This package provides tools to work with Cassini UVIS data.
It is recommended to manage your Python environments with conda or its new and faster sibling mamba.
After installation of mamba/conda, create a new Python environment with the name py38 (or whatever name you prefer) with:
conda create -n py38 python=3.8 -c conda-forge
{% include note.html content='If you installed mambaforge in the previous step, the conda-forge channel is the default source for conda packages and you wouldn’t need to add it here.' %}If you need a refresher or intro into managing your Python environment with conda, have a look at my tutorials here (long version) and here (short version).
Once you have created the new environment, activate it:
conda activate py38
(or your <env> name)
and install the latest requirements for the package:
mamba install -c conda-forge hvplot xarray pandas
followed by a pip install for this package (a pyuvis conda package will be created later):
pip install pyuvis
The package depends on planetarypy to organize data discovery, download, and local data management and the pip install should pull in planetarypy with the minimum version of 0.15.
When launched for the first time, the planetarypy package will ask for a general storage path for all data managed by planetarypy.
You should give it a folder path where you have sufficient space to store the planetary data you want to work with.
The different available modules were born out of special use cases for analysis of UVIS data, but for the general user the most important classes are UVPDS and the UVISObs.
The UVPDS class is designed to deal with exactly one PDS product for either EUV or FUV data:
from pyuvis import UVPDSpid = "FUV2005_172_03_35"
uv = UVPDS(pid)If the data file does not exist on your local storage yet, planetarypy will go ahead and download it for you and provide the local path to UVPDS for reading it.
You don't have to do anything to get the data and you don't have to decide anything about where to store the data. :)
If you are interested in seeing how the planetarypy.uvis module achieves this, have a look at https://michaelaye.github.io/nbplanetary/cassini_uvis
While the data qube itself is accessible as a numpy.array via the data attribute:
uv.data[100]array([[0, 0, 0, ..., 0, 2, 1],
[0, 1, 0, ..., 0, 1, 0],
[0, 0, 0, ..., 0, 1, 2],
...,
[0, 0, 1, ..., 0, 0, 0],
[0, 1, 0, ..., 0, 1, 0],
[0, 0, 0, ..., 0, 0, 0]], dtype=uint16)
... it is recommended to use the xarray attribute for a more advanced data container with labeled coordinate axes
("spectral", "spatial", "samples")
:
uv.xarray
<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.
*
*/
:root { --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1)); --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54)); --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38)); --xr-border-color: var(--jp-border-color2, #e0e0e0); --xr-disabled-color: var(--jp-layout-color3, #bdbdbd); --xr-background-color: var(--jp-layout-color0, white); --xr-background-color-row-even: var(--jp-layout-color1, white); --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee); }
html[theme=dark], body.vscode-dark { --xr-font-color0: rgba(255, 255, 255, 1); --xr-font-color2: rgba(255, 255, 255, 0.54); --xr-font-color3: rgba(255, 255, 255, 0.38); --xr-border-color: #1F1F1F; --xr-disabled-color: #515151; --xr-background-color: #111111; --xr-background-color-row-even: #111111; --xr-background-color-row-odd: #313131; }
.xr-wrap { display: block; min-width: 300px; max-width: 700px; }
.xr-text-repr-fallback { /* fallback to plain text repr when CSS is not injected (untrusted notebook) */ display: none; }
.xr-header { padding-top: 6px; padding-bottom: 6px; margin-bottom: 4px; border-bottom: solid 1px var(--xr-border-color); }
.xr-header > div, .xr-header > ul { display: inline; margin-top: 0; margin-bottom: 0; }
.xr-obj-type, .xr-array-name { margin-left: 2px; margin-right: 10px; }
.xr-obj-type { color: var(--xr-font-color2); }
.xr-sections { padding-left: 0 !important; display: grid; grid-template-columns: 150px auto auto 1fr 20px 20px; }
.xr-section-item { display: contents; }
.xr-section-item input { display: none; }
.xr-section-item input + label { color: var(--xr-disabled-color); }
.xr-section-item input:enabled + label { cursor: pointer; color: var(--xr-font-color2); }
.xr-section-item input:enabled + label:hover { color: var(--xr-font-color0); }
.xr-section-summary { grid-column: 1; color: var(--xr-font-color2); font-weight: 500; }
.xr-section-summary > span { display: inline-block; padding-left: 0.5em; }
.xr-section-summary-in:disabled + label { color: var(--xr-font-color2); }
.xr-section-summary-in + label:before { display: inline-block; content: 'â–ş'; font-size: 11px; width: 15px; text-align: center; }
.xr-section-summary-in:disabled + label:before { color: var(--xr-disabled-color); }
.xr-section-summary-in:checked + label:before { content: 'â–Ľ'; }
.xr-section-summary-in:checked + label > span { display: none; }
.xr-section-summary, .xr-section-inline-details { padding-top: 4px; padding-bottom: 4px; }
.xr-section-inline-details { grid-column: 2 / -1; }
.xr-section-details { display: none; grid-column: 1 / -1; margin-bottom: 5px; }
.xr-section-summary-in:checked ~ .xr-section-details { display: contents; }
.xr-array-wrap { grid-column: 1 / -1; display: grid; grid-template-columns: 20px auto; }
.xr-array-wrap > label { grid-column: 1; vertical-align: top; }
