Python gdal get nodata value


GDAL is a software library for creating and manipulating geospatial data. PCRaster operations work on raster datasets which have the same grid size and have the same spatial extent. GIS-data from different sources need to be clipped to the same region and resampled to the same grid size for usage in PCRaster models. Furthermore, when combining maps with a different map projections, maps have to be projected to a common map projection.

This is a set of command-line tools with all the required GDAL commands. When using linux, add. You will need this manual for the assignments. For this tutorial, you need several datasets. These datasets are available in a. Download and extract the. In this section, you will use DEM tiles to create a single elevation model for a catchment in the Alps. This is an elevation model at 30 m resolution that is available for the entire world.

This dataset can be downloaded as indicidual tiles that together cover the world. Adjust the folder location after cd to where the data is located. This will show a list of files in the folder. These files are already a selection of the original dataset to save space. Open QGIS and start a new project. Add all four files to the project, these tiles are next to each other.

In this tutorial, you will make a model for the Anza Catchment in Italy. The contour of this catchment is available in the file anzacatchment. Add this file to QGIS. You will see that the catchment is on the intersection of the four tiles. Using the mouse-pointer and coordinte box at the bottom of the screen in QGIS, you can lookup coordinates on the map.

Question: Tiled datasets have systematic filenames, which makes it easy to retreive the right data for the right location. The filenames contain a latitude N… and longitude E…. What coordinates do the nummers of the filename represent? This command has to be executed from the dataprocessing folder: :.

Close a raster dataset

The last part of the command is list of filenames to be merged. Now, add the merged file to QGIS to see the result of the merge operation. Adjust the visualisation to your liking. The next step is to crop the DEM to the modelling area the catchment. For modelling of smaller areas, it is more convenient to work in a local coordinate system. Both cropping and projecting can be done with one command. First, we must determine which coordinate system to use for the final maps.I cannot find an adequate tool to do some interpolation on no data based on surrounding rastervalues.

Is that correct? Four years later we've gotten our long-awaited raster support in the fmeobjects Python API, it makes it a lot easier to manipulate raster contents, although you'll probably need some Python experience to fully profit from it. An FYI for this Hypothetically, you could take a copy of the original raster and use the convolver to calculate the new values for nodata.

Hope this gives everyone some new ideas for this issue. Land Acknowledgement — Safe Software respectfully acknowledges that we live, learn and work on the traditional and unceded territories of the Kwantlen, Katzie, and Semiahmoo First Nations.

The FME Community uses cookies to enhance your experience. To learn more, read our Privacy Policy. Expand search. Search Loading. Log in. View This Post. November 3, at PM. Hi, i have raster file where i need to fill some nodata pixels with value calculated from neighborhood pixels. Idea is to get average9, min or max value of surrounding pixels.

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I have tried several different offsetter-clipper-rasterexpressionevaluator loops. Unfortunately workspace is now very slow and result is not either perfect. Have anyone already solved this kind of problem? Added an improvement. This is just an example. You can modify the calculation rule here. Hi Arto, A long story. Step 1 1 Extract these raster properties. The properties will be used to re-create a raster finally.

Use 8 AttributeCreators the screenshot shows 4 for sample : 1. The NotMerged points weren't adjoining a point having data, they can be discarded. There may be more efficient and elegant way Sadly, the fmeobjects API is as of FME pretty lacking in the raster department, so your options there are very limited. But if you've got the necessary Python skills, you will find the GDAL library very fast and, combined with e.

You can also easily integrate it into a workspace using the PythonCreator, etc. I have now succeeded to get workspace much faster by using Takashi example workspace with some minor changes to get it work with my raster file.

Running time is still some hours, but its not a problem anymore. It works and result is perfect. I agree that it's worth to consider introducing GDAL. Another thought. It's an interesting usage of the point cloud technology in FME. Log In to Answer. Related Questions Nothing found. Getting Started Forums Knowledge Base.Close a raster dataset. Get Raster Metadata. Get Raster Band. Loop Through All Raster Bands.

Get Raster Band Information. Polygonize a Raster Band. Clip a GeoTiff with Shapefile. Calculate zonal statistics. Raster to vector line. Create raster from array. Create least cost path. Replace No Data Value of Raster with new value. This recipe shows how to close a raster dataset. It is useful in the middle of a script, to recover the resources held by accessing the dataset, remove file locks, etc.

