# Xarray

## Numpy N-dimensional Array to Xarray

The following python 3 modules will be used:

```python
import numpy as np
import xarray as xr
import pandas as pd
```

An outline of the data format as derived from

We know that the shape of the data is (136, 994), this data will be stored in an ndarray across all 4 frequencies. Using the following guide

* <https://xarray.pydata.org/en/stable/data-structures.html#coordinates>

we know that the data needs to have dimension:

> 136 vertical samples x 994 horizonal samples x 4 frequencies

```python
numOberservations = data.shape[0] # represents vertical pixels
numWatercolumns = data.shape[1] # represents horizontal pixels
numFrequencies = 5
frequencyNames = ['18 kHz', '38 kHz', '70 kHz', '120 kHz', '200 kHz']
```

Generate some synthetic data:

```python
## generate some synthetic data
data_00 = np.random.rand(numFrequencies, numOberservations, numWatercolumns)
data_01 = np.random.rand(numFrequencies, numOberservations*5, numWatercolumns)
data_02 = np.random.rand(numFrequencies, numOberservations*5, numWatercolumns)

time = pd.date_range('2000-01-01', periods=numWatercolumns)

latitude = np.arange(numWatercolumns) + 1
longitude = np.arange(numWatercolumns) + 2

raw_depth = (np.arange(numOberservations) + 1.) / 2
resampled_depth = (np.arange(numOberservations*5) + 1.) / 2
```

```python
#cruise_name = "gu1002"

ds = xr.Dataset(
    data_vars={
        "level_00": (("frequency", "raw_depth", "time"), data_00),
        "level_01": (("frequency", "resampled_depth", "time"), data_01),
        "level_02": (("frequency", "resampled_depth", "time"), data_02),
        "latitude": ("time", latitude),
        "longitude": ("time", longitude)
    },
    coords={
        "frequency": frequency,
        "raw_depth": raw_depth,
        "resampled_depth": resampled_depth,
        "time": time
    },
    attrs={
        'Title': 'Sub-Surface Oil Monitoring Cruise (GU1002, EK60)',
        'Organization Name': 'DOC/NOAA/NESDIS/NCEI > National Centers for Environmental Information, NESDIS, NOAA, U.S. Department of Commerce',
        'File Identifier': 'gov.noaa.ngdc.mgg.wcd:GU1002_EK60',
        'E-Mail Address': 'wcd.info@example.gov',
        'Version': '0.1.0'
    }
)

ds = ds.chunk({
    "frequency": 1,
    "raw_depth": 2,
    "resampled_depth": 2
    "time": 1
})
```

```python
>>> ds
<xarray.Dataset>
Dimensions:          (frequency: 4, raw_depth: 5, resampled_depth: 25, time: 3)
Coordinates:
  * frequency        (frequency) <U7 '18 kHz' '38 kHz' '120 kHz' '200 kHz'
  * raw_depth        (raw_depth) float64 0.5 1.0 1.5 2.0 2.5
  * resampled_depth  (resampled_depth) float64 0.5 1.0 1.5 ... 11.5 12.0 12.5
  * time             (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03
Data variables:
    level_00         (frequency, raw_depth, time) float64 dask.array<chunksize=(1, 2, 1), meta=np.ndarray>
    level_01         (frequency, resampled_depth, time) float64 dask.array<chunksize=(1, 2, 1), meta=np.ndarray>
    level_02         (frequency, resampled_depth, time) float64 dask.array<chunksize=(1, 2, 1), meta=np.ndarray>
Attributes:
    Title:              Sub-Surface Oil Monitoring Cruise (GU1002, EK60)
    Organization Name:  DOC/NOAA/NESDIS/NCEI > National Centers for Environme...
    File Identifier:    gov.noaa.ngdc.mgg.wcd:GU1002_EK60
    E-Mail Address:     wcd.info@example.gov
    Version:            0.1.0
>>>
```

To read an products at level 0,  level 1, etc., the user can parse those entries as follows:

```python
>>> ds.data_vars.get('level_00')
<xarray.DataArray 'level_00' (frequency: 4, depth1: 5, time: 3)>
dask.array<xarray-level_00, shape=(4, 5, 3), dtype=float64, chunksize=(1, 2, 1), chunktype=numpy.ndarray>
Coordinates:
  * frequency  (frequency) <U7 '18 kHz' '38 kHz' '120 kHz' '200 kHz'
  * depth1     (depth1) float64 0.5 1.0 1.5 2.0 2.5
  * time       (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03


>>> ds.data_vars.get('level_01')
<xarray.DataArray 'level_01' (frequency: 4, depth2: 25, time: 3)>
dask.array<xarray-level_01, shape=(4, 25, 3), dtype=float64, chunksize=(1, 2, 1), chunktype=numpy.ndarray>
Coordinates:
  * frequency  (frequency) <U7 '18 kHz' '38 kHz' '120 kHz' '200 kHz'
  * depth2     (depth2) float64 0.5 1.0 1.5 2.0 2.5 ... 10.5 11.0 11.5 12.0 12.5
  * time       (time) datetime64[ns] 2000-01-01 2000-01-02 2000-01-03
```

The dtype of the data can be modified for products beyond level 0. See here for reference:

* <https://numpy.org/doc/stable/user/basics.types.html>

To get the raw data from the level 1 product:

```python
ds.data_vars.get('level_01').values
```


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://cires.gitbook.io/zarr/preprocessing/xarray.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.