Merge pull request #155 from nyx-space/154-pandas-may-fail-to-convert…

…-hifitime-epochs-into-datetime

Fix #154

README.md

@@ -1,5 +1,5 @@
 # nyx
-[Nyx](https://nyxspace.com) is a high fidelity, fast, reliable and **[validated]([https://nyxspace.com/MathSpec/](https://nyxspace.com/nyxspace/MathSpec/))** astrodynamics toolkit library written in Rust. It provides convenient interfaces to both Rust and Python.
+[Nyx](https://nyxspace.com) is a high fidelity, fast, reliable and **[validated]([https://nyxspace.com/MathSpec/](https://nyxspace.com/nyxspace/MathSpec/))** astrodynamics toolkit library written in Rust and available in Python.
 
 The target audience is mission designers, astrodynamics engineers/hobbyists, and GNC engineers. The rationale for using Rust is to allow for very fast computations, guaranteed thread safety,
 and portability to all platforms supported by [Rust](https://forge.rust-lang.org/platform-support.html).
@@ -50,49 +50,4 @@ The [AGPLv3 LICENSE](https://nyxspace.com/license/) is enforced.
 - [x] Frame rotations
 
 # Who am I?
-An astrodynamics engineer with a heavy background in software. I currently work for Rocket Lab USA on the GNC of the Blue Ghost lunar lander.
-
-# Examples
-Refer to the [showcase](https://nyxspace.com/showcase/).
-
-# Python todo
-
-The "[maturin](https://crates.io/crates/maturin)" python package is used to build the python bindings.
-
-```sh
-pip install maturin
-```
-
-Build the python bindings using the following command.
-```sh
-maturin build --cargo-extra-args="--features python"
-```
-
-This creates a wheel file in `./target/wheels/` which can be installed using `pip install <filename.whl>`.
-
-For development mode, the following command may be used that automatically installs the python module
-
-```sh
-maturin develop --cargo-extra-args="--features python"
-```
-
-## Python code example
-
-This minimal example runs the scenario defined in `data/simple-scenario.toml` using the Python bindings.
-
-```py
-from nyx_space import md
-from nyx_space import io
-from nyx_space import cosmic
-
-# Initialize the cosm which stores the ephemeris
-cosm = cosmic.Cosm.de438()
-
-with open('data/simple-scenario.toml', 'r') as f:
-    scen_data = f.read()
-
-scenario = io.ScenarioSerde.from_toml_str(scen_data)
-
-md.MDProcess.execute_all_in_scenario(scenario, cosm)
-
-```
+An astrodynamics engineer with a heavy background in software. I currently work for Rocket Lab USA on the GNC of the Blue Ghost lunar lander. -- Find me on [LinkedIn](https://www.linkedin.com/in/chrisrabotin/).

python/nyx_space/plots/od.py

@@ -88,7 +88,13 @@ def plot_estimates(
             orig_tim_col = df[col_name]
 
         # Build a Python datetime column
-        time_col = pd.to_datetime(orig_tim_col)
+        pd_ok_epochs = []
+        for epoch in orig_tim_col:
+            epoch = epoch.replace("UTC", "").strip()
+            if '.' not in epoch:
+                epoch += ".0"
+            pd_ok_epochs += [epoch]
+        time_col = pd.to_datetime(pd_ok_epochs)
         x_title = "Epoch {}".format(time_col_name[-3:])
 
         # Check that the requested covariance frame exists
@@ -115,9 +121,14 @@ def plot_estimates(
             "cz_dot_z_dot": "Covariance VzVz",
         }.items():
             # Create a new column with the transformed covariance. (e.g. "Covariance VzVz (RIC) 1.0-sigma sqrt")
-            cov_col_name = f"{covar_col} ({cov_frame}) {cov_sigma}-sigma {cov_fmt}"
-            # Transform the current column
-            df[cov_col_name] = eval(f"np.{cov_fmt}")(df[f"{covar_col} ({cov_frame})"])
+            if cov_fmt is None:
+                cov_col_name = f"{covar_col} ({cov_frame}) {cov_sigma}-sigma"
+                # No transformation here
+                df[cov_col_name] = df[f"{covar_col} ({cov_frame})"]
+            else:
+                cov_col_name = f"{covar_col} ({cov_frame}) {cov_sigma}-sigma {cov_fmt}"
+                # Transform the current column
+                df[cov_col_name] = eval(f"np.{cov_fmt}")(df[f"{covar_col} ({cov_frame})"])
             covar[f"{covar_var}"] = cov_col_name
 
         plt_df = df
@@ -315,7 +326,13 @@ def plot_covar(
             orig_tim_col = df[col_name]
 
