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fixing stats + residual estimation #15

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Mar 28, 2024
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fixing stats + residual estimation #15

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16 changes: 13 additions & 3 deletions gnssanalysis/gn_diffaux.py
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Original file line number Diff line number Diff line change
Expand Up @@ -626,12 +626,13 @@ def rac_df_to_rms_df(rac_df):
RMS of dX, dY, dZ and R (Radial), A (Along-track), C (Cross-track) per satellite (PRN).
Additionally, a 1D mean difference, (dX + dY + dZ)/3, and 3D difference, sqrt(R^2 + A^2 + C^2), are computed for each PRN.
"""

merged_data = rac_df.join(rac_df.attrs["diff_eci"])
rms_df = merged_data.pow(2).groupby("PRN").mean().pow(0.5)
std_df = merged_data.groupby("PRN").std(ddof=0)

rms_df["EST", "MEAN"] = rms_df.EST.mean(axis=1).groupby("PRN").mean()
rms_df["EST", "AVG"] = rac_df.attrs["diff_eci"].EST.mean(axis=1).groupby("PRN").mean()
std_df["EST", "3D_RMS"] = rac_df.attrs["diff_eci"].EST.pow(2).sum(axis=1).pow(0.5).groupby("PRN").std(ddof=0)
rms_df["EST", "3D_RMS"] = rac_df.attrs["diff_eci"].EST.pow(2).sum(axis=1).groupby("PRN").mean().pow(0.5)
rms_df = rms_df.droplevel(0, axis=1).rename(
columns={
Expand All @@ -643,10 +644,19 @@ def rac_df_to_rms_df(rac_df):
"Z": "dZ_RMS",
}
)

std_df = std_df.droplevel(0, axis=1).rename(
columns={
"Radial": "R_RMS",
"Along-track": "A_RMS",
"Cross-track": "C_RMS",
"X": "dX_RMS",
"Y": "dY_RMS",
"Z": "dZ_RMS",
}
)
# summarising over all SVs
summary_df = _pd.DataFrame(
[rms_df.mean(axis=0), rms_df.std(axis=0), rms_df.pow(2).mean(axis=0).pow(0.5)], index=["AVG", "STD", "RMS"]
[rms_df.mean(axis=0), std_df.mean(axis=0), rms_df.pow(2).mean(axis=0).pow(0.5)], index=["AVG", "STD", "RMS"]
)

rms_df.attrs["summary"] = summary_df
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2 changes: 1 addition & 1 deletion gnssanalysis/gn_transform.py
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ def get_helmert7(pt1:_np.ndarray, pt2:_np.ndarray, scale_in_ppm:bool = True):
"""inversion of 7 Helmert parameters between 2 sets of points. pt1@hlm -> pt2"""
A, rhs = gen_helm_aux(pt1, pt2)
sol = list(_np.linalg.lstsq(A, rhs, rcond=-1)) # parameters
sol[0] = sol[0].flatten() # flattening the HLM params arr to [Tx, Ty, Tz, Rx, Ry, Rz, Scale/mu]
sol[0] = sol[0].flatten()*-1.0 # flattening the HLM params arr to [Tx, Ty, Tz, Rx, Ry, Rz, Scale/mu]
if scale_in_ppm:
sol[0][-1] *= 1e6 # scale in ppm
res = rhs - A @ sol[0] # computing residuals for pt2
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