else: # issue #785: always return masked array, if no values masked data = ma.masked_array(data) # issue 515 scalar array with mask=True should be converted # to numpy.ma.MaskedConstant to be consistent with slicing # behavior of masked arrays. if data.shape == () and data.mask.all(): # return a scalar numpy masked constant not a 0-d masked array, # so that data == numpy.ma.masked. data = data[()] # changed from [...] (issue #662) elif not self.always_mask and not masked_values: # issue #809: return a regular numpy array if requested # and there are no missing values data = numpy.array(data, copy=False) return data def _pack(self,data): # pack non-masked values using scale_factor and add_offset if hasattr(self, 'scale_factor') and hasattr(self, 'add_offset'): data = (data - self.add_offset)/self.scale_factor if self.dtype.kind in 'iu': data = numpy.around(data) elif hasattr(self, 'scale_factor'): data = data/self.scale_factor if self.dtype.kind in 'iu': data = numpy.around(data) elif hasattr(self, 'add_offset'): data = data - self.add_offset if self.dtype.kind in 'iu': data = numpy.around(data) if self.dtype != data.dtype: data = data.astype(self.dtype) # cast data to var type, if necessary. if ma.isMA(data): # if underlying data in masked regions of masked array # corresponds to missing values, don't fill masked array - # just use underlying data instead if hasattr(self, 'missing_value') and \ numpy.all(numpy.isin(data.data[data.mask],self.missing_value)): data = data.data else: if hasattr(self, 'missing_value'): # if missing value is a scalar, use it as fill_value. # if missing value is a vector, raise an exception # since we then don't know how to fill in masked values. if numpy.array(self.missing_value).shape == (): fillval = self.missing_value else: msg="cannot assign fill_value for masked array when missing_value attribute is not a scalar" raise RuntimeError(msg) if numpy.array(fillval).shape != (): fillval = fillval[0] elif hasattr(self, '_FillValue'): fillval = self._FillValue else: fillval = default_fillvals[self.dtype.str[1:]] # some versions of numpy have trouble handling # MaskedConstants when filling - this is is # a workaround (issue #850) if data.shape == (1,) and data.mask.all(): data = numpy.array([fillval],self.dtype) else: data = data.filled(fill_value=fillval) return data def _assign_vlen(self, elem, data): """private method to assign data to a single item in a VLEN variable""" cdef size_t *startp cdef size_t *countp cdef int ndims, n cdef nc_vlen_t *vldata cdef char **strdata cdef ndarray data2 if not self._isvlen: raise TypeError('_assign_vlen method only for use with VLEN variables') ndims = self.ndim msg="data can only be assigned to VLEN variables using integer indices" # check to see that elem is a tuple of integers. # handle negative integers. if _is_int(elem): if ndims > 1: raise IndexError(msg) if elem < 0: if self.shape[0]+elem >= 0: elem = self.shape[0]+elem else: raise IndexError("Illegal index") elif isinstance(elem, tuple): if len(elem) != ndims: raise IndexError("Illegal index") elemnew = [] for n,e in enumerate(elem): if not _is_int(e): raise IndexError(msg) elif e < 0: enew = self.shape[n]+e if enew < 0: raise IndexError("Illegal index") else: elemnew.append(self.shape[n]+e) else: elemnew.append(e) elem = tuple(elemnew) else: raise IndexError(msg) # set start, count if isinstance(elem, tuple): start = list(elem) else: start = [elem] count = [1]*ndims startp = malloc(sizeof(size_t) * ndims) countp = malloc(sizeof(size_t) * ndims) for n in range(ndims): startp[n] = start[n] countp[n] = count[n] if self.dtype == str: # VLEN string strdata = malloc(sizeof(char *)) # use _Encoding attribute to specify string encoding - if # not given, use 'utf-8'. encoding = getattr(self,'_Encoding','utf-8') bytestr = _strencode(data,encoding=encoding) strdata[0] = bytestr with nogil: ierr = nc_put_vara(self._grpid, self._varid, startp, countp, strdata) _ensure_nc_success(ierr) free(strdata) else: # regular VLEN if data.dtype != self.dtype: raise TypeError("wrong data type: should be %s, got %s" % (self.dtype,data.dtype)) data2 = data vldata = malloc(sizeof(nc_vlen_t)) vldata[0].len = PyArray_SIZE(data2) vldata[0].p = PyArray_DATA(data2) with nogil: ierr = nc_put_vara(self._grpid, self._varid, startp, countp, vldata) _ensure_nc_success(ierr) free(vldata) free(startp) free(countp) def _check_safecast(self, attname): # check to see that variable attribute exists # can can be safely cast to variable data type. msg="""WARNING: %s not used since it cannot be safely cast to variable data type""" % attname if hasattr(self, attname): att = numpy.array(self.getncattr(attname)) else: return False try: atta = numpy.array(att, self.dtype) except ValueError: is_safe = False warnings.warn(msg) return is_safe is_safe = _safecast(att,atta) if not is_safe: warnings.warn(msg) return is_safe def __setitem__(self, elem, data): # This special method is used to assign to the netCDF variable # using "extended slice syntax". The extended slice syntax # is a perfect match for the "start", "count" and "stride" # arguments to the nc_put_var() function, and is much more easy # to use. # if _Encoding is specified for a character variable, convert # numpy array of strings to a numpy array of characters with one more # dimension. if self.chartostring and getattr(self.dtype,'kind',None) == 'S' and\ getattr(self.dtype,'itemsize',None) == 1: # NC_CHAR variable encoding = getattr(self,'_Encoding',None) if encoding is not None: # _Encoding attribute is set # if data is a string or a bytes object, convert to a numpy string array # whose length is equal to the rightmost dimension of the # variable. if type(data) in [str,bytes]: if encoding == 'ascii': data = numpy.asarray(data,dtype='S'+repr(self.shape[-1])) else: data = numpy.asarray(data,dtype='U'+repr(self.shape[-1])) if data.dtype.kind in ['S','U'] and data.dtype.itemsize > 1: # if data is a numpy string array, convert it to an array # of characters with one more dimension. data = stringtochar(data, encoding=encoding,n_strlen=self.shape[-1]) # if structured data has strings (and _Encoding att set), create view as char arrays # (issue #773) if self._iscompound and \ self._cmptype.dtype != self._cmptype.dtype_view and \ _set_viewdtype(data.dtype) == self._cmptype.dtype_view and \ self.chartostring: # self.chartostring and getattr(self,'_Encoding',None) is not None: # may need to cast input data to aligned type data = data.astype(self._cmptype.dtype_view).view(self._cmptype.dtype) if self._isvlen: # if vlen, should be object array (don't try casting) if self.dtype == str: # for string vars, if data is not an array # assume it is a python string and raise an error # if it is an array, but not an object array. if not isinstance(data, numpy.ndarray): # issue 458, allow Ellipsis to be used for scalar var if type(elem) == type(Ellipsis) and not\ len(self.dimensions): elem = 0 self._assign_vlen(elem, data) return elif data.dtype.kind in ['S', 'U']: if ma.isMA(data): msg='masked arrays cannot be assigned by VLEN str slices' raise TypeError(msg) data = data.astype(object) elif data.dtype.kind != 'O': msg = ('only numpy string, unicode or object arrays can ' 'be assigned to VLEN str var slices') raise TypeError(msg) else: # for non-string vlen arrays, if data is not multi-dim, or # not an object array, assume it represents a single element # of the vlen var. if not isinstance(data, numpy.ndarray) or data.dtype.kind != 'O': # issue 458, allow Ellipsis to be used for scalar var if type(elem) == type(Ellipsis) and not\ len(self.dimensions): elem = 0 # pack as integers if desired. if self.scale: data = self._pack(data) self._assign_vlen(elem, data) return # A numpy or masked array (or an object supporting the buffer interface) is needed. # Convert if necessary. if not ma.isMA(data) and not (hasattr(data,'data') and isinstance(data.data,memoryview)): # if auto scaling is to be done, don't cast to an integer yet. if self.scale and self.dtype.kind in 'iu' and \ hasattr(self, 'scale_factor') or hasattr(self, 'add_offset'): data = numpy.array(data,numpy.float64) else: data = numpy.array(data,self.dtype) # for Enum variable, make sure data is valid. if self._isenum: test = numpy.zeros(data.shape,numpy.bool_) if ma.isMA(data): # fix for new behaviour in numpy.ma in 1.13 (issue #662) for val in self.datatype.enum_dict.values(): test += data.filled() == val else: for val in self.datatype.enum_dict.values(): test += data == val if not numpy.all(test): msg="trying