Find collocations with typhon


This tutorial is still under development and contains invalid code. It needs to get updated to work under the new typhon versions.


NOTE: The code in the jupyter notebooks is old and needs to be updated! If you want to run the code from this tutorial on your machine as well, download and unzip it. You can find the code examples for this tutorial in the jupyter notebook file collocations.ipynb. You will need the jupyter engine for this.

Collocations between two data arrays

Let’s try out the simplest case: You have two data arrays with temporal-spatial data and you want to find collocations between them.

At first, we create two example data arrays with faked measurements. Let’s assume, these data arrays represent measurements from two different instruments (e.g. on satellites). Each measurement has a time attribute indicating when it was taken and a geo-location (latitude and longitude) indicating where this happened.

import as projections
from typhon.plots import worldmap

# Create the data
primary = {
    "time": np.arange("2018-01-01", "2018-01-02", dtype="datetime64[h]"),
    "lat": 30.*np.sin(np.linspace(-3.14, 3.14, 24))+20,
    "lon": np.linspace(0, 90, 24),
secondary = {
    "time": np.arange("2018-01-01", "2018-01-02", dtype="datetime64[h]"),
    "lat": 30.*np.sin(np.linspace(-3.14, 3.14, 24)+1.)+20,
    "lon": np.linspace(0, 90, 24),

# Plot the data
fig = plt.figure(figsize=(10, 10))
wmap,_ = worldmap(primary["lat"], primary["lon"], s=24, background=True)
worldmap(secondary["lat"], secondary["lon"], s=24, ax=wmap,)
wmap.set_extent([0, 90, -10, 50])

Now, let’s find all measurements of primary that have a maximmum distance of 300 kilometers to the measurements of secondary:

from typhon.collocations import collocate

indices = collocate([primary, secondary], max_distance=500,)
[[ 4 15 15 16]
 [ 4 15 16 15]]

This means, that the 4th point of primary collocates with the 4th point of secondary and the 15th point of primary collocates with the 15th point of secondary, etc.

Let’s mark the collocations with red crosses on the map:

fig = plt.figure(figsize=(10, 10))

# Plot the collocations
wmap,_ = worldmap(
    primary["lat"][indices[0]], primary["lon"][indices[0]],
    c="r", s=128, marker='x', background=True
    secondary["lat"][indices[1]], secondary["lon"][indices[1]],
    c="r", s=128, marker='x', ax=wmap,)

# Plot all points:
worldmap(primary["lat"], primary["lon"], s=24, ax=wmap,)
worldmap(secondary["lat"], secondary["lon"], s=24, ax=wmap,)
wmap.set_extent([0, 90, -10, 50])

We can also add a temporal filter that filters out all points which difference in time is bigger than a time interval. We are doing this by using max_interval:

indices = collocate([primary, secondary], max_distance=300, max_interval="1 hour")

If we are not interested in spatial collocations but only in temporal ones, we can leave max_distance out:

# Find temporal collocations (without regarding the location)
indices = collocate([primary, secondary], max_interval="1 hour")

# Plot intervals
interval = (primary["time"][indices[0]] - secondary["time"][indices[1]]).astype("int")
plt.scatter(range(indices.shape[1]), interval)
plt.xlabel("collocation id")
plt.ylabel("interval [h]")

Find collocations between two filesets


This section is not up-to-date and will not work with the newest version of typhon. Please wait for an update.

Normally, one has the data stored in a set of many files. typhon provides an object to handle those filesets (see the typhon doc). It is very simple to find collocations between them.

Firstly, we need to create FileSet objects and let them know where to find their files:

from typhon.files import FileSet

# Create the filesets objects and point them to the input files
a_fileset = FileSet(
b_fileset = FileSet(

If you do not know how to deal with those FileSet objects, try this tutorial.

Now, we can search for collocations between a_dataset and b_dataset and store them to ab_collocations.

from typhon.collocations import Collocations

# Create the output dataset:
ab_collocations = Collocations(
  [a_fileset, b_fileset], start="2018", end="2018-01-02",
  max_interval="1h", max_distance=300
Find collocations between SatelliteA and SatelliteB from 2018-01-01 00:00:00 to 2018-01-02 00:00:00
Retrieve time coverages from files...


TypeError                                 Traceback (most recent call last)

<ipython-input-13-e1ef9a1d68df> in <module>()
      1 collocate_datasets(
      2     [a_dataset, b_dataset], start="2018", end="2018-01-02",
----> 3     output=ab_collocations, max_interval="1h", max_distance=300
      4 )

~/Projects/typhon/typhon/spareice/collocations/ in collocate_datasets(datasets, start, end, output, verbose, **collocate_args)
    701         print("Retrieve time coverages from files...")
--> 703     for data, files in DataSlider(start, end, *datasets):
    705         primary_start, primary_end = data[].get_range("time")

~/Projects/typhon/typhon/spareice/ in move(self)
   2675             data = self._align_to_primary(data, primary_data)
-> 2676             data[self.datasets[0].name] = primary_data
   2678             yield data, files

TypeError: 'NoneType' object does not support item assignment
from typhon.spareice import collocate

a_data = a_dataset.collect("2018", "2018-01-02")
b_data = b_dataset.collect("2018", "2018-01-02")
collocate(a_data, b_data, max_interval="1hour", max_distance=300)
array([[ 69,  69,  79,  79,  79,  80,  80,  80,  89,  89,  89,  90,  90,
       [110, 109, 129, 130, 131, 129, 130, 131, 150, 149, 148, 150, 149,

Find collocations between more than two datasets

How about finding collocations between more than two datasets? Let’s assume we have an additional dataset from Satellite C:

from typhon.spareice.handlers import CSV

c_dataset = Dataset(
    # If you do not know, why we have to add these lines, try the tutorial link from above.
    handler=CSV(read_csv={"parse_dates":["time", ]}),
    time_coverage="05:59:59 hours",

Collocating multiple datasets could mean two things: 1. Only find the subset of collocations that have all collocated datasets in common. This is not yet implemented 2. Find collocations of one dataset that has been already collocated with a third dataset. This means for our example, we would use the a_dataset data points from ab_collocations and collocate them with the c_dataset.

1. Find the subset of all collocations

Point 1 is still not implemented. However, it is planned to do it like this: Simply pass more datasets objects to the method.

# Create the output dataset:
abc_collocations = CollocatedDataset(

#, end, [a_dataset, b_dataset, c_dataset], output=abc_collocations, only_primary=False)

2. Find collocations with an already-collocated dataset

This is easy to achieve. We have already collocated a_dataset with b_dataset. Now, we can ‘add’ the collocations from c_dataset with ab_collocations. We can decide which spatial-temporal information we want to use as reference from the a_dataset or b_dataset by setting the parameter primary of ab_collocations:

# Using the Satellite A dataset (a_dataset) as reference:
ab_collocations.primary = "SatelliteA"

# Create the output dataset:
ac_collocations = CollocatedDataset(

Now, let’s find the collocations: