From edbda0b83a50e1baa49d07aa775925e7de634435 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 11:25:58 +0200 Subject: [PATCH 01/94] add info on sensor configuration --- .../tutorials/working_with_expedition_yaml.md | 28 ++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/docs/user-guide/tutorials/working_with_expedition_yaml.md b/docs/user-guide/tutorials/working_with_expedition_yaml.md index af059b3f..d0825794 100644 --- a/docs/user-guide/tutorials/working_with_expedition_yaml.md +++ b/docs/user-guide/tutorials/working_with_expedition_yaml.md @@ -49,8 +49,13 @@ instruments_config: # <-- 2. instrument configuration section num_bins: 40 max_depth_meter: -1000.0 period_minutes: 5.0 + sensors: + - VELOCITY ship_underwater_st_config: period_minutes: 5.0 + sensors: + - TEMPERATURE + - SALINITY argo_float_config: ... ctd_config: ... drifter_config: ... @@ -88,6 +93,27 @@ You can do multiple `DRIFTER` deployments at the same waypoint by adding multipl This section defines the configuration settings for each instrument used in the expedition. Each instrument has its own subsection where specific parameters can be set. +##### Sensors + +For most users, the most important instrument configuration settings to consider are the **sensors** for each instrument, which control what type of measurements/variables the instrument records in the simulation. For example, for the `CTD` instrument, you can specify which sensors to include in the simulation (e.g., `TEMPERATURE`, `SALINITY`, `OXYGEN`, etc.) by adding or removing entries from the `sensors` list in the `ctd_config` section. These must be added on _new lines_ and be in _uppercase_, for example: + +```yaml +ctd_config: + max_depth_meter: -2000.0 + min_depth_meter: -11.0 + stationkeeping_time_minutes: 50.0 + sensors: + - TEMPERATURE + - SALINITY + - OXYGEN +``` + +```{important} +See [here](../documentation/full_sensor_list.md) for a full list of available sensors for each instrument. Trying to add a sensor to an instrument that does not support it will result in errors in VirtualShip. +``` + +##### Underway Instruments + Because **underway instruments** (e.g., ADCP, Ship Underwater ST) collect data continuously while the ship is moving, their deployment is not tied to specific waypoints. Instead, the presence of their configuration sections in `instruments_config` indicates that they will be active throughout the expedition. This means that if you wish to turn off an underway instrument, you can remove its configuration section or simply set it to `null`, for example: ```yaml @@ -96,7 +122,7 @@ instruments_config: ship_underwater_st_config: null ``` -For **all other instruments**, e.g. CTD, ARGO_FLOAT etc., the parameters can often be left as the default values unless advanced customisations are required. +For **all other instruments**, e.g. CTD, ARGO_FLOAT etc., the parameters can often be left as the default values unless further, advanced customisations are required. #### 3. `ship_config` From cd5b523f83046ca1e3ec5ab42a32af36ee5aa967 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 11:26:26 +0200 Subject: [PATCH 02/94] add list of available sensors to documentation --- .../documentation/full_sensor_list.md | 26 +++++++++++++++++++ docs/user-guide/index.md | 1 + 2 files changed, 27 insertions(+) create mode 100644 docs/user-guide/documentation/full_sensor_list.md diff --git a/docs/user-guide/documentation/full_sensor_list.md b/docs/user-guide/documentation/full_sensor_list.md new file mode 100644 index 00000000..1cc072a7 --- /dev/null +++ b/docs/user-guide/documentation/full_sensor_list.md @@ -0,0 +1,26 @@ +# Full list of available instrument sensors + +The following table provides a comprehensive list of available sensors for each instrument. These sensors can be specified via the `virtualship plan` tool (see the [Quickstart guide](../quickstart.md)) or in the `sensors` section of the respective instrument configuration in the `expedition.yaml` file (see the [working with expedition.yaml tutorial](../tutorials/working_with_expedition_yaml.md)). + +```{note} +Trying to add a sensor to an instrument that does not support it will result in errors in VirtualShip. Always refer to this table to check which sensors are available for each instrument. +``` + +| Instrument | Sensor Name | Description | Units | Category | +| :--------------------- | :----------------- | :-------------------------------------------------- | :----------------------------------- | :-------------- | +| **ADCP** | VELOCITY | Current velocities (eastward (u) and northward (v)) | m/s | Physical | +| **Ship Underwater ST** | TEMPERATURE | Temperature | °C | Physics | +| | SALINITY | Salinity | psu | Physics | +| **CTD** | TEMPERATURE | Temperature | °C | Physics | +| | SALINITY | Salinity | psu | Physics | +| | OXYGEN | Oxygen concentration | mmol m-3 | Biogeochemistry | +| | CHLOROPHYLL | Chlorophyll concentration | mmol m-3 | Biogeochemistry | +| | NITRATE | Nitrate concentration | mmol m-3 | Biogeochemistry | +| | PHOSPHATE | Phosphate concentration | mmol m-3 | Biogeochemistry | +| | PH | pH | - | Biogeochemistry | +| | PHYTOPLANKTON | Phytoplankton concentration in carbon | mmol m-3 | Biogeochemistry | +| | PRIMARY_PRODUCTION | Net primary production | mmol m-3 day-1 | Biogeochemistry | +| **ARGO_FLOAT** | TEMPERATURE | Temperature | °C | Physics | +| | SALINITY | Salinity | psu | Physics | +| **DRIFTER** | TEMPERATURE | Temperature | °C | Physics | +| **XBT** | TEMPERATURE | Temperature | °C | Physics | diff --git a/docs/user-guide/index.md b/docs/user-guide/index.md index fac1c26c..d5d4a124 100644 --- a/docs/user-guide/index.md +++ b/docs/user-guide/index.md @@ -17,4 +17,5 @@ assignments/index documentation/copernicus_products.md documentation/pre_download_data.md documentation/example_copernicus_download.ipynb +documentation/full_sensor_list.md ``` From 4668e548e63dcd27457c6493e7245a8fcea34bc2 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 11:27:16 +0200 Subject: [PATCH 03/94] remove redundant TODO --- src/virtualship/static/expedition.yaml | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/virtualship/static/expedition.yaml b/src/virtualship/static/expedition.yaml index 3be45c4d..acb16dcf 100644 --- a/src/virtualship/static/expedition.yaml +++ b/src/virtualship/static/expedition.yaml @@ -1,7 +1,5 @@ # see https://virtualship.readthedocs.io/en/latest/user-guide/tutorials/working_with_expedition_yaml.html for more details on how to edit this file # -# TODO: add a link to docs where lists what sensors are supported for each instrument -# schedule: waypoints: - instrument: From ae2d528edc38f735d296a0a1577e31195aa33a34 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 11:52:09 +0200 Subject: [PATCH 04/94] improve phrasing --- docs/user-guide/tutorials/working_with_expedition_yaml.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/user-guide/tutorials/working_with_expedition_yaml.md b/docs/user-guide/tutorials/working_with_expedition_yaml.md index d0825794..6bbf181e 100644 --- a/docs/user-guide/tutorials/working_with_expedition_yaml.md +++ b/docs/user-guide/tutorials/working_with_expedition_yaml.md @@ -95,7 +95,7 @@ This section defines the configuration settings for each instrument used in the ##### Sensors -For most users, the most important instrument configuration settings to consider are the **sensors** for each instrument, which control what type of measurements/variables the instrument records in the simulation. For example, for the `CTD` instrument, you can specify which sensors to include in the simulation (e.g., `TEMPERATURE`, `SALINITY`, `OXYGEN`, etc.) by adding or removing entries from the `sensors` list in the `ctd_config` section. These must be added on _new lines_ and be in _uppercase_, for example: +For most users, the most important instrument configuration setting to consider is the list of **sensors** for each instrument, which controls what type of measurements/variables the instrument records in the simulation. For example, for the `CTD` instrument, you can specify which sensors to include in the simulation (e.g., `TEMPERATURE`, `SALINITY`, `OXYGEN`, etc.) by adding or removing entries from the `sensors` list in the `ctd_config` section. These must be added on _new lines_ and be in _uppercase_, for example: ```yaml ctd_config: From 17d5b40bc335976b229a9cd4610d68039b1a8046 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 11:52:25 +0200 Subject: [PATCH 05/94] add details on sensor configurations to quickstart guide --- docs/user-guide/quickstart.md | 25 +++++++++++++++++++------ 1 file changed, 19 insertions(+), 6 deletions(-) diff --git a/docs/user-guide/quickstart.md b/docs/user-guide/quickstart.md index cc43b8a0..a4f75997 100644 --- a/docs/user-guide/quickstart.md +++ b/docs/user-guide/quickstart.md @@ -56,7 +56,7 @@ This will create a folder/directory called `EXPEDITION_NAME` with a single file: For advanced users: it is also possible to run the expedition initialisation step without an MFP .xlsx export file. In this case you should simply run `virtualship init EXPEDITION_NAME` in the CLI. This will write an example `expedition.yaml` file in the `EXPEDITION_NAME` folder/directory. This file contains example waypoints, timings, instrument selections, and ship configuration, but can be edited or propagated through the rest of the workflow unedited to run a sample expedition. ``` -## 3) Expedition scheduling & ship configuration +## 3) Expedition scheduling & configuration ```{important} This section describes the process of finalising the expedition schedule and instrument selection using the `virtualship plan` application. This is the recommended way for most users but when expeditions become larger with many waypoints, it can become cumbersome to use the planning tool (note, using VirtualShip in a remote terminal / cloud-based environment can also introduce lag in the user-interface). **In this case, you may prefer to edit the `expedition.yaml` file directly (see [here](./tutorials/working_with_expedition_yaml.md) for more details on how to do so)**. @@ -82,6 +82,23 @@ VirtualShip is capable of taking underway temperature and salinity measurements, For the underway ADCP, there is a choice of using the 38 kHz OceanObserver or the 300 kHz SeaSeven version (see [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#ADCP) for more detail on the two ADCP types). +### Instrument configuration + +The most important instrument configuration setting to consider is the list of **sensors** for each instrument, which controls what type of measurements/variables the instrument records in the simulation and therefore what output data you will receive for each instrument. + +Sensor lists can be configured for each instrument under _Ship Config Editor_ > _Instrument Configurations_. For example, for the CTD instrument, you can specify which sensors to include in the simulation (e.g., `TEMPERATURE`, `SALINITY`, `OXYGEN`, etc.) by toggling the respective switches on or off. + +```{note} +Sensor choices are only relevant for the instruments you plan to deploy as [underway measurements](#underway-measurements) or at waypoints across your expedition schedule [(see below)](#instrument-selection). For example, if you do not select to deploy a CTD at any of your waypoints, the CTD sensor choices will not affect any output data. +``` + +```{tip} +See [here](../documentation/full_sensor_list.md) for more information on the sensors available for each instrument. + +``` + +There are other instrument configurations settings that can be adjusted in the editor as well (e.g. `max_depth` for the CTD), but these are more advanced and in most cases do not need to be changed from the default values. + ### Waypoint datetimes @@ -107,15 +124,11 @@ The MFP route planning tool will give estimated durations of sailing between sit You should now consider which measurements are to be taken at each sampling site, and therefore which instruments need to be selected in the planning tool. ```{tip} -Click [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Measurement-Options) for more information on what measurement options are available, and a brief introduction to each instrument. +Click [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Measurement-Options) for more information on what instrument options are available, and a brief introduction to each instrument. ``` You can make instrument selections for each waypoint in the same sub-panels as the [waypoint time](#waypoint-datetimes) selection by simply switching each on or off. Multiple instruments are allowed at each waypoint. -```{note} -For advanced users: you can also make further customisations to behaviours of all instruments under _Ship Config Editor_ > _Instrument Configurations_. -``` - ### Save changes When you are happy with your ship configuration and schedule plan, press _Save Changes_. From baa982aa43d99a8080b0b146164a03c81c10cd77 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 12:04:37 +0200 Subject: [PATCH 06/94] update sail_the_ship with sensor configuration instructions --- .../assignments/Sail_the_ship.ipynb | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/docs/user-guide/assignments/Sail_the_ship.ipynb b/docs/user-guide/assignments/Sail_the_ship.ipynb index 1e1f439d..b4b94f3d 100644 --- a/docs/user-guide/assignments/Sail_the_ship.ipynb +++ b/docs/user-guide/assignments/Sail_the_ship.ipynb @@ -157,7 +157,7 @@ "The next step is to finalise the expedition schedule plan, including setting times and instrument selection choices for each waypoint, as well as configuring the ship (including any underway measurement instruments). \n", "\n", "
\n", - "**NOTE**: This section describes the process of finalising the expedition schedule and instrument selection using the `virtualship plan` application. For expeditions with many waypoints, it can become cumbersome to use the planning tool (note, using VirtualShip in a remote terminal / cloud-based environment can also introduce lag in the user-interface). **In this case, you may prefer to edit the** `expedition.yaml` **file directly (see [here](../tutorials/working_with_expedition_yaml.md) for more details on how to do so)**.\n", + "**Note**: This section describes the process of finalising the expedition schedule and instrument selection using the `virtualship plan` application. For expeditions with many waypoints, it can become cumbersome to use the planning tool (note, using VirtualShip in a remote terminal / cloud-based environment can also introduce lag in the user-interface). **In this case, you may prefer to edit the** `expedition.yaml` **file directly (see [here](../tutorials/working_with_expedition_yaml.md) for more details on how to do so)**.\n", "
\n", "\n", "\n", @@ -177,6 +177,22 @@ "\n", "For the underway ADCP, there is a choice of using the 38 kHz OceanObserver or the 300 kHz SeaSeven version (see [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#ADCP) for more detail on the two ADCP types).\n", "\n", + "### Instrument/sensor configuration\n", + "\n", + "The most important instrument configuration setting to consider is the list of **sensors** for each instrument, which controls what type of measurements/variables the instrument records in the simulation and therefore what output data you will receive for each instrument.\n", + "\n", + "Sensor lists can be configured for each instrument under _Ship Config Editor_ > _Instrument Configurations_. For example, for the CTD instrument, you can specify which sensors to include in the simulation (e.g., `TEMPERATURE`, `SALINITY`, `OXYGEN`, etc.) by toggling the respective switches on or off.\n", + "\n", + "
\n", + "**Note**: Sensor choices are only relevant for the instruments you plan to deploy as [underway measurements](#underway-measurements) or at waypoints across your expedition schedule [(see below)](#instrument-selection). For example, if you do not select to deploy a CTD at any of your waypoints, the CTD sensor choices will not affect any output data.\n", + "
\n", + "\n", + "
\n", + "**TIP**: See [here](../documentation/full_sensor_list.md) for more information on the sensors available for each instrument.\n", + "
\n", + "\n", + "There are other instrument configurations settings that can be adjusted in the editor as well (e.g. `max_depth` for the CTD), but these are more advanced and in most cases do not need to be changed from the default values.\n", + "\n", "### Waypoint datetimes\n", "\n", "
\n", @@ -200,7 +216,7 @@ "You should now consider which measurements are to be taken at each sampling site (think about those required for your chosen research question), and therefore which instruments need to be selected in the planning tool at each waypoint.\n", "\n", "
\n", - "**Tip**: Click [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Measurement-Options) for more information on what measurement options are available, and a brief introduction to each instrument.\n", + "**Tip**: Click [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Measurement-Options) for more information on which instruments are available in VirtualShip, and a brief introduction to each.\n", "
\n", "\n", "You can make instrument selections for each waypoint in the same sub-panels as the [waypoint time](#waypoint-datetimes) selection by simply switching each on or off. Multiple instruments are allowed at each waypoint.\n", From a8a4978e8fb1dfd631388af9372fb7c71a33630b Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 12:04:44 +0200 Subject: [PATCH 07/94] enahnce phrasing --- docs/user-guide/quickstart.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/user-guide/quickstart.md b/docs/user-guide/quickstart.md index a4f75997..7d984111 100644 --- a/docs/user-guide/quickstart.md +++ b/docs/user-guide/quickstart.md @@ -82,7 +82,7 @@ VirtualShip is capable of taking underway temperature and salinity measurements, For the underway ADCP, there is a choice of using the 38 kHz OceanObserver or the 300 kHz SeaSeven version (see [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#ADCP) for more detail on the two ADCP types). -### Instrument configuration +### Instrument/sensor configuration The most important instrument configuration setting to consider is the list of **sensors** for each instrument, which controls what type of measurements/variables the instrument records in the simulation and therefore what output data you will receive for each instrument. @@ -94,7 +94,6 @@ Sensor choices are only relevant for the instruments you plan to deploy as [unde ```{tip} See [here](../documentation/full_sensor_list.md) for more information on the sensors available for each instrument. - ``` There are other instrument configurations settings that can be adjusted in the editor as well (e.g. `max_depth` for the CTD), but these are more advanced and in most cases do not need to be changed from the default values. @@ -124,7 +123,7 @@ The MFP route planning tool will give estimated durations of sailing between sit You should now consider which measurements are to be taken at each sampling site, and therefore which instruments need to be selected in the planning tool. ```{tip} -Click [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Measurement-Options) for more information on what instrument options are available, and a brief introduction to each instrument. +Click [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Measurement-Options) for more information on what instrument options are available in VirtualShip, and a brief introduction to each. ``` You can make instrument selections for each waypoint in the same sub-panels as the [waypoint time](#waypoint-datetimes) selection by simply switching each on or off. Multiple instruments are allowed at each waypoint. From 6b3628ed1d123d00fee7f3639753d28c6d0b199b Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 13:05:42 +0200 Subject: [PATCH 08/94] add testing that docs match the code for sensor options, ensure instrument column entries are consistent --- .../documentation/full_sensor_list.md | 38 ++++++----- tests/test_utils.py | 65 ++++++++++++++++++- 2 files changed, 84 insertions(+), 19 deletions(-) diff --git a/docs/user-guide/documentation/full_sensor_list.md b/docs/user-guide/documentation/full_sensor_list.md index 1cc072a7..7e108b67 100644 --- a/docs/user-guide/documentation/full_sensor_list.md +++ b/docs/user-guide/documentation/full_sensor_list.md @@ -6,21 +6,23 @@ The following table provides a comprehensive list of available sensors for each Trying to add a sensor to an instrument that does not support it will result in errors in VirtualShip. Always refer to this table to check which sensors are available for each instrument. ``` -| Instrument | Sensor Name | Description | Units | Category | -| :--------------------- | :----------------- | :-------------------------------------------------- | :----------------------------------- | :-------------- | -| **ADCP** | VELOCITY | Current velocities (eastward (u) and northward (v)) | m/s | Physical | -| **Ship Underwater ST** | TEMPERATURE | Temperature | °C | Physics | -| | SALINITY | Salinity | psu | Physics | -| **CTD** | TEMPERATURE | Temperature | °C | Physics | -| | SALINITY | Salinity | psu | Physics | -| | OXYGEN | Oxygen concentration | mmol m-3 | Biogeochemistry | -| | CHLOROPHYLL | Chlorophyll concentration | mmol m-3 | Biogeochemistry | -| | NITRATE | Nitrate concentration | mmol m-3 | Biogeochemistry | -| | PHOSPHATE | Phosphate concentration | mmol m-3 | Biogeochemistry | -| | PH | pH | - | Biogeochemistry | -| | PHYTOPLANKTON | Phytoplankton concentration in carbon | mmol m-3 | Biogeochemistry | -| | PRIMARY_PRODUCTION | Net primary production | mmol m-3 day-1 | Biogeochemistry | -| **ARGO_FLOAT** | TEMPERATURE | Temperature | °C | Physics | -| | SALINITY | Salinity | psu | Physics | -| **DRIFTER** | TEMPERATURE | Temperature | °C | Physics | -| **XBT** | TEMPERATURE | Temperature | °C | Physics | + + +| Instrument | Sensor Name | Description | Units | Category | +| :------------------------------------- | :----------------- | :-------------------------------------------------- | :----------------------------------- | :-------------- | +| **ADCP** | VELOCITY | Current velocities (eastward (u) and northward (v)) | m/s | Physical | +| **UNDERWATER_ST** (Ship Underwater ST) | TEMPERATURE | Temperature | °C | Physics | +| | SALINITY | Salinity | psu | Physics | +| **CTD** | TEMPERATURE | Temperature | °C | Physics | +| | SALINITY | Salinity | psu | Physics | +| | OXYGEN | Oxygen concentration | mmol m-3 | Biogeochemistry | +| | CHLOROPHYLL | Chlorophyll concentration | mmol m-3 | Biogeochemistry | +| | NITRATE | Nitrate concentration | mmol m-3 | Biogeochemistry | +| | PHOSPHATE | Phosphate concentration | mmol m-3 | Biogeochemistry | +| | PH | pH | - | Biogeochemistry | +| | PHYTOPLANKTON | Phytoplankton concentration in carbon | mmol m-3 | Biogeochemistry | +| | PRIMARY_PRODUCTION | Net primary production | mmol m-3 day-1 | Biogeochemistry | +| **ARGO_FLOAT** | TEMPERATURE | Temperature | °C | Physics | +| | SALINITY | Salinity | psu | Physics | +| **DRIFTER** | TEMPERATURE | Temperature | °C | Physics | +| **XBT** | TEMPERATURE | Temperature | °C | Physics | diff --git a/tests/test_utils.py b/tests/test_utils.py index fde6796f..3196fa03 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -1,12 +1,13 @@ import datetime +import re from pathlib import Path import numpy as np import pytest import xarray as xr -from parcels import FieldSet, JITParticle, ScipyParticle, Variable import virtualship.utils +from parcels import FieldSet, JITParticle, ScipyParticle, Variable from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.models.expedition import Expedition, SensorConfig @@ -399,3 +400,65 @@ def test_build_particle_class_scipy_base(): ParticleClass = build_particle_class_from_sensors(sensors, nonsensor, ScipyParticle) assert issubclass(ParticleClass, ScipyParticle) + + +def test_allowed_sensors_matches_docs(): + """Test that SUPPORTED_SENSORS_MAP (sensors allowed for each instrument) matches the sensor table in full_sensor_list.md.""" + # local imports to trigger instrument registration and avoid potential circular imports + import virtualship.instruments # noqa: F401 - ensures all @register_instrument decorators run + from virtualship.utils import INSTRUMENT_CLASS_MAP, SUPPORTED_SENSORS_MAP + + docs_path = ( + Path(__file__).parent.parent + / "docs/user-guide/documentation/full_sensor_list.md" + ) + content = docs_path.read_text(encoding="utf-8") + + display_name_to_instrument_type: dict[str, InstrumentType] = { + instrument_type.value: instrument_type + for instrument_type in INSTRUMENT_CLASS_MAP + if isinstance(instrument_type, InstrumentType) + } # all instruments should use their enum value as the bold display name in the markdown table + + # parse markdown table rows + row_pattern = re.compile(r"^\|([^|]*)\|([^|]*)\|.*$", re.MULTILINE) + + expected: dict[InstrumentType, set[SensorType]] = {} + current_instrument: InstrumentType | None = None + + for match in row_pattern.finditer(content): + instrument_cell = match.group(1).strip() + sensor_cell = match.group(2).strip() + + # extract only the **bold** text from the cell (e.g. "**UNDERWATER_ST** (Ship Underwater ST)" -> "UNDERWATER_ST") + bold_match = re.search(r"\*\*(.+?)\*\*", instrument_cell) + instrument_name = bold_match.group(1).strip() if bold_match else "" + + if instrument_name and instrument_name in display_name_to_instrument_type: + current_instrument = display_name_to_instrument_type[instrument_name] + if current_instrument not in expected: + expected[current_instrument] = set() + + # skip irrelevant cells + if ( + not sensor_cell + or sensor_cell.startswith(":") + or sensor_cell == "Sensor Name" + ): + continue + + sensor_name = sensor_cell.strip() + if current_instrument is not None and sensor_name: + expected[current_instrument].add(SensorType(sensor_name)) + + # verify each instrument in the docs is registered and has matching sensors + for instrument_type, doc_sensors in expected.items(): + assert instrument_type in SUPPORTED_SENSORS_MAP, ( + f"{instrument_type} is listed in full_sensor_list.md but not found in SUPPORTED_SENSORS_MAP." + ) + registered_sensors = set(SUPPORTED_SENSORS_MAP[instrument_type]) + assert registered_sensors == doc_sensors, ( + f"Sensor mismatch for {instrument_type}:\n" + f" In docs: {sorted(s.value for s in doc_sensors)}\n" + f" In code: {sorted(s.value for s in registered_sensors)}\n" + ) From e23087b61cae509ec7be54be780d57f4af21f48c Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 13:10:46 +0200 Subject: [PATCH 09/94] add check that all instruments in code are addressed in sensor table --- tests/test_utils.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/tests/test_utils.py b/tests/test_utils.py index 3196fa03..4ab96654 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -462,3 +462,9 @@ def test_allowed_sensors_matches_docs(): f" In docs: {sorted(s.value for s in doc_sensors)}\n" f" In code: {sorted(s.value for s in registered_sensors)}\n" ) + + # verify each instrument registered in code is also covered in the docs + for instrument_type in SUPPORTED_SENSORS_MAP: + assert instrument_type in expected, ( + f"{instrument_type} is registered in SUPPORTED_SENSORS_MAP but not listed in full_sensor_list.md." + ) From a5ff3a44362f0cd688f03836ae2ec41bb4f27c82 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 14:17:44 +0200 Subject: [PATCH 10/94] update environments to pull parcels v4 alpha --- pixi.toml | 24 +++++++++++------------- pyproject.toml | 6 +++--- 2 files changed, 14 insertions(+), 16 deletions(-) diff --git a/pixi.toml b/pixi.toml index ba024968..0d4b8d87 100644 --- a/pixi.toml +++ b/pixi.toml @@ -1,7 +1,7 @@ [workspace] name = "VirtualShip" preview = ["pixi-build"] -channels = ["conda-forge"] +channels = ["https://repo.prefix.dev/parcels", "conda-forge"] platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"] exclude-newer = "5d" # security pre-caution against compromised packages requires-pixi = ">=0.67.0" @@ -18,9 +18,9 @@ setuptools = "*" setuptools_scm = "*" [package.run-dependencies] # Keep in sync with `pyproject.toml` and feedstock recipe -python = ">=3.10" +python = "3.11.*" click = "*" -parcels = ">3.1.0" +parcels = ">=4.0.0alpha" pyproj = ">=3,<4" sortedcontainers = "==2.4.0" opensimplex = "==0.4.5" @@ -34,14 +34,15 @@ textual = "*" [dependencies] virtualship = { path = "." } -[feature.py310.dependencies] -python = "3.10.*" +# Commented out whilst parcels v4 alpha only supports Python 3.11 +# [feature.py310.dependencies] +# python = "3.10.*" -[feature.py311.dependencies] -python = "3.11.*" +# [feature.py311.dependencies] +# python = "3.11.*" -[feature.py312.dependencies] -python = "3.12.*" +# [feature.py312.dependencies] +# python = "3.12.*" [feature.test.dependencies] pytest = "*" @@ -98,11 +99,8 @@ lxml = "*" typing = "mypy src/virtualship --install-types" [environments] -default = { features = ["test", "notebooks", "typing", "pre-commit", "analysis"] } +default = { features = ["test", "notebooks", "typing", "pre-commit", "analysis"] } test-latest = { features = ["test"], solve-group = "test" } -test-py310 = { features = ["test", "py310"] } -test-py311 = { features = ["test", "py311"] } -test-py312 = { features = ["test", "py312"] } test-notebooks = { features = ["test", "notebooks"], solve-group = "test" } analysis = { features = ["analysis"], solve-group = "analysis" } docs = { features = ["docs"], solve-group = "docs" } diff --git a/pyproject.toml b/pyproject.toml index 7f9a2108..bc9346d0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ name = "virtualship" description = "Code for the Virtual Ship Classroom, where Marine Scientists can combine Copernicus Marine Data with an OceanParcels ship to go on a virtual expedition." readme = "README.md" dynamic = ["version"] -authors = [{ name = "oceanparcels.org team" }] +authors = [{ name = "parcels-code.org team" }] requires-python = ">=3.10" license = { file = "LICENSE" } classifiers = [ @@ -26,7 +26,7 @@ classifiers = [ ] dependencies = [ "click", - "parcels >3.1.0", + "parcels >=4.0.0alpha", "pyproj >= 3, < 4", "sortedcontainers == 2.4.0", "opensimplex == 0.4.5", @@ -40,7 +40,7 @@ dependencies = [ ] [project.urls] -Homepage = "https://oceanparcels.org/" # TODO: Update this to just be repo? +Homepage = "https://virtualship.parcels-code.org/" Repository = "https://github.com/OceanParcels/virtualship" Documentation = "https://virtualship.readthedocs.io/" "Bug Tracker" = "https://github.com/OceanParcels/virtualship/issues" From db443243b2d2d6c55031c7bfff7b0e893b2a1acc Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 14:18:34 +0200 Subject: [PATCH 11/94] changed parcels logging api --- src/virtualship/cli/_run.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/src/virtualship/cli/_run.py b/src/virtualship/cli/_run.py index f2622be3..703502f2 100644 --- a/src/virtualship/cli/_run.py +++ b/src/virtualship/cli/_run.py @@ -35,11 +35,9 @@ get_instrument_class, ) -# parcels logger (suppress INFO messages to prevent log being flooded) -external_logger = logging.getLogger("parcels.tools.loggers") -external_logger.setLevel(logging.WARNING) - -# copernicusmarine logger (suppress INFO messages to prevent log being flooded) +# suppress INFO messages from copernicusmarine and parcels loggers; prevent log flooding +parcels_logger = logging.getLogger("parcels._logger") +parcels_logger.setLevel(logging.WARNING) logging.getLogger("copernicusmarine").setLevel("ERROR") From 6f732d3511a53760ed99e86531d0a4eadc596b26 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 15:36:47 +0200 Subject: [PATCH 12/94] first wave of changes to the instrument logic with v4 logic, and particle building --- src/virtualship/cli/_run.py | 1 + src/virtualship/instruments/adcp.py | 14 +- src/virtualship/instruments/argo_float.py | 197 ++++++++++-------- src/virtualship/instruments/ctd.py | 92 +++++--- src/virtualship/instruments/drifter.py | 25 ++- .../instruments/ship_underwater_st.py | 16 +- src/virtualship/instruments/xbt.py | 35 ++-- src/virtualship/utils.py | 7 +- 8 files changed, 220 insertions(+), 167 deletions(-) diff --git a/src/virtualship/cli/_run.py b/src/virtualship/cli/_run.py index 703502f2..10afc5c5 100644 --- a/src/virtualship/cli/_run.py +++ b/src/virtualship/cli/_run.py @@ -200,6 +200,7 @@ def _run( ) # execute simulation + # TODO: outpath will be Parquet with v4... instrument.execute( measurements=measurements, out_path=expedition_dir.joinpath(RESULTS, f"{itype.name.lower()}.zarr"), diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index b2da6582..7ee718f6 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -3,8 +3,8 @@ from typing import ClassVar import numpy as np -from parcels import ParticleSet, ScipyParticle +from parcels import ParticleSet from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType @@ -35,9 +35,13 @@ class ADCP: # ===================================================== -def _sample_velocity(particle, fieldset, time): - particle.U, particle.V = fieldset.UV.eval( - time, particle.depth, particle.lat, particle.lon, applyConversion=False +def _sample_velocity(particles, fieldset): + particles.U, particles.V = fieldset.UV.eval( + particles.time, + particles.z, + particles.lat, + particles.lon, + applyConversion=False, ) @@ -96,7 +100,7 @@ def simulate(self, measurements, out_path) -> None: # build dynamic particle class from the active sensors adcp_config = self.expedition.instruments_config.adcp_config _ADCPParticle = build_particle_class_from_sensors( - adcp_config.sensors, _ADCP_NONSENSOR_VARIABLES, ScipyParticle + adcp_config.sensors, _ADCP_NONSENSOR_VARIABLES ) bins = np.linspace(MAX_DEPTH, MIN_DEPTH, NUM_BINS) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 8c90cfb2..70fcb146 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -1,12 +1,11 @@ -import math from collections.abc import Callable from dataclasses import dataclass from datetime import timedelta from typing import ClassVar import numpy as np -from parcels import AdvectionRK4, JITParticle, ParticleSet, StatusCode, Variable +from parcels import AdvectionRK4, ParticleSet, StatusCode, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType @@ -53,103 +52,125 @@ class ArgoFloat: # SECTION: Kernels # ===================================================== - -def _argo_float_vertical_movement(particle, fieldset, time): - if particle.cycle_phase == 0: - # Phase 0: Sinking with vertical_speed until depth is drift_depth - particle_ddepth += ( # noqa - particle.vertical_speed * particle.dt +# TODO: need to add back in the shallow bathymetry checks (to phases 0 and 2?!) +# TODO: can this be refactored as well to a helper function? + + +def _argo_float_vertical_movement(particles, fieldset): + # Split particles based on their current cycle_phase + ptcls0 = particles[particles.cycle_phase == 0] + ptcls1 = particles[particles.cycle_phase == 1] + ptcls2 = particles[particles.cycle_phase == 2] + ptcls3 = particles[particles.cycle_phase == 3] + ptcls4 = particles[particles.cycle_phase == 4] + + # Phase 0: Sinking with vertical_speed until depth is driftdepth + ptcls0.dz += particles.vertical_speed * ptcls0.dt + loc_bathy = fieldset.bathymetry.eval(ptcls0.time, ptcls0.z, ptcls0.lat, ptcls0.lon) + driftdepth_mask = ptcls0.z + ptcls0.dz >= particles.drift_depth + bathy_mask = ptcls0.z + ptcls0.dz >= loc_bathy + next_phase = np.logical_and( + driftdepth_mask, bathy_mask + ) # combined mask; not at drift depth yet and not hitting bathymetry + ptcls0.cycle_phase[next_phase] = 1 + ptcls0.dz[next_phase] = ( + particles.drift_depth - ptcls0.z[next_phase] + ) # avoid overshoot + + # Phase 0.5: Check for grounding at bathymetry and raise if necessary + ptcls0.grounded[~bathy_mask] = 1 + if np.any(~bathy_mask): + print( + "Shallow bathymetry warning: Argo float grounded at bathymetry depth during sinking to drift depth. Raising by 50m above bathymetry and continuing cycle." ) - - # bathymetry at particle location - loc_bathy = fieldset.bathymetry.eval( - time, particle.depth, particle.lat, particle.lon + ptcls0.dz[~bathy_mask] = ( + loc_bathy[~bathy_mask] - ptcls0.z[~bathy_mask] + 50.0 + ) # raise to 50m above bathymetry + ptcls0.cycle_phase[~bathy_mask] = 1 + + # Phase 1: Drifting at depth for drifttime seconds + ptcls1.drift_age += ptcls1.dt + next_phase = ptcls1.drift_age >= particles.drift_days * 86400 # [seconds] + ptcls1.cycle_phase[next_phase] = 2 + ptcls1.drift_age[next_phase] = 0 # reset drift_age for next cycle + + # Phase 2: Sinking further to maxdepth + ptcls2.dz += particles.vertical_speed * ptcls2.dt + loc_bathy = fieldset.bathymetry.eval(ptcls2.time, ptcls2.z, ptcls2.lat, ptcls2.lon) + maxdepth_mask = ptcls2.z + ptcls2.dz >= particles.max_depth + bathy_mask = ptcls2.z + ptcls2.dz >= loc_bathy + next_phase = np.logical_and( + maxdepth_mask, bathy_mask + ) # combined mask; not at max depth yet and not hitting bathymetry + ptcls2.cycle_phase[next_phase] = 3 + ptcls2.dz[next_phase] = ( + particles.max_depth - ptcls2.z[next_phase] + ) # avoid overshoot + + # Phase 2.5: Check for grounding at bathymetry and raise if necessary + ptcls2.grounded[~bathy_mask] = 1 + if np.any(~bathy_mask): + print( + "Shallow bathymetry warning: Argo float grounded at bathymetry depth during sinking to max depth. Raising by 50m above bathymetry and continuing cycle." ) - if particle.depth + particle_ddepth <= loc_bathy: - particle_ddepth = loc_bathy - particle.depth + 50.0 # 50m above bathy - particle.cycle_phase = 1 - particle.grounded = 1 - print( - "Shallow bathymetry warning: Argo float grounded at bathymetry depth during sinking to drift depth. Raising by 50m above bathymetry and continuing cycle." - ) + ptcls2.dz[~bathy_mask] = ( + loc_bathy[~bathy_mask] - ptcls2.z[~bathy_mask] + 50.0 + ) # raise to 50m above bathymetry + ptcls2.cycle_phase[~bathy_mask] = 3 - elif particle.depth + particle_ddepth <= particle.drift_depth: - particle_ddepth = particle.drift_depth - particle.depth - particle.cycle_phase = 1 - - elif particle.cycle_phase == 1: - # Phase 1: Drifting at depth for drifttime seconds - particle.drift_age += particle.dt - if particle.drift_age >= particle.drift_days * 86400: - particle.drift_age = 0 # reset drift_age for next cycle - particle.cycle_phase = 2 - - elif particle.cycle_phase == 2: - # Phase 2: Sinking further to max_depth - particle_ddepth += particle.vertical_speed * particle.dt - loc_bathy = fieldset.bathymetry.eval( - time, particle.depth, particle.lat, particle.lon - ) - if particle.depth + particle_ddepth <= loc_bathy: - particle_ddepth = loc_bathy - particle.depth + 50.0 # 50m above bathy - particle.cycle_phase = 3 - particle.grounded = 1 - print( - "Shallow bathymetry warning: Argo float grounded at bathymetry depth during sinking to max depth. Raising by 50m above bathymetry and continuing cycle." - ) - elif particle.depth + particle_ddepth <= particle.max_depth: - particle_ddepth = particle.max_depth - particle.depth - particle.cycle_phase = 3 - - elif particle.cycle_phase == 3: - # Phase 3: Rising with vertical_speed until at surface - particle_ddepth -= particle.vertical_speed * particle.dt - particle.cycle_age += ( - particle.dt - ) # solve issue of not updating cycle_age during ascent - particle.grounded = 0 - if particle.depth + particle_ddepth >= particle.min_depth: - particle_ddepth = particle.min_depth - particle.depth - particle.cycle_phase = 4 + # Phase 3: Rising with vertical_speed until at surface + ptcls3.dz -= particles.vertical_speed * ptcls3.dt + ptcls3.temp = fieldset.thetao[ptcls3.time, ptcls3.z, ptcls3.lat, ptcls3.lon] + next_phase = ptcls3.z + ptcls3.dz <= particles.min_depth + ptcls3.cycle_phase[next_phase] = 4 + ptcls3.dz[next_phase] = ( + particles.min_depth - ptcls3.z[next_phase] + ) # avoid overshoot - elif particle.cycle_phase == 4: - # Phase 4: Transmitting at surface until cycletime is reached - if particle.cycle_age > particle.cycle_days * 86400: - particle.cycle_phase = 0 - particle.cycle_age = 0 + # Phase 4: Transmitting at surface until cycletime is reached + next_phase = ptcls4.cycle_age >= particles.cycle_days * 86400 + ptcls4.cycle_phase[next_phase] = 0 + ptcls4.cycle_age[next_phase] = 0 # reset cycle_age for next cycle + ptcls4.temp = np.nan # no temperature measurement when at surface - if particle.state == StatusCode.Evaluate: - particle.cycle_age += particle.dt # update cycle_age + particles.cycle_age += particles.dt # update cycle_age -def _keep_at_surface(particle, fieldset, time): - # Prevent error when float reaches surface - if particle.state == StatusCode.ErrorThroughSurface: - particle.depth = particle.min_depth - particle.state = StatusCode.Success +def _keep_at_surface(particles, fieldset): + through_surface = particles.state == StatusCode.ErrorThroughSurface + particles.z[through_surface] = particles.min_depth[through_surface] + particles.state[through_surface] = StatusCode.Success -def _check_error(particle, fieldset, time): - if particle.state >= 50: # This captures all Errors - particle.delete() +def _check_error(particles, fieldset): + errors = particles.state >= 50 # captures all Errors + particles.state[errors] = StatusCode.Delete -def _argo_sample_temperature(particle, fieldset, time): +def _argo_sample_temperature(particles, fieldset): # Phase 3: ascending — sample temperature; NaN otherwise - if particle.cycle_phase == 3 and particle.depth < particle.min_depth: - particle.temperature = fieldset.T[ - time, particle.depth, particle.lat, particle.lon - ] - else: - particle.temperature = math.nan - - -def _argo_sample_salinity(particle, fieldset, time): + phase_mask = particles.cycle_phase == 3 + depth_mask = particles.depth < particles.min_depth + sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] + sampling_particles.temperature = fieldset.T[ + sampling_particles.time, + sampling_particles.depth, + sampling_particles.lat, + sampling_particles.lon, + ] + + +def _argo_sample_salinity(particles, fieldset): # Phase 3: ascending — sample salinity; NaN otherwise - if particle.cycle_phase == 3 and particle.depth < particle.min_depth: - particle.salinity = fieldset.S[time, particle.depth, particle.lat, particle.lon] - else: - particle.salinity = math.nan + phase_mask = particles.cycle_phase == 3 + depth_mask = particles.depth < particles.min_depth + sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] + sampling_particles.salinity = fieldset.S[ + sampling_particles.time, + sampling_particles.depth, + sampling_particles.lat, + sampling_particles.lon, + ] # ===================================================== @@ -229,9 +250,7 @@ def simulate(self, measurements, out_path) -> None: # build dynamic particle class from the active sensors argo_float_config = self.expedition.instruments_config.argo_float_config _ArgoParticle = build_particle_class_from_sensors( - argo_float_config.sensors, - _ARGO_NONSENSOR_VARIABLES, - JITParticle, + argo_float_config.sensors, _ARGO_NONSENSOR_VARIABLES ) # define parcel particles diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 583a099c..a7a4218f 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -4,13 +4,13 @@ from typing import TYPE_CHECKING, ClassVar import numpy as np -from parcels import JITParticle, ParticleSet, Variable +from parcels._core.statuscodes import StatusCode +from parcels import ParticleSet, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.utils import ( - add_dummy_UV, build_particle_class_from_sensors, register_instrument, ) @@ -52,60 +52,87 @@ class CTD: ## physical variables -def _sample_temperature(particle, fieldset, time): - particle.temperature = fieldset.T[time, particle.depth, particle.lat, particle.lon] +def _sample_temperature(particles, fieldset): + particles.temperature = fieldset.T[ + particles.time, particles.z, particles.lat, particles.lon + ] -def _sample_salinity(particle, fieldset, time): - particle.salinity = fieldset.S[time, particle.depth, particle.lat, particle.lon] +def _sample_salinity(particles, fieldset): + particles.salinity = fieldset.S[ + particles.time, particles.z, particles.lat, particles.lon + ] ## bgc variables -def _sample_o2(particle, fieldset, time): - particle.o2 = fieldset.o2[time, particle.depth, particle.lat, particle.lon] +def _sample_o2(particles, fieldset): + particles.o2 = fieldset.o2[ + particles.time, particles.z, particles.lat, particles.lon + ] -def _sample_chlorophyll(particle, fieldset, time): - particle.chl = fieldset.chl[time, particle.depth, particle.lat, particle.lon] +def _sample_chlorophyll(particles, fieldset): + particles.chl = fieldset.chl[ + particles.time, particles.z, particles.lat, particles.lon + ] -def _sample_nitrate(particle, fieldset, time): - particle.no3 = fieldset.no3[time, particle.depth, particle.lat, particle.lon] +def _sample_nitrate(particles, fieldset): + particles.no3 = fieldset.no3[ + particles.time, particles.z, particles.lat, particles.lon + ] -def _sample_phosphate(particle, fieldset, time): - particle.po4 = fieldset.po4[time, particle.depth, particle.lat, particle.lon] +def _sample_phosphate(particles, fieldset): + particles.po4 = fieldset.po4[ + particles.time, particles.z, particles.lat, particles.lon + ] -def _sample_ph(particle, fieldset, time): - particle.ph = fieldset.ph[time, particle.depth, particle.lat, particle.lon] +def _sample_ph(particles, fieldset): + particles.ph = fieldset.ph[ + particles.time, particles.z, particles.lat, particles.lon + ] -def _sample_phytoplankton(particle, fieldset, time): - particle.phyc = fieldset.phyc[time, particle.depth, particle.lat, particle.lon] +def _sample_phytoplankton(particles, fieldset): + particles.phyc = fieldset.phyc[ + particles.time, particles.z, particles.lat, particles.lon + ] -def _sample_primary_production(particle, fieldset, time): - particle.nppv = fieldset.nppv[time, particle.depth, particle.lat, particle.lon] +def _sample_primary_production(particles, fieldset): + particles.nppv = fieldset.nppv[ + particles.time, particles.z, particles.lat, particles.lon + ] ## cast -def _ctd_cast(particle, fieldset, time): +def _ctd_cast(particles, fieldset): + particles_lowering = particles[particles.raising == 0] + particles_raising = particles[particles.raising == 1] + + # TODO: change to boolean masking, like with Argo Floats? + # lowering - if particle.raising == 0: - particle_ddepth = -particle.winch_speed * particle.dt - if particle.depth + particle_ddepth < particle.max_depth: - particle.raising = 1 - particle_ddepth = -particle_ddepth + particles_lowering.dz = -particles_lowering.winch_speed * particles_lowering.dt + particles_lowering.raising = np.where( + particles_lowering.z + particles_lowering.dz < particles_lowering.max_depth, + 1, + particles_lowering.raising, + ) + # raising - else: - particle_ddepth = particle.winch_speed * particle.dt - if particle.depth + particle_ddepth > particle.min_depth: - particle.delete() + particles_raising.dz = particles_raising.winch_speed * particles_raising.dt + particles_raising.state = np.where( + particles_raising.z + particles_raising.dz > particles_raising.min_depth, + StatusCode.Delete, + particles_raising.state, + ) # ===================================================== @@ -162,9 +189,6 @@ def simulate(self, measurements, out_path) -> None: fieldset = self.load_input_data() - # add dummy U - add_dummy_UV(fieldset) # TODO: parcels v3 bodge; remove when parcels v4 is used - # use first active field for time reference _time_ref_key = next(iter(self.variables)) _time_ref_field = getattr(fieldset, _time_ref_key) @@ -208,7 +232,7 @@ def simulate(self, measurements, out_path) -> None: # build dynamic particle class from the active sensors ctd_config = self.expedition.instruments_config.ctd_config _CTDParticle = build_particle_class_from_sensors( - ctd_config.sensors, _CTD_NONSENSOR_VARIABLES, JITParticle + ctd_config.sensors, _CTD_NONSENSOR_VARIABLES ) # define parcel particles diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index 379334b3..46ae82e9 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -4,8 +4,9 @@ from typing import ClassVar import numpy as np -from parcels import AdvectionRK4, JITParticle, ParticleSet, Variable +from parcels._core.statuscodes import StatusCode +from parcels import AdvectionRK4, ParticleSet, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType @@ -46,15 +47,21 @@ class Drifter: # ===================================================== -def _sample_temperature(particle, fieldset, time): - particle.temperature = fieldset.T[time, particle.depth, particle.lat, particle.lon] +def _sample_temperature(particles, fieldset): + particles.temperature = fieldset.T[ + particles.time, particles.z, particles.lat, particles.lon + ] -def _check_lifetime(particle, fieldset, time): - if particle.has_lifetime == 1: - particle.age += particle.dt - if particle.age >= particle.lifetime: - particle.delete() +def _check_lifetime(particles, fieldset): + particles_wlifetime = particles[particles.has_lifetime == 1] + + particles_wlifetime.age += particles_wlifetime.dt + particles_wlifetime.state = np.where( + particles_wlifetime.age >= particles_wlifetime.lifetime, + StatusCode.Delete, + particles_wlifetime.state, + ) # ===================================================== @@ -123,7 +130,7 @@ def simulate(self, measurements, out_path) -> None: # build dynamic particle class from the active sensors drifter_config = self.expedition.instruments_config.drifter_config _DrifterParticle = build_particle_class_from_sensors( - drifter_config.sensors, _DRIFTER_NONSENSOR_VARIABLES, JITParticle + drifter_config.sensors, _DRIFTER_NONSENSOR_VARIABLES ) # define parcel particles diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index 6a564cc0..78c757d2 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -3,13 +3,12 @@ from typing import ClassVar import numpy as np -from parcels import ParticleSet, ScipyParticle +from parcels import ParticleSet from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.utils import ( - add_dummy_UV, build_particle_class_from_sensors, register_instrument, ) @@ -40,13 +39,13 @@ class Underwater_ST: # define function sampling Salinity -def _sample_salinity(particle, fieldset, time): - particle.salinity = fieldset.S[time, particle.depth, particle.lat, particle.lon] +def _sample_salinity(particles, fieldset): + particles.S = fieldset.S[particles.time, particles.z, particles.lat, particles.lon] # define function sampling Temperature -def _sample_temperature(particle, fieldset, time): - particle.temperature = fieldset.T[time, particle.depth, particle.lat, particle.lon] +def _sample_temperature(particles, fieldset): + particles.T = fieldset.T[particles.time, particles.z, particles.lat, particles.lon] # ===================================================== @@ -95,13 +94,10 @@ def simulate(self, measurements, out_path) -> None: fieldset = self.load_input_data() - # add dummy U - add_dummy_UV(fieldset) # TODO: parcels v3 bodge; remove when parcels v4 is used - # build dynamic particle class from the active sensors st_config = self.expedition.instruments_config.ship_underwater_st_config _ShipSTParticle = build_particle_class_from_sensors( - st_config.sensors, _ST_NONSENSOR_VARIABLES, ScipyParticle + st_config.sensors, _ST_NONSENSOR_VARIABLES ) particleset = ParticleSet.from_list( diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index 051bf1fa..06e862a6 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -4,14 +4,14 @@ from typing import ClassVar import numpy as np -from parcels import JITParticle, ParticleSet, Variable +from parcels._core.statuscodes import StatusCode +from parcels import ParticleSet, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.models.spacetime import Spacetime from virtualship.utils import ( - add_dummy_UV, build_particle_class_from_sensors, register_instrument, ) @@ -50,26 +50,32 @@ class XBT: # ===================================================== -def _sample_temperature(particle, fieldset, time): - particle.temperature = fieldset.T[time, particle.depth, particle.lat, particle.lon] +def _sample_temperature(particles, fieldset): + particles.temperature = fieldset.T[ + particles.time, particles.z, particles.lat, particles.lon + ] -def _xbt_cast(particle, fieldset, time): - particle_ddepth = -particle.fall_speed * particle.dt +def _xbt_cast(particles, fieldset): + particles.dz = -particles.fall_speed * particles.dt # update the fall speed from the quadractic fall-rate equation # check https://doi.org/10.5194/os-7-231-2011 - particle.fall_speed = ( - particle.fall_speed - 2 * particle.deceleration_coefficient * particle.dt + particles.fall_speed = ( + particles.fall_speed - 2 * particles.deceleration_coefficient * particles.dt ) # delete particle if depth is exactly max_depth - if particle.depth == particle.max_depth: - particle.delete() + particles.state = np.where( + particles.z == particles.max_depth, StatusCode.Delete, particles.state + ) # set particle depth to max depth if it's too deep - if particle.depth + particle_ddepth < particle.max_depth: - particle_ddepth = particle.max_depth - particle.depth + particles.dz = np.where( + particles.z + particles.dz < particles.max_depth, + particles.max_depth - particles.z, + particles.z, + ) # ===================================================== @@ -117,9 +123,6 @@ def simulate(self, measurements, out_path) -> None: fieldset = self.load_input_data() - # add dummy U - add_dummy_UV(fieldset) # TODO: parcels v3 bodge; remove when parcels v4 is used - # use first active field for time reference _time_ref_key = next(iter(self.variables)) _time_ref_field = getattr(fieldset, _time_ref_key) @@ -166,7 +169,7 @@ def simulate(self, measurements, out_path) -> None: # build dynamic particle class from the active sensors xbt_config = self.expedition.instruments_config.xbt_config _XBTParticle = build_particle_class_from_sensors( - xbt_config.sensors, _XBT_NONSENSOR_VARIABLES, JITParticle + xbt_config.sensors, _XBT_NONSENSOR_VARIABLES ) # define xbt particles diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 7ad275cd..fb585934 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -15,8 +15,8 @@ import numpy as np import pyproj import xarray as xr -from parcels import FieldSet, Variable +from parcels import FieldSet, Particle, Variable from virtualship.errors import CopernicusCatalogueError if TYPE_CHECKING: @@ -677,14 +677,13 @@ def _make_hash(s: str, length: int) -> str: def build_particle_class_from_sensors( sensors: list[SensorConfig], nonsensor_variables: list[Variable], - particle_class: type, # generic type annotation needed for v3 particle class behaviour # TODO: Update with Parcels v4 ) -> type: - """Build a Particle class (JITParticle or ScipyParticle) from nonsensor variables and active sensors.""" + """Build a Particle class from nonsensor variables and active sensors.""" sensor_variables = [ variable for sc in sensors if sc.enabled for variable in sc.meta.particle_vars ] - return particle_class.add_variables(nonsensor_variables + sensor_variables) + return Particle.add_variables(nonsensor_variables + sensor_variables) # ===================================================== From 71881ccc921374fd8d4ea5b8d662499229250361 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 15:37:14 +0200 Subject: [PATCH 13/94] remove add dummy UV func, shouldn't be needed in v4 (?) --- src/virtualship/utils.py | 23 ----------------------- 1 file changed, 23 deletions(-) diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index fb585934..3dab6b70 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -340,29 +340,6 @@ def _get_expedition(expedition_dir: Path) -> Expedition: ) from e -def add_dummy_UV(fieldset: FieldSet): - """Add a dummy U and V field to a FieldSet to satisfy parcels FieldSet completeness checks.""" - if "U" not in fieldset.__dict__.keys(): - for uv_var in ["U", "V"]: - dummy_field = getattr( - FieldSet.from_data( - {"U": 0, "V": 0}, {"lon": 0, "lat": 0}, mesh="spherical" - ), - uv_var, - ) - fieldset.add_field(dummy_field) - try: - fieldset.time_origin = ( - fieldset.T.grid.time_origin - if "T" in fieldset.__dict__.keys() - else fieldset.o2.grid.time_origin - ) - except Exception: - raise ValueError( - "Cannot determine time_origin for dummy UV fields. Assert T or o2 exists in fieldset." - ) from None - - def _select_product_id( physical: bool, schedule_start, From 62b2b1b62187bfb3bf0744ffec5525c6e336a633 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 16:54:31 +0200 Subject: [PATCH 14/94] pull v4 from parcels/main --- pixi.toml | 12 +++++++++--- pyproject.toml | 2 +- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/pixi.toml b/pixi.toml index 0d4b8d87..b86b8f60 100644 --- a/pixi.toml +++ b/pixi.toml @@ -1,7 +1,7 @@ [workspace] name = "VirtualShip" preview = ["pixi-build"] -channels = ["https://repo.prefix.dev/parcels", "conda-forge"] +channels = ["conda-forge"] platforms = ["win-64", "linux-64", "osx-64", "osx-arm64"] exclude-newer = "5d" # security pre-caution against compromised packages requires-pixi = ">=0.67.0" @@ -20,11 +20,10 @@ setuptools_scm = "*" [package.run-dependencies] # Keep in sync with `pyproject.toml` and feedstock recipe python = "3.11.*" click = "*" -parcels = ">=4.0.0alpha" pyproj = ">=3,<4" sortedcontainers = "==2.4.0" opensimplex = "==0.4.5" -numpy = ">=1,<2" +numpy = ">=2.1.0" pydantic = ">=2,<3" pyyaml = "*" copernicusmarine = ">=2.2.2" @@ -33,6 +32,13 @@ textual = "*" [dependencies] virtualship = { path = "." } +# Pre-install as conda packages to avoid PyPI source builds +netcdf4 = "*" +numpy = ">=2.1.0" +dask = "*" + +[pypi-dependencies] +parcels = { git = "https://github.com/Parcels-code/Parcels", branch = "main" } # Commented out whilst parcels v4 alpha only supports Python 3.11 # [feature.py310.dependencies] diff --git a/pyproject.toml b/pyproject.toml index bc9346d0..e19024d3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -30,7 +30,7 @@ dependencies = [ "pyproj >= 3, < 4", "sortedcontainers == 2.4.0", "opensimplex == 0.4.5", - "numpy >=1, < 2", + "numpy >=2.1.0", "pydantic >=2, <3", "PyYAML", "copernicusmarine >= 2.2.2", From 38afc560ab5c614244b2413e31ba14847b3d98fc Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 16:56:40 +0200 Subject: [PATCH 15/94] use AdvectionRK2 --- src/virtualship/instruments/argo_float.py | 5 +++-- src/virtualship/instruments/drifter.py | 5 +++-- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 70fcb146..d1644849 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -4,8 +4,9 @@ from typing import ClassVar import numpy as np +from parcels.kernels import AdvectionRK2 -from parcels import AdvectionRK4, ParticleSet, StatusCode, Variable +from parcels import ParticleSet, StatusCode, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType @@ -291,7 +292,7 @@ def simulate(self, measurements, out_path) -> None: [ _argo_float_vertical_movement, *sampling_kernels, - AdvectionRK4, + AdvectionRK2, _keep_at_surface, _check_error, ], diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index 46ae82e9..46689e79 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -5,8 +5,9 @@ import numpy as np from parcels._core.statuscodes import StatusCode +from parcels.kernels import AdvectionRK2 -from parcels import AdvectionRK4, ParticleSet, Variable +from parcels import ParticleSet, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType @@ -176,7 +177,7 @@ def simulate(self, measurements, out_path) -> None: # execute simulation drifter_particleset.execute( - [AdvectionRK4, *sampling_kernels, _check_lifetime], + [AdvectionRK2, *sampling_kernels, _check_lifetime], endtime=endtime, dt=DT, output_file=out_file, From 573da9ed17b25b2b0327ff5f7f29f881ed88322a Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 20 May 2026 16:58:40 +0200 Subject: [PATCH 16/94] migrate fieldset ingestion protocol --- src/virtualship/instruments/base.py | 29 ++++++++++++++-------------- src/virtualship/models/expedition.py | 10 +--------- src/virtualship/utils.py | 21 +++++--------------- 3 files changed, 21 insertions(+), 39 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index d4e078e6..a3b7adb4 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -9,9 +9,9 @@ import copernicusmarine import xarray as xr -from parcels import FieldSet from yaspin import yaspin +import parcels from virtualship.errors import CopernicusCatalogueError from virtualship.utils import ( COPERNICUSMARINE_PHYS_VARIABLES, @@ -86,7 +86,7 @@ def __init__( self.min_lat, self.max_lat = min(wp_lats), max(wp_lats) self.min_lon, self.max_lon = min(wp_lons), max(wp_lons) - def load_input_data(self) -> FieldSet: + def load_input_data(self) -> parcels.FieldSet: """Load and return the input data as a FieldSet for the instrument.""" try: fieldset = self._generate_fieldset() @@ -97,7 +97,7 @@ def load_input_data(self) -> FieldSet: # interpolation methods for var in (v for v in self.variables if v not in ("U", "V")): - getattr(fieldset, var).interp_method = "linear_invdist_land_tracer" + getattr(fieldset, var).interp_method = parcels.interpolators.XLinear # depth negative for g in fieldset.gridset.grids: @@ -183,11 +183,11 @@ def _get_copernicus_ds( coordinates_selection_method="outside", ) - def _generate_fieldset(self) -> FieldSet: + def _generate_fieldset(self) -> parcels.FieldSet: """ Create and combine FieldSets for each variable, supporting both local and Copernicus Marine data sources. - Per variable avoids issues when using copernicusmarine and creating directly one FieldSet of ds's sourced from different Copernicus Marine product IDs, which is often the case for BGC variables. + N.B. Per variable avoids issues when using copernicusmarine and creating directly one FieldSet of ds's sourced from different Copernicus Marine product IDs (which can also have different temporal resolutions), which is often the case for BGC variables. """ fieldsets_list = [] keys = list(self.variables.keys()) @@ -217,12 +217,11 @@ def _generate_fieldset(self) -> FieldSet: [data_dir.joinpath(f) for f in files] ) # using: ds --> .from_xarray_dataset seems more robust than .from_netcdf for handling different temporal resolutions for different variables ... - fs = FieldSet.from_xarray_dataset( - ds, - variables={key: full_var_name}, - dimensions=self.dimensions, - mesh="spherical", - ) + # TODO: do docs on pre-downloading data need to be updated for these changes? Anything about conventions etc.? + fields = {key: ds[full_var_name]} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) + else: # stream via Copernicus Marine Service physical = var in COPERNICUSMARINE_PHYS_VARIABLES ds = self._get_copernicus_ds( @@ -230,9 +229,11 @@ def _generate_fieldset(self) -> FieldSet: physical=physical, var=var, ) - fs = FieldSet.from_xarray_dataset( - ds, {key: var}, self.dimensions, mesh="spherical" - ) + + fields = {key: ds[var]} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) + fieldsets_list.append(fs) base_fieldset = fieldsets_list[0] diff --git a/src/virtualship/models/expedition.py b/src/virtualship/models/expedition.py index eef23c76..da084328 100644 --- a/src/virtualship/models/expedition.py +++ b/src/virtualship/models/expedition.py @@ -17,7 +17,6 @@ _calc_sail_time, _calc_wp_stationkeeping_time, _get_bathy_data, - _get_waypoint_latlons, _validate_numeric_to_timedelta, get_supported_sensors, register_instrument_config, @@ -131,14 +130,7 @@ def verify( land_waypoints = [] if not ignore_land_test: try: - wp_lats, wp_lons = _get_waypoint_latlons(self.waypoints) - bathymetry_field = _get_bathy_data( - min(wp_lats), - max(wp_lats), - min(wp_lons), - max(wp_lons), - from_data=from_data, - ).bathymetry + bathymetry_field = _get_bathy_data(from_data=from_data).bathymetry except Exception as e: raise ScheduleError( f"Problem loading bathymetry data (used to verify waypoints are in water) directly via copernicusmarine. \n\n original message: {e}" diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 3dab6b70..6ba2711e 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -16,6 +16,7 @@ import pyproj import xarray as xr +import parcels from parcels import FieldSet, Particle, Variable from virtualship.errors import CopernicusCatalogueError @@ -425,13 +426,7 @@ def _start_end_in_product_timerange( ) -def _get_bathy_data( - min_lat: float, - max_lat: float, - min_lon: float, - max_lon: float, - from_data: Path | None = None, -) -> FieldSet: +def _get_bathy_data(from_data: Path | None = None) -> FieldSet: """Bathymetry data from local or 'streamed' directly from Copernicus Marine.""" if from_data is not None: # load from local data var = "deptho" @@ -443,11 +438,6 @@ def _get_bathy_data( f"\n\n❗️ Could not find bathymetry variable '{var}' in data directory '{from_data}/bathymetry/'.\n\n❗️ Is the pre-downloaded data directory structure compliant with VirtualShip expectations?\n\n❗️ See the docs for more information on expectations: https://virtualship.readthedocs.io/en/latest/user-guide/index.html#documentation\n" ) from e ds_bathymetry = xr.open_dataset(bathy_dir.joinpath(filename)) - bathymetry_variables = {"bathymetry": "deptho"} - bathymetry_dimensions = {"lon": "longitude", "lat": "latitude"} - return FieldSet.from_xarray_dataset( - ds_bathymetry, bathymetry_variables, bathymetry_dimensions - ) else: # stream via Copernicus Marine Service ds_bathymetry = copernicusmarine.open_dataset( @@ -455,12 +445,11 @@ def _get_bathy_data( variables=["deptho"], coordinates_selection_method="outside", ) - bathymetry_variables = {"bathymetry": "deptho"} - bathymetry_dimensions = {"lon": "longitude", "lat": "latitude"} - return FieldSet.from_xarray_dataset( - ds_bathymetry, bathymetry_variables, bathymetry_dimensions + ds_fset = parcels.convert.copernicusmarine_to_sgrid( + fields={var: ds_bathymetry[var]} ) + return FieldSet.from_sgrid_conventions(ds_fset) def expedition_cost(schedule_results: ScheduleOk, time_past: timedelta) -> float: From d13a0369c4b7d7c7b26cf20063ec9552a4e6fb96 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Wed, 20 May 2026 15:02:29 +0000 Subject: [PATCH 17/94] [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --- src/virtualship/instruments/adcp.py | 2 +- src/virtualship/instruments/argo_float.py | 2 +- src/virtualship/instruments/base.py | 2 +- src/virtualship/instruments/ctd.py | 2 +- src/virtualship/instruments/drifter.py | 2 +- src/virtualship/instruments/ship_underwater_st.py | 2 +- src/virtualship/instruments/xbt.py | 2 +- src/virtualship/utils.py | 4 ++-- tests/test_utils.py | 2 +- 9 files changed, 10 insertions(+), 10 deletions(-) diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index 7ee718f6..626ca354 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -3,8 +3,8 @@ from typing import ClassVar import numpy as np - from parcels import ParticleSet + from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index d1644849..4eae6350 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -4,9 +4,9 @@ from typing import ClassVar import numpy as np +from parcels import ParticleSet, StatusCode, Variable from parcels.kernels import AdvectionRK2 -from parcels import ParticleSet, StatusCode, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index a3b7adb4..f6afd3e0 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -8,10 +8,10 @@ from typing import TYPE_CHECKING, ClassVar import copernicusmarine +import parcels import xarray as xr from yaspin import yaspin -import parcels from virtualship.errors import CopernicusCatalogueError from virtualship.utils import ( COPERNICUSMARINE_PHYS_VARIABLES, diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index a7a4218f..d8f229c1 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -4,9 +4,9 @@ from typing import TYPE_CHECKING, ClassVar import numpy as np +from parcels import ParticleSet, Variable from parcels._core.statuscodes import StatusCode -from parcels import ParticleSet, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index 46689e79..79a1e34b 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -4,10 +4,10 @@ from typing import ClassVar import numpy as np +from parcels import ParticleSet, Variable from parcels._core.statuscodes import StatusCode from parcels.kernels import AdvectionRK2 -from parcels import ParticleSet, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index 78c757d2..d844b7a2 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -3,8 +3,8 @@ from typing import ClassVar import numpy as np - from parcels import ParticleSet + from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index 06e862a6..d02afb7a 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -4,9 +4,9 @@ from typing import ClassVar import numpy as np +from parcels import ParticleSet, Variable from parcels._core.statuscodes import StatusCode -from parcels import ParticleSet, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 6ba2711e..85b3ad75 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -13,11 +13,11 @@ import copernicusmarine import numpy as np +import parcels import pyproj import xarray as xr - -import parcels from parcels import FieldSet, Particle, Variable + from virtualship.errors import CopernicusCatalogueError if TYPE_CHECKING: diff --git a/tests/test_utils.py b/tests/test_utils.py index 4ab96654..2628793d 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -5,9 +5,9 @@ import numpy as np import pytest import xarray as xr +from parcels import FieldSet, JITParticle, ScipyParticle, Variable import virtualship.utils -from parcels import FieldSet, JITParticle, ScipyParticle, Variable from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.models.expedition import Expedition, SensorConfig From 5c7fd290c5b272193f3a5163991f7719d6d76c4f Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 21 May 2026 09:11:53 +0200 Subject: [PATCH 18/94] standardise variable naming for bathy data ingestion --- src/virtualship/utils.