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Standardize parameter names #178

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merged 16 commits into from
Jul 28, 2023
Merged

Standardize parameter names #178

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ba40184
Also verify timedeltas between forward and backward when merging sing…
bdestombe Jul 21, 2023
d9f4f88
Merge branch 'main' into merge-single-ended-to-double-check-dt
bdestombe Jul 22, 2023
c970508
Remove restrictions for xarray and pandas versions. Removed resample …
bdestombe Jul 22, 2023
6f5ff25
Removed output example notebook 2
bdestombe Jul 22, 2023
b466d08
Updated resample test
bdestombe Jul 22, 2023
55bf8b0
Removed deprecated pandas read_csv argument from import ts example
bdestombe Jul 22, 2023
0f80ebb
Standardize the parameter names
bdestombe Jul 23, 2023
520c075
Update 08Calibrate_double_ended.ipynb
bdestombe Jul 24, 2023
e327b68
Update 14Lossy_splices.ipynb
bdestombe Jul 24, 2023
a115b56
Updated notebooks 14, 15, and 16
bdestombe Jul 24, 2023
544d61f
Also standardize x and time dim names
bdestombe Jul 27, 2023
97f92ef
removed final bits of get_time_dim
bdestombe Jul 27, 2023
63fe767
Update CHANGELOG.rst
bdestombe Jul 27, 2023
b72d407
Merge branch 'main' into reduce-freedom-in-parameter-naming
bdestombe Jul 27, 2023
26223aa
Error in solving merge conflicts
bdestombe Jul 27, 2023
d7026b8
Cleaning up arguments for calc_alpha_double()
bdestombe Jul 28, 2023
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4 changes: 4 additions & 0 deletions CHANGELOG.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,10 @@ Bug fixes

* Single-ended measurements with `fix_alpha` failed due to a bug introduced in v2.0.0 ([#173](https://github.com/dtscalibration/python-dts-calibration/pull/173)).

Introduced limitations

* Standardized parameter names. Reduced the freedom in choosing parameter names and dimension names in favor of simplifying the code.