It is not necessary at the end of the script, as the Python garbage collector will do the same thing automatically when the script exits. Using the documentation on the Band API we can write a script that dumps out single band information. Go find them on your computer, read the source code and mine them for API tricks. This recipe takes in a OGR file e. Most of the following workflow came from this geospatialpython post. However, the source code on that site assumes your clipping polygon is the same extent as the input geotiff.

The modified script below takes this into account and sets the correct x,y offsets for the clipped geotiff.

osgeo.gdal.GetDriverByName

Note, in the following example we are assuming you have the Python Imaging Library installed. After Image: the clipped geotiff with the timezone border overlayed in orange on top of input geotiff:. This recipe calculates statistics on values of a raster within the zones of a vector dataset.

This recipe converts raster pixels with a specified value to vector lines. This recipe creates a least cost path between two coordinates based on a raster cost surface. In the example below, a cost path between point 1 and point 2 is created based on a slope raster. El objetivo de esta pregunta es muy simple, es determinar si se considera un entero positivo dado. El programa anterior genera el archivo myData. Find the k th largest element in an unsorted array.

Note that it is the kth largest element in the sorted order, not the kth distinct element. Open 'test. Notice how we are handling runtime errors this function might throw. GetCount for i in range 0, ctable. The raster we are going to polygonize: from osgeo import gdal, ogr import sys this allows GDAL to throw Python Exceptions gdal.

GetRasterBand 1 band. Before Image: the input Natural Earth 10m geotiff with the timezone overlay we want to clip out: from osgeo import gdal, gdalnumeric, ogr, osr import Image, ImageDraw import os, sys gdal. Open gdalnumeric.While it is easy to load a shp file into QGIS as a vector layer, I'm lost as to how to export the geometry, or even if it is possible.

First of all, you will need to open the vector layer to QGIS canvas. Repeat the same process with another cost surface. In the popout, choose the raster cost surface we created as the cost raster layer. Add the field you need and press ok when done. If you want to install QGis Server in a Windows computer, you will start a phoenix bios updates road full of pitfalls.

A dialog box will open. The input layer and drawing order field need to be set, but there is also a field that allows for the grouping of features, which outputs individual line features per group as well as a field to enter a storage location for the resulting vector file. Next, we have to define the number of contour lines. Home Addressusing the road network to each of the end points represented by the School locations. Click on Actions on the properties window.

Overview of the task We will load a vector point layer representing all major airports and use python scripting to create a text file with the airport name, airport code, latitude and longitude for each of the airport Point data, for example, can be from a vector file that can be input using QGIS or it can be from the result of a database query. A bit of python enables you to do this quickly. Do you guys know any QGIS 3 plugins to find shortest path through all points in layer, without going twice through the same point?

Also, I used Shortest path point to layer and the result is shown in a picture upper picture is showing line layer that represents network, and lower pictures shows the shortest pathbut I need my shortest path not to go back to the points that … Suppose I have 10 points.

Either you had some database which was pgrouting enabled, or you had some network and used this via the roadgraph plugin. We selected the Shortest option in the Path type to calculate. We use it for many applications such as: to know the elevation variation for a street, getting to know a landscape height variation between two points, pipe line or sewer planning, and so on.

Each line layer node should contain the z value of the point layer. Step 2. QGIS is used to create maps, edit maps, view maps and do all types of digital geo drawings. Civil 3D Python will return a list of paths. This is a giant leap forward for the project - our first Long Term Release based on the 3. To implement these actions, we're going to need a new map tool that lets the user click on a track vertex to select the starting or ending points.

This paper proposes a QGIS plugin that determines MST on The completed path is the smallest sum of raster cell values between the two points and it has the lowest cost. These are often referred to as Scratch Layers. Once the processing finishes, click Close. There are two plugins highlighted in this task sheet: the road graph plugin which allows you to find the shortest path between two points along a line road network, and OpenLayers Plugin which allows you to access common basemaps from Google, Bing, and … Select in Path Selection Tool.

Mari Spasialkan! If playback doesn't begin shortly, try restarting your device. QGIS software allows us to digitize using either hardcopy or softcopy sources.

In this plugin, it is not possible to save a temporary file.When working with raster data, you may sometimes need to deal with data gaps. These could be the result of sensor malfunction, processing errors or data corruption. Below is an example of data gap i. If the data gap is small, it can be effectively addressed by interpolating values from neighboring pixels.