to assign illegal value to Enum variable" raise ValueError(msg) start, count, stride, put_ind =\ _StartCountStride(elem,self.shape,self.dimensions,self._grp,datashape=data.shape,put=True,use_get_vars=self._use_get_vars) datashape = _out_array_shape(count) # if a numpy scalar, create an array of the right size # and fill with scalar values. if data.shape == (): data = numpy.tile(data,datashape) # reshape data array if needed to conform with start,count,stride. if data.ndim != len(datashape) or\ (data.shape != datashape and data.ndim > 1): # issue #1083 # create a view so shape in caller is not modified (issue 90) try: # if extra singleton dims, just reshape data = data.view() data.shape = tuple(datashape) except ValueError: # otherwise broadcast data = numpy.broadcast_to(data, datashape) # Reshape these arrays so we can iterate over them. start = start.reshape((-1, self.ndim or 1)) count = count.reshape((-1, self.ndim or 1)) stride = stride.reshape((-1, self.ndim or 1)) put_ind = put_ind.reshape((-1, self.ndim or 1)) # quantize data if least_significant_digit attribute # exists (improves compression). if self._has_lsd: data = _quantize(data,self.least_significant_digit) if self.scale and self._isprimitive: # pack non-masked values using scale_factor and add_offset data = self._pack(data) # Fill output array with data chunks. for (a,b,c,i) in zip(start, count, stride, put_ind): dataput = data[tuple(i)] if dataput.size == 0: continue # nothing to write # convert array scalar to regular array with one element. if dataput.shape == (): if self._isvlen: dataput=numpy.array(dataput,'O') else: dataput=numpy.array(dataput,dataput.dtype) self._put(dataput,a,b,c) def __len__(self): if not self.shape: raise TypeError('len() of unsized object') else: return self.shape[0] def assignValue(self,val): """ **`assignValue(self, val)`** assign a value to a scalar variable. Provided for compatibility with Scientific.IO.NetCDF, can also be done by assigning to an Ellipsis slice ([...]).""" if len(self.dimensions): raise IndexError('to assign values to a non-scalar variable, use a slice') self[:]=val def getValue(self): """ **`getValue(self)`** get the value of a scalar variable. Provided for compatibility with Scientific.IO.NetCDF, can also be done by slicing with an Ellipsis ([...]).""" if len(self.dimensions): raise IndexError('to retrieve values from a non-scalar variable, use slicing') return self[slice(None)] def set_auto_chartostring(self,chartostring): """ **`set_auto_chartostring(self,chartostring)`** turn on or off automatic conversion of character variable data to and from numpy fixed length string arrays when the `_Encoding` variable attribute is set. If `chartostring` is set to `True`, when data is read from a character variable (dtype = `S1`) that has an `_Encoding` attribute, it is converted to a numpy fixed length unicode string array (dtype = `UN`, where `N` is the length of the the rightmost dimension of the variable). The value of `_Encoding` is the unicode encoding that is used to decode the bytes into strings. When numpy string data is written to a variable it is converted back to individual bytes, with the number of bytes in each string equalling the rightmost dimension of the variable. The default value of `chartostring` is `True` (automatic conversions are performed). """ self.chartostring = bool(chartostring) def use_nc_get_vars(self,use_nc_get_vars): """ **`use_nc_get_vars(self,_use_get_vars)`** enable the use of netcdf library routine `nc_get_vars` to retrieve strided variable slices. By default, `nc_get_vars` may not used by default (depending on the version of the netcdf-c library being used) since it may be slower than multiple calls to the unstrided read routine `nc_get_vara`. """ self._use_get_vars = bool(use_nc_get_vars) def set_auto_maskandscale(self,maskandscale): """ **`set_auto_maskandscale(self,maskandscale)`** turn on or off automatic conversion of variable data to and from masked arrays, automatic packing/unpacking of variable data using `scale_factor` and `add_offset` attributes and automatic conversion of signed integer data to unsigned integer data if the `_Unsigned` attribute exists and is set to "true" (or "True"). If `maskandscale` is set to `True`, when data is read from a variable it is converted to a masked array if any of the values are exactly equal to the either the netCDF _FillValue or the value specified by the missing_value variable attribute. The fill_value of the masked array is set to the missing_value attribute (if it exists), otherwise the netCDF _FillValue attribute (which has a default value for each data type). If the variable has no missing_value attribute, the _FillValue is used instead. If the variable has valid_min/valid_max and missing_value attributes, data outside the specified range will be masked. When data is written to a variable, the masked array is converted back to a regular numpy array by replacing all the masked values by the missing_value attribute of the variable (if it exists). If the variable has no missing_value attribute, the _FillValue is used instead. If `maskandscale` is set to `True`, and the variable has a `scale_factor` or an `add_offset` attribute, then data read from that variable is unpacked using:: data = self.scale_factor*data + self.add_offset When data is written to a variable it is packed using:: data = (data - self.add_offset)/self.scale_factor If either scale_factor is present, but add_offset is missing, add_offset is assumed zero. If add_offset is present, but scale_factor is missing, scale_factor is assumed to be one. For more information on how `scale_factor` and `add_offset` can be used to provide simple compression, see the [PSL metadata conventions](http://www.esrl.noaa.gov/psl/data/gridded/conventions/cdc_netcdf_standard.shtml). In addition, if `maskandscale` is set to `True`, and if the variable has an attribute `_Unsigned` set to "true", and the variable has a signed integer data type, a view to the data is returned with the corresponding unsigned integer data type. This convention is used by the netcdf-java library to save unsigned integer data in `NETCDF3` or `NETCDF4_CLASSIC` files (since the `NETCDF3` data model does not have unsigned integer data types). The default value of `maskandscale` is `True` (automatic conversions are performed). """ self.scale = self.mask = bool(maskandscale) def set_auto_scale(self,scale): """ **`set_auto_scale(self,scale)`** turn on or off automatic packing/unpacking of variable data using `scale_factor` and `add_offset` attributes. Also turns on and off automatic conversion of signed integer data to unsigned integer data if the variable has an `_Unsigned` attribute set to "true" or "True". If `scale` is set to `True`, and the variable has a `scale_factor` or an `add_offset` attribute, then data read from that variable is unpacked using:: data = self.scale_factor*data + self.add_offset When data is written to a variable it is packed using:: data = (data - self.add_offset)/self.scale_factor If either scale_factor is present, but add_offset is missing, add_offset is assumed zero. If add_offset is present, but scale_factor is missing, scale_factor is assumed to be one. For more information on how `scale_factor` and `add_offset` can be used to provide simple compression, see the [PSL metadata conventions](http://www.esrl.noaa.gov/psl/data/gridded/conventions/cdc_netcdf_standard.shtml). In addition, if `scale` is set to `True`, and if the variable has an attribute `_Unsigned` set to "true", and the variable has a signed integer data type, a view to the data is returned with the corresponding unsigned integer datatype. This convention is used by the netcdf-java library to save unsigned integer data in `NETCDF3` or `NETCDF4_CLASSIC` files (since the `NETCDF3` data model does not have unsigned integer data types). The default value of `scale` is `True` (automatic conversions are performed). """ self.scale = bool(scale) def set_auto_mask(self,mask): """ **`set_auto_mask(self,mask)`** turn on or off automatic conversion of variable data to and from masked arrays . If `mask` is set to `True`, when data is read from a variable it is converted to a masked array if any of the values are exactly equal to the either the netCDF _FillValue or the value specified by the missing_value variable attribute. The fill_value of the masked array is set to the missing_value attribute (if it exists), otherwise the netCDF _FillValue attribute (which has a