diff --git a/docker/xps/Dockerfile b/docker/xps/Dockerfile
new file mode 100644
index 0000000000000000000000000000000000000000..436f59e069f7e7af64e261340ef3c2b6f86941b0
--- /dev/null
+++ b/docker/xps/Dockerfile
@@ -0,0 +1,13 @@
+FROM jupyter/scipy-notebook
+
+ENV HOME=/home/jovyan
+WORKDIR $HOME
+
+ADD requirements.txt .
+# Install packages and setup jupyter extensions
+RUN pip install -r requirements.txt \
+ && jupyter lab build \
+ && jupyter nbextension enable --py widgetsnbextension \
+ && jupyter serverextension enable jupyterlab_h5web \
+ && pip install git+https://github.com/nomad-coe/nomad-parser-nexus.git@XPS_datamodel_with_cls_AP1
+RUN rm requirements.txt
\ No newline at end of file
diff --git a/docker/xps/docker-compose.yml b/docker/xps/docker-compose.yml
new file mode 100644
index 0000000000000000000000000000000000000000..53fea18ad649ac2a311d89616bba3d79e3e10961
--- /dev/null
+++ b/docker/xps/docker-compose.yml
@@ -0,0 +1,10 @@
+version: "3.9"
+
+services:
+ xps:
+ image: gitlab-registry.mpcdf.mpg.de/nomad-lab/nomad-remote-tools-hub/xps-jupyter
+ ports:
+ - 8888:8888
+ volumes:
+ - ./example:/home/jovyan/work
+ working_dir: /home/jovyan/work
\ No newline at end of file
diff --git a/docker/xps/example/In-situ PBTTT XPS (SPECS).xml b/docker/xps/example/In-situ PBTTT XPS (SPECS).xml
new file mode 100644
index 0000000000000000000000000000000000000000..0573397d139a4fa54e82675a4a7b88194c64e901
--- /dev/null
+++ b/docker/xps/example/In-situ PBTTT XPS (SPECS).xml
@@ -0,0 +1,355955 @@
+<?xml version="1.0"?>
+<!-- CORBA XML document created by XMLSerializer2 1.6 at 2022-04-08 11:47:02 UTC, from SL 2.81-r39274 built 2013-12-15 02:02:53 UTC -->
+<!DOCTYPE any [
+<!ELEMENT any (any|typecode|sequence|array|struct|union|exception|string|objectref|enum|boolean|char|octet|short|ushort|long|ulong|float|double)?>
+<!ATTLIST any name NMTOKEN #IMPLIED type_id CDATA #IMPLIED type_name NMTOKEN #IMPLIED label NMTOKEN #IMPLIED version CDATA #IMPLIED>
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+<!ATTLIST objectref name NMTOKEN #IMPLIED type_id CDATA #REQUIRED type_name NMTOKEN #IMPLIED label NMTOKEN #IMPLIED>
+<!ELEMENT enum (#PCDATA)>
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+<!ATTLIST float name NMTOKEN #IMPLIED type_id CDATA #IMPLIED type_name NMTOKEN #IMPLIED label NMTOKEN #IMPLIED>
+<!ELEMENT double (#PCDATA)>
+<!ATTLIST double name NMTOKEN #IMPLIED type_id CDATA #IMPLIED type_name NMTOKEN #IMPLIED label NMTOKEN #IMPLIED>
+]>
+<any version="1.6">
+ <sequence length="4" type_id="IDL:specs.de/Serializer/RegionGroupSeq:1.0" type_name="RegionGroupSeq">
+ <struct type_id="IDL:specs.de/Serializer/RegionGroup:1.0" type_name="RegionGroup">
+ <string name="name">PBTTT 1.2 V</string>
+ <sequence name="regions" length="4" type_id="IDL:specs.de/Serializer/RegionDataSeq:1.0" type_name="RegionDataSeq">
+ <struct type_id="IDL:specs.de/Serializer/RegionData:1.0" type_name="RegionData">
+ <string name="name">Survey</string>
+ <struct name="region" type_id="IDL:specs.de/SurfaceAnalysis/RegionDef:1.0" type_name="RegionDef">
+ <string name="analysis_method">XPS</string>
+ <string name="analyzer_lens">MediumMagnification:1.5kV</string>
+ <string name="detector_dataset">XPS_MedMag</string>
+ <string name="detector_dataset_directory">C:\Program Files (x86)\SPECS\SpecsLab2\database\datasetCalib1d\2021_06</string>
+ <string name="analyzer_slit">1:0.2x20c\1:open</string>
+ <struct name="scan_mode" type_id="IDL:specs.de/SurfaceAnalysis/ScanMode:1.0" type_name="ScanMode">
+ <string name="name">FixedAnalyzerTransmission</string>
+ <ulong name="flags" type_id="IDL:specs.de/SurfaceAnalysis/ScanModeFlags:1.0" type_name="ScanModeFlags">2</ulong>
+ </struct>
+ <ulong name="num_scans">1</ulong>
+ <ulong name="curves_per_scan">1</ulong>
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+ <double name="kinetic_energy">553.6</double>
+ <double name="kinetic_energy_base">0</double>
+ <double name="pass_energy">50</double>
+ <double name="bias_voltage">90</double>
+ <double name="detector_voltage">2400</double>
+ <double name="effective_workfunction">4.37</double>
+ </struct>