I will outline 2 approaches for fixing this. As pointed out in the documentationthis is suitable for filling missing regions in continuous raster data such as elevation. It also works for very small gaps in varying data such as aerial imagery. If the source raster has a nodata value set and it is the same as the missing data value, then you can skip this step. In our example the nodata pixel value is 0. Set the value 0 for Assign a specified nodata value to output bands option and enter a filename for the converted raster.

Now we are ready to run the Fill nodata tool from the Processing Toolbox. This tool works on 1 band at a time. Select Band 1 Red. Set the Maximum distance to search out for values to interpolate to 1since we have only 1 pixel gap.

Repeat the process for Band 2 Green and Band 2 Bluechoosing appropriate file names for them. You should have 3 separate rasters with no data values filled.

Now we can merge them to a single file. Search and locate the Merge tool from the Processing Toolbox. In the Merge tool, select all 3 individual rasters. Check the Place each input file into a separate band box. Enter a filename for the output and click Run. The resulting merged raster will have 3 bands and the no-data gaps will be filled with interpolated values from neighboring pixels. Usually when you have such issues in your dataset, it is persistent across all source images.

If you have large amounts of data with such data gaps, fixing them in QGIS manually is not feasible. Those who have taken my Python Foundation for Spatial Analysis course, would know that this particular problem motivated me to learn Python 15 years back since there was no readily available solution. Below is a script that shows how to solve this problem in Python with the help of rasterio and numpy libraries.

Import geotiff into arcgis pro

If you want to try out this example, you can download the demo dataset which has the aerial image and Jupyter notebook k3 ve engine spark plug the Python code. Skip to content When working with raster data, you may sometimes need to deal with data gaps. Note: The data gap is simulated using a python script and is not part of the original dataset If the data gap is small, it can be effectively addressed by interpolating values from neighboring pixels.

Now we are ready to run the Fill nodata tool from the Processing Toolbox This tool works on 1 band at a time. Fixing Data Gaps with Python Usually when you have such issues in your dataset, it is persistent across all source images.You can download them here:.

DataSource is a wrapper for the OGR data source object that supports reading data from a variety of OGR-supported geospatial file formats and data sources using a consistent interface. Each data source is represented by a DataSource object which contains one or more layers of data. Each layer, represented by a Layer object, contains some number of geographic features Featureinformation about the type of features contained in that layer e. The constructor for DataSource only requires one parameter: the path of the file you want to read.

However, OGR also supports a variety of more complex data sources, including databases, that may be accessed by passing a special name string instead of a path. The name property of a DataSource instance gives the OGR name of the underlying data source that it is using. The optional encoding parameter allows you to specify a non-standard encoding of the strings in the source. This is typically useful when you obtain DjangoUnicodeDecodeError exceptions while reading field values.

For information on accessing the layers of data themselves, see the next section:. Support for pathlib. Layer is a wrapper for a layer of data in a DataSource object. You never create a Layer object directly. Instead, you retrieve them from a DataSource object, which is essentially a standard Python container of Layer objects. For example, you can access a specific layer by its index e. The Layer itself acts as a container for geometric features. Typically, all the features in a given layer have the same geometry type.

We can use it to print out some basic information about each layer in a DataSource :. The example output is from 4900 dt466 cities data source, loaded above, which evidently contains one layer, called "cities"which contains three point features.

Returns the number of features in the layer. Same as len layer :. Returns the number of fields in the layer, i. Returns a list of the data types of each of the fields in this layer. These are subclasses of Fielddiscussed below:. Returns a list of the numeric precisions for each of the fields in this layer. This is meaningless and set to zero for non-numeric fields:. Returns the spatial extent of this layer, as an Envelope object:.

Property that returns the SpatialReference associated with this layer:. If the Layer has no spatial reference information associated with it, None is returned. Property that may be used to retrieve or set a spatial filter for this layer.

When set with something other than Noneonly features that intersect the filter will be returned when iterating over the layer:.It is a common need to summarize information from a gridded dataset within an irregularly shaped area. While at first glance this may seem simple, reconciling differences between raster gridded and vector polygon datatypes can quickly become complicated. This article shows how to implement a zonal statistics algorithm in Python in 4 steps. Now set the file paths for the raster and vector data and use gdal and ogr to load the raster and vector data, respectively.