default value for each data type). If the variable has no missing_value attribute, the _FillValue is used instead. If the variable has valid_min/valid_max and missing_value attributes, data outside the specified range will be masked. When data is written to a variable, the masked array is converted back to a regular numpy array by replacing all the masked values by the missing_value attribute of the variable (if it exists). If the variable has no missing_value attribute, the _FillValue is used instead. The default value of `mask` is `True` (automatic conversions are performed). """ self.mask = bool(mask) def set_always_mask(self,always_mask): """ **`set_always_mask(self,always_mask)`** turn on or off conversion of data without missing values to regular numpy arrays. `always_mask` is a Boolean determining if automatic conversion of masked arrays with no missing values to regular numpy arrays shall be applied. Default is True. Set to False to restore the default behaviour in versions prior to 1.4.1 (numpy array returned unless missing values are present, otherwise masked array returned). """ self.always_mask = bool(always_mask) def set_ncstring_attrs(self,ncstring_attrs): """ **`set_always_mask(self,ncstring_attrs)`** turn on or off creating NC_STRING string attributes. If `ncstring_attrs` is set to `True` then text attributes will be variable-length NC_STRINGs. The default value of `ncstring_attrs` is `False` (writing ascii text attributes as NC_CHAR). """ self._ncstring_attrs__ = bool(ncstring_attrs) def _put(self,ndarray data,start,count,stride): """Private method to put data into a netCDF variable""" cdef int ierr, ndims cdef npy_intp totelem, dataelem, i cdef size_t *startp cdef size_t *countp cdef ptrdiff_t *stridep cdef char **strdata cdef void* elptr cdef char* databuff cdef ndarray dataarr cdef nc_vlen_t *vldata # rank of variable. ndims = len(self.dimensions) # make sure data is contiguous. # if not, make a local copy. if not PyArray_ISCONTIGUOUS(data): data = data.copy() # fill up startp,countp,stridep. totelem = 1 negstride = 0 sl = [] startp = malloc(sizeof(size_t) * ndims) countp = malloc(sizeof(size_t) * ndims) stridep = malloc(sizeof(ptrdiff_t) * ndims) for n in range(ndims): count[n] = abs(count[n]) # make -1 into +1 countp[n] = count[n] # for neg strides, reverse order (then flip that axis after data read in) if stride[n] < 0: negstride = 1 stridep[n] = -stride[n] startp[n] = start[n]+stride[n]*(count[n]-1) stride[n] = -stride[n] sl.append(slice(None, None, -1)) # this slice will reverse the data else: startp[n] = start[n] stridep[n] = stride[n] sl.append(slice(None,None, 1)) totelem = totelem*countp[n] # check to see that size of data array is what is expected # for slice given. dataelem = PyArray_SIZE(data) if totelem != dataelem: raise IndexError('size of data array does not conform to slice') if negstride: # reverse data along axes with negative strides. data = data[tuple(sl)].copy() # make sure a copy is made. if self._isprimitive or self._iscompound or self._isenum: # primitive, enum or compound data type. # if data type of array doesn't match variable, # try to cast the data. if self.dtype != data.dtype: data = data.astype(self.dtype) # cast data, if necessary. # byte-swap data in numpy array so that is has native # endian byte order (this is what netcdf-c expects - # issue #554, pull request #555) if not data.dtype.isnative: data = data.byteswap() # strides all 1 or scalar variable, use put_vara (faster) if self._grp.auto_complex: with nogil: ierr = pfnc_put_vars(self._grpid, self._varid, startp, countp, stridep, PyArray_DATA(data)) elif sum(stride) == ndims or ndims == 0: with nogil: ierr = nc_put_vara(self._grpid, self._varid, startp, countp, PyArray_DATA(data)) else: with nogil: ierr = nc_put_vars(self._grpid, self._varid, startp, countp, stridep, PyArray_DATA(data)) _ensure_nc_success(ierr) elif self._isvlen: if data.dtype.char !='O': raise TypeError('data to put in string variable must be an object array containing Python strings') # flatten data array. data = data.flatten() if self.dtype == str: # convert all elements from strings to bytes # use _Encoding attribute to specify string encoding - if # not given, use 'utf-8'. encoding = getattr(self,'_Encoding','utf-8') for n in range(data.shape[0]): data[n] = _strencode(data[n],encoding=encoding) # vlen string (NC_STRING) # loop over elements of object array, put data buffer for # each element in struct. # allocate struct array to hold vlen data. strdata = malloc(sizeof(char *)*totelem) for i in range(totelem): strdata[i] = data[i] # strides all 1 or scalar variable, use put_vara (faster) if sum(stride) == ndims or ndims == 0: with nogil: ierr = nc_put_vara(self._grpid, self._varid, startp, countp, strdata) else: with nogil: ierr = nc_put_vars(self._grpid, self._varid, startp, countp, stridep, strdata) _ensure_nc_success(ierr) free(strdata) else: # regular vlen. # loop over elements of object array, put data buffer for # each element in struct. databuff = PyArray_BYTES(data) # allocate struct array to hold vlen data. vldata = malloc(totelem*sizeof(nc_vlen_t)) for i in range(totelem): elptr = (databuff)[0] dataarr = elptr if self.dtype != dataarr.dtype.str[1:]: #dataarr = dataarr.astype(self.dtype) # cast data, if necessary. # casting doesn't work ?? just raise TypeError raise TypeError("wrong data type in object array: should be %s, got %s" % (self.dtype,dataarr.dtype)) vldata[i].len = PyArray_SIZE(dataarr) vldata[i].p = PyArray_DATA(dataarr) databuff = databuff + PyArray_STRIDES(data)[0] # strides all 1 or scalar variable, use put_vara (faster) if sum(stride) == ndims or ndims == 0: with nogil: ierr = nc_put_vara(self._grpid, self._varid, startp, countp, vldata) else: with nogil: ierr = nc_put_vars(self._grpid, self._varid, startp, countp, stridep, vldata) _ensure_nc_success(ierr) # free the pointer array. free(vldata) free(startp) free(countp) free(stridep) def _get(self,start,count,stride): """Private method to retrieve data from a netCDF variable""" cdef int ierr, ndims cdef npy_intp totelem, i cdef size_t *startp cdef size_t *countp cdef ptrdiff_t *stridep cdef ndarray data, dataarr cdef void *elptr cdef char **strdata cdef nc_vlen_t *vldata # if one of the counts is negative, then it is an index # and not a slice so the resulting array # should be 'squeezed' to remove the singleton dimension. shapeout = () squeeze_out = False for lendim in count: if lendim == -1: shapeout = shapeout + (1,) squeeze_out = True else: shapeout = shapeout + (lendim,) # rank of variable. ndims = len(self.dimensions) # fill up startp,countp,stridep. negstride = 0 sl = [] startp = malloc(sizeof(size_t) * ndims) countp = malloc(sizeof(size_t) * ndims) stridep = malloc(sizeof(ptrdiff_t) * ndims) for n in range(ndims): count[n] = abs(count[n]) # make -1 into +1 countp[n] = count[n] # for neg strides, reverse order (then flip that axis after data read in) if stride[n] < 0: negstride = 1 stridep[n] = -stride[n] startp[n] = start[n]+stride[n]*(count[n]-1) stride[n] = -stride[n] sl.append(slice(None, None, -1)) # this slice will reverse the data else: startp[n] = start[n] stridep[n] = stride[n] sl.append(slice(None,None, 1)) if self._isprimitive or self._iscompound or self._isenum: data = numpy.empty(shapeout, self.dtype) # strides all 1 or scalar variable, use get_vara (faster) # if count contains a zero element, no data is being read if 0 not in count: if self._grp.auto_complex: with nogil: ierr = pfnc_get_vars(self._grpid, self._varid, startp, countp, stridep, PyArray_DATA(data)) elif sum(stride) == ndims or ndims == 0: with nogil: ierr = nc_get_vara(self._grpid, self._varid, startp, countp, PyArray_DATA(data)) else: with nogil: ierr = nc_get_vars(self._grpid, self._varid, startp, countp, stridep, PyArray_DATA(data)) else: ierr = 0 if ierr == NC_EINVALCOORDS: raise IndexError('index exceeds dimension bounds') elif ierr != NC_NOERR: _ensure_nc_success(ierr) elif self._isvlen: # allocate array of correct primitive type. data = numpy.empty(shapeout, 'O') # flatten data array. data = data.flatten() totelem = PyArray_SIZE(data) if self.dtype == str: # vlen string (NC_STRING) # allocate pointer array to hold string data. strdata = malloc(sizeof(char *) * totelem) # strides all 1 or scalar variable, use get_vara (faster) if sum(stride) == ndims or ndims == 0: with nogil: ierr = nc_get_vara(self._grpid, self._varid, startp, countp, strdata) else: with nogil: ierr = nc_get_vars(self._grpid, self._varid, startp, countp, stridep, strdata) if