Skip to content

Commit

Permalink
update notebook and add offset to save ecalib
Browse files Browse the repository at this point in the history
  • Loading branch information
@steinnymir
steinnymir committed Nov 1, 2023
1 parent 2538e28 commit 3dafe4e
Show file tree
Hide file tree
Showing 3 changed files with 35 additions and 176 deletions.
11 changes: 9 additions & 2 deletions sed/core/processor.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -1117,7 +1117,13 @@ def save_energy_calibration(
"Energy calibration parameters not found, need to generate parameters first!",
) from exc

config = {"energy": {"calibration": calibration}}
config = {
"energy": {
"calibration": calibration,
},
}
if isinstance(self.ec.offset, dict):
config["energy"]["offset"] = self.ec.offset
save_config(config, filename, overwrite)

# 4. Apply energy calibration to the dataframe
Expand Down Expand Up @@ -1204,7 +1210,8 @@ def add_energy_offset(
metadata,
"add_energy_offset",
# TODO: allow only appending when no offset along this column(s) was applied
duplicate_policy="raise",
# TODO: clear memory of modifications if the energy axis is recalculated
duplicate_policy="append",
)
self._dataframe = df
else:
Expand Down
181 changes: 26 additions & 155 deletions tutorial/5 - hextof workflow.ipynb
View file
Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,8 @@
},
"outputs": [],
"source": [
"config_file = Path(sed.__file__).parent.parent/'tutorial/hextof_config.yaml'\n",
"local_path = Path(sed.__file__).parent.parent/'tutorial/'\n",
"config_file = local_path/'hextof_config.yaml'\n",
"assert config_file.exists()"
]
},
Expand Down Expand Up @@ -148,19 +149,7 @@
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"dres = res.copy()\n",
"dres.data = np.gradient(res.data, axis=1)\n",
"dres.plot.line(x='dldTimeSteps')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sp.load_bias_series(binned_data=dres)"
"sp.load_bias_series(binned_data=res)"
]
},
{
Expand All @@ -180,85 +169,17 @@
"metadata": {},
"outputs": [],
"source": [
"axes = ['sampleBias', 'dldTimeSteps']\n",
"bins = [5, 2000]\n",
"ranges = [[28,33], [0, 2000]]\n",
"res = sp.compute(bins=bins, axes=axes, ranges=ranges)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"res.plot.line(x='dldTimeSteps')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# ref_id=0\n",
"# ref_energy=0\n",
"# sp.calibrate_energy_axis(\n",
"# ref_id=ref_id,\n",
"# ref_energy=ref_energy,\n",
"# method=\"lmfit\",\n",
"# energy_scale='kinetic',\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from sed.calibrator.energy import tof2ev, tof2ns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"ph_peak = 1e-9 * tof2ns(binwidth=sp.config['dataframe']['tof_binwidth'], binning=3,t=219)\n",
"sp.calibrate_energy_axis(\n",
" ref_id=0,\n",
" ref_energy=0,\n",
" ref_id=2,\n",
" ref_energy=-1,\n",
" method=\"lmfit\",\n",
" energy_scale='kinetic',\n",
" d={'value':1.,'min': .8, 'max':5, 'vary':True},\n",
" t0={'value':ph_peak, 'min': ph_peak*0.1, 'max': ph_peak*1.9, 'vary':False},\n",
" d={'value':1.,'min': .2, 'max':1.0, 'vary':True},\n",
" t0={'value':5e-7, 'min': 1e-7, 'max': 1e-6, 'vary':True},\n",
" E0={'value': 0., 'min': -100, 'max': 100, 'vary': True},\n",
"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# tof = np.linspace(4000,5000,500)\n",
"# plt.figure()\n",
"# plt.plot(tof, tof2ev(\n",
"# t=tof, \n",
"# tof_distance=1, \n",
"# time_offset=1e-9*tof2ns(binwidth=sp.config['dataframe']['tof_binwidth'], binning=3,t=218), \n",
"# energy_offset=0,\n",
"# binning=3,\n",
"# energy_scale='kinetic',\n",
"# binwidth=sp.config['dataframe']['tof_binwidth'])\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand All @@ -268,15 +189,6 @@