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 6ba2711e..021a8979 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -428,27 +428,28 @@ def _start_end_in_product_timerange( def _get_bathy_data(from_data: Path | None = None) -> FieldSet: """Bathymetry data from local or 'streamed' directly from Copernicus Marine.""" + VAR = "deptho" if from_data is not None: # load from local data - var = "deptho" bathy_dir = from_data.joinpath("bathymetry") try: - filename, _ = _find_nc_file_with_variable(bathy_dir, var) + filename, _ = _find_nc_file_with_variable(bathy_dir, VAR) except Exception as e: raise RuntimeError( - f"\n\n❗️ Could not find bathymetry variable '{var}' in data directory '{from_data}/bathymetry/'.\n\n❗️ Is the pre-downloaded data directory structure compliant with VirtualShip expectations?\n\n❗️ See the docs for more information on expectations: https://virtualship.readthedocs.io/en/latest/user-guide/index.html#documentation\n" + f"\n\n❗️ Could not find bathymetry variable '{VAR}' in data directory '{from_data}/bathymetry/'.\n\n❗️ Is the pre-downloaded data directory structure compliant with VirtualShip expectations?\n\n❗️ See the docs for more information on expectations: https://virtualship.readthedocs.io/en/latest/user-guide/index.html#documentation\n" ) from e ds_bathymetry = xr.open_dataset(bathy_dir.joinpath(filename)) else: # stream via Copernicus Marine Service ds_bathymetry = copernicusmarine.open_dataset( dataset_id=BATHYMETRY_ID, - variables=["deptho"], + variables=[VAR], coordinates_selection_method="outside", ) ds_fset = parcels.convert.copernicusmarine_to_sgrid( - fields={var: ds_bathymetry[var]} + fields={"bathymetry": ds_bathymetry[VAR]} ) + return FieldSet.from_sgrid_conventions(ds_fset) From ee172304a6c127ba164330ca081fd7de385a47c0 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 21 May 2026 10:23:20 +0200 Subject: [PATCH 19/94] env fixes --- pixi.toml | 3 ++- pyproject.toml | 3 ++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/pixi.toml b/pixi.toml index b86b8f60..0f003e8e 100644 --- a/pixi.toml +++ b/pixi.toml @@ -36,11 +36,12 @@ virtualship = { path = "." } netcdf4 = "*" numpy = ">=2.1.0" dask = "*" +zarr = ">=3" [pypi-dependencies] parcels = { git = "https://github.com/Parcels-code/Parcels", branch = "main" } -# Commented out whilst parcels v4 alpha only supports Python 3.11 +# Commented out whilst parcels v4 alpha only supports Python 3.11 (?) # [feature.py310.dependencies] # python = "3.10.*" diff --git a/pyproject.toml b/pyproject.toml index e19024d3..fd0d612b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -69,7 +69,8 @@ filterwarnings = [ "error", "default::DeprecationWarning", "error::DeprecationWarning:virtualship", - "ignore:ParticleSet is empty.*:RuntimeWarning" # TODO: Probably should be ignored in the source code + "ignore:ParticleSet is empty.*:RuntimeWarning", # TODO: Probably should be ignored in the source code + "ignore:This is an alpha version of Parcels v4.*:UserWarning" # TODO: necessary whilst Parcels v4 is still alpha ] log_cli_level = "INFO" testpaths = [ From 92e74bd552c09c88de15bbf24222f014c5004107 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 21 May 2026 16:33:22 +0200 Subject: [PATCH 20/94] add ipdb for devs --- pixi.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pixi.toml b/pixi.toml index 0f003e8e..6cd97d08 100644 --- a/pixi.toml +++ b/pixi.toml @@ -37,6 +37,7 @@ netcdf4 = "*" numpy = ">=2.1.0" dask = "*" zarr = ">=3" +ipdb = ">=0.13.13,<0.14" [pypi-dependencies] parcels = { git = "https://github.com/Parcels-code/Parcels", branch = "main" } From 0a83437dca2f4620ff5bf01c84a5bc103a94f602 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 21 May 2026 17:22:26 +0200 Subject: [PATCH 21/94] further changes to suit v4 --- src/virtualship/instruments/base.py | 16 ++++++---------- src/virtualship/models/expedition.py | 2 +- src/virtualship/utils.py | 8 ++++++-- 3 files changed, 13 insertions(+), 13 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index a3b7adb4..583f62ed 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -8,10 +8,10 @@ from typing import TYPE_CHECKING, ClassVar import copernicusmarine +import parcels import xarray as xr from yaspin import yaspin -import parcels from virtualship.errors import CopernicusCatalogueError from virtualship.utils import ( COPERNICUSMARINE_PHYS_VARIABLES, @@ -100,19 +100,15 @@ def load_input_data(self) -> parcels.FieldSet: getattr(fieldset, var).interp_method = parcels.interpolators.XLinear # depth negative - for g in fieldset.gridset.grids: + for g in fieldset.gridset: g.negate_depth() # bathymetry data if self.add_bathymetry: - bathymetry_field = _get_bathy_data( - self.min_lat, - self.max_lat, - self.min_lon, - self.max_lon, - from_data=self.from_data, - ).bathymetry - bathymetry_field.data = -bathymetry_field.data + bathymetry_field = _get_bathy_data(from_data=self.from_data).bathymetry + bathymetry_field.data = ( + -bathymetry_field.data + ) # TODO: how does v4 handle? positive up or down? fieldset.add_field(bathymetry_field) return fieldset diff --git a/src/virtualship/models/expedition.py b/src/virtualship/models/expedition.py index da084328..d212a686 100644 --- a/src/virtualship/models/expedition.py +++ b/src/virtualship/models/expedition.py @@ -139,7 +139,7 @@ def verify( for wp_i, wp in enumerate(self.waypoints): try: value = bathymetry_field.eval( - 0, # time + np.float64(0.0), # time 0, # depth (surface) wp.location.lat, wp.location.lon, diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 021a8979..78657de5 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -13,11 +13,11 @@ import copernicusmarine import numpy as np +import parcels import pyproj import xarray as xr - -import parcels from parcels import FieldSet, Particle, Variable + from virtualship.errors import CopernicusCatalogueError if TYPE_CHECKING: @@ -446,6 +446,10 @@ def _get_bathy_data(from_data: Path | None = None) -> FieldSet: coordinates_selection_method="outside", ) + ds_bathymetry = ds_bathymetry.expand_dims( + {"depth": 1} + ) # TODO: bodge whilst parcels v4 does not support 2D fields and seeks depth dim; change when parcels v4 released + ds_fset = parcels.convert.copernicusmarine_to_sgrid( fields={"bathymetry": ds_bathymetry[VAR]} ) From ca9e306641540d0ba88d5f1c822f3f2b3fff2c24 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 22 May 2026 12:43:44 +0200 Subject: [PATCH 22/94] first steps considering handling depth positive up or down --- src/virtualship/instruments/base.py | 4 ---- src/virtualship/instruments/ctd.py | 5 ++++- tests/instruments/test_base.py | 1 - 3 files changed, 4 insertions(+), 6 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 583f62ed..f2ad8a47 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -99,10 +99,6 @@ def load_input_data(self) -> parcels.FieldSet: for var in (v for v in self.variables if v not in ("U", "V")): getattr(fieldset, var).interp_method = parcels.interpolators.XLinear - # depth negative - for g in fieldset.gridset: - g.negate_depth() - # bathymetry data if self.add_bathymetry: bathymetry_field = _get_bathy_data(from_data=self.from_data).bathymetry diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index a7a4218f..b48677ad 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -4,9 +4,9 @@ from typing import TYPE_CHECKING, ClassVar import numpy as np +from parcels import ParticleSet, Variable from parcels._core.statuscodes import StatusCode -from parcels import ParticleSet, Variable from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType @@ -117,6 +117,9 @@ def _ctd_cast(particles, fieldset): particles_raising = particles[particles.raising == 1] # TODO: change to boolean masking, like with Argo Floats? + # TODO: different handling of positive down for z now?! Doing positive down now... think kernels need adjusting... + # TODO: need to check on all other instrument kernels as well... + # TODO: plus how the configs are inputted in e.g. expedition.yaml # lowering particles_lowering.dz = -particles_lowering.winch_speed * particles_lowering.dt diff --git a/tests/instruments/test_base.py b/tests/instruments/test_base.py index bbcfea44..a17f95bf 100644 --- a/tests/instruments/test_base.py +++ b/tests/instruments/test_base.py @@ -33,7 +33,6 @@ def test_load_input_data(mock_copernicusmarine, mock_select_product_id, mock_Fie mock_fieldset = MagicMock() mock_FieldSet.from_netcdf.return_value = mock_fieldset mock_FieldSet.from_xarray_dataset.return_value = mock_fieldset - mock_fieldset.gridset.grids = [MagicMock(negate_depth=MagicMock())] mock_fieldset.__getitem__.side_effect = lambda k: MagicMock() mock_copernicusmarine.open_dataset.return_value = MagicMock() # Create a mock waypoint with latitude and longitude From 63122b9e84c26e4c302d001659dde00516ff7a55 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 26 May 2026 16:05:01 +0100 Subject: [PATCH 23/94] refactor to _compute_max_depths; more changes in line with Parcels v4 API --- src/virtualship/cli/_run.py | 1 - src/virtualship/instruments/ctd.py | 27 ++++++++------------------- src/virtualship/instruments/xbt.py | 14 ++------------ src/virtualship/utils.py | 20 +++++++++++++++++++- 4 files changed, 29 insertions(+), 33 deletions(-) diff --git a/src/virtualship/cli/_run.py b/src/virtualship/cli/_run.py index 10afc5c5..703502f2 100644 --- a/src/virtualship/cli/_run.py +++ b/src/virtualship/cli/_run.py @@ -200,7 +200,6 @@ def _run( ) # execute simulation - # TODO: outpath will be Parquet with v4... instrument.execute( measurements=measurements, out_path=expedition_dir.joinpath(RESULTS, f"{itype.name.lower()}.zarr"), diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index b48677ad..6162667e 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -11,6 +11,7 @@ from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.utils import ( + _compute_max_depths, build_particle_class_from_sensors, register_instrument, ) @@ -195,12 +196,9 @@ def simulate(self, measurements, out_path) -> None: # use first active field for time reference _time_ref_key = next(iter(self.variables)) _time_ref_field = getattr(fieldset, _time_ref_key) - fieldset_starttime = _time_ref_field.grid.time_origin.fulltime( - _time_ref_field.grid.time_full[0] - ) - fieldset_endtime = _time_ref_field.grid.time_origin.fulltime( - _time_ref_field.grid.time_full[-1] - ) + + fieldset_starttime = _time_ref_field.data.time.isel(time=0) + fieldset_endtime = _time_ref_field.data.time.isel(time=-1) # deploy time for all ctds should be later than fieldset start time if not all( @@ -212,18 +210,7 @@ def simulate(self, measurements, out_path) -> None: raise ValueError("CTD deployed before fieldset starts.") # depth the ctd will go to. shallowest between ctd max depth and bathymetry. - max_depths = [ - max( - ctd.max_depth, - fieldset.bathymetry.eval( - z=0, - y=ctd.spacetime.location.lat, - x=ctd.spacetime.location.lon, - time=0, - ), - ) - for ctd in measurements - ] + max_depths = _compute_max_depths(measurements, fieldset) # CTD depth can not be too shallow, because kernel would break. # This shallow is not useful anyway, no need to support. @@ -245,7 +232,9 @@ def simulate(self, measurements, out_path) -> None: lon=[ctd.spacetime.location.lon for ctd in measurements], lat=[ctd.spacetime.location.lat for ctd in measurements], depth=[ctd.min_depth for ctd in measurements], - time=[ctd.spacetime.time for ctd in measurements], + time=[ + np.datetime64(ctd.spacetime.time) for ctd in measurements + ], # TODO: v4 question... docstring says takes datetime, but here requires -> np.datetime64? max_depth=max_depths, min_depth=[ctd.min_depth for ctd in measurements], winch_speed=[WINCH_SPEED for _ in measurements], diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index d02afb7a..fc52696d 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -12,6 +12,7 @@ from virtualship.instruments.types import InstrumentType from virtualship.models.spacetime import Spacetime from virtualship.utils import ( + _compute_max_depths, build_particle_class_from_sensors, register_instrument, ) @@ -143,18 +144,7 @@ def simulate(self, measurements, out_path) -> None: raise ValueError("XBT deployed before fieldset starts.") # depth the xbt will go to. shallowest between xbt max depth and bathymetry. - max_depths = [ - max( - xbt.max_depth, - fieldset.bathymetry.eval( - z=0, - y=xbt.spacetime.location.lat, - x=xbt.spacetime.location.lon, - time=0, - ), - ) - for xbt in measurements - ] + max_depths = _compute_max_depths(measurements, fieldset) # initial fall speeds initial_fall_speeds = [xbt.fall_speed for xbt in measurements] diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 78657de5..9b7c2b8b 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -560,6 +560,24 @@ def _find_files_in_timerange( return [fname for _, fname in files_with_dates] +def _compute_max_depths(measurements, fieldset) -> list[float]: + """Compute the effective max depth for each measurement, capped by bathymetry. Return as list of floats for best Parcels compatibility.""" + return [ + max( + m.max_depth, + float( + fieldset.bathymetry.eval( + z=0, + y=m.spacetime.location.lat, + x=m.spacetime.location.lon, + time=np.float64(0), + ) + ), + ) + for m in measurements + ] + + def _random_noise(scale: float = 0.05, limit: float = 0.1) -> float: """Generate a small random noise value for drifter seeding locations.""" value = np.random.normal(loc=0.0, scale=scale) @@ -654,7 +672,7 @@ def build_particle_class_from_sensors( variable for sc in sensors if sc.enabled for variable in sc.meta.particle_vars ] - return Particle.add_variables(nonsensor_variables + sensor_variables) + return Particle.add_variable(nonsensor_variables + sensor_variables) # ===================================================== From a6ad6317e445931ade22d14c68c3de6b3e07913b Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 26 May 2026 16:09:33 +0100 Subject: [PATCH 24/94] depth -> z in ParticleSet's --- src/virtualship/instruments/adcp.py | 4 ++-- src/virtualship/instruments/argo_float.py | 2 +- src/virtualship/instruments/ctd.py | 2 +- src/virtualship/instruments/drifter.py | 2 +- src/virtualship/instruments/ship_underwater_st.py | 2 +- src/virtualship/instruments/xbt.py | 2 +- 6 files changed, 7 insertions(+), 7 deletions(-) diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index 626ca354..4be4b33c 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -105,14 +105,14 @@ def simulate(self, measurements, out_path) -> None: bins = np.linspace(MAX_DEPTH, MIN_DEPTH, NUM_BINS) num_particles = len(bins) - particleset = ParticleSet.from_list( + particleset = ParticleSet( fieldset=fieldset, pclass=_ADCPParticle, lon=np.full( num_particles, 0.0 ), # initial lat/lon are irrelevant and will be overruled later.s lat=np.full(num_particles, 0.0), - depth=bins, + z=bins, time=0, ) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 4eae6350..490cf150 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -260,7 +260,7 @@ def simulate(self, measurements, out_path) -> None: pclass=_ArgoParticle, lat=[argo.spacetime.location.lat for argo in measurements], lon=[argo.spacetime.location.lon for argo in measurements], - depth=[argo.min_depth for argo in measurements], + z=[argo.min_depth for argo in measurements], time=[argo.spacetime.time for argo in measurements], min_depth=[argo.min_depth for argo in measurements], max_depth=[argo.max_depth for argo in measurements], diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 6162667e..094f08c9 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -231,7 +231,7 @@ def simulate(self, measurements, out_path) -> None: pclass=_CTDParticle, lon=[ctd.spacetime.location.lon for ctd in measurements], lat=[ctd.spacetime.location.lat for ctd in measurements], - depth=[ctd.min_depth for ctd in measurements], + z=[ctd.min_depth for ctd in measurements], time=[ np.datetime64(ctd.spacetime.time) for ctd in measurements ], # TODO: v4 question... docstring says takes datetime, but here requires -> np.datetime64? diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index 79a1e34b..67e84780 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -147,7 +147,7 @@ def simulate(self, measurements, out_path) -> None: pclass=_DrifterParticle, lat=lat_release, lon=lon_release, - depth=[drifter.depth for drifter in measurements], + z=[drifter.depth for drifter in measurements], time=[drifter.spacetime.time for drifter in measurements], has_lifetime=[ 1 if drifter.lifetime is not None else 0 for drifter in measurements diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index d844b7a2..bccaa2ce 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -100,7 +100,7 @@ def simulate(self, measurements, out_path) -> None: st_config.sensors, _ST_NONSENSOR_VARIABLES ) - particleset = ParticleSet.from_list( + particleset = ParticleSet( fieldset=fieldset, pclass=_ShipSTParticle, lon=0.0, diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index fc52696d..04efc80c 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -168,7 +168,7 @@ def simulate(self, measurements, out_path) -> None: pclass=_XBTParticle, lon=[xbt.spacetime.location.lon for xbt in measurements], lat=[xbt.spacetime.location.lat for xbt in measurements], - depth=[xbt.min_depth for xbt in measurements], + z=[xbt.min_depth for xbt in measurements], time=[xbt.spacetime.time for xbt in measurements], max_depth=max_depths, min_depth=[xbt.min_depth for xbt in measurements], From 8e0a33f57039fa4c92143be8476b676d73de4ce0 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 28 May 2026 10:32:42 +0100 Subject: [PATCH 25/94] times to datetime64 and update fieldset eval output --- src/virtualship/instruments/adcp.py | 1 - src/virtualship/instruments/argo_float.py | 2 +- src/virtualship/instruments/base.py | 17 +++++++++++------ src/virtualship/instruments/ctd.py | 4 +--- src/virtualship/instruments/drifter.py | 2 +- .../instruments/ship_underwater_st.py | 1 - src/virtualship/instruments/xbt.py | 2 +- src/virtualship/utils.py | 16 +++++++--------- 8 files changed, 22 insertions(+), 23 deletions(-) diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index 4be4b33c..ee910e02 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -113,7 +113,6 @@ def simulate(self, measurements, out_path) -> None: ), # initial lat/lon are irrelevant and will be overruled later.s lat=np.full(num_particles, 0.0), z=bins, - time=0, ) out_file = particleset.ParticleFile(name=out_path, outputdt=np.inf) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 490cf150..eae21624 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -261,7 +261,7 @@ def simulate(self, measurements, out_path) -> None: lat=[argo.spacetime.location.lat for argo in measurements], lon=[argo.spacetime.location.lon for argo in measurements], z=[argo.min_depth for argo in measurements], - time=[argo.spacetime.time for argo in measurements], + time=[np.datetime64(argo.spacetime.time) for argo in measurements], min_depth=[argo.min_depth for argo in measurements], max_depth=[argo.max_depth for argo in measurements], drift_depth=[argo.drift_depth for argo in measurements], diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index f2ad8a47..3e49850b 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -120,14 +120,19 @@ def simulate( def execute(self, measurements: list, out_path: str | Path) -> None: """Run instrument simulation.""" + TMP = False + if not self.verbose_progress: - with yaspin( - text=f"Simulating {self.__class__.__name__.split('Instrument')[0]} measurements... ", - side="right", - spinner=ship_spinner, - ) as spinner: + if TMP: + with yaspin( + text=f"Simulating {self.__class__.__name__.split('Instrument')[0]} measurements... ", + side="right", + spinner=ship_spinner, + ) as spinner: + self.simulate(measurements, out_path) + spinner.ok("✅\n") + else: self.simulate(measurements, out_path) - spinner.ok("✅\n") else: print( f"Simulating {self.__class__.__name__.split('Instrument')[0]} measurements... " diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 094f08c9..91c71e59 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -232,9 +232,7 @@ def simulate(self, measurements, out_path) -> None: lon=[ctd.spacetime.location.lon for ctd in measurements], lat=[ctd.spacetime.location.lat for ctd in measurements], z=[ctd.min_depth for ctd in measurements], - time=[ - np.datetime64(ctd.spacetime.time) for ctd in measurements - ], # TODO: v4 question... docstring says takes datetime, but here requires -> np.datetime64? + time=[np.datetime64(ctd.spacetime.time) for ctd in measurements], max_depth=max_depths, min_depth=[ctd.min_depth for ctd in measurements], winch_speed=[WINCH_SPEED for _ in measurements], diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index 67e84780..95c6132f 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -148,7 +148,7 @@ def simulate(self, measurements, out_path) -> None: lat=lat_release, lon=lon_release, z=[drifter.depth for drifter in measurements], - time=[drifter.spacetime.time for drifter in measurements], + time=[np.datetime64(drifter.spacetime.time) for drifter in measurements], has_lifetime=[ 1 if drifter.lifetime is not None else 0 for drifter in measurements ], diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index bccaa2ce..829d1ad7 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -106,7 +106,6 @@ def simulate(self, measurements, out_path) -> None: lon=0.0, lat=0.0, depth=DEPTH, - time=0, ) out_file = particleset.ParticleFile(name=out_path, outputdt=np.inf) diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index 04efc80c..d738a1fd 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -169,7 +169,7 @@ def simulate(self, measurements, out_path) -> None: lon=[xbt.spacetime.location.lon for xbt in measurements], lat=[xbt.spacetime.location.lat for xbt in measurements], z=[xbt.min_depth for xbt in measurements], - time=[xbt.spacetime.time for xbt in measurements], + time=[np.datetime64(xbt.spacetime.time) for xbt in measurements], max_depth=max_depths, min_depth=[xbt.min_depth for xbt in measurements], fall_speed=[xbt.fall_speed for xbt in measurements], diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 9b7c2b8b..df5d153c 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -561,18 +561,16 @@ def _find_files_in_timerange( def _compute_max_depths(measurements, fieldset) -> list[float]: - """Compute the effective max depth for each measurement, capped by bathymetry. Return as list of floats for best Parcels compatibility.""" + """Compute the effective max depth for each measurement, capped by bathymetry.""" return [ max( m.max_depth, - float( - fieldset.bathymetry.eval( - z=0, - y=m.spacetime.location.lat, - x=m.spacetime.location.lon, - time=np.float64(0), - ) - ), + fieldset.bathymetry.eval( + z=0, + y=m.spacetime.location.lat, + x=m.spacetime.location.lon, + time=np.float64(0), + )[0], ) for m in measurements ] From 8b76854e2874ce994e43161b337f1799afb920a1 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 29 May 2026 12:32:54 +0100 Subject: [PATCH 26/94] tmp ds.load() step for v4.0 --- src/virtualship/instruments/base.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 3e49850b..74ad7bc4 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -210,9 +210,8 @@ def _generate_fieldset(self) -> parcels.FieldSet: data_dir, var ) # get full variable name from one of the files; var may only appear as substring in variable name in file - ds = xr.open_mfdataset( - [data_dir.joinpath(f) for f in files] - ) # using: ds --> .from_xarray_dataset seems more robust than .from_netcdf for handling different temporal resolutions for different variables ... + ds = xr.open_mfdataset([data_dir.joinpath(f) for f in files]) + ds.load() # TODO: tmp step during v4 alpha stage... probably to be updated on the Parcels end # TODO: do docs on pre-downloading data need to be updated for these changes? Anything about conventions etc.? fields = {key: ds[full_var_name]} @@ -226,6 +225,7 @@ def _generate_fieldset(self) -> parcels.FieldSet: physical=physical, var=var, ) + ds.load() # TODO: tmp step during v4 alpha stage... probably to be updated on the Parcels end fields = {key: ds[var]} ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) From 81f857f166de793ce3f0f1ce2160572effab56bc Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 11:30:21 +0200 Subject: [PATCH 27/94] error messaging in argo floats --- src/virtualship/instruments/argo_float.py | 34 ++++++++--------------- 1 file changed, 12 insertions(+), 22 deletions(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index f5fbd858..86b0faf1 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -13,6 +13,11 @@ from virtualship.models.spacetime import Spacetime from virtualship.utils import build_particle_class_from_sensors, register_instrument +# mapping from StatusCode integer value to attribute name (e.g. 60 -> "ErrorOutOfBounds") +_STATUS_CODE_NAMES: dict[int, str] = { + v: k for k, v in vars(StatusCode).items() if not k.startswith("_") +} + # ===================================================== # SECTION: Dataclass # ===================================================== @@ -145,31 +150,16 @@ def _keep_at_surface(particles, fieldset): def _check_error(particles, fieldset): errors = particles.state >= 50 # captures all Errors + # TODO: check print statements are as expected + print( + "WARNING: Error(s) found during Argo Float simulation but the expedition will continue..." + f"\n\nError code(s): {', '.join(_STATUS_CODE_NAMES.get(error, str(error)) + 'at time: ' + str(particles.time[errors][i]) + ', lat: ' + str(particles.lat[errors][i]) + ', lon: ' + str(particles.lon[errors][i]) for i, error in enumerate(particles.state[errors]))}" + "\n\nIf ErrorOutOfBounds, consider reducing the lifetime in Argo Float config (the fieldset spatial bounds are constrained under-the-hood). For further advice please contact the VirtualShip team via GitHub (https://github.com/Parcels-code/virtualship/issues) or email (virtualship@uu.nl)." + "\nCarrying on with the expedition..." + ) particles.state[errors] = StatusCode.Delete -def _check_error(particle, fieldset, time): - if particle.state >= 50: # This captures all Errors - if particle.state == 50: - print("WARNING: Error during Argo Float simulation...") - elif particle.state == 51: - print("WARNING: ErrorInterpolation during Argo Float simulation...") - elif particle.state == 60: - print("WARNING: ErrorOutOfBounds during Argo Float simulation...") - elif particle.state == 61: - print("WARNING: ErrorThroughSurface during Argo Float simulation...") - elif particle.state == 70: - print("WARNING: ErrorTimeExtrapolation during Argo Float simulation...") - else: - print("Unknown error during Argo Float simulation...") - print( - "WARNING: An error occured during simulation but the expedition will continue. If ErrorOutOfBounds, consider reducing the lifetime in Argo Float config (the fieldset spatial bounds are constrained under-the-hood). For further advice please contact the VirtualShip team via GitHub (https://github.com/Parcels-code/virtualship/issues) or email (virtualship@uu.nl). Carrying on with the expedition..." - ) - # TODO: warnings are a bit limited in Parcels v3, but v4 should allow more informative (+ not all these if statements) when e.g. f-strings are supported in kernels - - particle.delete() - - def _argo_sample_temperature(particles, fieldset): # Phase 3: ascending — sample temperature; NaN otherwise phase_mask = particles.cycle_phase == 3 From cd2f9ffb203b8d3f7d8a4df77e9ce016c289c9e5 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 11:38:29 +0200 Subject: [PATCH 28/94] refactor _generate_fieldset() --- src/virtualship/instruments/base.py | 28 +++++++++++----------------- 1 file changed, 11 insertions(+), 17 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 74ad7bc4..81e8f4f4 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -193,12 +193,11 @@ def _generate_fieldset(self) -> parcels.FieldSet: for key in keys: var = self.variables[key] + physical = var in COPERNICUSMARINE_PHYS_VARIABLES + + # TODO: do docs on pre-downloading data need to be updated for these changes? Anything about conventions etc.? if self.from_data is not None: # load from local data - physical = var in COPERNICUSMARINE_PHYS_VARIABLES - if physical: - data_dir = self.from_data.joinpath("phys") - else: - data_dir = self.from_data.joinpath("bgc") + data_dir = self.from_data.joinpath("phys" if physical else "bgc") files = _find_files_in_timerange( data_dir, @@ -206,30 +205,25 @@ def _generate_fieldset(self) -> parcels.FieldSet: self.max_time + timedelta(days=time_buffer), ) - _, full_var_name = _find_nc_file_with_variable( + _, field_var_name = _find_nc_file_with_variable( data_dir, var ) # get full variable name from one of the files; var may only appear as substring in variable name in file ds = xr.open_mfdataset([data_dir.joinpath(f) for f in files]) - ds.load() # TODO: tmp step during v4 alpha stage... probably to be updated on the Parcels end - - # TODO: do docs on pre-downloading data need to be updated for these changes? Anything about conventions etc.? - fields = {key: ds[full_var_name]} - ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) - fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) else: # stream via Copernicus Marine Service - physical = var in COPERNICUSMARINE_PHYS_VARIABLES ds = self._get_copernicus_ds( time_buffer, physical=physical, var=var, ) - ds.load() # TODO: tmp step during v4 alpha stage... probably to be updated on the Parcels end + field_var_name = var + + ds.load() # TODO: tmp step during v4 alpha stage... probably to be updated on the Parcels end - fields = {key: ds[var]} - ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) - fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) + fields = {key: ds[field_var_name]} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) fieldsets_list.append(fs) From 39e63d19d9fbc18911e785f996a9fb743054c3ee Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 11:50:04 +0200 Subject: [PATCH 29/94] small tidy up --- src/virtualship/instruments/base.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 81e8f4f4..dd38f032 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -102,9 +102,7 @@ def load_input_data(self) -> parcels.FieldSet: # bathymetry data if self.add_bathymetry: bathymetry_field = _get_bathy_data(from_data=self.from_data).bathymetry - bathymetry_field.data = ( - -bathymetry_field.data - ) # TODO: how does v4 handle? positive up or down? + bathymetry_field.data = -bathymetry_field.data fieldset.add_field(bathymetry_field) return fieldset @@ -120,12 +118,13 @@ def simulate( def execute(self, measurements: list, out_path: str | Path) -> None: """Run instrument simulation.""" - TMP = False + TMP = False # TODO: just for dev; remove before merging + instrument_name = self.__class__.__name__.split("Instrument")[0] if not self.verbose_progress: if TMP: with yaspin( - text=f"Simulating {self.__class__.__name__.split('Instrument')[0]} measurements... ", + text=f"Simulating {instrument_name} measurements... ", side="right", spinner=ship_spinner, ) as spinner: @@ -134,9 +133,7 @@ def execute(self, measurements: list, out_path: str | Path) -> None: else: self.simulate(measurements, out_path) else: - print( - f"Simulating {self.__class__.__name__.split('Instrument')[0]} measurements... " - ) + print(f"Simulating {instrument_name} measurements... ") self.simulate(measurements, out_path) print("\n") From 0dc5975aff44ac99b0c630c04be610ee4d17145c Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 11:55:13 +0200 Subject: [PATCH 30/94] from zarr -> parquet output --- src/virtualship/cli/_run.py | 4 +++- src/virtualship/instruments/adcp.py | 4 ++-- src/virtualship/instruments/argo_float.py | 4 ++-- src/virtualship/instruments/ctd.py | 4 ++-- src/virtualship/instruments/drifter.py | 4 ++-- src/virtualship/instruments/ship_underwater_st.py | 4 ++-- src/virtualship/instruments/xbt.py | 4 ++-- 7 files changed, 15 insertions(+), 13 deletions(-) diff --git a/src/virtualship/cli/_run.py b/src/virtualship/cli/_run.py index 703502f2..a320acad 100644 --- a/src/virtualship/cli/_run.py +++ b/src/virtualship/cli/_run.py @@ -202,7 +202,9 @@ def _run( # execute simulation instrument.execute( measurements=measurements, - out_path=expedition_dir.joinpath(RESULTS, f"{itype.name.lower()}.zarr"), + out_path=expedition_dir.joinpath( + RESULTS, f"{itype.name.lower()}.parquet" + ), ) except Exception as e: # clean up if unexpected error occurs diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index ee910e02..ca83f3d7 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -3,7 +3,7 @@ from typing import ClassVar import numpy as np -from parcels import ParticleSet +from parcels import ParticleFile, ParticleSet from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType @@ -115,7 +115,7 @@ def simulate(self, measurements, out_path) -> None: z=bins, ) - out_file = particleset.ParticleFile(name=out_path, outputdt=np.inf) + out_file = ParticleFile(name=out_path, outputdt=np.inf) # build kernel list from active sensors only sampling_kernels = [ diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 86b0faf1..03801038 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -4,7 +4,7 @@ from typing import ClassVar import numpy as np -from parcels import ParticleSet, StatusCode, Variable +from parcels import ParticleFile, ParticleSet, StatusCode, Variable from parcels.kernels import AdvectionRK2 from virtualship.instruments.base import Instrument @@ -283,7 +283,7 @@ def simulate(self, measurements, out_path) -> None: ) # define output file for the simulation - out_file = argo_float_particleset.ParticleFile( + out_file = ParticleFile( name=out_path, outputdt=OUTPUT_DT, chunks=[len(argo_float_particleset), 100], diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 91c71e59..c63a09b5 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -4,7 +4,7 @@ from typing import TYPE_CHECKING, ClassVar import numpy as np -from parcels import ParticleSet, Variable +from parcels import ParticleFile, ParticleSet, Variable from parcels._core.statuscodes import StatusCode from virtualship.instruments.base import Instrument @@ -239,7 +239,7 @@ def simulate(self, measurements, out_path) -> None: ) # define output file for the simulation - out_file = ctd_particleset.ParticleFile(name=out_path, outputdt=OUTPUT_DT) + out_file = ParticleFile(name=out_path, outputdt=OUTPUT_DT) # build kernel list from active sensors only sampling_kernels = [ diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index 95c6132f..e1114394 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -4,7 +4,7 @@ from typing import ClassVar import numpy as np -from parcels import ParticleSet, Variable +from parcels import ParticleFile, ParticleSet, Variable from parcels._core.statuscodes import StatusCode from parcels.kernels import AdvectionRK2 @@ -159,7 +159,7 @@ def simulate(self, measurements, out_path) -> None: ) # define output file for the simulation - out_file = drifter_particleset.ParticleFile( + out_file = ParticleFile( name=out_path, outputdt=OUTPUT_DT, chunks=[len(drifter_particleset), 100], diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index 829d1ad7..51c9f068 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -3,7 +3,7 @@ from typing import ClassVar import numpy as np -from parcels import ParticleSet +from parcels import ParticleFile, ParticleSet from virtualship.instruments.base import Instrument from virtualship.instruments.sensors import SensorType @@ -108,7 +108,7 @@ def simulate(self, measurements, out_path) -> None: depth=DEPTH, ) - out_file = particleset.ParticleFile(name=out_path, outputdt=np.inf) + out_file = ParticleFile(name=out_path, outputdt=np.inf) # build kernel list from active sensors only sampling_kernels = [ diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index d738a1fd..f03c8f50 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -4,7 +4,7 @@ from typing import ClassVar import numpy as np -from parcels import ParticleSet, Variable +from parcels import ParticleFile, ParticleSet, Variable from parcels._core.statuscodes import StatusCode from virtualship.instruments.base import Instrument @@ -175,7 +175,7 @@ def simulate(self, measurements, out_path) -> None: fall_speed=[xbt.fall_speed for xbt in measurements], ) - out_file = xbt_particleset.ParticleFile(name=out_path, outputdt=OUTPUT_DT) + out_file = ParticleFile(name=out_path, outputdt=OUTPUT_DT) # build kernel list from active sensors only sampling_kernels = [ From 3e52b1067c9c40d7daa09e3000fc860db62a5bfc Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 13:25:46 +0200 Subject: [PATCH 31/94] endtime/time_origin API update --- src/virtualship/instruments/adcp.py | 5 ++++- src/virtualship/instruments/argo_float.py | 7 +++---- src/virtualship/instruments/ctd.py | 2 +- src/virtualship/instruments/drifter.py | 5 ++--- src/virtualship/instruments/ship_underwater_st.py | 5 ++++- src/virtualship/instruments/xbt.py | 11 ++++------- 6 files changed, 18 insertions(+), 17 deletions(-) diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index ca83f3d7..e9a55a8d 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -115,7 +115,7 @@ def simulate(self, measurements, out_path) -> None: z=bins, ) - out_file = ParticleFile(name=out_path, outputdt=np.inf) + out_file = ParticleFile(path=out_path, outputdt=np.inf) # build kernel list from active sensors only sampling_kernels = [ @@ -124,6 +124,9 @@ def simulate(self, measurements, out_path) -> None: if sc.enabled and sc.sensor_type in self.sensor_kernels ] + # TODO: need to overhaul ADCP/underway instruments generally... don't think this Parcels API works anymore + # TODO: a good time to implement https://github.com/Parcels-code/virtualship/issues/231 + for point in measurements: particleset.lon_nextloop[:] = point.location.lon particleset.lat_nextloop[:] = point.location.lat diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 03801038..2a694466 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -247,7 +247,7 @@ def simulate(self, measurements, out_path) -> None: shallow_waypoints = {} for i, m in enumerate(measurements): loc_bathy = fieldset.bathymetry.eval( - time=0, + time=np.float64(0), z=0, y=m.spacetime.location.lat, x=m.spacetime.location.lon, @@ -284,13 +284,12 @@ def simulate(self, measurements, out_path) -> None: # define output file for the simulation out_file = ParticleFile( - name=out_path, + path=out_path, outputdt=OUTPUT_DT, - chunks=[len(argo_float_particleset), 100], ) # endtime - endtime = fieldset.time_origin.fulltime(fieldset.U.grid.time_full[-1]) + endtime = fieldset.U.data.time.isel(time=-1) # build kernel list from active sensors only sampling_kernels = [ diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index c63a09b5..2a139abe 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -239,7 +239,7 @@ def simulate(self, measurements, out_path) -> None: ) # define output file for the simulation - out_file = ParticleFile(name=out_path, outputdt=OUTPUT_DT) + out_file = ParticleFile(path=out_path, outputdt=OUTPUT_DT) # build kernel list from active sensors only sampling_kernels = [ diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index e1114394..901c866e 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -160,13 +160,12 @@ def simulate(self, measurements, out_path) -> None: # define output file for the simulation out_file = ParticleFile( - name=out_path, + path=out_path, outputdt=OUTPUT_DT, - chunks=[len(drifter_particleset), 100], ) # determine end time for simulation, from fieldset (which itself is controlled by drifter lifetimes) - endtime = fieldset.time_origin.fulltime(fieldset.U.grid.time_full[-1]) + endtime = fieldset.U.data.time.isel(time=-1) # build kernel list from active sensors only sampling_kernels = [ diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index 51c9f068..c5149a7a 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -108,7 +108,7 @@ def simulate(self, measurements, out_path) -> None: depth=DEPTH, ) - out_file = ParticleFile(name=out_path, outputdt=np.inf) + out_file = ParticleFile(path=out_path, outputdt=np.inf) # build kernel list from active sensors only sampling_kernels = [ @@ -117,6 +117,9 @@ def simulate(self, measurements, out_path) -> None: if sc.enabled and sc.sensor_type in self.sensor_kernels ] + # TODO: need to overhaul UNDERWATER_ST/underway instruments generally... don't think this Parcels API works anymore + # TODO: a good time to implement https://github.com/Parcels-code/virtualship/issues/231 + for point in measurements: particleset.lon_nextloop[:] = point.location.lon particleset.lat_nextloop[:] = point.location.lat diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index f03c8f50..abfd08ed 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -127,12 +127,9 @@ def simulate(self, measurements, out_path) -> None: # use first active field for time reference _time_ref_key = next(iter(self.variables)) _time_ref_field = getattr(fieldset, _time_ref_key) - fieldset_starttime = _time_ref_field.grid.time_origin.fulltime( - _time_ref_field.grid.time_full[0] - ) - fieldset_endtime = _time_ref_field.grid.time_origin.fulltime( - _time_ref_field.grid.time_full[-1] - ) + + fieldset_starttime = _time_ref_field.data.time.isel(time=0) + fieldset_endtime = _time_ref_field.data.time.isel(time=-1) # deploy time for all xbts should be later than fieldset start time if not all( @@ -175,7 +172,7 @@ def simulate(self, measurements, out_path) -> None: fall_speed=[xbt.fall_speed for xbt in measurements], ) - out_file = ParticleFile(name=out_path, outputdt=OUTPUT_DT) + out_file = ParticleFile(path=out_path, outputdt=OUTPUT_DT) # build kernel list from active sensors only sampling_kernels = [ From 07209bf03556f0472fdf38c3574956ec7c24dbce Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 13:44:25 +0200 Subject: [PATCH 32/94] no temp sampling in phase 3 --- src/virtualship/instruments/argo_float.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 2a694466..18a06f02 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -126,7 +126,6 @@ def _argo_float_vertical_movement(particles, fieldset): # Phase 3: Rising with vertical_speed until at surface ptcls3.dz -= particles.vertical_speed * ptcls3.dt - ptcls3.temp = fieldset.thetao[ptcls3.time, ptcls3.z, ptcls3.lat, ptcls3.lon] next_phase = ptcls3.z + ptcls3.dz <= particles.min_depth ptcls3.cycle_phase[next_phase] = 4 ptcls3.dz[next_phase] = ( @@ -289,7 +288,7 @@ def simulate(self, measurements, out_path) -> None: ) # endtime - endtime = fieldset.U.data.time.isel(time=-1) + endtime = fieldset.U.data.time.isel(time=-1).values # build kernel list from active sensors only sampling_kernels = [ From 9cb321cd63371812105a51d980e5d7a1a83666bf Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 13:44:46 +0200 Subject: [PATCH 33/94] endtimes access scalars directly --- src/virtualship/instruments/ctd.py | 4 ++-- src/virtualship/instruments/drifter.py | 2 +- src/virtualship/instruments/xbt.py | 4 ++-- 3 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 2a139abe..8e529f5b 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -197,8 +197,8 @@ def simulate(self, measurements, out_path) -> None: _time_ref_key = next(iter(self.variables)) _time_ref_field = getattr(fieldset, _time_ref_key) - fieldset_starttime = _time_ref_field.data.time.isel(time=0) - fieldset_endtime = _time_ref_field.data.time.isel(time=-1) + fieldset_starttime = _time_ref_field.data.time.isel(time=0).values + fieldset_endtime = _time_ref_field.data.time.isel(time=-1).values # deploy time for all ctds should be later than fieldset start time if not all( diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index 901c866e..c18088ee 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -165,7 +165,7 @@ def simulate(self, measurements, out_path) -> None: ) # determine end time for simulation, from fieldset (which itself is controlled by drifter lifetimes) - endtime = fieldset.U.data.time.isel(time=-1) + endtime = fieldset.U.data.time.isel(time=-1).values # build kernel list from active sensors only sampling_kernels = [ diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index abfd08ed..bc5f5ecf 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -128,8 +128,8 @@ def simulate(self, measurements, out_path) -> None: _time_ref_key = next(iter(self.variables)) _time_ref_field = getattr(fieldset, _time_ref_key) - fieldset_starttime = _time_ref_field.data.time.isel(time=0) - fieldset_endtime = _time_ref_field.data.time.isel(time=-1) + fieldset_starttime = _time_ref_field.data.time.isel(time=0).values + fieldset_endtime = _time_ref_field.data.time.isel(time=-1).values # deploy time for all xbts should be later than fieldset start time if not all( From 137978df6e6a9993a91b01ef209f7fa96e84829d Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 13:54:23 +0200 Subject: [PATCH 34/94] fix wrong var name in kernel --- src/virtualship/instruments/argo_float.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 18a06f02..be2da444 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -136,7 +136,7 @@ def _argo_float_vertical_movement(particles, fieldset): next_phase = ptcls4.cycle_age >= particles.cycle_days * 86400 ptcls4.cycle_phase[next_phase] = 0 ptcls4.cycle_age[next_phase] = 0 # reset cycle_age for next cycle - ptcls4.temp = np.nan # no temperature measurement when at surface + ptcls4.T = np.nan # no temperature measurement when at surface particles.cycle_age += particles.dt # update cycle_age From c29efe6a23c47e0891b61df99afa55b97c654367 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 14:32:23 +0200 Subject: [PATCH 35/94] combined vector fields for instruments --- src/virtualship/instruments/base.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index dd38f032..9cbb5699 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -228,6 +228,17 @@ def _generate_fieldset(self) -> parcels.FieldSet: for fs, key in zip(fieldsets_list[1:], keys[1:], strict=False): base_fieldset.add_field(getattr(fs, key)) + # some instruments use AdvectionRKn kernels which require a combined UV vector field + # fieldsets are created per variable and thus are not seen by from_sgrid_conventions at the same time, therefore build combined VectorField here in FieldSet + if "U" in keys and "V" in keys: + uv = parcels.VectorField( + "UV", + base_fieldset.U, + base_fieldset.V, + vector_interp_method=parcels.interpolators.XLinear_Velocity, + ) + base_fieldset.add_field(uv) + return base_fieldset def _get_spec_value(self, spec_type: str, key: str, default=None): From a8242c1fe61072fb69f5713065f1016a2675e9e7 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 14:34:59 +0200 Subject: [PATCH 36/94] small tweaks/fixes --- src/virtualship/instruments/argo_float.py | 2 +- src/virtualship/instruments/base.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index be2da444..96adcd74 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -136,7 +136,7 @@ def _argo_float_vertical_movement(particles, fieldset): next_phase = ptcls4.cycle_age >= particles.cycle_days * 86400 ptcls4.cycle_phase[next_phase] = 0 ptcls4.cycle_age[next_phase] = 0 # reset cycle_age for next cycle - ptcls4.T = np.nan # no temperature measurement when at surface + ptcls4.temperature = np.nan # no temperature measurement when at surface particles.cycle_age += particles.dt # update cycle_age diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 9cbb5699..8e482495 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -216,6 +216,7 @@ def _generate_fieldset(self) -> parcels.FieldSet: ) field_var_name = var + # TODO: I think this is potentially slowing down simulations slightly... compared to v0.3 anyway for *drifters* ds.load() # TODO: tmp step during v4 alpha stage... probably to be updated on the Parcels end fields = {key: ds[field_var_name]} From 95d84b7b03f266c99591af6ccf6fc13c7cd56cc2 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 5 Jun 2026 15:39:18 +0200 Subject: [PATCH 37/94] cmo to pixi.toml and tweaked particleset API --- pixi.toml | 1 + src/virtualship/instruments/base.py | 2 +- src/virtualship/instruments/ctd.py | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/pixi.toml b/pixi.toml index 6cd97d08..240c4047 100644 --- a/pixi.toml +++ b/pixi.toml @@ -38,6 +38,7 @@ numpy = ">=2.1.0" dask = "*" zarr = ">=3" ipdb = ">=0.13.13,<0.14" +cmocean = ">=4.0.3,<5" [pypi-dependencies] parcels = { git = "https://github.com/Parcels-code/Parcels", branch = "main" } diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 8e482495..39dd7419 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -118,7 +118,7 @@ def simulate( def execute(self, measurements: list, out_path: str | Path) -> None: """Run instrument simulation.""" - TMP = False # TODO: just for dev; remove before merging + TMP = True # TODO: just for dev; remove before merging instrument_name = self.__class__.__name__.split("Instrument")[0] if not self.verbose_progress: diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 8e529f5b..9f93b6ac 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -258,7 +258,7 @@ def simulate(self, measurements, out_path) -> None: ) # there should be no particles left, as they delete themselves when they resurface - if len(ctd_particleset.particledata) != 0: + if len(ctd_particleset.lon) != 0: raise ValueError( "Simulation ended before CTD resurfaced. This most likely means the field time dimension did not match the simulation time span." ) From 7f68c834b46e0c9a5b66a8cbfc080c5e0391ee39 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 10 Jun 2026 16:54:29 +0200 Subject: [PATCH 38/94] depth -> z for argo sampling, update interp method in base, fix ctd vertical movement --- src/virtualship/instruments/argo_float.py | 8 ++++---- src/virtualship/instruments/base.py | 7 ++++--- src/virtualship/instruments/ctd.py | 9 ++------- 3 files changed, 10 insertions(+), 14 deletions(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 96adcd74..d1856b02 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -162,11 +162,11 @@ def _check_error(particles, fieldset): def _argo_sample_temperature(particles, fieldset): # Phase 3: ascending — sample temperature; NaN otherwise phase_mask = particles.cycle_phase == 3 - depth_mask = particles.depth < particles.min_depth + depth_mask = particles.z < particles.min_depth sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.temperature = fieldset.T[ sampling_particles.time, - sampling_particles.depth, + sampling_particles.z, sampling_particles.lat, sampling_particles.lon, ] @@ -175,11 +175,11 @@ def _argo_sample_temperature(particles, fieldset): def _argo_sample_salinity(particles, fieldset): # Phase 3: ascending — sample salinity; NaN otherwise phase_mask = particles.cycle_phase == 3 - depth_mask = particles.depth < particles.min_depth + depth_mask = particles.z < particles.min_depth sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.salinity = fieldset.S[ sampling_particles.time, - sampling_particles.depth, + sampling_particles.z, sampling_particles.lat, sampling_particles.lon, ] diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 39dd7419..c651645a 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -97,7 +97,9 @@ def load_input_data(self) -> parcels.FieldSet: # interpolation methods for var in (v for v in self.variables if v not in ("U", "V")): - getattr(fieldset, var).interp_method = parcels.interpolators.XLinear + getattr( + fieldset, var + ).interp_method = parcels.interpolators.XLinearInvdistLandTracer # bathymetry data if self.add_bathymetry: @@ -118,7 +120,7 @@ def simulate( def execute(self, measurements: list, out_path: str | Path) -> None: """Run instrument simulation.""" - TMP = True # TODO: just for dev; remove before merging + TMP = False # TODO: just for dev; remove before merging instrument_name = self.__class__.__name__.split("Instrument")[0] if not self.verbose_progress: @@ -192,7 +194,6 @@ def _generate_fieldset(self) -> parcels.FieldSet: var = self.variables[key] physical = var in COPERNICUSMARINE_PHYS_VARIABLES - # TODO: do docs on pre-downloading data need to be updated for these changes? Anything about conventions etc.? if self.from_data is not None: # load from local data data_dir = self.from_data.joinpath("phys" if physical else "bgc") diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 9f93b6ac..6823cca8 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -117,13 +117,8 @@ def _ctd_cast(particles, fieldset): particles_lowering = particles[particles.raising == 0] particles_raising = particles[particles.raising == 1] - # TODO: change to boolean masking, like with Argo Floats? - # TODO: different handling of positive down for z now?! Doing positive down now... think kernels need adjusting... - # TODO: need to check on all other instrument kernels as well... - # TODO: plus how the configs are inputted in e.g. expedition.yaml - # lowering - particles_lowering.dz = -particles_lowering.winch_speed * particles_lowering.dt + particles_lowering.dz += -particles_lowering.winch_speed * particles_lowering.dt particles_lowering.raising = np.where( particles_lowering.z + particles_lowering.dz < particles_lowering.max_depth, 1, @@ -131,7 +126,7 @@ def _ctd_cast(particles, fieldset): ) # raising - particles_raising.dz = particles_raising.winch_speed * particles_raising.dt + particles_raising.dz += particles_raising.winch_speed * particles_raising.dt particles_raising.state = np.where( particles_raising.z + particles_raising.dz > particles_raising.min_depth, StatusCode.Delete, From 003f79fbdf3551a10c21f5c7572465c26c90d8e7 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 10 Jun 2026 17:36:56 +0200 Subject: [PATCH 39/94] move to positive down API throughout virtualship --- src/virtualship/instruments/adcp.py | 4 ++-- src/virtualship/instruments/argo_float.py | 4 ++-- src/virtualship/instruments/base.py | 1 - src/virtualship/instruments/ctd.py | 13 ++++++------ .../instruments/ship_underwater_st.py | 2 +- src/virtualship/instruments/xbt.py | 6 +++--- src/virtualship/models/expedition.py | 20 +++++++++---------- src/virtualship/static/expedition.yaml | 18 ++++++++--------- src/virtualship/utils.py | 2 +- .../expedition/expedition_dir/expedition.yaml | 14 ++++++------- 10 files changed, 42 insertions(+), 42 deletions(-) diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index e9a55a8d..94027aaf 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -82,7 +82,7 @@ def simulate(self, measurements, out_path) -> None: self.expedition.instruments_config.adcp_config.max_depth_meter ) - if config_max_depth < -1600.0: + if config_max_depth > 1600.0: print( f"\n\n⚠️ Warning: The configured ADCP max depth of {abs(config_max_depth)} m exceeds the 1600 m limit for the technology (e.g. https://www.geomar.de/en/research/fb1/fb1-po/observing-systems/adcp)." "\n\n This expedition will continue using the prescribed configuration. However, note, the results will not necessarily represent authentic ADCP instrument readings and could also lead to slower simulations ." @@ -90,7 +90,7 @@ def simulate(self, measurements, out_path) -> None: ) MAX_DEPTH = config_max_depth - MIN_DEPTH = -5.0 + MIN_DEPTH = 5.0 NUM_BINS = self.expedition.instruments_config.adcp_config.num_bins measurements.sort(key=lambda p: p.time) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index d1856b02..97c35181 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -162,7 +162,7 @@ def _check_error(particles, fieldset): def _argo_sample_temperature(particles, fieldset): # Phase 3: ascending — sample temperature; NaN otherwise phase_mask = particles.cycle_phase == 3 - depth_mask = particles.z < particles.min_depth + depth_mask = particles.z > particles.min_depth sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.temperature = fieldset.T[ sampling_particles.time, @@ -175,7 +175,7 @@ def _argo_sample_temperature(particles, fieldset): def _argo_sample_salinity(particles, fieldset): # Phase 3: ascending — sample salinity; NaN otherwise phase_mask = particles.cycle_phase == 3 - depth_mask = particles.z < particles.min_depth + depth_mask = particles.z > particles.min_depth sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.salinity = fieldset.S[ sampling_particles.time, diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index c651645a..4d487c46 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -104,7 +104,6 @@ def load_input_data(self) -> parcels.FieldSet: # bathymetry data if self.add_bathymetry: bathymetry_field = _get_bathy_data(from_data=self.from_data).bathymetry - bathymetry_field.data = -bathymetry_field.data fieldset.add_field(bathymetry_field) return fieldset diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 6823cca8..c2b24742 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -118,17 +118,17 @@ def _ctd_cast(particles, fieldset): particles_raising = particles[particles.raising == 1] # lowering - particles_lowering.dz += -particles_lowering.winch_speed * particles_lowering.dt + particles_lowering.dz += particles_lowering.winch_speed * particles_lowering.dt particles_lowering.raising = np.where( - particles_lowering.z + particles_lowering.dz < particles_lowering.max_depth, + particles_lowering.z + particles_lowering.dz > particles_lowering.max_depth, 1, particles_lowering.raising, ) # raising - particles_raising.dz += particles_raising.winch_speed * particles_raising.dt + particles_raising.dz += -particles_raising.winch_speed * particles_raising.dt particles_raising.state = np.where( - particles_raising.z + particles_raising.dz > particles_raising.min_depth, + particles_raising.z + particles_raising.dz < particles_raising.min_depth, StatusCode.Delete, particles_raising.state, ) @@ -209,9 +209,10 @@ def simulate(self, measurements, out_path) -> None: # CTD depth can not be too shallow, because kernel would break. # This shallow is not useful anyway, no need to support. - if not all([max_depth <= -DT * WINCH_SPEED for max_depth in max_depths]): + # TODO: should make this say which CTD(s) are the issue, and which max depth(s) are the issue, to make it easier for users to fix + if not all([max_depth >= DT * WINCH_SPEED for max_depth in max_depths]): raise ValueError( - f"CTD max_depth or bathymetry shallower than maximum {-DT * WINCH_SPEED}" + f"CTD max_depth or bathymetry shallower than maximum {DT * WINCH_SPEED}" ) # build dynamic particle class from the active sensors diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index c5149a7a..0c099003 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -88,7 +88,7 @@ def __init__(self, expedition, from_data): def simulate(self, measurements, out_path) -> None: """Simulate underway salinity and temperature measurements.""" - DEPTH = -2.0 + DEPTH = 2.0 measurements.sort(key=lambda p: p.time) diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index bc5f5ecf..cfa7283d 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -58,7 +58,7 @@ def _sample_temperature(particles, fieldset): def _xbt_cast(particles, fieldset): - particles.dz = -particles.fall_speed * particles.dt + particles.dz = particles.fall_speed * particles.dt # update the fall speed from the quadractic fall-rate equation # check https://doi.org/10.5194/os-7-231-2011 @@ -73,9 +73,9 @@ def _xbt_cast(particles, fieldset): # set particle depth to max depth if it's too deep particles.dz = np.where( - particles.z + particles.dz < particles.max_depth, + particles.z + particles.dz > particles.max_depth, particles.max_depth - particles.z, - particles.z, + particles.dz, ) diff --git a/src/virtualship/models/expedition.py b/src/virtualship/models/expedition.py index b7269373..7bcf6208 100644 --- a/src/virtualship/models/expedition.py +++ b/src/virtualship/models/expedition.py @@ -255,10 +255,10 @@ class ArgoFloatConfig(_InstrumentConfigMixin, pydantic.BaseModel): _instrument_type: ClassVar[InstrumentType] = InstrumentType.ARGO_FLOAT _instrument_name: ClassVar[str] = "ArgoFloat" - min_depth_meter: float = pydantic.Field(le=0.0) - max_depth_meter: float = pydantic.Field(le=0.0) - drift_depth_meter: float = pydantic.Field(le=0.0) - vertical_speed_meter_per_second: float = pydantic.Field(lt=0.0) + min_depth_meter: float = pydantic.Field(ge=0.0) + max_depth_meter: float = pydantic.Field(ge=0.0) + drift_depth_meter: float = pydantic.Field(ge=0.0) + vertical_speed_meter_per_second: float = pydantic.Field(gt=0.0) cycle_days: float = pydantic.Field(gt=0.0) drift_days: float = pydantic.Field(gt=0.0) lifetime: timedelta = pydantic.Field( @@ -302,7 +302,7 @@ class ADCPConfig(_InstrumentConfigMixin, pydantic.BaseModel): _instrument_type: ClassVar[InstrumentType] = InstrumentType.ADCP _instrument_name: ClassVar[str] = "ADCP" - max_depth_meter: float = pydantic.Field(le=0.0) + max_depth_meter: float = pydantic.Field(ge=0.0) num_bins: int = pydantic.Field(gt=0.0) period: timedelta = pydantic.Field( serialization_alias="period_minutes", @@ -346,8 +346,8 @@ class CTDConfig(_InstrumentConfigMixin, pydantic.BaseModel): validation_alias="stationkeeping_time_minutes", gt=timedelta(), ) - min_depth_meter: float = pydantic.Field(le=0.0) - max_depth_meter: float = pydantic.Field(le=0.0) + min_depth_meter: float = pydantic.Field(ge=0.0) + max_depth_meter: float = pydantic.Field(ge=0.0) sensors: list[SensorConfig] = pydantic.Field( default_factory=lambda: [ @@ -402,7 +402,7 @@ class DrifterConfig(_InstrumentConfigMixin, pydantic.BaseModel): _instrument_type: ClassVar[InstrumentType] = InstrumentType.DRIFTER _instrument_name: ClassVar[str] = "Drifter" - depth_meter: float = pydantic.Field(le=0.0) + depth_meter: float = pydantic.Field(ge=0.0) lifetime: timedelta = pydantic.Field( serialization_alias="lifetime_days", validation_alias="lifetime_days", @@ -429,8 +429,8 @@ class XBTConfig(_InstrumentConfigMixin, pydantic.BaseModel): _instrument_type: ClassVar[InstrumentType] = InstrumentType.XBT _instrument_name: ClassVar[str] = "XBT" - min_depth_meter: float = pydantic.Field(le=0.0) - max_depth_meter: float = pydantic.Field(le=0.0) + min_depth_meter: float = pydantic.Field(ge=0.0) + max_depth_meter: float = pydantic.Field(ge=0.0) fall_speed_meter_per_second: float = pydantic.Field(gt=0.0) deceleration_coefficient: float = pydantic.Field(gt=0.0) diff --git a/src/virtualship/static/expedition.yaml b/src/virtualship/static/expedition.yaml index acb16dcf..9615ccb1 100644 --- a/src/virtualship/static/expedition.yaml +++ b/src/virtualship/static/expedition.yaml @@ -34,25 +34,25 @@ schedule: instruments_config: adcp_config: num_bins: 40 - max_depth_meter: -1000.0 + max_depth_meter: 1000.0 period_minutes: 5.0 sensors: - VELOCITY argo_float_config: cycle_days: 10.0 drift_days: 9.0 - drift_depth_meter: -1000.0 - max_depth_meter: -2000.0 + drift_depth_meter: 1000.0 + max_depth_meter: 2000.0 min_depth_meter: 0.0 - vertical_speed_meter_per_second: -0.1 + vertical_speed_meter_per_second: 0.1 stationkeeping_time_minutes: 20.0 lifetime_days: 63.0 sensors: - TEMPERATURE - SALINITY ctd_config: - max_depth_meter: -2000.0 - min_depth_meter: -11.0 + max_depth_meter: 2000.0 + min_depth_meter: 11.0 stationkeeping_time_minutes: 50.0 sensors: - TEMPERATURE @@ -65,14 +65,14 @@ instruments_config: - PHYTOPLANKTON - PRIMARY_PRODUCTION drifter_config: - depth_meter: -1.0 + depth_meter: 1.0 lifetime_days: 42.0 stationkeeping_time_minutes: 20.0 sensors: - TEMPERATURE xbt_config: - max_depth_meter: -285.0 - min_depth_meter: -2.0 + max_depth_meter: 285.0 + min_depth_meter: 2.0 fall_speed_meter_per_second: 6.7 deceleration_coefficient: 0.00225 sensors: diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index df5d153c..03283023 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -563,7 +563,7 @@ def _find_files_in_timerange( def _compute_max_depths(measurements, fieldset) -> list[float]: """Compute the effective max depth for each measurement, capped by bathymetry.""" return [ - max( + min( # min because depth is positive down m.max_depth, fieldset.bathymetry.eval( z=0, diff --git a/tests/expedition/expedition_dir/expedition.yaml b/tests/expedition/expedition_dir/expedition.yaml index 6392076b..e8393ed1 100644 --- a/tests/expedition/expedition_dir/expedition.yaml +++ b/tests/expedition/expedition_dir/expedition.yaml @@ -22,23 +22,23 @@ schedule: instruments_config: adcp_config: num_bins: 40 - max_depth_meter: -1000.0 + max_depth_meter: 1000.0 period_minutes: 5.0 argo_float_config: cycle_days: 10.0 drift_days: 9.0 - drift_depth_meter: -1000.0 - max_depth_meter: -2000.0 + drift_depth_meter: 1000.0 + max_depth_meter: 2000.0 min_depth_meter: 0.0 - vertical_speed_meter_per_second: -0.1 + vertical_speed_meter_per_second: 0.1 stationkeeping_time_minutes: 20.0 lifetime_days: 63.0 ctd_config: - max_depth_meter: -2000.0 - min_depth_meter: -11.0 + max_depth_meter: 2000.0 + min_depth_meter: 11.0 stationkeeping_time_minutes: 50.0 drifter_config: - depth_meter: -1.0 + depth_meter: 1.0 lifetime_days: 28.0 stationkeeping_time_minutes: 20.0 ship_underwater_st_config: From da866131286cc24b95d7ff479c81c02fdc3342c4 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 11 Jun 2026 13:33:05 +0200 Subject: [PATCH 40/94] Revert "move to positive down API throughout virtualship" This reverts commit 003f79fbdf3551a10c21f5c7572465c26c90d8e7. --- src/virtualship/instruments/adcp.py | 4 ++-- src/virtualship/instruments/argo_float.py | 4 ++-- src/virtualship/instruments/base.py | 1 + src/virtualship/instruments/ctd.py | 13 ++++++------ .../instruments/ship_underwater_st.py | 2 +- src/virtualship/instruments/xbt.py | 6 +++--- src/virtualship/models/expedition.py | 20 +++++++++---------- src/virtualship/static/expedition.yaml | 18 ++++++++--------- src/virtualship/utils.py | 2 +- .../expedition/expedition_dir/expedition.yaml | 14 ++++++------- 10 files changed, 42 insertions(+), 42 deletions(-) diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index 94027aaf..e9a55a8d 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -82,7 +82,7 @@ def simulate(self, measurements, out_path) -> None: self.expedition.instruments_config.adcp_config.max_depth_meter ) - if config_max_depth > 1600.0: + if config_max_depth < -1600.0: print( f"\n\n⚠️ Warning: The configured ADCP max depth of {abs(config_max_depth)} m exceeds the 1600 m limit for the technology (e.g. https://www.geomar.de/en/research/fb1/fb1-po/observing-systems/adcp)." "\n\n This expedition will continue using the prescribed configuration. However, note, the results will not necessarily represent authentic ADCP instrument readings and could also lead to slower simulations ." @@ -90,7 +90,7 @@ def simulate(self, measurements, out_path) -> None: ) MAX_DEPTH = config_max_depth - MIN_DEPTH = 5.0 + MIN_DEPTH = -5.0 NUM_BINS = self.expedition.instruments_config.adcp_config.num_bins measurements.sort(key=lambda p: p.time) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 97c35181..d1856b02 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -162,7 +162,7 @@ def _check_error(particles, fieldset): def _argo_sample_temperature(particles, fieldset): # Phase 3: ascending — sample temperature; NaN otherwise phase_mask = particles.cycle_phase == 3 - depth_mask = particles.z > particles.min_depth + depth_mask = particles.z < particles.min_depth sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.temperature = fieldset.T[ sampling_particles.time, @@ -175,7 +175,7 @@ def _argo_sample_temperature(particles, fieldset): def _argo_sample_salinity(particles, fieldset): # Phase 3: ascending — sample salinity; NaN otherwise phase_mask = particles.cycle_phase == 3 - depth_mask = particles.z > particles.min_depth + depth_mask = particles.z < particles.min_depth sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.salinity = fieldset.S[ sampling_particles.time, diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 4d487c46..c651645a 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -104,6 +104,7 @@ def load_input_data(self) -> parcels.FieldSet: # bathymetry data if self.add_bathymetry: bathymetry_field = _get_bathy_data(from_data=self.from_data).bathymetry + bathymetry_field.data = -bathymetry_field.data fieldset.add_field(bathymetry_field) return fieldset diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index c2b24742..6823cca8 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -118,17 +118,17 @@ def _ctd_cast(particles, fieldset): particles_raising = particles[particles.raising == 1] # lowering - particles_lowering.dz += particles_lowering.winch_speed * particles_lowering.dt + particles_lowering.dz += -particles_lowering.winch_speed * particles_lowering.dt particles_lowering.raising = np.where( - particles_lowering.z + particles_lowering.dz > particles_lowering.max_depth, + particles_lowering.z + particles_lowering.dz < particles_lowering.max_depth, 1, particles_lowering.raising, ) # raising - particles_raising.dz += -particles_raising.winch_speed * particles_raising.dt + particles_raising.dz += particles_raising.winch_speed * particles_raising.dt particles_raising.state = np.where( - particles_raising.z + particles_raising.dz < particles_raising.min_depth, + particles_raising.z + particles_raising.dz > particles_raising.min_depth, StatusCode.Delete, particles_raising.state, ) @@ -209,10 +209,9 @@ def simulate(self, measurements, out_path) -> None: # CTD depth can not be too shallow, because kernel would break. # This shallow is not useful anyway, no need to support. - # TODO: should make this say which CTD(s) are the issue, and which max depth(s) are the issue, to make it easier for users to fix - if not all([max_depth >= DT * WINCH_SPEED for max_depth in max_depths]): + if not all([max_depth <= -DT * WINCH_SPEED for max_depth in max_depths]): raise ValueError( - f"CTD max_depth or bathymetry shallower than maximum {DT * WINCH_SPEED}" + f"CTD max_depth or bathymetry shallower than maximum {-DT * WINCH_SPEED}" ) # build dynamic particle class from the active sensors diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index 0c099003..c5149a7a 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -88,7 +88,7 @@ def __init__(self, expedition, from_data): def simulate(self, measurements, out_path) -> None: """Simulate underway salinity and temperature measurements.""" - DEPTH = 2.0 + DEPTH = -2.0 measurements.sort(key=lambda p: p.time) diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index cfa7283d..bc5f5ecf 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -58,7 +58,7 @@ def _sample_temperature(particles, fieldset): def _xbt_cast(particles, fieldset): - particles.dz = particles.fall_speed * particles.dt + particles.dz = -particles.fall_speed * particles.dt # update the fall speed from the quadractic fall-rate equation # check https://doi.org/10.5194/os-7-231-2011 @@ -73,9 +73,9 @@ def _xbt_cast(particles, fieldset): # set particle depth to max depth if it's too deep particles.dz = np.where( - particles.z + particles.dz > particles.max_depth, + particles.z + particles.dz < particles.max_depth, particles.max_depth - particles.z, - particles.dz, + particles.z, ) diff --git a/src/virtualship/models/expedition.py b/src/virtualship/models/expedition.py index 7bcf6208..b7269373 100644 --- a/src/virtualship/models/expedition.py +++ b/src/virtualship/models/expedition.py @@ -255,10 +255,10 @@ class ArgoFloatConfig(_InstrumentConfigMixin, pydantic.BaseModel): _instrument_type: ClassVar[InstrumentType] = InstrumentType.ARGO_FLOAT _instrument_name: ClassVar[str] = "ArgoFloat" - min_depth_meter: float = pydantic.Field(ge=0.0) - max_depth_meter: float = pydantic.Field(ge=0.0) - drift_depth_meter: float = pydantic.Field(ge=0.0) - vertical_speed_meter_per_second: float = pydantic.Field(gt=0.0) + min_depth_meter: float = pydantic.Field(le=0.0) + max_depth_meter: float = pydantic.Field(le=0.0) + drift_depth_meter: float = pydantic.Field(le=0.0) + vertical_speed_meter_per_second: float = pydantic.Field(lt=0.0) cycle_days: float = pydantic.Field(gt=0.0) drift_days: float = pydantic.Field(gt=0.0) lifetime: timedelta = pydantic.Field( @@ -302,7 +302,7 @@ class ADCPConfig(_InstrumentConfigMixin, pydantic.BaseModel): _instrument_type: ClassVar[InstrumentType] = InstrumentType.ADCP _instrument_name: ClassVar[str] = "ADCP" - max_depth_meter: float = pydantic.Field(ge=0.0) + max_depth_meter: float = pydantic.Field(le=0.0) num_bins: int = pydantic.Field(gt=0.0) period: timedelta = pydantic.Field( serialization_alias="period_minutes", @@ -346,8 +346,8 @@ class CTDConfig(_InstrumentConfigMixin, pydantic.BaseModel): validation_alias="stationkeeping_time_minutes", gt=timedelta(), ) - min_depth_meter: float = pydantic.Field(ge=0.0) - max_depth_meter: float = pydantic.Field(ge=0.0) + min_depth_meter: float = pydantic.Field(le=0.0) + max_depth_meter: float = pydantic.Field(le=0.0) sensors: list[SensorConfig] = pydantic.Field( default_factory=lambda: [ @@ -402,7 +402,7 @@ class DrifterConfig(_InstrumentConfigMixin, pydantic.BaseModel): _instrument_type: ClassVar[InstrumentType] = InstrumentType.DRIFTER _instrument_name: ClassVar[str] = "Drifter" - depth_meter: float = pydantic.Field(ge=0.0) + depth_meter: float = pydantic.Field(le=0.0) lifetime: timedelta = pydantic.Field( serialization_alias="lifetime_days", validation_alias="lifetime_days", @@ -429,8 +429,8 @@ class XBTConfig(_InstrumentConfigMixin, pydantic.BaseModel): _instrument_type: ClassVar[InstrumentType] = InstrumentType.XBT _instrument_name: ClassVar[str] = "XBT" - min_depth_meter: float = pydantic.Field(ge=0.0) - max_depth_meter: float = pydantic.Field(ge=0.0) + min_depth_meter: float = pydantic.Field(le=0.0) + max_depth_meter: float = pydantic.Field(le=0.0) fall_speed_meter_per_second: float = pydantic.Field(gt=0.0) deceleration_coefficient: float = pydantic.Field(gt=0.0) diff --git a/src/virtualship/static/expedition.yaml b/src/virtualship/static/expedition.yaml index 9615ccb1..acb16dcf 100644 --- a/src/virtualship/static/expedition.yaml +++ b/src/virtualship/static/expedition.yaml @@ -34,25 +34,25 @@ schedule: instruments_config: adcp_config: num_bins: 40 - max_depth_meter: 1000.0 + max_depth_meter: -1000.0 period_minutes: 5.0 sensors: - VELOCITY argo_float_config: cycle_days: 10.0 drift_days: 9.0 - drift_depth_meter: 1000.0 - max_depth_meter: 2000.0 + drift_depth_meter: -1000.0 + max_depth_meter: -2000.0 min_depth_meter: 0.0 - vertical_speed_meter_per_second: 0.1 + vertical_speed_meter_per_second: -0.1 stationkeeping_time_minutes: 20.0 lifetime_days: 63.0 sensors: - TEMPERATURE - SALINITY ctd_config: - max_depth_meter: 2000.0 - min_depth_meter: 11.0 + max_depth_meter: -2000.0 + min_depth_meter: -11.0 stationkeeping_time_minutes: 50.0 sensors: - TEMPERATURE @@ -65,14 +65,14 @@ instruments_config: - PHYTOPLANKTON - PRIMARY_PRODUCTION drifter_config: - depth_meter: 1.0 + depth_meter: -1.0 lifetime_days: 42.0 stationkeeping_time_minutes: 20.0 sensors: - TEMPERATURE xbt_config: - max_depth_meter: 285.0 - min_depth_meter: 2.0 + max_depth_meter: -285.0 + min_depth_meter: -2.0 fall_speed_meter_per_second: 6.7 deceleration_coefficient: 0.00225 sensors: diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 03283023..df5d153c 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -563,7 +563,7 @@ def _find_files_in_timerange( def _compute_max_depths(measurements, fieldset) -> list[float]: """Compute the effective max depth for each measurement, capped by bathymetry.""" return [ - min( # min because depth is positive down + max( m.max_depth, fieldset.bathymetry.eval( z=0, diff --git a/tests/expedition/expedition_dir/expedition.yaml b/tests/expedition/expedition_dir/expedition.yaml index e8393ed1..6392076b 100644 --- a/tests/expedition/expedition_dir/expedition.yaml +++ b/tests/expedition/expedition_dir/expedition.yaml @@ -22,23 +22,23 @@ schedule: instruments_config: adcp_config: num_bins: 40 - max_depth_meter: 1000.0 + max_depth_meter: -1000.0 period_minutes: 5.0 argo_float_config: cycle_days: 10.0 drift_days: 9.0 - drift_depth_meter: 1000.0 - max_depth_meter: 2000.0 + drift_depth_meter: -1000.0 + max_depth_meter: -2000.0 min_depth_meter: 0.0 - vertical_speed_meter_per_second: 0.1 + vertical_speed_meter_per_second: -0.1 stationkeeping_time_minutes: 20.0 lifetime_days: 63.0 ctd_config: - max_depth_meter: 2000.0 - min_depth_meter: 11.0 + max_depth_meter: -2000.0 + min_depth_meter: -11.0 stationkeeping_time_minutes: 50.0 drifter_config: - depth_meter: 1.0 + depth_meter: -1.0 lifetime_days: 28.0 stationkeeping_time_minutes: 20.0 ship_underwater_st_config: From a468adeb38b70bdba733db61ae477c2acb77ae76 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 11 Jun 2026 15:11:53 +0200 Subject: [PATCH 41/94] negate *and* reindex depth in ds before fieldset creation --- src/virtualship/instruments/base.