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I need to read a multiple netCDF4 files using python 3. The files are in different folder namedwith months and days. And the files have the same variables I use to read and plot it.

I can do that just for one file. How I can read all files and variable IR in all files in all folder? You can use the glob. Then, if there is a dimension in your netcdfs that is unlimited, you can open all the files in one line like this:. So it's probably better to load small parts at a time by replacing [:] with, for example, [].

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I would recommend xarray. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 1 year ago. Active 2 months ago. Viewed 2k times. Sarah Tohami. Sarah Tohami Sarah Tohami 97 8 8 bronze badges. Active Oldest Votes. Bert Coerver Bert Coerver 4 4 silver badges 11 11 bronze badges. Dataset" instead of "netCDF4. MFDataset" and load a single nc-file at a time.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

It only takes a minute to sign up. I need to read a multiple netCDF4 files using python 3. The files are in different folder namedwith months and days. And the files have the same variables I use to read and plot it. I can do that just for one file. How I can read all files and variable IR in all files in all folder? You can use the glob. Then, if there is a dimension in your netcdfs that is unlimited, you can open all the files in one line like this:.

So it's probably better to load small parts at a time by replacing [:] with, for example, [].

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I would recommend xarray. Sign up to join this community. The best answers are voted up and rise to the top. Home Questions Tags Users Unanswered. Asked 1 year ago. Active 2 months ago. Viewed 2k times. Sarah Tohami. Sarah Tohami Sarah Tohami 97 8 8 bronze badges. Active Oldest Votes. Bert Coerver Bert Coerver 4 4 silver badges 11 11 bronze badges. Dataset" instead of "netCDF4. MFDataset" and load a single nc-file at a time.

Sign up or log in Sign up using Google. Sign up using Facebook.For how to read a netCDF data, please refer to the previous post.

It is safe to check whether the netCDF file has closed, using the try and except statement. We can now fill the netCDF files opened with the dimensions, variables, and attributes.

read multiple netcdf files python

Every dimension has a name and length. If we set the dimension length to be 0 or None, then it takes it as of unlimited size and can grow. Since we are following the netCDF classic format, only one dimension can be unlimited. To make more than one dimension to be unlimited follow the other format. Here, we will constrain to the classic format only as it is the simplest one.

One of the nice features of netCDF data format is that we can also store the meta-data information along with the data.

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This information can be stored as attributes. Now, let us add some variables to store some data in them. A variable has a name, a type, a shape and some data values. The shape of the variable can be stated using the tuple of the dimension names. The variable should also contain some attributes such as units to describe the data.

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Here, we create the variable using the createVariable method. This method takes 3 arguments: a variable name string typedata types, a tuple containing the dimension.

Also, notice the units of the time variable. The length of the lat and lon variable will be equal to its dimension. Since the length of the time variable is unlimited and is subject to grow, we can give it any size.

We can treat netCDF array as a numpy array and add data to it.

netcdf 0.2.1

The above statement writes all the data at once, but we can do it iteratively as well. The dashes indicate that there is no data available. Also, notice the 4 dashes corresponding to the four levels in of the time stacks.By using our site, you acknowledge that you have read and understand our Cookie PolicyPrivacy Policyand our Terms of Service.

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read multiple netcdf files python

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I am attempting to make a plot of mean temperatures of multiple yearsthe only problem I am having is attempting to read multiple NetCDF. At the moment my program will plot a single file, but I do not understand how to make it read all files in a folder one for each year. I want to find the mean for all of the years. I left out the plot data, because that is fine, the only help I need is in looping through all files in a single folder.

The netcdf4-python library has a class that can read multiple netcdf files making variables that has a record dimension appear as a single big variable. Learn more. Asked 5 years ago.

Active 5 years ago. Viewed 6k times. Colton Rogers Colton Rogers 41 1 1 silver badge 2 2 bronze badges. Active Oldest Votes. Favo Favo 5 5 silver badges 12 12 bronze badges. Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password.

Post as a guest Name. Email Required, but never shown. The Overflow Blog. The Overflow How many jobs can be done at home? Featured on Meta. Community and Moderator guidelines for escalating issues via new response…. Feedback on Q2 Community Roadmap. Technical site integration observational experiment live on Stack Overflow.With the data, we make a contour fill map in a Mollweide equal area projection using the Matplotlib toolkit Basemap. After this, we will write the air temperature profile for Darwin, Australia We will create a simple line plot to visualize this data.