+ <ulong name="mcd_head">8</ulong>
+ <ulong name="mcd_tail">7</ulong>
+ <struct name="analyzer_info" type_id="IDL:specs.de/SurfaceAnalysis/AnalyzerInfo:1.0" type_name="AnalyzerInfo">
+ <string name="name">PHOIBOS HSA3500 100 R6-HORN[HWType 30:7] MCD-5</string>
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+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Threshold [mV]</string>
+ <any name="value">
+ <double>2.87109375</double>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Deadtime [ns]</string>
+ <any name="value">
+ <double>-0</double>
+ </any>
+ </struct>
+ </sequence>
+ </struct>
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+ <string name="name">Threshold [mV]</string>
+ <any name="value">
+ <double>2.87109375</double>
+ </any>
+ </struct>
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+ <any name="value">
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+ </any>
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+ </any>
+ </struct>
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+ </any>
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+ </sequence>
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+ </any>
+ </struct>
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+ </sequence>
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+ </struct>
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+ </struct>
+ </sequence>
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+ </any>
+ </struct>
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+ </any>
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+ </sequence>
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+ </sequence>
+ <sequence name="scaling_factors" length="0" type_id="IDL:specs.de/SurfaceAnalysis/DoubleSeq:1.0" type_name="DoubleSeq"/>
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+ </sequence>
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+ </sequence>
+ <sequence name="scaling_factors" length="0" type_id="IDL:specs.de/SurfaceAnalysis/DoubleSeq:1.0" type_name="DoubleSeq"/>
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+ </sequence>
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+ </sequence>
+ <sequence name="scaling_factors" length="0" type_id="IDL:specs.de/SurfaceAnalysis/DoubleSeq:1.0" type_name="DoubleSeq"/>
+ </struct>
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+ </sequence>
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+ </sequence>
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+ </sequence>
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+ </sequence>
+ <sequence name="scaling_factors" length="0" type_id="IDL:specs.de/SurfaceAnalysis/DoubleSeq:1.0" type_name="DoubleSeq"/>
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+ <enum values="Mg Al Ag AlMono AgMono Y_M_zeta Zr_M_zeta Nb_M_zeta Mo_M_zeta Ti_L_alpha Cr_L_alpha Ni_L_alpha Cu_L_alpha Mg_K_alpha Al_K_alpha Si_K_alpha Y_L_alpha Zr_L_alpha Au_M_alpha Mo_L_alpha Ag_L_alpha Ti_K_alpha Cr_K_alpha Cu_K_alpha AlMicro1 AlMicro2 AlMicro3 AlMicro4 UserDefined" type_id="IDL:specs.de/XRayAnode/Anode:1.0" type_name="Anode">Mg</enum>
+ </any>
+ </struct>
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+ <string name="name">Power [W]</string>
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+ <double>0</double>
+ </any>
+ </struct>
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+ <string name="name">Voltage [kV]</string>
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+ <double>0</double>
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+ <double>45</double>
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+ <string name="name">End of acquisition</string>
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+ <string name="name"/>
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+ </struct>
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+ </ulong>
+ </sequence>
+ <sequence name="scaling_factors" length="0" type_id="IDL:specs.de/SurfaceAnalysis/DoubleSeq:1.0" type_name="DoubleSeq"/>
+ </struct>
+ </sequence>
+ <sequence name="y_curves" length="0" type_id="IDL:specs.de/Serializer/YCurveSeq:1.0" type_name="YCurveSeq"/>