ierr == NC_EINVALCOORDS: raise IndexError elif ierr != NC_NOERR: _ensure_nc_success(ierr) # loop over elements of object array, fill array with # contents of strdata. # use _Encoding attribute to decode string to bytes - if # not given, use 'utf-8'. encoding = getattr(self,'_Encoding','utf-8') for i in range(totelem): if strdata[i]: data[i] = strdata[i].decode(encoding) else: data[i] = "" # issue 915 # reshape the output array data = numpy.reshape(data, shapeout) # free string data internally allocated in netcdf C lib with nogil: ierr = nc_free_string(totelem, strdata) # free the pointer array free(strdata) else: # regular vlen # allocate struct array to hold vlen data. vldata = malloc(totelem*sizeof(nc_vlen_t)) for i in range(totelem): vldata[i].len = 0 vldata[i].p = 0 # strides all 1 or scalar variable, use get_vara (faster) if sum(stride) == ndims or ndims == 0: with nogil: ierr = nc_get_vara(self._grpid, self._varid, startp, countp, vldata) else: with nogil: ierr = nc_get_vars(self._grpid, self._varid, startp, countp, stridep, vldata) if ierr == NC_EINVALCOORDS: raise IndexError elif ierr != NC_NOERR: _ensure_nc_success(ierr) # loop over elements of object array, fill array with # contents of vlarray struct, put array in object array. for i in range(totelem): arrlen = vldata[i].len dataarr = numpy.empty(arrlen, self.dtype) #dataarr.data = vldata[i].p memcpy(PyArray_DATA(dataarr), vldata[i].p, dataarr.nbytes) data[i] = dataarr # reshape the output array data = numpy.reshape(data, shapeout) # free vlen data internally allocated in netcdf C lib with nogil: ierr = nc_free_vlens(totelem, vldata) # free the pointer array free(vldata) free(startp) free(countp) free(stridep) if negstride: # reverse data along axes with negative strides. data = data[tuple(sl)].copy() # make a copy so data is contiguous. # netcdf-c always returns data in native byte order, # regardless of variable endian-ness. Here we swap the # bytes if the variable dtype is not native endian, so the # dtype of the returned numpy array matches the variable dtype. # (pull request #555, issue #554). if not data.dtype.isnative: data.byteswap(True) # in-place byteswap if not self.dimensions: return data[0] # a scalar elif squeeze_out: return numpy.squeeze(data) else: return data def set_collective(self, value): """**`set_collective(self,True_or_False)`** turn on or off collective parallel IO access. Ignored if file is not open for parallel access. """ if not __has_parallel_support__: return mode = NC_COLLECTIVE if value else NC_INDEPENDENT with nogil: ierr = nc_var_par_access(self._grpid, self._varid, mode) _ensure_nc_success(ierr) def get_dims(self): """ **`get_dims(self)`** return a tuple of `Dimension` instances associated with this `Variable`. """ return tuple(_find_dim(self._grp, dim) for dim in self.dimensions) def __reduce__(self): # raise error is user tries to pickle a Variable object. raise NotImplementedError('Variable is not picklable') # Compound datatype support. cdef class CompoundType: """ A `CompoundType` instance is used to describe a compound data type, and can be passed to the the `Dataset.createVariable` method of a `Dataset` or `Group` instance. Compound data types map to numpy structured arrays. See `CompoundType.__init__` for more details. The instance variables `dtype` and `name` should not be modified by the user. """ cdef public nc_type _nc_type cdef public dtype, dtype_view, name def __init__(self, grp, object dt, object dtype_name, **kwargs): """ ***`__init__(group, datatype, datatype_name)`*** CompoundType constructor. **`grp`**: `Group` instance to associate with the compound datatype. **`dt`**: A numpy dtype object describing a structured (a.k.a record) array. Can be composed of homogeneous numeric or character data types, or other structured array data types. **`dtype_name`**: a Python string containing a description of the compound data type. ***Note 1***: When creating nested compound data types, the inner compound data types must already be associated with CompoundType instances (so create CompoundType instances for the innermost structures first). ***Note 2***: `CompoundType` instances should be created using the `Dataset.createCompoundType` method of a `Dataset` or `Group` instance, not using this class directly. """ cdef nc_type xtype # convert dt to a numpy datatype object # and make sure the isalignedstruct flag is set to True # (so padding is added to the fields to match what a # C compiler would output for a similar C-struct). # This is needed because nc_get_vara is # apparently expecting the data buffer to include # padding to match what a C struct would have. # (this may or may not be still true, but empirical # evidence suggests that segfaults occur if this # alignment step is skipped - see issue #705). # numpy string subdtypes (i.e. 'S80') are # automatically converted to character array # subtypes (i.e. ('S1',80)). If '_Encoding' # variable attribute is set, data will be converted # to and from the string array representation with views. dt = _set_alignment(numpy.dtype(dt)) # create a view datatype for converting char arrays to/from strings dtview = _set_viewdtype(numpy.dtype(dt)) if 'typeid' in kwargs: xtype = kwargs['typeid'] else: xtype = _def_compound(grp, dt, dtype_name) self._nc_type = xtype self.dtype = dt self.dtype_view = dtview self.name = dtype_name def __repr__(self): return self.__str__() def __str__(self): typ = repr(type(self)).replace("._netCDF4", "") return "%s: name = '%s', numpy dtype = %s" %\ (typ, self.name, self.dtype) def __reduce__(self): # raise error is user tries to pickle a CompoundType object. raise NotImplementedError('CompoundType is not picklable') def _set_alignment(dt): # recursively set alignment flag in nested structured data type names = dt.names; formats = [] for name in names: fmt = dt.fields[name][0] if fmt.kind == 'V': if fmt.shape == (): dtx = _set_alignment(dt.fields[name][0]) else: if fmt.subdtype[0].kind == 'V': # structured dtype raise TypeError('nested structured dtype arrays not supported') else: dtx = dt.fields[name][0] else: # convert character string elements to char arrays if fmt.kind == 'S' and fmt.itemsize != 1: dtx = numpy.dtype('(%s,)S1' % fmt.itemsize) else: # primitive data type dtx = dt.fields[name][0] formats.append(dtx) # leave out offsets, they will be re-computed to preserve alignment. dtype_dict = {'names':names,'formats':formats} return numpy.dtype(dtype_dict, align=True) def _set_viewdtype(dt): # recursively change character array dtypes to string dtypes names = dt.names; formats = [] for name in names: fmt = dt.fields[name][0] if fmt.kind == 'V': if fmt.shape == (): dtx = _set_viewdtype(dt.fields[name][0]) else: if fmt.subdtype[0].kind == 'V': # structured dtype raise TypeError('nested structured dtype arrays not supported') elif fmt.subdtype[0].kind == 'S' and len(dt.fields[name][0].shape) == 1: lenchar = dt.fields[name][0].shape[0] dtx = numpy.dtype('S%s' % lenchar) else: dtx = dt.fields[name][0] else: # primitive data type dtx = dt.fields[name][0] formats.append(dtx) dtype_dict = {'names':names,'formats':formats} return numpy.dtype(dtype_dict, align=True) cdef _def_compound(grp, object dt, object dtype_name): # private function used to construct a netcdf compound data type # from a numpy dtype object by CompoundType.