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index c651645a..6f898e39 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -120,7 +120,7 @@ def simulate( def execute(self, measurements: list, out_path: str | Path) -> None: """Run instrument simulation.""" - TMP = False # TODO: just for dev; remove before merging + TMP = True # TODO: just for dev; remove before merging instrument_name = self.__class__.__name__.split("Instrument")[0] if not self.verbose_progress: @@ -220,6 +220,10 @@ def _generate_fieldset(self) -> parcels.FieldSet: # TODO: I think this is potentially slowing down simulations slightly... compared to v0.3 anyway for *drifters* ds.load() # TODO: tmp step during v4 alpha stage... probably to be updated on the Parcels end + # negate depth and reindex (to suit Parcels XGrid strictly increasing depth convention) + ds["depth"] = -ds["depth"] + ds = ds.reindex(depth=ds["depth"][::-1]) # + fields = {key: ds[field_var_name]} ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) From fd2232a2ca35b3e2df02810a67527b0f0a615e4c Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 11 Jun 2026 16:42:03 +0200 Subject: [PATCH 42/94] fill land/nans with 0s --- src/virtualship/instruments/base.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 6f898e39..f6d0c9a2 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -222,7 +222,10 @@ def _generate_fieldset(self) -> parcels.FieldSet: # negate depth and reindex (to suit Parcels XGrid strictly increasing depth convention) ds["depth"] = -ds["depth"] - ds = ds.reindex(depth=ds["depth"][::-1]) # + ds = ds.reindex(depth=ds["depth"][::-1]) + + # TODO: update when decision on handling of nans/0s in v4 is made (i.e. https://github.com/Parcels-code/Parcels/issues/2393) + ds = ds.fillna(0) fields = {key: ds[field_var_name]} ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) From 0be180d9aa1d63a18471449aaceb62c0100a7729 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 12 Jun 2026 15:51:26 +0200 Subject: [PATCH 43/94] fixes to argo kernels + some refactoring --- src/virtualship/instruments/argo_float.py | 146 ++++++++++++++-------- 1 file changed, 95 insertions(+), 51 deletions(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index d1856b02..7ed59710 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -58,9 +58,6 @@ class ArgoFloat: # SECTION: Kernels # ===================================================== -# TODO: need to add back in the shallow bathymetry checks (to phases 0 and 2?!) -# TODO: can this be refactored as well to a helper function? - def _argo_float_vertical_movement(particles, fieldset): # Split particles based on their current cycle_phase @@ -73,26 +70,21 @@ def _argo_float_vertical_movement(particles, fieldset): # Phase 0: Sinking with vertical_speed until depth is driftdepth ptcls0.dz += particles.vertical_speed * ptcls0.dt loc_bathy = fieldset.bathymetry.eval(ptcls0.time, ptcls0.z, ptcls0.lat, ptcls0.lon) - driftdepth_mask = ptcls0.z + ptcls0.dz >= particles.drift_depth - bathy_mask = ptcls0.z + ptcls0.dz >= loc_bathy - next_phase = np.logical_and( - driftdepth_mask, bathy_mask - ) # combined mask; not at drift depth yet and not hitting bathymetry + driftdepth_mask = ptcls0.z + ptcls0.dz <= particles.drift_depth # noqa:has reached drift depth + bathysafe_mask = ptcls0.z + ptcls0.dz >= loc_bathy # noqa:has not reached bathymetry + next_phase = np.logical_and(driftdepth_mask, bathysafe_mask) ptcls0.cycle_phase[next_phase] = 1 - ptcls0.dz[next_phase] = ( - particles.drift_depth - ptcls0.z[next_phase] - ) # avoid overshoot + ptcls0.dz[next_phase] = particles.drift_depth - ptcls0.z[next_phase] # noqa:avoid overshoot # Phase 0.5: Check for grounding at bathymetry and raise if necessary - ptcls0.grounded[~bathy_mask] = 1 - if np.any(~bathy_mask): - print( - "Shallow bathymetry warning: Argo float grounded at bathymetry depth during sinking to drift depth. Raising by 50m above bathymetry and continuing cycle." - ) - ptcls0.dz[~bathy_mask] = ( - loc_bathy[~bathy_mask] - ptcls0.z[~bathy_mask] + 50.0 - ) # raise to 50m above bathymetry - ptcls0.cycle_phase[~bathy_mask] = 1 + _handle_grounding( + ptcls0, + bathysafe_mask, + loc_bathy, + fieldset, + "sinking to drift depth", + target_phase=1, + ) # Phase 1: Drifting at depth for drifttime seconds ptcls1.drift_age += ptcls1.dt @@ -103,34 +95,27 @@ def _argo_float_vertical_movement(particles, fieldset): # Phase 2: Sinking further to maxdepth ptcls2.dz += particles.vertical_speed * ptcls2.dt loc_bathy = fieldset.bathymetry.eval(ptcls2.time, ptcls2.z, ptcls2.lat, ptcls2.lon) - maxdepth_mask = ptcls2.z + ptcls2.dz >= particles.max_depth - bathy_mask = ptcls2.z + ptcls2.dz >= loc_bathy - next_phase = np.logical_and( - maxdepth_mask, bathy_mask - ) # combined mask; not at max depth yet and not hitting bathymetry + maxdepth_mask = ptcls2.z + ptcls2.dz <= particles.max_depth # noqa:has reached max depth + bathysafe_mask = ptcls2.z + ptcls2.dz >= loc_bathy # noqa:has not reached bathymetry + next_phase = np.logical_and(maxdepth_mask, bathysafe_mask) ptcls2.cycle_phase[next_phase] = 3 - ptcls2.dz[next_phase] = ( - particles.max_depth - ptcls2.z[next_phase] - ) # avoid overshoot + ptcls2.dz[next_phase] = particles.max_depth - ptcls2.z[next_phase] # noqa:avoid overshoot # Phase 2.5: Check for grounding at bathymetry and raise if necessary - ptcls2.grounded[~bathy_mask] = 1 - if np.any(~bathy_mask): - print( - "Shallow bathymetry warning: Argo float grounded at bathymetry depth during sinking to max depth. Raising by 50m above bathymetry and continuing cycle." - ) - ptcls2.dz[~bathy_mask] = ( - loc_bathy[~bathy_mask] - ptcls2.z[~bathy_mask] + 50.0 - ) # raise to 50m above bathymetry - ptcls2.cycle_phase[~bathy_mask] = 3 + _handle_grounding( + ptcls2, + bathysafe_mask, + loc_bathy, + fieldset, + "sinking to max depth", + target_phase=3, + ) # Phase 3: Rising with vertical_speed until at surface ptcls3.dz -= particles.vertical_speed * ptcls3.dt - next_phase = ptcls3.z + ptcls3.dz <= particles.min_depth + next_phase = ptcls3.z + ptcls3.dz >= particles.min_depth ptcls3.cycle_phase[next_phase] = 4 - ptcls3.dz[next_phase] = ( - particles.min_depth - ptcls3.z[next_phase] - ) # avoid overshoot + ptcls3.dz[next_phase] = particles.min_depth - ptcls3.z[next_phase] # noqa:avoid overshoot # Phase 4: Transmitting at surface until cycletime is reached next_phase = ptcls4.cycle_age >= particles.cycle_days * 86400 @@ -148,21 +133,38 @@ def _keep_at_surface(particles, fieldset): def _check_error(particles, fieldset): - errors = particles.state >= 50 # captures all Errors - # TODO: check print statements are as expected + errors = particles.state >= 50 + if not np.any(errors): + return + + error_ints = particles.state[errors].astype(int) + error_times, error_lats, error_lons = _format_log_metadata( + particles, errors, fieldset + ) + + error_details = ", ".join( + f"{_STATUS_CODE_NAMES.get(err, str(err))} at time(s): {t}, lat(s): {lat}, lon(s): {lon}" + for err, lat, lon, t in zip( + error_ints, error_lats, error_lons, error_times, strict=True + ) + ) print( - "WARNING: Error(s) found during Argo Float simulation but the expedition will continue..." - f"\n\nError code(s): {', '.join(_STATUS_CODE_NAMES.get(error, str(error)) + 'at time: ' + str(particles.time[errors][i]) + ', lat: ' + str(particles.lat[errors][i]) + ', lon: ' + str(particles.lon[errors][i]) for i, error in enumerate(particles.state[errors]))}" - "\n\nIf ErrorOutOfBounds, consider reducing the lifetime in Argo Float config (the fieldset spatial bounds are constrained under-the-hood). For further advice please contact the VirtualShip team via GitHub (https://github.com/Parcels-code/virtualship/issues) or email (virtualship@uu.nl)." - "\nCarrying on with the expedition..." + "WARNING: Error(s) found during Argo Float simulation but the expedition will continue...\n\n" + f"Error code(s): {error_details}\n\n" + "If ErrorOutOfBounds, consider reducing the lifetime in Argo Float config " + "(the fieldset spatial bounds are constrained under-the-hood). For further advice " + "please contact the VirtualShip team via GitHub (https://github.com/Parcels-code/virtualship/issues) " + "or email (virtualship@uu.nl).\n" + "Carrying on with the expedition..." ) + particles.state[errors] = StatusCode.Delete def _argo_sample_temperature(particles, fieldset): - # Phase 3: ascending — sample temperature; NaN otherwise + # Phase 3: ascending — sample temperature phase_mask = particles.cycle_phase == 3 - depth_mask = particles.z < particles.min_depth + depth_mask = particles.z < particles.min_depth # still ascending sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.temperature = fieldset.T[ sampling_particles.time, @@ -173,9 +175,9 @@ def _argo_sample_temperature(particles, fieldset): def _argo_sample_salinity(particles, fieldset): - # Phase 3: ascending — sample salinity; NaN otherwise + # Phase 3: ascending — sample salinity phase_mask = particles.cycle_phase == 3 - depth_mask = particles.z < particles.min_depth + depth_mask = particles.z < particles.min_depth # still ascending sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.salinity = fieldset.S[ sampling_particles.time, @@ -185,6 +187,48 @@ def _argo_sample_salinity(particles, fieldset): ] +# ===================================================== +# SECTION: Helper Functions +# ===================================================== + + +def _handle_grounding( + ptcls_subset, bathysafe_mask, loc_bathy, fieldset, phase_name, target_phase +): + """Handle grounding logic, logging warnings, and raising particles above bathymetry.""" + grounded_mask = ~bathysafe_mask + if not np.any(grounded_mask): + return + + ptcls_subset.grounded[grounded_mask] = 1 + + # extract log data + times, lats, lons = _format_log_metadata(ptcls_subset, grounded_mask, fieldset) + + print( + f"Shallow bathymetry warning: Argo float grounded at bathymetry during {phase_name} " + f"(time(s): {times}, lat(s): {lats}, lon(s): {lons}). " + f"Raising by 50m above bathymetry and continuing cycle." + ) + + # adjust vertical displacement to be 50m above bathymetry and transition phase + ptcls_subset.dz[grounded_mask] = ( + loc_bathy[grounded_mask] - ptcls_subset.z[grounded_mask] + 50.0 + ) + ptcls_subset.cycle_phase[grounded_mask] = target_phase + + +def _format_log_metadata(ptcls_subset, mask, fieldset): + """Extracts and formats timestamps, latitudes, and longitudes for particles.""" + lats = ptcls_subset.lat[mask].astype(float) + lons = ptcls_subset.lon[mask].astype(float) + + time_origin = fieldset.U.data.time[0].values + times = ptcls_subset.time[mask].astype("timedelta64[s]") + time_origin + + return times, lats, lons + + # ===================================================== # SECTION: Instrument Class # ===================================================== From c05d2f7634a08012fdaebe5926151ed350ba5c18 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 13 Jul 2026 15:21:50 +0200 Subject: [PATCH 44/94] switch to using .to_windowed_arrays() for performance --- src/virtualship/instruments/base.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index f6d0c9a2..baa36e9d 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -217,9 +217,6 @@ def _generate_fieldset(self) -> parcels.FieldSet: ) field_var_name = var - # TODO: I think this is potentially slowing down simulations slightly... compared to v0.3 anyway for *drifters* - ds.load() # TODO: tmp step during v4 alpha stage... probably to be updated on the Parcels end - # negate depth and reindex (to suit Parcels XGrid strictly increasing depth convention) ds["depth"] = -ds["depth"] ds = ds.reindex(depth=ds["depth"][::-1]) @@ -229,7 +226,9 @@ def _generate_fieldset(self) -> parcels.FieldSet: fields = {key: ds[field_var_name]} ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) + fs.to_windowed_arrays() # TODO: for enhanced performance in Parcels v4, Parcels-side recommendations may change in the future... fieldsets_list.append(fs) From cff02401c4dc77ec8534b822b7a60dfa25926795 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 13 Jul 2026 16:11:52 +0200 Subject: [PATCH 45/94] neater way of negating depth --- src/virtualship/utils.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index df5d153c..98fdb866 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -450,6 +450,9 @@ def _get_bathy_data(from_data: Path | None = None) -> FieldSet: {"depth": 1} ) # TODO: bodge whilst parcels v4 does not support 2D fields and seeks depth dim; change when parcels v4 released + # Negate bathymetry to convert from depth below geoid to negative depth (Parcels convention) + ds_bathymetry[VAR] = -ds_bathymetry[VAR] + ds_fset = parcels.convert.copernicusmarine_to_sgrid( fields={"bathymetry": ds_bathymetry[VAR]} ) From d9df5b6dddbea0022ba3eefec08f0870fe54afc0 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 13 Jul 2026 16:13:26 +0200 Subject: [PATCH 46/94] use interpolator object --- src/virtualship/instruments/base.py | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index baa36e9d..4986eae9 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -99,12 +99,11 @@ def load_input_data(self) -> parcels.FieldSet: for var in (v for v in self.variables if v not in ("U", "V")): getattr( fieldset, var - ).interp_method = parcels.interpolators.XLinearInvdistLandTracer + ).interp_method = parcels.interpolators.XLinearInvdistLandTracer() # bathymetry data if self.add_bathymetry: bathymetry_field = _get_bathy_data(from_data=self.from_data).bathymetry - bathymetry_field.data = -bathymetry_field.data fieldset.add_field(bathymetry_field) return fieldset @@ -236,16 +235,16 @@ def _generate_fieldset(self) -> parcels.FieldSet: for fs, key in zip(fieldsets_list[1:], keys[1:], strict=False): base_fieldset.add_field(getattr(fs, key)) - # some instruments use AdvectionRKn kernels which require a combined UV vector field - # fieldsets are created per variable and thus are not seen by from_sgrid_conventions at the same time, therefore build combined VectorField here in FieldSet - if "U" in keys and "V" in keys: - uv = parcels.VectorField( - "UV", - base_fieldset.U, - base_fieldset.V, - vector_interp_method=parcels.interpolators.XLinear_Velocity, - ) - base_fieldset.add_field(uv) + # # some instruments use AdvectionRKn kernels which require a combined UV vector field + # # fieldsets are created per variable and thus are not seen by from_sgrid_conventions at the same time, therefore build combined VectorField here in FieldSet + # if "U" in keys and "V" in keys: + # uv = parcels.VectorField( + # "UV", + # base_fieldset.U, + # base_fieldset.V, + # interp_method=parcels.interpolators.XLinear_Velocity, + # ) + # base_fieldset.add_field(uv) return base_fieldset From f89ce7362584df659e550e9ce2b99d28f81da61b Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 14 Jul 2026 11:54:36 +0200 Subject: [PATCH 47/94] lat/lon/time -> y/x/t --- src/virtualship/instruments/adcp.py | 12 +++--- src/virtualship/instruments/argo_float.py | 32 ++++++++------- src/virtualship/instruments/ctd.py | 40 ++++++------------- src/virtualship/instruments/drifter.py | 8 ++-- .../instruments/ship_underwater_st.py | 10 ++--- src/virtualship/instruments/xbt.py | 8 ++-- 6 files changed, 48 insertions(+), 62 deletions(-) diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index e9a55a8d..49a08120 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -37,10 +37,10 @@ class ADCP: def _sample_velocity(particles, fieldset): particles.U, particles.V = fieldset.UV.eval( - particles.time, + particles.t, particles.z, - particles.lat, - particles.lon, + particles.x, + particles.y, applyConversion=False, ) @@ -108,10 +108,10 @@ def simulate(self, measurements, out_path) -> None: particleset = ParticleSet( fieldset=fieldset, pclass=_ADCPParticle, - lon=np.full( + x=np.full( num_particles, 0.0 - ), # initial lat/lon are irrelevant and will be overruled later.s - lat=np.full(num_particles, 0.0), + ), # initial lat/lon are irrelevant and will be overruled later + y=np.full(num_particles, 0.0), z=bins, ) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 7ed59710..bca082e9 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -60,6 +60,8 @@ class ArgoFloat: def _argo_float_vertical_movement(particles, fieldset): + breakpoint() + # Split particles based on their current cycle_phase ptcls0 = particles[particles.cycle_phase == 0] ptcls1 = particles[particles.cycle_phase == 1] @@ -69,7 +71,7 @@ def _argo_float_vertical_movement(particles, fieldset): # Phase 0: Sinking with vertical_speed until depth is driftdepth ptcls0.dz += particles.vertical_speed * ptcls0.dt - loc_bathy = fieldset.bathymetry.eval(ptcls0.time, ptcls0.z, ptcls0.lat, ptcls0.lon) + loc_bathy = fieldset.bathymetry.eval(ptcls0.t, ptcls0.z, ptcls0.y, ptcls0.x) driftdepth_mask = ptcls0.z + ptcls0.dz <= particles.drift_depth # noqa:has reached drift depth bathysafe_mask = ptcls0.z + ptcls0.dz >= loc_bathy # noqa:has not reached bathymetry next_phase = np.logical_and(driftdepth_mask, bathysafe_mask) @@ -94,7 +96,7 @@ def _argo_float_vertical_movement(particles, fieldset): # Phase 2: Sinking further to maxdepth ptcls2.dz += particles.vertical_speed * ptcls2.dt - loc_bathy = fieldset.bathymetry.eval(ptcls2.time, ptcls2.z, ptcls2.lat, ptcls2.lon) + loc_bathy = fieldset.bathymetry.eval(ptcls2.t, ptcls2.z, ptcls2.y, ptcls2.x) maxdepth_mask = ptcls2.z + ptcls2.dz <= particles.max_depth # noqa:has reached max depth bathysafe_mask = ptcls2.z + ptcls2.dz >= loc_bathy # noqa:has not reached bathymetry next_phase = np.logical_and(maxdepth_mask, bathysafe_mask) @@ -167,10 +169,10 @@ def _argo_sample_temperature(particles, fieldset): depth_mask = particles.z < particles.min_depth # still ascending sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.temperature = fieldset.T[ - sampling_particles.time, + sampling_particles.t, sampling_particles.z, - sampling_particles.lat, - sampling_particles.lon, + sampling_particles.y, + sampling_particles.x, ] @@ -180,10 +182,10 @@ def _argo_sample_salinity(particles, fieldset): depth_mask = particles.z < particles.min_depth # still ascending sampling_particles = particles[np.logical_and(phase_mask, depth_mask)] sampling_particles.salinity = fieldset.S[ - sampling_particles.time, + sampling_particles.t, sampling_particles.z, - sampling_particles.lat, - sampling_particles.lon, + sampling_particles.y, + sampling_particles.x, ] @@ -220,11 +222,11 @@ def _handle_grounding( def _format_log_metadata(ptcls_subset, mask, fieldset): """Extracts and formats timestamps, latitudes, and longitudes for particles.""" - lats = ptcls_subset.lat[mask].astype(float) - lons = ptcls_subset.lon[mask].astype(float) + lats = ptcls_subset.y[mask].astype(float) + lons = ptcls_subset.x[mask].astype(float) time_origin = fieldset.U.data.time[0].values - times = ptcls_subset.time[mask].astype("timedelta64[s]") + time_origin + times = ptcls_subset.t[mask].astype("timedelta64[s]") + time_origin return times, lats, lons @@ -290,7 +292,7 @@ def simulate(self, measurements, out_path) -> None: shallow_waypoints = {} for i, m in enumerate(measurements): loc_bathy = fieldset.bathymetry.eval( - time=np.float64(0), + t=np.float64(0), z=0, y=m.spacetime.location.lat, x=m.spacetime.location.lon, @@ -313,10 +315,10 @@ def simulate(self, measurements, out_path) -> None: argo_float_particleset = ParticleSet( fieldset=fieldset, pclass=_ArgoParticle, - lat=[argo.spacetime.location.lat for argo in measurements], - lon=[argo.spacetime.location.lon for argo in measurements], + y=[argo.spacetime.location.lat for argo in measurements], + x=[argo.spacetime.location.lon for argo in measurements], z=[argo.min_depth for argo in measurements], - time=[np.datetime64(argo.spacetime.time) for argo in measurements], + t=[np.datetime64(argo.spacetime.time) for argo in measurements], min_depth=[argo.min_depth for argo in measurements], max_depth=[argo.max_depth for argo in measurements], drift_depth=[argo.drift_depth for argo in measurements], diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 6823cca8..1dfda753 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -55,59 +55,43 @@ class CTD: def _sample_temperature(particles, fieldset): particles.temperature = fieldset.T[ - particles.time, particles.z, particles.lat, particles.lon + particles.t, particles.z, particles.y, particles.x ] def _sample_salinity(particles, fieldset): - particles.salinity = fieldset.S[ - particles.time, particles.z, particles.lat, particles.lon - ] + particles.salinity = fieldset.S[particles.t, particles.z, particles.y, particles.x] ## bgc variables def _sample_o2(particles, fieldset): - particles.o2 = fieldset.o2[ - particles.time, particles.z, particles.lat, particles.lon - ] + particles.o2 = fieldset.o2[particles.t, particles.z, particles.y, particles.x] def _sample_chlorophyll(particles, fieldset): - particles.chl = fieldset.chl[ - particles.time, particles.z, particles.lat, particles.lon - ] + particles.chl = fieldset.chl[particles.t, particles.z, particles.y, particles.x] def _sample_nitrate(particles, fieldset): - particles.no3 = fieldset.no3[ - particles.time, particles.z, particles.lat, particles.lon - ] + particles.no3 = fieldset.no3[particles.t, particles.z, particles.y, particles.x] def _sample_phosphate(particles, fieldset): - particles.po4 = fieldset.po4[ - particles.time, particles.z, particles.lat, particles.lon - ] + particles.po4 = fieldset.po4[particles.t, particles.z, particles.y, particles.x] def _sample_ph(particles, fieldset): - particles.ph = fieldset.ph[ - particles.time, particles.z, particles.lat, particles.lon - ] + particles.ph = fieldset.ph[particles.t, particles.z, particles.y, particles.x] def _sample_phytoplankton(particles, fieldset): - particles.phyc = fieldset.phyc[ - particles.time, particles.z, particles.lat, particles.lon - ] + particles.phyc = fieldset.phyc[particles.t, particles.z, particles.y, particles.x] def _sample_primary_production(particles, fieldset): - particles.nppv = fieldset.nppv[ - particles.time, particles.z, particles.lat, particles.lon - ] + particles.nppv = fieldset.nppv[particles.t, particles.z, particles.y, particles.x] ## cast @@ -224,10 +208,10 @@ def simulate(self, measurements, out_path) -> None: ctd_particleset = ParticleSet( fieldset=fieldset, pclass=_CTDParticle, - lon=[ctd.spacetime.location.lon for ctd in measurements], - lat=[ctd.spacetime.location.lat for ctd in measurements], + x=[ctd.spacetime.location.lon for ctd in measurements], + y=[ctd.spacetime.location.lat for ctd in measurements], z=[ctd.min_depth for ctd in measurements], - time=[np.datetime64(ctd.spacetime.time) for ctd in measurements], + t=[np.datetime64(ctd.spacetime.time) for ctd in measurements], max_depth=max_depths, min_depth=[ctd.min_depth for ctd in measurements], winch_speed=[WINCH_SPEED for _ in measurements], diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index c18088ee..a771b246 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -50,7 +50,7 @@ class Drifter: def _sample_temperature(particles, fieldset): particles.temperature = fieldset.T[ - particles.time, particles.z, particles.lat, particles.lon + particles.t, particles.z, particles.y, particles.x ] @@ -145,10 +145,10 @@ def simulate(self, measurements, out_path) -> None: drifter_particleset = ParticleSet( fieldset=fieldset, pclass=_DrifterParticle, - lat=lat_release, - lon=lon_release, + y=lat_release, + x=lon_release, z=[drifter.depth for drifter in measurements], - time=[np.datetime64(drifter.spacetime.time) for drifter in measurements], + t=[np.datetime64(drifter.spacetime.time) for drifter in measurements], has_lifetime=[ 1 if drifter.lifetime is not None else 0 for drifter in measurements ], diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index c5149a7a..302d991f 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -40,12 +40,12 @@ class Underwater_ST: # define function sampling Salinity def _sample_salinity(particles, fieldset): - particles.S = fieldset.S[particles.time, particles.z, particles.lat, particles.lon] + particles.S = fieldset.S[particles.t, particles.z, particles.y, particles.x] # define function sampling Temperature def _sample_temperature(particles, fieldset): - particles.T = fieldset.T[particles.time, particles.z, particles.lat, particles.lon] + particles.T = fieldset.T[particles.t, particles.z, particles.y, particles.x] # ===================================================== @@ -103,9 +103,9 @@ def simulate(self, measurements, out_path) -> None: particleset = ParticleSet( fieldset=fieldset, pclass=_ShipSTParticle, - lon=0.0, - lat=0.0, - depth=DEPTH, + x=0.0, + y=0.0, + z=DEPTH, ) out_file = ParticleFile(path=out_path, outputdt=np.inf) diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index bc5f5ecf..92f6b3f2 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -53,7 +53,7 @@ class XBT: def _sample_temperature(particles, fieldset): particles.temperature = fieldset.T[ - particles.time, particles.z, particles.lat, particles.lon + particles.t, particles.z, particles.y, particles.x ] @@ -163,10 +163,10 @@ def simulate(self, measurements, out_path) -> None: xbt_particleset = ParticleSet( fieldset=fieldset, pclass=_XBTParticle, - lon=[xbt.spacetime.location.lon for xbt in measurements], - lat=[xbt.spacetime.location.lat for xbt in measurements], + x=[xbt.spacetime.location.lon for xbt in measurements], + y=[xbt.spacetime.location.lat for xbt in measurements], z=[xbt.min_depth for xbt in measurements], - time=[np.datetime64(xbt.spacetime.time) for xbt in measurements], + t=[np.datetime64(xbt.spacetime.time) for xbt in measurements], max_depth=max_depths, min_depth=[xbt.min_depth for xbt in measurements], fall_speed=[xbt.fall_speed for xbt in measurements], From d8bd0743e163873192b7087ce4e9de37afdc1c86 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 14 Jul 2026 11:55:05 +0200 Subject: [PATCH 48/94] performance step: windowed arrays --- src/virtualship/instruments/base.py | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 4986eae9..b84cc854 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -227,7 +227,7 @@ def _generate_fieldset(self) -> parcels.FieldSet: ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) - fs.to_windowed_arrays() # TODO: for enhanced performance in Parcels v4, Parcels-side recommendations may change in the future... + fs = fs.to_windowed_arrays() # TODO: for enhanced performance in Parcels v4, Parcels-side recommendations may change in the future... fieldsets_list.append(fs) @@ -235,16 +235,16 @@ def _generate_fieldset(self) -> parcels.FieldSet: for fs, key in zip(fieldsets_list[1:], keys[1:], strict=False): base_fieldset.add_field(getattr(fs, key)) - # # some instruments use AdvectionRKn kernels which require a combined UV vector field - # # fieldsets are created per variable and thus are not seen by from_sgrid_conventions at the same time, therefore build combined VectorField here in FieldSet - # if "U" in keys and "V" in keys: - # uv = parcels.VectorField( - # "UV", - # base_fieldset.U, - # base_fieldset.V, - # interp_method=parcels.interpolators.XLinear_Velocity, - # ) - # base_fieldset.add_field(uv) + # some instruments use AdvectionRKn kernels which require a combined UV vector field + # fieldsets are created per variable and thus are not seen by from_sgrid_conventions at the same time, therefore build combined VectorField here in FieldSet + if "U" in keys and "V" in keys: + uv = parcels.VectorField( + "UV", + base_fieldset.U, + base_fieldset.V, + interp_method=parcels.interpolators.XLinear_Velocity(), + ) + base_fieldset.add_field(uv) return base_fieldset From ca3ba1f20f26fceddde3f1fdab38d1f6b2404a73 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 14 Jul 2026 11:55:12 +0200 Subject: [PATCH 49/94] time -> t --- src/virtualship/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 98fdb866..9cb028f3 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -572,7 +572,7 @@ def _compute_max_depths(measurements, fieldset) -> list[float]: z=0, y=m.spacetime.location.lat, x=m.spacetime.location.lon, - time=np.float64(0), + t=np.float64(0), )[0], ) for m in measurements From c7406308e60136ae0117f059540243ab9da67a1a Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 16 Jul 2026 09:31:30 +0200 Subject: [PATCH 50/94] fix argo bug, min_depth spec to avoid immediately error out of bounds --- src/virtualship/instruments/argo_float.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index bca082e9..b18c91ba 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -60,8 +60,6 @@ class ArgoFloat: def _argo_float_vertical_movement(particles, fieldset): - breakpoint() - # Split particles based on their current cycle_phase ptcls0 = particles[particles.cycle_phase == 0] ptcls1 = particles[particles.cycle_phase == 1] @@ -151,7 +149,7 @@ def _check_error(particles, fieldset): ) ) print( - "WARNING: Error(s) found during Argo Float simulation but the expedition will continue...\n\n" + "\nWARNING: Error(s) found during Argo Float simulation but the expedition will continue...\n\n" f"Error code(s): {error_details}\n\n" "If ErrorOutOfBounds, consider reducing the lifetime in Argo Float config " "(the fieldset spatial bounds are constrained under-the-hood). For further advice " @@ -311,15 +309,18 @@ def simulate(self, measurements, out_path) -> None: argo_float_config.sensors, _ARGO_NONSENSOR_VARIABLES ) + # in case fieldset depth is smaller than the config min_depth, possible when min_depth config is 0 and fieldset surface is ~ -0.4... + grid_shallowest = fieldset.U.grid.depth[-1] + # define parcel particles argo_float_particleset = ParticleSet( fieldset=fieldset, pclass=_ArgoParticle, y=[argo.spacetime.location.lat for argo in measurements], x=[argo.spacetime.location.lon for argo in measurements], - z=[argo.min_depth for argo in measurements], + z=[min(argo.min_depth, grid_shallowest) for argo in measurements], t=[np.datetime64(argo.spacetime.time) for argo in measurements], - min_depth=[argo.min_depth for argo in measurements], + min_depth=[min(argo.min_depth, grid_shallowest) for argo in measurements], max_depth=[argo.max_depth for argo in measurements], drift_depth=[argo.drift_depth for argo in measurements], vertical_speed=[argo.vertical_speed for argo in measurements], From abb5380a3b32efe68de2294ebb45bbad92683e9c Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 17 Jul 2026 11:57:17 +0200 Subject: [PATCH 51/94] remove ds.load/to_windowed_arrays experiments --- src/virtualship/instruments/base.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index b84cc854..cc406def 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -208,6 +208,8 @@ def _generate_fieldset(self) -> parcels.FieldSet: ds = xr.open_mfdataset([data_dir.joinpath(f) for f in files]) + # TODO: for the local data it's useful to sel the relevant depth layer(s), in case the user's data is full depth + else: # stream via Copernicus Marine Service ds = self._get_copernicus_ds( time_buffer, @@ -227,7 +229,6 @@ def _generate_fieldset(self) -> parcels.FieldSet: ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) - fs = fs.to_windowed_arrays() # TODO: for enhanced performance in Parcels v4, Parcels-side recommendations may change in the future... fieldsets_list.append(fs) From 583fc7b038853af3ff34c706b1a3f79cae78c696 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 20 Jul 2026 13:20:15 +0200 Subject: [PATCH 52/94] lon -> x --- src/virtualship/instruments/ctd.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 1dfda753..48164ca7 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -237,7 +237,7 @@ def simulate(self, measurements, out_path) -> None: ) # there should be no particles left, as they delete themselves when they resurface - if len(ctd_particleset.lon) != 0: + if len(ctd_particleset.x) != 0: raise ValueError( "Simulation ended before CTD resurfaced. This most likely means the field time dimension did not match the simulation time span." ) From f2becc0c7dd540e219a862826d85c83a2cdb7460 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 20 Jul 2026 15:15:45 +0200 Subject: [PATCH 53/94] use vertical_axis arg for depth convention --- src/virtualship/instruments/base.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index cc406def..1595f5e4 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -176,6 +176,9 @@ def _get_copernicus_ds( minimum_depth=depth_min, maximum_depth=depth_max, coordinates_selection_method="outside", + service="arco-geo-series", + chunk_size_limit=1, + vertical_axis="elevation", ) def _generate_fieldset(self) -> parcels.FieldSet: @@ -218,9 +221,9 @@ def _generate_fieldset(self) -> parcels.FieldSet: ) field_var_name = var - # negate depth and reindex (to suit Parcels XGrid strictly increasing depth convention) - ds["depth"] = -ds["depth"] - ds = ds.reindex(depth=ds["depth"][::-1]) + # # negate depth and reindex (to suit Parcels XGrid strictly increasing depth convention) + # ds["depth"] = -ds["depth"] + # ds = ds.reindex(depth=ds["depth"][::-1]) # TODO: update when decision on handling of nans/0s in v4 is made (i.e. https://github.com/Parcels-code/Parcels/issues/2393) ds = ds.fillna(0) From ae980592e251f2c2f10626e0020ce46e213b1f18 Mon Sep 17 00:00:00 2001 From: Jamie Atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 22 Jul 2026 14:44:14 +0200 Subject: [PATCH 54/94] Refactor dataset retrieval specs (#359) * small refactor: consolidate spacetime_buffer_size and limit_spec dicts for clarity * to FetchSpec dataclass * new test for FetchSpec class --- src/virtualship/instruments/adcp.py | 8 +--- src/virtualship/instruments/argo_float.py | 16 +++----- src/virtualship/instruments/base.py | 37 ++++++++++--------- src/virtualship/instruments/ctd.py | 8 +--- src/virtualship/instruments/drifter.py | 20 ++++------ .../instruments/ship_underwater_st.py | 12 +----- src/virtualship/instruments/xbt.py | 8 +--- tests/instruments/test_base.py | 17 ++++++++- 8 files changed, 57 insertions(+), 69 deletions(-) diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index 49a08120..26c8122d 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -5,7 +5,7 @@ import numpy as np from parcels import ParticleFile, ParticleSet -from virtualship.instruments.base import Instrument +from virtualship.instruments.base import FetchSpec, Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.utils import build_particle_class_from_sensors, register_instrument @@ -61,9 +61,6 @@ class ADCPInstrument(Instrument): def __init__(self, expedition, from_data): """Initialize ADCPInstrument.""" variables = expedition.instruments_config.adcp_config.active_variables() - limit_spec = { - "spatial": True - } # spatial limits; lat/lon constrained to waypoint locations + buffer super().__init__( expedition, @@ -71,8 +68,7 @@ def __init__(self, expedition, from_data): add_bathymetry=False, allow_time_extrapolation=True, verbose_progress=False, - spacetime_buffer_size=None, - limit_spec=limit_spec, + fetch_spec=FetchSpec(), from_data=from_data, ) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index b18c91ba..96b8e4f0 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -7,7 +7,7 @@ from parcels import ParticleFile, ParticleSet, StatusCode, Variable from parcels.kernels import AdvectionRK2 -from virtualship.instruments.base import Instrument +from virtualship.instruments.base import FetchSpec, Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.models.spacetime import Spacetime @@ -253,14 +253,11 @@ def __init__(self, expedition, from_data): "V": "vo", **sensor_variables, } # advection variables (U and V) are always required for argo float simulation; sensor variables come from config - spacetime_buffer_size = { - "latlon": 3.0, # [degrees] - "time": expedition.instruments_config.argo_float_config.lifetime.total_seconds() + fetch_spec = FetchSpec( + latlon_buffer=3.0, # [degrees] + time_buffer=expedition.instruments_config.argo_float_config.lifetime.total_seconds() / (24 * 3600), # [days] - } - limit_spec = { - "spatial": True, # spatial limits; lat/lon constrained to waypoint locations + buffer - } + ) super().__init__( expedition, @@ -268,8 +265,7 @@ def __init__(self, expedition, from_data): add_bathymetry=True, allow_time_extrapolation=False, verbose_progress=True, - spacetime_buffer_size=spacetime_buffer_size, - limit_spec=limit_spec, + fetch_spec=fetch_spec, from_data=from_data, ) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 1595f5e4..f07bc0cf 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -2,6 +2,7 @@ import abc import collections +from dataclasses import dataclass from datetime import timedelta from itertools import pairwise from pathlib import Path @@ -28,6 +29,17 @@ from virtualship.models import Expedition +@dataclass +class FetchSpec: + """Fetch constraints and parameters for dataset retrieval.""" + + spatial: bool = True + latlon_buffer: float = 0.25 # degrees + time_buffer: float = 0.0 # days + depth_min: float | None = None + depth_max: float | None = None + + class Instrument(abc.ABC): """Base class for instruments and their simulation.""" @@ -50,8 +62,7 @@ def __init__( allow_time_extrapolation: bool, verbose_progress: bool, from_data: Path | None, - spacetime_buffer_size: dict | None = None, - limit_spec: dict | None = None, + fetch_spec: FetchSpec | None = None, ): """Initialise instrument.""" self.expedition = expedition @@ -67,8 +78,7 @@ def __init__( self.add_bathymetry = add_bathymetry self.allow_time_extrapolation = allow_time_extrapolation self.verbose_progress = verbose_progress - self.spacetime_buffer_size = spacetime_buffer_size - self.limit_spec = limit_spec + self.fetch_spec = fetch_spec or FetchSpec() wp_lats, wp_lons = _get_waypoint_latlons(expedition.schedule.waypoints) wp_times = [ @@ -152,12 +162,10 @@ def _get_copernicus_ds( variable=var if not physical else None, ) - latlon_buffer = self._get_spec_value( - "buffer", "latlon", 0.25 - ) # [degrees]; default 0.25 deg buffer to ensure coverage in field cell edge cases - depth_min = self._get_spec_value("limit", "depth_min", None) - depth_max = self._get_spec_value("limit", "depth_max", None) - spatial_constraint = self._get_spec_value("limit", "spatial", True) + latlon_buffer = self.fetch_spec.latlon_buffer + depth_min = self.fetch_spec.depth_min + depth_max = self.fetch_spec.depth_max + spatial_constraint = self.fetch_spec.spatial min_lon_bound = self.min_lon - latlon_buffer if spatial_constraint else None max_lon_bound = self.max_lon + latlon_buffer if spatial_constraint else None @@ -176,8 +184,6 @@ def _get_copernicus_ds( minimum_depth=depth_min, maximum_depth=depth_max, coordinates_selection_method="outside", - service="arco-geo-series", - chunk_size_limit=1, vertical_axis="elevation", ) @@ -190,7 +196,7 @@ def _generate_fieldset(self) -> parcels.FieldSet: fieldsets_list = [] keys = list(self.variables.keys()) - time_buffer = self._get_spec_value("buffer", "time", 0.0) + time_buffer = self.fetch_spec.time_buffer for key in keys: var = self.variables[key] @@ -251,8 +257,3 @@ def _generate_fieldset(self) -> parcels.FieldSet: base_fieldset.add_field(uv) return base_fieldset - - def _get_spec_value(self, spec_type: str, key: str, default=None): - """Helper to extract a value from spacetime_buffer_size or limit_spec.""" - spec = self.spacetime_buffer_size if spec_type == "buffer" else self.limit_spec - return spec.get(key) if spec and spec.get(key) is not None else default diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 48164ca7..d6764130 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -7,7 +7,7 @@ from parcels import ParticleFile, ParticleSet, Variable from parcels._core.statuscodes import StatusCode -from virtualship.instruments.base import Instrument +from virtualship.instruments.base import FetchSpec, Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.utils import ( @@ -142,9 +142,6 @@ class CTDInstrument(Instrument): def __init__(self, expedition, from_data): """Initialize CTDInstrument.""" variables = expedition.instruments_config.ctd_config.active_variables() - limit_spec = { - "spatial": True - } # spatial limits; lat/lon constrained to waypoint locations + buffer super().__init__( expedition, @@ -152,8 +149,7 @@ def __init__(self, expedition, from_data): add_bathymetry=True, allow_time_extrapolation=True, verbose_progress=False, - spacetime_buffer_size=None, - limit_spec=limit_spec, + fetch_spec=FetchSpec(), from_data=from_data, ) diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index a771b246..6319831e 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -8,7 +8,7 @@ from parcels._core.statuscodes import StatusCode from parcels.kernels import AdvectionRK2 -from virtualship.instruments.base import Instrument +from virtualship.instruments.base import FetchSpec, Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.models.spacetime import Spacetime @@ -88,20 +88,17 @@ def __init__(self, expedition, from_data): "V": "vo", **sensor_variables, } # advection variables (U and V) are always required for drifter simulation; sensor variables come from config - spacetime_buffer_size = { - "latlon": None, - "time": expedition.instruments_config.drifter_config.lifetime.total_seconds() + fetch_spec = FetchSpec( + latlon_buffer=30.0, # TODO: generous buffer to limit tmp file size download, can potentially be removed in the future as and when Parcels streaming performance improves (see #358) + time_buffer=expedition.instruments_config.drifter_config.lifetime.total_seconds() / (24 * 3600), # [days] - } - limit_spec = { - "spatial": False, # no spatial limits; generate global fieldset - "depth_min": abs( + depth_min=abs( expedition.instruments_config.drifter_config.depth_meter ), # [meters] - "depth_max": abs( + depth_max=abs( expedition.instruments_config.drifter_config.depth_meter ), # [meters] - } + ) super().__init__( expedition, @@ -109,8 +106,7 @@ def __init__(self, expedition, from_data): add_bathymetry=False, allow_time_extrapolation=False, verbose_progress=True, - spacetime_buffer_size=spacetime_buffer_size, - limit_spec=limit_spec, + fetch_spec=fetch_spec, from_data=from_data, ) diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index 302d991f..1dc7522a 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -5,7 +5,7 @@ import numpy as np from parcels import ParticleFile, ParticleSet -from virtualship.instruments.base import Instrument +from virtualship.instruments.base import FetchSpec, Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.utils import ( @@ -67,13 +67,6 @@ def __init__(self, expedition, from_data): variables = ( expedition.instruments_config.ship_underwater_st_config.active_variables() ) - spacetime_buffer_size = { - "latlon": 0.25, # [degrees] - "time": 0.0, # [days] - } - limit_spec = { - "spatial": True - } # spatial limits; lat/lon constrained to waypoint locations + buffer super().__init__( expedition, @@ -81,8 +74,7 @@ def __init__(self, expedition, from_data): add_bathymetry=False, allow_time_extrapolation=True, verbose_progress=False, - spacetime_buffer_size=spacetime_buffer_size, - limit_spec=limit_spec, + fetch_spec=FetchSpec(), from_data=from_data, ) diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index 92f6b3f2..7046fde1 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -7,7 +7,7 @@ from parcels import ParticleFile, ParticleSet, Variable from parcels._core.statuscodes import StatusCode -from virtualship.instruments.base import Instrument +from virtualship.instruments.base import FetchSpec, Instrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.models.spacetime import Spacetime @@ -95,9 +95,6 @@ class XBTInstrument(Instrument): def __init__(self, expedition, from_data): """Initialize XBTInstrument.""" variables = expedition.instruments_config.xbt_config.active_variables() - limit_spec = { - "spatial": True - } # spatial limits; lat/lon constrained to waypoint locations + buffer super().__init__( expedition, @@ -105,8 +102,7 @@ def __init__(self, expedition, from_data): add_bathymetry=True, allow_time_extrapolation=True, verbose_progress=False, - spacetime_buffer_size=None, - limit_spec=limit_spec, + fetch_spec=FetchSpec(), from_data=from_data, ) diff --git a/tests/instruments/test_base.py b/tests/instruments/test_base.py index a17f95bf..1b090cb9 100644 --- a/tests/instruments/test_base.py +++ b/tests/instruments/test_base.py @@ -2,11 +2,26 @@ import pytest -from virtualship.instruments.base import Instrument +from virtualship.instruments.base import FetchSpec, Instrument from virtualship.instruments.types import InstrumentType from virtualship.utils import get_instrument_class +def test_FetchSpec(): + fetch_spec = FetchSpec() + + # test that default values are set + assert fetch_spec.latlon_buffer is not None + assert fetch_spec.time_buffer is not None + + # test setting values (in new instance) and that original is unchanged in memory + fetch_spec2 = FetchSpec(latlon_buffer=0.5, time_buffer=1.0) + assert fetch_spec2.latlon_buffer == 0.5 + assert fetch_spec2.time_buffer == 1.0 + + assert fetch_spec.latlon_buffer != fetch_spec2.latlon_buffer + + def test_all_instruments_have_instrument_class(): for instrument in InstrumentType: instrument_class = get_instrument_class(instrument) From 391b9305cb021d83f4ee8fabc85f55f3fad47539 Mon Sep 17 00:00:00 2001 From: Jamie Atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 23 Jul 2026 11:19:35 +0200 Subject: [PATCH 55/94] add tmp file write/read step (#358) * add tmp file write/read step * small refactor: consolidate spacetime_buffer_size and limit_spec dicts for clarity * to FetchSpec dataclass * add tmp file write/read step * remove depth axis reversal now using depth='elevation' in copernicusmarine.open_dataset() * tidy up todo * small update: fetch spec to match changes implemented in #359 * add test for _via_tmp_ds * add to_windowed_arrays() back in --- src/virtualship/instruments/base.py | 21 ++++++++++++----- src/virtualship/instruments/drifter.py | 2 +- tests/instruments/test_base.py | 31 +++++++++++++++++++++----- 3 files changed, 42 insertions(+), 12 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index f07bc0cf..bbb32889 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -2,6 +2,7 @@ import abc import collections +import tempfile from dataclasses import dataclass from datetime import timedelta from itertools import pairwise @@ -192,6 +193,9 @@ def _generate_fieldset(self) -> parcels.FieldSet: Create and combine FieldSets for each variable, supporting both local and Copernicus Marine data sources. N.B. Per variable avoids issues when using copernicusmarine and creating directly one FieldSet of ds's sourced from different Copernicus Marine product IDs (which can also have different temporal resolutions), which is often the case for BGC variables. + + Includes an intermediate step of writing to tmp files, as per https://github.com/Parcels-code/parcels-benchmarks/pull/49 + TODO: the need for this step may be removed as Parcels x copernicusmarine integration improves, tracked in https://github.com/Parcels-code/Parcels/issues/2756 and xref'd in VirtualShip #357 (https://github.com/Parcels-code/virtualship/issues/357) """ fieldsets_list = [] keys = list(self.variables.keys()) @@ -227,17 +231,15 @@ def _generate_fieldset(self) -> parcels.FieldSet: ) field_var_name = var - # # negate depth and reindex (to suit Parcels XGrid strictly increasing depth convention) - # ds["depth"] = -ds["depth"] - # ds = ds.reindex(depth=ds["depth"][::-1]) - - # TODO: update when decision on handling of nans/0s in v4 is made (i.e. https://github.com/Parcels-code/Parcels/issues/2393) + # TODO: to be removed when Parcels #2746 is merged (i.e. https://github.com/Parcels-code/Parcels/pull/2746) ds = ds.fillna(0) fields = {key: ds[field_var_name]} ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + ds_fset = self._via_tmp_ds(ds_fset) fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) + fs.to_windowed_arrays() # always to windowed arrays, just in case any ds is Dask backed fieldsets_list.append(fs) @@ -257,3 +259,12 @@ def _generate_fieldset(self) -> parcels.FieldSet: base_fieldset.add_field(uv) return base_fieldset + + @staticmethod + def _via_tmp_ds(ds) -> xr.Dataset: + """Create and re-load a temporary local dataset.""" + tmpdir = tempfile.TemporaryDirectory() + tmp_fpath = Path(tmpdir.name).joinpath("tmp.nc") + ds.to_netcdf(tmp_fpath) + del ds + return xr.open_dataset(tmp_fpath) diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index 6319831e..3b52dc71 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -89,7 +89,7 @@ def __init__(self, expedition, from_data): **sensor_variables, } # advection variables (U and V) are always required for drifter simulation; sensor variables come from config fetch_spec = FetchSpec( - latlon_buffer=30.0, # TODO: generous buffer to limit tmp file size download, can potentially be removed in the future as and when Parcels streaming performance improves (see #358) + latlon_buffer=30.0, # TODO: generous buffer to reduce tmp file footprint, can potentially be removed in the future as/when Parcels streaming performance improves (see #358) time_buffer=expedition.instruments_config.drifter_config.lifetime.total_seconds() / (24 * 3600), # [days] depth_min=abs( diff --git a/tests/instruments/test_base.py b/tests/instruments/test_base.py index 1b090cb9..93a38e90 100644 --- a/tests/instruments/test_base.py +++ b/tests/instruments/test_base.py @@ -1,6 +1,7 @@ from unittest.mock import MagicMock, patch import pytest +import xarray as xr from virtualship.instruments.base import FetchSpec, Instrument from virtualship.instruments.types import InstrumentType @@ -89,25 +90,43 @@ def test_execute_calls_simulate(monkeypatch): dummy.simulate.assert_called_once() -def test_get_spec_value_buffer_and_limit(): +def test_fetch_spec_applied_to_instrument(): + """FetchSpec values are correctly stored on the instrument.""" mock_waypoint = MagicMock() mock_waypoint.location.latitude = 1.0 mock_waypoint.location.longitude = 2.0 mock_schedule = MagicMock() mock_schedule.waypoints = [mock_waypoint] + fetch_spec = FetchSpec(latlon_buffer=5.0, depth_min=10.0) dummy = DummyInstrument( expedition=MagicMock(schedule=mock_schedule), variables={"A": "a"}, add_bathymetry=False, allow_time_extrapolation=False, verbose_progress=False, - spacetime_buffer_size={"latlon": 5.0}, - limit_spec={"depth_min": 10.0}, + fetch_spec=fetch_spec, from_data=None, ) - assert dummy._get_spec_value("buffer", "latlon", 0.0) == 5.0 - assert dummy._get_spec_value("limit", "depth_min", None) == 10.0 - assert dummy._get_spec_value("buffer", "missing", 42) == 42 + assert dummy.fetch_spec.latlon_buffer == 5.0 + assert dummy.fetch_spec.depth_min == 10.0 + # unset values use dataclass defaults + assert dummy.fetch_spec.time_buffer == 0.0 + assert dummy.fetch_spec.depth_max is None + + +def test_via_tmp_ds_roundtrip(): + """_via_tmp_ds writes to a tmp file and re-opens it.""" + ds = xr.Dataset( + {"temperature": (["x", "y"], [[1.0, 2.0], [3.0, 4.0]])}, + coords={"x": [0, 1], "y": [10, 20]}, + ) + result = Instrument._via_tmp_ds(ds) + + assert isinstance(result, xr.Dataset) + assert "temperature" in result + assert ( + result is not ds + ) # result is new object loaded from tmp file, not the original def test_generate_fieldset_combines_fields(monkeypatch): From 1ca6860f8daf64a128c9333dedae3fc9fa5f51ef Mon Sep 17 00:00:00 2001 From: Jamie Atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 27 Jul 2026 13:31:04 +0200 Subject: [PATCH 56/94] `to_windowed_arrays` only for non-underway instruments (#361) * instrument type property, conditional windowed arrays * add instrument_type/is_underway property tests to ADCP instrument tests * update tests across all instruments for instrument type property/is underway, incl. some refactoring * clean-up for review * Apply suggestions from code review Co-authored-by: Erik van Sebille --------- Co-authored-by: Erik van Sebille --- src/virtualship/instruments/base.py | 13 +- tests/instruments/test_adcp.py | 87 +++++++------ tests/instruments/test_argo_float.py | 14 +++ tests/instruments/test_ctd.py | 125 +++++++++---------- tests/instruments/test_drifter.py | 10 ++ tests/instruments/test_ship_underwater_st.py | 73 +++++++---- tests/instruments/test_xbt.py | 74 +++++++---- 7 files changed, 242 insertions(+), 154 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index bbb32889..80deeb02 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -15,8 +15,10 @@ from yaspin import yaspin from virtualship.errors import CopernicusCatalogueError +from virtualship.instruments.types import InstrumentType from virtualship.utils import ( COPERNICUSMARINE_PHYS_VARIABLES, + INSTRUMENT_CLASS_MAP, _find_files_in_timerange, _find_nc_file_with_variable, _get_bathy_data, @@ -239,7 +241,11 @@ def _generate_fieldset(self) -> parcels.FieldSet: ds_fset = self._via_tmp_ds(ds_fset) fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) - fs.to_windowed_arrays() # always to windowed arrays, just in case any ds is Dask backed + + # non-underway instruments to windowed arrays, just in case any ds is Dask backed + # underway instruments should not to converted to windowed arrays, as they use one direct fieldset.eval() call which could cause a big memory usage if the fieldset is windowed + if not self.instrument_type.is_underway: + fs = fs.to_windowed_arrays() fieldsets_list.append(fs) @@ -268,3 +274,8 @@ def _via_tmp_ds(ds) -> xr.Dataset: ds.to_netcdf(tmp_fpath) del ds return xr.open_dataset(tmp_fpath) + + @property + def instrument_type(self) -> InstrumentType: + """Return the InstrumentType for this instrument instance.""" + return next(k for k, v in INSTRUMENT_CLASS_MAP.items() if type(self) is v) diff --git a/tests/instruments/test_adcp.py b/tests/instruments/test_adcp.py index 48e9e17c..16604b67 100644 --- a/tests/instruments/test_adcp.py +++ b/tests/instruments/test_adcp.py @@ -1,32 +1,64 @@ """Test the simulation of ADCP instruments.""" import datetime +from typing import ClassVar import numpy as np import pydantic import pytest import xarray as xr - from parcels import FieldSet + from virtualship.instruments.adcp import ADCPInstrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.models import Location, Spacetime, Waypoint from virtualship.models.expedition import ADCPConfig, InstrumentsConfig, SensorConfig +# ===================================================== +# Shared constants and fixtures +# ===================================================== -def test_simulate_adcp(tmpdir) -> None: - MAX_DEPTH = -1000 - MIN_DEPTH = -5 - NUM_BINS = 40 +BASE_TIME = datetime.datetime.strptime( + "1950-01-01", "%Y-%m-%d" +) # arbitrary time offset for the dummy fieldset +MAX_DEPTH = -1000 +NUM_BINS = 40 + + +@pytest.fixture +def adcp_expedition(): + """Minimal Expedition for ADCPInstrument instantiation.""" + + class DummyExpedition: + class schedule: + waypoints: ClassVar[list] = [ + Waypoint( + location=Location(1, 2), + time=BASE_TIME, + instrument=InstrumentType.ADCP, + ), + ] + + instruments_config = InstrumentsConfig( + adcp_config=ADCPConfig( + max_depth_meter=MAX_DEPTH, + num_bins=NUM_BINS, + period_minutes=5.0, + sensors=[SensorConfig(sensor_type=SensorType.VELOCITY)], + ) + ) + + return DummyExpedition() - # arbitrary time offset for the dummy fieldset - base_time = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") + +def test_simulate_adcp(tmpdir, adcp_expedition) -> None: + MIN_DEPTH = -5 # where to sample sample_points = [ - Spacetime(Location(1, 2), base_time + datetime.timedelta(seconds=0)), - Spacetime(Location(3, 4), base_time + datetime.timedelta(seconds=1)), + Spacetime(Location(1, 2), BASE_TIME + datetime.timedelta(seconds=0)), + Spacetime(Location(3, 4), BASE_TIME + datetime.timedelta(seconds=1)), ] # expected observations at sample points @@ -36,14 +68,14 @@ def test_simulate_adcp(tmpdir) -> None: "U": {"surface": 7, "max_depth": 8}, "lat": sample_points[0].location.lat, "lon": sample_points[0].location.lon, - "time": base_time + datetime.timedelta(seconds=0), + "time": BASE_TIME + datetime.timedelta(seconds=0), }, { "V": {"surface": 9, "max_depth": 10}, "U": {"surface": 11, "max_depth": 12}, "lat": sample_points[1].location.lat, "lon": sample_points[1].location.lon, - "time": base_time + datetime.timedelta(seconds=1), + "time": BASE_TIME + datetime.timedelta(seconds=1), }, ] @@ -79,31 +111,7 @@ def test_simulate_adcp(tmpdir) -> None: }, ) - # dummy expedition for ADCPInstrument - class DummyExpedition: - class schedule: - # ruff: noqa - waypoints = [ - Waypoint( - location=Location(1, 2), - time=base_time, - instrument=InstrumentType.ADCP, - ), - ] - - instruments_config = InstrumentsConfig( - adcp_config=ADCPConfig( - max_depth_meter=MAX_DEPTH, - num_bins=NUM_BINS, - period_minutes=5.0, - sensors=[SensorConfig(sensor_type=SensorType.VELOCITY)], - ) - ) - - expedition = DummyExpedition() - from_data = None - - adcp_instrument = ADCPInstrument(expedition, from_data) + adcp_instrument = ADCPInstrument(adcp_expedition, from_data=None) out_path = tmpdir.join("out.zarr") adcp_instrument.load_input_data = lambda: fieldset @@ -183,3 +191,10 @@ def test_adcp_config_unsupported_sensor_rejected(): period_minutes=30.0, sensors=[SensorConfig(sensor_type=SensorType.TEMPERATURE)], ) + + +def test_adcp_instrument_type(adcp_expedition): + """ADCPInstrument returns the correct InstrumentType and if is underway instrument.""" + adcp_instrument = ADCPInstrument(adcp_expedition, from_data=None) + assert adcp_instrument.instrument_type == InstrumentType.ADCP + assert adcp_instrument.instrument_type.is_underway diff --git a/tests/instruments/test_argo_float.py b/tests/instruments/test_argo_float.py index 1a61673a..c56b6d4c 100644 --- a/tests/instruments/test_argo_float.py +++ b/tests/instruments/test_argo_float.py @@ -10,6 +10,7 @@ from virtualship.instruments.argo_float import ArgoFloat, ArgoFloatInstrument from virtualship.instruments.sensors import SensorType +from virtualship.instruments.types import InstrumentType from virtualship.models import Location, Spacetime from virtualship.models.expedition import ( ArgoFloatConfig, @@ -239,3 +240,16 @@ def test_argo_fieldoutofbounds_error(tmpdir) -> None: # TODO: capturing the warnings in the tests is complicated by the Parcels C-level print statements; but the logic of not crashing on out-of-bounds is tested if the test simulation runs # TODO: when using Parcels v4, this test can become much more robust by capturing the specific warning as well + + +def test_argo_float_instrument_type(): + """ArgoFloatInstrument returns the correct InstrumentType and if is underway instrument.""" + sensors = [ + SensorConfig(sensor_type=SensorType.TEMPERATURE), + SensorConfig(sensor_type=SensorType.SALINITY), + ] + expedition = create_dummy_expedition(sensors) + + argo_instrument = ArgoFloatInstrument(expedition, from_data=None) + assert argo_instrument.instrument_type == InstrumentType.ARGO_FLOAT + assert not argo_instrument.instrument_type.is_underway diff --git a/tests/instruments/test_ctd.py b/tests/instruments/test_ctd.py index c080f0f5..29eb758a 100644 --- a/tests/instruments/test_ctd.py +++ b/tests/instruments/test_ctd.py @@ -10,8 +10,8 @@ import pydantic import pytest import xarray as xr - from parcels import Field, FieldSet + from virtualship.instruments.ctd import CTD, CTDInstrument from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType @@ -23,18 +23,43 @@ Waypoint, ) +BASE_TIME = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") +MIN_DEPTH = -11 +MAX_DEPTH = -2000 +STATIONKEEPING_TIME = 50 + + +def create_dummy_expedition( + sensors, lifetime=datetime.timedelta(days=1), location=(1, 2) +): + """Create a DummyExpedition class with specified sensors and parameters.""" + + class DummyExpedition: + class schedule: + waypoints: list[Waypoint] = [ # noqa: RUF012 + Waypoint(location=Location(*location), time=BASE_TIME) + ] + + instruments_config = InstrumentsConfig( + ctd_config=CTDConfig( + stationkeeping_time_minutes=STATIONKEEPING_TIME, + min_depth_meter=MIN_DEPTH, + max_depth_meter=MAX_DEPTH, + sensors=sensors, + ) + ) + + return DummyExpedition() + def test_simulate_ctds(tmpdir) -> None: """Test that CTDInstrument simulates