+ <sequence name="xy_curves" length="0" type_id="IDL:specs.de/Serializer/XYCurveSeq:1.0" type_name="XYCurveSeq"/>
+ <sequence name="parameters" length="0" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq"/>
+ </struct>
+ </sequence>
+ <sequence name="compact_cycles" length="0" type_id="IDL:specs.de/Serializer/CompactCycleSeq:1.0" type_name="CompactCycleSeq"/>
+ <sequence name="transmission" length="35" type_id="IDL:specs.de/SurfaceAnalysis/DoubleSeq:1.0" type_name="DoubleSeq">
+ <double>
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+ </double>
+ </sequence>
+ <sequence name="parameters" length="6" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">File</string>
+ <any name="value">
+ <string>C:\Users\Big Omicron\Dropbox\Ahmed\2022\Cambridge Collaboration\PBTTT XPS.xml</string>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Group</string>
+ <any name="value">
+ <string>in-situ PBTTT</string>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">First of group</string>
+ <any name="value">
+ <boolean>0</boolean>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Interpolation</string>
+ <any name="value">
+ <boolean>1</boolean>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Color</string>
+ <any name="value">
+ <ulong>21930</ulong>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Style</string>
+ <any name="value">
+ <string>solid_line:8</string>
+ </any>
+ </struct>
+ </sequence>
+ </struct>
+ <struct type_id="IDL:specs.de/Serializer/RegionData:1.0" type_name="RegionData">
+ <string name="name">Region4</string>
+ <struct name="region" type_id="IDL:specs.de/SurfaceAnalysis/RegionDef:1.0" type_name="RegionDef">
+ <string name="analysis_method">XPS</string>
+ <string name="analyzer_lens">MediumMagnification:1.5kV</string>
+ <string name="detector_dataset">XPS_MedMag</string>
+ <string name="detector_dataset_directory">C:\Program Files (x86)\SPECS\SpecsLab2\database\datasetCalib1d\2021_06</string>
+ <string name="analyzer_slit">1:0.2x20c\1:open</string>
+ <struct name="scan_mode" type_id="IDL:specs.de/SurfaceAnalysis/ScanMode:1.0" type_name="ScanMode">
+ <string name="name">FixedEnergies</string>
+ <ulong name="flags" type_id="IDL:specs.de/SurfaceAnalysis/ScanModeFlags:1.0" type_name="ScanModeFlags">0</ulong>
+ </struct>
+ <ulong name="num_scans">1</ulong>
+ <ulong name="curves_per_scan">1</ulong>
+ <ulong name="values_per_curve">158</ulong>
+ <double name="dwell_time">0.1</double>
+ <double name="scan_delta">1</double>
+ <double name="excitation_energy">1253.6</double>
+ <double name="kinetic_energy">1089</double>
+ <double name="kinetic_energy_base">0</double>
+ <double name="pass_energy">50</double>
+ <double name="bias_voltage">90</double>
+ <double name="detector_voltage">2400</double>
+ <double name="effective_workfunction">4.37</double>
+ </struct>
+ <ulong name="mcd_head">0</ulong>
+ <ulong name="mcd_tail">0</ulong>
+ <struct name="analyzer_info" type_id="IDL:specs.de/SurfaceAnalysis/AnalyzerInfo:1.0" type_name="AnalyzerInfo">
+ <string name="name">PHOIBOS HSA3500 100 R6-HORN[HWType 30:7] MCD-5</string>
+ <sequence name="detectors" length="5" type_id="IDL:specs.de/SurfaceAnalysis/DetectorSeq:1.0" type_name="DetectorSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Detector:1.0" type_name="Detector">
+ <double name="position">-16</double>
+ <double name="shift">-0.0744451</double>
+ <double name="gain">1</double>
+ <sequence name="parameters" length="2" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Threshold [mV]</string>
+ <any name="value">
+ <double>2.87109375</double>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Deadtime [ns]</string>
+ <any name="value">
+ <double>-0</double>
+ </any>
+ </struct>
+ </sequence>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Detector:1.0" type_name="Detector">
+ <double name="position">-8</double>
+ <double name="shift">-0.0348058</double>
+ <double name="gain">1</double>
+ <sequence name="parameters" length="2" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Threshold [mV]</string>
+ <any name="value">
+ <double>2.87109375</double>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Deadtime [ns]</string>