__init__. cdef nc_type xtype, xtype_tmp cdef int ierr, ndims, grpid cdef size_t offset, size cdef char *namstring cdef char *nested_namstring cdef int *dim_sizes bytestr = _strencode(dtype_name) namstring = bytestr size = dt.itemsize grpid = grp._grpid with nogil: ierr = nc_def_compound(grpid, size, namstring, &xtype) _ensure_nc_success(ierr) names = list(dt.fields.keys()) formats = [v[0] for v in dt.fields.values()] offsets = [v[1] for v in dt.fields.values()] # make sure entries in lists sorted by offset. # (don't know why this is necessary, but it is for version 4.0.1) names = _sortbylist(names, offsets) formats = _sortbylist(formats, offsets) offsets.sort() for name, format, offset in zip(names, formats, offsets): bytestr = _strencode(name) namstring = bytestr if format.kind != 'V': # scalar primitive type try: xtype_tmp = _nptonctype[format.str[1:]] except KeyError: raise ValueError('Unsupported compound type element') with nogil: ierr = nc_insert_compound(grpid, xtype, namstring, offset, xtype_tmp) _ensure_nc_success(ierr) else: if format.shape == (): # nested scalar compound type # find this compound type in this group or it's parents. xtype_tmp = _find_cmptype(grp, format) bytestr = _strencode(name) nested_namstring = bytestr with nogil: ierr = nc_insert_compound(grpid, xtype,\ nested_namstring,\ offset, xtype_tmp) _ensure_nc_success(ierr) else: # nested array compound element ndims = len(format.shape) dim_sizes = malloc(sizeof(int) * ndims) for n in range(ndims): dim_sizes[n] = format.shape[n] if format.subdtype[0].kind != 'V': # primitive type. try: xtype_tmp = _nptonctype[format.subdtype[0].str[1:]] except KeyError: raise ValueError('Unsupported compound type element') with nogil: ierr = nc_insert_array_compound(grpid,xtype,namstring, offset,xtype_tmp,ndims,dim_sizes) _ensure_nc_success(ierr) else: # nested array compound type. raise TypeError('nested structured dtype arrays not supported') # this code is untested and probably does not work, disable # for now... # # find this compound type in this group or it's parents. # xtype_tmp = _find_cmptype(grp, format.subdtype[0]) # bytestr = _strencode(name) # nested_namstring = bytestr # with nogil: # ierr = nc_insert_array_compound(grpid,xtype,\ # nested_namstring,\ # offset,xtype_tmp,\ # ndims,dim_sizes) # _ensure_nc_success(ierr) free(dim_sizes) return xtype cdef _find_cmptype(grp, dtype): # look for data type in this group and it's parents. # return datatype id when found, if not found, raise exception. cdef nc_type xtype match = False for cmpname, cmpdt in grp.cmptypes.items(): xtype = cmpdt._nc_type names1 = dtype.fields.keys() names2 = cmpdt.dtype.fields.keys() formats1 = [v[0] for v in dtype.fields.values()] formats2 = [v[0] for v in cmpdt.dtype.fields.values()] formats2v = [v[0] for v in cmpdt.dtype_view.fields.values()] # match names, formats, but not offsets (they may be changed # by netcdf lib). if names1==names2 and formats1==formats2 or (formats1 == formats2v): match = True break if not match: try: parent_grp = grp.parent except AttributeError: raise ValueError("cannot find compound type in this group or parent groups") if parent_grp is None: raise ValueError("cannot find compound type in this group or parent groups") else: xtype = _find_cmptype(parent_grp,dtype) return xtype cdef _read_compound(group, nc_type xtype, endian=None): # read a compound data type id from an existing file, # construct a corresponding numpy dtype instance, # then use that to create a CompoundType instance. # called by _get_vars, _get_types and _get_att. # Calls itself recursively for nested compound types. cdef int ierr, nf, numdims, ndim, classp, _grpid cdef size_t nfields, offset cdef nc_type field_typeid cdef int *dim_sizes cdef char field_namstring[NC_MAX_NAME+1] cdef char cmp_namstring[NC_MAX_NAME+1] # get name and number of fields. _grpid = group._grpid with nogil: ierr = nc_inq_compound(_grpid, xtype, cmp_namstring, NULL, &nfields) _ensure_nc_success(ierr) name = cmp_namstring.decode('utf-8') # loop over fields. names = [] formats = [] offsets = [] for nf in range(nfields): with nogil: ierr = nc_inq_compound_field(_grpid, xtype, nf, field_namstring, &offset, &field_typeid, &numdims, NULL) _ensure_nc_success(ierr) dim_sizes = malloc(sizeof(int) * numdims) with nogil: ierr = nc_inq_compound_field(_grpid, xtype, nf, field_namstring, &offset, &field_typeid, &numdims, dim_sizes) _ensure_nc_success(ierr) field_name = field_namstring.decode('utf-8') names.append(field_name) offsets.append(offset) # if numdims=0, not an array. field_shape = () if numdims != 0: for ndim in range(numdims): field_shape = field_shape + (dim_sizes[ndim],) free(dim_sizes) # check to see if this field is a nested compound type. try: field_type = _nctonptype[field_typeid] if endian is not None: format = endian + format except KeyError: with nogil: ierr = nc_inq_user_type(_grpid, field_typeid,NULL,NULL,NULL,NULL,&classp) if classp == NC_COMPOUND: # a compound type # recursively call this function? field_type = _read_compound(group, field_typeid, endian=endian) else: raise KeyError('compound field of an unsupported data type') if field_shape != (): formats.append((field_type,field_shape)) else: formats.append(field_type) # make sure entries in lists sorted by offset. names = _sortbylist(names, offsets) formats = _sortbylist(formats, offsets) offsets.sort() # create a dict that can be converted into a numpy dtype. dtype_dict = {'names':names,'formats':formats,'offsets':offsets} return CompoundType(group, dtype_dict, name, typeid=xtype) # VLEN datatype support. cdef class VLType: """ A `VLType` instance is used to describe a variable length (VLEN) data type, and can be passed to the the `Dataset.createVariable` method of a `Dataset` or `Group` instance. See `VLType.__init__` for more details. The instance variables `dtype` and `name` should not be modified by the user. """ cdef public nc_type _nc_type cdef public dtype, name def __init__(self, grp, object dt, object dtype_name, **kwargs): """ **`__init__(group, datatype, datatype_name)`** VLType constructor. **`group`**: `Group` instance to associate with the VLEN datatype. **`datatype`**: An numpy dtype object describing the component type for the variable length array. **`datatype_name`**: a Python string containing a description of the VLEN data type. ***`Note`***: `VLType` instances should be created using the `Dataset.createVLType` method of a `Dataset` or `Group` instance, not using this class directly. """ cdef nc_type xtype if 'typeid' in kwargs: xtype = kwargs['typeid'] else: xtype, dt = _def_vlen(grp, dt, dtype_name) self._nc_type = xtype self.dtype = dt if dt == str: self.name = None else: self.name = dtype_name def __repr__(self): return self.__str__() def __str__(self): typ = repr(type(self)).replace("._netCDF4", "") if self.dtype == str: return '%r: string type' % (typ,) else: return "%r: name = '%s', numpy dtype = %s" %\ (typ, self.name, self.dtype) def __reduce__(self): # raise error is user tries to pickle a VLType object. raise NotImplementedError('VLType is not picklable') cdef _def_vlen(grp, object dt, object dtype_name): # private function used to construct a netcdf VLEN data type # from a numpy dtype object or python str object by VLType.__init__. cdef nc_type xtype, xtype_tmp cdef int ierr, ndims, grpid cdef size_t offset, size cdef char *namstring cdef char *nested_namstring grpid = grp._grpid if dt == str: # python string, use NC_STRING xtype = NC_STRING # dtype_name ignored else: # numpy datatype bytestr = _strencode(dtype_name) namstring = bytestr dt = numpy.dtype(dt) # convert to numpy datatype. if dt.str[1:] in _supportedtypes: # find netCDF primitive data type corresponding to # specified numpy data type. xtype_tmp = _nptonctype[dt.str[1:]] with nogil: ierr = nc_def_vlen(grpid, namstring, xtype_tmp, &xtype); _ensure_nc_success(ierr) else: raise KeyError("unsupported datatype specified for VLEN") return xtype, dt cdef _read_vlen(group, nc_type xtype, endian=None): # read a VLEN data type id from an existing file, # construct a corresponding numpy dtype instance, # then use that to create a VLType instance. # called by _get_types, _get_vars. cdef int ierr, grpid cdef size_t vlsize cdef nc_type base_xtype cdef char vl_namstring[NC_MAX_NAME+1] grpid = group._grpid if xtype == NC_STRING: dt = str name = None else: with nogil: ierr = nc_inq_vlen(grpid, xtype, vl_namstring, &vlsize, &base_xtype) _ensure_nc_success(ierr) name = vl_namstring.decode('utf-8') try: datatype = _nctonptype[base_xtype] if endian is not None: datatype = endian + datatype dt = numpy.dtype(datatype) # see if it is a primitive type except KeyError: raise KeyError("unsupported component type for VLEN") return VLType(group, dt, name, typeid=xtype) # Enum datatype support. cdef class EnumType: """ A `EnumType` instance is used to describe an Enum data type, and can be passed to the the `Dataset.createVariable` method of a `Dataset` or `Group` instance. See `EnumType.__init__` for more details. The instance variables `dtype`, `name` and `enum_dict` should not be modified by the user. """ cdef public nc_type _nc_type cdef public dtype, name, enum_dict def __init__(self, grp, object dt, object dtype_name, object enum_dict, **kwargs): """ **`__init__(group, datatype, datatype_name, enum_dict)`** EnumType constructor. **`group`**: `Group` instance to associate with the VLEN datatype. **`datatype`**: An numpy integer dtype object describing the base type for the Enum. **`datatype_name`**: a Python string containing a description of the Enum data type. **`enum_dict`**: a Python dictionary containing the Enum field/value pairs. ***`Note`***: `EnumType` instances should be created using the `Dataset.createEnumType` method of a `Dataset` or `Group` instance, not using this class directly. """ cdef nc_type xtype if 'typeid' in kwargs: xtype = kwargs['typeid'] else: xtype, dt = _def_enum(grp, dt, dtype_name, enum_dict) self._nc_type = xtype self.dtype = dt self.name = dtype_name self.enum_dict = enum_dict def __repr__(self): return self.