measurements correctly, incuding sampling physical and bgc variables.""" - # arbitrary time offset for the dummy fieldset - base_time = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") - # where to cast CTDs ctds = [ CTD( spacetime=Spacetime( location=Location(latitude=0, longitude=1), - time=base_time + datetime.timedelta(hours=0), + time=BASE_TIME + datetime.timedelta(hours=0), ), min_depth=0, max_depth=float("-inf"), @@ -42,7 +67,7 @@ def test_simulate_ctds(tmpdir) -> None: CTD( spacetime=Spacetime( location=Location(latitude=1, longitude=0), - time=base_time, + time=BASE_TIME, ), min_depth=0, max_depth=float("-inf"), @@ -132,8 +157,8 @@ def test_simulate_ctds(tmpdir) -> None: {"V": v, "U": u, "T": t, "S": s, "o2": o2, "chl": chl, "no3": no3}, { "time": [ - np.datetime64(base_time + datetime.timedelta(hours=0)), - np.datetime64(base_time + datetime.timedelta(hours=1)), + np.datetime64(BASE_TIME + datetime.timedelta(hours=0)), + np.datetime64(BASE_TIME + datetime.timedelta(hours=1)), ], "depth": [-1000, 0], "lat": [0, 1], @@ -142,33 +167,15 @@ def test_simulate_ctds(tmpdir) -> None: ) fieldset.add_field(Field("bathymetry", [-1000], lon=0, lat=0)) - # dummy expedition for CTDInstrument - class DummyExpedition: - class schedule: - # ruff: noqa - waypoints = [ - Waypoint( - location=Location(1, 2), - time=base_time, - ), - ] - - instruments_config = InstrumentsConfig( - ctd_config=CTDConfig( - stationkeeping_time_minutes=50, - min_depth_meter=-11.0, - max_depth_meter=-2000.0, - sensors=[ - SensorConfig(sensor_type=SensorType.TEMPERATURE), - SensorConfig(sensor_type=SensorType.SALINITY), - SensorConfig(sensor_type=SensorType.OXYGEN), - SensorConfig(sensor_type=SensorType.CHLOROPHYLL), - SensorConfig(sensor_type=SensorType.NITRATE), - ], - ) - ) + sensors = [ + SensorConfig(sensor_type=SensorType.TEMPERATURE), + SensorConfig(sensor_type=SensorType.SALINITY), + SensorConfig(sensor_type=SensorType.OXYGEN), + SensorConfig(sensor_type=SensorType.CHLOROPHYLL), + SensorConfig(sensor_type=SensorType.NITRATE), + ] - expedition = DummyExpedition() + expedition = create_dummy_expedition(sensors) from_data = None ctd_instrument = CTDInstrument(expedition, from_data) @@ -278,22 +285,11 @@ def test_ctd_disabled_sensor_absent(tmpdir) -> None: ) fieldset.add_field(Field("bathymetry", [-1000], lon=0, lat=0)) - class DummyExpedition: - class schedule: - waypoints = [Waypoint(location=Location(1, 2), time=base_time)] - - instruments_config = InstrumentsConfig( - ctd_config=CTDConfig( - stationkeeping_time_minutes=50, - min_depth_meter=-11.0, - max_depth_meter=-2000.0, - sensors=[ - SensorConfig(sensor_type=SensorType.TEMPERATURE) - ], # SALINITY omitted = disabled - ) - ) + sensors = ( + [SensorConfig(sensor_type=SensorType.TEMPERATURE)], + ) # SALINITY omitted = disabled - expedition = DummyExpedition() + expedition = create_dummy_expedition(sensors) ctd_instrument = CTDInstrument(expedition, None) out_path = tmpdir.join("out_disabled.zarr") ctd_instrument.load_input_data = lambda: fieldset @@ -387,23 +383,12 @@ def test_sensor_absent(tmpdir) -> None: ) fieldset.add_field(Field("bathymetry", [-1000], lon=0, lat=0)) - class DummyExpedition: - class schedule: - waypoints = [Waypoint(location=Location(1, 2), time=base_time)] - - instruments_config = InstrumentsConfig( - ctd_config=CTDConfig( - stationkeeping_time_minutes=50, - min_depth_meter=-11.0, - max_depth_meter=-2000.0, - sensors=[ - SensorConfig(sensor_type=SensorType.OXYGEN), - # CHLOROPHYLL omitted = disabled - ], - ) - ) + sensors = [ + SensorConfig(sensor_type=SensorType.OXYGEN), + # CHLOROPHYLL omitted = disabled + ] - expedition = DummyExpedition() + expedition = create_dummy_expedition(sensors) ctd_instrument = CTDInstrument(expedition, None) out_path = tmpdir.join("out_bgc_disabled.zarr") ctd_instrument.load_input_data = lambda: fieldset @@ -412,3 +397,13 @@ class schedule: results = xr.open_zarr(out_path) assert "o2" in results, "Enabled BGC sensor variable must be present" assert "chl" not in results, "Disabled sensor variable must be absent from output" + + +def test_ctd_instrument_type(): + """CTDInstrument returns the correct InstrumentType and if is underway instrument.""" + sensors = [SensorConfig(sensor_type=SensorType.TEMPERATURE)] # only need one + expedition = create_dummy_expedition(sensors) + + ctd_instrument = CTDInstrument(expedition, from_data=None) + assert ctd_instrument.instrument_type == InstrumentType.CTD + assert not ctd_instrument.instrument_type.is_underway diff --git a/tests/instruments/test_drifter.py b/tests/instruments/test_drifter.py index 56f3257e..0b115374 100644 --- a/tests/instruments/test_drifter.py +++ b/tests/instruments/test_drifter.py @@ -11,6 +11,7 @@ from virtualship.instruments.drifter import Drifter, DrifterInstrument from virtualship.instruments.sensors import SensorType +from virtualship.instruments.types import InstrumentType from virtualship.models import Location, Spacetime from virtualship.models.expedition import ( DrifterConfig, @@ -232,3 +233,12 @@ def test_drifter_config_unsupported_sensor_rejected(): stationkeeping_time_minutes=10, sensors=[SensorConfig(sensor_type=SensorType.VELOCITY)], ) + + +def test_drifter_instrument_type(): + """DrifterInstrument returns the correct InstrumentType and if is underway instrument.""" + expedition = create_dummy_expedition() + + drifter_instrument = DrifterInstrument(expedition, from_data=None) + assert drifter_instrument.instrument_type == InstrumentType.DRIFTER + assert not drifter_instrument.instrument_type.is_underway diff --git a/tests/instruments/test_ship_underwater_st.py b/tests/instruments/test_ship_underwater_st.py index 9c879d48..016734a1 100644 --- a/tests/instruments/test_ship_underwater_st.py +++ b/tests/instruments/test_ship_underwater_st.py @@ -1,15 +1,17 @@ """Test the simulation of ship salinity temperature measurements.""" import datetime +from typing import ClassVar import numpy as np import pydantic import pytest import xarray as xr - from parcels import FieldSet -from virtualship.instruments.ship_underwater_st import Underwater_STInstrument + from virtualship.instruments.sensors import SensorType +from virtualship.instruments.ship_underwater_st import Underwater_STInstrument +from virtualship.instruments.types import InstrumentType from virtualship.models import Location, Spacetime from virtualship.models.expedition import ( InstrumentsConfig, @@ -18,8 +20,40 @@ Waypoint, ) +BASE_TIME = datetime.datetime.strptime( + "1950-01-01", "%Y-%m-%d" +) # arbitrary time offset for the dummy fieldset +PERIOD = 5.0 # minutes + + +@pytest.fixture +def underwater_st_expedition(): + """Minimal Expedition for Underwater_STInstrument instantiation.""" + + class DummyExpedition: + class schedule: + waypoints: ClassVar[list] = [ + Waypoint( + location=Location(1, 2), + time=BASE_TIME, + instrument=InstrumentType.UNDERWATER_ST, + ), + ] + + instruments_config = InstrumentsConfig( + ship_underwater_st_config=ShipUnderwaterSTConfig( + period_minutes=PERIOD, + sensors=[ + SensorConfig(sensor_type=SensorType.TEMPERATURE), + SensorConfig(sensor_type=SensorType.SALINITY), + ], + ) + ) -def test_simulate_ship_underwater_st(tmpdir) -> None: + return DummyExpedition() + + +def test_simulate_ship_underwater_st(tmpdir, underwater_st_expedition) -> None: # arbitrary time offset for the dummy fieldset base_time = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") @@ -76,31 +110,9 @@ def test_simulate_ship_underwater_st(tmpdir) -> None: }, ) - # dummy expedition for Underwater_STInstrument - class DummyExpedition: - class schedule: - # ruff: noqa - waypoints = [ - Waypoint( - location=Location(1, 2), - time=base_time, - ), - ] - - instruments_config = InstrumentsConfig( - ship_underwater_st_config=ShipUnderwaterSTConfig( - period_minutes=5.0, - sensors=[ - SensorConfig(sensor_type=SensorType.TEMPERATURE), - SensorConfig(sensor_type=SensorType.SALINITY), - ], - ) - ) - - expedition = DummyExpedition() from_data = None - st_instrument = Underwater_STInstrument(expedition, from_data) + st_instrument = Underwater_STInstrument(underwater_st_expedition, from_data) out_path = tmpdir.join("out.zarr") st_instrument.load_input_data = lambda: fieldset @@ -180,3 +192,12 @@ def test_underwater_st_config_unsupported_sensor_rejected(): period_minutes=5.0, sensors=[SensorConfig(sensor_type=SensorType.OXYGEN)], ) + + +def test_underwater_st_instrument_type(underwater_st_expedition): + """Underwater_STInstrument returns the correct InstrumentType and if is underway instrument.""" + underwater_st_instrument = Underwater_STInstrument( + underwater_st_expedition, from_data=None + ) + assert underwater_st_instrument.instrument_type == InstrumentType.UNDERWATER_ST + assert underwater_st_instrument.instrument_type.is_underway diff --git a/tests/instruments/test_xbt.py b/tests/instruments/test_xbt.py index 0ac3a7cb..ac4af1a7 100644 --- a/tests/instruments/test_xbt.py +++ b/tests/instruments/test_xbt.py @@ -5,15 +5,17 @@ """ import datetime +from typing import ClassVar import numpy as np import pydantic import pytest import xarray as xr - from parcels import Field, FieldSet -from virtualship.instruments.xbt import XBT, XBTInstrument + from virtualship.instruments.sensors import SensorType +from virtualship.instruments.types import InstrumentType +from virtualship.instruments.xbt import XBT, XBTInstrument from virtualship.models import Location, Spacetime from virtualship.models.expedition import ( InstrumentsConfig, @@ -22,8 +24,43 @@ XBTConfig, ) +BASE_TIME = datetime.datetime.strptime( + "1950-01-01", "%Y-%m-%d" +) # arbitrary time offset for the dummy fieldset +MIN_DEPTH = -2.0 +MAX_DEPTH = -285.0 +FALL_SPEED = 6.7 +DECELERATION_COEFFICIENT = 0.00225 + + +@pytest.fixture +def xbt_expedition(): + """Minimal Expedition for Underwater_STInstrument instantiation.""" + + class DummyExpedition: + class schedule: + waypoints: ClassVar[list] = [ + Waypoint( + location=Location(1, 2), + time=BASE_TIME, + instrument=InstrumentType.XBT, + ), + ] + + instruments_config = InstrumentsConfig( + xbt_config=XBTConfig( + min_depth_meter=MIN_DEPTH, + max_depth_meter=MAX_DEPTH, + fall_speed_meter_per_second=FALL_SPEED, + deceleration_coefficient=DECELERATION_COEFFICIENT, + sensors=[SensorConfig(sensor_type=SensorType.TEMPERATURE)], + ) + ) + + return DummyExpedition() + -def test_simulate_xbts(tmpdir) -> None: +def test_simulate_xbts(tmpdir, xbt_expedition) -> None: # arbitrary time offset for the dummy fieldset base_time = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") @@ -104,31 +141,9 @@ def test_simulate_xbts(tmpdir) -> None: ) fieldset.add_field(Field("bathymetry", [-1000], lon=0, lat=0)) - # dummy expedition for XBTInstrument - class DummyExpedition: - class schedule: - # ruff: noqa - waypoints = [ - Waypoint( - location=Location(1, 2), - time=base_time, - ), - ] - - instruments_config = InstrumentsConfig( - xbt_config=XBTConfig( - min_depth_meter=-2.0, - max_depth_meter=-285.0, - fall_speed_meter_per_second=6.7, - deceleration_coefficient=0.00225, - sensors=[SensorConfig(sensor_type=SensorType.TEMPERATURE)], - ) - ) - - expedition = DummyExpedition() from_data = None - xbt_instrument = XBTInstrument(expedition, from_data) + xbt_instrument = XBTInstrument(xbt_expedition, from_data) out_path = tmpdir.join("out.zarr") xbt_instrument.load_input_data = lambda: fieldset @@ -208,3 +223,10 @@ def test_xbt_config_unsupported_sensor_rejected(): deceleration_coefficient=0.00225, sensors=[SensorConfig(sensor_type=SensorType.SALINITY)], ) + + +def test_xbt_instrument_type(xbt_expedition): + """XBTInstrument returns the correct InstrumentType and if is underway instrument.""" + xbt_instrument = XBTInstrument(xbt_expedition, from_data=None) + assert xbt_instrument.instrument_type == InstrumentType.XBT + assert not xbt_instrument.instrument_type.is_underway From 4dfa96f414d5239b09f9b3efdb920f617d77a95c Mon Sep 17 00:00:00 2001 From: Jamie Atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 31 Jul 2026 11:31:19 +0200 Subject: [PATCH 57/94] Update underway (#360) * first changes; Parcels v4 API * move to using field.eval for UV sampling, plus call to custom write-to-parquet * custom write-to-parquet for underway instruments * instrument type property, conditional windowed arrays * tidy up * sample individual U and V fields for m s-1 units * fix u, v sampling to use correct sampling and conversions * new intermediate UnderwayInstrument base class (parquet writing moved over from utils), change kernels to special-case underway kernels * fix intermediate class logic so that sensor_kernels check is only triggered for final instrument child classes * remove support for depth as None * use coords for adcp 'kernel' * add validtion to UnderwayCoordinates class, rename func * migrate UnderwaterST instrument to new underway instrument logic * fix func name * add new tests and refine for intermediate UnderwayInstrument class * update ADCP tests * tidy up comments * update UnderwaterST tests * use fieldset.time_interval.left / right for fieldset start / end times * particle_id is constant for underway instruments * dt and state are unnecessary for public facing output * add test for monitoring for schema drift vs. parcels, plus some refactoring * Update tests/instruments/test_base.py Co-authored-by: Erik van Sebille * remove dev spinner bypass option * parcels simulation only needs one write step --------- Co-authored-by: Erik van Sebille --- src/virtualship/instruments/adcp.py | 125 ++++----- src/virtualship/instruments/base.py | 166 ++++++++++- src/virtualship/instruments/ctd.py | 8 +- .../instruments/ship_underwater_st.py | 115 ++++---- src/virtualship/instruments/xbt.py | 8 +- tests/instruments/test_adcp.py | 69 ++--- tests/instruments/test_base.py | 259 +++++++++++++++++- tests/instruments/test_ship_underwater_st.py | 95 +++---- 8 files changed, 599 insertions(+), 246 deletions(-) diff --git a/src/virtualship/instruments/adcp.py b/src/virtualship/instruments/adcp.py index 26c8122d..bd253230 100644 --- a/src/virtualship/instruments/adcp.py +++ b/src/virtualship/instruments/adcp.py @@ -1,48 +1,38 @@ from collections.abc import Callable -from dataclasses import dataclass from typing import ClassVar import numpy as np -from parcels import ParticleFile, ParticleSet +import parcels -from virtualship.instruments.base import FetchSpec, Instrument +from virtualship.instruments.base import ( + FetchSpec, + UnderwayCoordinates, + UnderwayInstrument, +) from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType -from virtualship.utils import build_particle_class_from_sensors, register_instrument +from virtualship.utils import register_instrument # ===================================================== -# SECTION: Dataclass +# SECTION: Kernels # ===================================================== -@dataclass -class ADCP: - """ADCP configuration.""" - - name: ClassVar[str] = "ADCP" - - -# ===================================================== -# SECTION: non-sensor Particle Variables (non-sampling) -# ===================================================== - -# ADCP has no non-sensor variables, only sensor variables. -_ADCP_NONSENSOR_VARIABLES: list = [] +# N.B. underway 'kernels' are special cases, where the particleset is not needed, and the kernel is not passed to `pset.execute()` as would be done for a typical Parcels workflow. +# Instead, the 'kernel' function is used only once to evaluate the fieldset at given times, depths, lats, lons. -# ===================================================== -# SECTION: Kernels -# ===================================================== +def _sample_underway_velocity(fieldset: parcels.FieldSet, coords: UnderwayCoordinates): + # eval + u, v = fieldset.UV.eval( + t=coords.times, z=coords.depths, x=coords.lons, y=coords.lats + ) + # convert from degrees s-1 to metres s-1 + u = u * 1852 * 60 * np.cos(np.deg2rad(coords.lats)) + v = v * 1852 * 60 -def _sample_velocity(particles, fieldset): - particles.U, particles.V = fieldset.UV.eval( - particles.t, - particles.z, - particles.x, - particles.y, - applyConversion=False, - ) + return u, v # ===================================================== @@ -51,11 +41,11 @@ def _sample_velocity(particles, fieldset): @register_instrument(InstrumentType.ADCP) -class ADCPInstrument(Instrument): +class ADCPInstrument(UnderwayInstrument): """ADCP instrument class.""" sensor_kernels: ClassVar[dict[SensorType, Callable]] = { - SensorType.VELOCITY: _sample_velocity, + SensorType.VELOCITY: _sample_underway_velocity, } def __init__(self, expedition, from_data): @@ -74,9 +64,9 @@ def __init__(self, expedition, from_data): def simulate(self, measurements, out_path) -> None: """Simulate ADCP measurements.""" - config_max_depth = ( - self.expedition.instruments_config.adcp_config.max_depth_meter - ) + adcp_config = self.expedition.instruments_config.adcp_config + + config_max_depth = adcp_config.max_depth_meter if config_max_depth < -1600.0: print( @@ -87,53 +77,42 @@ def simulate(self, measurements, out_path) -> None: MAX_DEPTH = config_max_depth MIN_DEPTH = -5.0 - NUM_BINS = self.expedition.instruments_config.adcp_config.num_bins + NUM_BINS = adcp_config.num_bins measurements.sort(key=lambda p: p.time) fieldset = self.load_input_data() - # build dynamic particle class from the active sensors - adcp_config = self.expedition.instruments_config.adcp_config - _ADCPParticle = build_particle_class_from_sensors( - adcp_config.sensors, _ADCP_NONSENSOR_VARIABLES + # times in seconds since fieldset time origin, expanded across depth bins + fieldset_starttime = fieldset.time_interval.left + times = np.array( + [ + (np.datetime64(point.time) - fieldset_starttime) + / np.timedelta64(1, "s") + for point in measurements + ] ) + lons = np.array([point.location.lon for point in measurements]) + lats = np.array([point.location.lat for point in measurements]) bins = np.linspace(MAX_DEPTH, MIN_DEPTH, NUM_BINS) - num_particles = len(bins) - particleset = ParticleSet( - fieldset=fieldset, - pclass=_ADCPParticle, - x=np.full( - num_particles, 0.0 - ), # initial lat/lon are irrelevant and will be overruled later - y=np.full(num_particles, 0.0), - z=bins, - ) - - out_file = ParticleFile(path=out_path, outputdt=np.inf) - # build kernel list from active sensors only - sampling_kernels = [ - self.sensor_kernels[sc.sensor_type] - for sc in adcp_config.sensors - if sc.enabled and sc.sensor_type in self.sensor_kernels - ] - - # TODO: need to overhaul ADCP/underway instruments generally... don't think this Parcels API works anymore - # TODO: a good time to implement https://github.com/Parcels-code/virtualship/issues/231 + # full sampling coordinates + coords = UnderwayCoordinates( + times=np.repeat(times, NUM_BINS), + lons=np.repeat(lons, NUM_BINS), + lats=np.repeat(lats, NUM_BINS), + depths=np.tile(bins, len(times)), + ) - for point in measurements: - particleset.lon_nextloop[:] = point.location.lon - particleset.lat_nextloop[:] = point.location.lat - particleset.time_nextloop[:] = fieldset.time_origin.reltime( - np.datetime64(point.time) - ) + sampled = self._sample_underway( + config_sensors=adcp_config.sensors, fieldset=fieldset, coords=coords + ) - particleset.execute( - sampling_kernels, - dt=1, - runtime=1, - verbose_progress=self.verbose_progress, - output_file=out_file, - ) + self._to_parquet( + dat_arrays=sampled, + var_names=self.variables.keys(), + fieldset_time_origin=fieldset_starttime, + out_path=out_path, + coords=coords, + ) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 80deeb02..ce0f8324 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -2,15 +2,19 @@ import abc import collections +import inspect import tempfile from dataclasses import dataclass from datetime import timedelta from itertools import pairwise from pathlib import Path -from typing import TYPE_CHECKING, ClassVar +from typing import TYPE_CHECKING, ClassVar, Literal import copernicusmarine +import numpy as np import parcels +import pyarrow as pa +import pyarrow.parquet as pq import xarray as xr from yaspin import yaspin @@ -50,8 +54,11 @@ class Instrument(abc.ABC): sensor_kernels: ClassVar[dict[SensorType, collections.abc.Callable]] def __init_subclass__(cls, **kwargs: object) -> None: - """Ensure subclasses define sensor_kernels as class attribute.""" + """Ensure non-abstract subclasses (i.e. final/concrete instrument classes) define sensor_kernels as a class attribute.""" super().__init_subclass__(**kwargs) + if inspect.isabstract(cls): + return + if "sensor_kernels" not in cls.__dict__: raise TypeError( f"Instrument subclass '{cls.__name__}' must define 'sensor_kernels' as a class attribute." @@ -132,20 +139,17 @@ def simulate( def execute(self, measurements: list, out_path: str | Path) -> None: """Run instrument simulation.""" - TMP = True # TODO: just for dev; remove before merging instrument_name = self.__class__.__name__.split("Instrument")[0] if not self.verbose_progress: - if TMP: - with yaspin( - text=f"Simulating {instrument_name} measurements... ", - side="right", - spinner=ship_spinner, - ) as spinner: - self.simulate(measurements, out_path) - spinner.ok("✅\n") - else: + with yaspin( + text=f"Simulating {instrument_name} measurements... ", + side="right", + spinner=ship_spinner, + ) as spinner: self.simulate(measurements, out_path) + spinner.ok("✅\n") + else: print(f"Simulating {instrument_name} measurements... ") self.simulate(measurements, out_path) @@ -279,3 +283,141 @@ def _via_tmp_ds(ds) -> xr.Dataset: def instrument_type(self) -> InstrumentType: """Return the InstrumentType for this instrument instance.""" return next(k for k, v in INSTRUMENT_CLASS_MAP.items() if type(self) is v) + + +@dataclass(frozen=True) +class UnderwayCoordinates: + """1D sampling location arrays for underway instruments.""" + + times: np.ndarray # seconds since origin + lons: np.ndarray + lats: np.ndarray + depths: np.ndarray + + def __post_init__(self): + """Validate that all arrays are 1D and have the same length.""" + shapes = { + "times": self.times.shape, + "lons": self.lons.shape, + "lats": self.lats.shape, + "depths": self.depths.shape, + } + + for name, shape in shapes.items(): + if len(shape) != 1: + raise ValueError(f"Array '{name}' must be 1D, but got shape {shape}.") + + n = len(self.times) + if not (len(self.lons) == len(self.lats) == len(self.depths) == n): + raise ValueError( + f"Array length mismatch in UnderwayCoordinates: " + f"times={len(self.times)}, lons={len(self.lons)}, " + f"lats={len(self.lats)}, depths={len(self.depths)}" + ) + + +class UnderwayInstrument(Instrument): + """Intermediate base class for underway instruments, which perform variable sampling without ParticleSets.""" + + def _sample_underway( + self, + config_sensors: list, + fieldset: parcels.FieldSet, + coords: UnderwayCoordinates, + ): + """Perform variable sampling for underway instruments and their active sensors.""" + sampling_kernels = [ + self.sensor_kernels[sc.sensor_type] + for sc in config_sensors + if sc.enabled and sc.sensor_type in self.sensor_kernels + ] # active sensors only + + sampled = [ + kernel(fieldset, coords) for kernel in sampling_kernels + ] # perform sampling + + # ensure that sampled is a flat list of arrays, even if some kernels return tuples/lists of arrays + # e.g. ADCP kernel returns (u, v) tuple of arrays, whilst UnderwaterST returns single array of temperature/salinity + sampled_flat = [ + arr + for item in sampled + for arr in (item if isinstance(item, (tuple, list)) else (item,)) + ] + + return sampled_flat + + @staticmethod + def _to_parquet( + dat_arrays: list[np.ndarray], + var_names: list[str], + fieldset_time_origin: np.datetime64, + out_path: Path | str, + coords: UnderwayCoordinates, + compression: Literal["zstd", "gzip", "snappy", "brotli", None] = "zstd", + ) -> None: + """ + Write underway instrument data to a Parquet file mirroring the Parcels v4 ParticleFile schema. + + Designed so that output files can be re-read back in with Parcels.read_particlefile for consistent downstream workflows with non-underway instruments. + """ + assert len(dat_arrays) == len(var_names), ( + "dat_arrays and var_names must have the same length" + ) + + n = len(coords.times) + + origin_str = str(fieldset_time_origin).replace("T", " ") + t_metadata = {"units": f"seconds since {origin_str}", "calendar": "standard"} + + # base schema mirroring Parcels ParticleFile schema, not yet with sampled variables + base_schema = pa.schema( + [ + pa.field("t", pa.float64(), metadata=t_metadata), + pa.field("z", pa.float32()), + pa.field("y", pa.float32()), + pa.field("x", pa.float32()), + pa.field("particle_id", pa.int64()), + ], + metadata={ + "feature_type": "trajectory", + "Conventions": "CF-1.6/CF-1.7", + "ncei_template_version": "NCEI_NetCDF_Trajectory_Template_v2.0", + "parcels_version": parcels.__version__, + "parcels_grid_mesh": "spherical", + }, + ) + + for var in var_names: + base_schema = base_schema.append( + pa.field(var, pa.float32()) + ) # add sampled variable to schema + + out_path = Path(out_path) + if out_path.suffix != ".parquet": + raise ValueError( + f"out_path must end in '.parquet', got {out_path.suffix!r}" + ) + + # build table with all data, including sampled variables + table = pa.table( + { + "t": pa.array(coords.times.astype(np.float64)), + "z": pa.array(coords.depths.astype(np.float32)) + if coords.depths is not None + else pa.array(np.full(n, np.nan, dtype=np.float32)), + "y": pa.array(coords.lats.astype(np.float32)), + "x": pa.array(coords.lons.astype(np.float32)), + "particle_id": pa.array( + np.zeros(n, dtype=np.int64) + ), # ship is a single 'particle' (here represented by a constant particle_id of 0) + "dt": pa.array(np.full(n, np.nan, dtype=np.float64)), + "state": pa.array(np.zeros(n, dtype=np.int32)), + **{ + var: pa.array(dat.astype(np.float32)) + for var, dat in zip(var_names, dat_arrays, strict=True) + }, + }, + schema=base_schema, + ) + + pq.write_table(table, out_path, compression=compression) diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index d6764130..5b8cba61 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -168,12 +168,8 @@ def simulate(self, measurements, out_path) -> None: fieldset = self.load_input_data() - # use first active field for time reference - _time_ref_key = next(iter(self.variables)) - _time_ref_field = getattr(fieldset, _time_ref_key) - - fieldset_starttime = _time_ref_field.data.time.isel(time=0).values - fieldset_endtime = _time_ref_field.data.time.isel(time=-1).values + fieldset_starttime = fieldset.time_interval.left + fieldset_endtime = fieldset.time_interval.right # deploy time for all ctds should be later than fieldset start time if not all( diff --git a/src/virtualship/instruments/ship_underwater_st.py b/src/virtualship/instruments/ship_underwater_st.py index 1dc7522a..4aff0af5 100644 --- a/src/virtualship/instruments/ship_underwater_st.py +++ b/src/virtualship/instruments/ship_underwater_st.py @@ -1,51 +1,40 @@ from collections.abc import Callable -from dataclasses import dataclass from typing import ClassVar import numpy as np -from parcels import ParticleFile, ParticleSet +import parcels -from virtualship.instruments.base import FetchSpec, Instrument +from virtualship.instruments.base import ( + FetchSpec, + UnderwayCoordinates, + UnderwayInstrument, +) from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.utils import ( - build_particle_class_from_sensors, register_instrument, ) -# ===================================================== -# SECTION: Dataclass -# ===================================================== - - -@dataclass -class Underwater_ST: - """Underwater_ST configuration.""" - - name: ClassVar[str] = "Underwater_ST" - - -# ===================================================== -# SECTION: non-sensor Particle Variables (non-sampling) -# ===================================================== - -# Underwater ST has no non-sensor variables, only sensor variables. -_ST_NONSENSOR_VARIABLES: list = [] - - # ===================================================== # SECTION: Kernels # ===================================================== +# N.B. underway 'kernels' are special cases, where the particleset is not needed, and the kernel is not passed to `pset.execute()` as would be done for a typical Parcels workflow. +# Instead, the 'kernel' function is used only once to evaluate the fieldset at given times, depths, lats, lons. -# define function sampling Salinity -def _sample_salinity(particles, fieldset): - particles.S = fieldset.S[particles.t, particles.z, particles.y, particles.x] +def _sample_underway_salinity(fieldset: parcels.FieldSet, coords: UnderwayCoordinates): + return fieldset.S.eval( + t=coords.times, z=coords.depths, x=coords.lons, y=coords.lats + ) -# define function sampling Temperature -def _sample_temperature(particles, fieldset): - particles.T = fieldset.T[particles.t, particles.z, particles.y, particles.x] + +def _sample_underway_temperature( + fieldset: parcels.FieldSet, coords: UnderwayCoordinates +): + return fieldset.T.eval( + t=coords.times, z=coords.depths, x=coords.lons, y=coords.lats + ) # ===================================================== @@ -54,12 +43,12 @@ def _sample_temperature(particles, fieldset): @register_instrument(InstrumentType.UNDERWATER_ST) -class Underwater_STInstrument(Instrument): +class Underwater_STInstrument(UnderwayInstrument): """Underwater_ST instrument class.""" sensor_kernels: ClassVar[dict[SensorType, Callable]] = { - SensorType.TEMPERATURE: _sample_temperature, - SensorType.SALINITY: _sample_salinity, + SensorType.TEMPERATURE: _sample_underway_temperature, + SensorType.SALINITY: _sample_underway_salinity, } def __init__(self, expedition, from_data): @@ -80,49 +69,37 @@ def __init__(self, expedition, from_data): def simulate(self, measurements, out_path) -> None: """Simulate underway salinity and temperature measurements.""" + st_config = self.expedition.instruments_config.ship_underwater_st_config + DEPTH = -2.0 measurements.sort(key=lambda p: p.time) fieldset = self.load_input_data() - # build dynamic particle class from the active sensors - st_config = self.expedition.instruments_config.ship_underwater_st_config - _ShipSTParticle = build_particle_class_from_sensors( - st_config.sensors, _ST_NONSENSOR_VARIABLES + # sampling times and locations + fieldset_starttime = fieldset.time_interval.left + times = np.array( + [ + (np.datetime64(point.time) - fieldset_starttime) + / np.timedelta64(1, "s") + for point in measurements + ] + ) + lons = np.array([point.location.lon for point in measurements]) + lats = np.array([point.location.lat for point in measurements]) + coords = UnderwayCoordinates( + times, lons, lats, depths=np.full_like(times, DEPTH) ) - particleset = ParticleSet( - fieldset=fieldset, - pclass=_ShipSTParticle, - x=0.0, - y=0.0, - z=DEPTH, + sampled = self._sample_underway( + config_sensors=st_config.sensors, fieldset=fieldset, coords=coords ) - out_file = ParticleFile(path=out_path, outputdt=np.inf) - - # build kernel list from active sensors only - sampling_kernels = [ - self.sensor_kernels[sc.sensor_type] - for sc in st_config.sensors - if sc.enabled and sc.sensor_type in self.sensor_kernels - ] - - # TODO: need to overhaul UNDERWATER_ST/underway instruments generally... don't think this Parcels API works anymore - # TODO: a good time to implement https://github.com/Parcels-code/virtualship/issues/231 - - for point in measurements: - particleset.lon_nextloop[:] = point.location.lon - particleset.lat_nextloop[:] = point.location.lat - particleset.time_nextloop[:] = fieldset.time_origin.reltime( - np.datetime64(point.time) - ) - - particleset.execute( - sampling_kernels, - dt=1, - runtime=1, - verbose_progress=self.verbose_progress, - output_file=out_file, - ) + self._to_parquet( + dat_arrays=sampled, + var_names=self.variables.keys(), + fieldset_time_origin=fieldset_starttime, + out_path=out_path, + coords=coords, + ) diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index 7046fde1..9de23092 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -120,12 +120,8 @@ def simulate(self, measurements, out_path) -> None: fieldset = self.load_input_data() - # use first active field for time reference - _time_ref_key = next(iter(self.variables)) - _time_ref_field = getattr(fieldset, _time_ref_key) - - fieldset_starttime = _time_ref_field.data.time.isel(time=0).values - fieldset_endtime = _time_ref_field.data.time.isel(time=-1).values + fieldset_starttime = fieldset.time_interval.left + fieldset_endtime = fieldset.time_interval.right # deploy time for all xbts should be later than fieldset start time if not all( diff --git a/tests/instruments/test_adcp.py b/tests/instruments/test_adcp.py index 16604b67..ab13f99c 100644 --- a/tests/instruments/test_adcp.py +++ b/tests/instruments/test_adcp.py @@ -4,10 +4,11 @@ from typing import ClassVar import numpy as np +import parcels +import polars as pl import pydantic import pytest import xarray as xr -from parcels import FieldSet from virtualship.instruments.adcp import ADCPInstrument from virtualship.instruments.sensors import SensorType @@ -22,6 +23,7 @@ BASE_TIME = datetime.datetime.strptime( "1950-01-01", "%Y-%m-%d" ) # arbitrary time offset for the dummy fieldset +MIN_DEPTH = -5 MAX_DEPTH = -1000 NUM_BINS = 40 @@ -53,8 +55,6 @@ class schedule: def test_simulate_adcp(tmpdir, adcp_expedition) -> None: - MIN_DEPTH = -5 - # where to sample sample_points = [ Spacetime(Location(1, 2), BASE_TIME + datetime.timedelta(seconds=0)), @@ -93,53 +93,58 @@ def test_simulate_adcp(tmpdir, adcp_expedition) -> None: u[1, 0, 1, 1] = expected_obs[1]["U"]["max_depth"] u[1, 1, 1, 1] = expected_obs[1]["U"]["surface"] - fieldset = FieldSet.from_data( - { - "V": v, - "U": u, + # make ds + times = np.array([expected_obs[0]["time"], expected_obs[1]["time"]]) + lats = np.array([expected_obs[0]["lat"], expected_obs[1]["lat"]]) + lons = np.array([expected_obs[0]["lon"], expected_obs[1]["lon"]]) + + ds_fields = xr.Dataset( + data_vars={ + "U": (["time", "depth", "lat", "lon"], u, {"units": "m s-1"}), + "V": (["time", "depth", "lat", "lon"], v, {"units": "m s-1"}), }, - { - "lat": np.array([expected_obs[0]["lat"], expected_obs[1]["lat"]]), - "lon": np.array([expected_obs[0]["lon"], expected_obs[1]["lon"]]), - "depth": np.array([MAX_DEPTH, MIN_DEPTH]), - "time": np.array( - [ - np.datetime64(expected_obs[0]["time"]), - np.datetime64(expected_obs[1]["time"]), - ] - ), + coords={ + "time": ("time", times, {"axis": "T"}), + "depth": ("depth", [MAX_DEPTH, MIN_DEPTH], {"units": "m", "axis": "Z"}), + "lat": ("lat", lats, {"units": "degrees_north"}), + "lon": ("lon", lons, {"units": "degrees_east"}), }, ) + # to fieldset + fields = {"U": ds_fields["U"], "V": ds_fields["V"]} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset) + adcp_instrument = ADCPInstrument(adcp_expedition, from_data=None) - out_path = tmpdir.join("out.zarr") + out_path = tmpdir.join("out.parquet") adcp_instrument.load_input_data = lambda: fieldset adcp_instrument.simulate(sample_points, out_path) - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) - # test if output is as expected - assert len(results.trajectory) == NUM_BINS + assert np.unique(results["z"].to_numpy()).size == NUM_BINS # for every obs, check if the variables match the expected observations # we only verify at the surface and max depth of the adcp, because in between is tricky - for traj, vert_loc in [ - (results.trajectory[0], "max_depth"), - (results.trajectory[-1], "surface"), + for df_depth, vert_loc in [ + (results.filter(pl.col("z") == MAX_DEPTH), "max_depth"), + (results.filter(pl.col("z") == MIN_DEPTH), "surface"), ]: - obs_all = results.sel(trajectory=traj).obs - assert len(obs_all) == len(sample_points) - for i, (obs_i, exp) in enumerate(zip(obs_all, expected_obs, strict=True)): - obs = results.sel(trajectory=traj, obs=obs_i) - for var in ["lat", "lon"]: - obs_value = obs[var].values.item() - exp_value = exp[var] + assert len(df_depth) == len(sample_points) + + for i, (obs_i, exp) in enumerate( + zip(df_depth.iter_rows(named=True), expected_obs, strict=True) + ): + for var in [("y", "lat"), ("x", "lon")]: + obs_value = obs_i[var[0]] + exp_value = exp[var[1]] assert np.isclose(obs_value, exp_value), ( f"Observation incorrect {vert_loc=} {obs_i=} {var=} {obs_value=} {exp_value=}." ) for var in ["V", "U"]: - obs_value = obs[var].values.item() + obs_value = obs_i[var] exp_value = exp[var][vert_loc] assert np.isclose(obs_value, exp_value), ( f"Observation incorrect {vert_loc=} {i=} {var=} {obs_value=} {exp_value=}." diff --git a/tests/instruments/test_base.py b/tests/instruments/test_base.py index 93a38e90..e14bb630 100644 --- a/tests/instruments/test_base.py +++ b/tests/instruments/test_base.py @@ -1,12 +1,27 @@ +from dataclasses import dataclass +from typing import ClassVar from unittest.mock import MagicMock, patch +import numpy as np +import parcels +import pyarrow.parquet as pq import pytest import xarray as xr -from virtualship.instruments.base import FetchSpec, Instrument +from virtualship.instruments.base import ( + FetchSpec, + Instrument, + UnderwayCoordinates, + UnderwayInstrument, +) +from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.utils import get_instrument_class +# ============================================================================= +# Instrument base class testing +# ============================================================================= + def test_FetchSpec(): fetch_spec = FetchSpec() @@ -193,3 +208,245 @@ def test_instrument_subclass_without_sensor_kernels_error(): class ErrorInstrument(Instrument): def simulate(self, data_dir, measurements, out_path): pass + + +# ============================================================================= +# UnderwayInstrument intermediate class testing +# ============================================================================= + + +@dataclass +class DummySensorConfig: + """Mock sensor configuration.""" + + sensor_type: SensorType + enabled: bool = True + + +class ConcreteUnderwayInstrument(UnderwayInstrument): + """Concrete subclass of UnderwayInstrument for testing.""" + + sensor_kernels: ClassVar = { + SensorType.TEMPERATURE: lambda fieldset, coords: np.array( + [15.0, 16.0], dtype=np.float32 + ), + SensorType.SALINITY: lambda fieldset, coords: np.array( + [35.0, 35.1], dtype=np.float32 + ), + SensorType.VELOCITY: lambda fieldset, coords: ( + np.array([0.5, 0.6], dtype=np.float32), # U vector component + np.array([-0.1, -0.2], dtype=np.float32), # V vector component + ), + } + + def simulate(self, measurements, out_path) -> None: # noqa + pass + + +@pytest.fixture +def sample_underway_coords(): + """Fixture providing valid 1D UnderwayCoordinates.""" + return UnderwayCoordinates( + times=np.array([0.0, 3600.0]), + lons=np.array([-5.0, -5.1]), + lats=np.array([50.0, 50.1]), + depths=np.array([-2.0, -2.0]), + ) + + +@pytest.fixture +def dummy_underway_inst(): + """Bypass __init__ and requirements for expedition object etc. for testing.""" + return ConcreteUnderwayInstrument.__new__(ConcreteUnderwayInstrument) + + +def test_underway_coordinates_validation(): + """UnderwayCoordinates validates array lengths upon instantiation.""" + # valid coordinates work cleanly + coords = UnderwayCoordinates( + times=np.array([0.0, 1.0]), + lons=np.array([10.0, 11.0]), + lats=np.array([20.0, 21.0]), + depths=np.array([-1.0, -1.0]), + ) + assert len(coords.times) == 2 + + # mismatched array lengths raise ValueError + with pytest.raises(ValueError, match="Array length mismatch"): + UnderwayCoordinates( + times=np.array([0.0, 1.0]), + lons=np.array([10.0]), # length 1 vs 2 + lats=np.array([20.0, 21.0]), + depths=np.array([-1.0, -1.0]), + ) + + +def test_sample_underway_filters_and_flattens( + dummy_underway_inst, sample_underway_coords +): + """_sample_underway evaluates active sensors and flattens multi-output tuple kernels.""" + configs = [ + DummySensorConfig(SensorType.VELOCITY, enabled=True), + DummySensorConfig(SensorType.TEMPERATURE, enabled=True), + DummySensorConfig(SensorType.SALINITY, enabled=True), + ] + + sampled = dummy_underway_inst._sample_underway( + config_sensors=configs, + fieldset=None, + coords=sample_underway_coords, + ) + + assert len(sampled) == 4 # total flattened arrays (u, v, temp, sal) + np.testing.assert_array_equal(sampled[0], np.array([0.5, 0.6], dtype=np.float32)) + np.testing.assert_array_equal(sampled[1], np.array([-0.1, -0.2], dtype=np.float32)) + np.testing.assert_array_equal(sampled[2], np.array([15.0, 16.0], dtype=np.float32)) + np.testing.assert_array_equal(sampled[3], np.array([35.0, 35.1], dtype=np.float32)) + + +def test_to_parquet_writes_valid_file( + tmp_path, dummy_underway_inst, sample_underway_coords +): + """_to_parquet writes a valid Parquet table with expected schema metadata and data values.""" + out_path = tmp_path / "output.parquet" + dat_arrays = [ + np.array([15.0, 16.0], dtype=np.float32), + np.array([35.0, 35.1], dtype=np.float32), + ] + var_names = ["temp", "sal"] + origin = np.datetime64("2026-01-01T00:00:00") + + dummy_underway_inst._to_parquet( + dat_arrays=dat_arrays, + var_names=var_names, + fieldset_time_origin=origin, + out_path=out_path, + coords=sample_underway_coords, + ) + + assert out_path.exists() + + # verify parquet table, metadata, and columns + table = pq.read_table(out_path) + schema = table.schema + + assert table.column_names == [ + "t", + "z", + "y", + "x", + "particle_id", + "temp", + "sal", + ] + assert schema.metadata[b"feature_type"] == b"trajectory" + assert b"units" in schema.field("t").metadata + + np.testing.assert_array_equal( + table["x"].to_numpy(), np.array(sample_underway_coords.lons, dtype=np.float32) + ) + np.testing.assert_array_equal(table["temp"].to_numpy(), dat_arrays[0]) + + +def _create_underway_parquet( + out_path, + var_names, + dat_arrays=None, + origin=np.datetime64("2026-01-01T00:00:00"), # noqa +): + """Helper to generate an UnderwayInstrument parquet output file.""" + coords = UnderwayCoordinates( + times=np.array([0.0, 3600.0]), + lons=np.array([-5.0, -5.1]), + lats=np.array([50.0, 50.1]), + depths=np.array([-2.0, -2.0]), + ) + + if dat_arrays is None: + dat_arrays = [ + np.array([15.0, 16.0], dtype=np.float32), + np.array([35.0, 35.1], dtype=np.float32), + ] + + UnderwayInstrument._to_parquet( + dat_arrays=dat_arrays, + var_names=var_names, + fieldset_time_origin=origin, + out_path=out_path, + coords=coords, + ) + + +def dummy_sample_temperature(particles, fieldset): + particles.T = fieldset.T[particles.t, particles.z, particles.y, particles.x] + + +def test_parquet_openable_by_parcels_read_particlefile(tmp_path): + """Test that a parquet file written by _to_parquet can be read back by parcels.read_particlefile.""" + parquet_path = tmp_path / "test_particles.parquet" + _create_underway_parquet( + out_path=parquet_path, + var_names=["temp", "sal"], + dat_arrays=[ + np.array([15.0, 16.0], dtype=np.float32), + np.array([35.0, 35.1], dtype=np.float32), + ], + ) + + # read back and assert values + results = parcels.read_particlefile(parquet_path) + assert len(results) == 2 + assert np.isclose(results["temp"][0], 15.0) + assert np.isclose(results["sal"][1], 35.1) + + +def test_underway_schema_matches_parcels(tmp_path): + """Verify that underway instrument parquet output base schema matches Parcels' ParticleFile.""" + # minimal Parcels FieldSet + T = np.zeros((2, 1, 1)) + T[0, 0, 0], T[1, 0, 0] = 15.0, 16.0 + + t1 = np.datetime64("2024-01-01T00:00:00") + t2 = np.datetime64("2024-01-02T00:00:00") + ds_fields = xr.Dataset( + data_vars={"temperature": (["time", "lat", "lon"], T, {"units": "degC"})}, + coords={ + "time": ("time", [t1, t2], {"axis": "T"}), + "lat": ("lat", [0.0], {"units": "degrees_north"}), + "lon": ("lon", [0.0], {"units": "degrees_east"}), + }, + ) + + fields = {"T": ds_fields["temperature"]} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset) + + # parcels simualtion + SampleParticle = parcels.Particle.add_variable(parcels.Variable("T")) + + pset = parcels.ParticleSet( + fieldset=fieldset, pclass=SampleParticle, t=t1, y=[0.0], x=[0.0] + ) + + parcels_path = tmp_path / "parcels_particles.parquet" + parcels_output = parcels.ParticleFile(parcels_path, outputdt=3600.0) + pset.execute( + [dummy_sample_temperature], + runtime=np.timedelta64(60, "m"), + dt=np.timedelta64(60, "m"), + output_file=parcels_output, + ) + parcels_df = parcels.read_particlefile(parcels_path) + + # UnderwayInstrument output + underway_path = tmp_path / "underway_particles.parquet" + _create_underway_parquet( + out_path=underway_path, + var_names=["T"], + dat_arrays=[np.array([15.0, 16.0], dtype=np.float32)], + origin=np.datetime64("2024-01-01T00:00:00"), + ) + underway_df = parcels.read_particlefile(underway_path) + + # assert schemas match + assert parcels_df.schema == underway_df.schema diff --git a/tests/instruments/test_ship_underwater_st.py b/tests/instruments/test_ship_underwater_st.py index 016734a1..1bf1689c 100644 --- a/tests/instruments/test_ship_underwater_st.py +++ b/tests/instruments/test_ship_underwater_st.py @@ -4,10 +4,10 @@ from typing import ClassVar import numpy as np +import parcels import pydantic import pytest import xarray as xr -from parcels import FieldSet from virtualship.instruments.sensors import SensorType from virtualship.instruments.ship_underwater_st import Underwater_STInstrument @@ -54,87 +54,88 @@ class schedule: def test_simulate_ship_underwater_st(tmpdir, underwater_st_expedition) -> None: - # arbitrary time offset for the dummy fieldset - base_time = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") - # where to sample sample_points = [ - Spacetime(Location(1, 2), base_time + datetime.timedelta(seconds=0)), - Spacetime(Location(3, 4), base_time + datetime.timedelta(seconds=1)), + Spacetime(Location(1, 2), BASE_TIME + datetime.timedelta(seconds=0)), + Spacetime(Location(3, 4), BASE_TIME + datetime.timedelta(seconds=1)), ] # expected observations at sample points expected_obs = [ { - "salinity": 5, - "temperature": 6, + "S": 5, + "T": 6, "lat": sample_points[0].location.lat, "lon": sample_points[0].location.lon, - "time": base_time + datetime.timedelta(seconds=0), + "time": BASE_TIME + datetime.timedelta(seconds=0), }, { - "salinity": 7, - "temperature": 8, + "S": 7, + "T": 8, "lat": sample_points[1].location.lat, "lon": sample_points[1].location.lon, - "time": base_time + datetime.timedelta(seconds=1), + "time": BASE_TIME + datetime.timedelta(seconds=1), }, ] # create fieldset based on the expected observations # indices are time, latitude, longitude salinity = np.zeros((2, 2, 2)) - salinity[0, 0, 0] = expected_obs[0]["salinity"] - salinity[1, 1, 1] = expected_obs[1]["salinity"] + salinity[0, 0, 0] = expected_obs[0]["S"] + salinity[1, 1, 1] = expected_obs[1]["S"] temperature = np.zeros((2, 2, 2)) - temperature[0, 0, 0] = expected_obs[0]["temperature"] - temperature[1, 1, 1] = expected_obs[1]["temperature"] - - fieldset = FieldSet.from_data( - { - "V": np.zeros((2, 2, 2)), - "U": np.zeros((2, 2, 2)), - "S": salinity, - "T": temperature, + temperature[0, 0, 0] = expected_obs[0]["T"] + temperature[1, 1, 1] = expected_obs[1]["T"] + + # make ds + times = np.array([expected_obs[0]["time"], expected_obs[1]["time"]]) + lats = np.array([expected_obs[0]["lat"], expected_obs[1]["lat"]]) + lons = np.array([expected_obs[0]["lon"], expected_obs[1]["lon"]]) + + ds_fields = xr.Dataset( + data_vars={ + "T": (["time", "lat", "lon"], temperature, {"units": "degC"}), + "S": (["time", "lat", "lon"], salinity, {"units": "psu"}), }, - { - "lat": np.array([expected_obs[0]["lat"], expected_obs[1]["lat"]]), - "lon": np.array([expected_obs[0]["lon"], expected_obs[1]["lon"]]), - "time": np.array( - [ - np.datetime64(expected_obs[0]["time"]), - np.datetime64(expected_obs[1]["time"]), - ] - ), + coords={ + "time": ("time", times, {"axis": "T"}), + "lat": ("lat", lats, {"units": "degrees_north"}), + "lon": ("lon", lons, {"units": "degrees_east"}), }, ) - from_data = None + # to fieldset + fields = {"T": ds_fields["T"], "S": ds_fields["S"]} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset) - st_instrument = Underwater_STInstrument(underwater_st_expedition, from_data) - out_path = tmpdir.join("out.zarr") + st_instrument = Underwater_STInstrument(underwater_st_expedition, from_data=None) + out_path = tmpdir.join("out.parquet") st_instrument.load_input_data = lambda: fieldset - # The instrument expects measurements as sample_points st_instrument.simulate(sample_points, out_path) - # test if output is as expected - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) - assert len(results.trajectory) == 1 # expect a single trajectory - traj = results.trajectory.item() - assert len(results.sel(trajectory=traj).obs) == len( - sample_points - ) # expect as many obs as sample points + # expect a single depth level + assert np.unique(results["z"].to_numpy()).size == 1 + + # expect as many obs as sample points (given the period is 5 minutes and the sample points are 1 second apart) + assert len(results) == len(sample_points) # for every obs, check if the variables match the expected observations for i, (obs_i, exp) in enumerate( - zip(results.sel(trajectory=traj).obs, expected_obs, strict=True) + zip(results.iter_rows(named=True), expected_obs, strict=True) ): - obs = results.sel(trajectory=traj, obs=obs_i) - for var in ["salinity", "temperature", "lat", "lon"]: - obs_value = obs[var].values.item() + for var in [("y", "lat"), ("x", "lon")]: + obs_value = obs_i[var[0]] + exp_value = exp[var[1]] + assert np.isclose(obs_value, exp_value), ( + f"Observation incorrect {obs_i=} {var=} {obs_value=} {exp_value=}." + ) + for var in ["T", "S"]: + obs_value = obs_i[var] exp_value = exp[var] assert np.isclose(obs_value, exp_value), ( f"Observation incorrect {i=} {var=} {obs_value=} {exp_value=}." From ebd2c0c1ddd4637973162b5fe477b3a85eb207a2 Mon Sep 17 00:00:00 2001 From: Jamie Atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 3 Aug 2026 16:05:06 +0200 Subject: [PATCH 58/94] Add initial Field sampling for non-underway instruments (#364) * new method for setting initial conditions for sampling variables * add initial sampling step to all (non-underway) instruments * move _sample_initial to staticmethod * refactor to use fixtures, add test for field initial condition sampling * use `pset` as sole argument Co-authored-by: Erik van Sebille --------- Co-authored-by: Erik van Sebille --- src/virtualship/instruments/argo_float.py | 5 ++ src/virtualship/instruments/base.py | 22 ++++++ src/virtualship/instruments/ctd.py | 3 + src/virtualship/instruments/drifter.py | 3 + src/virtualship/instruments/xbt.py | 3 + tests/instruments/test_base.py | 90 ++++++++++++++++------- 6 files changed, 98 insertions(+), 28 deletions(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 96b8e4f0..029a0aec 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -324,6 +324,11 @@ def simulate(self, measurements, out_path) -> None: drift_days=[argo.drift_days for argo in measurements], ) + # add initial conditions to sampling variables + self._sample_initial( + argo_float_particleset, fieldset, argo_float_config.sensors + ) + # define output file for the simulation out_file = ParticleFile( path=out_path, diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index ce0f8324..a9643718 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -279,6 +279,28 @@ def _via_tmp_ds(ds) -> xr.Dataset: del ds return xr.open_dataset(tmp_fpath) + @staticmethod + def _sample_initial( + pset: parcels.ParticleSet, + fieldset: parcels.FieldSet, + sensors_config: object, + ) -> parcels.ParticleSet: + """Perform initial Field sampling with ParticleSet.""" + for sensor in sensors_config: + if not sensor.enabled: + raise ValueError( + f"Attempted to initialise sensor '{sensor.sensor_type}' but it is not enabled in the expedition configuration." + ) + + fs_key = sensor.meta.fs_key + field = getattr(fieldset, fs_key) + particle_vars = [pv.name for pv in sensor.meta.particle_vars] + + for var in particle_vars: + setattr(pset, var, field[pset]) + + return pset + @property def instrument_type(self) -> InstrumentType: """Return the InstrumentType for this instrument instance.""" diff --git a/src/virtualship/instruments/ctd.py b/src/virtualship/instruments/ctd.py index 5b8cba61..1291890d 100644 --- a/src/virtualship/instruments/ctd.py +++ b/src/virtualship/instruments/ctd.py @@ -209,6 +209,9 @@ def simulate(self, measurements, out_path) -> None: winch_speed=[WINCH_SPEED for _ in measurements], ) + # add initial conditions to sampling variables + self._sample_initial(ctd_particleset, fieldset, ctd_config.sensors) + # define output file for the simulation out_file = ParticleFile(path=out_path, outputdt=OUTPUT_DT) diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index 3b52dc71..fe8b3738 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -154,6 +154,9 @@ def simulate(self, measurements, out_path) -> None: ], ) + # add initial conditions to sampling variables + self._sample_initial(drifter_particleset, fieldset, drifter_config.sensors) + # define output file for the simulation out_file = ParticleFile( path=out_path, diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index 9de23092..c59e6996 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -164,6 +164,9 @@ def simulate(self, measurements, out_path) -> None: fall_speed=[xbt.fall_speed for xbt in measurements], ) + # add initial conditions to sampling variables + self._sample_initial(xbt_particleset, fieldset, xbt_config.sensors) + out_file = ParticleFile(path=out_path, outputdt=OUTPUT_DT) # build kernel list from active sensors only diff --git a/tests/instruments/test_base.py b/tests/instruments/test_base.py index e14bb630..aa725fb4 100644 --- a/tests/instruments/test_base.py +++ b/tests/instruments/test_base.py @@ -16,8 +16,48 @@ ) from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType +from virtualship.models.expedition import SensorConfig from virtualship.utils import get_instrument_class +# ============================================================================= +# Fixtures +# ============================================================================= + + +@pytest.fixture() +def fieldset(): + """Minimal Parcels FieldSet containing a temperature field.""" + T = np.zeros((2, 1, 1)) + T[0, 0, 0], T[1, 0, 0] = 15.0, 16.0 + + t1 = np.datetime64("2024-01-01T00:00:00") + t2 = np.datetime64("2024-01-02T00:00:00") + + ds_fields = xr.Dataset( + data_vars={"temperature": (["time", "lat", "lon"], T, {"units": "degC"})}, + coords={ + "time": ("time", [t1, t2], {"axis": "T"}), + "lat": ("lat", [0.0], {"units": "degrees_north"}), + "lon": ("lon", [0.0], {"units": "degrees_east"}), + }, + ) + + fields = {"T": ds_fields["temperature"]} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + return parcels.FieldSet.from_sgrid_conventions(ds_fset) + + +@pytest.fixture() +def pset(fieldset): + """Minimal ParticleSet initialized with a custom Particle class and the fieldset fixture.""" + SampleParticle = parcels.Particle.add_variable(parcels.Variable("temperature")) + t1 = np.datetime64("2024-01-01T00:00:00") + + return parcels.ParticleSet( + fieldset=fieldset, pclass=SampleParticle, t=t1, y=[0.0], x=[0.0] + ) + + # ============================================================================= # Instrument base class testing # ============================================================================= @@ -210,6 +250,24 @@ def simulate(self, data_dir, measurements, out_path): pass +def test_instrument_samples_initial_conditions(fieldset, pset): + """_sample_initial adds initial conditions to particles.""" + psetT_preinit = pset.temperature.copy() # before sampling initial conditions + + sensor_config = SensorConfig(sensor_type=SensorType.TEMPERATURE, enabled=True) + pset = Instrument._sample_initial(pset, fieldset, [sensor_config]) + + psetT_postinit = pset.temperature # once initialised + + assert not np.array_equal(psetT_preinit, psetT_postinit), ( + "Initial conditions were not added." + ) + + assert np.allclose(psetT_postinit, [15.0]), ( + "Initial conditions do not match expected values." + ) + + # ============================================================================= # UnderwayInstrument intermediate class testing # ============================================================================= @@ -378,7 +436,9 @@ def _create_underway_parquet( def dummy_sample_temperature(particles, fieldset): - particles.T = fieldset.T[particles.t, particles.z, particles.y, particles.x] + particles.temperature = fieldset.T[ + particles.t, particles.z, particles.y, particles.x + ] def test_parquet_openable_by_parcels_read_particlefile(tmp_path): @@ -400,34 +460,8 @@ def test_parquet_openable_by_parcels_read_particlefile(tmp_path): assert np.isclose(results["sal"][1], 35.1) -def test_underway_schema_matches_parcels(tmp_path): +def test_underway_schema_matches_parcels(tmp_path, pset): """Verify that underway instrument parquet output base schema matches Parcels' ParticleFile.""" - # minimal Parcels FieldSet - T = np.zeros((2, 1, 1)) - T[0, 0, 0], T[1, 0, 0] = 15.0, 16.0 - - t1 = np.datetime64("2024-01-01T00:00:00") - t2 = np.datetime64("2024-01-02T00:00:00") - ds_fields = xr.Dataset( - data_vars={"temperature": (["time", "lat", "lon"], T, {"units": "degC"})}, - coords={ - "time": ("time", [t1, t2], {"axis": "T"}), - "lat": ("lat", [0.0], {"units": "degrees_north"}), - "lon": ("lon", [0.0], {"units": "degrees_east"}), - }, - ) - - fields = {"T": ds_fields["temperature"]} - ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) - fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset) - - # parcels simualtion - SampleParticle = parcels.Particle.add_variable(parcels.Variable("T")) - - pset = parcels.ParticleSet( - fieldset=fieldset, pclass=SampleParticle, t=t1, y=[0.0], x=[0.0] - ) - parcels_path = tmp_path / "parcels_particles.parquet" parcels_output = parcels.ParticleFile(parcels_path, outputdt=3600.0) pset.execute( From ac43f15365bf20786ded34b44f7ff65b8d0ad7c2 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 3 Aug 2026 16:46:29 +0200 Subject: [PATCH 59/94] remove .fillna() now that it's in Parcels internals --- src/virtualship/instruments/base.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index a9643718..86b76f8b 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -237,9 +237,6 @@ def _generate_fieldset(self) -> parcels.FieldSet: ) field_var_name = var - # TODO: to be removed when Parcels #2746 is merged (i.e. https://github.com/Parcels-code/Parcels/pull/2746) - ds = ds.fillna(0) - fields = {key: ds[field_var_name]} ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) ds_fset = self._via_tmp_ds(ds_fset) From 1e08b9656c95c4b24e45658150a3e2e065a84f68 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 4 Aug 2026 14:45:38 +0200 Subject: [PATCH 60/94] via_tmp_ds is only needed when streaming data --- src/virtualship/instruments/base.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 86b76f8b..7f20c146 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -239,7 +239,9 @@ def _generate_fieldset(self) -> parcels.FieldSet: fields = {key: ds[field_var_name]} ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) - ds_fset = self._via_tmp_ds(ds_fset) + + if self.from_data is None: + ds_fset = self._via_tmp_ds(ds_fset) fs = parcels.FieldSet.from_sgrid_conventions(ds_fset) From c202418d6d149a96f9e4738156193dfbb202e484 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 4 Aug 2026 14:46:36 +0200 Subject: [PATCH 61/94] FetchSpec takes the VirtualShip-native negative depth values --- src/virtualship/instruments/drifter.py | 8 ++------ tests/instruments/test_base.py | 4 ++-- 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/src/virtualship/instruments/drifter.py b/src/virtualship/instruments/drifter.py index fe8b3738..b72b8ab0 100644 --- a/src/virtualship/instruments/drifter.py +++ b/src/virtualship/instruments/drifter.py @@ -92,12 +92,8 @@ def __init__(self, expedition, from_data): latlon_buffer=30.0, # TODO: generous buffer to reduce tmp file footprint, can potentially be removed in the future as/when Parcels streaming performance improves (see #358) time_buffer=expedition.instruments_config.drifter_config.lifetime.total_seconds() / (24 * 3600), # [days] - depth_min=abs( - expedition.instruments_config.drifter_config.depth_meter - ), # [meters] - depth_max=abs( - expedition.instruments_config.drifter_config.depth_meter - ), # [meters] + depth_min=expedition.instruments_config.drifter_config.depth_meter, # [meters] + depth_max=expedition.instruments_config.drifter_config.depth_meter, # [meters] ) super().__init__( diff --git a/tests/instruments/test_base.py b/tests/instruments/test_base.py index aa725fb4..7d662076 100644 --- a/tests/instruments/test_base.py +++ b/tests/instruments/test_base.py @@ -152,7 +152,7 @@ def test_fetch_spec_applied_to_instrument(): mock_waypoint.location.longitude = 2.0 mock_schedule = MagicMock() mock_schedule.waypoints = [mock_waypoint] - fetch_spec = FetchSpec(latlon_buffer=5.0, depth_min=10.0) + fetch_spec = FetchSpec(latlon_buffer=5.0, depth_min=-10.0) dummy = DummyInstrument( expedition=MagicMock(schedule=mock_schedule), variables={"A": "a"}, @@ -163,7 +163,7 @@ def test_fetch_spec_applied_to_instrument(): from_data=None, ) assert dummy.fetch_spec.latlon_buffer == 5.0 - assert dummy.fetch_spec.depth_min == 10.0 + assert dummy.fetch_spec.depth_min == -10.0 # unset values use dataclass defaults assert dummy.fetch_spec.time_buffer == 0.0 assert dummy.fetch_spec.depth_max is None From 90573125073f76558da9ae1c6511bbf96f67018f Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 4 Aug 2026 14:47:32 +0200 Subject: [PATCH 62/94] bring --from-data methods up to date with new v4 workflows, also includes some refactoring/reorganisation --- src/virtualship/instruments/base.py | 135 +++++++++++++++++++--------- 1 file changed, 93 insertions(+), 42 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 7f20c146..4f3f4b1e 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -155,45 +155,6 @@ def execute(self, measurements: list, out_path: str | Path) -> None: self.simulate(measurements, out_path) print("\n") - def _get_copernicus_ds( - self, - time_buffer: float | None, - physical: bool, - var: str, - ) -> xr.Dataset: - """Get Copernicus Marine dataset for direct ingestion.""" - product_id = _select_product_id( - physical=physical, - schedule_start=self.min_time, - schedule_end=self.max_time, - variable=var if not physical else None, - ) - - latlon_buffer = self.fetch_spec.latlon_buffer - depth_min = self.fetch_spec.depth_min - depth_max = self.fetch_spec.depth_max - spatial_constraint = self.fetch_spec.spatial - - min_lon_bound = self.min_lon - latlon_buffer if spatial_constraint else None - max_lon_bound = self.max_lon + latlon_buffer if spatial_constraint else None - min_lat_bound = self.min_lat - latlon_buffer if spatial_constraint else None - max_lat_bound = self.max_lat + latlon_buffer if spatial_constraint else None - - return copernicusmarine.open_dataset( - dataset_id=product_id, - minimum_longitude=min_lon_bound, - maximum_longitude=max_lon_bound, - minimum_latitude=min_lat_bound, - maximum_latitude=max_lat_bound, - variables=[var], - start_datetime=self.min_time, - end_datetime=self.max_time + timedelta(days=time_buffer), - minimum_depth=depth_min, - maximum_depth=depth_max, - coordinates_selection_method="outside", - vertical_axis="elevation", - ) - def _generate_fieldset(self) -> parcels.FieldSet: """ Create and combine FieldSets for each variable, supporting both local and Copernicus Marine data sources. @@ -225,9 +186,7 @@ def _generate_fieldset(self) -> parcels.FieldSet: data_dir, var ) # get full variable name from one of the files; var may only appear as substring in variable name in file - ds = xr.open_mfdataset([data_dir.joinpath(f) for f in files]) - - # TODO: for the local data it's useful to sel the relevant depth layer(s), in case the user's data is full depth + ds = self._get_local_ds([data_dir.joinpath(f) for f in files]) else: # stream via Copernicus Marine Service ds = self._get_copernicus_ds( @@ -240,6 +199,7 @@ def _generate_fieldset(self) -> parcels.FieldSet: fields = {key: ds[field_var_name]} ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + # streaming data performance is improved by writing to a temporary file, unnecessary for local data if self.from_data is None: ds_fset = self._via_tmp_ds(ds_fset) @@ -269,6 +229,81 @@ def _generate_fieldset(self) -> parcels.FieldSet: return base_fieldset + def _get_copernicus_ds( + self, + time_buffer: float | None, + physical: bool, + var: str, + ) -> xr.Dataset: + """Get Copernicus Marine dataset for direct ingestion.""" + product_id = _select_product_id( + physical=physical, + schedule_start=self.min_time, + schedule_end=self.max_time, + variable=var if not physical else None, + ) + + # spatial bounds with buffer, if spatial constraints apply + min_lon_wbuf, max_lon_wbuf, min_lat_wbuf, max_lat_wbuf = self.spatial_bounds + + return copernicusmarine.open_dataset( + dataset_id=product_id, + minimum_longitude=min_lon_wbuf, + maximum_longitude=max_lon_wbuf, + minimum_latitude=min_lat_wbuf, + maximum_latitude=max_lat_wbuf, + variables=[var], + start_datetime=self.min_time, + end_datetime=self.max_time + timedelta(days=time_buffer), + minimum_depth=abs(self.fetch_spec.depth_min), + maximum_depth=abs(self.fetch_spec.depth_max), + coordinates_selection_method="outside", + vertical_axis="elevation", + ) + + def _get_local_ds(self, files: list[Path]) -> xr.Dataset: + """Get local dataset for direct ingestion.""" + # TODO: add flexibility to ingest one .nc file / not split across time? (i.e. #366) + ds = xr.open_mfdataset([f for f in files]) + + # TODO: update docs about the depth dimension metadata requirement, but will be superseded by #366 + try: + if ds["depth"].attrs.get("positive") == "down": + ds["depth"] = -ds["depth"] + ds = ds.reindex(depth=ds["depth"][::-1]) + ds["depth"].attrs["positive"] = "up" + elif ds["depth"].attrs.get("positive") != "up": + pass + + except Exception as e: + raise ValueError( + f"Missing or invalid 'positive' attribute for 'depth' coordinate in {files[0].parent}. Expected 'positive: up' or 'positive: down'. Original error: {e}" + ) from e + + # sel only relevant latlon and depth subsets, to speed up simulations (avoid bringing in potentially global data) + # spatial bounds with buffer, if spatial constraints apply + min_lon_wbuf, max_lon_wbuf, min_lat_wbuf, max_lat_wbuf = self.spatial_bounds + + depth_min = self.fetch_spec.depth_min + depth_max = self.fetch_spec.depth_max + if depth_min == depth_max: + depth_sel = { + "depth": [depth_min], + "method": "nearest", + } # preserve depth dim with square brackets + else: + # max, min slice because depth is negative and positive: up + depth_sel = {"depth": slice(depth_max, depth_min)} + + ds = ds.sel( + longitude=slice(min_lon_wbuf, max_lon_wbuf), + latitude=slice(min_lat_wbuf, max_lat_wbuf), + ) + # separate sel for depth to allow nearest selection if not using slices + ds = ds.sel(**depth_sel) + + return ds + @staticmethod def _via_tmp_ds(ds) -> xr.Dataset: """Create and re-load a temporary local dataset.""" @@ -305,6 +340,22 @@ def instrument_type(self) -> InstrumentType: """Return the InstrumentType for this instrument instance.""" return next(k for k, v in INSTRUMENT_CLASS_MAP.items() if type(self) is v) + @property + def spatial_bounds( + self, + ) -> tuple[float | None, float | None, float | None, float | None]: + """Return (min_lon, max_lon, min_lat, max_lat) bounds including buffer if spatial constraints apply.""" + if not self.fetch_spec.spatial: + return None, None, None, None + + buf = self.fetch_spec.latlon_buffer + return ( + self.min_lon - buf, + self.max_lon + buf, + self.min_lat - buf, + self.max_lat + buf, + ) + @dataclass(frozen=True) class UnderwayCoordinates: From d68aa097ef127cc03c59c5ddd01fa279721c4872 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 4 Aug 2026 16:06:35 +0200 Subject: [PATCH 63/94] update test_utils for new v4 workflows --- src/virtualship/utils.py | 15 ++++--- tests/test_utils.py | 94 ++++++++++++++-------------------------- 2 files changed, 41 insertions(+), 68 deletions(-) diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 9cb028f3..e74e4380 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -437,7 +437,9 @@ def _get_bathy_data(from_data: Path | None = None) -> FieldSet: raise RuntimeError( f"\n\n❗️ Could not find bathymetry variable '{VAR}' in data directory '{from_data}/bathymetry/'.\n\n❗️ Is the pre-downloaded data directory structure compliant with VirtualShip expectations?