"sp.append_energy_axis()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sp.ec.calibration"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -324,14 +236,23 @@
"metadata": {},
"outputs": [],
"source": [
"sp.apply_energy_offset(\n",
" constant=2,\n",
"sp.add_energy_offset(\n",
" constant=0,\n",
" columns=['sampleBias','monochromatorPhotonEnergy','tofVoltage'],\n",
" signs=[1,-1,-1],\n",
" subtract_mean=True,\n",
" preserve_mean=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sp.dataframe"
]
},
{
"cell_type": "code",
"execution_count": null,
Expand Down Expand Up @@ -361,7 +282,9 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"# Fit the falling edge at E=0 to evaluate the quality of the energy calibration"
"### save the calibration parameters\n",
"This is currently overwriting all other values in the config file you point at. Do not overwrite local settings! \n",
"This should be fixed in the future."
]
},
{
Expand All @@ -370,60 +293,7 @@
"metadata": {},
"outputs": [],
"source": [
"from lmfit.models import Gaussian2dModel, LorentzianModel, LinearModel"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"curve = res_fit.mean('sampleBias').sel(energy=slice(-0.15,1))\n",
"x = curve.energy\n",
"y = -np.gradient(curve.data)\n",
"full_y = np.gradient(res_fit.data, axis=1)\n",
"gm = LorentzianModel()\n",
"# lin = LinearModel()\n",
"model = gm #+ lin\n",
"params = gm.guess(y, x=x)\n",
"# params.update(lin.make_params())\n",
"result = model.fit(y, params, x=x)\n",
"\n",
"plt.figure()\n",
"plt.plot(x, y, 'bo')\n",
"plt.plot(x, result.best_fit, 'r-')\n",
"result.best_values\n",
"best = result.params"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"{'amplitude': 62.649384173227375,\n",
" 'center': 0.07618539040289204,\n",
" 'sigma': 0.0948828689156287}"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"plt.figure()\n",
"for i in range(len(res_fit.sampleBias)):\n",
" x = res_fit.isel(sampleBias=i).sel(energy=slice(-0.15,1)).energy\n",
" y = res_fit.isel(sampleBias=i).sel(energy=slice(-0.15,1)).data\n",
" y = -np.gradient(y)\n",
" y = y/np.max(y)\n",
" result = model.fit(y, best, x=x)\n",
" print(f'{i}: {result.best_values}')\n",
" plt.plot(x, y+i, 'bo')\n",
" plt.plot(x, result.best_fit+i, 'r-')\n"
"sp.save_energy_calibration(filename=local_path/'energy_calibration.yaml')"
]
},
{
Expand All @@ -443,11 +313,12 @@
},
"outputs": [],
"source": [
"config={\"core\": {\"paths\": {\n",
"config_dict={\"core\": {\"paths\": {\n",
" \"data_raw_dir\": \"/asap3/flash/gpfs/pg2/2023/data/11019101/raw/hdf/offline/fl1user3\", \n",
" \"data_parquet_dir\": \"/home/agustsss/temp/sed_parquet/\"\n",
"}}}\n",
"sp = SedProcessor(runs=[44797], config=config, user_config=config_file, system_config={}, collect_metadata=False)"
"# config = sed.core.config.parse_config(config=config_dict, folder_config=local_path/'energy_calibration.yaml', user_config={}, system_config={})\n",
"sp = SedProcessor(runs=[44797], config=config_dict, folder_config=local_path/'energy_calibration.yaml', user_config=config_file, collect_metadata=False)"
]
},
{
Expand Down
19 changes: 0 additions & 19 deletions tutorial/hextof_config.yaml
View file
Original file line number Diff line number Diff line change
Expand Up @@ -119,22 +119,3 @@ dataframe:
pg2: "/asap3/flash/gpfs/pg2/"
hextof: "/asap3/fs-flash-o/gpfs/hextof/"
wespe: "/asap3/fs-flash-o/gpfs/wespe/"

energy:
calibration:
d: 2.7342492951998603
t0: 3.6049383256584405e-08
E0: -51.289659014865784 # flip sign if switching between kinetic and binding energy
energy_scale: kinetic
refid: 3
offset:
constant: 2.0
sampleBias:
sign: 1
preserve_mean: True
monochromatorPhotonEnergy:
sign: -1
preserve_mean: True
tofVoltage:
sign: -1
preserve_mean: True

0 comments on commit 3dafe4e

Please sign in to comment.