__str__() def __str__(self): typ = repr(type(self)).replace("._netCDF4", "") return "%r: name = '%s', numpy dtype = %s, fields/values =%s" %\ (typ, self.name, self.dtype, self.enum_dict) def __reduce__(self): # raise error is user tries to pickle a EnumType object. raise NotImplementedError('EnumType is not picklable') cdef _def_enum(grp, object dt, object dtype_name, object enum_dict): # private function used to construct a netCDF Enum data type # from a numpy dtype object or python str object by EnumType.__init__. cdef nc_type xtype, xtype_tmp cdef int ierr, grpid cdef char *namstring cdef ndarray value_arr bytestr = _strencode(dtype_name) namstring = bytestr grpid = grp._grpid dt = numpy.dtype(dt) # convert to numpy datatype. if dt.str[1:] in _intnptonctype.keys(): # find netCDF primitive data type corresponding to # specified numpy data type. xtype_tmp = _intnptonctype[dt.str[1:]] with nogil: ierr = nc_def_enum(grpid, xtype_tmp, namstring, &xtype) _ensure_nc_success(ierr) else: msg="unsupported datatype specified for ENUM (must be integer)" raise KeyError(msg) # insert named members into enum type. for field in enum_dict: value_arr = numpy.array(enum_dict[field],dt) bytestr = _strencode(field) namstring = bytestr with nogil: ierr = nc_insert_enum(grpid, xtype, namstring, PyArray_DATA(value_arr)) _ensure_nc_success(ierr) return xtype, dt cdef _read_enum(group, nc_type xtype, endian=None): # read a Enum data type id from an existing file, # construct a corresponding numpy dtype instance, # then use that to create a EnumType instance. # called by _get_types, _get_vars. cdef int ierr, grpid, nmem cdef ndarray enum_val cdef nc_type base_xtype cdef char enum_namstring[NC_MAX_NAME+1] cdef size_t nmembers grpid = group._grpid # get name, datatype, and number of members. with nogil: ierr = nc_inq_enum(grpid, xtype, enum_namstring, &base_xtype, NULL,\ &nmembers) _ensure_nc_success(ierr) enum_name = enum_namstring.decode('utf-8') try: datatype = _nctonptype[base_xtype] if endian is not None: datatype = endian + datatype dt = numpy.dtype(datatype) # see if it is a primitive type except KeyError: raise KeyError("unsupported component type for ENUM") # loop over members, build dict. enum_dict = {} enum_val = numpy.empty(1,dt) for nmem in range(nmembers): with nogil: ierr = nc_inq_enum_member(grpid, xtype, nmem, \ enum_namstring,PyArray_DATA(enum_val)) _ensure_nc_success(ierr) name = enum_namstring.decode('utf-8') enum_dict[name] = enum_val.item() return EnumType(group, dt, enum_name, enum_dict, typeid=xtype) cdef _strencode(pystr,encoding=None): # encode a string into bytes. If already bytes, do nothing. # uses 'utf-8' for default encoding. if encoding is None: encoding = 'utf-8' try: return pystr.encode(encoding) except (AttributeError, UnicodeDecodeError): return pystr # already bytes or unicode? def _to_ascii(bytestr): # encode a byte string to an ascii encoded string. return str(bytestr,encoding='ascii') def stringtoarr(string,NUMCHARS,dtype='S'): """ **`stringtoarr(a, NUMCHARS,dtype='S')`** convert a string to a character array of length `NUMCHARS` **`a`**: Input python string. **`NUMCHARS`**: number of characters used to represent string (if len(a) < `NUMCHARS`, it will be padded on the right with blanks). **`dtype`**: type of numpy array to return. Default is `'S'`, which means an array of dtype `'S1'` will be returned. If dtype=`'U'`, a unicode array (dtype = `'U1'`) will be returned. returns a rank 1 numpy character array of length NUMCHARS with datatype `'S1'` (default) or `'U1'` (if dtype=`'U'`)""" if dtype not in ["S","U"]: raise ValueError("dtype must string or unicode ('S' or 'U')") arr = numpy.zeros(NUMCHARS,dtype+'1') arr[0:len(string)] = tuple(string) return arr def stringtochar(a,encoding='utf-8',n_strlen=None): """ **`stringtochar(a,encoding='utf-8',n_strlen=None)`** convert a string array to a character array with one extra dimension **`a`**: Input numpy string array with numpy datatype `'SN'` or `'UN'`, where N is the number of characters in each string. Will be converted to an array of characters (datatype `'S1'` or `'U1'`) of shape `a.shape + (N,)`. optional kwarg `encoding` can be used to specify character encoding (default `utf-8`). If `encoding` is 'none' or 'bytes', a `numpy.string_` the input array is treated a raw byte strings (`numpy.string_`). optional kwarg `n_strlen` is the number of characters in each string. Default is None, which means `n_strlen` will be set to a.itemsize (the number of bytes used to represent each string in the input array). returns a numpy character array with datatype `'S1'` or `'U1'` and shape `a.shape + (N,)`, where N is the length of each string in a.""" dtype = a.dtype.kind if n_strlen is None: n_strlen = a.dtype.itemsize if dtype not in ["S","U"]: raise ValueError("type must string or unicode ('S' or 'U')") if encoding in ['none','None','bytes']: b = numpy.array(tuple(a.tobytes()),'S1') elif encoding == 'ascii': b = numpy.array(tuple(a.tobytes().decode(encoding)),dtype+'1') b.shape = a.shape + (n_strlen,) else: if not a.ndim: a = numpy.array([a]) bbytes = [text.encode(encoding) for text in a] pad = b'\0' * n_strlen bbytes = [(x + pad)[:n_strlen] for x in bbytes] b = numpy.array([[bb[i:i+1] for i in range(n_strlen)] for bb in bbytes]) return b def chartostring(b,encoding='utf-8'): """ **`chartostring(b,encoding='utf-8')`** convert a character array to a string array with one less dimension. **`b`**: Input character array (numpy datatype `'S1'` or `'U1'`). Will be converted to a array of strings, where each string has a fixed length of `b.shape[-1]` characters. optional kwarg `encoding` can be used to specify character encoding (default `utf-8`). If `encoding` is 'none' or 'bytes', a `numpy.string_` byte array is returned. returns a numpy string array with datatype `'UN'` (or `'SN'`) and shape `b.shape[:-1]` where where `N=b.shape[-1]`.""" dtype = b.dtype.kind if dtype not in ["S","U"]: raise ValueError("type must be string or unicode ('S' or 'U')") bs = b.tobytes() slen = int(b.shape[-1]) if encoding in ['none','None','bytes']: a = numpy.array([bs[n1:n1+slen] for n1 in range(0,len(bs),slen)],'S'+repr(slen)) else: a = numpy.array([bs[n1:n1+slen].decode(encoding) for n1 in range(0,len(bs),slen)],'U'+repr(slen)) a.shape = b.shape[:-1] return a class MFDataset(Dataset): """ Class for reading multi-file netCDF Datasets, making variables spanning multiple files appear as if they were in one file. Datasets must be in `NETCDF4_CLASSIC`, `NETCDF3_CLASSIC`, `NETCDF3_64BIT_OFFSET` or `NETCDF3_64BIT_DATA` format (`NETCDF4` Datasets won't work). Adapted from [pycdf](http://pysclint.sourceforge.net/pycdf) by Andre Gosselin. Example usage (See `MFDataset.__init__` for more details): ```python >>> import numpy as np >>> # create a series of netCDF files with a variable sharing >>> # the same unlimited dimension. >>> for nf in range(10): ... with Dataset("mftest%s.nc" % nf, "w", format='NETCDF4_CLASSIC') as f: ... f.createDimension("x",None) ... x = f.createVariable("x","i",("x",)) ... x[0:10] = np.arange(nf*10,10*(nf+1)) >>> # now read all those files in at once, in one Dataset. >>> f = MFDataset("mftest*nc") >>> print(f.variables["x"][:]) [ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99] ``` """ def __init__(self, files, check=False, aggdim=None, exclude=[], master_file=None): """ **`__init__(self, files, check=False, aggdim=None, exclude=[], master_file=None)`** Open a Dataset spanning multiple files, making it look as if it was a single file. Variables in the list of files that share the same dimension (specified with the keyword `aggdim`) are aggregated. If `aggdim` is not specified, the unlimited is aggregated. Currently, `aggdim` must be the leftmost (slowest varying) dimension of each of the variables to be aggregated. **`files`**: either a sequence of netCDF files or a string with a wildcard (converted to a sorted list of files using glob) If the `master_file` kwarg is not specified, the first file in the list will become the "master" file, defining all the variables with an aggregation dimension which may span subsequent