\n\n❗️ See the docs for more information on expectations: https://virtualship.readthedocs.io/en/latest/user-guide/index.html#documentation\n" ) from e - ds_bathymetry = xr.open_dataset(bathy_dir.joinpath(filename)) + ds_bathymetry = xr.open_dataset( + bathy_dir.joinpath(filename), engine="h5netcdf" + ) # h5netcdf for more robust handling else: # stream via Copernicus Marine Service ds_bathymetry = copernicusmarine.open_dataset( @@ -446,11 +448,8 @@ def _get_bathy_data(from_data: Path | None = None) -> FieldSet: coordinates_selection_method="outside", ) - ds_bathymetry = ds_bathymetry.expand_dims( - {"depth": 1} - ) # TODO: bodge whilst parcels v4 does not support 2D fields and seeks depth dim; change when parcels v4 released - - # Negate bathymetry to convert from depth below geoid to negative depth (Parcels convention) + # give a depth dimension and make bathymetry negative + ds_bathymetry = ds_bathymetry.expand_dims({"depth": 1}) ds_bathymetry[VAR] = -ds_bathymetry[VAR] ds_fset = parcels.convert.copernicusmarine_to_sgrid( @@ -487,7 +486,9 @@ def _find_nc_file_with_variable(data_dir: Path, var: str) -> str | None: """Search for a .nc file in the given directory containing the specified variable.""" for nc_file in data_dir.glob("*.nc"): try: - with xr.open_dataset(nc_file, chunks={}) as ds: + with xr.open_dataset( + nc_file, engine="h5netcdf" + ) as ds: # h5netcdf for more robust handling matched_vars = [v for v in ds.variables if var in v] if matched_vars: return nc_file.name, matched_vars[0] diff --git a/tests/test_utils.py b/tests/test_utils.py index 2628793d..7a05ef4d 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -5,7 +5,7 @@ import numpy as np import pytest import xarray as xr -from parcels import FieldSet, JITParticle, ScipyParticle, Variable +from parcels import FieldSet, ParticleClass, Variable import virtualship.utils from virtualship.instruments.sensors import SensorType @@ -20,7 +20,6 @@ _get_bathy_data, _select_product_id, _start_end_in_product_timerange, - add_dummy_UV, build_particle_class_from_sensors, get_example_expedition, ) @@ -89,25 +88,6 @@ def test_instrument_registry_updates(dummy_instrument): assert utils.INSTRUMENT_CLASS_MAP["DUMMY_TYPE"] is dummy_instrument -def test_add_dummy_UV_adds_fields(): - fieldset = FieldSet.from_data({"T": 1}, {"lon": 0, "lat": 0}, mesh="spherical") - fieldset.__dict__.pop("U", None) - fieldset.__dict__.pop("V", None) - - # should not have U or V fields initially - assert "U" not in fieldset.__dict__ - assert "V" not in fieldset.__dict__ - - add_dummy_UV(fieldset) - - # now U and V should be present - assert "U" in fieldset.__dict__ - assert "V" in fieldset.__dict__ - - # should not raise error if U and V already present - add_dummy_UV(fieldset) - - @pytest.mark.usefixtures("copernicus_no_download") def test_select_product_id(expedition): """Should return the physical reanalysis product id via the timings prescribed.""" @@ -138,27 +118,31 @@ def test_start_end_in_product_timerange(expedition): def test_get_bathy_data_local(tmp_path): """Test that _get_bathy_data returns a FieldSet when given a local directory for --from-data.""" # dummy .nc file with 'deptho' variable - data = np.array([[1, 2], [3, 4]]) + + data = np.array( + [[1, 2], [3, 4]] + ) # positive values, to mock how most bathymetry datasets are supplied ds = xr.Dataset( { - "deptho": (("x", "y"), data), + "deptho": (("lat", "lon"), data), }, coords={ - "longitude": (("x", "y"), np.array([[0, 1], [0, 1]])), - "latitude": (("x", "y"), np.array([[0, 0], [1, 1]])), + "lon": (("lon"), np.array([0, 1]), {"units": "degrees_east"}), + "lat": (("lat"), np.array([0, 1]), {"units": "degrees_north"}), }, ) + nc_path = tmp_path / "bathymetry/dummy.nc" nc_path.parent.mkdir(parents=True, exist_ok=True) - ds.to_netcdf(nc_path) + ds.to_netcdf(nc_path, engine="h5netcdf") - # should return a FieldSet - fieldset = _get_bathy_data( - min_lat=0.25, max_lat=0.75, min_lon=0.25, max_lon=0.75, from_data=tmp_path - ) + fieldset = _get_bathy_data(from_data=tmp_path) assert isinstance(fieldset, FieldSet) assert hasattr(fieldset, "bathymetry") - assert np.allclose(fieldset.bathymetry.data, data) + + assert np.allclose( + fieldset.bathymetry.data.values, -ds["deptho"].values + ) # should be negated def test_get_bathy_data_copernicusmarine(monkeypatch): @@ -172,7 +156,7 @@ def dummy_copernicusmarine(*args, **kwargs): ) try: - _get_bathy_data(min_lat=0.25, max_lat=0.75, min_lon=0.25, max_lon=0.75) + _get_bathy_data(from_data=None) # None means call copernicusmarine except RuntimeError as e: assert "copernicusmarine called" in str(e) @@ -190,7 +174,7 @@ def test_find_nc_file_with_variable_substring(tmp_path): }, ) nc_path = tmp_path / "test.nc" - ds.to_netcdf(nc_path) + ds.to_netcdf(nc_path, engine="h5netcdf") # h5netcdf for more robust handling # should find 'uo_glor' when searching for 'uo' result = _find_nc_file_with_variable(tmp_path, "uo") @@ -220,35 +204,32 @@ def test_data_dir_and_filename_compliance(): utils_code = utils_path.read_text(encoding="utf-8") # Check for phys and bgc in Instrument._generate_fieldset - assert 'self.from_data.joinpath("phys")' in base_code, ( - "Expected 'phys' subdirectory not found in Instrument._generate_fieldset. This indicates a drift between docs and implementation." + assert 'self.from_data.joinpath("phys"' in base_code, ( + "Expected 'phys' subdirectory not found in Instrument._generate_fieldset. This could indicate a drift between docs and implementation." ) - assert 'self.from_data.joinpath("bgc")' in base_code, ( - "Expected 'bgc' subdirectory not found in Instrument._generate_fieldset. This indicates a drift between docs and implementation." + assert 'if physical else "bgc")' in base_code, ( + "Expected 'bgc' subdirectory not found in Instrument._generate_fieldset. This could indicate a drift between docs and implementation." ) # Check for bathymetry in _get_bathy_data assert 'from_data.joinpath("bathymetry")' in utils_code, ( - "Expected 'bathymetry' subdirectory not found in _get_bathy_data. This indicates a drift between docs and implementation." + "Expected 'bathymetry' subdirectory not found in _get_bathy_data. This could indicate a drift between docs and implementation." ) # Check for date_pattern in _find_files_in_timerange assert 'date_pattern=r"\\d{4}_\\d{2}_\\d{2}"' in utils_code, ( - "Expected date_pattern r'\\d{4}_\\d{2}_\\d{2}' not found in _find_files_in_timerange. This indicates a drift between docs and implementation." + "Expected date_pattern r'\\d{4}_\\d{2}_\\d{2}' not found in _find_files_in_timerange. This could indicate a drift between docs and implementation." ) # Check for P1D and P1M in t_resolution logic assert 'if all("P1D" in s for s in all_files):' in utils_code, ( - "Expected check for 'P1D' in all_files not found in _find_files_in_timerange. This indicates a drift between docs and implementation." + "Expected check for 'P1D' in all_files not found in _find_files_in_timerange. This could indicate a drift between docs and implementation." ) assert 'elif all("P1M" in s for s in all_files):' in utils_code, ( - "Expected check for 'P1M' in all_files not found in _find_files_in_timerange. This indicates a drift between docs and implementation." + "Expected check for 'P1M' in all_files not found in _find_files_in_timerange. This could indicate a drift between docs and implementation." ) -# TODO: test for calc_sail_time - - def test_calc_sail_time(projection=PROJECTION): LATITUDE = 0.0 # constant at equator @@ -366,8 +347,8 @@ def test_build_basic_particle_class(): nonsensor = [Variable("cycle_phase", dtype=np.int32, initial=0)] sensors = _make_sensors(SensorType.TEMPERATURE, SensorType.SALINITY) - ParticleClass = build_particle_class_from_sensors(sensors, nonsensor, JITParticle) - assert issubclass(ParticleClass, JITParticle) + pclass = build_particle_class_from_sensors(sensors, nonsensor) + assert isinstance(pclass, ParticleClass) def test_build_particle_class_disabled_sensors_excluded(): @@ -378,9 +359,9 @@ def test_build_particle_class_disabled_sensors_excluded(): SensorConfig(sensor_type=SensorType.SALINITY, enabled=False), ] - ParticleClass = build_particle_class_from_sensors(sensors, nonsensor, JITParticle) - assert hasattr(ParticleClass, "temperature") - assert not hasattr(ParticleClass, "salinity") + pclass = build_particle_class_from_sensors(sensors, nonsensor) + assert any(v.name == "temperature" for v in pclass.variables) + assert not any(v.name == "salinity" for v in pclass.variables) def test_build_particle_class_velocity_adds_U_V(): @@ -388,18 +369,9 @@ def test_build_particle_class_velocity_adds_U_V(): nonsensor = [] sensors = _make_sensors(SensorType.VELOCITY) - ParticleClass = build_particle_class_from_sensors(sensors, nonsensor, JITParticle) - assert hasattr(ParticleClass, "U") - assert hasattr(ParticleClass, "V") - - -def test_build_particle_class_scipy_base(): - """Should also work with ScipyParticle as the base class.""" - nonsensor = [] - sensors = _make_sensors(SensorType.TEMPERATURE) - - ParticleClass = build_particle_class_from_sensors(sensors, nonsensor, ScipyParticle) - assert issubclass(ParticleClass, ScipyParticle) + pclass = build_particle_class_from_sensors(sensors, nonsensor) + assert any(v.name == "U" for v in pclass.variables) + assert any(v.name == "V" for v in pclass.variables) def test_allowed_sensors_matches_docs(): From 707328d1265ddcfb802adb44e6b2bd24ee08d96b Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 4 Aug 2026 16:12:20 +0200 Subject: [PATCH 64/94] update test_expedition for v4 fieldset ingestion --- tests/expedition/test_expedition.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/tests/expedition/test_expedition.py b/tests/expedition/test_expedition.py index f6a84dfe..4bde12bd 100644 --- a/tests/expedition/test_expedition.py +++ b/tests/expedition/test_expedition.py @@ -3,10 +3,10 @@ from unittest.mock import patch import numpy as np +import parcels import pyproj import pytest import xarray as xr -from parcels import FieldSet from virtualship.errors import InstrumentsConfigError, ScheduleError from virtualship.models import ( @@ -105,8 +105,8 @@ def test_get_instruments() -> None: def test_verify_on_land(): """Test that schedule verification raises error for waypoints on land (0.0 m bathymetry).""" # bathymetry fieldset with NaNs at specific locations - latitude = np.array([0, 1.0, 2.0]) - longitude = np.array([0, 1.0, 2.0]) + lat = np.array([0, 1.0, 2.0]) + lon = np.array([0, 1.0, 2.0]) bathymetry = np.array( [ [100, 0.0, 100], @@ -117,20 +117,20 @@ def test_verify_on_land(): ds_bathymetry = xr.Dataset( { - "deptho": (("latitude", "longitude"), bathymetry), + "deptho": (("lat", "lon"), bathymetry), }, coords={ - "latitude": latitude, - "longitude": longitude, + "lon": (("lon"), lon, {"units": "degrees_east"}), + "lat": (("lat"), lat, {"units": "degrees_north"}), }, ) - bathymetry_variables = {"bathymetry": "deptho"} - bathymetry_dimensions = {"lon": "longitude", "lat": "latitude"} - bathymetry_fieldset = FieldSet.from_xarray_dataset( - ds_bathymetry, bathymetry_variables, bathymetry_dimensions + ds_fset = parcels.convert.copernicusmarine_to_sgrid( + fields={"bathymetry": ds_bathymetry["deptho"]}, ) + bathymetry_fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset) + # waypoints placed in NaN bathy cells waypoints = [ Waypoint( From 56bf4df388c2e2e1b557bdbdab98b5657d517570 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 4 Aug 2026 16:53:38 +0200 Subject: [PATCH 65/94] use context managers for _via_tmp_ds, fix using abs() for copernicusmarine open_dataset --- src/virtualship/instruments/base.py | 29 +++++++++++++++++++++-------- 1 file changed, 21 insertions(+), 8 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 4f3f4b1e..0d5edc82 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -246,6 +246,17 @@ def _get_copernicus_ds( # spatial bounds with buffer, if spatial constraints apply min_lon_wbuf, max_lon_wbuf, min_lat_wbuf, max_lat_wbuf = self.spatial_bounds + min_depth = ( + abs(self.fetch_spec.depth_min) + if self.fetch_spec.depth_min is not None + else None + ) + max_depth = ( + abs(self.fetch_spec.depth_max) + if self.fetch_spec.depth_max is not None + else None + ) + return copernicusmarine.open_dataset( dataset_id=product_id, minimum_longitude=min_lon_wbuf, @@ -255,8 +266,8 @@ def _get_copernicus_ds( variables=[var], start_datetime=self.min_time, end_datetime=self.max_time + timedelta(days=time_buffer), - minimum_depth=abs(self.fetch_spec.depth_min), - maximum_depth=abs(self.fetch_spec.depth_max), + minimum_depth=min_depth, + maximum_depth=max_depth, coordinates_selection_method="outside", vertical_axis="elevation", ) @@ -305,13 +316,15 @@ def _get_local_ds(self, files: list[Path]) -> xr.Dataset: return ds @staticmethod - def _via_tmp_ds(ds) -> xr.Dataset: + def _via_tmp_ds(ds: xr.Dataset) -> xr.Dataset: """Create and re-load a temporary local dataset.""" - tmpdir = tempfile.TemporaryDirectory() - tmp_fpath = Path(tmpdir.name).joinpath("tmp.nc") - ds.to_netcdf(tmp_fpath) - del ds - return xr.open_dataset(tmp_fpath) + with tempfile.TemporaryDirectory() as tmpdir: + tmp_fpath = Path(tmpdir) / "tmp.nc" + ds.to_netcdf(tmp_fpath) + + # Open and load into memory so the file handle closes before tmpdir exits + with xr.open_dataset(tmp_fpath) as loaded_ds: + return loaded_ds.load() @staticmethod def _sample_initial( From 8954456b0c0ab3823c2ff08bc7c2ebf79e6b3f62 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 4 Aug 2026 16:54:34 +0200 Subject: [PATCH 66/94] avoid annoying warnings concerning potential pixi build issues and compatibility with parcels v4 pull from main --- pixi.toml | 5 ++++- pyproject.toml | 3 ++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/pixi.toml b/pixi.toml index 152766f9..3d18b8b4 100644 --- a/pixi.toml +++ b/pixi.toml @@ -14,11 +14,12 @@ version = "dynamic" # dynamic versioning needs better support in pixi https://gi backend = { name = "pixi-build-python", version = "0.4.*" } [package.host-dependencies] +python = "3.12.*" setuptools = "*" setuptools_scm = "*" [package.run-dependencies] # Keep in sync with `pyproject.toml` and feedstock recipe -python = "3.11.*" +python = "3.12.*" click = "*" pyproj = ">=3,<4" sortedcontainers = "==2.4.0" @@ -32,6 +33,7 @@ textual = "*" [dependencies] virtualship = { path = "." } +python = "3.12.*" # Pre-install as conda packages to avoid PyPI source builds netcdf4 = "*" numpy = ">=2.1.0" @@ -39,6 +41,7 @@ dask = "*" zarr = ">=3" ipdb = ">=0.13.13,<0.14" cmocean = ">=4.0.3,<5" +numba = ">=0.59.0" [pypi-dependencies] parcels = { git = "https://github.com/Parcels-code/Parcels", branch = "main" } diff --git a/pyproject.toml b/pyproject.toml index 7e6d184b..d83dc25c 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,7 +70,8 @@ filterwarnings = [ "default::DeprecationWarning", "error::DeprecationWarning:virtualship", "ignore:ParticleSet is empty.*:RuntimeWarning", # TODO: Probably should be ignored in the source code - "ignore:This is an alpha version of Parcels v4.*:UserWarning" # TODO: necessary whilst Parcels v4 is still alpha + "ignore:This is an alpha version of Parcels v4.*:UserWarning", # TODO: necessary whilst Parcels v4 is still alpha + "ignore:numpy.ndarray size changed:RuntimeWarning" # TODO: annoying incompatability issue with pixi env, see if fixed when relying on a Parcels v4 release rather than pulling from main ] log_cli_level = "INFO" testpaths = [ From acca7e34649a552b03d83a411dd3573b9355363a Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 4 Aug 2026 16:55:00 +0200 Subject: [PATCH 67/94] revert need to specify h5netcdf engine --- src/virtualship/utils.py | 8 ++------ tests/test_utils.py | 4 ++-- 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index e74e4380..8ccf69a5 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -437,9 +437,7 @@ def _get_bathy_data(from_data: Path | None = None) -> FieldSet: raise RuntimeError( f"\n\n❗️ Could not find bathymetry variable '{VAR}' in data directory '{from_data}/bathymetry/'.\n\n❗️ Is the pre-downloaded data directory structure compliant with VirtualShip expectations?\n\n❗️ See the docs for more information on expectations: https://virtualship.readthedocs.io/en/latest/user-guide/index.html#documentation\n" ) from e - ds_bathymetry = xr.open_dataset( - bathy_dir.joinpath(filename), engine="h5netcdf" - ) # h5netcdf for more robust handling + ds_bathymetry = xr.open_dataset(bathy_dir.joinpath(filename)) else: # stream via Copernicus Marine Service ds_bathymetry = copernicusmarine.open_dataset( @@ -486,9 +484,7 @@ def _find_nc_file_with_variable(data_dir: Path, var: str) -> str | None: """Search for a .nc file in the given directory containing the specified variable.""" for nc_file in data_dir.glob("*.nc"): try: - with xr.open_dataset( - nc_file, engine="h5netcdf" - ) as ds: # h5netcdf for more robust handling + with xr.open_dataset(nc_file) as ds: matched_vars = [v for v in ds.variables if var in v] if matched_vars: return nc_file.name, matched_vars[0] diff --git a/tests/test_utils.py b/tests/test_utils.py index 7a05ef4d..63b5c4e9 100644 --- a/tests/test_utils.py +++ b/tests/test_utils.py @@ -134,7 +134,7 @@ def test_get_bathy_data_local(tmp_path): nc_path = tmp_path / "bathymetry/dummy.nc" nc_path.parent.mkdir(parents=True, exist_ok=True) - ds.to_netcdf(nc_path, engine="h5netcdf") + ds.to_netcdf(nc_path) fieldset = _get_bathy_data(from_data=tmp_path) assert isinstance(fieldset, FieldSet) @@ -174,7 +174,7 @@ def test_find_nc_file_with_variable_substring(tmp_path): }, ) nc_path = tmp_path / "test.nc" - ds.to_netcdf(nc_path, engine="h5netcdf") # h5netcdf for more robust handling + ds.to_netcdf(nc_path) # should find 'uo_glor' when searching for 'uo' result = _find_nc_file_with_variable(tmp_path, "uo") From fefcc206e67beb74044d8e5e7326db12be857013 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Tue, 4 Aug 2026 17:10:31 +0200 Subject: [PATCH 68/94] update test_base.py for new v4 workflows --- tests/instruments/test_base.py | 78 +++++++++++++++++++--------------- 1 file changed, 43 insertions(+), 35 deletions(-) diff --git a/tests/instruments/test_base.py b/tests/instruments/test_base.py index 7d662076..bcc741a5 100644 --- a/tests/instruments/test_base.py +++ b/tests/instruments/test_base.py @@ -93,36 +93,45 @@ def simulate(self, data_dir, measurements, out_path): """Dummy simulate implementation for test.""" self.simulate_called = True + @property + def instrument_type(self) -> InstrumentType: + """Return a valid InstrumentType for the test.""" + return InstrumentType.CTD -@patch("virtualship.instruments.base.FieldSet") -@patch( - "virtualship.instruments.base._select_product_id", return_value="dummy_product_id" -) -@patch("virtualship.instruments.base.copernicusmarine") -def test_load_input_data(mock_copernicusmarine, mock_select_product_id, mock_FieldSet): + +def test_load_input_data(): """Test Instrument.load_input_data with mocks.""" - mock_fieldset = MagicMock() - mock_FieldSet.from_netcdf.return_value = mock_fieldset - mock_FieldSet.from_xarray_dataset.return_value = mock_fieldset - mock_fieldset.__getitem__.side_effect = lambda k: MagicMock() - mock_copernicusmarine.open_dataset.return_value = MagicMock() - # Create a mock waypoint with latitude and longitude mock_waypoint = MagicMock() mock_waypoint.location.latitude = 1.0 mock_waypoint.location.longitude = 2.0 - mock_schedule = MagicMock() - mock_schedule.waypoints = [mock_waypoint] + dummy = DummyInstrument( - expedition=MagicMock(schedule=mock_schedule), + expedition=MagicMock(schedule=MagicMock(waypoints=[mock_waypoint])), variables={"A": "a"}, add_bathymetry=False, allow_time_extrapolation=False, verbose_progress=False, from_data=None, ) - fieldset = dummy.load_input_data() - assert mock_FieldSet.from_xarray_dataset.called - assert fieldset == mock_fieldset + + mock_fieldset = MagicMock() + mock_fieldset.to_windowed_arrays.return_value = mock_fieldset + + with ( + patch( + "virtualship.instruments.base._select_product_id", + return_value="dummy_product_id", + ), + patch("copernicusmarine.open_dataset"), + patch.object(dummy, "_via_tmp_ds", side_effect=lambda ds: ds), + patch("parcels.convert.copernicusmarine_to_sgrid"), + patch( + "parcels.FieldSet.from_sgrid_conventions", return_value=mock_fieldset + ) as mock_from_sgrid, + ): + fieldset = dummy.load_input_data() + + mock_from_sgrid.assert_called_once() assert fieldset == mock_fieldset @@ -184,35 +193,34 @@ def test_via_tmp_ds_roundtrip(): ) # result is new object loaded from tmp file, not the original -def test_generate_fieldset_combines_fields(monkeypatch): +def test_generate_fieldset_combines_fields(): mock_waypoint = MagicMock() mock_waypoint.location.latitude = 1.0 mock_waypoint.location.longitude = 2.0 - mock_schedule = MagicMock() - mock_schedule.waypoints = [mock_waypoint] + dummy = DummyInstrument( - expedition=MagicMock(schedule=mock_schedule), + expedition=MagicMock(schedule=MagicMock(waypoints=[mock_waypoint])), variables={"A": "a", "B": "b"}, add_bathymetry=False, allow_time_extrapolation=False, verbose_progress=False, from_data=None, ) - dummy.from_data = None - - monkeypatch.setattr( - dummy, "_get_copernicus_ds", lambda *args, **kwargs: MagicMock() - ) fs_A = MagicMock() fs_B = MagicMock() - fs_B.B = MagicMock() - monkeypatch.setattr( - "virtualship.instruments.base.FieldSet.from_xarray_dataset", - lambda ds, varmap, dims, mesh=None: fs_A if "A" in varmap else fs_B, - ) - monkeypatch.setattr(fs_A, "add_field", MagicMock()) - dummy._generate_fieldset() + + fs_A.to_windowed_arrays.return_value = fs_A + fs_B.to_windowed_arrays.return_value = fs_B + + with ( + patch.object(dummy, "_get_copernicus_ds"), + patch.object(dummy, "_via_tmp_ds"), + patch("parcels.convert.copernicusmarine_to_sgrid"), + patch("parcels.FieldSet.from_sgrid_conventions", side_effect=[fs_A, fs_B]), + ): + dummy._generate_fieldset() + fs_A.add_field.assert_called_once_with(fs_B.B) @@ -476,7 +484,7 @@ def test_underway_schema_matches_parcels(tmp_path, pset): underway_path = tmp_path / "underway_particles.parquet" _create_underway_parquet( out_path=underway_path, - var_names=["T"], + var_names=["temperature"], dat_arrays=[np.array([15.0, 16.0], dtype=np.float32)], origin=np.datetime64("2024-01-01T00:00:00"), ) From de8c627b0357e1c9ceac38f6d5dbb01966a33037 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 5 Aug 2026 11:09:04 +0200 Subject: [PATCH 69/94] update tests for v4 workflows and enhance out of bounds error checking --- tests/instruments/test_argo_float.py | 115 ++++++++++++++++++--------- 1 file changed, 79 insertions(+), 36 deletions(-) diff --git a/tests/instruments/test_argo_float.py b/tests/instruments/test_argo_float.py index c56b6d4c..c9e881d4 100644 --- a/tests/instruments/test_argo_float.py +++ b/tests/instruments/test_argo_float.py @@ -1,12 +1,14 @@ """Test the simulation of Argo floats.""" +import contextlib +import io from datetime import datetime, timedelta import numpy as np +import parcels import pydantic import pytest import xarray as xr -from parcels import FieldSet from virtualship.instruments.argo_float import ArgoFloat, ArgoFloatInstrument from virtualship.instruments.sensors import SensorType @@ -44,7 +46,10 @@ def argo_config_kwargs(): def create_fieldset( - lon_range=(0.0, 10.0), lat_range=(0.0, 10.0), include_salinity=True, lifetime_days=1 + lon_range=(0.0, 10.0), + lat_range=(0.0, 10.0), + include_salinity=True, + lifetime_days=0.1, ): """Create a test fieldset with optional salinity.""" v = np.full((2, 2, 2), 1.0) @@ -52,34 +57,62 @@ def create_fieldset( t = np.full((2, 2, 2), 1.0) bathy = np.full((2, 2), -5000.0) - data = {"V": v, "U": u, "T": t} + data_vars = { + "V": (("time", "lat", "lon"), v), + "U": (("time", "lat", "lon"), u), + "T": (("time", "lat", "lon"), t), + } + if include_salinity: - data["S"] = np.full((2, 2, 2), 1.0) - - fieldset = FieldSet.from_data( - data, - { - "lon": np.array(lon_range), - "lat": np.array(lat_range), - "time": [ - np.datetime64(BASE_TIME), - np.datetime64(BASE_TIME + timedelta(days=lifetime_days + 1)), - ], + data_vars["S"] = (("time", "lat", "lon"), np.full((2, 2, 2), 1.0)) + + ds_fields = xr.Dataset( + data_vars=data_vars, + coords={ + "lon": (("lon"), np.array(lon_range), {"units": "degrees_east"}), + "lat": (("lat"), np.array(lat_range), {"units": "degrees_north"}), + "time": ( + ("time"), + [ + np.datetime64(BASE_TIME), + np.datetime64(BASE_TIME + timedelta(days=lifetime_days)), + ], + {"axis": "T"}, + ), }, ) - fieldset.add_field( - FieldSet.from_data( - {"bathymetry": bathy}, - {"lon": np.array(lon_range), "lat": np.array(lat_range)}, - ).bathymetry + + fields = {var: ds_fields[var] for var in data_vars.keys()} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset) + + ds_bathymetry = xr.Dataset( + data_vars={"bathymetry": (("lat", "lon"), bathy)}, + coords={ + "lon": (("lon"), np.array(lon_range), {"units": "degrees_east"}), + "lat": (("lat"), np.array(lat_range), {"units": "degrees_north"}), + }, + ) + ds_bathymetry_fset = parcels.convert.copernicusmarine_to_sgrid( + fields={"bathymetry": ds_bathymetry["bathymetry"]} ) + bathymetry_fset = parcels.FieldSet.from_sgrid_conventions(ds_bathymetry_fset) + + fieldset.add_field(bathymetry_fset.bathymetry) + return fieldset -def create_argo_float(lat=0.0, lon=0.0): +def create_argo_float(waypoint): """Create a single ArgoFloat instance.""" return ArgoFloat( - spacetime=Spacetime(location=Location(latitude=lat, longitude=lon), time=0), + spacetime=Spacetime( + location=Location( + latitude=waypoint.location.latitude, + longitude=waypoint.location.longitude, + ), + time=waypoint.time, + ), min_depth=0.0, max_depth=MAX_DEPTH, drift_depth=DRIFT_DEPTH, @@ -118,7 +151,6 @@ class schedule: def test_simulate_argo_floats(tmpdir) -> None: """Test basic Argo float simulation with temperature and salinity sensors.""" fieldset = create_fieldset() - argo_floats = [create_argo_float()] sensors = [ SensorConfig(sensor_type=SensorType.TEMPERATURE), @@ -127,31 +159,32 @@ def test_simulate_argo_floats(tmpdir) -> None: expedition = create_dummy_expedition(sensors) argo_instrument = ArgoFloatInstrument(expedition, None) - out_path = tmpdir.join("out.zarr") + argo_floats = [create_argo_float(wp) for wp in expedition.schedule.waypoints] + out_path = tmpdir.join("out.parquet") argo_instrument.load_input_data = lambda: fieldset argo_instrument.simulate(argo_floats, out_path) - results = xr.open_zarr(out_path) - assert len(results.trajectory) == len(argo_floats) - for var in ["lon", "lat", "z", "temperature", "salinity"]: + results = parcels.read_particlefile(out_path) + assert np.unique(results["particle_id"].to_numpy()).size == len(argo_floats) + for var in ["x", "y", "z", "temperature", "salinity"]: assert var in results, f"Results don't contain {var}" def test_argo_float_disabled_sensor(tmpdir) -> None: """Variables for disabled sensors must not appear in the zarr output.""" fieldset = create_fieldset(include_salinity=False) - argo_floats = [create_argo_float()] # only temperature sensor enabled sensors = [SensorConfig(sensor_type=SensorType.TEMPERATURE)] expedition = create_dummy_expedition(sensors) argo_instrument = ArgoFloatInstrument(expedition, None) - out_path = tmpdir.join("out_disabled.zarr") + argo_floats = [create_argo_float(wp) for wp in expedition.schedule.waypoints] + out_path = tmpdir.join("out_disabled.parquet") argo_instrument.load_input_data = lambda: fieldset argo_instrument.simulate(argo_floats, out_path) - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) assert "temperature" in results, "Enabled sensor variable must be present" assert "salinity" not in results, ( "Disabled sensor variable must be absent from output" @@ -213,7 +246,6 @@ def test_argo_fieldoutofbounds_error(tmpdir) -> None: fieldset = create_fieldset( lon_range=(0.0, 0.1), lat_range=(0.0, 0.1), lifetime_days=lifetime.days ) - argo_floats = [create_argo_float()] sensors = [ SensorConfig(sensor_type=SensorType.TEMPERATURE), @@ -224,22 +256,33 @@ def test_argo_fieldoutofbounds_error(tmpdir) -> None: ) argo_instrument = ArgoFloatInstrument(expedition, None) - out_path = tmpdir.join("out.zarr") + argo_floats = [create_argo_float(wp) for wp in expedition.schedule.waypoints] + + out_path = tmpdir.join("out.parquet") argo_instrument.load_input_data = lambda: fieldset - argo_instrument.simulate(argo_floats, out_path) + + # capture stdout/stderr safely without breaking (i.e. using capsys interferes with print out stream...) + f = io.StringIO() + with contextlib.redirect_stdout(f), contextlib.redirect_stderr(f): + argo_instrument.simulate(argo_floats, out_path) + + output_log = f.getvalue() # results file should exist even if data is incomplete due to out-of-bounds error - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) # not reaching expected final time indicates simulation was stopped due to FieldOutOfBounds expected_final_time = np.datetime64(BASE_TIME + lifetime) - actual_final_time = results.time.values[np.isfinite(results.time.values)].max() + actual_final_time = ( + results["t"].to_numpy()[np.isfinite(results["t"].to_numpy())].max() + ) assert actual_final_time < expected_final_time, ( "Actual final time should be less than expected final time due to out-of-bounds error/warning" ) - # TODO: capturing the warnings in the tests is complicated by the Parcels C-level print statements; but the logic of not crashing on out-of-bounds is tested if the test simulation runs - # TODO: when using Parcels v4, this test can become much more robust by capturing the specific warning as well + assert "ErrorOutOfBounds" in output_log, ( + "Expected 'ErrorOutOfBounds' message to be printed during simulation." + ) def test_argo_float_instrument_type(): From f17b43e15e01df0b09ab625a3dc6fbf270d52dc1 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 5 Aug 2026 12:09:21 +0200 Subject: [PATCH 70/94] update tests for v4 workflows --- tests/instruments/test_ctd.py | 180 +++++++++++++++++++++------------- 1 file changed, 114 insertions(+), 66 deletions(-) diff --git a/tests/instruments/test_ctd.py b/tests/instruments/test_ctd.py index 29eb758a..845ac06d 100644 --- a/tests/instruments/test_ctd.py +++ b/tests/instruments/test_ctd.py @@ -7,10 +7,11 @@ import datetime import numpy as np +import parcels +import polars as pl import pydantic import pytest import xarray as xr -from parcels import Field, FieldSet from virtualship.instruments.ctd import CTD, CTDInstrument from virtualship.instruments.sensors import SensorType @@ -52,6 +53,64 @@ class schedule: return DummyExpedition() +def create_fieldset( + data_dict, + lon_range=(0.0, 1.0), + lat_range=(0.0, 1.0), + depth_range=(-1000, 0), + time_range=None, + bathymetry_val=-1000.0, +): + if time_range is None: + time_range = [ + np.datetime64(BASE_TIME), + np.datetime64(BASE_TIME + datetime.timedelta(hours=1)), + ] + + data_vars = {} + for key, val in data_dict.items(): + data_vars[key] = (("time", "depth", "lat", "lon"), val) + + ds_fields = xr.Dataset( + data_vars=data_vars, + coords={ + "lon": (("lon"), np.array(lon_range), {"units": "degrees_east"}), + "lat": (("lat"), np.array(lat_range), {"units": "degrees_north"}), + "depth": (("depth"), np.array(depth_range)), + "time": ( + ("time"), + time_range, + {"axis": "T"}, + ), + }, + ) + + fields = {var: ds_fields[var] for var in data_vars.keys()} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset) + + ds_bathymetry = xr.Dataset( + data_vars={ + "bathymetry": ( + ("lat", "lon"), + np.full((len(lat_range), len(lon_range)), bathymetry_val), + ) + }, + coords={ + "lon": (("lon"), np.array(lon_range), {"units": "degrees_east"}), + "lat": (("lat"), np.array(lat_range), {"units": "degrees_north"}), + }, + ) + ds_bathymetry_fset = parcels.convert.copernicusmarine_to_sgrid( + fields={"bathymetry": ds_bathymetry["bathymetry"]} + ) + bathymetry_fset = parcels.FieldSet.from_sgrid_conventions(ds_bathymetry_fset) + + fieldset.add_field(bathymetry_fset.bathymetry) + + return fieldset + + def test_simulate_ctds(tmpdir) -> None: """Test that CTDInstrument simulates measurements correctly, incuding sampling physical and bgc variables.""" # where to cast CTDs @@ -83,8 +142,8 @@ def test_simulate_ctds(tmpdir) -> None: "o2": 10.0, "chl": 20.0, "no3": 30.0, - "lat": ctds[0].spacetime.location.lat, - "lon": ctds[0].spacetime.location.lon, + "y": ctds[0].spacetime.location.lat, + "x": ctds[0].spacetime.location.lon, }, "maxdepth": { "salinity": 7, @@ -92,8 +151,8 @@ def test_simulate_ctds(tmpdir) -> None: "o2": 11.0, "chl": 21.0, "no3": 31.0, - "lat": ctds[0].spacetime.location.lat, - "lon": ctds[0].spacetime.location.lon, + "y": ctds[0].spacetime.location.lat, + "x": ctds[0].spacetime.location.lon, }, }, { @@ -103,8 +162,8 @@ def test_simulate_ctds(tmpdir) -> None: "o2": 12.0, "chl": 22.0, "no3": 32.0, - "lat": ctds[1].spacetime.location.lat, - "lon": ctds[1].spacetime.location.lon, + "y": ctds[1].spacetime.location.lat, + "x": ctds[1].spacetime.location.lon, }, "maxdepth": { "salinity": 7, @@ -112,8 +171,8 @@ def test_simulate_ctds(tmpdir) -> None: "o2": 13.0, "chl": 23.0, "no3": 33.0, - "lat": ctds[1].spacetime.location.lat, - "lon": ctds[1].spacetime.location.lon, + "y": ctds[1].spacetime.location.lat, + "x": ctds[1].spacetime.location.lon, }, }, ] @@ -153,19 +212,13 @@ def test_simulate_ctds(tmpdir) -> None: no3[:, 1, 1, 0] = ctd_exp[1]["surface"]["no3"] no3[:, 0, 1, 0] = ctd_exp[1]["maxdepth"]["no3"] - fieldset = FieldSet.from_data( + fieldset = create_fieldset( {"V": v, "U": u, "T": t, "S": s, "o2": o2, "chl": chl, "no3": no3}, - { - "time": [ - np.datetime64(BASE_TIME + datetime.timedelta(hours=0)), - np.datetime64(BASE_TIME + datetime.timedelta(hours=1)), - ], - "depth": [-1000, 0], - "lat": [0, 1], - "lon": [0, 1], - }, + time_range=[ + np.datetime64(BASE_TIME + datetime.timedelta(hours=0)), + np.datetime64(BASE_TIME + datetime.timedelta(hours=1)), + ], ) - fieldset.add_field(Field("bathymetry", [-1000], lon=0, lat=0)) sensors = [ SensorConfig(sensor_type=SensorType.TEMPERATURE), @@ -179,30 +232,31 @@ def test_simulate_ctds(tmpdir) -> None: from_data = None ctd_instrument = CTDInstrument(expedition, from_data) - out_path = tmpdir.join("out.zarr") + out_path = tmpdir.join("out.parquet") ctd_instrument.load_input_data = lambda: fieldset ctd_instrument.simulate(ctds, out_path) # test if output is as expected - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) - assert len(results.trajectory) == len(ctds) + assert np.unique(results["particle_id"].to_numpy()).size == len(ctds) - for ctd_i, (traj, exp_bothloc) in enumerate( - zip(results.trajectory, ctd_exp, strict=True) - ): - obs_surface = results.sel(trajectory=traj, obs=0) - min_index = np.argmin(results.sel(trajectory=traj)["z"].data) - obs_maxdepth = results.sel(trajectory=traj, obs=min_index) + for ctd_i, id in enumerate(np.unique(results["particle_id"].to_numpy())): + ctd_df = results.filter(pl.col("particle_id") == id) + ctd_surface = ctd_df.filter(pl.col("z") == ctd_df["z"].max())[ + 0 + ] # one row (there are two given ctd ascends back to surface) + ctd_maxdepth = ctd_df.filter(pl.col("z") == ctd_df["z"].min()) for obs, loc in [ - (obs_surface, "surface"), - (obs_maxdepth, "maxdepth"), + (ctd_surface, "surface"), + (ctd_maxdepth, "maxdepth"), ]: - exp = exp_bothloc[loc] - for var in ["salinity", "temperature", "o2", "chl", "no3", "lat", "lon"]: - obs_value = obs[var].values.item() + exp = ctd_exp[ctd_i][loc] + + for var in ["salinity", "temperature", "o2", "chl", "no3", "y", "x"]: + obs_value = obs[var].item() exp_value = exp[var] assert np.isclose(obs_value, exp_value), ( @@ -256,7 +310,7 @@ def test_ctd_sensor_config_yaml() -> None: def test_ctd_disabled_sensor_absent(tmpdir) -> None: - """Variables for disabled sensors must not appear in the zarr output.""" + """Variables for disabled sensors must not appear in the output.""" base_time = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") ctds = [ @@ -271,31 +325,28 @@ def test_ctd_disabled_sensor_absent(tmpdir) -> None: ] # Only temperature field, no salinty - t = np.full((2, 2, 2), 5.0) - fieldset = FieldSet.from_data( + t = np.full((2, 2, 2, 2), 5.0) + fieldset = create_fieldset( {"T": t}, - { - "lon": np.array([0.0, 1.0]), - "lat": np.array([0.0, 1.0]), - "time": [ - np.datetime64(base_time + datetime.timedelta(seconds=0)), - np.datetime64(base_time + datetime.timedelta(hours=4)), - ], - }, + time_range=[ + np.datetime64(base_time + datetime.timedelta(seconds=0)), + np.datetime64(base_time + datetime.timedelta(hours=4)), + ], + lat_range=np.array([0.0, 1.0]), + lon_range=np.array([0.0, 1.0]), ) - fieldset.add_field(Field("bathymetry", [-1000], lon=0, lat=0)) - sensors = ( - [SensorConfig(sensor_type=SensorType.TEMPERATURE)], - ) # SALINITY omitted = disabled + sensors = [ + SensorConfig(sensor_type=SensorType.TEMPERATURE) + ] # SALINITY omitted = disabled expedition = create_dummy_expedition(sensors) ctd_instrument = CTDInstrument(expedition, None) - out_path = tmpdir.join("out_disabled.zarr") + out_path = tmpdir.join("out_disabled.parquet") ctd_instrument.load_input_data = lambda: fieldset ctd_instrument.simulate(ctds, out_path) - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) assert "temperature" in results, "Enabled sensor variable must be present" assert "salinity" not in results, ( "Disabled sensor variable must be absent from output" @@ -355,7 +406,7 @@ def test_ctd_config_unsupported_sensor_rejected(): def test_sensor_absent(tmpdir) -> None: - """A (BGC) sensor that is disabled must not appear in the zarr output.""" + """A (BGC) sensor that is disabled must not appear in the output.""" base_time = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") ctds = [ @@ -369,32 +420,29 @@ def test_sensor_absent(tmpdir) -> None: ), ] - o2_data = np.full((2, 2, 2), 5.0) - fieldset = FieldSet.from_data( + o2_data = np.full((2, 2, 2, 2), 5.0) + fieldset = create_fieldset( {"o2": o2_data}, - { - "lon": np.array([0.0, 1.0]), - "lat": np.array([0.0, 1.0]), - "time": [ - np.datetime64(base_time + datetime.timedelta(seconds=0)), - np.datetime64(base_time + datetime.timedelta(hours=4)), - ], - }, + time_range=[ + np.datetime64(base_time + datetime.timedelta(seconds=0)), + np.datetime64(base_time + datetime.timedelta(hours=4)), + ], + lat_range=np.array([0.0, 1.0]), + lon_range=np.array([0.0, 1.0]), ) - fieldset.add_field(Field("bathymetry", [-1000], lon=0, lat=0)) sensors = [ - SensorConfig(sensor_type=SensorType.OXYGEN), + SensorConfig(sensor_type=SensorType.OXYGEN) # CHLOROPHYLL omitted = disabled ] expedition = create_dummy_expedition(sensors) ctd_instrument = CTDInstrument(expedition, None) - out_path = tmpdir.join("out_bgc_disabled.zarr") + out_path = tmpdir.join("out_bgc_disabled.parquet") ctd_instrument.load_input_data = lambda: fieldset ctd_instrument.simulate(ctds, out_path) - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) assert "o2" in results, "Enabled BGC sensor variable must be present" assert "chl" not in results, "Disabled sensor variable must be absent from output" From 52ff1fdaf1f8cf761306fef6dc14530504d7e0c9 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 5 Aug 2026 12:20:43 +0200 Subject: [PATCH 71/94] update drifter tests for v4 workflows --- tests/instruments/test_drifter.py | 141 +++++++++++++++++------------- 1 file changed, 80 insertions(+), 61 deletions(-) diff --git a/tests/instruments/test_drifter.py b/tests/instruments/test_drifter.py index 0b115374..21ac2afa 100644 --- a/tests/instruments/test_drifter.py +++ b/tests/instruments/test_drifter.py @@ -1,13 +1,13 @@ """Test the simulation of drifters.""" import datetime -from typing import ClassVar import numpy as np +import parcels +import polars as pl import pydantic import pytest import xarray as xr -from parcels import FieldSet from virtualship.instruments.drifter import Drifter, DrifterInstrument from virtualship.instruments.sensors import SensorType @@ -22,36 +22,75 @@ BASE_TIME = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") LIFETIME = datetime.timedelta(days=1) +DEPLOY_DEPTH = -1.0 -DEPLOY_DEPTH = -1.0 # default - -def create_dummy_expedition(): - # arbitrary time offset for the dummy fieldset +def create_dummy_expedition( + sensors=None, + lifetime=LIFETIME, + depth=DEPLOY_DEPTH, + location=(1, 2), +): + if sensors is None: + sensors = [SensorConfig(sensor_type=SensorType.TEMPERATURE)] class DummyExpedition: class schedule: - waypoints: ClassVar = [ - Waypoint( - location=Location( - 1, 2 - ), # any location is fine for dummy, actual drifter deployment locations are defined in the test functions - time=BASE_TIME, - ), + waypoints: list[Waypoint] = [ # noqa: RUF012 + Waypoint(location=Location(*location), time=BASE_TIME) ] instruments_config = InstrumentsConfig( drifter_config=DrifterConfig( - lifetime=LIFETIME, - depth_meter=DEPLOY_DEPTH, + lifetime=lifetime, + depth_meter=depth, stationkeeping_time_minutes=10, - sensors=[SensorConfig(sensor_type=SensorType.TEMPERATURE)], + sensors=sensors, ) ) return DummyExpedition() +def create_fieldset( + data_dict, + lon_range=(0.0, 10.0), + lat_range=(0.0, 10.0), + depth_range=None, + time_range=None, +): + if time_range is None: + time_range = [ + np.datetime64(BASE_TIME), + np.datetime64(BASE_TIME + datetime.timedelta(days=3)), + ] + + data_vars = {} + is_3d = depth_range is not None + + for key, val in data_dict.items(): + if is_3d: + data_vars[key] = (("time", "depth", "lat", "lon"), val) + else: + data_vars[key] = (("time", "lat", "lon"), val) + + coords = { + "lon": (("lon"), np.array(lon_range), {"units": "degrees_east"}), + "lat": (("lat"), np.array(lat_range), {"units": "degrees_north"}), + "time": (("time"), time_range, {"axis": "T"}), + } + if is_3d: + coords["depth"] = (("depth"), np.array(depth_range)) + + ds_fields = xr.Dataset(data_vars=data_vars, coords=coords) + + fields = {var: ds_fields[var] for var in data_vars.keys()} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset) + + return fieldset + + def test_simulate_drifters(tmpdir) -> None: CONST_TEMPERATURE = 1.0 # constant temperature in fieldset @@ -59,19 +98,8 @@ def test_simulate_drifters(tmpdir) -> None: u = np.full((2, 2, 2), 1.0) t = np.full((2, 2, 2), CONST_TEMPERATURE) - fieldset = FieldSet.from_data( - {"V": v, "U": u, "T": t}, - { - "lon": np.array([0.0, 10.0]), - "lat": np.array([0.0, 10.0]), - "time": [ - np.datetime64(BASE_TIME + datetime.timedelta(seconds=0)), - np.datetime64(BASE_TIME + datetime.timedelta(days=3)), - ], - }, - ) + fieldset = create_fieldset({"V": v, "U": u, "T": t}) - # drifters to deploy drifters = [ Drifter( spacetime=Spacetime( @@ -95,28 +123,29 @@ def test_simulate_drifters(tmpdir) -> None: from_data = None drifter_instrument = DrifterInstrument(expedition, from_data) - out_path = tmpdir.join("out.zarr") + out_path = tmpdir.join("out.parquet") drifter_instrument.load_input_data = lambda: fieldset drifter_instrument.simulate(drifters, out_path) - # test if output is as expected - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) - assert len(results.trajectory) == len(drifters) + assert np.unique(results["particle_id"].to_numpy()).size == len(drifters) - for drifter_i, traj in enumerate(results.trajectory): - # Check if drifters are moving - # lat, lon, should be increasing values (with the above positive VU fieldset) - dlat = np.diff(results.sel(trajectory=traj)["lat"].values) + for drifter_i, traj_id in enumerate(np.unique(results["particle_id"].to_numpy())): + traj_df = results.filter(pl.col("particle_id") == traj_id) + + dlat = np.diff(traj_df["y"].to_numpy()) assert np.all(dlat[np.isfinite(dlat)] > 0), ( f"Drifter is not moving over y {drifter_i=}" ) - dlon = np.diff(results.sel(trajectory=traj)["lon"].values) + + dlon = np.diff(traj_df["x"].to_numpy()) assert np.all(dlon[np.isfinite(dlon)] > 0), ( f"Drifter is not moving over x {drifter_i=}" ) - temp = results.sel(trajectory=traj)["temperature"].values + + temp = traj_df["temperature"].to_numpy() assert np.all(temp[np.isfinite(temp)] == CONST_TEMPERATURE), ( f"measured temperature does not match {drifter_i=}" ) @@ -130,25 +159,15 @@ def test_drifter_depths(tmpdir) -> None: u = np.full((2, 2, 2, 2), 1.0) t = np.full((2, 2, 2, 2), CONST_TEMPERATURE) - # different values at depth (random) v[:, -1, :, :] = 1.0 * DEPTH_FACTOR u[:, -1, :, :] = 1.0 * DEPTH_FACTOR t[:, -1, :, :] = CONST_TEMPERATURE * DEPTH_FACTOR - fieldset = FieldSet.from_data( + fieldset = create_fieldset( {"V": v, "U": u, "T": t}, - { - "time": [ - np.datetime64(BASE_TIME + datetime.timedelta(seconds=0)), - np.datetime64(BASE_TIME + datetime.timedelta(days=3)), - ], - "depth": np.array([-10, 0]), - "lat": np.array([0.0, 10.0]), - "lon": np.array([0.0, 10.0]), - }, + depth_range=(-10, 0), ) - # drifters to deploy (same time and location, but different depths) drifters = [ Drifter( spacetime=Spacetime( @@ -163,7 +182,7 @@ def test_drifter_depths(tmpdir) -> None: location=Location(latitude=5.0, longitude=5.0), time=BASE_TIME + datetime.timedelta(days=0), ), - depth=DEPLOY_DEPTH - 5.0, # different drogue depth + depth=DEPLOY_DEPTH - 5.0, lifetime=datetime.timedelta(hours=12), ), ] @@ -172,25 +191,25 @@ def test_drifter_depths(tmpdir) -> None: from_data = None drifter_instrument = DrifterInstrument(expedition, from_data) - out_path = tmpdir.join("out.zarr") + out_path = tmpdir.join("out.parquet") drifter_instrument.load_input_data = lambda: fieldset drifter_instrument.simulate(drifters, out_path) - # test if output is as expected - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) - assert len(results.trajectory) == len(drifters) + pids = np.unique(results["particle_id"].to_numpy()) + assert pids.size == len(drifters) - drifter_surface = results.isel(trajectory=0) - drifter_depth = results.isel(trajectory=1) + drifter_surface = results.filter(pl.col("particle_id") == pids[0]) + drifter_depth = results.filter(pl.col("particle_id") == pids[1]) - assert drifter_surface.z[0] > drifter_depth.z[0], ( + assert drifter_surface["z"][0] > drifter_depth["z"][0], ( "Surface drifter should be at shallower depth than deeper drifter" ) - surface_depths = drifter_surface.z.values - depth_depths = drifter_depth.z.values + surface_depths = drifter_surface["z"].to_numpy() + depth_depths = drifter_depth["z"].to_numpy() assert np.all(surface_depths[~np.isnan(surface_depths)] == surface_depths[0]), ( "Surface drifter depth should be constant" ) @@ -198,7 +217,7 @@ def test_drifter_depths(tmpdir) -> None: "Depth drifter depth should be constant" ) - assert drifter_surface.temperature[0] != drifter_depth.temperature[0], ( + assert drifter_surface["temperature"][0] != drifter_depth["temperature"][0], ( "Surface and deeper drifter should have different temperature measurements" ) From 90bef409180e1dd449f326d9ab9328ac6a751928 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 5 Aug 2026 14:25:26 +0200 Subject: [PATCH 72/94] fix bugs in xbt behavious, also update particleset inspection for v4 --- src/virtualship/instruments/xbt.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/src/virtualship/instruments/xbt.py b/src/virtualship/instruments/xbt.py index c59e6996..c07c385c 100644 --- a/src/virtualship/instruments/xbt.py +++ b/src/virtualship/instruments/xbt.py @@ -75,7 +75,7 @@ def _xbt_cast(particles, fieldset): particles.dz = np.where( particles.z + particles.dz < particles.max_depth, particles.max_depth - particles.z, - particles.z, + particles.dz, ) @@ -162,6 +162,9 @@ def simulate(self, measurements, out_path) -> None: max_depth=max_depths, min_depth=[xbt.min_depth for xbt in measurements], fall_speed=[xbt.fall_speed for xbt in measurements], + deceleration_coefficient=[ + xbt.deceleration_coefficient for xbt in measurements + ], ) # add initial conditions to sampling variables @@ -185,7 +188,7 @@ def simulate(self, measurements, out_path) -> None: ) # there should be no particles left, as they delete themselves when they finish profiling - if len(xbt_particleset.particledata) != 0: + if len(xbt_particleset._data["x"]) != 0: raise ValueError( "Simulation ended before XBT finished profiling. This most likely means the field time dimension did not match the simulation time span." ) From 12d87f6c4c5d2da1eacae7356bca860bafbe9e15 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 5 Aug 2026 14:32:57 +0200 Subject: [PATCH 73/94] update xbt tests for v4 workflows --- tests/instruments/test_xbt.py | 124 +++++++++++++++++++++++----------- 1 file changed, 84 insertions(+), 40 deletions(-) diff --git a/tests/instruments/test_xbt.py b/tests/instruments/test_xbt.py index ac4af1a7..e2d8525f 100644 --- a/tests/instruments/test_xbt.py +++ b/tests/instruments/test_xbt.py @@ -8,10 +8,11 @@ from typing import ClassVar import numpy as np +import parcels +import polars as pl import pydantic import pytest import xarray as xr -from parcels import Field, FieldSet from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType @@ -60,6 +61,63 @@ class schedule: return DummyExpedition() +def create_fieldset( + data_dict, + lon_range=(0.0, 1.0), + lat_range=(0.0, 1.0), + depth_range=(-1000, 0), + time_range=None, + bathymetry_val=-1000.0, +): + if time_range is None: + time_range = [ + np.datetime64(BASE_TIME), + np.datetime64(BASE_TIME + datetime.timedelta(hours=3)), + ] + data_vars = {} + for key, val in data_dict.items(): + data_vars[key] = (("time", "depth", "lat", "lon"), val) + + ds_fields = xr.Dataset( + data_vars=data_vars, + coords={ + "lon": (("lon"), np.array(lon_range), {"units": "degrees_east"}), + "lat": (("lat"), np.array(lat_range), {"units": "degrees_north"}), + "depth": (("depth"), np.array(depth_range)), + "time": ( + ("time"), + time_range, + {"axis": "T"}, + ), + }, + ) + + fields = {var: ds_fields[var] for var in data_vars.keys()} + ds_fset = parcels.convert.copernicusmarine_to_sgrid(fields=fields) + fieldset = parcels.FieldSet.from_sgrid_conventions(ds_fset) + + ds_bathymetry = xr.Dataset( + data_vars={ + "bathymetry": ( + ("lat", "lon"), + np.full((len(lat_range), len(lon_range)), bathymetry_val), + ) + }, + coords={ + "lon": (("lon"), np.array(lon_range), {"units": "degrees_east"}), + "lat": (("lat"), np.array(lat_range), {"units": "degrees_north"}), + }, + ) + ds_bathymetry_fset = parcels.convert.copernicusmarine_to_sgrid( + fields={"bathymetry": ds_bathymetry["bathymetry"]} + ) + bathymetry_fset = parcels.FieldSet.from_sgrid_conventions(ds_bathymetry_fset) + + fieldset.add_field(bathymetry_fset.bathymetry) + + return fieldset + + def test_simulate_xbts(tmpdir, xbt_expedition) -> None: # arbitrary time offset for the dummy fieldset base_time = datetime.datetime.strptime("1950-01-01", "%Y-%m-%d") @@ -73,18 +131,18 @@ def test_simulate_xbts(tmpdir, xbt_expedition) -> None: ), min_depth=0, max_depth=float("-inf"), - fall_speed=6.553, - deceleration_coefficient=0.00242, + fall_speed=FALL_SPEED, + deceleration_coefficient=DECELERATION_COEFFICIENT, ), XBT( spacetime=Spacetime( location=Location(latitude=1, longitude=0), - time=base_time, + time=base_time + datetime.timedelta(hours=1), ), min_depth=0, max_depth=float("-inf"), - fall_speed=6.553, - deceleration_coefficient=0.00242, + fall_speed=FALL_SPEED, + deceleration_coefficient=DECELERATION_COEFFICIENT, ), ] @@ -93,25 +151,25 @@ def test_simulate_xbts(tmpdir, xbt_expedition) -> None: { "surface": { "temperature": 6, - "lat": xbts[0].spacetime.location.lat, - "lon": xbts[0].spacetime.location.lon, + "y": xbts[0].spacetime.location.lat, + "x": xbts[0].spacetime.location.lon, }, "maxdepth": { "temperature": 8, - "lat": xbts[0].spacetime.location.lat, - "lon": xbts[0].spacetime.location.lon, + "y": xbts[0].spacetime.location.lat, + "x": xbts[0].spacetime.location.lon, }, }, { "surface": { "temperature": 6, - "lat": xbts[1].spacetime.location.lat, - "lon": xbts[1].spacetime.location.lon, + "y": xbts[1].spacetime.location.lat, + "x": xbts[1].spacetime.location.lon, }, "maxdepth": { "temperature": 8, - "lat": xbts[1].spacetime.location.lat, - "lon": xbts[1].spacetime.location.lon, + "y": xbts[1].spacetime.location.lat, + "x": xbts[1].spacetime.location.lon, }, }, ] @@ -127,49 +185,35 @@ def test_simulate_xbts(tmpdir, xbt_expedition) -> None: t[:, 1, 1, 0] = xbt_exp[1]["surface"]["temperature"] t[:, 0, 1, 0] = xbt_exp[1]["maxdepth"]["temperature"] - fieldset = FieldSet.from_data( - {"V": v, "U": u, "T": t}, - { - "time": [ - np.datetime64(base_time + datetime.timedelta(hours=0)), - np.datetime64(base_time + datetime.timedelta(hours=1)), - ], - "depth": [-1000, 0], - "lat": [0, 1], - "lon": [0, 1], - }, - ) - fieldset.add_field(Field("bathymetry", [-1000], lon=0, lat=0)) + fieldset = create_fieldset({"V": v, "U": u, "T": t}) from_data = None xbt_instrument = XBTInstrument(xbt_expedition, from_data) - out_path = tmpdir.join("out.zarr") + out_path = tmpdir.join("out.parquet") xbt_instrument.load_input_data = lambda: fieldset xbt_instrument.simulate(xbts, out_path) # test if output is as expected - results = xr.open_zarr(out_path) + results = parcels.read_particlefile(out_path) - assert len(results.trajectory) == len(xbts) + assert np.unique(results["particle_id"].to_numpy()).size == len(xbts) - for xbt_i, (traj, exp_bothloc) in enumerate( - zip(results.trajectory, xbt_exp, strict=True) - ): - obs_surface = results.sel(trajectory=traj, obs=0) - min_index = np.argmin(results.sel(trajectory=traj)["z"].data) - obs_maxdepth = results.sel(trajectory=traj, obs=min_index) + for xbt_i, id in enumerate(np.unique(results["particle_id"].to_numpy())): + xbt_df = results.filter(pl.col("particle_id") == id) + obs_surface = xbt_df.filter(pl.col("z") == xbt_df["z"].max())[0] + obs_maxdepth = xbt_df.filter(pl.col("z") == xbt_df["z"].min())[0] for obs, loc in [ (obs_surface, "surface"), (obs_maxdepth, "maxdepth"), ]: - exp = exp_bothloc[loc] - for var in ["temperature", "lat", "lon"]: - obs_value = obs[var].values.item() + exp = xbt_exp[xbt_i][loc] + for var in ["temperature", "y", "x"]: + obs_value = obs[var].item() exp_value = exp[var] - assert np.isclose(obs_value, exp_value), ( + assert np.isclose(obs_value, exp_value, rtol=0.1), ( f"Observation incorrect {xbt_i=} {loc=} {var=} {obs_value=} {exp_value=}." ) From 4e075a2ccd3f47ce703274c95ad6f172f8de710b Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 5 Aug 2026 15:08:23 +0200 Subject: [PATCH 74/94] attempt to fix pixi env and CI --- .github/workflows/ci.yml | 2 +- pixi.toml | 38 +++++++++++++++++++------------------- pyproject.toml | 22 ++++++++++------------ 3 files changed, 30 insertions(+), 32 deletions(-) diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 9b93379b..85a1f227 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -55,7 +55,7 @@ jobs: pixi-environment: ["test-latest"] runs-on: [ubuntu-latest, windows-latest, macos-14] include: - - pixi-environment: "test-py310" + - pixi-environment: "test-py311" runs-on: ubuntu-latest steps: - uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 diff --git a/pixi.toml b/pixi.toml index 3d18b8b4..a373ec3f 100644 --- a/pixi.toml +++ b/pixi.toml @@ -15,11 +15,11 @@ backend = { name = "pixi-build-python", version = "0.4.*" } [package.host-dependencies] python = "3.12.*" -setuptools = "*" -setuptools_scm = "*" +setuptools = ">=61.0" +setuptools_scm = ">=8.0" [package.run-dependencies] # Keep in sync with `pyproject.toml` and feedstock recipe -python = "3.12.*" +python = ">=3.11,<3.13" click = "*" pyproj = ">=3,<4" sortedcontainers = "==2.4.0" @@ -33,12 +33,12 @@ textual = "*" [dependencies] virtualship = { path = "." } -python = "3.12.*" # Pre-install as conda packages to avoid PyPI source builds netcdf4 = "*" numpy = ">=2.1.0" dask = "*" -zarr = ">=3" +pandas = "*" +pyarrow = "*" ipdb = ">=0.13.13,<0.14" cmocean = ">=4.0.3,<5" numba = ">=0.59.0" @@ -46,15 +46,13 @@ numba = ">=0.59.0" [pypi-dependencies] parcels = { git = "https://github.com/Parcels-code/Parcels", branch = "main" } -# Commented out whilst parcels v4 alpha only supports Python 3.11 (?) -# [feature.py310.dependencies] -# python = "3.10.*" +[feature.py311.dependencies] +python = "3.11.*" +zarr = ">=3.0.0" -# [feature.py311.dependencies] -# python = "3.11.*" - -# [feature.py312.dependencies] -# python = "3.12.*" +[feature.py312.dependencies] +python = "3.12.*" +zarr = ">=3.0.0" [feature.test.dependencies] pytest = "*" @@ -120,10 +118,12 @@ types-xlrd = "*" typing = "mypy src/virtualship --install-types" [environments] -default = { features = ["test", "notebooks", "typing", "pre-commit", "analysis"] } -test-latest = { features = ["test"], solve-group = "test" } -test-notebooks = { features = ["test", "notebooks"], solve-group = "test" } -analysis = { features = ["analysis"], solve-group = "analysis" } -docs = { features = ["docs"], solve-group = "docs" } -typing = { features = ["typing"], solve-group = "typing" } +default = { features = ["py312", "test", "notebooks", "typing", "pre-commit", "analysis"] } +test-latest = { features = ["py312", "test"], solve-group = "test-py312" } +test-py311 = { features = ["py311", "test"], solve-group = "test-py311" } +test-py312 = { features = ["py312", "test"], solve-group = "test-py312" } +test-notebooks = { features = ["py312", "test", "notebooks"], solve-group = "test-py312" } +analysis = { features = ["py312", "analysis"], solve-group = "analysis" } +docs = { features = ["py312", "docs"], solve-group = "docs" } +typing = { features = ["py312", "typing"], solve-group = "typing" } pre-commit = { features = ["pre-commit"], no-default-feature = true } diff --git a/pyproject.toml b/pyproject.toml index d83dc25c..1fbeff00 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,11 +8,11 @@ description = "Code for the Virtual Ship Classroom, where Marine Scientists can readme = "README.md" dynamic = ["version"] authors = [{ name = "parcels-code.org team" }] -requires-python = ">=3.10" -license = { file = "LICENSE" } +requires-python = ">=3.10, <3.13" +license = "MIT" +license-files = ["LICENSE"] classifiers = [ "Development Status :: 3 - Alpha", - "License :: OSI Approved :: MIT License", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3 :: Only", @@ -41,11 +41,10 @@ dependencies = [ [project.urls] Homepage = "https://virtualship.parcels-code.org/" -Repository = "https://github.com/OceanParcels/virtualship" +Repository = "https://github.com/Parcels-code/virtualship" Documentation = "https://virtualship.readthedocs.io/" -"Bug Tracker" = "https://github.com/OceanParcels/virtualship/issues" -Changelog = "https://github.com/OceanParcels/virtualship/releases" - +"Bug Tracker" = "https://github.com/Parcels-code/virtualship/issues" +Changelog = "https://github.com/Parcels-code/virtualship/releases" [tool.setuptools.packages.find] where = ["src"] @@ -60,7 +59,6 @@ local_scheme = "no-local-version" [project.scripts] virtualship = "virtualship.cli.main:cli" - [tool.pytest.ini_options] minversion = "6.0" addopts = ["-ra", "--showlocals", "--strict-markers", "--strict-config"] @@ -78,10 +76,11 @@ testpaths = [ "tests", ] +[tool.coverage.run] +source = ["virtualship"] -[tool.coverage] -run.source = ["virtualship"] -report.exclude_also = [ +[tool.coverage.report] +exclude_also = [ '\.\.\.', 'if typing.TYPE_CHECKING:', ] @@ -120,7 +119,6 @@ ignore = [ "D100", "D103" ] - [tool.mypy] files = ['src'] disable_error_code = "import-untyped" From dc783c03ac8b1d399684b4321ee14240d3989127 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 6 Aug 2026 09:16:25 +0200 Subject: [PATCH 75/94] update pixi and CI to python 3.14 --- pixi.toml | 27 +++++++++++++-------------- pyproject.toml | 31 +++++++++++++------------------ 2 files changed, 26 insertions(+), 32 deletions(-) diff --git a/pixi.toml b/pixi.toml index a373ec3f..a429025d 100644 --- a/pixi.toml +++ b/pixi.toml @@ -14,12 +14,11 @@ version = "dynamic" # dynamic versioning needs better support in pixi https://gi backend = { name = "pixi-build-python", version = "0.4.*" } [package.host-dependencies] -python = "3.12.*" -setuptools = ">=61.0" -setuptools_scm = ">=8.0" +setuptools = "*" +setuptools_scm = "*" -[package.run-dependencies] # Keep in sync with `pyproject.toml` and feedstock recipe -python = ">=3.11,<3.13" +[package.run-dependencies] # Keep in sync with `pyproject.toml` dependencies +python = ">=3.11" click = "*" pyproj = ">=3,<4" sortedcontainers = "==2.4.0" @@ -50,8 +49,8 @@ parcels = { git = "https://github.com/Parcels-code/Parcels", branch = "main" } python = "3.11.*" zarr = ">=3.0.0" -[feature.py312.dependencies] -python = "3.12.*" +[feature.py314.dependencies] +python = "3.14.*" zarr = ">=3.0.0" [feature.test.dependencies] @@ -118,12 +117,12 @@ types-xlrd = "*" typing = "mypy src/virtualship --install-types" [environments] -default = { features = ["py312", "test", "notebooks", "typing", "pre-commit", "analysis"] } -test-latest = { features = ["py312", "test"], solve-group = "test-py312" } +default = { features = ["py314", "test", "notebooks", "typing", "pre-commit", "analysis"], solve-group = "main" } +test-latest = { features = ["py314", "test"], solve-group = "test-py314" } test-py311 = { features = ["py311", "test"], solve-group = "test-py311" } -test-py312 = { features = ["py312", "test"], solve-group = "test-py312" } -test-notebooks = { features = ["py312", "test", "notebooks"], solve-group = "test-py312" } -analysis = { features = ["py312", "analysis"], solve-group = "analysis" } -docs = { features = ["py312", "docs"], solve-group = "docs" } -typing = { features = ["py312", "typing"], solve-group = "typing" } +test-py314 = { features = ["py314", "test"], solve-group = "test-py314" } +test-notebooks = { features = ["py314", "test", "notebooks"], solve-group = "test-py314" } +analysis = { features = ["py314", "analysis"], solve-group = "analysis" } +docs = { features = ["py314", "docs"], solve-group = "docs" } +typing = { features = ["py314", "typing"], solve-group = "main" } pre-commit = { features = ["pre-commit"], no-default-feature = true } diff --git a/pyproject.toml b/pyproject.toml index 1fbeff00..a5f1f8e6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,24 +1,21 @@ [build-system] -requires = ["setuptools >= 61.0", "setuptools_scm[toml]>=6.2"] +requires = ["setuptools>=61.0", "setuptools_scm>=8.0"] build-backend = "setuptools.build_meta" [project] name = "virtualship" -description = "Code for the Virtual Ship Classroom, where Marine Scientists can combine Copernicus Marine Data with an OceanParcels ship to go on a virtual expedition." +description = "Code for VirtualShip, where Marine Scientists can combine Copernicus Marine Data with a Parcels ship to go on a virtual expedition." readme = "README.md" dynamic = ["version"] authors = [{ name = "parcels-code.org team" }] -requires-python = ">=3.10, <3.13" -license = "MIT" -license-files = ["LICENSE"] +requires-python = ">=3.11" +license = { file = "LICENSE" } classifiers = [ - "Development Status :: 3 - Alpha", - "Programming Language :: Python", "Programming Language :: Python :: 3", - "Programming Language :: Python :: 3 :: Only", - "Programming Language :: Python :: 3.10", "Programming Language :: Python :: 3.11", "Programming Language :: Python :: 3.12", + "Programming Language :: Python :: 3.13", + "Programming Language :: Python :: 3.14", "Operating System :: OS Independent", "Topic :: Scientific/Engineering", "Topic :: Education", @@ -26,17 +23,15 @@ classifiers = [ ] dependencies = [ "click", - "parcels >=4.0.0alpha", - "pyproj >= 3, < 4", - "sortedcontainers == 2.4.0", - "opensimplex == 0.4.5", - "numpy >=2.1.0", - "pydantic >=2, <3", - "PyYAML", - "copernicusmarine >= 2.2.2", + "pyproj>=3,<4", + "sortedcontainers==2.4.0", + "opensimplex==0.4.5", + "numpy>=2.1.0", + "pydantic>=2,<3", + "pyyaml", + "copernicusmarine>=2.2.2", "yaspin", "textual", - "openpyxl", ] [project.urls] From 1d3955a919da97a652e0f615308c49dbcbe5d438 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 6 Aug 2026 09:39:12 +0200 Subject: [PATCH 76/94] update contribution guide for pixi instructions --- docs/contributing/index.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/contributing/index.md b/docs/contributing/index.md index 171d714e..47db0282 100644 --- a/docs/contributing/index.md +++ b/docs/contributing/index.md @@ -51,9 +51,8 @@ You can use the following Pixi commands to run common development tasks. VirtualShip supports testing against different environments (e.g., different Python versions) with different feature sets. In CI we test against these environments, and you can too locally. For example: -- `pixi run -e test-py310 tests` - Run tests using Python 3.10 -- `pixi run -e test-py311 tests` - Run tests using Python 3.11 -- `pixi run -e test-py312 tests` - Run tests using Python 3.12 +- `pixi run -e test-py311 tests` - Run tests using Python 3.11 (lower bound) +- `pixi run -e test-py314 tests` - Run tests using Python 3.14 The name of the workflow on GitHub contains the command you have to run locally to recreate the workflow - making it super easy to reproduce CI failures locally. From 616f5d824c6463e4379411e392ebf395d6ddb585 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 6 Aug 2026 10:36:44 +0200 Subject: [PATCH 77/94] fix pre-commit issue --- src/virtualship/instruments/sensors.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/virtualship/instruments/sensors.py b/src/virtualship/instruments/sensors.py index 2db148d8..1205880f 100644 --- a/src/virtualship/instruments/sensors.py +++ b/src/virtualship/instruments/sensors.py @@ -1,7 +1,7 @@ from __future__ import annotations from dataclasses import dataclass -from enum import Enum +from enum import StrEnum from functools import lru_cache from typing import Literal @@ -9,7 +9,7 @@ from parcels import Variable -class SensorType(str, Enum): +class SensorType(StrEnum): """Sensors available. Different intstruments mix and match these sensors as needed.""" TEMPERATURE = "TEMPERATURE" From 9a53435e2c7e7a928f1cdf13946a1adcf62b3f35 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 6 Aug 2026 10:37:58 +0200 Subject: [PATCH 78/94] sort out encoding to avoid UserWarnings --- src/virtualship/instruments/base.py | 7 +++++-- src/virtualship/utils.py | 14 ++++++++++++++ 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/src/virtualship/instruments/base.py b/src/virtualship/instruments/base.py index 0d5edc82..e06ae344 100644 --- a/src/virtualship/instruments/base.py +++ b/src/virtualship/instruments/base.py @@ -28,6 +28,7 @@ _get_bathy_data, _get_waypoint_latlons, _select_product_id, + get_clean_encoding, ship_spinner, ) @@ -318,11 +319,13 @@ def _get_local_ds(self, files: list[Path]) -> xr.Dataset: @staticmethod def _via_tmp_ds(ds: xr.Dataset) -> xr.Dataset: """Create and re-load a temporary local dataset.""" + encoding = get_clean_encoding(ds) + with tempfile.TemporaryDirectory() as tmpdir: tmp_fpath = Path(tmpdir) / "tmp.nc" - ds.to_netcdf(tmp_fpath) + ds.to_netcdf(tmp_fpath, encoding=encoding) - # Open and load into memory so the file handle closes before tmpdir exits + # context manage to ensure file closure with xr.open_dataset(tmp_fpath) as loaded_ds: return loaded_ds.load() diff --git a/src/virtualship/utils.py b/src/virtualship/utils.py index 8ccf69a5..30f3dffc 100644 --- a/src/virtualship/utils.py +++ b/src/virtualship/utils.py @@ -673,6 +673,20 @@ def build_particle_class_from_sensors( return Particle.add_variable(nonsensor_variables + sensor_variables) +def get_clean_encoding(ds): + """ + Clean existing encodings and supply explicit native endianness to prevent netCDF4 UserWarnings. + + Helps avoid annoying user warnings when writing tmp files to disk. + """ + encoding = {} + for var_name, var in ds.variables.items(): + var.encoding.pop("endian", None) + encoding[var_name] = {"endian": "native"} + + return encoding + + # ===================================================== # SECTION: misc. # ===================================================== From f37ce1d1974095854c786a7b58b316348e7114c8 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 6 Aug 2026 11:44:20 +0200 Subject: [PATCH 79/94] use fieldset.time_interval.left notation --- src/virtualship/instruments/argo_float.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 029a0aec..eafeb612 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -223,7 +223,7 @@ def _format_log_metadata(ptcls_subset, mask, fieldset): lats = ptcls_subset.y[mask].astype(float) lons = ptcls_subset.x[mask].astype(float) - time_origin = fieldset.U.data.time[0].values + time_origin = fieldset.time_interval.left times = ptcls_subset.t[mask].astype("timedelta64[s]") + time_origin return times, lats, lons From b3fe2de8b1bdc38edeb382fbb7234beaf663525f Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Thu, 6 Aug 2026 11:44:30 +0200 Subject: [PATCH 80/94] add _handle_grounding test --- tests/instruments/test_argo_float.py | 60 +++++++++++++++++++++++++++- 1 file changed, 59 insertions(+), 1 deletion(-) diff --git a/tests/instruments/test_argo_float.py b/tests/instruments/test_argo_float.py index c9e881d4..b7da7a60 100644 --- a/tests/instruments/test_argo_float.py +++ b/tests/instruments/test_argo_float.py @@ -10,7 +10,11 @@ import pytest import xarray as xr -from virtualship.instruments.argo_float import ArgoFloat, ArgoFloatInstrument +from virtualship.instruments.argo_float import ( + ArgoFloat, + ArgoFloatInstrument, + _handle_grounding, +) from virtualship.instruments.sensors import SensorType from virtualship.instruments.types import InstrumentType from virtualship.models import Location, Spacetime @@ -296,3 +300,57 @@ def test_argo_float_instrument_type(): argo_instrument = ArgoFloatInstrument(expedition, from_data=None) assert argo_instrument.instrument_type == InstrumentType.ARGO_FLOAT assert not argo_instrument.instrument_type.is_underway + + +def test_handle_grounding(): + """Test that _handle_grounding sets grounded status, logs warnings, adjusts dz, and updates cycle phase.""" + + class DummyParticles: + def __init__(self): + self.grounded = np.array([0, 0]) + self.z = np.array([-1200.0, -1500.0]) + self.dz = np.array([0.0, 0.0]) + self.cycle_phase = np.array([1, 1]) + self.t = np.array([0.0, 0.0]) + self.y = np.array([10.0, 11.0]) + self.x = np.array([50.0, 51.0]) + + ptcls = DummyParticles() + + # index 0 is grounded (-1000m bathymetry vs -1200m particle depth) + # index 1 is safe (-2000m bathymetry vs -1500m particle depth) + loc_bathy = np.array([-1000.0, -2000.0]) + bathysafe_mask = np.array([False, True]) + + target_phase = 2 + fieldset = create_fieldset() + + # capture print outputs + log_stream = io.StringIO() + with contextlib.redirect_stdout(log_stream): + _handle_grounding( + ptcls_subset=ptcls, + bathysafe_mask=bathysafe_mask, + loc_bathy=loc_bathy, + fieldset=fieldset, + phase_name="descent", + target_phase=target_phase, + ) + + output = log_stream.getvalue() + + # grounding mask applied correctly + np.testing.assert_array_equal(ptcls.grounded, np.array([1, 0])) + + # dz updated (target depth = bathy + 50m = -1000 + 50 = -950m; dz = -950 - (-1200) = 250m) + expected_dz = np.array([250.0, 0.0]) + np.testing.assert_allclose(ptcls.dz, expected_dz) + + # cycle phase updated + np.testing.assert_array_equal(ptcls.cycle_phase, np.array([target_phase, 1])) + + # warning message printed + assert ( + "Shallow bathymetry warning: Argo float grounded at bathymetry during descent" + in output + ) From 84db2eb3ea23e840fc472a3106eede5899b8ec56 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 7 Aug 2026 11:48:31 +0200 Subject: [PATCH 81/94] remove docstring for consistency --- src/virtualship/cli/_run.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/virtualship/cli/_run.py b/src/virtualship/cli/_run.py index a320acad..6969d32f 100644 --- a/src/virtualship/cli/_run.py +++ b/src/virtualship/cli/_run.py @@ -1,5 +1,3 @@ -"""do_expedition function.""" - import logging import os import shutil From fb789fc507f88d38e455c6dc8625232a34f753bd Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 7 Aug 2026 11:53:38 +0200 Subject: [PATCH 82/94] filter out parcels v4 user warning for (temporary measure) --- src/virtualship/cli/main.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/src/virtualship/cli/main.py b/src/virtualship/cli/main.py index a02a5ffb..a055429f 100644 --- a/src/virtualship/cli/main.py +++ b/src/virtualship/cli/main.py @@ -1,6 +1,15 @@ -import click +import warnings -from . import commands +# TODO: remove this when Parcels v4 is no longer alpha and the warning is no longer issued +warnings.filterwarnings( + "ignore", + message="This is an alpha version of Parcels v4.*", + category=UserWarning, +) + +import click # noqa: E402 + +from . import commands # noqa: E402 @click.group() From 495c1437d28a42c7c6585e8744b1a0612d304db0 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 10 Aug 2026 13:13:18 +0200 Subject: [PATCH 83/94] remove initial sampling from argo floats --- src/virtualship/instruments/argo_float.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index eafeb612..4ae81966 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -324,10 +324,8 @@ def simulate(self, measurements, out_path) -> None: drift_days=[argo.drift_days for argo in measurements], ) - # add initial conditions to sampling variables - self._sample_initial( - argo_float_particleset, fieldset, argo_float_config.sensors - ) + # N.B. whilst some instruments need sample initial conditions (`_sample_initial`), Argo floats should not; + # as this would result in sampling at the initial release, which is not authentic. The sampling should occur during the ascent phase of the cycles. # define output file for the simulation out_file = ParticleFile( From 26d6681b761722d436ab82e0babc97f6d6445a46 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 10 Aug 2026 14:44:19 +0200 Subject: [PATCH 84/94] argo kernel: solve issue of not updating cycle_age during ascent --- src/virtualship/instruments/argo_float.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/virtualship/instruments/argo_float.py b/src/virtualship/instruments/argo_float.py index 4ae81966..66e7ca3d 100644 --- a/src/virtualship/instruments/argo_float.py +++ b/src/virtualship/instruments/argo_float.py @@ -113,6 +113,7 @@ def _argo_float_vertical_movement(particles, fieldset): # Phase 3: Rising with vertical_speed until at surface ptcls3.dz -= particles.vertical_speed * ptcls3.dt + ptcls3.cycle_age += ptcls3.dt # solve issue of not updating cycle_age during ascent next_phase = ptcls3.z + ptcls3.dz >= particles.min_depth ptcls3.cycle_phase[next_phase] = 4 ptcls3.dz[next_phase] = particles.min_depth - ptcls3.z[next_phase] # noqa:avoid overshoot From e40defd994a5cb4fde17762ec8ca8b1a970473c7 Mon Sep 17 00:00:00 2001 From: Jamie Atkins <106238905+j-atkins@users.noreply.github.com> Date: Wed, 12 Aug 2026 13:50:16 +0200 Subject: [PATCH 85/94] Update docs for v4 migration (#371) * docs: copy updates from old/broken branch * Update depth dimension attribute explanation --- .../assignments/Sail_the_ship.ipynb | 10 +- .../documentation/pre_download_data.md | 1 + docs/user-guide/index.md | 1 - docs/user-guide/quickstart.md | 6 +- docs/user-guide/teacher-content/index.md | 17 +- .../user-guide/tutorials/ADCP_transects.ipynb | 257 +++++++++++++----- .../tutorials/Argo_data_tutorial.ipynb | 112 ++++---- docs/user-guide/tutorials/CTD_transects.ipynb | 190 ++++++------- .../tutorials/Drifter_data_tutorial.ipynb | 87 +++--- .../Ship_underwater_ST_plotting.ipynb | 53 ++-- docs/user-guide/tutorials/index.md | 3 - docs/user-guide/tutorials/xbt_plotting.ipynb | 31 ++- 12 files changed, 456 insertions(+), 312 deletions(-) diff --git a/docs/user-guide/assignments/Sail_the_ship.ipynb b/docs/user-guide/assignments/Sail_the_ship.ipynb index b4b94f3d..db2562b7 100644 --- a/docs/user-guide/assignments/Sail_the_ship.ipynb +++ b/docs/user-guide/assignments/Sail_the_ship.ipynb @@ -251,10 +251,6 @@ "\n", "Small simulations (e.g. small space-time domains and fewer instrument deployments) will be relatively fast. For large, complex expeditions, it _could_ take up to an hour to simulate the measurements depending on your choices. Waiting for simulation is a great time to practice your level of patience. A skill much needed in oceanographic fieldwork ;-)\n", "\n", - "