Access the layer which contains the polygon data from the vector data source that was loaded. Then get the GeoTransform information positions the gridded raster data and specifies cell sizes and no data value for the raster data. This will be the most complicated and intensive part of the algorithm. To start, read the first polygon feature from the vector layer. Then start a while loop that will continue as long as there is another feature in the vector layer.

Below is the basic way to set up the loop. This loop will be modified as we continue.

Getting started with GDAL

We need to create some functions that will help us create a new raster to store the rasterized polygon features.

These functions will make more sense when we call them with actual data. For now, add the function definitions at the top of the script. First we need to convert the convert the coordinates of the bounding box containing a polygon feature to cell coordinates, or offsets, row and column numbers that to correspond the input raster.

The bounding box of a polygon is the rectangle that contains the entire polygon feature, it consists of minimum and maximum X and Y coordinates. Next write a function that creates a new GeoTransform with the cell offsets that were calculated in the function above.

These two functions will allow us to create a new, smaller raster that only covers the area overlapped by a polygon monta caini. Now comes the most complicated part of the algorithm: creating a new empty raster in memory and converting the polygon features to rasters.

Follow the comments in code snippet below to understand what is accomplished by each line of code. First, make sure that the input raster exists.

Next create a mask array. Write one more function that takes a number of values as inputs and creates a dictionary of values. Place this function with the others at the top of the zonal statistics script. Now, calculate the values from maskarray and pass them to the setFeatureStats function. Only do this if the array exists is not None. Otherwise, pass the no data value to set the statistics. You have created a Python zonal statistics script that can be applied to many different applications.

The full script is available below. Konrad is a natural resources scientist. He develops models and analysis workflows to predict and evaluate changes to landscapes and water resources.

This article still receives many views on my website and on Medium. However, since then, I've found an even better way to Skip to content It is a common need to summarize information from a gridded dataset within an irregularly shaped area.

Load raster data and vector polygons Rasterize polygon features Mask input data to polygon extent Calculate zonal statistics for the polygon extent 1. Load raster data and vector polygons Start by importing the necessary Python modules. GetRasterBand 1. GetGeometryRef is not None: if os. Get raster metadata for quick-and-dirty resolution checks from osgeo import gdal, ogr # Define pixel_size and NoData value of new raster pixel_size = RasterCount # Set NoData Value band = enerbiom.euterBand(1) ndv = e+38 enerbiom.euataValue(ndv) # Get Statistics stats = band.

enerbiom.eu › questions › inserting-nodata-value-using-gdal. RasterCount + 1): # set the nodata value of the band ras. if you have gdal in python (you can get enerbiom.eu file here if needed). enerbiom.eu › development › rfc › rfc58_removing_dataset_nodata_value. Newly created GeoTIFF files can have no nodata value (no tag), but once a nodata value is set and stored it can only be given new values, it can not be removed.

Band NODATA value. Band overview resolutions available. Band unit type (i.e. “meters” or “feet” for elevation bands). The maximum and minimum values here do not include “meaningless values”! This is the NoDataValue shown above. Label: PythonGDAL. Nodata masks allow you to identify regions of valid data values. One is the the valid data mask from GDAL, an unsigned byte array with the same number.

In terms of maintaining the NoData value from the ar. book Geoprocessing with Python and for a quick reference this gdal/ogr cookbook page is a great. I am trying to reproject and resample (m ->> 30m) a raster image (shape X) using enerbiom.euectImage?() function in Python. Calculate zonal statistics. Raster to vector line. Create raster from array. Create least cost path. Replace No Data Value of Raster with new value. A cookbook full of recipes for using the Python GDAL/OGR bindings. Define pixel_size and NoData value of new raster pixel_size = 25 NoData_value = Getting information/metadata- I have used an example raster for this article #Fill NoData values with zerogdalwarp -srcnodata e+ In such cases, use the refresh argument to get updated values and store them in the cache.

For empty bands (where all pixel values are “no data”), all. Then you'll see how to use Python and GDAL to read these datasets into memory Pixels must have a value, but a specific value can be specified as NoData. output file, ignoring what would have been derived from the source file. -a_nodata value: Assign a specified nodata value to output bands.

CHM, Slope Aspect) into Python numpy arrays with gdal. Once we generate the array, we want to set No Data Values to NaN, and bet254 jackpot the. so you have to change the nodata value from to before using fill nodata function. Usually there are several ways to do this in QGIS. For example. You should have 3 separate rasters with no data values filled. Now we can merge them to a single file. Search and locate the Merge tool from.