files. Attribute access returns attributes only from "master" file. The files are always opened in read-only mode. **`check`**: True if you want to do consistency checking to ensure the correct variables structure for all of the netcdf files. Checking makes the initialization of the MFDataset instance much slower. Default is False. **`aggdim`**: The name of the dimension to aggregate over (must be the leftmost dimension of each of the variables to be aggregated). If None (default), aggregate over the unlimited dimension. **`exclude`**: A list of variable names to exclude from aggregation. Default is an empty list. **`master_file`**: file to use as "master file", defining all the variables with an aggregation dimension and all global attributes. """ # Open the master file in the base class, so that the CDFMF instance # can be used like a CDF instance. if isinstance(files, str): if files.startswith('http'): msg='cannot using file globbing for remote (OPeNDAP) datasets' raise ValueError(msg) else: files = sorted(glob(files)) if not files: msg='no files specified (file list is empty)' raise OSError(msg) if master_file is not None: if master_file not in files: raise ValueError('master_file not in files list') else: master = master_file else: master = files[0] # Open the master again, this time as a classic CDF instance. This will avoid # calling methods of the CDFMF subclass when querying the master file. cdfm = Dataset(master) # copy attributes from master. for name, value in cdfm.__dict__.items(): self.__dict__[name] = value # Make sure the master defines a dim with name aggdim, # or an unlimited dimension. aggDimId = None for dimname,dim in cdfm.dimensions.items(): if aggdim is None: if dim.isunlimited(): aggDimId = dim aggDimName = dimname else: if dimname == aggdim: aggDimId = dim aggDimName = dimname if aggDimId is None: raise OSError("master dataset %s does not have a aggregation dimension" % master) # Get info on all aggregation variables defined in the master. # Make sure the master defines at least one aggregation variable. masterRecVar = {} for vName,v in cdfm.variables.items(): # skip variables specified in exclude list. if vName in exclude: continue dims = v.dimensions shape = v.shape dtype = v.dtype # Be careful: we may deal with a scalar (dimensionless) variable. # Unlimited dimension always occupies index 0. if (len(dims) > 0 and aggDimName == dims[0]): masterRecVar[vName] = (dims, shape, dtype) if len(masterRecVar) == 0: raise OSError("master dataset %s does not have any variables to aggregate" % master) # Create the following: # cdf list of Dataset instances # cdfVLen list unlimited dimension lengths in each CDF instance # cdfRecVar dictionary indexed by the aggregation var names; each key holds # a list of the corresponding Variable instance, one for each # cdf file of the file set cdf = [] self._cdf = cdf # Store this now, because dim() method needs it cdfVLen = [] cdfRecVar = {} # Open each remaining file in read-only mode. # Make sure each file defines the same aggregation variables as the master # and that the variables are defined in the same way (name, shape and type) for f in files: if f == master: part = cdfm else: part = Dataset(f) if cdfRecVar == {}: empty_cdfRecVar = True else: empty_cdfRecVar = False varInfo = part.variables for v in masterRecVar.keys(): if check: # Make sure master rec var is also defined here. if v not in varInfo.keys(): raise OSError("aggregation variable %s not defined in %s" % (v, f)) #if not vInst.dimensions[0] != aggDimName: masterDims, masterShape, masterType = masterRecVar[v][:3] extDims = varInfo[v].dimensions extShape = varInfo[v].shape extType = varInfo[v].dtype # Check that dimension names are identical. if masterDims != extDims: raise OSError("variable %s : dimensions mismatch between " "master %s (%s) and extension %s (%s)" % (v, master, masterDims, f, extDims)) # Check that the ranks are identical, and the dimension lengths are # identical (except for that of the unlimited dimension, which of # course may vary. if len(masterShape) != len(extShape): raise OSError("variable %s : rank mismatch between " "master %s (%s) and extension %s (%s)" % (v, master, len(masterShape), f, len(extShape))) if masterShape[1:] != extShape[1:]: raise OSError("variable %s : shape mismatch between " "master %s (%s) and extension %s (%s)" % (v, master, masterShape, f, extShape)) # Check that the data types are identical. if masterType != extType: raise OSError("variable %s : data type mismatch between " "master %s (%s) and extension %s (%s)" % (v, master, masterType, f, extType)) # Everything ok. if empty_cdfRecVar: cdfRecVar[v] = [part.variables[v]] else: cdfRecVar[v].append(part.variables[v]) else: # No making sure of anything -- assume this is ok.. if empty_cdfRecVar: cdfRecVar[v] = [part.variables[v]] else: cdfRecVar[v].append(part.variables[v]) cdf.append(part) cdfVLen.append(len(part.dimensions[aggDimName])) # Attach attributes to the MFDataset instance. # A local __setattr__() method is required for them. self._files = files # list of cdf file names in the set self._cdfVLen = cdfVLen # list of unlimited lengths self._cdfTLen = sum(cdfVLen) # total length self._cdfRecVar = cdfRecVar # dictionary of Variable instances for all # the aggregation variables self._dims = cdfm.dimensions self._grps = cdfm.groups for dimname, dim in self._dims.items(): if dimname == aggDimName: self._dims[dimname] = _Dimension(dimname, dim, self._cdfVLen, self._cdfTLen) self._vars = cdfm.variables for varname,var in self._vars.items(): if varname in self._cdfRecVar.keys(): self._vars[varname] = _Variable(self, varname, var, aggDimName) self._file_format = [] self._data_model = [] self._disk_format = [] for dset in self._cdf: if dset.file_format == 'NETCDF4' or dset.data_model == 'NETCDF4': raise ValueError('MFNetCDF4 only works with NETCDF3_* and NETCDF4_CLASSIC formatted files, not NETCDF4') self._file_format.append(dset.file_format) self._data_model.append(dset.data_model) self._disk_format.append(dset.disk_format) self._path = '/' def __setattr__(self, name, value): """override base class attribute creation""" self.__dict__[name] = value def __getattribute__(self, name): if name in ['variables','dimensions','file_format','groups',\ 'data_model','disk_format','path']: if name == 'dimensions': return self._dims if name == 'variables': return self._vars if name == 'file_format': return self._file_format if name == 'data_model': return self._data_model if name == 'disk_format': return self._disk_format if name == 'path': return self._path if name == 'groups': return self._grps else: return Dataset.__getattribute__(self, name) def ncattrs(self): """ **`ncattrs(self)`** return the netcdf attribute names from the master file. """ return list(self._cdf[0].__dict__) def close(self): """ **`close(self)`** close all the open files. """ for dset in self._cdf: dset.close() def isopen(self): """ **`isopen(self)`** True if all files are open, False otherwise. """ return all(map(lambda dset: dset.isopen(), self._cdf)) def __repr__(self): ncdump = [repr(type(self)).replace("._netCDF4", "")] dimnames = tuple(str(dimname) for dimname in self.dimensions.keys()) varnames = tuple(str(varname) for varname in self.variables.keys()) grpnames = () if self.path == '/': ncdump.append('root group (%s data model, file format %s):' % (self.data_model[0], self.disk_format[0])) else: ncdump.append('group %s:' % self.path) for name in self.ncattrs(): ncdump.append(' %s: %s' % (name, self.__dict__[name])) ncdump.append(' dimensions = %s' % str(dimnames)) ncdump.append(' variables = %s' % str(varnames)) ncdump.append(' groups = %s' % str(grpnames)) return '\n'.join(ncdump) def __reduce__(self): # raise error is user tries to pickle a MFDataset object. raise NotImplementedError('MFDataset is not picklable') class _Dimension: def __init__(self, dimname, dim, dimlens, dimtotlen): self.dimlens = dimlens self.dimtotlen = dimtotlen self._name = dimname def __len__(self): return self.dimtotlen def isunlimited(self): return True def __repr__(self): typ = repr(type(self)).replace("._netCDF4", "") if self.isunlimited(): return "%r (unlimited): name = '%s', size = %s" %\ (typ, self._name, len(self)) else: return "%r: name = '%s', size = %s" %\ (typ, self._name, len(self)) class _Variable: def __init__(self, dset, varname, var, recdimname): self.dimensions = var.dimensions self._dset = dset self._grp = dset self._mastervar = var self._recVar = dset._cdfRecVar[varname] self._recdimname = recdimname self._recLen = dset._cdfVLen self.dtype = var.dtype self._name = var._name # copy attributes from master. for name, value in var.