Removed

* Removed ds.resample_datastore() in favor of using xarray's resample function. See example notebook 2.
Expand Down
2 changes: 0 additions & 2 deletions docs/notebooks/08Calibrate_double_ended.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -241,8 +241,6 @@
" ast_var=ast_var,\n",
" rst_var=rst_var,\n",
" rast_var=rast_var,\n",
" mc_sample_size=10000, # Optional\n",
" mc_conf_ints=[2.5, 97.5], # Optional\n",
")"
]
},
Expand Down
16 changes: 5 additions & 11 deletions docs/notebooks/14Lossy_splices.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -122,7 +122,7 @@
"ds.isel(time=0).ast.plot(label=\"ast\")\n",
"ds.isel(time=0).rst.plot(label=\"rst\")\n",
"ds.isel(time=0).rast.plot(label=\"rast\")\n",
"plt.legend()"
"plt.legend();"
]
},
{
Expand Down Expand Up @@ -157,12 +157,9 @@
" ast_var=ast_var,\n",
" rst_var=rst_var,\n",
" rast_var=rast_var,\n",
" store_tmpw=\"tmpw\",\n",
" method=\"wls\",\n",
" solver=\"sparse\",\n",
")\n",
"\n",
"ds_a.isel(time=0).tmpw.plot(label=\"calibrated\")"
"ds_a.isel(time=0).tmpw.plot(label=\"calibrated\");"
]
},
{
Expand Down Expand Up @@ -198,14 +195,11 @@
" rst_var=rst_var,\n",
" rast_var=rast_var,\n",
" trans_att=[50.0],\n",
" store_tmpw=\"tmpw\",\n",
" method=\"wls\",\n",
" solver=\"sparse\",\n",
")\n",
"\n",
"ds_a.isel(time=0).tmpw.plot(label=\"no trans. att.\")\n",
"ds.isel(time=0).tmpw.plot(label=\"with trans. att.\")\n",
"plt.legend()"
"plt.legend();"
]
},
{
Expand All @@ -218,7 +212,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -232,7 +226,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.11"
"version": "3.10.10"
}
},
"nbformat": 4,
Expand Down
12 changes: 3 additions & 9 deletions docs/notebooks/15Matching_sections.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -132,10 +132,7 @@
" st_var=st_var,\n",
" ast_var=ast_var,\n",
" rst_var=rst_var,\n",
" rast_var=rast_var,\n",
" store_tmpw=\"tmpw\",\n",
" method=\"wls\",\n",
" solver=\"sparse\",\n",
" rast_var=rast_var\n",
")\n",
"\n",
"ds_a.isel(time=0).tmpw.plot(label=\"calibrated\")"
Expand Down Expand Up @@ -183,9 +180,6 @@
" rast_var=rast_var,\n",
" trans_att=[50.0],\n",
" matching_sections=matching_sections,\n",
" store_tmpw=\"tmpw\",\n",
" method=\"wls\",\n",
" solver=\"sparse\",\n",
")\n",
"\n",
"ds_a.isel(time=0).tmpw.plot(label=\"normal calibration\")\n",
Expand All @@ -203,7 +197,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -217,7 +211,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.11"
"version": "3.10.10"
}
},
"nbformat": 4,
Expand Down
7 changes: 2 additions & 5 deletions docs/notebooks/16Averaging_temperatures.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -104,9 +104,6 @@
" ast_var=ast_var,\n",
" rst_var=rst_var,\n",
" rast_var=rast_var,\n",
" store_tmpw=\"tmpw\",\n",
" method=\"wls\",\n",
" solver=\"sparse\",\n",
")"
]
},
Expand Down Expand Up @@ -386,7 +383,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -400,7 +397,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.11"
"version": "3.10.10"
}
},
"nbformat": 4,
Expand Down
71 changes: 30 additions & 41 deletions src/dtscalibration/calibrate_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -390,12 +390,12 @@ def calibration_double_ended_solver( # noqa: MC0001
# Calculate E as initial estimate for the E calibration.
# Does not require ta to be passed on
E_all_guess, E_all_var_guess = calc_alpha_double(
'guess',
ds,
st_var,
ast_var,
rst_var,
rast_var,
mode='guess',
ds=ds,
st_var=st_var,
ast_var=ast_var,
rst_var=rst_var,
rast_var=rast_var,
ix_alpha_is_zero=ix_alpha_is_zero)
df_est, db_est = calc_df_db_double_est(ds, ix_alpha_is_zero, 485.)

Expand Down Expand Up @@ -633,22 +633,17 @@ def calibration_double_ended_solver( # noqa: MC0001
# put E outside of reference section in solution
# concatenating makes a copy of the data instead of using a pointer
ds_sub = ds[['st', 'ast', 'rst', 'rast', 'trans_att']]
time_dim = ds_sub.get_time_dim()
ds_sub['df'] = ((time_dim,), p_sol[1:1 + nt])
ds_sub['df_var'] = ((time_dim,), p_var[1:1 + nt])
ds_sub['db'] = ((time_dim,), p_sol[1 + nt:1 + 2 * nt])
ds_sub['db_var'] = ((time_dim,), p_var[1 + nt:1 + 2 * nt])
ds_sub['df'] = (('time',), p_sol[1:1 + nt])
ds_sub['df_var'] = (('time',), p_var[1:1 + nt])
ds_sub['db'] = (('time',), p_sol[1 + nt:1 + 2 * nt])
ds_sub['db_var'] = (('time',), p_var[1 + nt:1 + 2 * nt])
E_all_exact, E_all_var_exact = calc_alpha_double(
'exact',
ds_sub,
st_var,
ast_var,
rst_var,
rast_var,
'df',
'db',
'df_var',
'db_var',
mode='exact',
ds=ds_sub,
st_var=st_var,
ast_var=ast_var,
rst_var=rst_var,
rast_var=rast_var,
ix_alpha_is_zero=ix_alpha_is_zero,
talpha_fw=talpha_fw,
talpha_bw=talpha_bw,
Expand Down Expand Up @@ -1223,10 +1218,6 @@ def calc_alpha_double(
ast_var=None,
rst_var=None,
rast_var=None,
D_F_label=None,
D_B_label=None,
D_F_var_label=None,
D_B_var_label=None,
ix_alpha_is_zero=-1,
talpha_fw=None,
talpha_bw=None,
Expand All @@ -1238,8 +1229,6 @@ def calc_alpha_double(
assert ix_alpha_is_zero >= 0, 'Define ix_alpha_is_zero' + \
str(ix_alpha_is_zero)