Visit cfconventions. Lastly, we will compute the global air temperature departure from its value at Darwin, Australia for all of We will create a corresponding NetCDF file entitled '. In addition, we will create a contour fill plot of the departure. Please feel free to contact me with any feedback, questions, comments, or concerns. My contact information can be found on my about page.

Combining multiple files in one Netcdf/csv files in Matlab

After the data are read using Python, the air temperature is plotted using a Mollweide projection. These are the 0. Plotting using Matplotlib and Basemap is also shown. The information is similar to that of NCAR's ncdump utility. One with the global air temperature departure from its value at Darwin, Australia. The other with the temperature profile for the entire year at Darwin. Open a new NetCDF file to write the data to.Will be converted to a array of strings, where each string has a fixed length of b.

If encoding is 'none' or 'bytes', a numpy. The datetime objects should not include a time-zone offset. The nctime object must have a units attribute. If calendar is None, its value is given by nctime. Return numeric time values given datetime objects. The units of the numeric time values are described by the units argument and the calendar keyword. The datetime objects must be in UTC with no time-zone offset. If there is a time-zone offset in unitsit will be applied to the returned numeric values.

Return datetime objects given numeric time values. The returned datetime objects represent UTC with no time-zone offset, even if the specified units contain a time-zone offset. Default True. Default False. Otherwise, they are ctime. The datetime instances do not contain a time-zone offset, even if the specified units contains one.

Default is 'S'which means an array of dtype 'S1' will be returned. Will be converted to an array of characters datatype 'S1' or 'U1' of shape a. A CompoundType instance is used to describe a compound data type, and can be passed to the the createVariable method of a Dataset or Group instance.

Compound data types map to numpy structured arrays. The instance variables dtype and name should not be modified by the user. Can be composed of homogeneous numeric or character data types, or other structured array data types.Since writing my original tutorial Python - NetCDF reading and writing example with plottingI have received a lot of questions and feedback. As a result, I decided to expand my original tutorial into a multi-part blog post.

In this series, we will cover.

Reading NetCDF4 Data in Python

At the most basic level, NetCDF files are trying to avoid a new file format popping up for each new data set. Each file format requires its own drivers and utilities. This is problematic for anyone. Users need to not only learn the format but must write new code to read the files. This can be very time consuming. Enter NetCDF! As the name suggests, its goal is to make a universal data file format.

One format to rule them all, one format to I digress. Dealing with them might be a different story. We are talking about large, multidimensional data sets. In weather and climate work, the state of the atmosphere is represented by state variables that are typically defined at points of latitude, longitude, height, and time. These data sets can have file sizes that quickly grow into the gigabytes.

OK, it is a universal file format which works well for the types of data used in weather and climate. However, NetCDF doesn't stop there. As highlighted in the scientific journal Nature special Challenges in irreproducible researchthe academic community is quickly moving to enact standards to address problems related with irreproducibility. The result is the many journals are mandating that data used in the research be included with the manuscript submission.

As we will discuss in more detail shortly, NetCDF by its construction assists in achieving these goals because the files are self-describing, portable, sharable, and archivable. Yes, they do! Many agencies and groups created NetCDF conventions.

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However, if you are curious or encounter data using a different convention, Unidata maintains a list you can use to find out more information. In this series, we will generate files that are CF compliant. If you are not in a field associated with weather or climate, the CF Conventions have general data guidelines that can be adapted to your purposes.

I think the best thing to do is walk through an example. In this example, we will be looking at output generated by a Python function called ncdump. This function mimics the header output of the Unidata ncdump utility. Please note: at this stage, I will only be discussing the output from this code.

In the output generated by the short snippet of code, we see that there a three main section Global attributes, dimensions, and variables.

read multiple netcdf files python

Under each of the primary sections, you will see additional information. In the global attribute section, you will see attributes as the name suggests. In the dimension and variable sections, you will see the name of the dimension and variable followed by attributes. Variables are distinguished from dimensions because variables are typically functions of one or more dimensions.

In our example, 'air' has time, lat, and lon as its dimensions. The classic format has its roots in the original version of the NetCDF standard.