__dict__.items(): self.__dict__[name] = value def typecode(self): return self.dtype def ncattrs(self): return list(self._mastervar.__dict__.keys()) def __getattr__(self,name): if name == 'shape': return self._shape() if name == 'ndim': return len(self._shape()) if name == 'name': return self._name try: return self.__dict__[name] except: raise AttributeError(name) def __repr__(self): ncdump = [repr(type(self)).replace("._netCDF4", "")] dimnames = tuple(str(dimname) for dimname in self.dimensions) ncdump.append('%s %s%s' % (self.dtype, self._name, dimnames)) for name in self.ncattrs(): ncdump.append(' %s: %s' % (name, self.__dict__[name])) unlimdims = [] for dimname in self.dimensions: dim = _find_dim(self._grp, dimname) if dim.isunlimited(): unlimdims.append(str(dimname)) ncdump.append('unlimited dimensions = %r' % (tuple(unlimdims),)) ncdump.append('current size = %r' % (self.shape,)) return '\n'.join(ncdump) def __len__(self): if not self._shape: raise TypeError('len() of unsized object') else: return self._shape()[0] def _shape(self): recdimlen = len(self._dset.dimensions[self._recdimname]) return (recdimlen,) + self._mastervar.shape[1:] def set_auto_chartostring(self,val): for v in self._recVar: v.set_auto_chartostring(val) def set_auto_maskandscale(self,val): for v in self._recVar: v.set_auto_maskandscale(val) def set_auto_mask(self,val): for v in self._recVar: v.set_auto_mask(val) def set_auto_scale(self,val): for v in self._recVar: v.set_auto_scale(val) def set_always_mask(self,val): for v in self._recVar: v.set_always_mask(val) def __getitem__(self, elem): """Get records from a concatenated set of variables.""" # This special method is used to index the netCDF variable # using the "extended slice syntax". The extended slice syntax # is a perfect match for the "start", "count" and "stride" # arguments to the nc_get_var() function, and is much more easy # to use. start, count, stride, put_ind =\ _StartCountStride(elem, self.shape) datashape = _out_array_shape(count) data = ma.empty(datashape, dtype=self.dtype) # Determine which dimensions need to be squeezed # (those for which elem is an integer scalar). # The convention used is that for those cases, # put_ind for this dimension is set to -1 by _StartCountStride. squeeze = data.ndim * [slice(None),] for i,n in enumerate(put_ind.shape[:-1]): if n == 1 and put_ind[...,i].ravel()[0] == -1: squeeze[i] = 0 # Reshape the arrays so we can iterate over them. strt = start.reshape((-1, self.ndim or 1)) cnt = count.reshape((-1, self.ndim or 1)) strd = stride.reshape((-1, self.ndim or 1)) put_ind = put_ind.reshape((-1, self.ndim or 1)) # Fill output array with data chunks. # Number of variables making up the MFVariable.Variable. nv = len(self._recLen) for (start,count,stride,ind) in zip(strt, cnt, strd, put_ind): # make sure count=-1 becomes count=1 count = [abs(cnt) for cnt in count] if (numpy.array(stride) < 0).any(): raise IndexError('negative strides not allowed when slicing MFVariable Variable instance') # Start, stop and step along 1st dimension, eg the unlimited # dimension. sta = start[0] step = stride[0] stop = sta + count[0] * step # Build a list representing the concatenated list of all records in # the MFVariable variable set. The list is composed of 2-elem lists # each holding: # the record index inside the variables, from 0 to n # the index of the Variable instance to which each record belongs idx = [] # list of record indices vid = [] # list of Variable indices for n in range(nv): k = self._recLen[n] # number of records in this variable idx.extend(range(k)) vid.extend([n] * k) # Merge the two lists to get a list of 2-elem lists. # Slice this list along the first dimension. lst = list(zip(idx, vid)).__getitem__(slice(sta, stop, step)) # Rebuild the slicing expression for dimensions 1 and ssq. newSlice = [slice(None, None, None)] for n in range(1, len(start)): # skip dimension 0 s = slice(start[n],start[n] + count[n] * stride[n], stride[n]) newSlice.append(s) # Apply the slicing expression to each var in turn, extracting records # in a list of arrays. lstArr = [] ismasked = False for n in range(nv): # Get the list of indices for variable 'n'. idx = [i for i,numv in lst if numv == n] if idx: # Rebuild slicing expression for dimension 0. newSlice[0] = slice(idx[0], idx[-1] + 1, step) # Extract records from the var, and append them to a list # of arrays. dat = Variable.__getitem__(self._recVar[n],tuple(newSlice)) if ma.isMA(dat) and not ismasked: ismasked=True fill_value = dat.fill_value lstArr.append(dat) if ismasked: lstArr = ma.concatenate(lstArr) else: lstArr = numpy.concatenate(lstArr) if lstArr.dtype != data.dtype: data = data.astype(lstArr.dtype) # sometimes there are legitimate singleton dimensions, in which # case the array shapes won't conform. If so, a ValueError will # result, and no squeeze will be done. try: data[tuple(ind)] = lstArr.squeeze() except ValueError: data[tuple(ind)] = lstArr # Remove extra singleton dimensions. data = data[tuple(squeeze)] # if no masked elements, return numpy array. if ma.isMA(data) and not data.mask.any(): data = data.filled() return data class MFTime(_Variable): """ Class providing an interface to a MFDataset time Variable by imposing a unique common time unit and/or calendar to all files. Example usage (See `MFTime.__init__` for more details): ```python >>> import numpy as np >>> f1 = Dataset("mftest_1.nc","w", format="NETCDF4_CLASSIC") >>> f2 = Dataset("mftest_2.nc","w", format="NETCDF4_CLASSIC") >>> f1.createDimension("time",None) >>> f2.createDimension("time",None) >>> t1 = f1.createVariable("time","i",("time",)) >>> t2 = f2.createVariable("time","i",("time",)) >>> t1.units = "days since 2000-01-01" >>> t2.units = "days since 2000-02-01" >>> t1.calendar = "standard" >>> t2.calendar = "standard" >>> t1[:] = np.arange(31) >>> t2[:] = np.arange(30) >>> f1.close() >>> f2.close() >>> # Read the two files in at once, in one Dataset. >>> f = MFDataset("mftest_*nc") >>> t = f.variables["time"] >>> print(t.units) days since 2000-01-01 >>> print(t[32]) # The value written in the file, inconsistent with the MF time units. 1 >>> T = MFTime(t) >>> print(T[32]) 32 ``` """ def __init__(self, time, units=None, calendar=None): """ **`__init__(self, time, units=None, calendar=None)`** Create a time Variable with units consistent across a multifile dataset. **`time`**: Time variable from a `MFDataset`. **`units`**: Time units, for example, `'days since 1979-01-01'`. If `None`, use the units from the master variable. **`calendar`**: Calendar overload to use across all files, for example, `'standard'` or `'gregorian'`. If `None`, check that the calendar attribute is present on each variable and values are unique across files raising a `ValueError` otherwise. """ import datetime self.__time = time # copy attributes from master time variable. for name, value in time.__dict__.items(): self.__dict__[name] = value # Make sure calendar attribute present in all files if no default calendar # is provided. Also assert this value is the same across files. if calendar is None: calendars = [None] * len(self._recVar) for idx, t in enumerate(self._recVar): if not hasattr(t, 'calendar'): msg = 'MFTime requires that the time variable in all files ' \ 'have a calendar attribute if no default calendar is provided.' raise ValueError(msg) else: calendars[idx] = t.calendar calendars = set(calendars) if len(calendars) > 1: msg = 'MFTime requires that the same time calendar is ' \ 'used by all files if no default calendar is provided.' raise ValueError(msg) else: calendar = list(calendars)[0] # Set calendar using the default or the unique calendar value across all files. self.calendar = calendar # Override units if units is specified. self.units = units or time.units # Reference date to compute the difference between different time units. ref_date = datetime.datetime(1900,1,1) ref_num = date2num(ref_date, self.units, self.calendar) # Create delta vector: delta = ref_num(ref_date) - num(ref_date) # So that ref_num(date) = num(date) + delta self.__delta = numpy.empty(len(self), time.dtype) i0 = 0; i1 = 0 for i,v in enumerate(self._recVar): n = self._recLen[i] # Length of time vector. num = date2num(ref_date, v.units, self.calendar) i1 += n self.__delta[i0:i1] = ref_num - num i0 += n def __getitem__(self, elem): return self.__time[elem] + self.__delta[elem]