\n", - "**Tip**: Not using underway instruments will speed up the simulation time considerably. So, if you do not plan to use underway temperature/salinity or ADCP measurements, make sure to switch these off in the planning tool before running the expedition.\n", - "
\n", - "\n", "
\n", "**Important**: VirtualShip may encounter 'real-life challenges' during the expedition, which simulate the various problems and unexpected events that can occur during real-life oceanographic expeditions (e.g. instrument and/or equipment failure, logistical challenges etc.). These may require your intervention to ensure your expedition schedule can continue!\n", "
" @@ -266,11 +262,11 @@ "source": [ "## 7) Results\n", "\n", - "Upon successfully completing the simulation, results from the expedition will be stored in the `EXPEDITION_NAME/results` directory, written as [Zarr](https://zarr.dev/) files.\n", + "Upon successfully completing the simulation, results from the expedition will be stored in the `EXPEDITION_NAME/results` directory, written in `.parquet` [format](https://parquet.apache.org/).\n", "\n", - "From here you can carry on your analysis. In general, we encourage you to explore and analyse these data using [Xarray](https://docs.xarray.dev/en/stable/). We also provide various further [VirtualShip tutorials](https://virtualship.readthedocs.io/en/latest/user-guide/tutorials/index.html) which provide examples of how to visualise data recorded by the VirtualShip instruments. Use these to help you get started!\n", + "From here you can carry on your analysis. In general, we encourage you to use [Parcels](https://Parcels-code.org/) (i.e. `parcels.read_particlefile()`) to read in VirtualShip output files, and tools such as [Polars](https://www.pola.rs/) and/or [Pandas](https://pandas.pydata.org/) for further data analysis. We also provide various further [VirtualShip tutorials](https://virtualship.readthedocs.io/en/latest/user-guide/tutorials/index.html) which provide examples of how to visualise data recorded by the VirtualShip instruments. Use these to help you get started!\n", "\n", - "If you are using VirtualShip in class, the same tutorial notebooks will be uploaded in your SURF RC environment for you to use and interact directly with the code. These should be available in e.g. the `data/storage/tutorials/` directory. You will notice that there is a notebook file dedicated to visualising each of the different instruments available in VirtualShip. \n", + "If you are using VirtualShip in class, the same tutorial notebooks may be uploaded in your SURF RC environment for you to use and interact directly with the code. These should be available in e.g. the `data/storage/tutorials/` directory. You will notice that there is a notebook file dedicated to visualising each of the different instruments available in VirtualShip. \n", "\n", "To run these notebooks with your own data, you will need to copy the them over to your expedition working directory (i.e. `data/storage/{your-group-name}`). This can be done by either 1) using the file explorer panel in JupyterLab to copy the relevant files or the via the command line in Terminal. In the terminal, running `cp -r /data/storage/tutorials/* /data/storage/{your-group-name}/` would copy __all__ the tutorial notebooks to your group's directory, so if you only want to copy specific ones, make sure to adjust the command accordingly." ] diff --git a/docs/user-guide/documentation/pre_download_data.md b/docs/user-guide/documentation/pre_download_data.md index d58100cc..5e43fc1b 100644 --- a/docs/user-guide/documentation/pre_download_data.md +++ b/docs/user-guide/documentation/pre_download_data.md @@ -77,6 +77,7 @@ The following assumptions are also made about the data: 3. If using BGC-enabled instruments (e.g. BGC variables on the `CTD`), the relevant biogeochemical data files must contain the following variables: `o2`, `chl`, `no3`, `po4`, `nppv`, `ph`, `phyc`. - Or these strings must appear as substrings within the variable names (e.g. `o2_glor` is acceptable for `o2`). 4. Bathymetry data files must contain a variable named `deptho`. +5. Pre-downloaded data files must have a `"positive"` attribute for the depth dimension (e.g. `"positive": "down"` or `"positive": "up"`) in order to ensure that the depth dimension is correctly interpreted under-the-hood. #### Also of note diff --git a/docs/user-guide/index.md b/docs/user-guide/index.md index d7b25e4f..24eee0c8 100644 --- a/docs/user-guide/index.md +++ b/docs/user-guide/index.md @@ -18,5 +18,4 @@ documentation/full_sensor_list.md documentation/copernicus_products.md documentation/pre_download_data.md documentation/example_copernicus_download.ipynb -documentation/full_sensor_list.md ``` diff --git a/docs/user-guide/quickstart.md b/docs/user-guide/quickstart.md index 7d984111..5f913a79 100644 --- a/docs/user-guide/quickstart.md +++ b/docs/user-guide/quickstart.md @@ -178,6 +178,8 @@ See the relevant [documentation](https://virtualship.readthedocs.io/en/latest/us ## 5) Results -Upon successfully completing the simulation, results from the expedition will be stored in the `EXPEDITION_NAME/results` directory, written as [Zarr](https://zarr.dev/) files. +Upon successfully completing the simulation, results from the expedition will be stored in the `EXPEDITION_NAME/results` directory, written in `.parquet` [format](https://parquet.apache.org/). -From here you can carry on your analysis (offline). We encourage you to explore and analyse these data using [Xarray](https://docs.xarray.dev/en/stable/). We also provide various further [VirtualShip tutorials](https://virtualship.readthedocs.io/en/latest/user-guide/tutorials/index.html) which provide examples of how to visualise data recorded by the VirtualShip instruments. +From here you can carry on your analysis (offline). We encourage you to use [Parcels](https://Parcels-code.org/) (i.e. `parcels.read_particlefile()`) to read in VirtualShip output files, and tools such as [Polars](https://www.pola.rs/) and/or [Pandas](https://pandas.pydata.org/) for further data analysis. + +We also provide various [VirtualShip tutorials](https://virtualship.readthedocs.io/en/latest/user-guide/tutorials/index.html) with further examples of how to visualise data recorded by the VirtualShip instruments. diff --git a/docs/user-guide/teacher-content/index.md b/docs/user-guide/teacher-content/index.md index d6a39eb0..89389d8e 100644 --- a/docs/user-guide/teacher-content/index.md +++ b/docs/user-guide/teacher-content/index.md @@ -1,5 +1,7 @@ # Teacher content +### Overview + VirtualShip is used as part of the VirtualShip Classroom, that combines authentic tools with VR to create a virtual fieldwork experience and allows you to teach about sea-based research from your regular classroom. All VirtualShip Classroom (VSC) material is open under an MIT licence and freely available! You can use the VSC to teach anything from a 4 hour masterclass up to an open assignment of more than 40 hours. Example assignments are available below and please feel free to customize anything offline or [contribute](../../contributing/index.md) to the assignments provided here. @@ -16,17 +18,26 @@ The VSC design focuses on creating didactically sound, authentic learning experi We evaluated in several (under)graduate courses and find that the VirtualShip Classroom is highly engaging, and students report on enhanced confidence and knowledge [(Daniels et al. 2025)](https://current-journal.com/articles/10.5334/cjme.121). +### Teaching materials + ```{toctree} :maxdepth: 1 -:caption: Teaching materials ILOs.ipynb letter.md ``` +### Previous implementations + +```{warning} +The following implementations are examples of how the VSC has been used in the past. They are provided here for reference only and may not be up to date with the latest version of the VSC and/or VirtualShip software. +``` + +#### Utrecht University + ```{nbgallery} --- -caption: UU Ocean of the Future +caption: Ocean of the Future (BSc), 2025-26 --- UU-ocean-of-future/Tutorial1.ipynb UU-ocean-of-future/CoordinatesExport-Transect.xlsx @@ -37,7 +48,7 @@ UU-ocean-of-future/plot_slider.py ```{nbgallery} --- -caption: UU Dynamical Oceanography +caption: Dynamical Oceanography (MSc), 2025-26 --- UU-dyoc/example_expedition.md UU-dyoc/file_permissions.md diff --git a/docs/user-guide/tutorials/ADCP_transects.ipynb b/docs/user-guide/tutorials/ADCP_transects.ipynb index b35a916b..d07ecddf 100644 --- a/docs/user-guide/tutorials/ADCP_transects.ipynb +++ b/docs/user-guide/tutorials/ADCP_transects.ipynb @@ -5,15 +5,13 @@ "id": "bad21046", "metadata": {}, "source": [ - "# ADCP Transect Plotting\n", + "# ADCP Plotting\n", "\n", - "This notebook demonstrates a simple plotting exercise for ADCP data across a transect, using the output of a VirtualShip expedition. There are example plots embedded at the end, but these will ultimately be replaced by your own versions as you work through the notebook.\n", + "This notebook demonstrates a simple plotting exercise for ADCP data across a ship track, using the output of a VirtualShip expedition. There are example plots embedded at the end, but these will ultimately be replaced by your own versions as you work through the notebook.\n", "\n", - "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the start, and are intended to be a starting point for your analysis. Because the `ADCP` instrument is an underway/onboard instrument, this means we benefit from continuous recordings across the length of the ship's track (unlike overboard instruments such as CTDs which have to deployed at individual sampling sites).\n", + "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance travelled by the ship, and are intended to be a starting point for your analysis. \n", "\n", - "
\n", - "Note: This notebook assumes that each point along the expedition track is further from the start than the previous point. The code will still work if not, but the resultant plots might not be very intuitive.\n", - "
" + "Because the `ADCP` instrument is an underway/onboard instrument, this means we benefit from continuous recordings across the length of the ship's track (unlike overboard instruments such as CTDs which have to deployed at individual sampling sites)." ] }, { @@ -34,7 +32,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "f6c87472", "metadata": {}, "outputs": [], @@ -43,8 +41,10 @@ "import matplotlib.colors as mcolors\n", "import matplotlib.patches as mpatches\n", "import numpy as np\n", + "from matplotlib import pyplot as plt\n", "import xarray as xr\n", - "from matplotlib import pyplot as plt" + "import polars as pl\n", + "import parcels" ] }, { @@ -65,12 +65,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "0cb630f6", "metadata": {}, "outputs": [], "source": [ - "data_dir = \"/path/to/EXPEDITION/results/\" # set this to be where your expedition output data is located on your (virtual) machine" + "data_dir = \"/path/to/EXPEDITION/results\" # set this to be where your expedition output data is located on your (virtual) machine" ] }, { @@ -80,20 +80,71 @@ "source": [ "## Load data\n", "\n", - "We are now ready to read in the data. You can carry on executing the next cells without making changes to the code..." + "We are now ready to read in the ADCP data. You can carry on executing the next cells without making changes to the code..." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "654fb036", "metadata": {}, "outputs": [], "source": [ "# load ADCP data\n", - "adcp_ds = xr.open_dataset(f\"{data_dir}/adcp.zarr\")\n", - "if adcp_ds[\"obs\"].size <= 1:\n", - " raise ValueError(\"Number of waypoints must be > 1\")" + "adcp_df = parcels.read_particlefile(f\"{data_dir}/adcp.parquet\")\n", + "if len(adcp_df) <= 1:\n", + " raise ValueError(\"Number of observations must be > 1\")" + ] + }, + { + "cell_type": "markdown", + "id": "97cbee6a", + "metadata": {}, + "source": [ + "\n", + "And here is what the first few lines of the data look like, with recordings of time (t), latitude (y), longitude (x), depth (z), plus the U and V velocity components:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a3cf571", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Below are various helper functions which perform tasks such as calculating the ship's distance from the start of the transect at each point and calculating the various velocity components from the ADCP data." + "Before we can continue, we need to do some post-processing to get it ready for plotting as a 2D distance × depth plot. Below are various helper functions which perform tasks such as calculating the ship's cumulative travel distance and calculating the various velocity components from the ADCP data." ] }, { @@ -125,31 +176,104 @@ " return 6371000 * c\n", "\n", "\n", - "def distance_from_start(ds):\n", - " \"\"\"Array of meters from first waypoint.\"\"\"\n", - " lon0, lat0 = ds.isel(obs=0)[\"lon\"].values, ds.isel(obs=0)[\"lat\"].values\n", - " d = np.zeros_like(ds[\"lon\"].values, dtype=float)\n", - " for ob, (lon, lat) in enumerate(zip(ds[\"lon\"], ds[\"lat\"], strict=False)):\n", - " d[ob] = haversine(lon, lat, lon0, lat0)\n", + "def distance_along_expedition(df):\n", + " \"\"\"Array of cumulative meters travelled along ADCP measurement locations.\"\"\"\n", + " d = np.zeros_like(df[\"x\"], dtype=float)\n", + " for ob in range(1, len(df[\"x\"])):\n", + " d[ob] = d[ob - 1] + haversine(\n", + " df[\"x\"][ob - 1], df[\"y\"][ob - 1], df[\"x\"][ob], df[\"y\"][ob]\n", + " )\n", " return d\n", "\n", "\n", - "def calc_velocities(ds):\n", - " \"\"\"Calculate absolute, parallel and perpendicular (to the ship trajectory) velocities, as well as (compass) direction of flow.\"\"\"\n", - " Uabs = np.sqrt(ds[\"U\"] ** 2 + ds[\"V\"] ** 2)\n", - " ds_surface = ds.isel(trajectory=0)\n", - " dlon = np.deg2rad(ds_surface[\"lon\"].differentiate(\"obs\"))\n", - " dlat = np.deg2rad(ds_surface[\"lat\"].differentiate(\"obs\"))\n", - " lat = np.deg2rad(ds_surface[\"lat\"])\n", - " alpha = np.arctan(dlat / (dlon * np.cos(lat))).mean(\"obs\") # cruise direction angle\n", - " Uparallel = np.cos(alpha) * ds[\"U\"] + np.sin(alpha) * ds[\"V\"]\n", - " Uperp = -np.sin(alpha) * ds[\"U\"] + np.cos(alpha) * ds[\"V\"]\n", - " direction_rad = np.arctan2(\n", - " ds[\"U\"], ds[\"V\"]\n", - " ) # direction of flow [degrees from north]\n", - " direction_deg = (np.degrees(direction_rad) + 360) % 360\n", - "\n", - " return Uabs, Uparallel, Uperp, direction_deg" + "def calc_velocities(df):\n", + " \"\"\"From U and V in df, calculate absolute, parallel, and perpendicular velocities, as well as compass flow direction, using a per-waypoint ship angle alpha.\"\"\"\n", + " traj_df = (\n", + " df.select([\"t\", \"x\", \"y\"])\n", + " .unique(subset=[\"t\"])\n", + " .sort(\"t\")\n", + " .with_columns(\n", + " # next waypoint (x1, y1)\n", + " pl.col(\"x\").radians().alias(\"lon_rad\"),\n", + " pl.col(\"y\").radians().alias(\"lat_rad\"),\n", + " pl.col(\"x\").shift(-1).radians().alias(\"lon1_rad\"),\n", + " pl.col(\"y\").shift(-1).radians().alias(\"lat1_rad\"),\n", + " )\n", + " .with_columns(\n", + " # forward differences to next waypoint\n", + " (pl.col(\"lon1_rad\") - pl.col(\"lon_rad\")).alias(\"dlon\"),\n", + " (pl.col(\"lat1_rad\") - pl.col(\"lat_rad\")).alias(\"dlat\"),\n", + " )\n", + " .with_columns(\n", + " # alpha between (x, y) and (x1, y1) using arctan2\n", + " pl.arctan2(pl.col(\"dlat\"), pl.col(\"dlon\") * pl.col(\"lat_rad\").cos()).alias(\n", + " \"alpha\"\n", + " )\n", + " )\n", + " # fill final location alpha (where shift(-1) is Null) with backward fill\n", + " .with_columns(pl.col(\"alpha\").backward_fill())\n", + " .select([\"t\", \"alpha\"])\n", + " )\n", + "\n", + " # join per-timestep alpha back to main DataFrame and calculate velocity components\n", + " return df.join(traj_df, on=\"t\", how=\"left\").with_columns(\n", + " # Uabs = sqrt(U^2 + V^2)\n", + " (pl.col(\"U\").pow(2) + pl.col(\"V\").pow(2)).sqrt().alias(\"Uabs\"),\n", + " # Uparallel = cos(alpha)*U + sin(alpha)*V\n", + " (\n", + " pl.col(\"alpha\").cos() * pl.col(\"U\") + pl.col(\"alpha\").sin() * pl.col(\"V\")\n", + " ).alias(\"Uparallel\"),\n", + " # Uperp = -sin(alpha)*U + cos(alpha)*V\n", + " (\n", + " -pl.col(\"alpha\").sin() * pl.col(\"U\") + pl.col(\"alpha\").cos() * pl.col(\"V\")\n", + " ).alias(\"Uperp\"),\n", + " # compass direction in degrees [0, 360)\n", + " ((pl.arctan2(pl.col(\"U\"), pl.col(\"V\")).degrees() + 360) % 360).alias(\n", + " \"direction_deg\"\n", + " ),\n", + " )\n", + "\n", + "\n", + "def get_velocity_2d_array(df, var_name, units):\n", + " \"\"\"Extract a single velocity variable as a 2D xarray DataArray (dims: t, z).\"\"\"\n", + " # deduplicate (t, z) pairs in case of duplicate sampling\n", + " df_clean = df.unique(subset=[\"t\", \"z\"])\n", + "\n", + " # to 2D grid matrix (rows = t, columns = z)\n", + " pivot_df = df_clean.pivot(on=\"z\", index=\"t\", values=var_name).sort(\"t\")\n", + "\n", + " # map distance along track to each timestamp (t)\n", + " dist_map = df_clean.select([\"t\", \"distance\"]).unique(subset=[\"t\"]).sort(\"t\")\n", + " pivot_df = pivot_df.join(dist_map, on=\"t\", how=\"left\")\n", + "\n", + " # extract coordinates and matrix\n", + " z_coords = sorted(\n", + " [float(c) for c in pivot_df.columns if c not in (\"t\", \"distance\")]\n", + " )\n", + " dist_coords = pivot_df[\"distance\"].to_numpy()\n", + " t_coords = pivot_df[\"t\"].to_numpy()\n", + "\n", + " data_matrix = pivot_df.select([pl.col(str(z)) for z in z_coords]).to_numpy()\n", + "\n", + " # create/return da\n", + " return xr.DataArray(\n", + " data=data_matrix,\n", + " dims=[\"distance\", \"z\"],\n", + " coords={\n", + " \"distance\": (\n", + " \"distance\",\n", + " dist_coords,\n", + " {\"long_name\": \"Distance along track\"},\n", + " ),\n", + " \"z\": (\"z\", z_coords, {\"units\": \"m\", \"long_name\": \"Depth\"}),\n", + " \"t\": (\n", + " \"distance\",\n", + " t_coords,\n", + " ), # Preserves time reference along distance dimension\n", + " },\n", + " name=var_name,\n", + " attrs={\"units\": units, \"long_name\": f\"{var_name} velocity component\"},\n", + " )" ] }, { @@ -163,19 +287,28 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 30, "id": "6433742a", "metadata": {}, "outputs": [], "source": [ - "# distance from start as 1d array\n", - "distance_1d = distance_from_start(adcp_ds.isel(trajectory=0))\n", - "\n", - "# calculate velocity components and direction\n", - "Uabs, Uparallel, Uperp, direction = calc_velocities(adcp_ds)\n", - "\n", - "# land / sea bed mask\n", - "landmask = xr.where(((adcp_ds[\"U\"] == 0) & (adcp_ds[\"V\"] == 0)), 1, np.nan)" + "# distance from start as 1d array, and add as new column to adcp_df\n", + "distance_1d = distance_along_expedition(adcp_df)\n", + "adcp_df = adcp_df.with_columns(pl.Series(\"distance\", distance_1d))\n", + "\n", + "# calculate and add velocity components and direction to adcp_df\n", + "adcp_df = calc_velocities(adcp_df)\n", + "\n", + "# convert velocity components to 2D xarray DataArrays (dims: distance, z)\n", + "Uabs = get_velocity_2d_array(adcp_df, \"Uabs\", units=\"m/s\")\n", + "Uparallel = get_velocity_2d_array(adcp_df, \"Uparallel\", units=\"m/s\")\n", + "Uperp = get_velocity_2d_array(adcp_df, \"Uperp\", units=\"m/s\")\n", + "direction = get_velocity_2d_array(adcp_df, \"direction_deg\", units=\"degrees\")\n", + "\n", + "# coord arrays and landmask/seabed mask\n", + "distance = Uabs[\"distance\"].values\n", + "depth = Uabs[\"z\"].values\n", + "landmask = np.where(Uabs == 0, 1, np.nan)" ] }, { @@ -186,21 +319,21 @@ "## Plotting\n", "\n", "
\n", - "Note: The plots produced next are a starting point for your analysis. You are encouraged to make adjustments, for example axis limits and scaling if the defaults not best suited to your specific data. Use your preferred AI coding assistant for help!\n", + "Note: The plots produced next are a starting point for your analysis. You are encouraged to make adjustments, for example axis limits and scaling if the defaults not best suited to your specific data.\n", "
\n", "\n", - "We are now ready to plot our transect data. We will use distance from the start of the transect/expedition for the x-axis, and water column depth for the y-axis. The ADCP data will then be plotted according to the colour map for diagnostic. The profiles across the transect are likely to be different depths because some parts of the ocean are of course shallower than others." + "We are now ready to plot our data. We will use distance travelled by the ship for the x-axis, and water column depth for the y-axis. The ADCP data will then be plotted according to the colour map for each diagnostic. The profiles across the trajectory are likely to be different depths because some parts of the ocean are of course shallower than others." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 31, "id": "93693258", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -245,18 +378,18 @@ "for idx, ((key, var), ax) in enumerate(zip(PLOT_DICT.items(), axs, strict=False)):\n", " # adcp data\n", " mesh = ax.pcolormesh(\n", - " distance_1d / 1000,\n", - " adcp_ds[\"z\"],\n", - " var[\"data\"],\n", + " distance / 1000, # distance in km\n", + " depth,\n", + " var[\"data\"].T.values, # transpose to match (distance, z) dims\n", " cmap=var[\"cmap\"],\n", " norm=var[\"norm\"] if var[\"norm\"] is not None else None,\n", " )\n", "\n", " # seabed\n", " ax.pcolormesh(\n", - " distance_1d / 1000, # distance in km\n", - " adcp_ds[\"z\"],\n", - " landmask,\n", + " distance / 1000, # distance in km\n", + " depth,\n", + " landmask.T,\n", " cmap=mcolors.ListedColormap([mcolors.to_rgba(\"tan\"), mcolors.to_rgba(\"white\")]),\n", " )\n", "\n", @@ -281,7 +414,7 @@ " # axis labels\n", " ax.set_ylabel(\"Depth (m)\")\n", " if idx == len(axs) - 1: # bottom panel only for single column of subplots\n", - " ax.set_xlabel(\"Distance from start (km)\")\n", + " ax.set_xlabel(\"Distance (km)\")\n", "\n", "# legend for sea bed\n", "tan_patch = mpatches.Patch(color=mcolors.to_rgba(\"tan\"), label=\"Land / sea bed\")\n", @@ -299,8 +432,8 @@ "The resultant figure shows various components of the velocity field, derived from ADCP data.\n", "\n", "1) Absolute velocity\n", - "2) Along-track velocity (where positive values indicate flow in the overall direction of the ship's track across the the transect)\n", - "3) Cross-track velocity (where postive values indicate flow to the left of the ship's direction).\n", + "2) Along-track velocity (where positive values indicate flow in the ship's travel direction between waypoints, i.e. the angle changes as the ship turns).\n", + "3) Cross-track velocity (where positive values indicate flow to the left of the ship's direction).\n", "4) The direction of the flow, expressed as degrees from North.\n", "\n", "You can use these plots as a starting point to consider how flow dynamics vary over the cross-section. You may find some diagnostics more useful than others, depending on your specific aims!" @@ -309,7 +442,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ship", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -323,7 +456,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.14.6" } }, "nbformat": 4, diff --git a/docs/user-guide/tutorials/Argo_data_tutorial.ipynb b/docs/user-guide/tutorials/Argo_data_tutorial.ipynb index d1e4470d..de30d535 100644 --- a/docs/user-guide/tutorials/Argo_data_tutorial.ipynb +++ b/docs/user-guide/tutorials/Argo_data_tutorial.ipynb @@ -30,21 +30,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", + "import parcels\n", + "import polars as pl\n", "import matplotlib.pyplot as plt\n", - "import cmocean.cm as cmo\n", "import numpy as np\n", "import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", - "import matplotlib.colors as mcolors\n", - "from matplotlib.collections import LineCollection\n", - "from mpl_toolkits.mplot3d import Axes3D\n", "import plotly.graph_objects as go\n", - "\n", "import plotly.io as pio\n", "from IPython.display import HTML" ] @@ -79,18 +75,18 @@ "source": [ "#### Load data\n", "\n", - "We are now ready to read in the data using the `xarray` package. You can carry on executing the next cells without making changes to the code…" + "We are now ready to read in the data using `Parcels`, specifically the `parcels.read_particlefile()` function. You can carry on executing the next cells without making changes to the code…" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 78, "metadata": {}, "outputs": [], "source": [ "# load argo data\n", "\n", - "argo_ds = xr.open_dataset(f\"{data_dir}/argo_float.zarr\")" + "argo_df = parcels.read_particlefile(f\"{data_dir}/argo_float.parquet\")" ] }, { @@ -108,12 +104,12 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 79, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -128,11 +124,12 @@ "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", "\n", "# plot trajectory\n", - "for i, traj in enumerate(argo_ds[\"trajectory\"]):\n", - " # extract trajectory data\n", - " lons = argo_ds[\"lon\"][:].sel(trajectory=traj).squeeze()\n", - " lats = argo_ds[\"lat\"][:].sel(trajectory=traj).squeeze()\n", - " cycle_phase = argo_ds[\"cycle_phase\"][:].sel(trajectory=traj).squeeze()\n", + "for i, argo_id in enumerate(np.unique(argo_df[\"particle_id\"])):\n", + " # extract argo data\n", + " argo_dat = argo_df.filter(pl.col(\"particle_id\") == argo_id)\n", + " lons = argo_dat[\"x\"]\n", + " lats = argo_dat[\"y\"]\n", + " cycle_phase = argo_dat[\"cycle_phase\"]\n", "\n", " # plot\n", " ax.plot(\n", @@ -157,14 +154,14 @@ " label=\"Waypoint\" if i == 0 else None, # only label first for legend\n", " )\n", "\n", - " # add marker where cycle_phase == 0 and temperature is not nan\n", - " temp = argo_ds[\"temperature\"][:].sel(trajectory=traj).squeeze()\n", - " mask = ~np.isnan(temp)\n", + " # add marker where cycle_phase == 3 and temperature is not nan (i.e. vertical profiling sites)\n", + " temp = argo_dat[\"temperature\"].to_numpy()\n", + " mask = ~np.isnan(temp) & (cycle_phase.to_numpy() == 3)\n", " if np.any(mask):\n", " ax.scatter(\n", " np.array(lons)[mask],\n", " np.array(lats)[mask],\n", - " s=10,\n", + " s=20,\n", " color=\"crimson\",\n", " zorder=5,\n", " transform=ccrs.PlateCarree(),\n", @@ -176,10 +173,10 @@ "latlon_buffer = 10.0 # degrees (adjust this to 'zoom' in/out in the plot)\n", "ax.set_extent(\n", " [\n", - " argo_ds.lon.min() - latlon_buffer,\n", - " argo_ds.lon.max() + latlon_buffer,\n", - " argo_ds.lat.min() - latlon_buffer,\n", - " argo_ds.lat.max() + latlon_buffer,\n", + " argo_df[\"x\"].min() - latlon_buffer,\n", + " argo_df[\"x\"].max() + latlon_buffer,\n", + " argo_df[\"y\"].min() - latlon_buffer,\n", + " argo_df[\"y\"].max() + latlon_buffer,\n", " ],\n", " crs=ccrs.PlateCarree(),\n", ")\n", @@ -198,11 +195,17 @@ "\n", "ax.legend(loc=\"upper right\", fontsize=12)\n", "\n", - "n_days = float(\n", - " (argo_ds[\"time\"][0].max(skipna=True) - argo_ds[\"time\"][0].min(skipna=True))\n", + "argo_lifetimes = [\n", + " (\n", + " argo_df.filter(pl.col(\"particle_id\") == argo_id)[\"t\"].max()\n", + " - argo_df.filter(pl.col(\"particle_id\") == argo_id)[\"t\"].min()\n", + " )\n", " / np.timedelta64(1, \"D\")\n", - ")\n", - "plt.title(f\"[{n_days:.1f} day(s) Argo Float lifetime]\", fontsize=12)\n", + " for argo_id in np.unique(argo_df[\"particle_id\"])\n", + "]\n", + "u, counts = np.unique(argo_lifetimes, return_counts=True)\n", + "n_days = u[np.argmax(counts)] # use the most common drifter lifetime for the title\n", + "plt.title(f\"[{round(n_days, 1)} day Argo Float lifetime]\", fontsize=12)\n", "\n", "plt.tight_layout()" ] @@ -221,12 +224,12 @@ "**TIP**: This 3D plot is interactive! You can click and drag to rotate the view, scroll to zoom in/out, and hover over points to see more information.\n", "
\n", "\n", - "**Important**: This plot supports plotting only one Argo float/waypoint at a time. If you have deployed multiple Argo floats at different waypoints, you will need to make your selection via the `WHICH_ARGO` variable below. (Hint: you can see how many waypoints with Argo floats there are by running the cell immediately after this one first.)" + "**Important**: This plot supports plotting only one Argo float deployment/waypoint at a time. If you have deployed multiple Argo floats at different waypoints, you will need to make your selection via the `WHICH_ARGO` variable below. (Hint: you can see how many waypoints with Argo floats there are by running the cell immediately after this one first.)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 80, "metadata": {}, "outputs": [ { @@ -239,13 +242,13 @@ ], "source": [ "print(\n", - " f\"There is/are {len(argo_ds['trajectory'])} Argo float(s) waypoints in this dataset.\"\n", + " f\"There is/are {len(argo_df['particle_id'].unique())} Argo float(s) waypoints in this dataset.\"\n", ")" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -254,7 +257,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 82, "metadata": {}, "outputs": [ { @@ -263,8 +266,8 @@ "\n", "\n", "\n", - "
\n", - "
\n", + "
\n", + "
\n", "\n", "" ], @@ -279,10 +282,10 @@ "source": [ "fig = go.Figure()\n", "\n", - "traj = argo_ds[\"trajectory\"][WHICH_ARGO]\n", - "lons = argo_ds[\"lon\"][:].sel(trajectory=traj).squeeze()\n", - "lats = argo_ds[\"lat\"][:].sel(trajectory=traj).squeeze()\n", - "depth = argo_ds[\"z\"][:].sel(trajectory=traj).squeeze()\n", + "argo_da = argo_df.filter(pl.col(\"particle_id\") == WHICH_ARGO)\n", + "lons = argo_da[\"x\"]\n", + "lats = argo_da[\"y\"]\n", + "depth = argo_da[\"z\"]\n", "\n", "fig.add_trace(\n", " go.Scatter3d(\n", @@ -301,10 +304,7 @@ " )\n", ")\n", "\n", - "n_days = float(\n", - " (argo_ds[\"time\"][0].max(skipna=True) - argo_ds[\"time\"][0].min(skipna=True))\n", - " / np.timedelta64(1, \"D\")\n", - ")\n", + "n_days = float((argo_da[\"t\"].max() - argo_da[\"t\"].min()) / np.timedelta64(1, \"D\"))\n", "\n", "fig.update_layout(\n", " scene=dict(\n", @@ -330,7 +330,7 @@ "\n", "Let's now have a look at the vertical profiles of temperature and salinity collected by the Argo floats during the expedition. We can produce plots for each variable, showing how they vary with depth. \n", "\n", - "Choose below which variable you would like to plot by setting the `PLOT_VARIABLE` variable to either `\"temperature\"` or `\"salinity\"`.\n", + "Choose below which variable you would like to plot by setting the `PLOT_VARIABLE` variable to either `\"temperature\"` or `\"salinity\"` (depending on which [sensors](../documentation/full_sensor_list.md) you configured for your VirtualShip expedition).\n", "\n", "The vertical profiles are recorded by the Argo float at each of the ascent/descent locations we've seen in the previous 3D plot. This time the drift periods are not shown.\n", "\n", @@ -341,7 +341,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -350,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 84, "metadata": {}, "outputs": [ { @@ -359,8 +359,8 @@ "\n", "\n", "\n", - "
\n", - "
\n", + "
\n", + "
\n", "\n", "" ], @@ -388,11 +388,11 @@ " },\n", "}\n", "\n", - "traj = argo_ds[\"trajectory\"][WHICH_ARGO]\n", - "lons = argo_ds[\"lon\"][:].sel(trajectory=traj).squeeze()\n", - "lats = argo_ds[\"lat\"][:].sel(trajectory=traj).squeeze()\n", - "depth = argo_ds[\"z\"][:].sel(trajectory=traj).squeeze()\n", - "var = argo_ds[VARIABLES[PLOT_VARIABLE][\"ds_name\"]][:].sel(trajectory=traj).squeeze()\n", + "argo_da = argo_df.filter(pl.col(\"particle_id\") == WHICH_ARGO)\n", + "lons = argo_da[\"x\"]\n", + "lats = argo_da[\"y\"]\n", + "depth = argo_da[\"z\"]\n", + "var = argo_da[PLOT_VARIABLE].to_numpy()\n", "\n", "# mask out NaNs (temp/salinity not recorded when drifting)\n", "mask = ~np.isnan(var)\n", @@ -447,7 +447,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ship", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -461,7 +461,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.14.6" } }, "nbformat": 4, diff --git a/docs/user-guide/tutorials/CTD_transects.ipynb b/docs/user-guide/tutorials/CTD_transects.ipynb index bcefcb82..b51e17ee 100644 --- a/docs/user-guide/tutorials/CTD_transects.ipynb +++ b/docs/user-guide/tutorials/CTD_transects.ipynb @@ -9,12 +9,12 @@ "\n", "This notebook demonstrates a simple plotting exercise for CTD data across a transect, using the output of a VirtualShip expedition. There are example plots embedded at the end, but these will ultimately be replaced by your own versions as you work through the notebook.\n", "\n", - "We can plot physical (temperature, salinity) or biogeochemical data (oxygen, chlorophyll, primary production, phytoplankton, nutrients, pH) as measured by the VirtualShip `CTD` instrument.\n", + "We can plot physical (temperature, salinity) or biogeochemical data (oxygen, chlorophyll, primary production, phytoplankton, nutrients, pH) as measured by the VirtualShip `CTD` instrument (dependent on which [sensors](../documentation/full_sensor_list.md) you have configured).\n", "\n", - "The plot(s) we will produce are simple plots which follow the trajectory of the expedition as a function of distance from the first waypoint, and are intended to be a starting point for your analysis. \n", + "The plots we will produce are simple plots which follow the trajectory of the expedition as a function of distance travelled by the ship, and are intended to be a starting point for your analysis. \n", "\n", "
\n", - "Note: This notebook assumes that each waypoint in the expedition is further from the start than the last waypoint. The code will still work if not, but the resultant plots might not be very intuitive.\n", + "**Note**: This notebook assumes that each waypoint in the expedition is further from the start than the last waypoint (i.e. waypoints arranged as a transect). The code will still work if not, but the resultant plots might not be very intuitive.\n", "
" ] }, @@ -30,13 +30,13 @@ "The first step is to import the Python packages required for post-processing the data and plotting. \n", "\n", "
\n", - "Tip: You may need to set the Kernel to the relevant (Conda) environment in the top right of this notebook to access the required packages! \n", + "**Tip**: You may need to set the Kernel to the relevant (Conda) environment in the top right of this notebook to access the required packages! \n", "
" ] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "id": "c7f9f2ee", "metadata": {}, "outputs": [], @@ -45,8 +45,9 @@ "import matplotlib.colors as mcolors\n", "import matplotlib.patches as mpatches\n", "import numpy as np\n", - "import xarray as xr\n", - "from matplotlib import pyplot as plt" + "from matplotlib import pyplot as plt\n", + "import polars as pl\n", + "import parcels" ] }, { @@ -60,7 +61,7 @@ "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n", "\n", "
\n", - "Tip: You can get the path to your expedition results by navigating to the `results` folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", + "**Tip**: You can get the path to your expedition results by navigating to the `results` folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", "
\n", "\n" ] @@ -84,7 +85,6 @@ "\n", "You should now consider which variable from your CTD casts you would like to plot. Which ones are available to you will depend on which sensors you deployed the `CTD` instrument with (via the `virtualship plan` tool and/or your `expedition.yaml` file). Below is the full list of valid variable choices...\n", "\n", - "`CTD`:\n", "- \"temperature\"\n", "- \"salinity\"\n", "- \"oxygen\"\n", @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 44, "id": "8de8b4ae", "metadata": {}, "outputs": [], @@ -117,13 +117,13 @@ "We also define the `VARIABLES` dictionary here, which we use to store some parameters for the plots related to each variable choice (e.g. labels, what units each is in, and which colour map we should use for the plots).\n", "\n", "
\n", - "Tip: You don't need to change anything here, but should you wish to change the colour scheme (`cmap`) for any CTD variable you can do so. At the moment it's set to use relevant cmaps from the cmocean Python package, which has developed specialist colour schemes for oceanographic data applications.\n", + "**Tip**: You don't need to change anything here, but should you wish to change the colour scheme (`cmap`) for any CTD variable you can do so. At the moment it's set to use relevant cmaps from the [cmocean](https://matplotlib.org/cmocean/) Python package, which has developed specialist colour schemes for oceanographic data applications.\n", "
" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "b32d2730", "metadata": {}, "outputs": [], @@ -195,9 +195,8 @@ "outputs": [], "source": [ "# load CTD data\n", - "filename = \"ctd.zarr\"\n", - "ctd_ds = xr.open_dataset(f\"{data_dir}/{filename}\")\n", - "if ctd_ds[\"trajectory\"].size <= 1:\n", + "ctd_df = parcels.read_particlefile(f\"{data_dir}/ctd.parquet\")\n", + "if np.unique(ctd_df[\"particle_id\"]).size <= 1:\n", " raise ValueError(\"Number of waypoints must be > 1\")" ] }, @@ -208,12 +207,12 @@ "source": [ "## Data post-processing\n", "\n", - "Before we can continue, we need to do some post-processing to get it ready for plotting. Below are various helper functions which perform tasks such as calculating the distance of each waypoint from the start, capturing only the downcasts of the CTD casts, as well as some other utility methods. " + "Before we can continue, we need to do some post-processing to get it ready for plotting as a 2D distance × depth plot. Below are various helper functions which perform tasks such as calculating the ship's cumulative travel distance, as well as some other utility methods. " ] }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 51, "id": "785b2b35", "metadata": {}, "outputs": [], @@ -230,74 +229,76 @@ " return 6371000 * c\n", "\n", "\n", - "def distance_from_start(ds):\n", - " \"\"\"Add 'distance' variable: meters from first waypoint.\"\"\"\n", - " lon0, lat0 = (\n", - " ds.isel(trajectory=0)[\"lon\"].values[0],\n", - " ds.isel(trajectory=0)[\"lat\"].values[0],\n", - " )\n", - " d = np.zeros_like(ds[\"lon\"].values, dtype=float)\n", - " for ob, (lon, lat) in enumerate(zip(ds[\"lon\"], ds[\"lat\"], strict=False)):\n", - " d[ob] = haversine(lon, lat, lon0, lat0)\n", - " ds[\"distance\"] = xr.DataArray(\n", - " d,\n", - " dims=ds[\"lon\"].dims,\n", - " attrs={\"long_name\": \"distance from first waypoint\", \"units\": \"m\"},\n", - " )\n", - " return ds\n", - "\n", - "\n", - "def descent_only(ds, variable):\n", - " \"\"\"Extract descending CTD data (downcast), pad with NaNs for alignment.\"\"\"\n", - " min_z_idx = ds[\"z\"].argmin(\"obs\")\n", - " da_clean = []\n", - " for i, traj in enumerate(ds[\"trajectory\"].values):\n", - " idx = min_z_idx.sel(trajectory=traj).item()\n", - " descent_vals = ds[variable][\n", - " i, : idx + 1\n", - " ] # take values from surface to min_z_idx (inclusive)\n", - " da_clean.append(descent_vals)\n", - " max_len = max(len(arr[~np.isnan(arr)]) for arr in da_clean)\n", - " da_padded = np.full((ds[\"trajectory\"].size, max_len), np.nan)\n", - " for i, arr in enumerate(da_clean):\n", - " da_dropna = arr[~np.isnan(arr)]\n", - " da_padded[i, : len(da_dropna)] = da_dropna\n", - " return xr.DataArray(\n", - " da_padded,\n", - " dims=[\"trajectory\", \"obs\"],\n", - " coords={\"trajectory\": ds[\"trajectory\"], \"obs\": np.arange(max_len)},\n", + "def distance_along_expedition(df):\n", + " \"\"\"Array of cumulative meters travelled across CTD waypoints.\"\"\"\n", + " d = np.zeros_like(df[\"x\"], dtype=float)\n", + " for ob in range(1, len(df[\"x\"])):\n", + " d[ob] = d[ob - 1] + haversine(\n", + " df[\"x\"][ob - 1], df[\"y\"][ob - 1], df[\"x\"][ob], df[\"y\"][ob]\n", + " )\n", + " return d\n", + "\n", + "\n", + "def ctd_build_2d_arrays(df, varname):\n", + " \"\"\"Build 2D (n_casts x uniform-depth) arrays from df. Ordered by their cumulative expedition distance.\"\"\"\n", + "\n", + " # by cumulative distance\n", + " cast_order = (\n", + " df.group_by(\"particle_id\").agg(pl.col(\"distance\").first()).sort(\"distance\")\n", " )\n", "\n", + " profiles_z = []\n", + " profiles_var = []\n", + " cast_distances = []\n", + "\n", + " for row in cast_order.iter_rows(named=True):\n", + " pid = row[\"particle_id\"]\n", + " cast = df.filter(pl.col(\"particle_id\") == pid).sort(\n", + " \"z\", descending=True\n", + " ) # sort by depth (descending)\n", + " profiles_z.append(cast[\"z\"].to_numpy())\n", + " profiles_var.append(cast[varname].to_numpy())\n", + " cast_distances.append(row[\"distance\"])\n", + "\n", + " # pad to uniform depth dimension with NaN\n", + " max_len = max(len(p) for p in profiles_z)\n", + " n_casts = len(profiles_z)\n", + "\n", + " z_2d = np.full((n_casts, max_len), np.nan)\n", + " var_2d = np.full((n_casts, max_len), np.nan)\n", "\n", - "def build_masked_array(data_up, profile_indices, n_profiles):\n", - " arr = np.full((n_profiles, data_up.shape[1]), np.nan)\n", + " for i, (pz, pv) in enumerate(zip(profiles_z, profiles_var)):\n", + " z_2d[i, : len(pz)] = pz\n", + " var_2d[i, : len(pv)] = pv\n", + "\n", + " return z_2d, np.array(cast_distances), var_2d\n", + "\n", + "\n", + "def build_masked_array(var_2d, profile_indices, n_profiles):\n", + " arr = np.full((n_profiles, var_2d.shape[1]), np.nan)\n", " for i, idx in enumerate(profile_indices):\n", " if idx is not None:\n", - " arr[i, :] = data_up.values[idx, :]\n", + " arr[i, :] = var_2d[idx, :]\n", " return arr\n", "\n", "\n", - "def get_profile_indices(distance_1d):\n", - " \"\"\"\n", - " Returns regular distance bins and profile indices for CTD transect plotting.\n", - "\n", - " Bin size is set to one order of magnitude lower than max distance.\n", - " \"\"\"\n", - " dist_min, dist_max = float(distance_1d.min()), float(distance_1d.max())\n", + "def get_profile_indices(distances):\n", + " \"\"\"Regular distance bins and profile indices for CTD transect plotting. Bin size is set to one order of magnitude lower than max distance.\"\"\"\n", + " dist_min, dist_max = float(np.min(distances)), float(np.max(distances))\n", " if dist_max > 1e6:\n", - " dist_step = 1e5\n", + " dist_step = 1.0e5\n", " elif dist_max > 1e5:\n", - " dist_step = 1e4\n", + " dist_step = 1.0e4\n", " elif dist_max > 1e4:\n", - " dist_step = 1e3\n", + " dist_step = 1.0e3\n", " else:\n", - " dist_step = 1e2 # fallback for very short transects\n", + " dist_step = 1.0e2 # fallback for very short transects\n", "\n", " distance_regular = np.arange(dist_min, dist_max + dist_step, dist_step)\n", " threshold = dist_step / 2\n", " profile_indices = [\n", - " np.argmin(np.abs(distance_1d.values - d))\n", - " if np.min(np.abs(distance_1d.values - d)) < threshold\n", + " np.argmin(np.abs(distances - d))\n", + " if np.min(np.abs(distances - d)) < threshold\n", " else None\n", " for d in distance_regular\n", " ]\n", @@ -315,25 +316,24 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 52, "id": "f59824a1", "metadata": {}, "outputs": [], "source": [ - "# add distance from start\n", - "ctd_distance = distance_from_start(ctd_ds)\n", + "# add distance along expedition\n", + "ctd_distance = distance_along_expedition(ctd_df)\n", + "ctd_df = ctd_df.with_columns(pl.Series(\"distance\", ctd_distance))\n", "\n", - "# exract descent-only data\n", - "z_up = descent_only(ctd_distance, \"z\")\n", - "d_up = descent_only(ctd_distance, \"distance\")\n", - "var_up = descent_only(ctd_distance, VARIABLES[plot_variable][\"ds_name\"])\n", + "# downcast only data\n", + "ctd_down = ctd_df.filter(pl.col(\"raising\") == 0)\n", "\n", - "# 1d array of depth dimension (from deepest trajectory)\n", - "traj_idx, obs_idx = np.where(z_up == np.nanmin(z_up))\n", - "z1d = z_up.values[traj_idx[0], :]\n", + "# build 2D arrays: (n_casts x uniform-depth)\n", + "z_2d, distances, var_2d = ctd_build_2d_arrays(ctd_down, plot_variable)\n", "\n", - "# distance as 1d array\n", - "distance_1d = d_up.isel(obs=0)" + "# 1D depth array from the deepest cast\n", + "deepest_idx = np.argmax(np.sum(~np.isnan(z_2d), axis=1))\n", + "z1d = z_2d[deepest_idx, :]" ] }, { @@ -344,10 +344,10 @@ "## Plotting\n", "\n", "
\n", - "Note: The plots produced next are a starting point for your analysis. You are encouraged to make adjustments, for example axis limits and scaling if the defaults not best suited to your specific data. Use your preferred AI coding assistant for help!\n", + "**Note**: The plots produced next are a starting point for your analysis. You are encouraged to make adjustments, for example axis limits and scaling if the defaults not best suited to your specific data.\n", "
\n", "\n", - "We are now ready to plot our transect data. We will use distance from the first waypoint/CTD cast for the x-axis, and water column depth for the y-axis. The data for the chosen variable will then be plotted according to the colour map. The CTD casts are likely to be different depths because some parts of the ocean are of course shallower than others.\n", + "We are now ready to plot our transect data. We will use distance travelled by the ship for the x-axis, and water column depth for the y-axis. The data for the chosen variable will then be plotted according to the colour map. The CTD casts are likely to be different depths because some parts of the ocean are of course shallower than others.\n", "\n", "There are a few extra steps below which arrange the CTD casts into regular distance bins, so as to clearly demonstrate where along the transect we made CTD casts and indeed where there are gaps.\n" ] @@ -360,7 +360,7 @@ "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -371,8 +371,8 @@ ], "source": [ "# regularised transect\n", - "profile_indices, distance_regular = get_profile_indices(distance_1d)\n", - "var_masked = build_masked_array(var_up, profile_indices, len(distance_regular))\n", + "profile_indices, distance_regular = get_profile_indices(distances)\n", + "var_masked = build_masked_array(var_2d, profile_indices, len(distance_regular))\n", "\n", "xticks_reg = np.linspace(\n", " float(distance_regular.min()),\n", @@ -409,7 +409,7 @@ "We can also also plot a 'filled' version without the distance bins, to give an alternative view of the evolution across the transect which is not dominated by gaps and white space. This time we will also add a 'sea bed' to the plot.\n", "\n", "
\n", - "Note: It is important to remember that the gaps do actually exist in reality and this is a caveat which must be considered when interpreting the transect derived from CTD casts. Indeed, if you look at the x-axis of the plot below you will see that the deployments are not necessarily regularly spaced and some gaps are larger than others.\n", + "**Note**: It is important to remember that the gaps do actually exist in reality and this is a caveat which must be considered when interpreting the transect derived from CTD casts. Indeed, if you look at the x-axis of the plot below you will see that the deployments are not necessarily regularly spaced and some gaps are larger than others.\n", "
" ] }, @@ -421,7 +421,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -435,15 +435,15 @@ "fig, ax = plt.subplots(figsize=(10, 6), dpi=96)\n", "\n", "mesh = ax.pcolormesh(\n", - " distance_1d / 1000, # distance in km\n", + " distances / 1000, # distance in km\n", " z1d,\n", - " var_up.T,\n", + " var_2d.T,\n", " cmap=VARIABLES[plot_variable][\"cmap\"],\n", ")\n", "\n", - "seabed = xr.where(np.isnan(var_up), 1, np.nan) # sea bed\n", + "seabed = np.where(np.isnan(var_2d), 1, np.nan) # sea bed\n", "ax.pcolormesh(\n", - " distance_1d / 1000, # distance in km\n", + " distances / 1000, # distance in km\n", " z1d,\n", " seabed.T,\n", " cmap=mcolors.ListedColormap([mcolors.to_rgba(\"tan\"), mcolors.to_rgba(\"white\")]),\n", @@ -452,7 +452,7 @@ "tan_patch = mpatches.Patch(color=mcolors.to_rgba(\"tan\"), label=\"Land / sea bed\")\n", "ax.legend(handles=[tan_patch], loc=\"lower right\")\n", "\n", - "ax.set_xticks(distance_1d / 1000)\n", + "ax.set_xticks(distances / 1000)\n", "\n", "ax.set_ylabel(\"Depth (m)\")\n", "ax.set_xlabel(\"Distance from start (km)\")\n", @@ -464,7 +464,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ship", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -478,7 +478,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.12" + "version": "3.14.6" } }, "nbformat": 4, diff --git a/docs/user-guide/tutorials/Drifter_data_tutorial.ipynb b/docs/user-guide/tutorials/Drifter_data_tutorial.ipynb index ec22454e..13479eca 100755 --- a/docs/user-guide/tutorials/Drifter_data_tutorial.ipynb +++ b/docs/user-guide/tutorials/Drifter_data_tutorial.ipynb @@ -28,11 +28,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", + "import parcels\n", + "import polars as pl\n", "import matplotlib.pyplot as plt\n", "import cmocean.cm as cmo\n", "import numpy as np\n", @@ -63,7 +64,7 @@ "source": [ "# set data directory path\n", "\n", - "data_dir = \"/path/to/EXPEDITION/results/\" # set this to be where your expedition output data is located on your (virtual) machine" + "data_dir = \"/path/to/EXPEDITION/results\" # set this to be where your expedition output data is located on your (virtual) machine" ] }, { @@ -72,18 +73,18 @@ "source": [ "### Load data\n", "\n", - "We are now ready to read in the data using the `xarray` package. You can carry on executing the next cells without making changes to the code…" + "We are now ready to read in the data using `Parcels`, specifically the `parcels.read_particlefile()` function. You can carry on executing the next cells without making changes to the code…" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ "# load drifter data\n", "\n", - "drifter_ds = xr.open_dataset(f\"{data_dir}/drifter.zarr\")" + "drifter_df = parcels.read_particlefile(f\"{data_dir}/drifter.parquet\")" ] }, { @@ -107,12 +108,12 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 42, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -127,10 +128,11 @@ "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", "\n", "# plot trajectory\n", - "for i, traj in enumerate(drifter_ds[\"trajectory\"]):\n", + "for i, drifter_id in enumerate(np.unique(drifter_df[\"particle_id\"])):\n", " # extract trajectory data\n", - " lons = drifter_ds[\"lon\"][:].sel(trajectory=traj).squeeze()\n", - " lats = drifter_ds[\"lat\"][:].sel(trajectory=traj).squeeze()\n", + " drifter_dat = drifter_df.filter(pl.col(\"particle_id\") == drifter_id)\n", + " lons = drifter_dat[\"x\"].to_numpy()\n", + " lats = drifter_dat[\"y\"].to_numpy()\n", "\n", " # plot\n", " ax.plot(\n", @@ -160,10 +162,10 @@ "latlon_buffer = 3.0 # degrees (adjust this to 'zoom' in/out in the plot)\n", "ax.set_extent(\n", " [\n", - " drifter_ds.lon.min() - latlon_buffer,\n", - " drifter_ds.lon.max() + latlon_buffer,\n", - " drifter_ds.lat.min() - latlon_buffer,\n", - " drifter_ds.lat.max() + latlon_buffer,\n", + " drifter_df[\"x\"].min() - latlon_buffer,\n", + " drifter_df[\"x\"].max() + latlon_buffer,\n", + " drifter_df[\"y\"].min() - latlon_buffer,\n", + " drifter_df[\"y\"].max() + latlon_buffer,\n", " ],\n", " crs=ccrs.PlateCarree(),\n", ")\n", @@ -182,10 +184,16 @@ "\n", "ax.legend(loc=\"upper right\", fontsize=12)\n", "\n", - "n_days = float(\n", - " (drifter_ds[\"time\"][0].max(skipna=True) - drifter_ds[\"time\"][0].min(skipna=True))\n", + "drifter_lifetimes = [\n", + " (\n", + " drifter_df.filter(pl.col(\"particle_id\") == drifter_id)[\"t\"].max()\n", + " - drifter_df.filter(pl.col(\"particle_id\") == drifter_id)[\"t\"].min()\n", + " )\n", " / np.timedelta64(1, \"D\")\n", - ")\n", + " for drifter_id in np.unique(drifter_df[\"particle_id\"])\n", + "]\n", + "u, counts = np.unique(drifter_lifetimes, return_counts=True)\n", + "n_days = u[np.argmax(counts)] # use the most common drifter lifetime for the title\n", "plt.title(f\"[{round(n_days, 1)} day drifter lifetime]\", fontsize=12)\n", "\n", "plt.tight_layout()" @@ -197,17 +205,17 @@ "source": [ "### Add temperature data to the trajectories\n", "\n", - "The VirtualShip drifters will sample sea surface temperature (SST) as they flow throught the ocean. We can add this information to our trajectory plot by colouring the drifter trajectories by the temperature recorded at each time step." + "The VirtualShip drifters will sample sea surface temperature (SST) as they flow through the ocean. We can add this information to our trajectory plot by colouring the drifter trajectories by the temperature recorded at each time step." ] }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 43, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", "text/plain": [ "