.xr-preview { color: var(--xr-font-color3); }
.xr-array-preview, .xr-array-data { padding: 0 5px !important; grid-column: 2; }
.xr-array-data, .xr-array-in:checked ~ .xr-array-preview { display: none; }
.xr-array-in:checked ~ .xr-array-data, .xr-array-preview { display: inline-block; }
.xr-dim-list { display: inline-block !important; list-style: none; padding: 0 !important; margin: 0; }
.xr-dim-list li { display: inline-block; padding: 0; margin: 0; }
.xr-dim-list:before { content: '('; }
.xr-dim-list:after { content: ')'; }
.xr-dim-list li:not(:last-child):after { content: ','; padding-right: 5px; }
.xr-has-index { font-weight: bold; }
.xr-var-list, .xr-var-item { display: contents; }
.xr-var-item > div, .xr-var-item label, .xr-var-item > .xr-var-name span { background-color: var(--xr-background-color-row-even); margin-bottom: 0; }
.xr-var-item > .xr-var-name:hover span { padding-right: 5px; }
.xr-var-list > li:nth-child(odd) > div, .xr-var-list > li:nth-child(odd) > label, .xr-var-list > li:nth-child(odd) > .xr-var-name span { background-color: var(--xr-background-color-row-odd); }
.xr-var-name { grid-column: 1; }
.xr-var-dims { grid-column: 2; }
.xr-var-dtype { grid-column: 3; text-align: right; color: var(--xr-font-color2); }
.xr-var-preview { grid-column: 4; }
.xr-var-name, .xr-var-dims, .xr-var-dtype, .xr-preview, .xr-attrs dt { white-space: nowrap; overflow: hidden; text-overflow: ellipsis; padding-right: 10px; }
.xr-var-name:hover, .xr-var-dims:hover, .xr-var-dtype:hover, .xr-attrs dt:hover { overflow: visible; width: auto; z-index: 1; }
.xr-var-attrs, .xr-var-data { display: none; background-color: var(--xr-background-color) !important; padding-bottom: 5px !important; }
.xr-var-attrs-in:checked ~ .xr-var-attrs, .xr-var-data-in:checked ~ .xr-var-data { display: block; }
.xr-var-data > table { float: right; }
.xr-var-name span, .xr-var-data, .xr-attrs { padding-left: 25px !important; }
.xr-attrs, .xr-var-attrs, .xr-var-data { grid-column: 1 / -1; }
dl.xr-attrs { padding: 0; margin: 0; display: grid; grid-template-columns: 125px auto; }
.xr-attrs dt, .xr-attrs dd { padding: 0; margin: 0; float: left; padding-right: 10px; width: auto; }
.xr-attrs dt { font-weight: normal; grid-column: 1; }
.xr-attrs dt:hover span { display: inline-block; background: var(--xr-background-color); padding-right: 10px; }
.xr-attrs dd { grid-column: 2; white-space: pre-wrap; word-break: break-all; }
.xr-icon-database, .xr-icon-file-text2 { display: inline-block; vertical-align: middle; width: 1em; height: 1.5em !important; stroke-width: 0; stroke: currentColor; fill: currentColor; } </style>
<xarray.DataArray 'FUV2005_172_03_35' (spectral: 1024, spatial: 60, samples: 164)> 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Coordinates:
-
spectral (spectral) float64 111.5 111.6 111.7 111.8 ... 191.1 191.2 191.3
-
spatial (spatial) int64 2 3 4 5 6 7 8 9 10 ... 53 54 55 56 57 58 59 60 61
-
samples (samples) int64 1 2 3 4 5 6 7 8 ... 158 159 160 161 162 163 164 Attributes: units: Counts long_name: FUV raw data n_bands: 1024 integration_duration: Quantity(value=240.0, units='SECOND')
xarray.DataArray
'FUV2005_172_03_35'
- spectral: 1024
- spatial: 60
- samples: 164
- 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
array([[[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]],
[[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]],
[[0, 0, 0, ..., 0, 1, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., ... ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]],
[[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]],
[[0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], ..., [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0], [0, 0, 0, ..., 0, 0, 0]]], dtype=uint16)
- Coordinates: (3)
- spectral(spectral)float64111.5 111.6 111.7 ... 191.2 191.3
- units :
- nm
- long_name :
- Wavelength
array([111.535, 111.613, 111.69 , ..., 191.131, 191.209, 191.287])
- spatial(spatial)int642 3 4 5 6 7 8 ... 56 57 58 59 60 61
- long_name :
- Spatial lines
array([ 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])
- samples(samples)int641 2 3 4 5 6 ... 160 161 162 163 164
array([ 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, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164])
- Attributes: (4)
- units :
- Counts
- long_name :
- FUV raw data
- n_bands :
- 1024
- integration_duration :
- Quantity(value=240.0, units='SECOND')
One can see that the spatial range was already reduced to the valid range of 2..61 according to the label data.
Note that the sample count starts at 1, to be fitting to the UVIS manual.
I usually story the xarray.DataArray into a da or arr variable for further analysis and plotting:
arr = uv.xarrayA built-in example plot method shows you how to use hvplot to generate interactive plots:
uv.plot()The widget to the right allows you to scroll through the samples dimension.
Hint:You can look at the source code of any method using the double question mark:
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ďż˝[0;31mFile:ďż˝[0m ~/Dropbox/src/pyuvis/pyuvis/io.py
ďż˝[0;31mType:ďż˝[0m method
I chose to use the less precise (due to our non-equidistant wavelengths) raster plot by default, instead of the more precise quadmesh, because the latter creates an annoying aliasing effect,possibly due to gridlines, I'm am investigating.
If you need to be really precise on the wavelength position, use the precise=True keyword:
uv.plot(precise=True)