+ <any name="value">
+ <double>-0</double>
+ </any>
+ </struct>
+ </sequence>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Detector:1.0" type_name="Detector">
+ <double name="position">0</double>
+ <double name="shift">0.00538331</double>
+ <double name="gain">1</double>
+ <sequence name="parameters" length="2" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Threshold [mV]</string>
+ <any name="value">
+ <double>2.87109375</double>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Deadtime [ns]</string>
+ <any name="value">
+ <double>-0</double>
+ </any>
+ </struct>
+ </sequence>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Detector:1.0" type_name="Detector">
+ <double name="position">8</double>
+ <double name="shift">0.0444291</double>
+ <double name="gain">1</double>
+ <sequence name="parameters" length="2" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Threshold [mV]</string>
+ <any name="value">
+ <double>2.87109375</double>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Deadtime [ns]</string>
+ <any name="value">
+ <double>-0</double>
+ </any>
+ </struct>
+ </sequence>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Detector:1.0" type_name="Detector">
+ <double name="position">16</double>
+ <double name="shift">0.0824935</double>
+ <double name="gain">1</double>
+ <sequence name="parameters" length="2" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Threshold [mV]</string>
+ <any name="value">
+ <double>2.87109375</double>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Deadtime [ns]</string>
+ <any name="value">
+ <double>-0</double>
+ </any>
+ </struct>
+ </sequence>
+ </struct>
+ </sequence>
+ <sequence name="parameters" length="0" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq"/>
+ </struct>
+ <struct name="source_info" type_id="IDL:specs.de/SurfaceAnalysis/SourceInfo:1.0" type_name="SourceInfo">
+ <string name="name">XRayDummy</string>
+ <sequence name="excitations" length="1" type_id="IDL:specs.de/SurfaceAnalysis/ExcitationSeq:1.0" type_name="ExcitationSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Excitation:1.0" type_name="Excitation">
+ <double name="energy">1253.6</double>
+ <double name="lorentz_width">0.7</double>
+ <double name="gauss_width">0</double>
+ <double name="intensity">1</double>
+ </struct>
+ </sequence>
+ <sequence name="parameters" length="5" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Anode</string>
+ <any name="value">
+ <enum values="Mg Al Ag AlMono AgMono Y_M_zeta Zr_M_zeta Nb_M_zeta Mo_M_zeta Ti_L_alpha Cr_L_alpha Ni_L_alpha Cu_L_alpha Mg_K_alpha Al_K_alpha Si_K_alpha Y_L_alpha Zr_L_alpha Au_M_alpha Mo_L_alpha Ag_L_alpha Ti_K_alpha Cr_K_alpha Cu_K_alpha AlMicro1 AlMicro2 AlMicro3 AlMicro4 UserDefined" type_id="IDL:specs.de/XRayAnode/Anode:1.0" type_name="Anode">Mg</enum>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Power [W]</string>
+ <any name="value">
+ <double>0</double>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Voltage [kV]</string>
+ <any name="value">
+ <double>0</double>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Angle [deg]</string>
+ <any name="value">
+ <double>45</double>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">End of acquisition</string>
+ <any name="value">
+ <enum values="Off Standby Operate" type_id="IDL:specs.de/SurfaceAnalysis/SourceState:1.0" type_name="SourceState">Off</enum>
+ </any>
+ </struct>
+ </sequence>
+ <boolean name="intensity_scaling">0</boolean>
+ </struct>
+ <struct name="remote_info" type_id="IDL:specs.de/SurfaceAnalysis/RemoteInfo:1.0" type_name="RemoteInfo">
+ <string name="name"/>
+ <enum name="frequency" values="Never PerRegion PerScan PerValue AfterRegion AfterScan AfterValue PerValueAndScan" type_id="IDL:specs.de/SurfaceAnalysis/RemoteFrequency:1.0" type_name="RemoteFrequency">Never</enum>
+ <sequence name="parameters" length="0" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq"/>
+ <sequence name="channel_names" length="0" type_id="IDL:specs.de/SurfaceAnalysis/StringSeq:1.0" type_name="StringSeq"/>