" ] @@ -224,11 +232,12 @@ "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", "\n", "# plot trajectory colored by temperature / salinity\n", - "for i, traj in enumerate(drifter_ds[\"trajectory\"]):\n", + "for i, drifter_id in enumerate(np.unique(drifter_df[\"particle_id\"])):\n", " # extract trajectory data\n", - " lons = drifter_ds[\"lon\"][:].sel(trajectory=traj).squeeze().values\n", - " lats = drifter_ds[\"lat\"][:].sel(trajectory=traj).squeeze().values\n", - " var = drifter_ds[PLOT_VARIABLE][:].sel(trajectory=traj).squeeze().values\n", + " drifter_dat = drifter_df.filter(pl.col(\"particle_id\") == drifter_id)\n", + " lons = drifter_dat[\"x\"].to_numpy()\n", + " lats = drifter_dat[\"y\"].to_numpy()\n", + " var = drifter_dat[PLOT_VARIABLE].to_numpy()\n", "\n", " # segments for LineCollection\n", " points = np.array([lons, lats]).T.reshape(-1, 1, 2)\n", @@ -265,10 +274,10 @@ "latlon_buffer = 1.0 # degrees (adjust this to 'zoom' in/out in the plot)\n", "ax.set_extent(\n", " [\n", - " drifter_ds.lon.min() - latlon_buffer,\n", - " drifter_ds.lon.max() + latlon_buffer,\n", - " drifter_ds.lat.min() - latlon_buffer,\n", - " drifter_ds.lat.max() + latlon_buffer,\n", + " drifter_df[\"x\"].min() - latlon_buffer,\n", + " drifter_df[\"x\"].max() + latlon_buffer,\n", + " drifter_df[\"y\"].min() - latlon_buffer,\n", + " drifter_df[\"y\"].max() + latlon_buffer,\n", " ],\n", " crs=ccrs.PlateCarree(),\n", ")\n", @@ -289,8 +298,8 @@ "sm = plt.cm.ScalarMappable(\n", " cmap=cmo.thermal,\n", " norm=mcolors.Normalize(\n", - " vmin=float(drifter_ds.temperature.min()),\n", - " vmax=float(drifter_ds.temperature.max()),\n", + " vmin=float(drifter_df[PLOT_VARIABLE].min()),\n", + " vmax=float(drifter_df[PLOT_VARIABLE].max()),\n", " ),\n", ")\n", "sm._A = []\n", @@ -298,10 +307,16 @@ "\n", "ax.legend(loc=\"upper right\", fontsize=12)\n", "\n", - "n_days = float(\n", - " (drifter_ds[\"time\"][0].max(skipna=True) - drifter_ds[\"time\"][0].min(skipna=True))\n", + "drifter_lifetimes = [\n", + " (\n", + " drifter_df.filter(pl.col(\"particle_id\") == drifter_id)[\"t\"].max()\n", + " - drifter_df.filter(pl.col(\"particle_id\") == drifter_id)[\"t\"].min()\n", + " )\n", " / np.timedelta64(1, \"D\")\n", - ")\n", + " for drifter_id in np.unique(drifter_df[\"particle_id\"])\n", + "]\n", + "u, counts = np.unique(drifter_lifetimes, return_counts=True)\n", + "n_days = u[np.argmax(counts)] # use the most common drifter lifetime for the title\n", "plt.title(f\"[{round(n_days, 1)} day drifter lifetime]\", fontsize=12)\n", "\n", "plt.tight_layout()" @@ -344,7 +359,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ship", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -358,7 +373,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.14.6" } }, "nbformat": 4, diff --git a/docs/user-guide/tutorials/Ship_underwater_ST_plotting.ipynb b/docs/user-guide/tutorials/Ship_underwater_ST_plotting.ipynb index 5b2559ad..7be07acc 100644 --- a/docs/user-guide/tutorials/Ship_underwater_ST_plotting.ipynb +++ b/docs/user-guide/tutorials/Ship_underwater_ST_plotting.ipynb @@ -30,13 +30,13 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "c7abdbb5", "metadata": {}, "outputs": [], "source": [ + "import parcels\n", "import numpy as np\n", - "import xarray as xr\n", "import cmocean.cm as cmo\n", "import matplotlib.pyplot as plt\n", "from cartopy import crs as ccrs\n", @@ -66,8 +66,6 @@ "metadata": {}, "outputs": [], "source": [ - "# set data dir path\n", - "\n", "data_dir = \"/path/to/EXPEDITION/results/\" # set this to be where your expedition output data is located on your (virtual) machine" ] }, @@ -78,19 +76,18 @@ "source": [ "#### Load data\n", "\n", - "We are now ready to read in the data using the `xarray` package." + "We are now ready to read in the data using `Parcels`, specifically the `parcels.read_particlefile()` function." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "2cd63a8f", "metadata": {}, "outputs": [], "source": [ "# load underway temperature and salinity data\n", - "\n", - "underway_st_ds = xr.open_dataset(f\"{data_dir}/underwater_st.zarr\")" + "underway_st_df = parcels.read_particlefile(f\"{data_dir}/underwater_st.parquet\")" ] }, { @@ -121,13 +118,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 16, "id": "78d5bd8d", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -155,14 +152,9 @@ "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", "\n", "# extract trajectory data\n", - "lons = underway_st_ds[\"lon\"][:].sel(trajectory=0).squeeze().values\n", - "lats = underway_st_ds[\"lat\"][:].sel(trajectory=0).squeeze().values\n", - "var = (\n", - " underway_st_ds[VARIABLES[PLOT_VARIABLE][\"ds_name\"]][:]\n", - " .sel(trajectory=0)\n", - " .squeeze()\n", - " .values\n", - ")\n", + "lons = underway_st_df[\"x\"]\n", + "lats = underway_st_df[\"y\"]\n", + "var = underway_st_df[VARIABLES[PLOT_VARIABLE][\"ds_name\"]]\n", "\n", "# segments for LineCollection\n", "points = np.array([lons, lats]).T.reshape(-1, 1, 2)\n", @@ -182,13 +174,13 @@ "ax.add_collection(lc)\n", "\n", "# additional map features\n", - "latlon_buffer = 7.5 # degrees (adjust this to 'zoom' in/out in the plot)\n", + "latlon_buffer = 1.0 # degrees (adjust this to 'zoom' in/out in the plot)\n", "ax.set_extent(\n", " [\n", - " underway_st_ds.lon.min() - latlon_buffer,\n", - " underway_st_ds.lon.max() + latlon_buffer,\n", - " underway_st_ds.lat.min() - latlon_buffer,\n", - " underway_st_ds.lat.max() + latlon_buffer,\n", + " underway_st_df[\"x\"].min() - latlon_buffer,\n", + " underway_st_df[\"x\"].max() + latlon_buffer,\n", + " underway_st_df[\"y\"].min() - latlon_buffer,\n", + " underway_st_df[\"y\"].max() + latlon_buffer,\n", " ],\n", " crs=ccrs.PlateCarree(),\n", ")\n", @@ -209,8 +201,8 @@ "sm = plt.cm.ScalarMappable(\n", " cmap=VARIABLES[PLOT_VARIABLE][\"cmap\"],\n", " norm=mcolors.Normalize(\n", - " vmin=float(underway_st_ds[VARIABLES[PLOT_VARIABLE][\"ds_name\"]].min()),\n", - " vmax=float(underway_st_ds[VARIABLES[PLOT_VARIABLE][\"ds_name\"]].max()),\n", + " vmin=var.min(),\n", + " vmax=var.max(),\n", " ),\n", ")\n", "sm._A = []\n", @@ -218,13 +210,10 @@ " sm, ax=ax, orientation=\"vertical\", label=VARIABLES[PLOT_VARIABLE][\"label\"]\n", ")\n", "\n", - "dates = (\n", - " underway_st_ds[\"time\"][:].sel(trajectory=0).squeeze().min(skipna=True).values,\n", - " underway_st_ds[\"time\"][:].sel(trajectory=0).squeeze().max(skipna=True).values,\n", - ")\n", + "dates = (underway_st_df[\"t\"].min(), underway_st_df[\"t\"].max())\n", "n_days = (np.datetime64(dates[1]) - np.datetime64(dates[0])) / np.timedelta64(1, \"D\")\n", "plt.title(\n", - " f\"{dates[0].astype('datetime64[D]')} to {dates[1].astype('datetime64[D]')} [{n_days:.1f} day(s)]\",\n", + " f\"{np.datetime64(dates[0], 'D')} to {np.datetime64(dates[1], 'D')} [{n_days:.1f} day(s)]\",\n", " fontsize=12,\n", ")\n", "\n", @@ -234,7 +223,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ship", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -248,7 +237,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.14.6" } }, "nbformat": 4, diff --git a/docs/user-guide/tutorials/index.md b/docs/user-guide/tutorials/index.md index 35b04234..444742de 100644 --- a/docs/user-guide/tutorials/index.md +++ b/docs/user-guide/tutorials/index.md @@ -5,8 +5,6 @@ maxdepth: 1 caption: Post-processing results --- -ADCP_data_tutorial.ipynb -CTD_data_tutorial.ipynb Drifter_data_tutorial.ipynb Argo_data_tutorial.ipynb CTD_transects.ipynb @@ -21,7 +19,6 @@ maxdepth: 1 caption: SURF Research Cloud set up --- surf_research_cloud_setup.ipynb -surf_collaborative_setup.ipynb ``` ```{nbgallery} diff --git a/docs/user-guide/tutorials/xbt_plotting.ipynb b/docs/user-guide/tutorials/xbt_plotting.ipynb index b10e0f72..4dfc17be 100644 --- a/docs/user-guide/tutorials/xbt_plotting.ipynb +++ b/docs/user-guide/tutorials/xbt_plotting.ipynb @@ -32,12 +32,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "63bef1a3", "metadata": {}, "outputs": [], "source": [ - "import xarray as xr\n", + "import parcels\n", + "import polars as pl\n", + "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import math" ] @@ -63,8 +65,6 @@ "metadata": {}, "outputs": [], "source": [ - "# set data dir path\n", - "\n", "data_dir = \"/path/to/EXPEDITION/results/\" # set this to be where your expedition output data is located on your (virtual) machine" ] }, @@ -75,19 +75,20 @@ "source": [ "#### Load data\n", "\n", - "We are now ready to read in the data using the `xarray` package. You can carry on executing the next cells without making changes to the code…" + "We are now ready to read in the data using `Parcels`, specifically the `parcels.read_particlefile()` function. \n", + "\n", + "You can carry on executing the next cells without making changes to the code…" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 16, "id": "18ea91b0", "metadata": {}, "outputs": [], "source": [ "# load argo data\n", - "\n", - "xbt_ds = xr.open_dataset(f\"{data_dir}/xbt.zarr\")" + "xbt_df = parcels.read_particlefile(f\"{data_dir}/xbt.parquet\")" ] }, { @@ -112,13 +113,13 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, "id": "b9f71a4c", "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -129,7 +130,7 @@ ], "source": [ "# make as 'square' a grid of subplots as possible\n", - "n_profiles = len(xbt_ds[\"temperature\"])\n", + "n_profiles = np.unique(xbt_df[\"particle_id\"]).size\n", "ncols = math.ceil(math.sqrt(n_profiles))\n", "nrows = math.ceil(n_profiles / ncols)\n", "\n", @@ -145,7 +146,7 @@ "\n", "for i, ax in enumerate(axs.flat):\n", " if i < n_profiles:\n", - " profile = xbt_ds.isel(trajectory=i)\n", + " profile = xbt_df.filter(pl.col(\"particle_id\") == i)\n", "\n", " # plot\n", " ax.scatter(\n", @@ -161,7 +162,7 @@ " )\n", "\n", " # extras\n", - " ax.set_title(f\"Waypoint {int(profile['trajectory'].values) + 1}\", fontsize=14)\n", + " ax.set_title(f\"Waypoint {int(profile['particle_id'][0]) + 1}\", fontsize=14)\n", " ax.set_facecolor(\"gainsboro\")\n", " ax.grid(visible=True, which=\"both\", color=\"white\", linewidth=0.5)\n", "\n", @@ -186,7 +187,7 @@ ], "metadata": { "kernelspec": { - "display_name": "ship", + "display_name": "Python 3", "language": "python", "name": "python3" }, @@ -200,7 +201,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.9" + "version": "3.14.6" } }, "nbformat": 4, From d047bb4641679f7dacb4077549a6f21cd17b33bb Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 21 Aug 2026 10:25:42 +0200 Subject: [PATCH 86/94] make train the teacher section --- docs/conf.py | 1 + docs/user-guide/teacher-content/index.md | 15 ++++- .../train-the-teacher/train_the_teacher.md | 67 +++++++++++++++++++ 3 files changed, 82 insertions(+), 1 deletion(-) create mode 100644 docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md diff --git a/docs/conf.py b/docs/conf.py index ea12ec89..4c809bc7 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -84,6 +84,7 @@ "user-guide/tutorials/working_with_expedition_yaml": "user-guide/_images/AnnaWeber.jpeg", "user-guide/teacher-content/UU-dyoc/example_expedition": "user-guide/_images/AnnaWeber.jpeg", "user-guide/teacher-content/UU-dyoc/file_permissions": "user-guide/_images/AnnaWeber.jpeg", + "user-guide/teacher-content/train-the-teacher/surf_set_up": "user-guide/_images/AnnaWeber.jpeg", } sphinx_gallery_conf = {"default_thumb_file": "_static/virtual_ship_logo.png"} diff --git a/docs/user-guide/teacher-content/index.md b/docs/user-guide/teacher-content/index.md index 89389d8e..6d3c45a0 100644 --- a/docs/user-guide/teacher-content/index.md +++ b/docs/user-guide/teacher-content/index.md @@ -18,7 +18,20 @@ The VSC design focuses on creating didactically sound, authentic learning experi We evaluated in several (under)graduate courses and find that the VirtualShip Classroom is highly engaging, and students report on enhanced confidence and knowledge [(Daniels et al. 2025)](https://current-journal.com/articles/10.5334/cjme.121). -### Teaching materials +### Train the teacher + +This section provides a step-by-step guide for teachers to get started with the VirtualShip Classroom. It includes instructions on how to set up the software, navigate the tooling, and use the teaching materials effectively. + +```{toctree} +:maxdepth: 2 + +train-the-teacher/train_the_teacher.md + +``` + +### Further teaching materials + + ```{toctree} :maxdepth: 1 diff --git a/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md b/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md new file mode 100644 index 00000000..dba65316 --- /dev/null +++ b/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md @@ -0,0 +1,67 @@ +# Train the teacher + +We're pleased that you've chosen to use VirtualShip in your teaching! + +This guide is designed to help you get started with the platform and make the most of its features in your classroom. It is currently tailored primarily for educators at partner instutions, as part of the [VirtualShip NKO Scale Up project](https://virtualship.parcels-code.org/blog/scaleup-grant). However, we welcome all educators to explore the guide and adapt it to their own teaching contexts. + +Here, we will assume that you're familiar with the purpose and motivations for using VirtualShip. We will be going through all the practical steps to get you up and running. + +```{tip} +This is a long guide... use the table of contents (on the right) to navigate to the sections that are most relevant to you! +``` + +## Foreword + +In our experience, the most successful implementations of VirtualShip are those where the activities have a strong **narrative** ("You have been granted _ weeks of ship time!") and where students are given ample **freedom**, for example to choose their own research question, location and timing (perhaps from a selection of [case studies](../assignments/case_studies_virtualship.ipynb)). + +That being said, VirtualShip is a flexible platform and can be used in a variety of ways, from highly structured to more open-ended activities. + +## Setting up a programming environment + +There are broadly two ways to set up VirtualShip for teaching: + +1. Each student uses a local installation of the software (on their own device), installed via a package manager such as `pip`, `conda` or `pixi`. +2. A software environment is pre-configured on a cloud-based platform. + +Option 1) requires less preparation but can be more challenging for students to set up (especially if inexperienced) with frequent machine-dependent issues (and a lot of time spent on troubleshooting during lesson time!). Option 2) requires more preparation as the course convenor but is generally easier to support in-class, especially for larger groups. It also has the advantage that all students are working with the same resources, versions and infrastructure, which is generally important for reproducibility and fairness. + +In previous implementations at Utrecht University (where VirtualShip originated), we have primarily used Option 2) on the [SURF Research Cloud](https://www.surf.nl/en/services/compute/surf-research-cloud)). + +### Local installation + + + +```{tip} +If you have access to a computer lab, you may also consider installing the software on the those machines. This is similar to a local installation but can bring similar benefits to a cloud-based environment (i.e. each student has the same resources), but with less flexibility for students to work from home or on their own devices. +``` + +### Pre-configured environment (cloud based) + +This documentation focuses on a set up specifically on the [SURF Research Cloud](https://www.surf.nl/en/services/compute/surf-research-cloud)). The concepts are similar for other cloud-based platforms, but you may need to adapt them to your own context. + +```{note} +Note, the SURF Research Cloud is only available to Dutch institutions. Other cloud-based platforms (e.g. Google Colab, Binder, etc.) could be used as well but we have not extensively tested these platforms. +``` + +For detailed instructions on how to set up the pre-configured environment on the SURF Research Cloud, please refer to the SURF Research Cloud set up guide: + +```{nbgallery} + +surf_set_up.md +``` + +## Simulating Real Life Challenges + + + +## ❗️ End-of-course survey + +```{important} +We would be really grateful for your help in collecting feedback from your students on their experience with VirtualShip. This will help us to improve the platform, research its impact and to better understand how it is being used in different contexts. + +Please distribute the following survey link to your students at the end of the course: _____ +``` + +## Feedback + +If you have any feedback on this guide, or if you have suggestions for improvements, please reach out to us via our [GitHub issue tracker](https://github.com/Parcels-code/virtualship/issues) or by email: [virtualship@uu.nl](mailto:virtualship@uu.nl). From 00c215feb20219fa2e79e0c0cee5fb792a27b16c Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 21 Aug 2026 10:26:03 +0200 Subject: [PATCH 87/94] new page for surf specific set up --- .../train-the-teacher/surf_set_up.md | 92 +++++++++++++++++++ 1 file changed, 92 insertions(+) create mode 100644 docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md diff --git a/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md new file mode 100644 index 00000000..580d5420 --- /dev/null +++ b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md @@ -0,0 +1,92 @@ +# Set up the SURF Research Cloud (RC) + +```{note} +For this guide, we will assume that you already have access to the SURF Research Cloud and have credits available. +``` + +```{tip} +For general information on how to use the SURF Research Cloud, please refer to their [documentation](https://servicedesk.surf.nl/wiki/spaces/WIKI/pages/9798172/SURF+Research+Cloud). +``` + +The VirtualShip Team has created a pre-configured "Catalog item" on the SURF Research Cloud that contains all the necessary software and dependencies for running VirtualShip. This includes a JupyterLab environment, the VirtualShip software itself, and a selection of useful post-processing packages (`xarray`, `matplotlib`, `cartopy`, `plotly` etc.). + +This means you can deploy VirtualShip on a SURF Research Cloud workspace 'out of the box'. This guide will go through the steps you'll need to take to get access to this pre-configured environment and to set up a workspace for your students. + +## The VirtualShip Catalogue item + +First please log in to the SURF Research Cloud [dashboard/portal](https://portal.live.surfresearchcloud.nl/dashboard/workspaces) and navigate to the "Catalog" heading (Figure 1). + +![](_images/catalog.png) +_Figure 1. Navigating to the Catalogue (screenshot)._ + +You should see a wide selection of available catalogue items. You can search for the VirtualShip catalogue item by typing "VirtualShip" in the search bar (Figure 2). When you find the VirtualShip catalogue item, click on it to view more details and to **request access**. The VirtualShip Team will then review your request and grant accesss. + +![](_images/virtualship_catalog.png) +_Figure 2. The pre-configured VirtualShip Catalogue item (screenshot)._ + +## Arranging storage space + +```{important} +The persistent storage space is different from the `home` directory of the workspace, which you first enter when launching the workspace. `home` is not persistent and its contents will be lost when the workspace is stopped! +``` + +It is important to have a persistent storage space associated with your workspace. This is where students should base their work and save any expedition, configuration or output files. You can request a persistent storage space via the SURF Research Cloud dashboard (Figure 3). + +![](_images/surf_storage.png) +_Figure 3. Creating persistent storage space via the SURF Research Cloud dashboard (screenshot)._ + +As a rough rule of thumb, a 50GB storage space should be sufficient for a classroom activity, as the VirtualShip output files are generally not very large. + +```{tip} +Once attached to the workspace you create (see the next section), the storage space should be available under the `/data` directory in the workspace. Typical VirtualShip workflows will then get (groups of) students to make their own subdirectory in `/data` for their expeditions. +``` + +## Creating a new workspace + +Next, return to the main dashboard and click to create a new `workspace` (Figure 4). + +![](_images/new_workspace.png) +_Figure 4. Creating a new workspace via the SURF Research Cloud dashboard (screenshot)._ + +From here, you can run through the steps to create a new workspace. You will be prompted to select a catalogue item, and you should select the VirtualShip catalogue item that you requested access to in the previous step. You will probably have to use the search bar again and it should be visible once you have been granted access. + +You should also attach the storage space you created in the previous step. This ensures persistent storage for students across sessions. You can also select the size of the workspace (CPU, RAM, storage) and the duration for which it will be available. + +```{tip} +When it comes to selecting a "Cloud Provider" (and if you have multiple choices), we recommend simply sticking to the SURF HPC Cloud for reduced credit consumption. + +Generally, a classroom VirtualShip activity will not require large amounts of resource, so you can also usually select a smaller workspace size (e.g. 2 or 4 CPU, 16 GB RAM). Choosing a higher CPU count will use up more credits! + +You can always "pause" a workspace when it is not in use, which will reduce credit consumption, and then "resume" it when needed again. +``` + +## Inviting students to the workspace + +Once your workspace is set up, you can invite students to join it. This is facilitated through the separate [SURF Research Access Management (SRAM)](https://sram.surf.nl/collaborations-overview) platform. + +From here select to your collaboration and, as an admin, you should be able to navigate to the "Members" tab and invite new members to the collaboration (Figure 5). + +![](_images/surf_invite.png) +_Figure 5. Inviting students to the collaboration via SRAM (screenshot)._ + +You will need to provide the email addresses of your students and they will receive an invitation to join the collaboration. Once they have accepted the invitation, they should be able to log in to the SURF Research Cloud via their institutional credentials and see/access the workspace you created before. + +## Updating the workspace + +The VirtualShip Team will be responsible for maintaining and keeping the catalogue item up to date. However, if you ever come across a problem with the software (e.g. suspected bugs) or you would like to request a new feature which should be added to the version in the catalogue item, please get in touch with the Team via our [GitHub issue tracker](https://github.com/Parcels-code/virtualship/issues) or by email: [virtualship@uu.nl](mailto:virtualship@uu.nl). We are open to requests and will try to accommodate them as quickly as possible! + +If/when the software has been updated in the catalogue item, you will need to update your own version in the workspace to use the new version. + +You can do so by running the following commands in the Terminal in your workspace (see the Note block below, as you will need to replace `{branch-name}` with the name of the branch you want to install): + +```bash +# activiate the VirtualShip environment +conda activate virtualship + +# this will install the updated version of VirtualShip +sudo /etc/miniconda/envs/virtualship/bin/pip install --upgrade git+https://github.com/Parcels-code/virtualship@{branch-name} +``` + +```{note} +The specific branch name to use (`{branch-name}`) will depend on the version of VirtualShip you want to install. For example, if we have coordinated to add a new feature which is not yet in the `main` branch, we may ask you to install from a specific branch. If you are unsure which branch to use, please contact the VirtualShip Team. +``` From 9b7eed189617ec40837cfc4db3af03147d84a665 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 21 Aug 2026 10:26:11 +0200 Subject: [PATCH 88/94] screenshots for surf set up --- .../train-the-teacher/_images/catalog.png | Bin 0 -> 27182 bytes .../_images/new_workspace.png | Bin 0 -> 20893 bytes .../train-the-teacher/_images/surf_invite.png | Bin 0 -> 251709 bytes .../train-the-teacher/_images/surf_storage.png | Bin 0 -> 37083 bytes .../_images/virtualship_catalog.png | Bin 0 -> 144242 bytes 5 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 docs/user-guide/teacher-content/train-the-teacher/_images/catalog.png create mode 100644 docs/user-guide/teacher-content/train-the-teacher/_images/new_workspace.png create mode 100644 docs/user-guide/teacher-content/train-the-teacher/_images/surf_invite.png create mode 100644 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zdYkJqKVQ}Gvt)tqQ2vGG26li4X5>yUOY_$oK^d5Vp1;2RZPlZm1a8q3E_(HrX#%iJ zl@AU6vTBlM!Q#yxqkLh)uW4Q)qoA*X^nkIP5Pf<7w~VmNCv5NldvE_&?^%A5Pe=g! zT~9}O^&|y=2LiTS`VK#QAulN$bc0KXC#;^NO7K8ssM6|rPYVD_3WV#eo+LDQfL6PE z`DyuzXMSvl)y(aW?eG=b{IA{pV>^5i>Hlkf{w+&?XopouzaQG+i Date: Fri, 21 Aug 2026 10:49:17 +0200 Subject: [PATCH 89/94] additions to surf guide --- .../train-the-teacher/surf_set_up.md | 26 ++++++++++++------- 1 file changed, 17 insertions(+), 9 deletions(-) diff --git a/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md index 580d5420..d338f699 100644 --- a/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md +++ b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md @@ -1,23 +1,25 @@ # Set up the SURF Research Cloud (RC) ```{note} -For this guide, we will assume that you already have access to the SURF Research Cloud and have credits available. +For this guide, we will assume that you are the course convenor, already have access to the SURF Research Cloud and have credits available. ``` ```{tip} For general information on how to use the SURF Research Cloud, please refer to their [documentation](https://servicedesk.surf.nl/wiki/spaces/WIKI/pages/9798172/SURF+Research+Cloud). ``` +In this documentation, we will primarily be working from the SURF Research Cloud dashboard (or "portal") which is available at: [https://portal.live.surfresearchcloud.nl/dashboard/workspaces](https://portal.live.surfresearchcloud.nl/dashboard/workspaces). You can log in to the dashboard using your institutional credentials. + +## The VirtualShip Catalog item + The VirtualShip Team has created a pre-configured "Catalog item" on the SURF Research Cloud that contains all the necessary software and dependencies for running VirtualShip. This includes a JupyterLab environment, the VirtualShip software itself, and a selection of useful post-processing packages (`xarray`, `matplotlib`, `cartopy`, `plotly` etc.). This means you can deploy VirtualShip on a SURF Research Cloud workspace 'out of the box'. This guide will go through the steps you'll need to take to get access to this pre-configured environment and to set up a workspace for your students. -## The VirtualShip Catalogue item - First please log in to the SURF Research Cloud [dashboard/portal](https://portal.live.surfresearchcloud.nl/dashboard/workspaces) and navigate to the "Catalog" heading (Figure 1). ![](_images/catalog.png) -_Figure 1. Navigating to the Catalogue (screenshot)._ +_Figure 1. Navigating to the Catalogue via the SURF Research Cloud dashboard (screenshot)._ You should see a wide selection of available catalogue items. You can search for the VirtualShip catalogue item by typing "VirtualShip" in the search bar (Figure 2). When you find the VirtualShip catalogue item, click on it to view more details and to **request access**. The VirtualShip Team will then review your request and grant accesss. @@ -30,7 +32,7 @@ _Figure 2. The pre-configured VirtualShip Catalogue item (screenshot)._ The persistent storage space is different from the `home` directory of the workspace, which you first enter when launching the workspace. `home` is not persistent and its contents will be lost when the workspace is stopped! ``` -It is important to have a persistent storage space associated with your workspace. This is where students should base their work and save any expedition, configuration or output files. You can request a persistent storage space via the SURF Research Cloud dashboard (Figure 3). +It is important to have persistent `storage` associated with your workspace. This is where students should base their work and save any expedition, configuration or output files. You can request a persistent storage space via the SURF Research Cloud dashboard (Figure 3). ![](_images/surf_storage.png) _Figure 3. Creating persistent storage space via the SURF Research Cloud dashboard (screenshot)._ @@ -50,7 +52,7 @@ _Figure 4. Creating a new workspace via the SURF Research Cloud dashboard (scree From here, you can run through the steps to create a new workspace. You will be prompted to select a catalogue item, and you should select the VirtualShip catalogue item that you requested access to in the previous step. You will probably have to use the search bar again and it should be visible once you have been granted access. -You should also attach the storage space you created in the previous step. This ensures persistent storage for students across sessions. You can also select the size of the workspace (CPU, RAM, storage) and the duration for which it will be available. +You should also attach the `storage` you created in the previous step. This ensures persistent storage for students across sessions. You can also select the size of the workspace (CPU, RAM, storage) and the duration for which it will be available. ```{tip} When it comes to selecting a "Cloud Provider" (and if you have multiple choices), we recommend simply sticking to the SURF HPC Cloud for reduced credit consumption. @@ -64,7 +66,7 @@ You can always "pause" a workspace when it is not in use, which will reduce cred Once your workspace is set up, you can invite students to join it. This is facilitated through the separate [SURF Research Access Management (SRAM)](https://sram.surf.nl/collaborations-overview) platform. -From here select to your collaboration and, as an admin, you should be able to navigate to the "Members" tab and invite new members to the collaboration (Figure 5). +After logging in, select to your collaboration and, as an admin, you should be able to navigate to the "Members" tab and invite new members to the collaboration (Figure 5). ![](_images/surf_invite.png) _Figure 5. Inviting students to the collaboration via SRAM (screenshot)._ @@ -75,9 +77,9 @@ You will need to provide the email addresses of your students and they will rece The VirtualShip Team will be responsible for maintaining and keeping the catalogue item up to date. However, if you ever come across a problem with the software (e.g. suspected bugs) or you would like to request a new feature which should be added to the version in the catalogue item, please get in touch with the Team via our [GitHub issue tracker](https://github.com/Parcels-code/virtualship/issues) or by email: [virtualship@uu.nl](mailto:virtualship@uu.nl). We are open to requests and will try to accommodate them as quickly as possible! -If/when the software has been updated in the catalogue item, you will need to update your own version in the workspace to use the new version. +If the software has been updated when your workspace is already active, you will need to update your own version in the workspace to use the new version. -You can do so by running the following commands in the Terminal in your workspace (see the Note block below, as you will need to replace `{branch-name}` with the name of the branch you want to install): +You can do so by running the following commands in the Terminal in your launched workspace (see the Note block below though as you will need to replace `{branch-name}` with the name of the branch you want to install): ```bash # activiate the VirtualShip environment @@ -87,6 +89,12 @@ conda activate virtualship sudo /etc/miniconda/envs/virtualship/bin/pip install --upgrade git+https://github.com/Parcels-code/virtualship@{branch-name} ``` +After a successful update, you should restart the workspace to ensure that the new version is being used. Students should then also have access to the updated version of VirtualShip. + ```{note} The specific branch name to use (`{branch-name}`) will depend on the version of VirtualShip you want to install. For example, if we have coordinated to add a new feature which is not yet in the `main` branch, we may ask you to install from a specific branch. If you are unsure which branch to use, please contact the VirtualShip Team. ``` + +```{important} +This instruction involves the use of `sudo` to install the updated version of VirtualShip, so this should only be done by the course convenor (or someone with admin privileges). +``` From 431ca687db86ef0f28de60b1390e3adb2711fda6 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 21 Aug 2026 11:05:43 +0200 Subject: [PATCH 90/94] more information on file permissions on surf --- .../train-the-teacher/file_permissions.md | 31 +++++++++++++++++++ .../train-the-teacher/surf_set_up.md | 8 ++++- 2 files changed, 38 insertions(+), 1 deletion(-) create mode 100644 docs/user-guide/teacher-content/train-the-teacher/file_permissions.md diff --git a/docs/user-guide/teacher-content/train-the-teacher/file_permissions.md b/docs/user-guide/teacher-content/train-the-teacher/file_permissions.md new file mode 100644 index 00000000..ce94cfe4 --- /dev/null +++ b/docs/user-guide/teacher-content/train-the-teacher/file_permissions.md @@ -0,0 +1,31 @@ +# File Permissions on the SURF Research Cloud + +The shared storage directory in the SURF RC virtual machine (e.g. `data/virtualship-storage/`) is configured such that all users of the workspace can read and access the files within it, but only the owner of a file can edit it. This can prevent seamless collaboration on the same expedition content, for example within your group. + +## How to share and edit files within the shared storage + +To enable collaboration on expedition content within your group, you can change the permissions of files within the shared storage directory to allow editing by all users. This can be done using the `chmod` command in the terminal (see [here](https://en.wikipedia.org/wiki/Chmod) for more detail on the `chmod` command). + +For example, for your `expedition.yaml` file, you can run the following command in the terminal (after navigating to your group's directory and replacing `EXPEDITION_NAME` with your actual expedition directory): + +``` +chmod 777 /EXPEDITION_NAME/expedition.yaml +``` + +This will allow _all_ users in the SURF environment to edit the `expedition.yaml` file. You can repeat this process for any other files within the shared storage that you wish to collaborate on with your group members. + +```{warning} +Be careful when using `chmod 777`, as it grants read, write, and execute permissions to **all** users. This means _everyone_ who has access to the SURF environment can edit the file (i.e. the whole class), which could cause accidental changes or deletions if not used carefully. We recommend you make backups of important files before changing permissions. + +This is generally fine for the purposes of this unit where the virtual environment is a controlled setting, but in other contexts, it can pose security risks. Always ensure you understand the implications of changing file permissions and consider more restrictive permissions when necessary. + +**TL;DR the `chmod 777` command is fine for this unit, but be very careful when using it in other contexts!** +``` + +## Reverting the file permissions + +If you wish to revert the file permissions back to only allowing the owner to edit, you can run the following command in the terminal: + +``` +chmod 644 /EXPEDITION_NAME/expedition.yaml +``` diff --git a/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md index d338f699..0e30390e 100644 --- a/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md +++ b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md @@ -40,7 +40,7 @@ _Figure 3. Creating persistent storage space via the SURF Research Cloud dashboa As a rough rule of thumb, a 50GB storage space should be sufficient for a classroom activity, as the VirtualShip output files are generally not very large. ```{tip} -Once attached to the workspace you create (see the next section), the storage space should be available under the `/data` directory in the workspace. Typical VirtualShip workflows will then get (groups of) students to make their own subdirectory in `/data` for their expeditions. +Once attached to the workspace you create (see the next section), the storage space (with the name you chose during set up) should be available under the `/data` directory in the workspace. Typical VirtualShip workflows will then get (groups of) students to make their own subdirectory in `/data/{storage_name}` for their expeditions. ``` ## Creating a new workspace @@ -73,6 +73,12 @@ _Figure 5. Inviting students to the collaboration via SRAM (screenshot)._ You will need to provide the email addresses of your students and they will receive an invitation to join the collaboration. Once they have accepted the invitation, they should be able to log in to the SURF Research Cloud via their institutional credentials and see/access the workspace you created before. +## Collaboration amongst students + +We often recommend that students work in small groups (e.g. 2-3 students) for their VirtualShip projects. Each student should have their own account/access to the workspace and they can work from the same sub-directory in the `/data` storage space. + +Unfortunately, though, the SURF Research Cloud does not currently support smooth, simultaneous collaboration on the same files in the workspace. This means that students will need to coordinate amongst themselves to ensure that they are not overwriting each other's work. You can refer the students to the [File permissions tutorial](file_permissions.md) for more information on how to arrange access to each other's files and directories in the workspace. + ## Updating the workspace The VirtualShip Team will be responsible for maintaining and keeping the catalogue item up to date. However, if you ever come across a problem with the software (e.g. suspected bugs) or you would like to request a new feature which should be added to the version in the catalogue item, please get in touch with the Team via our [GitHub issue tracker](https://github.com/Parcels-code/virtualship/issues) or by email: [virtualship@uu.nl](mailto:virtualship@uu.nl). We are open to requests and will try to accommodate them as quickly as possible! From 6ca09f4c0e6d92cd4085d1a9538f51a6ab626ac7 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 21 Aug 2026 11:06:15 +0200 Subject: [PATCH 91/94] more information on tailored support --- .../train-the-teacher/train_the_teacher.md | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md b/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md index dba65316..988dd37a 100644 --- a/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md +++ b/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md @@ -2,7 +2,7 @@ We're pleased that you've chosen to use VirtualShip in your teaching! -This guide is designed to help you get started with the platform and make the most of its features in your classroom. It is currently tailored primarily for educators at partner instutions, as part of the [VirtualShip NKO Scale Up project](https://virtualship.parcels-code.org/blog/scaleup-grant). However, we welcome all educators to explore the guide and adapt it to their own teaching contexts. +This guide is designed to help you get started with the platform and make the most of its features in your classroom. It is currently tailored primarily for educators at partner instutions, as part of the [VirtualShip NKO Scale Up project](https://virtualship.parcels-code.org/blog/scaleup-grant). However, we welcome all educators to explore the guide and adapt it to their own teaching contexts. For more tailored support, please see the [Feedback & support](#feedback-support) section at the end of this guide! Here, we will assume that you're familiar with the purpose and motivations for using VirtualShip. We will be going through all the practical steps to get you up and running. @@ -23,7 +23,7 @@ There are broadly two ways to set up VirtualShip for teaching: 1. Each student uses a local installation of the software (on their own device), installed via a package manager such as `pip`, `conda` or `pixi`. 2. A software environment is pre-configured on a cloud-based platform. -Option 1) requires less preparation but can be more challenging for students to set up (especially if inexperienced) with frequent machine-dependent issues (and a lot of time spent on troubleshooting during lesson time!). Option 2) requires more preparation as the course convenor but is generally easier to support in-class, especially for larger groups. It also has the advantage that all students are working with the same resources, versions and infrastructure, which is generally important for reproducibility and fairness. +Option 1) requires less preparation but can be more challenging for students to set up (especially if inexperienced) with frequent machine-dependent issues (and a lot of time spent on troubleshooting during lesson time!). Option 2) requires more preparation as the course convenor but is generally easier to support in-class, especially for larger groups. It also has the advantage that all students are working with the same resources, versions and infrastructure, which is beneficial for reproducibility and fairness. In previous implementations at Utrecht University (where VirtualShip originated), we have primarily used Option 2) on the [SURF Research Cloud](https://www.surf.nl/en/services/compute/surf-research-cloud)). @@ -54,6 +54,11 @@ surf_set_up.md + + + + + ## ❗️ End-of-course survey ```{important} @@ -62,6 +67,8 @@ We would be really grateful for your help in collecting feedback from your stude Please distribute the following survey link to your students at the end of the course: _____ ``` -## Feedback +## Feedback & support + +If you have any feedback on this guide, would like additional support or if you have suggestions for improvements, please reach out to us via our [GitHub issue tracker](https://github.com/Parcels-code/virtualship/issues) or by email: [virtualship@uu.nl](mailto:virtualship@uu.nl). -If you have any feedback on this guide, or if you have suggestions for improvements, please reach out to us via our [GitHub issue tracker](https://github.com/Parcels-code/virtualship/issues) or by email: [virtualship@uu.nl](mailto:virtualship@uu.nl). +We are always happy to hear from educators and will do our best to support you in your teaching with VirtualShip! From e5f779c45e76d62a68278cf05e9936f166a5c561 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 21 Aug 2026 14:00:21 +0200 Subject: [PATCH 92/94] further additions; including new documents for lesson plans and student-perspective surf set up --- docs/conf.py | 2 + .../train-the-teacher/lesson_plans.md | 79 ++++++++++++++ .../train-the-teacher/surf_set_up.md | 8 +- .../train-the-teacher/surf_student_access.md | 82 ++++++++++++++ .../train-the-teacher/train_the_teacher.md | 102 ++++++++++++++++-- 5 files changed, 258 insertions(+), 15 deletions(-) create mode 100644 docs/user-guide/teacher-content/train-the-teacher/lesson_plans.md create mode 100644 docs/user-guide/teacher-content/train-the-teacher/surf_student_access.md diff --git a/docs/conf.py b/docs/conf.py index 4c809bc7..9820c305 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -85,6 +85,8 @@ "user-guide/teacher-content/UU-dyoc/example_expedition": "user-guide/_images/AnnaWeber.jpeg", "user-guide/teacher-content/UU-dyoc/file_permissions": "user-guide/_images/AnnaWeber.jpeg", "user-guide/teacher-content/train-the-teacher/surf_set_up": "user-guide/_images/AnnaWeber.jpeg", + "user-guide/teacher-content/train-the-teacher/file_permissions": "user-guide/_images/AnnaWeber.jpeg", + "user-guide/teacher-content/train-the-teacher/surf_student_access": "user-guide/_images/AnnaWeber.jpeg", } sphinx_gallery_conf = {"default_thumb_file": "_static/virtual_ship_logo.png"} diff --git a/docs/user-guide/teacher-content/train-the-teacher/lesson_plans.md b/docs/user-guide/teacher-content/train-the-teacher/lesson_plans.md new file mode 100644 index 00000000..9e82f85e --- /dev/null +++ b/docs/user-guide/teacher-content/train-the-teacher/lesson_plans.md @@ -0,0 +1,79 @@ +# Example lesson plans + +## Using the `VirtualShip` software + +This example lesson plan is suitable for students with programming knowledge. Students hand in a short research proposal and expedition plan for feedback. They use the `VirtualShip` software to conduct their virtual expedition and analyse the results. They show their results in a presentation that can be assessed. This assignment is recommended to be undertaken in groups, because collaborative learning is beneficial. Suggested learning goals are that students can: + +- identify and address (practical) challenges involved in sea-based research; +- give examples of uncertainty and the synoptic nature of sea-based observations; +- analyze and interpret sea-based observations; +- plan a comprehensive research expedition. + +**An example lesson plan is as follows:** + +| Background lecture on observing the ocean | +| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| _1-2 hours depending on the amount of detail, can also be viewed by student before class as preparation_ | +| Presentation about observing and measuring the ocean, covering different types of observations and instruments followed by steps to conduct ocean research. Sample presentation sides are available on [Edusources](https://edusources.nl/materials/90a4df16-427b-4f95-b2c2-b7bc9b2c6b81/presentation-about-observing-and-measuring-the-ocean). | + +| Prep work / homework | +| ------------------------------------------------------------------------------------------------------------------ | +| _1 hour of homework_ | +| Introduction to VirtualShip research proposals, available [here](../../assignments/Research_proposal_intro.ipynb). | + +| Tutorial 1 | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| _2 hours in class, 4 hours of homework_ | +| Students hand in a short VirtualShip [research proposal](../../assignments/Virtualship_research_proposal.ipynb) for the lecturer (or an expert colleague) to provide feedback. We recommend ensuring that the research questions are focused enough to be researchable in the granted ship time (typically up to three weeks). | + +```{note} +Because data are taken from the Copernicus Marine Data Store (see [here](../../documentation/copernicus_products.md) for more technical details), expeditions can take place from 1993 to present (up to 2 weeks into the future). +``` + +```{tip} +An interesting addition can be to let students use the [MyOcean Pro viewer](https://data.marine.copernicus.eu/viewer/) to, for example, track an eddy using sea surface height and deploy their instruments therein. +``` + +| Tutorial 2 | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | +| _4 hours in class_ | +| Students begin their expedition simulations. Please refer back to the main train-the-teacher [documentation](train_the_teacher.md/#sailing-the-ship) for further instructions on running the software. | + +| VR component | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| A range of 360° videos are available on [YouTube](https://www.youtube.com/@VirtualShipClassroom). If time allows, let students watch one or more of the ship tours and a-day-at-sea videos. | + +| Tutorial 3 | +| ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| _2-4 hours in class, 10 hours of homework_ | +| Encourage the students to analyze their own data using the [example tutorials](../../tutorials/index.md). For more advanced courses, students can be asked to produce further, derived quantities as part of their analysis. | + +| Presentations | +| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| _Number of hours in class will depend on the number of groups that present, we suggest at least 10-15 minutes for each group including questions_ | +| Students present their results. An example presentation rubric is available on [Edusources](https://edusources.nl/materials/44ff66eb-537d-4ab9-a844-0fba9bd8d240/rubric-to-grade-advanced-student-presentations). | + +## _Not_ using the software + +It is also possible to skip using the `VirtuaShip` software entirely and instead use the Open Education Resources (and potentially the VR component) to teach about oceanography and research methods. This is a good option if you want to focus on the learning outcomes without the technical overhead of using the software. + +This example lesson plan is suitable for all students, regardless of programming knowledge. Students hand in a research proposal and an accompanying expedition plan that can be assessed. The assignment can be conducted in groups or individually. Suggested learning goals are that students can: + +- identify practical challenges of planning sea-based research; +- plan a comprehensive research expedition. + +**An example lesson plan is as follows:** + +| Background lecture on observing the ocean | +| ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| _1-2 hours depending on the amount of detail, can also be viewed by student before class as preparation_ | +| Presentation about observing and measuring the ocean, covering different types of observations and instruments followed by steps to conduct ocean research. Sample presentation sides are available on [Edusources](https://edusources.nl/materials/90a4df16-427b-4f95-b2c2-b7bc9b2c6b81/presentation-about-observing-and-measuring-the-ocean). | + +| Tutorial | +| --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| _2 hours in class, 4-8 hours of homework_ | +| In class, students discuss their research questions and approach with an expert before submitting a VirtualShip [research proposal](../../assignments/Research_Proposal_only.ipynb). This is a good opportunity to invite oceanography experts to share their perspectives and insights, if you are not one yourself. | + +| VR component | +| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | +| A range of 360° videos are available on [YouTube](https://www.youtube.com/@VirtualShipClassroom). If time allows, let students watch one or more of the ship tours and a-day-at-sea videos. | diff --git a/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md index 0e30390e..b1ed0fa2 100644 --- a/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md +++ b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md @@ -40,7 +40,7 @@ _Figure 3. Creating persistent storage space via the SURF Research Cloud dashboa As a rough rule of thumb, a 50GB storage space should be sufficient for a classroom activity, as the VirtualShip output files are generally not very large. ```{tip} -Once attached to the workspace you create (see the next section), the storage space (with the name you chose during set up) should be available under the `/data` directory in the workspace. Typical VirtualShip workflows will then get (groups of) students to make their own subdirectory in `/data/{storage_name}` for their expeditions. +Once attached to the workspace you create (see the next section), the storage space (with the name you chose during set up) should be available under the `/data` directory in the workspace. Typical VirtualShip workflows will then get (groups of) students to make their own subdirectory in `/data/{storage-name}` for their expeditions. ``` ## Creating a new workspace @@ -73,11 +73,9 @@ _Figure 5. Inviting students to the collaboration via SRAM (screenshot)._ You will need to provide the email addresses of your students and they will receive an invitation to join the collaboration. Once they have accepted the invitation, they should be able to log in to the SURF Research Cloud via their institutional credentials and see/access the workspace you created before. -## Collaboration amongst students +## Student access to the workspace -We often recommend that students work in small groups (e.g. 2-3 students) for their VirtualShip projects. Each student should have their own account/access to the workspace and they can work from the same sub-directory in the `/data` storage space. - -Unfortunately, though, the SURF Research Cloud does not currently support smooth, simultaneous collaboration on the same files in the workspace. This means that students will need to coordinate amongst themselves to ensure that they are not overwriting each other's work. You can refer the students to the [File permissions tutorial](file_permissions.md) for more information on how to arrange access to each other's files and directories in the workspace. +Please refer back to the main train-the-teacher [documentation](train_the_teacher.md/#student-access-to-the-workspace) for information on how students can access the workspace and perform the final steps to use the VirtualShip software. ## Updating the workspace diff --git a/docs/user-guide/teacher-content/train-the-teacher/surf_student_access.md b/docs/user-guide/teacher-content/train-the-teacher/surf_student_access.md new file mode 100644 index 00000000..30e5b806 --- /dev/null +++ b/docs/user-guide/teacher-content/train-the-teacher/surf_student_access.md @@ -0,0 +1,82 @@ +# Student guide: Accessing the SURF Research Cloud + +## Accepting SURF Research Cloud invite + +In your student email you will have an invite from SURF Research Access Management (SRAM) to join a project on the SURF Research Cloud. Please accept this invite. + +## Open the environment + +Navigate to the [SURF Research Cloud Dashboard](https://portal.live.surfresearchcloud.nl/), or click on the link in the email, and click "access" on the shared workspace. + +```{important} +A known issue is that you may hit a server error when accessing the workspace. If this happens, keep on trying (refresh), as the server spin-up can be a bit overloaded at times, but should get through eventually. + +Unfortunately this is out of our control. Clearing your browser cache and cookies, and/or trying via an incognito/private window may also help. We find that with persistence, the workspace will eventually load. +``` + +## The JupyterLab workspace layout and additional config + +```{note} +This only needs to be done once during setup! +``` + +In the JupyterLab workspace, you'll see the following (or similar) in your file explorer (left-hand side of the screen): + +``` +. +├── KERNEL-README.ipynb +├── data +│ └── datasets +| └── virtualship-storage <--- The shared persistent storage +└── scratch +``` + +```{note} +The persistent storage folder may be called something slightly different in your instance, for example it may have a name specific to the course you are enrolled on, such as `data/storage-osl`, `data/storage-dyoc` or `data/storage-1-sept`. +``` + +In the Jupyter launcher, you can open a Terminal session by clicking on "Terminal" button under the "Other" section, or by going to the "File" menu --> "New" --> "Terminal". From here you can navigate the workspace directory structure and run commands. + +```{tip} +`VirtualShip` is a command line interface (CLI) based tool. We will be working predominantly via the command line in Terminal (typing out commands instead of pointing and clicking). If you are unfamiliar with what a CLI is, see [here](https://www.w3schools.com/whatis/whatis_cli.asp) for more information. In our case, the Terminal is just a way to access the CLI on the SURF Research Cloud virtual machine. +``` + +The `data/virtualship-storage` folder is your persistent storage. Here you can make a folder (e.g., by running `mkdir data/virtualship-storage/{your-group-name}` as a command in the Terminal, replacing `{your-group-name}` with your group name) to house your work for the unit. + +This folder will be visible to anyone using the workspace, but only you will be able to make edits to it. This is the primary place you should store your `virtualship` configs and content relevant to this unit. + +## Initialize conda + +To be able to run VirtualShip from the Terminal, we need to take some additional steps. To make the already installed conda-tool available for yourself, you have to initialise your Terminal shell. + +Back in the "Terminal" tab, type: `/etc/miniconda/bin/conda init` + +Close the Terminal tab and start a new one. +You will see that the Terminal prompt has changed to something like + +```bash +(base) metheuser@mywsp: +``` + +This is conda telling you that you are currently in the "base" environment. + +From here, you already have another environment set up for you. Running `conda env list` in the Terminal, you should see: + +```bash +conda env list + +# conda environments: +# +base * /etc/miniconda +virtualship /etc/miniconda/envs/virtualship` +``` + +Here you can do `conda activate virtualship` to activate the environment called "virtualship". This environment is a shared environment among all workspace users that can be centrally updated. If you want, you can create and manage your own environments by running the relevant conda commands. + +With the `virtualship` environment, you now have access to the `virtualship` command in your Terminal, which can be confirmed by running `virtualship --help`. + +From here you can `cd` ('change directory') into `data/virtualship-storage/{your-group-name}` and run `virtualship` commands as you would on your local machine (see the VirtualShip [quickstart guide](https://virtualship.readthedocs.io/en/latest/user-guide/quickstart.html)). + +## Extra tip: Working in Jupyter _Notebooks_ + +Finally, when you're working in Jupyter _Notebooks_ (`*.ipynb` files), you are able to access the Conda environment with `virtualship` and related dependencies by switching the Kernel in the top right of the UI. diff --git a/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md b/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md index 988dd37a..1a1cd07a 100644 --- a/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md +++ b/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md @@ -1,35 +1,80 @@ # Train the teacher -We're pleased that you've chosen to use VirtualShip in your teaching! +We're pleased that you've chosen to use VirtualShip in your teaching! This guide is designed to help you get started with the platform and make the most of its features in your classroom. -This guide is designed to help you get started with the platform and make the most of its features in your classroom. It is currently tailored primarily for educators at partner instutions, as part of the [VirtualShip NKO Scale Up project](https://virtualship.parcels-code.org/blog/scaleup-grant). However, we welcome all educators to explore the guide and adapt it to their own teaching contexts. For more tailored support, please see the [Feedback & support](#feedback-support) section at the end of this guide! +The instructions are currently tailored primarily for educators at partner instutions, as part of the [VirtualShip NKO Scale Up project](https://virtualship.parcels-code.org/blog/scaleup-grant). However, we welcome all educators to explore the guide and adapt it to their own teaching contexts. For more tailored support, please see the [Feedback & support](#feedback-support) section at the end of this guide! -Here, we will assume that you're familiar with the purpose and motivations for using VirtualShip. We will be going through all the practical steps to get you up and running. +For this guide, we will assume that you're familiar with the purpose and motivations for using VirtualShip. We will be going through all the practical steps to get you up and running. ```{tip} -This is a long guide... use the table of contents (on the right) to navigate to the sections that are most relevant to you! +This is a long guide, intended as a blueprint for setting up your teaching... use the table of contents (on the right) to navigate to the sections that are most relevant to you! ``` ## Foreword +### Introduction + +As a reminder, where we refer to the VirtualShip Classroom, we refer to the combination of three core pillars: the `VirtualShip` software, VR / 360° videos and the Open Education Resources. The VirtualShip Classroom is designed to be flexible, the different components interchangable and can be used in a variety of ways, from highly structured to more open-ended activities. We discuss some example lesson plans [below](#lesson-plan-approaches), but we encourage you to adapt these to your own teaching context and learning objectives. As educators, we encourage you to think about the intended learning outcomes (ILOs) for your students and adapt the activities accoridingly. + +### Our advice + In our experience, the most successful implementations of VirtualShip are those where the activities have a strong **narrative** ("You have been granted _ weeks of ship time!") and where students are given ample **freedom**, for example to choose their own research question, location and timing (perhaps from a selection of [case studies](../assignments/case_studies_virtualship.ipynb)). -That being said, VirtualShip is a flexible platform and can be used in a variety of ways, from highly structured to more open-ended activities. +```{note} +Check out evaluations of the VirtualShip Classroom in published paper(s) [here](https://virtualship.parcels-code.org/publications), for more information on the pedagogical approach! +``` + +That being said, the VirtualShip Classroom is a flexible platform and can be used in a variety of ways, from highly structured to more open-ended activities. + +## Lesson plan approaches + +The 'core' implementation of the VirtualShip Classroom has traditionally followed a structure of: + +1. Background lecture on observing the ocean and research methods +2. Introduction to the VirtualShip software (if applicable) +3. In-class tutorials and exercises +4. Assignment hand-in (presentation or article) and feedback. + +**More detail on all of these components (including links to lecture slides etc.) can be found in the [example lesson plans](lesson_plans.md) documentation.** + +### Adding a VR component + +The example lesson plans referred to above suggest including the VR component in your teaching. This is not a requirement, but we do recommend it as it can enhance the learning experience and provide students with a more immersive understanding of the challenges of sea-based research. + +All the videos, available on [YouTube](https://www.youtube.com/@VirtualShipClassroom), can be viewed on students' own devices using their mouse to view in 360° if on a laptop/desktop or by moving their devices if watching on a mobile device. However, if the facilities exist at your instition, or you have access to VR headsets, you could use videos in a full VR environment. For more advice on how to set this up, please get in touch with the VirtualShip Team at [virtualship@uu.nl](mailto:virtualship.uu.nl). + + ## Setting up a programming environment -There are broadly two ways to set up VirtualShip for teaching: +```{tip} +If you are opting to use the VirtualShip Classroom without the `VirtualShip` software, there is no need to set up a programming environment for your students. You can refer to the Open Education Resources mentioned in the relevant [example lesson plan](./lesson_plans.md/#not-using-the-software) and/or the [VR component](#adding-a-vr-component) (if you choose to use it). + +❗️ Please do still ask students to complete the end-of-course survey (see [below](#end-of-course-survey)) so that we can collect feedback on their experience with the VirtualShip Classroom. +``` + +There are broadly two ways to set up the `VirtualShip` software for teaching: 1. Each student uses a local installation of the software (on their own device), installed via a package manager such as `pip`, `conda` or `pixi`. 2. A software environment is pre-configured on a cloud-based platform. Option 1) requires less preparation but can be more challenging for students to set up (especially if inexperienced) with frequent machine-dependent issues (and a lot of time spent on troubleshooting during lesson time!). Option 2) requires more preparation as the course convenor but is generally easier to support in-class, especially for larger groups. It also has the advantage that all students are working with the same resources, versions and infrastructure, which is beneficial for reproducibility and fairness. -In previous implementations at Utrecht University (where VirtualShip originated), we have primarily used Option 2) on the [SURF Research Cloud](https://www.surf.nl/en/services/compute/surf-research-cloud)). +In previous implementations at Utrecht University (where `VirtualShip` originated), we have primarily used Option 2) on the [SURF Research Cloud](https://www.surf.nl/en/services/compute/surf-research-cloud)). ### Local installation - +Students can install the `VirtualShip` software on their own devices using `conda` from the command line: + +```bash +# create a new conda environment called 'virtualship' and install the software from the conda-forge channel +conda create -n virtualship -c conda-forge virtualship + +# activate the environment +conda activate virtualship +``` + +This creates an environment named `virtualship` with the latest version of the `VirtualShip` software installed. Students can then run the software from the command line in this environment. ```{tip} If you have access to a computer lab, you may also consider installing the software on the those machines. This is similar to a local installation but can bring similar benefits to a cloud-based environment (i.e. each student has the same resources), but with less flexibility for students to work from home or on their own devices. @@ -43,14 +88,50 @@ This documentation focuses on a set up specifically on the [SURF Research Cloud] Note, the SURF Research Cloud is only available to Dutch institutions. Other cloud-based platforms (e.g. Google Colab, Binder, etc.) could be used as well but we have not extensively tested these platforms. ``` -For detailed instructions on how to set up the pre-configured environment on the SURF Research Cloud, please refer to the SURF Research Cloud set up guide: +For detailed instructions on how to set up the pre-configured VirtualShip environment on the SURF Research Cloud, please refer to the set up guide below: ```{nbgallery} surf_set_up.md ``` -## Simulating Real Life Challenges +#### Student access to the workspace + +When students log in to the SURF Research Cloud and click to access the workspace, they will be taken to a JupyterLab environment. This is where they can run the VirtualShip software and work on their expeditions. + +```{important} +A known issue is that students may hit a server error when accessing the workspace. If this happens, keep on trying (refresh), as the server spin-up can be a bit overloaded at times, but should get through eventually. + +Unfortunately this is out of our control. Clearing your browser cache and cookies, and/or trying via an incognito/private window may also help. We find that with persistence, the workspace will eventually load for all users. +``` + +We recommend distributing the following instructions sheet to your students once you have invited them to the workspace and ahead of first using the `VirtualShip` software, which outlines how to access the workspace and initialise the pre-configured environment in their respective account spaces: + +```{nbgallery} + +surf_student_access.md +``` + +```{note} +If you, as the course convenor/workspace owner, would also like to use the `VirtualShip` software, you will also need to carry out the steps in the instructions sheet above to initialise the environment in your own account space, as a one-time set up step. +``` + +#### Collaboration amongst students + +We often recommend that students work in small groups (e.g. 2-3 students) for their VirtualShip projects. Each student should have their own account/access to the workspace and they can work from the same sub-directory in the `/data/{storage-name}` storage space. + +Unfortunately, though, the SURF Research Cloud does not currently support smooth, simultaneous collaboration on the same files in the workspace. This means that students will need to coordinate amongst themselves to ensure that they are not overwriting each other's work. You can refer the students to the file permissions tutorial below for more information on how to arrange access to each other's files and directories in the workspace: + +```{nbgallery} + +file_permissions.md +``` + +## Sailing the ship + +### Information on MFP + +### Simulating Real Life Challenges @@ -64,6 +145,7 @@ surf_set_up.md ```{important} We would be really grateful for your help in collecting feedback from your students on their experience with VirtualShip. This will help us to improve the platform, research its impact and to better understand how it is being used in different contexts. + Please distribute the following survey link to your students at the end of the course: _____ ``` From 550cfc0c134464abd9e73d712fb3e2707943a64a Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Fri, 21 Aug 2026 14:48:00 +0200 Subject: [PATCH 93/94] more content to train the teacher; update the sail the ship instructions to make more generally applicable --- docs/conf.py | 3 +- .../assignments/Sail_the_ship.ipynb | 322 ------------------ docs/user-guide/assignments/sail_the_ship.md | 216 ++++++++++++ .../train-the-teacher/surf_set_up.md | 2 +- .../train-the-teacher/train_the_teacher.md | 36 +- 5 files changed, 254 insertions(+), 325 deletions(-) delete mode 100644 docs/user-guide/assignments/Sail_the_ship.ipynb create mode 100644 docs/user-guide/assignments/sail_the_ship.md diff --git a/docs/conf.py b/docs/conf.py index 9820c305..7438c0af 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -70,11 +70,12 @@ always_document_param_types = True nbsphinx_thumbnails = { + "user-guide/quickstart": "user-guide/_images/AnnaWeber.jpeg", "user-guide/assignments/Research_proposal_intro": "user-guide/_images/MFPtimeline.jpg", "user-guide/assignments/Research_Proposal_only": "user-guide/_images/MFP.jpg", "user-guide/assignments/Virtualship_research_proposal": "user-guide/_images/AnnaWeber.jpeg", "user-guide/assignments/sciencecommunication_assignment": "user-guide/_images/marine_ss.jpg", - "user-guide/assignments/Sail_the_ship": "user-guide/_images/freepik_research_vessel.jpg", + "user-guide/assignments/sail_the_ship": "user-guide/_images/freepik_research_vessel.jpg", "user-guide/assignments/Code_of_conduct": "user-guide/_images/freepik_code_of_conduct.jpg", "user-guide/teacher-content/ILOs": "user-guide/_images/ILOs.jpg", "user-guide/teacher-content/UU-ocean-of-future/Tutorial1": "user-guide/_images/freepik_assignment.png", diff --git a/docs/user-guide/assignments/Sail_the_ship.ipynb b/docs/user-guide/assignments/Sail_the_ship.ipynb deleted file mode 100644 index db2562b7..00000000 --- a/docs/user-guide/assignments/Sail_the_ship.ipynb +++ /dev/null @@ -1,322 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Sail the ship\n", - "\n", - "```\n", - "Note: This guide is specific to students who are enrolled at Utrecht University.\n", - "```\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Welcome aboard VirtualShip!\n", - "\n", - "Welcome aboard, oceanography students, to our scientific research vessel! We're thrilled to have you join us for this exciting 3-week journey into the depths of the ocean. As we embark on this voyage of exploration and discovery, there are a few things we'd like to share with you to ensure a smooth and enriching experience:\n", - "\n", - "**Introduction to the Vessel:** Take some time to familiarize yourselves with the layout of the ship. Get to know key areas such as the laboratories, living quarters, dining area, and deck spaces. E.g. at https://viewer.foleon.com/preview/vo2PZClgA/rv-wim-wolff\n", - "\n", - "**Daily Routine:** Life on a research vessel follows a structured daily routine. We'll have designated times for meals, research activities, data analysis, and downtime. It's important to maintain this routine to ensure that our work is conducted efficiently and that everyone onboard has the opportunity to rest and recharge.\n", - "\n", - "**Safety Orientation:** Safety is our top priority. As we set sail, we'll conduct a comprehensive safety orientation. This will cover important topics such as emergency procedures, the location of safety equipment, and proper use of personal protective gear. Please pay close attention during this orientation to ensure your safety and the safety of others on board.\n", - "\n", - "**Respect for the Environment:** As we explore the ocean, it's essential to maintain a deep respect for the marine environment. We'll adhere to strict environmental protocols to minimize our impact on marine ecosystems and wildlife. Remember to dispose of waste properly and avoid disturbing marine life whenever possible.\n", - "\n", - "**Teamwork and Collaboration:** Oceanographic research is a collaborative effort that requires teamwork and cooperation. You'll be working closely with your fellow students. Embrace the opportunity to learn from each other and support one another throughout the journey." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Emergency procedures\n", - "\n", - "Before going on any research expedition you need follow a one day Safety at Sea course and get a medical check-up.\n", - "\n", - "Of course this is not needed for your virtual fieldwork, but we would like to draw for your attention to the following on-board emergency procedures.\n", - "\n", - "**Safety Drills:** We conduct regular safety drills to ensure that everyone on board is well-prepared in case of an emergency. Specifically, fire and boat drills are held once a week. These drills are not just routine; they are essential survival training and should be taken seriously. To minimize disruption to the research program, science party members are usually notified in advance of the scheduled drills. In the event that you must continue working during a drill, prior arrangements can be made through the Chief Scientist.\n", - "\n", - "**Emergency Signal:** In the event of an emergency signal, your immediate action is crucial. Don life jackets, put on long-sleeved garments, and wear a hat or head covering if available. Then, proceed to the designated station indicated on the station card located next to your bunk.\n", - "\n", - "**On-Call Readiness:** Please keep in mind that while on board the ship, you may be called upon without warning to assist during your off-watch periods. Emergencies can happen at any time, and your readiness to respond promptly and efficiently is essential to the safety of all aboard.\n", - "\n", - "**Boat Drill (Abandon Ship):** The signal for abandon ship is seven or more short blasts followed by one long blast of the ship’s whistle and general alarm. When this signal is heard, report to your designated life raft station. There the Mate in charge will explain the procedures for launching and embarking into the life rafts. The rafts will not be launched during a drill.\n", - "\n", - "**Fire and Emergency Drills:** The signal is one long blast on the ship’s whistle and general alarm bell, lasting for ten seconds or more. During this drill, members of the science party muster in the designated area. Attendance will be taken and reported to the bridge.\n", - "\n", - "**Man Overboard:** If someone falls overboard, throw a life-ring into the water towards the person. Keep your eye on the person at all times and point towards the person. Shout “MAN OVERBOARD, STARBOARD (or PORT),” and call the bridge on the sound powered phone or squawk box to inform them without losing sight of the person if possible. If you hear someone hail \"Man Overboard,\" pass the word to the bridge." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Your virtual expedition...\n", - "\n", - "Now let's get started on running your VirtualShip expedition. Follow the steps below to set up your coding environment, plan your expedition, and launch your simulation. \n", - "\n", - "

\n", - "Before that though, make sure you have had your research question approved by your instructor!\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1) Register with the Copernicus Marine Data Store\n", - "\n", - "You will need to register for **Copernicus Marine Service** account (see [here](https://data.marine.copernicus.eu/register)), if you have not done so already. This is required to access the oceanographic data that VirtualShip uses to run your expedition." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2) Set up your virtual machine\n", - "\n", - "In the class, we will use VirtualShip in the cloud (in this case, SURF Research Cloud - called SURF RC from here-on). This has several advantages:\n", - "\n", - "- You aren't limited to the power of your laptop.\n", - "- The environment is pre-configured with all necessary software and dependencies.\n", - "\n", - "**Follow the instructions [here](https://virtualship.readthedocs.io/en/latest/user-guide/tutorials/surf_research_cloud_setup.html) to set up your SURF RC environment for VirtualShip.**\n", - "\n", - "