time_dim = ds.get_time_dim()

if st_var is not None:
if callable(st_var):
st_var_val = st_var(ds.st)
Expand Down Expand Up @@ -1271,26 +1260,26 @@ def calc_alpha_double(
A = (i_bw - i_fw) / 2

elif mode == 'exact':
D_F = ds[D_F_label]
D_B = ds[D_B_label]
D_F_var = ds[D_F_var_label]
D_B_var = ds[D_B_var_label]
D_F = ds["df"]
D_B = ds["db"]
D_F_var = ds["df_var"]
D_B_var = ds["db_var"]

if ds.trans_att.size > 0:
# Can be improved by including covariances. That reduces the
# uncert.

ta_arr_fw = np.zeros((ds.x.size, ds[time_dim].size))
ta_arr_fw_var = np.zeros((ds.x.size, ds[time_dim].size))
ta_arr_fw = np.zeros((ds.x.size, ds['time'].size))
ta_arr_fw_var = np.zeros((ds.x.size, ds['time'].size))
for tai, taxi, tai_var in zip(
talpha_fw.T, ds.trans_att.values, talpha_fw_var.T):
ta_arr_fw[ds.x.values >= taxi] = \
ta_arr_fw[ds.x.values >= taxi] + tai
ta_arr_fw_var[ds.x.values >= taxi] = \
ta_arr_fw_var[ds.x.values >= taxi] + tai_var

ta_arr_bw = np.zeros((ds.x.size, ds[time_dim].size))
ta_arr_bw_var = np.zeros((ds.x.size, ds[time_dim].size))
ta_arr_bw = np.zeros((ds.x.size, ds['time'].size))
ta_arr_bw_var = np.zeros((ds.x.size, ds['time'].size))
for tai, taxi, tai_var in zip(
talpha_bw.T, ds.trans_att.values, talpha_bw_var.T):
ta_arr_bw[ds.x.values < taxi] = \
Expand All @@ -1308,8 +1297,8 @@ def calc_alpha_double(
A_var = (i_var_fw + i_var_bw + D_B_var + D_F_var) / 2
A = (i_bw - i_fw) / 2 + (D_B - D_F) / 2

E_var = 1 / (1 / A_var).sum(dim=time_dim)
E = (A / A_var).sum(dim=time_dim) * E_var
E_var = 1 / (1 / A_var).sum(dim='time')
E = (A / A_var).sum(dim='time') * E_var

else:
i_fw = np.log(ds.st / ds.ast)
Expand All @@ -1318,12 +1307,12 @@ def calc_alpha_double(
if mode == 'guess':
A = (i_bw - i_fw) / 2
elif mode == 'exact':
D_F = ds[D_F_label]
D_B = ds[D_B_label]
D_F = ds["df"]
D_B = ds["db"]
A = (i_bw - i_fw) / 2 + (D_B - D_F) / 2

E_var = A.var(dim=time_dim)
E = A.mean(dim=time_dim)
E_var = A.var(dim='time')
E = A.mean(dim='time')

# E is defined zero at the first index of the reference sections
if mode == 'guess':
Expand Down
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