         # Build a Python datetime column
-        time_col = pd.to_datetime(orig_tim_col)
+        pd_ok_epochs = []
+        for epoch in orig_tim_col:
+            epoch = epoch.replace("UTC", "").strip()
+            if '.' not in epoch:
+                epoch += ".0"
+            pd_ok_epochs += [epoch]
+        time_col = pd.to_datetime(pd_ok_epochs)
         x_title = "Epoch {}".format(time_col_name[-3:])
 
         # Check that the requested covariance frame exists
@@ -494,7 +511,13 @@ def plot_measurements(
             orig_tim_col = df[col_name]
 
         # Build a Python datetime column
-        time_col = pd.to_datetime(orig_tim_col)
+        pd_ok_epochs = []
+        for epoch in orig_tim_col:
+            epoch = epoch.replace("UTC", "").strip()
+            if '.' not in epoch:
+                epoch += ".0"
+            pd_ok_epochs += [epoch]
+        time_col = pd.to_datetime(pd_ok_epochs)
         x_title = "Epoch {}".format(time_col_name[-3:])
 
         # Diff the epochs of the measurements to find when there is a start and end.
@@ -585,7 +608,13 @@ def plot_residuals(
         orig_tim_col = df[col_name]
 
     # Build a Python datetime column
-    time_col = pd.to_datetime(orig_tim_col)
+    pd_ok_epochs = []
+    for epoch in orig_tim_col:
+        epoch = epoch.replace("UTC", "").strip()
+        if '.' not in epoch:
+            epoch += ".0"
+        pd_ok_epochs += [epoch]
+    time_col = pd.to_datetime(pd_ok_epochs)
     x_title = "Epoch {}".format(time_col_name[-3:])
 
     plt_any = False

python/nyx_space/plots/traj.py

@@ -216,7 +216,13 @@ def plot_orbit_elements(
         dfs = [dfs]
 
     for df in dfs:
-        df["Epoch"] = pd.to_datetime(df["Epoch:Gregorian UTC"])
+        pd_ok_epochs = []
+        for epoch in df["Epoch:Gregorian UTC"]:
+            epoch = epoch.replace("UTC", "").strip()
+            if '.' not in epoch:
+                epoch += ".0"
+            pd_ok_epochs += [epoch]
+        df["Epoch"] = pd.to_datetime(pd_ok_epochs)
 
     if not isinstance(names, list):
         names = [names]

src/od/process/mod.rs

@@ -631,10 +631,15 @@ where
     }
 
     /// Continuously predicts the trajectory until the provided end epoch, with covariance mapping at each step. In other words, this performs a time update.
-    pub fn predict_until(&mut self, max_step: Duration, end_epoch: Epoch) -> Result<(), NyxError> {
+    pub fn predict_until(
+        &mut self,
+        step: Duration,
+        fixed_step: bool,
+        end_epoch: Epoch,
+    ) -> Result<(), NyxError> {
         let prop_time = end_epoch - self.kf.previous_estimate().epoch();
         info!("Propagating for {prop_time} seconds and mapping covariance",);
-        self.prop.set_step(max_step, false);
+        self.prop.set_step(step, fixed_step);
 
         loop {
             let mut epoch = self.prop.state.epoch();
@@ -671,10 +676,15 @@ where
     }
 
     /// Continuously predicts the trajectory for the provided duration, with covariance mapping at each step. In other words, this performs a time update.
-    pub fn predict_for(&mut self, max_step: Duration, duration: Duration) -> Result<(), NyxError> {
+    pub fn predict_for(
+        &mut self,
+        step: Duration,
+        fixed_step: bool,
+        duration: Duration,
+    ) -> Result<(), NyxError> {
         let end_epoch = self.kf.previous_estimate().epoch() + duration;
 
-        self.predict_until(max_step, end_epoch)
+        self.predict_until(step, fixed_step, end_epoch)
     }
 