\n", - "**Note**: If you have Anaconda installed on your local machine and would like to run VirtualShip locally instead of on SURF RC, please see [VirtualShip - Installation](https://virtualship.readthedocs.io/en/latest/#installation) for instructions.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 3) Expedition route planning\n", - "\n", - "### NIOZ MFP tool\n", - "\n", - "The first step is to plan the expedition route for your chosen research question, bearing in mind the time needed for your sampling strategy, and traveling to and from a port suitable for research vessels (remember though, you have a three week ship time limit).\n", - "\n", - "Your route can be created with the online [NIOZ MFP tool](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#). Documentation on how to use the website can be found [here](https://surfdrive.surf.nl/files/index.php/s/84TFmsAAzcSD56F). Alternatively, you can watch this [video](https://www.youtube.com/watch?v=yIpYX2xCvsM&list=PLE-LzO7kk1gLM74U4PLDh8RywYXmZcloz&ab_channel=VirtualShipClassroom), which runs through how to use the MFP tool.\n", - "\n", - "\n", - "### Export the coordinates from MFP\n", - "\n", - "Once you have finalised your MFP expedition route, select \"Export\" on the right hand side of the window --> \"Export Coordinates\" --> \"DD\". This will download your coordinates as an .xlsx (Excel) file, which we will later feed into the VirtualShip protocol to initialise the expedition.\\\n", - "\n", - "### Upload the coordinates to your virtual machine\n", - "\n", - "
\n", - "**Important**: _If you have not done so already_, make sure you create a folder for your group's expedition data in the persistent storage on SURF RC (i.e. the `data/storage/` folder). You can do so by running `mkdir /data/storage/{your-group-name}` in Terminal, replacing `{your-group-name}` with your actual group name, or by using the \"New Folder\" button in the JupyterLab file explorer panel.\n", - "
\n", - "\n", - "Back in the SURF RC JupyterLab interface, use the **file explorer** on the left hand side to navigate to the directory where your group will be running your expedition (i.e. `data/storage/{your-group-name}`). \n", - "\n", - "Then upload the exported .xlsx file (it will be called something like \"Coordinates-20251125T1403.xlsx\") by either dragging and dropping it from your laptop's Downloads into the file explorer, or by using the \"Upload Files\" button (the icon with an upward arrow) at the top of the file explorer panel." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 4) Expedition initialisation\n", - "\n", - "Open a Terminal window if you do not already have one open. Remember, this can be done from the Launcher tab by clicking on \"Terminal\" button under the \"Other\" section, or by going to the \"File\" menu --> \"New\" --> \"Terminal\".\n", - "\n", - "
\n", - "**Important**: Once in Terminal, navigate to where you would like your expedition to be run on your (virtual) machine. You can do so by `cd /data/storage/{your-group-name}`, replacing `{your-group-name}` with your actual group name. This is where you will be working from for the rest of the session.\n", - "
\n", - "\n", - "Now enter the following command in the Terminal (changing `EXPEDITION_NAME` to something more meaningful for your group's expedition):\n", - "\n", - "`virtualship init EXPEDITION_NAME --from-mfp {CoordinatesExport}.xlsx`\n", - "\n", - "
\n", - "**Tip**: The `{CoordinatesExport}.xlsx` in the command above refers to the .xlsx file exported from MFP and uploaded to your virtual machine earlier. Replace the filename with the name of your .xlsx file.\n", - "
\n", - "\n", - "This will create a folder/directory called `EXPEDITION_NAME` (or what you have changed this to) with a single file: `expedition.yaml`. This file contains details on the ship and instrument configurations, as well as the expedition schedule based on the sampling site coordinates that you specified in your MFP export. The `--from-mfp` flag indicates that the exported coordinates should be used." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 5) Expedition scheduling & ship configuration\n", - "\n", - "
\n", - "**Tip**: From here, you should replace any references to `EXPEDITION_NAME` with the actual name you used for your expedition when running any `virtualship` commands.\n", - "
\n", - "\n", - "The next step is to finalise the expedition schedule plan, including setting times and instrument selection choices for each waypoint, as well as configuring the ship (including any underway measurement instruments). \n", - "\n", - "
\n", - "**Note**: This section describes the process of finalising the expedition schedule and instrument selection using the `virtualship plan` application. For expeditions with many waypoints, it can become cumbersome to use the planning tool (note, using VirtualShip in a remote terminal / cloud-based environment can also introduce lag in the user-interface). **In this case, you may prefer to edit the** `expedition.yaml` **file directly (see [here](../tutorials/working_with_expedition_yaml.md) for more details on how to do so)**.\n", - "
\n", - "\n", - "\n", - "The easiest way to do so is to use the bespoke VirtualShip planning tool. Enter the following command in Terminal: `virtualship plan EXPEDITION_NAME`.\n", - "\n", - "
\n", - "**TIP**: Using the `virtualship plan` tool is optional. Advanced users can also edit the `expedition.yaml` file directly if preferred.\n", - "
\n", - "\n", - "### Ship speed\n", - "\n", - "In the planning tool which appears, under _Ship Config Editor_ > _Ship Speed & Onboard Measurements_, there is an option to change the ship speed. However, for this course, you should leave this as the default **10 knots** value.\n", - "\n", - "### Underway measurements\n", - "\n", - "VirtualShip is capable of taking underway temperature and salinity measurements, as well as onboard ADCP measurements, as the ship sails across the length of the expedition (see [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Underway-Data) for more detail). These underway measurements can be switched on/off under _Ship Config Editor_ > _Ship Speed & Onboard Measurements_ as well.\n", - "\n", - "For the underway ADCP, there is a choice of using the 38 kHz OceanObserver or the 300 kHz SeaSeven version (see [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#ADCP) for more detail on the two ADCP types).\n", - "\n", - "### Instrument/sensor configuration\n", - "\n", - "The most important instrument configuration setting to consider is the list of **sensors** for each instrument, which controls what type of measurements/variables the instrument records in the simulation and therefore what output data you will receive for each instrument.\n", - "\n", - "Sensor lists can be configured for each instrument under _Ship Config Editor_ > _Instrument Configurations_. For example, for the CTD instrument, you can specify which sensors to include in the simulation (e.g., `TEMPERATURE`, `SALINITY`, `OXYGEN`, etc.) by toggling the respective switches on or off.\n", - "\n", - "
\n", - "**Note**: Sensor choices are only relevant for the instruments you plan to deploy as [underway measurements](#underway-measurements) or at waypoints across your expedition schedule [(see below)](#instrument-selection). For example, if you do not select to deploy a CTD at any of your waypoints, the CTD sensor choices will not affect any output data.\n", - "
\n", - "\n", - "
\n", - "**TIP**: See [here](../documentation/full_sensor_list.md) for more information on the sensors available for each instrument.\n", - "
\n", - "\n", - "There are other instrument configurations settings that can be adjusted in the editor as well (e.g. `max_depth` for the CTD), but these are more advanced and in most cases do not need to be changed from the default values.\n", - "\n", - "### Waypoint datetimes\n", - "\n", - "
\n", - "**Note**: VirtualShip supports running experiments in the years 1993 through to the present day by leveraging the suite of products available on the Copernicus Marine Data Store.\n", - "
\n", - "\n", - "You will need to enter dates and times for each of the sampling stations/waypoints selected in the MFP route planning stage. This can be done under _Schedule Editor_ > _Waypoints & Instrument Selection_ in the planning tool.\n", - "\n", - "Each waypoint has its own sub-panel for parameter inputs (click on it to expand the selection options). Here, the time for each waypoint can be inputted. There is also an option to adjust the latitude/longitude coordinates and you can add or remove waypoints.\n", - "\n", - "
\n", - "**Note**: It is important to ensure that the timings for each station are realistic. There must be enough time for the ship to travel to each site at the prescribed speed (10 knots). The expedition schedule will be automatically verified when you press _Save Changes_ in the planning tool.\n", - "
\n", - "\n", - "
\n", - "**Tip**: The MFP route planning tool will give estimated durations of sailing between sites at the 10 knots sailing speed. This can be useful to refer back to when planning the expedition timings and entering these into the `virtualship plan` tool.\n", - "
\n", - "\n", - "### Instrument selection\n", - "\n", - "You should now consider which measurements are to be taken at each sampling site (think about those required for your chosen research question), and therefore which instruments need to be selected in the planning tool at each waypoint.\n", - "\n", - "
\n", - "**Tip**: Click [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Measurement-Options) for more information on which instruments are available in VirtualShip, and a brief introduction to each.\n", - "
\n", - "\n", - "You can make instrument selections for each waypoint in the same sub-panels as the [waypoint time](#waypoint-datetimes) selection by simply switching each on or off. Multiple instruments are allowed at each waypoint.\n", - "\n", - "\n", - "### Save changes\n", - "\n", - "When you are happy with your ship configuration and schedule plan, press _Save Changes_ at the bottom of the planning tool.\n", - "\n", - "
\n", - "**Note**: On pressing _Save Changes_ the tool will check the selections are valid (for example that the ship will be able to reach each waypoint in time). If they are, the changes will be saved to the `expedition.yaml` file, ready for the next steps. If your selections are invalid you should be provided with information on how to fix them.\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 6) Run the expedition\n", - "\n", - "You are now ready to run your virtual expedition! This stage will take all the measurements for each of instruments you selected at each waypoint in your expedition schedule, using input data sourced from the [Copernicus Marine Data Store](https://data.marine.copernicus.eu/products).\n", - "\n", - "
\n", - "**Note**: You will need to register for a Copernicus Marine Service account (you can do so [here](https://data.marine.copernicus.eu/register)), if you have not done so already.\n", - "
\n", - "\n", - "You can run your expedition simulation using the command: \n", - "\n", - "`virtualship run EXPEDITION_NAME`\n", - "\n", - "If this is your first time running VirtualShip, you will be prompted to enter your own Copernicus Marine Data Store credentials (these will be saved automatically for future use).\n", - "\n", - "Small simulations (e.g. small space-time domains and fewer instrument deployments) will be relatively fast. For large, complex expeditions, it _could_ take up to an hour to simulate the measurements depending on your choices. Waiting for simulation is a great time to practice your level of patience. A skill much needed in oceanographic fieldwork ;-)\n", - "\n", - "
\n", - "**Important**: VirtualShip may encounter 'real-life challenges' during the expedition, which simulate the various problems and unexpected events that can occur during real-life oceanographic expeditions (e.g. instrument and/or equipment failure, logistical challenges etc.). These may require your intervention to ensure your expedition schedule can continue!\n", - "
" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 7) Results\n", - "\n", - "Upon successfully completing the simulation, results from the expedition will be stored in the `EXPEDITION_NAME/results` directory, written in `.parquet` [format](https://parquet.apache.org/).\n", - "\n", - "From here you can carry on your analysis. In general, we encourage you to use [Parcels](https://Parcels-code.org/) (i.e. `parcels.read_particlefile()`) to read in VirtualShip output files, and tools such as [Polars](https://www.pola.rs/) and/or [Pandas](https://pandas.pydata.org/) for further data analysis. We also provide various further [VirtualShip tutorials](https://virtualship.readthedocs.io/en/latest/user-guide/tutorials/index.html) which provide examples of how to visualise data recorded by the VirtualShip instruments. Use these to help you get started!\n", - "\n", - "If you are using VirtualShip in class, the same tutorial notebooks may be uploaded in your SURF RC environment for you to use and interact directly with the code. These should be available in e.g. the `data/storage/tutorials/` directory. You will notice that there is a notebook file dedicated to visualising each of the different instruments available in VirtualShip. \n", - "\n", - "To run these notebooks with your own data, you will need to copy the them over to your expedition working directory (i.e. `data/storage/{your-group-name}`). This can be done by either 1) using the file explorer panel in JupyterLab to copy the relevant files or the via the command line in Terminal. In the terminal, running `cp -r /data/storage/tutorials/* /data/storage/{your-group-name}/` would copy __all__ the tutorial notebooks to your group's directory, so if you only want to copy specific ones, make sure to adjust the command accordingly." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Reporting\n", - "\n", - "Reporting your journey is an essential aspect of our oceanographic research expedition. It allows us to share our experiences, communicate our findings, and contribute to the broader scientific community. After each scientific expedition a cruise report should be written (or in the case of this course, a presentation).\n", - "\n", - "You can find many cruise [reports](https://www.bodc.ac.uk/resources/inventories/cruise_inventory/reports/pe358.pdf) and [blogs](https://www.nioz.nl/en/news-and-blogs) online from many different cruises.\n", - "\n", - "Reporting our journey allows us to validate the data collected during our research activities. It provides context for our findings and helps ensure that our results are accurately interpreted and understood. Detailed reports enable us to cross-reference our observations with environmental conditions, sampling locations, and other relevant factors, enhancing the reliability and credibility of our data. \n", - "\n", - "Our reports also serve as valuable educational resources for students, educators, and the general public. They provide insights into the process of scientific inquiry, the challenges of conducting research at sea, and the significance of oceanographic discoveries. \n", - "\n", - "We look forward to seeing the impact of your collective efforts during the presentations in a few weeks time!\n", - "\n", - "Please don't worry if your results are insufficient to answer your research question. Share your failure and things you would do different a next time instead!\n", - "\n", - "For example:\n", - "\n", - "- [Normalizing failure: when things go wrong in participatory marine social science fieldwork](https://publications.csiro.au/publications/publication/PIcsiro:EP2022-3465).\n", - "- [Emotions and failure in academic life: Normalising the experience and building resilience](https://www.cambridge.org/core/journals/journal-of-management-and-organization/article/emotions-and-failure-in-academic-life-normalising-the-experience-and-building-resilience/91FD71A50A32404D8EDFFB7886FF3521).\n", - "\n", - "---" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "ship", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/docs/user-guide/assignments/sail_the_ship.md b/docs/user-guide/assignments/sail_the_ship.md new file mode 100644 index 00000000..9765dd6f --- /dev/null +++ b/docs/user-guide/assignments/sail_the_ship.md @@ -0,0 +1,216 @@ +# Sail the ship + +## Welcome aboard VirtualShip! + +Welcome aboard, oceanography students, to our scientific research vessel! We're thrilled to have you join us for this exciting journey into the depths of the ocean. As we embark on this voyage of exploration and discovery, there are a few things we'd like to share with you to ensure a smooth and enriching experience: + +**Introduction to the Vessel:** Take some time to familiarise yourselves with the layout of the ship. Get to know key areas such as the laboratories, living quarters, dining area, and deck spaces. + +**Daily Routine:** Life on a research vessel follows a structured daily routine. We'll have designated times for meals, research activities, data analysis, and downtime. It's important to maintain this routine to ensure that our work is conducted efficiently and that everyone onboard has the opportunity to rest and recharge. + +**Safety Orientation:** Safety is our top priority. As we set sail, we'll conduct a comprehensive safety orientation. This will cover important topics such as emergency procedures, the location of safety equipment, and proper use of personal protective gear. Please pay close attention during this orientation to ensure your safety and the safety of others on board. + +**Respect for the Environment:** As we explore the ocean, it's essential to maintain a deep respect for the marine environment. We'll adhere to strict environmental protocols to minimize our impact on marine ecosystems and wildlife. Remember to dispose of waste properly and avoid disturbing marine life whenever possible. + +**Teamwork and Collaboration:** Oceanographic research is a collaborative effort that requires teamwork and cooperation. You'll be working closely with your fellow students. Embrace the opportunity to learn from each other and support one another throughout the journey. + +## Emergency procedures + +Before going on any research expedition you need follow a one day Safety at Sea course and get a medical check-up. + +Of course this is not needed for your virtual fieldwork, but we would like to draw for your attention to the following on-board emergency procedures. + +**Safety Drills:** We conduct regular safety drills to ensure that everyone on board is well-prepared in case of an emergency. Specifically, fire and boat drills are held once a week. These drills are not just routine; they are essential survival training and should be taken seriously. To minimize disruption to the research program, science party members are usually notified in advance of the scheduled drills. In the event that you must continue working during a drill, prior arrangements can be made through the Chief Scientist. + +**Emergency Signal:** In the event of an emergency signal, your immediate action is crucial. Don life jackets, put on long-sleeved garments, and wear a hat or head covering if available. Then, proceed to the designated station indicated on the station card located next to your bunk. + +**On-Call Readiness:** Please keep in mind that while on board the ship, you may be called upon without warning to assist during your off-watch periods. Emergencies can happen at any time, and your readiness to respond promptly and efficiently is essential to the safety of all aboard. + +**Boat Drill (Abandon Ship):** The signal for abandon ship is seven or more short blasts followed by one long blast of the ship’s whistle and general alarm. When this signal is heard, report to your designated life raft station. There the Mate in charge will explain the procedures for launching and embarking into the life rafts. The rafts will not be launched during a drill. + +**Fire and Emergency Drills:** The signal is one long blast on the ship’s whistle and general alarm bell, lasting for ten seconds or more. During this drill, members of the science party muster in the designated area. Attendance will be taken and reported to the bridge. + +**Man Overboard:** If someone falls overboard, throw a life-ring into the water towards the person. Keep your eye on the person at all times and point towards the person. Shout “MAN OVERBOARD, STARBOARD (or PORT),” and call the bridge on the sound powered phone or squawk box to inform them without losing sight of the person if possible. If you hear someone hail "Man Overboard," pass the word to the bridge. + +## Your virtual expedition... + +Now let's get started on running your VirtualShip expedition. Follow the steps below to set up your coding environment, plan your expedition, and launch your simulation. + +```{note} +Before that though, make sure you have had your research question approved by your instructor! +``` + +## 1) Register with the Copernicus Marine Data Store + +You will need to register for **Copernicus Marine Service** account (see [here](https://data.marine.copernicus.eu/register)), if you have not done so already. This is required to access the oceanographic data that VirtualShip uses to run your expedition. + +## 2) Set up your virtual machine + +You will be informed by your teacher if you will be using a cloud-based, pre-configured environment for VirtualShip (e.g., SURF Research Cloud) or if you should set up a local installation of the software. Please follow the instructions provided by your teacher to set up your virtual machine accordingly. + +## 3) Expedition route planning + +### NIOZ MFP tool + +The first step is to plan the expedition route for your chosen research question, bearing in mind the time needed for your sampling strategy, and traveling to and from a port suitable for research vessels. Remember, your approved proposal from your instructor may limit the amount of ship time you have received, so make sure to plan your route accordingly! + +Your route can be created with the online [NIOZ MFP tool](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#). Documentation on how to use the website can be found [here](https://surfdrive.surf.nl/files/index.php/s/84TFmsAAzcSD56F). Alternatively, you can watch this [video](https://www.youtube.com/watch?v=yIpYX2xCvsM&list=PLE-LzO7kk1gLM74U4PLDh8RywYXmZcloz&ab_channel=VirtualShipClassroom), which runs through how to use the MFP tool. + +### Export the coordinates from MFP + +Once you have finalised your MFP expedition route, select "Export" on the right hand side of the window --> "Export Coordinates" --> "DD". This will download your coordinates as an .xlsx (Excel) file, which we will later feed into the VirtualShip protocol to initialise the expedition.\ + +### _If using the SURF Research Cloud_... upload the coordinates to your virtual machine + +```{important} +If you have not done so already, make sure you create a folder for your group's expedition data in the persistent storage space. You can do so by running e.g. `mkdir /data/virtualship-storage/{your-group-name}` in Terminal, replacing `{your-group-name}` with your actual group name, or by using the "New Folder" button in the JupyterLab file explorer panel. +``` + +Back in the JupyterLab interface, use the **file explorer** on the left hand side to navigate to the directory where your group will be running your expedition (e.g. `data/virtualship-storage/{your-group-name}`). + +Then upload the exported `.xlsx` file (it will be called something like `Coordinates-20251125T1403.xlsx`) by either dragging and dropping it from your laptop's Downloads into the file explorer, or by using the "Upload Files" button (the icon with an upward arrow) at the top of the file explorer panel. + +## 4) Expedition initialisation + +Open a Terminal window if you do not already have one open. Remember, this can be done from the Launcher tab by clicking on "Terminal" button under the "Other" section, or by going to the "File" menu --> "New" --> "Terminal". + +```{important} +Once in Terminal, navigate to where you would like your expedition to be run on your (virtual) machine. You can do so by `cd /data/virtualship-storage/{your-group-name}`, replacing `{your-group-name}` with your actual group name. This is where you will be working from for the rest of the session. +``` + +Now enter the following command in the Terminal (changing `EXPEDITION_NAME` to something more meaningful for your group's expedition): + +`virtualship init EXPEDITION_NAME --from-mfp {CoordinatesExport}.xlsx` + +```{tip} +The `{CoordinatesExport}.xlsx` in the command above refers to the `.xlsx` file exported from MFP and uploaded to your virtual machine earlier. Replace the filename with the name of your own file. +``` + +This will create a folder/directory called `EXPEDITION_NAME` (or what you have changed this to) with a single file: `expedition.yaml`. This file contains details on the ship and instrument configurations, as well as the expedition schedule based on the sampling site coordinates that you specified in your MFP export. The `--from-mfp` flag indicates that the exported coordinates should be used. + +## 5) Expedition scheduling & ship configuration + +```{tip} +From here, you should replace any references to `EXPEDITION_NAME` with the actual name you used for your expedition when running any `virtualship` commands. +``` + + + +The next step is to finalise the expedition schedule plan, including setting times and instrument selection choices for each waypoint, as well as configuring the ship (including any underway measurement instruments). + +```{note} +This section describes the process of finalising the expedition schedule and instrument selection using the `virtualship plan` application. For expeditions with many waypoints, it can become cumbersome to use the planning tool (note, using VirtualShip in a remote terminal / cloud-based environment can also introduce lag in the user-interface). **In this case, you may prefer to edit the** `expedition.yaml` **file directly (see [here](../tutorials/working_with_expedition_yaml.md) for more details on how to do so)**. +``` + +The easiest way to do so is to use the bespoke VirtualShip planning tool. Enter the following command in Terminal: `virtualship plan EXPEDITION_NAME`. + +### Ship speed + +In the planning tool which appears, under _Ship Config Editor_ > _Ship Speed & Onboard Measurements_, there is an option to change the ship speed. However, for this course, you should leave this as the default **10 knots** value. + +### Underway measurements + +VirtualShip is capable of taking underway temperature and salinity measurements, as well as onboard ADCP measurements, as the ship sails across the length of the expedition (see [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Underway-Data) for more detail). These underway measurements can be switched on/off under _Ship Config Editor_ > _Ship Speed & Onboard Measurements_ as well. + +For the underway ADCP, there is a choice of using the 38 kHz OceanObserver or the 300 kHz SeaSeven version (see [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#ADCP) for more detail on the two ADCP types). + +### Instrument/sensor configuration + +The most important instrument configuration setting to consider is the list of **sensors** for each instrument, which controls what type of measurements/variables the instrument records in the simulation and therefore what output data you will receive for each instrument. + +Sensor lists can be configured for each instrument under _Ship Config Editor_ > _Instrument Configurations_. For example, for the CTD instrument, you can specify which sensors to include in the simulation (e.g., `TEMPERATURE`, `SALINITY`, `OXYGEN`, etc.) by toggling the respective switches on or off. + +```{note} +Sensor choices are only relevant for the instruments you plan to deploy as [underway measurements](#underway-measurements) or at waypoints across your expedition schedule [(see below)](#instrument-selection). For example, if you do not select to deploy a CTD at any of your waypoints, the CTD sensor choices will not affect any output data. +``` + +```{tip} +See [here](../documentation/full_sensor_list.md) for more information on the sensors available for each instrument. +``` + +There are other instrument configurations settings that can be adjusted in the editor as well (e.g. `max_depth` for the CTD), but these are more advanced and in most cases do not need to be changed from the default values. + +### Waypoint datetimes + +```{note} +VirtualShip supports running experiments in the years 1993 through to the present day by leveraging the suite of products available on the Copernicus Marine Data Store. +``` + +You will need to enter dates and times for each of the sampling stations/waypoints selected in the MFP route planning stage. This can be done under _Schedule Editor_ > _Waypoints & Instrument Selection_ in the planning tool. + +Each waypoint has its own sub-panel for parameter inputs (click on it to expand the selection options). Here, the time for each waypoint can be inputted. There is also an option to adjust the latitude/longitude coordinates and you can add or remove waypoints. + +```{note} +It is important to ensure that the timings for each station are realistic. There must be enough time for the ship to travel to each site at the prescribed speed (10 knots). The expedition schedule will be automatically verified when you press _Save Changes_ in the planning tool. +``` + +```{tip} +The MFP route planning tool will give estimated durations of sailing between sites at the 10 knots sailing speed. This can be useful to refer back to when planning the expedition timings and entering these into the `virtualship plan` tool. +``` + +### Instrument selection + +You should now consider which measurements are to be taken at each sampling site (think about those required for your chosen research question), and therefore which instruments need to be selected in the planning tool at each waypoint. + +```{tip} +Click [here](https://virtualship.readthedocs.io/en/latest/user-guide/assignments/Research_proposal_intro.html#Measurement-Options) for more information on which instruments are available in VirtualShip, and a brief introduction to each. +``` + +You can make instrument selections for each waypoint in the same sub-panels as the [waypoint time](#waypoint-datetimes) selection by simply switching each on or off. Multiple instruments are allowed at each waypoint. + +### Save changes + +When you are happy with your ship configuration and schedule plan, press _Save Changes_ at the bottom of the planning tool. + +```{note} +On pressing _Save Changes_ the tool will check the selections are valid (for example that the ship will be able to reach each waypoint in time). If they are, the changes will be saved to the `expedition.yaml` file, ready for the next steps. If your selections are invalid you should be provided with information on how to fix them. +``` + +## 6) Run the expedition + +You are now ready to run your virtual expedition! This stage will take all the measurements for each of instruments you selected at each waypoint in your expedition schedule, using input data sourced from the [Copernicus Marine Data Store](https://data.marine.copernicus.eu/products). + +```{note} +You will need to register for a Copernicus Marine Service account (you can do so [here](https://data.marine.copernicus.eu/register)), if you have not done so already. +``` + +You can run your expedition simulation using the command: + +`virtualship run EXPEDITION_NAME` + +If this is your first time running VirtualShip, you will be prompted to enter your own Copernicus Marine Data Store credentials (these will be saved automatically for future use). + +Small simulations (e.g. small space-time domains and fewer instrument deployments) will be relatively fast. For large, complex expeditions, it _could_ take up to an hour to simulate the measurements depending on your choices. Waiting for simulation is a great time to practice your level of patience. A skill much needed in oceanographic fieldwork ;-) + +```{important} +VirtualShip may encounter 'real-life challenges' during the expedition, which simulate the various problems and unexpected events that can occur during real-life oceanographic expeditions (e.g. instrument and/or equipment failure, logistical challenges etc.). These may require your intervention to ensure your expedition schedule can continue! +``` + +## 7) Results + +Upon successfully completing the simulation, results from the expedition will be stored in the `EXPEDITION_NAME/results` directory, written in `.parquet` [format](https://parquet.apache.org/). + +From here you can carry on your analysis. In general, we encourage you to use [Parcels](https://Parcels-code.org/) (i.e. `parcels.read_particlefile()`) to read in VirtualShip output files, and tools such as [Polars](https://www.pola.rs/) and/or [Pandas](https://pandas.pydata.org/) for further data analysis. We also provide various further [VirtualShip tutorials](https://virtualship.readthedocs.io/en/latest/user-guide/tutorials/index.html) which provide examples of how to visualise data recorded by the VirtualShip instruments. Use these to help you get started! + +If you are using VirtualShip in class, the same tutorial notebooks may be uploaded in your SURF Research Cloud environment for you to use and interact directly with the code (ask your teacher!). If so, these should be available in e.g. the `data/virtualship-storage/tutorials/` directory. You will notice that there is a notebook file dedicated to visualising each of the different instruments available in VirtualShip. + +To run these notebooks with your own data, you will need to copy the them over to your expedition working directory (i.e. `data/storage/{your-group-name}`). This can be done by either 1) using the file explorer panel in JupyterLab to copy the relevant files or the via the command line in Terminal. In the terminal, running `cp -r /data/storage/tutorials/* /data/storage/{your-group-name}/` would copy **all** the tutorial notebooks to your group's directory, so if you only want to copy specific ones, make sure to adjust the command accordingly. + +## Reporting + +Reporting your journey is an essential aspect of our oceanographic research expedition. It allows us to share our experiences, communicate our findings, and contribute to the broader scientific community. After each scientific expedition a cruise report should be written (or potentially in the case of this course, a presentation). + +You can find many cruise [reports](https://www.bodc.ac.uk/resources/inventories/cruise_inventory/reports/pe358.pdf) and [blogs](https://www.nioz.nl/en/news-and-blogs) online from many different cruises. + +Reporting our journey allows us to validate the data collected during our research activities. It provides context for our findings and helps ensure that our results are accurately interpreted and understood. Detailed reports enable us to cross-reference our observations with environmental conditions, sampling locations, and other relevant factors, enhancing the reliability and credibility of our data. + +Our reports also serve as valuable educational resources for students, educators, and the general public. They provide insights into the process of scientific inquiry, the challenges of conducting research at sea, and the significance of oceanographic discoveries. + +If your course assignment involves a presentation, we look forward to seeing the impact of your collective efforts during the presentations in a few weeks time! + +Please don't worry if your results are insufficient to answer your research question. Share your failure and things you would do different a next time instead! + +For example: + +- [Normalizing failure: when things go wrong in participatory marine social science fieldwork](https://publications.csiro.au/publications/publication/PIcsiro:EP2022-3465). +- [Emotions and failure in academic life: Normalising the experience and building resilience](https://www.cambridge.org/core/journals/journal-of-management-and-organization/article/emotions-and-failure-in-academic-life-normalising-the-experience-and-building-resilience/91FD71A50A32404D8EDFFB7886FF3521). diff --git a/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md index b1ed0fa2..28c4a22a 100644 --- a/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md +++ b/docs/user-guide/teacher-content/train-the-teacher/surf_set_up.md @@ -1,4 +1,4 @@ -# Set up the SURF Research Cloud (RC) +# Educator guide: Set up the SURF Research Cloud (RC) ```{note} For this guide, we will assume that you are the course convenor, already have access to the SURF Research Cloud and have credits available. diff --git a/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md b/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md index 1a1cd07a..d226109f 100644 --- a/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md +++ b/docs/user-guide/teacher-content/train-the-teacher/train_the_teacher.md @@ -129,10 +129,39 @@ file_permissions.md ## Sailing the ship -### Information on MFP +Now that the technical set up is complete, we are ready to start getting students going with using the `VirtualShip` software! 🚢 🥳 + +The Quickstart guide below provides a minimal overview of the basic commands and workflow to get started with the software... perhaps useful for you as the course convenor to get a quick overview of the software. + +However, for teaching applications we recommend distributing the student-focused **"Sail the ship"** guide below, which is designed to be more accessible and includes additional context and narrative elements for students. Many of the narrative elements won't actually be enacted, of course, but they are included to help students immerse themselves! + +```{nbgallery} + +../../quickstart.md +../../assignments/sail_the_ship.md + +``` + +### Reviewing Expedition proposals + +The "Sail the ship" guide is designed to be used in conjunction with a lesson plan similar to that presented in the [example lesson plans](lesson_plans.md) documentation. It relies on students having already chosen a research question, submitting a proposal and having it approved by their instructor/you. + +Reviewing and approving the proposals is a good time to check that students have a realistic plan for their expedition, and we find it is beneficial to provide a maximum ship time limit (e.g. 3 weeks) to ensure that students are thinking about the practicalities of their research question and sampling strategy. + +### Additional resources used in the VirtualShip workflow + +You will notice in the "Quickstart" and "Sail the Ship" guides that there are a number of additional resources used in the VirtualShip workflow. These include: + +- [Copernicus Marine Data Store](https://data.marine.copernicus.eu/) - the source of the oceanographic data used in VirtualShip +- [NIOZ Marine Facilities Planning (MFP) tool](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#) - used to plan the expedition route and generate the coordinates for the VirtualShip protocol + + + ### Simulating Real Life Challenges + + @@ -140,6 +169,11 @@ file_permissions.md +### VirtualShip output + + + + ## ❗️ End-of-course survey ```{important} From 30ed5f6e6d0240c3fc03a079617ce9b9fe9c3e78 Mon Sep 17 00:00:00 2001 From: j-atkins <106238905+j-atkins@users.noreply.github.com> Date: Mon, 24 Aug 2026 11:55:01 +0200 Subject: [PATCH 94/94] batch of updates to the train the teacher guide and associated docs --- docs/conf.py | 1 + docs/user-guide/assignments/sail_the_ship.md | 4 + .../Drifter_data_tutorial.ipynb | 366 ++++++++++++++++++ .../_images/new_workspace.png | Bin 20893 -> 20956 bytes .../train-the-teacher/file_permissions.md | 6 +- .../train-the-teacher/lesson_plans.md | 12 +- .../train-the-teacher/surf_set_up.md | 2 +- .../train-the-teacher/surf_student_access.md | 14 +- .../train-the-teacher/train_the_teacher.md | 109 ++++-- 9 files changed, 464 insertions(+), 50 deletions(-) create mode 100644 docs/user-guide/teacher-content/Drifter_data_tutorial.ipynb diff --git a/docs/conf.py b/docs/conf.py index 7438c0af..c4bc6704 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -71,6 +71,7 @@ nbsphinx_thumbnails = { "user-guide/quickstart": "user-guide/_images/AnnaWeber.jpeg", + "user-guide/tutorials/index": "user-guide/_images/AnnaWeber.jpeg", "user-guide/assignments/Research_proposal_intro": "user-guide/_images/MFPtimeline.jpg", "user-guide/assignments/Research_Proposal_only": "user-guide/_images/MFP.jpg", "user-guide/assignments/Virtualship_research_proposal": "user-guide/_images/AnnaWeber.jpeg", diff --git a/docs/user-guide/assignments/sail_the_ship.md b/docs/user-guide/assignments/sail_the_ship.md index 9765dd6f..d1b7cd9e 100644 --- a/docs/user-guide/assignments/sail_the_ship.md +++ b/docs/user-guide/assignments/sail_the_ship.md @@ -56,6 +56,10 @@ The first step is to plan the expedition route for your chosen research question Your route can be created with the online [NIOZ MFP tool](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#). Documentation on how to use the website can be found [here](https://surfdrive.surf.nl/files/index.php/s/84TFmsAAzcSD56F). Alternatively, you can watch this [video](https://www.youtube.com/watch?v=yIpYX2xCvsM&list=PLE-LzO7kk1gLM74U4PLDh8RywYXmZcloz&ab_channel=VirtualShipClassroom), which runs through how to use the MFP tool. +```{note} +The MFP tool is used by professional oceanographers to plan research expeditions, so this is a great opportunity to get a feel for how real-world oceanographic research is planned! +``` + ### Export the coordinates from MFP Once you have finalised your MFP expedition route, select "Export" on the right hand side of the window --> "Export Coordinates" --> "DD". This will download your coordinates as an .xlsx (Excel) file, which we will later feed into the VirtualShip protocol to initialise the expedition.\ diff --git a/docs/user-guide/teacher-content/Drifter_data_tutorial.ipynb b/docs/user-guide/teacher-content/Drifter_data_tutorial.ipynb new file mode 100644 index 00000000..ec22454e --- /dev/null +++ b/docs/user-guide/teacher-content/Drifter_data_tutorial.ipynb @@ -0,0 +1,366 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Drifter Trajectory Plotting\n", + "\n", + "This notebook demonstrates a simple plotting exercise for drifter trajectory data, using the output of a VirtualShip expedition. There are example plots embedded in this notebook, but these will ultimately be replaced by your own versions if you are working through the notebook with your own expedition output.\n", + "\n", + "The plots we will produce are simple plots which visualise the trajectories of the drifters released at each waypoint of the VirtualShip expedition. We will also have a look at adding the sea surface temperature recorded by the drifters as they move through the ocean. Finally, the notebook will conclude with some example questions that you can think about as you interpret the drifter trajectories, using the Agulhas region as a case study." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set up\n", + "\n", + "### Imports\n", + "\n", + "The first step is to import the Python packages required for post-processing the data and plotting. \n", + "\n", + "
\n", + "**TIP**: You may need to set the Kernel to the relevant (Conda) environment in the top right of this notebook to access the required packages! \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "import matplotlib.pyplot as plt\n", + "import cmocean.cm as cmo\n", + "import numpy as np\n", + "import cartopy.crs as ccrs\n", + "import cartopy.feature as cfeature\n", + "import matplotlib.colors as mcolors\n", + "from matplotlib.collections import LineCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Data directory\n", + "\n", + "Next, you should set `data_dir` to be the path to your expedition results in the code block below. You should replace `\"/path/to/EXPEDITION/results/\"` with the path for your machine.\n", + "\n", + "
\n", + "**TIP**: You can get the path to your expedition results by navigating to the `results` folder in Terminal (using `cd`) and then using the `pwd` command. This will print your working directory which you can copy to the `data_dir` variable in this notebook. Don't forget to keep it as a string (in \"quotation\" marks)!\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# set data directory path\n", + "\n", + "data_dir = \"/path/to/EXPEDITION/results/\" # set this to be where your expedition output data is located on your (virtual) machine" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load data\n", + "\n", + "We are now ready to read in the data using the `xarray` package. You can carry on executing the next cells without making changes to the code…" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load drifter data\n", + "\n", + "drifter_ds = xr.open_dataset(f\"{data_dir}/drifter.zarr\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting\n", + "\n", + "
\n", + "**NOTE**: The plots produced next are a starting point for your analysis. You are encouraged to make further adjustments and enhancements to suit your own data and research question.\n", + "
\n", + "\n", + "We will now produce a plot of the drifters: their release locations at each waypoint (scatter markers), and their trajectories as they move through the ocean (coloured lines).\n", + "\n", + "From this we'll be able to get a first impression of the flow dynamics in the region. Are there any eddies or other features that stand out? Do the drifters stay together or spread out over time?\n", + "\n", + "
\n", + "**TIP**: You can adjust the lifetime of your simulated drifters by changing the \"Lifetime\" parameter in the VirtualShip expedition setup (see the _Instrument Configurations_ > _Drifter_ section in the `virtualship plan` tool), if you want them to flow for longer. Note, however, this does mean you would need to re-run the expedition to generate new data with the updated lifetime.\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# fig\n", + "fig = plt.figure(figsize=(10, 6), dpi=96)\n", + "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + "\n", + "# plot trajectory\n", + "for i, traj in enumerate(drifter_ds[\"trajectory\"]):\n", + " # extract trajectory data\n", + " lons = drifter_ds[\"lon\"][:].sel(trajectory=traj).squeeze()\n", + " lats = drifter_ds[\"lat\"][:].sel(trajectory=traj).squeeze()\n", + "\n", + " # plot\n", + " ax.plot(\n", + " lons,\n", + " lats,\n", + " linestyle=\"-\",\n", + " linewidth=1.25,\n", + " zorder=3,\n", + " transform=ccrs.PlateCarree(),\n", + " )\n", + "\n", + " # add release location\n", + " MARKERSIZE = 25\n", + " ax.scatter(\n", + " lons[0],\n", + " lats[0],\n", + " marker=\"o\",\n", + " s=MARKERSIZE,\n", + " color=\"black\",\n", + " zorder=4,\n", + " transform=ccrs.PlateCarree(),\n", + " label=\"Waypoint\" if i == 0 else None, # only label first for legend\n", + " )\n", + "\n", + "\n", + "# additional map features\n", + "latlon_buffer = 3.0 # degrees (adjust this to 'zoom' in/out in the plot)\n", + "ax.set_extent(\n", + " [\n", + " drifter_ds.lon.min() - latlon_buffer,\n", + " drifter_ds.lon.max() + latlon_buffer,\n", + " drifter_ds.lat.min() - latlon_buffer,\n", + " drifter_ds.lat.max() + latlon_buffer,\n", + " ],\n", + " crs=ccrs.PlateCarree(),\n", + ")\n", + "ax.coastlines(linewidth=0.5, color=\"black\")\n", + "ax.add_feature(cfeature.LAND, facecolor=\"tan\")\n", + "gl = ax.gridlines(\n", + " draw_labels=True,\n", + " linewidth=0.5,\n", + " color=\"gainsboro\",\n", + " alpha=1.0,\n", + " linestyle=\"-\",\n", + " zorder=0,\n", + ")\n", + "gl.top_labels = False\n", + "gl.right_labels = False\n", + "\n", + "ax.legend(loc=\"upper right\", fontsize=12)\n", + "\n", + "n_days = float(\n", + " (drifter_ds[\"time\"][0].max(skipna=True) - drifter_ds[\"time\"][0].min(skipna=True))\n", + " / np.timedelta64(1, \"D\")\n", + ")\n", + "plt.title(f\"[{round(n_days, 1)} day drifter lifetime]\", fontsize=12)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add temperature data to the trajectories\n", + "\n", + "The VirtualShip drifters will sample sea surface temperature (SST) as they flow throught the ocean. We can add this information to our trajectory plot by colouring the drifter trajectories by the temperature recorded at each time step." + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "PLOT_VARIABLE = \"temperature\"\n", + "\n", + "# fig\n", + "fig = plt.figure(figsize=(10, 6), dpi=96)\n", + "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", + "\n", + "# plot trajectory colored by temperature / salinity\n", + "for i, traj in enumerate(drifter_ds[\"trajectory\"]):\n", + " # extract trajectory data\n", + " lons = drifter_ds[\"lon\"][:].sel(trajectory=traj).squeeze().values\n", + " lats = drifter_ds[\"lat\"][:].sel(trajectory=traj).squeeze().values\n", + " var = drifter_ds[PLOT_VARIABLE][:].sel(trajectory=traj).squeeze().values\n", + "\n", + " # segments for LineCollection\n", + " points = np.array([lons, lats]).T.reshape(-1, 1, 2)\n", + " segments = np.concatenate([points[:-1], points[1:]], axis=1)\n", + "\n", + " # coloured by temperature\n", + " lc = LineCollection(\n", + " segments,\n", + " cmap=cmo.thermal,\n", + " norm=mcolors.Normalize(vmin=np.nanmin(var), vmax=np.nanmax(var)),\n", + " array=var[:-1],\n", + " linewidth=2.5,\n", + " zorder=3,\n", + " transform=ccrs.PlateCarree(),\n", + " )\n", + " ax.add_collection(lc)\n", + "\n", + " # add release location\n", + " MARKERSIZE = 45\n", + " ax.scatter(\n", + " lons[0],\n", + " lats[0],\n", + " marker=\"o\",\n", + " s=MARKERSIZE,\n", + " color=\"white\",\n", + " edgecolor=\"black\",\n", + " zorder=4,\n", + " transform=ccrs.PlateCarree(),\n", + " label=\"Waypoint\" if i == 0 else None, # only label first for legend\n", + " )\n", + "\n", + "\n", + "# additional map features\n", + "latlon_buffer = 1.0 # degrees (adjust this to 'zoom' in/out in the plot)\n", + "ax.set_extent(\n", + " [\n", + " drifter_ds.lon.min() - latlon_buffer,\n", + " drifter_ds.lon.max() + latlon_buffer,\n", + " drifter_ds.lat.min() - latlon_buffer,\n", + " drifter_ds.lat.max() + latlon_buffer,\n", + " ],\n", + " crs=ccrs.PlateCarree(),\n", + ")\n", + "ax.coastlines(linewidth=0.5, color=\"black\")\n", + "ax.add_feature(cfeature.LAND, facecolor=\"tan\")\n", + "gl = ax.gridlines(\n", + " draw_labels=True,\n", + " linewidth=0.5,\n", + " color=\"gainsboro\",\n", + " alpha=1.0,\n", + " linestyle=\"-\",\n", + " zorder=0,\n", + ")\n", + "gl.top_labels = False\n", + "gl.right_labels = False\n", + "\n", + "# add colorbar\n", + "sm = plt.cm.ScalarMappable(\n", + " cmap=cmo.thermal,\n", + " norm=mcolors.Normalize(\n", + " vmin=float(drifter_ds.temperature.min()),\n", + " vmax=float(drifter_ds.temperature.max()),\n", + " ),\n", + ")\n", + "sm._A = []\n", + "cbar = plt.colorbar(sm, ax=ax, orientation=\"vertical\", label=\"Temperature (°C)\")\n", + "\n", + "ax.legend(loc=\"upper right\", fontsize=12)\n", + "\n", + "n_days = float(\n", + " (drifter_ds[\"time\"][0].max(skipna=True) - drifter_ds[\"time\"][0].min(skipna=True))\n", + " / np.timedelta64(1, \"D\")\n", + ")\n", + "plt.title(f\"[{round(n_days, 1)} day drifter lifetime]\", fontsize=12)\n", + "\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Case study: Interpreting drifter trajectories\n", + "\n", + "
\n", + "**NOTE**: This next section has no code to run and will likely look different from your own results - you will have run your own drifter simulations with different initial positions and times! This is just an example to demonstrate the kinds of drifter flow dynamics questions you can start to think about.\n", + "
\n", + "\n", + "The Atlantic and Indian Ocean meet around South Africa, and this is one of the most dynamic and energetic regions in the world ocean. The Agulhas retroflection is a region where the Agulhas current retroflects back into the Indian Ocean. This region is known for its strong currents and eddies, and is a region where many drifters have been deployed.\n", + "\n", + "Below is an example of a previous VirtualShip drifter experiment in the Agulhas region, showing the trajectories of 20 virtual drifters launched from a line at 31S between 31E and 32E (see red dots) on the 2 and 21 July 2023, one each day at midnight, simulated forward in time 90 days.\n", + "\n", + "\n", + "\n", + "![trajectories](./assets/trajan_drifters.png)\n", + "\n", + "As you see, the drifters all start in the Agulhas Current (red dots at 30S) and most are initially advected southwestwards (although some first move northeastwards). At least two drifters take a path farther offshore, where their trajectories are much more eddying. When the inshore drifters reach approximately 25E, some of them start to circulate in eddies, and their tracks become even more convoluted.\n", + "\n", + "One potentially interesting analysis could be to compare the starting longitude to the final longitude. Do the drifters that start on the inshore side of the Agulhas Current have a higher chance to end up in the Atlantic Ocean (aka Agulhas leakage) than the drifters that start on the offshore side?\n", + "\n", + "![final_vs_start_lon](./assets/initial_vs_final_drifters.png)\n", + "\n", + "How do you interpret this plot? Is it what you expected? \n", + "\n", + "What other analyses could be interesting to do with this data? Would it be interesting to look at the temperature or salinity that the drifters experience along their trajectories?" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "ship", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.9" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/user-guide/teacher-content/train-the-teacher/_images/new_workspace.png b/docs/user-guide/teacher-content/train-the-teacher/_images/new_workspace.png index 5695de92e7f1deab2d128f53425134a75dff1a22..f1bbf256bd3caddf631fbfac3d04f9a21525de4e 100644 GIT binary patch literal 20956 zcmeFZWmr^Q8#W9BC=4JdEv=|DD%~w1-JQ}YDIHRRgi_Mo-7qj9Akv*fiuAzHIn=wk 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zmywU`&gnahrFMTj#{cceViN{L33M?=z}XWVUlBlbW{it{c&zelGEMeZzD}xfoPc7) zeAv?j`}X5rWWm8AQKuKCbGWJ)H1R-3Yo?OIus)`0?rkM zR|iN#vk$n+)7S(o^CuqE1lHfMya=qxRe_Ul>%T29gVYOPBNYNtfU&I994v!u(GhQ8 z0K~m@%tdGh)}kkXopo2X9VlnZ2?Py(NbvF?Dz`*}*KSJp=G1j!0Rko^F9M{^ z^XBl%SX^Lt-C5QBiLWc ## Setting up a programming environment ```{tip} -If you are opting to use the VirtualShip Classroom without the `VirtualShip` software, there is no need to set up a programming environment for your students. You can refer to the Open Education Resources mentioned in the relevant [example lesson plan](./lesson_plans.md/#not-using-the-software) and/or the [VR component](#adding-a-vr-component) (if you choose to use it). +As mentioned in the [Lesson plans section](#lesson-plans), it is not necessary to use the `VirtualShip` software component as part of the VirtualShip Classroom. If this is the case, you can skip the next sections, and refer to the Open Education Resources mentioned in the relevant [example lesson plan](./lesson_plans.md/#not-using-the-software). -❗️ Please do still ask students to complete the end-of-course survey (see [below](#end-of-course-survey)) so that we can collect feedback on their experience with the VirtualShip Classroom. +❗️ Please do still ask students to complete the end-of-course survey (see [below](#end-of-course-survey)) though so that we can collect feedback on their experience with the VirtualShip Classroom. ``` There are broadly two ways to set up the `VirtualShip` software for teaching: @@ -60,7 +72,7 @@ There are broadly two ways to set up the `VirtualShip` software for teaching: Option 1) requires less preparation but can be more challenging for students to set up (especially if inexperienced) with frequent machine-dependent issues (and a lot of time spent on troubleshooting during lesson time!). Option 2) requires more preparation as the course convenor but is generally easier to support in-class, especially for larger groups. It also has the advantage that all students are working with the same resources, versions and infrastructure, which is beneficial for reproducibility and fairness. -In previous implementations at Utrecht University (where `VirtualShip` originated), we have primarily used Option 2) on the [SURF Research Cloud](https://www.surf.nl/en/services/compute/surf-research-cloud)). +In previous implementations at Utrecht University (where the VirtualShip Classroom originated), we have primarily used Option 2) on the [SURF Research Cloud](https://www.surf.nl/en/services/compute/surf-research-cloud). ### Local installation @@ -77,14 +89,14 @@ conda activate virtualship This creates an environment named `virtualship` with the latest version of the `VirtualShip` software installed. Students can then run the software from the command line in this environment. ```{tip} -If you have access to a computer lab, you may also consider installing the software on the those machines. This is similar to a local installation but can bring similar benefits to a cloud-based environment (i.e. each student has the same resources), but with less flexibility for students to work from home or on their own devices. +If you have access to a computer lab, you may also consider installing the software on the those machines. This is similar to a local installation but can bring similar benefits to a cloud-based environment (i.e. the environment is prepared ahead of the lesson, each student has the same resources), but with less flexibility for students to work from home or on their own devices. ``` ### Pre-configured environment (cloud based) This documentation focuses on a set up specifically on the [SURF Research Cloud](https://www.surf.nl/en/services/compute/surf-research-cloud)). The concepts are similar for other cloud-based platforms, but you may need to adapt them to your own context. -```{note} +```{important} Note, the SURF Research Cloud is only available to Dutch institutions. Other cloud-based platforms (e.g. Google Colab, Binder, etc.) could be used as well but we have not extensively tested these platforms. ``` @@ -100,7 +112,7 @@ surf_set_up.md When students log in to the SURF Research Cloud and click to access the workspace, they will be taken to a JupyterLab environment. This is where they can run the VirtualShip software and work on their expeditions. ```{important} -A known issue is that students may hit a server error when accessing the workspace. If this happens, keep on trying (refresh), as the server spin-up can be a bit overloaded at times, but should get through eventually. +A known issue is that students may hit a "server error" when accessing the workspace. If this happens, keep on trying (refresh), as the server spin-up can be a bit overloaded at times, but should get through eventually. Unfortunately this is out of our control. Clearing your browser cache and cookies, and/or trying via an incognito/private window may also help. We find that with persistence, the workspace will eventually load for all users. ``` @@ -116,7 +128,7 @@ surf_student_access.md If you, as the course convenor/workspace owner, would also like to use the `VirtualShip` software, you will also need to carry out the steps in the instructions sheet above to initialise the environment in your own account space, as a one-time set up step. ``` -#### Collaboration amongst students +#### Collaboration within groups We often recommend that students work in small groups (e.g. 2-3 students) for their VirtualShip projects. Each student should have their own account/access to the workspace and they can work from the same sub-directory in the `/data/{storage-name}` storage space. @@ -131,9 +143,9 @@ file_permissions.md Now that the technical set up is complete, we are ready to start getting students going with using the `VirtualShip` software! 🚢 🥳 -The Quickstart guide below provides a minimal overview of the basic commands and workflow to get started with the software... perhaps useful for you as the course convenor to get a quick overview of the software. +The general-purpose **Quickstart guide** below provides a minimal overview of the basic commands and workflow to get started with the software... perhaps useful for you as the course convenor to get a quick overview of the software. -However, for teaching applications we recommend distributing the student-focused **"Sail the ship"** guide below, which is designed to be more accessible and includes additional context and narrative elements for students. Many of the narrative elements won't actually be enacted, of course, but they are included to help students immerse themselves! +However, for teaching applications we recommend distributing the student-focused **"Sail the ship" guide** below, which is designed to be more accessible and includes additional context and narrative elements for students. ```{nbgallery} @@ -144,43 +156,76 @@ However, for teaching applications we recommend distributing the student-focused ### Reviewing Expedition proposals -The "Sail the ship" guide is designed to be used in conjunction with a lesson plan similar to that presented in the [example lesson plans](lesson_plans.md) documentation. It relies on students having already chosen a research question, submitting a proposal and having it approved by their instructor/you. +The "Sail the ship" guide above is designed to be used in conjunction with a lesson plan similar to that presented in the [example lesson plans](lesson_plans.md) documentation. It relies on students having already chosen a research question, submitting a proposal and having it approved by their instructor/you. -Reviewing and approving the proposals is a good time to check that students have a realistic plan for their expedition, and we find it is beneficial to provide a maximum ship time limit (e.g. 3 weeks) to ensure that students are thinking about the practicalities of their research question and sampling strategy. +Reviewing and approving the proposals is a good time to check that students have a realistic plan for their expedition, and we find it is beneficial to prescribe a maximum ship time limit (e.g. 3 weeks) to ensure that students are thinking about the practicalities of their research question and sampling strategy. -### Additional resources used in the VirtualShip workflow +```{tip} +The ship time limit can not currently be set in the `VirtualShip` software, but you could enforce it as part of your assignment instructions. +``` -You will notice in the "Quickstart" and "Sail the Ship" guides that there are a number of additional resources used in the VirtualShip workflow. These include: +### Additional resources used in the VirtualShip workflow -- [Copernicus Marine Data Store](https://data.marine.copernicus.eu/) - the source of the oceanographic data used in VirtualShip -- [NIOZ Marine Facilities Planning (MFP) tool](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#) - used to plan the expedition route and generate the coordinates for the VirtualShip protocol +You will notice in the "Quickstart" and "Sail the Ship" guides that there are a number of additional resources that get used in the VirtualShip workflow. These include the: - - +- [Copernicus Marine Data Store](https://data.marine.copernicus.eu/). + - This source of the oceanographic data used in VirtualShip (streamed under-the-hood in the `VirtualShip` software). + - As mentioned in the guides, students will need to set up a _free_ account to access the data. We recommend asking students to do this ahead of time, to avoid delays during the lesson. + - Users are prompted to enter their credentials when they first run the `VirtualShip` software, and the credentials are then stored for future use. +- [Marine Facilities Planning (MFP) tool](https://nioz.marinefacilitiesplanning.com/cruiselocationplanning#) + - This tool is used to plan the expedition route and generate the coordinates for the VirtualShip protocol. + - It is an authentic tool used by real-life oceanographers to plan their research expedtions, and is a good example of the type of software that students may encounter in their future careers. + - There is no sign-up required to use the tool, but students may need some time to get familiar with it. + - As mentioned in the guides, the `VirtualShip` software can ingest exported coordinate files straight from MFP. ### Simulating Real Life Challenges - +You will notice mentions to "Real Life Challenges" (RLCs) in the "Quickstart" and "Sail the Ship" guides. These are a module in the `VirtualShip` software that can be used to simulate real-life challenges that oceanographers may encounter during their research expeditions. These include things like equipment failures, bad weather, and other unexpected events. They usually require active intervention from the students to resolve. - +They are not 'bugs' and are instead a feature that can be used to teach students about the challenges of oceanographic research: that things rarely go to plan, the scheudle will probably have to adapted and that some contingency planning is required. - - - - +The RLCs can be configured by setting the difficulty level (`--difficulty-level`) parameter in the virtualship run command. It can be set to `“easy”` (no problems, default in the main software distribution), `“medium”` or `“hard”` (e.g. `virtualship run EXPEDITION_NAME --difficulty-level medium`). + +For maximum authenticity, you can set `--difficulty-level hard`, which will scale the number of problems encountered by the complexity of the expedition (longer duration, more waypoints, more instruments will lead to more problems). `--difficulty-level medium` will limit the number of problems to a maximum of 2, regardless of the expedition complexity. + +```{tip} +We can arrange that the default difficulty level is set to `medium` for your course, if you would like to use the RLCs in your teaching without having to ask students to add the `--difficulty-level` parameter themselves on each run. This can enhance immersivity as the RLCs appear more unexpected from the students' perspective. Please [get in touch](train_the_teacher.md/#feedback-support) if this is something you would like to do. +``` + +```{note} +It's possible that students will explore this VirtualShip documentation site and understand that they can disable the RLCs by setting `--difficulty-level easy`. If you would like to ensure that students must encounter the RLCs, we suggest making a discussion of how they dealt with these issues part of their assignment. Similar to the ship time limit mentioned previously. +``` ### VirtualShip output - - +Once the simulations have run, the VirtualShip output files will be available in the workspace. These are in `.parquet` format. + +```{tip} +`VirtualShip` depends heavily on `Parcels` under-the-hood for simulating the instrument behaviours. As such, the VirtualShip output is built on `Parcels` output formats. See the `Parcels` [documentation](https://docs.oceanparcels.org/en/main/user_guide/getting_started/tutorial_output.html) for more information on how to work with the `.parquet` files. +``` + +VirtualShip does not provide explicit tooling for analysis, as this will be dependent on the specific learning objectives and research questions of the students. However, we have provided a number of **example tutorials** (see below), which provide sample code for simple first analysis of the VirtualShip output, for each instrument type. + +```{nbgallery} + +../../tutorials/index.md + +``` + +We suggest that you encourage students to explore these tutorials and use them as a starting point for their own analysis. You might consider uploading copies of these notebooks to the shared storage space if you are using a cloud-based environment, so that students can access them without having to copy them from the documentation site. The easiest way to do so is to 'wget' the raw notebooks from the codebase, for example: + +```bash + +# copy the drifter data tutorial to the current directory +wget http://raw.githubusercontent.com/Parcels-code/virtualship/refs/heads/main/docs/user-guide/tutorials/Drifter_data_tutorial.ipynb +``` ## ❗️ End-of-course survey ```{important} We would be really grateful for your help in collecting feedback from your students on their experience with VirtualShip. This will help us to improve the platform, research its impact and to better understand how it is being used in different contexts. - -Please distribute the following survey link to your students at the end of the course: _____ +Please distribute the following survey link to your students at the end of the course: https://survey.uu.nl/jfe/form/SV_0OLu4lKYPyLhAxM ``` ## Feedback & support