     /// Builds the navigation trajectory for the estimated state only

src/python/orbit_determination/process.rs

@@ -37,7 +37,7 @@ use super::{estimate::OrbitEstimate, GroundStation};
 /// You must also provide an export path and optionally and export configuration to export the results to a Parquet file.
 #[pyfunction]
 #[pyo3(
-    text_signature = "(dynamics, spacecraft, initial_estimate, measurement_noise, arc, export_path, export_cfg, ekf_num_meas=None, ekf_disable_time=None, resid_crit=None, predict_until=None, predict_for=None, predict_step=None)"
+    text_signature = "(dynamics, spacecraft, initial_estimate, measurement_noise, arc, export_path, export_cfg, ekf_num_meas=None, ekf_disable_time=None, resid_crit=None, predict_until=None, predict_for=None, predict_step=None, fixed_step=False)"
 )]
 pub(crate) fn process_tracking_arc(
     dynamics: SpacecraftDynamics,
@@ -53,6 +53,7 @@ pub(crate) fn process_tracking_arc(
     predict_until: Option<Epoch>,
     predict_for: Option<Duration>,
     predict_step: Option<Duration>,
+    fixed_step: Option<bool>,
 ) -> Result<String, NyxError> {
     // TODO: Return a navigation trajectory or use a class that mimics the better ODProcess -- https://github.com/nyx-space/nyx/issues/134
     let msr_noise = Matrix2::from_iterator(measurement_noise);
@@ -87,11 +88,13 @@ pub(crate) fn process_tracking_arc(
     if let Some(epoch) = predict_until {
         let max_step =
             predict_step.ok_or_else(|| NyxError::CustomError("predict_step unset".to_string()))?;
-        odp.predict_until(max_step, epoch).unwrap();
+        odp.predict_until(max_step, fixed_step.unwrap_or_else(|| false), epoch)
+            .unwrap();
     } else if let Some(duration) = predict_for {
         let max_step =
             predict_step.ok_or_else(|| NyxError::CustomError("predict_step unset".to_string()))?;
-        odp.predict_for(max_step, duration).unwrap();
+        odp.predict_for(max_step, fixed_step.unwrap_or_else(|| false), duration)
+            .unwrap();
     }
 
     let maybe = odp.to_parquet(
@@ -109,7 +112,7 @@ pub(crate) fn process_tracking_arc(
 /// You must also provide an export path and optionally and export configuration to export the results to a Parquet file.
 #[pyfunction]
 #[pyo3(
-    text_signature = "(dynamics, spacecraft, initial_estimate, step, export_path, export_cfg, predict_until=None, predict_for=None)"
+    text_signature = "(dynamics, spacecraft, initial_estimate, step, export_path, export_cfg, predict_until=None, predict_for=None, fixed_step=False)"
 )]
 pub(crate) fn predictor(
     dynamics: SpacecraftDynamics,
@@ -120,6 +123,7 @@ pub(crate) fn predictor(
     export_cfg: Option<ExportCfg>,
     predict_until: Option<Epoch>,
     predict_for: Option<Duration>,
+    fixed_step: Option<bool>,
 ) -> Result<String, NyxError> {
     // TODO: Return a navigation trajectory or use a class that mimics the better ODProcess -- https://github.com/nyx-space/nyx/issues/134
     let msr_noise = Matrix2::from_iterator(vec![1e-10, 0.0, 0.0, 1e-10]);
@@ -143,9 +147,11 @@ pub(crate) fn predictor(
     let mut odp = ODProcess::ckf(prop_est, kf, None, Cosm::de438());
 
     if let Some(epoch) = predict_until {
-        odp.predict_until(step, epoch).unwrap();
+        odp.predict_until(step, fixed_step.unwrap_or_else(|| false), epoch)
+            .unwrap();
     } else if let Some(duration) = predict_for {
-        odp.predict_for(step, duration).unwrap();
+        odp.predict_for(step, fixed_step.unwrap_or_else(|| false), duration)
+            .unwrap();
     }
 
     let maybe = odp.to_parquet(

tests/orbit_determination/two_body.rs

@@ -956,7 +956,7 @@ fn od_tb_ckf_map_covar() {
         KF<Orbit, nalgebra::Const<3>, nalgebra::Const<2>>,
     > = ODProcess::ckf(prop_est, ckf, None, cosm);
 
-    odp.predict_for(30.seconds(), duration).unwrap();
+    odp.predict_for(30.seconds(), false, duration).unwrap();
 
     // Check that the covariance inflated (we don't get the norm of the estimate because it's zero without any truth data)
     let estimates = odp.estimates;