+ <sequence name="channel_units" length="0" type_id="IDL:specs.de/SurfaceAnalysis/StringSeq:1.0" type_name="StringSeq"/>
+ </struct>
+ <sequence name="cycles" length="0" type_id="IDL:specs.de/Serializer/CycleSeq:1.0" type_name="CycleSeq"/>
+ <sequence name="compact_cycles" length="0" type_id="IDL:specs.de/Serializer/CompactCycleSeq:1.0" type_name="CompactCycleSeq"/>
+ <sequence name="transmission" length="158" type_id="IDL:specs.de/SurfaceAnalysis/DoubleSeq:1.0" type_name="DoubleSeq">
+ <double>
+10.7353003675253
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+ </double>
+ </sequence>
+ <sequence name="parameters" length="6" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">File</string>
+ <any name="value">
+ <string>C:\Users\Big Omicron\Dropbox\Ahmed\2022\Cambridge Collaboration\PBTTT XPS.xml</string>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Group</string>
+ <any name="value">
+ <string>in-situ PBTTT</string>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">First of group</string>
+ <any name="value">
+ <boolean>0</boolean>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Interpolation</string>
+ <any name="value">
+ <boolean>1</boolean>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Color</string>
+ <any name="value">
+ <ulong>0</ulong>
+ </any>
+ </struct>
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">Style</string>
+ <any name="value">
+ <string>solid_line:4</string>
+ </any>
+ </struct>
+ </sequence>
+ </struct>
+ </sequence>
+ <sequence name="parameters" length="1" type_id="IDL:specs.de/SurfaceAnalysis/ParameterSeq:1.0" type_name="ParameterSeq">
+ <struct type_id="IDL:specs.de/SurfaceAnalysis/Parameter:1.0" type_name="Parameter">
+ <string name="name">File</string>
+ <any name="value">
+ <string>C:\Users\Big Omicron\Dropbox\Ahmed\2022\Cambridge Collaboration\PBTTT XPS.xml</string>
+ </any>
+ </struct>
+ </sequence>
+ </struct>
+ </sequence>
+</any>
\ No newline at end of file
diff --git a/docker/xps/example/PBTTT.nxs b/docker/xps/example/PBTTT.nxs
new file mode 100644
index 0000000000000000000000000000000000000000..bc5afe555fdfec348f78abf545599dd20f89909f
Binary files /dev/null and b/docker/xps/example/PBTTT.nxs differ
diff --git a/docker/xps/example/README.md b/docker/xps/example/README.md
new file mode 100644
index 0000000000000000000000000000000000000000..63cef6fb77512937d18a7e8c96a03d13b7d4cb43
--- /dev/null
+++ b/docker/xps/example/README.md
@@ -0,0 +1,31 @@
+# Introduction
+
+This example presents the capabilities of the NOMAD platform to store and standardize XPS data. It shows the generation of a NeXus file according to the [NXmpes](https://manual.nexusformat.org/classes/contributed_definitions/NXmpes.html#nxmpes) application definition and a successive analysis of an example data set.
+
+# Viewing uploaded data
+
+Below, you find an overview of your uploaded data.
+Click on the `> /` button to get a list of your data or select **FILES** from the top menu of this upload.
+You may add your own files to the upload or experiment with the pre-existing electronic lab notebook (ELN) example.
+The ELN follows the general structure of NOMAD ELN templates and you may refer to the [documentation](https://nomad-lab.eu/prod/v1/staging/docs/archive.html) or a [YouTube tutorial](https://youtu.be/o5ETHmGmnaI) (~1h)
+for further information.
+When the ELN is saved a NeXus file will be generated from the provided example data.
+You may also view your supplied or generated NeXus files here with the H5Web viewer.
+To do so open the **FILES** tab and just select a `.nxs` file.
+
+# Analyzing the data
+
+The examples work through the use of NOMAD remote tools hub (NORTH) containers, i.e. besides using and dealing with the uploaded XPS data, an analysis container can be started. If you want to execute the examples locally you may also use your local python and jupyterlab installation. Please refer to the documentation of [nexusutils](https://github.com/nomad-coe/nomad-parser-nexus) and [h5web](https://github.com/nomad-coe/nomad-parser-nexus) on how to install it on your machine.
+
+To start an analysis, note your upload id (which you find on top of this explanation) and select **ANALYZE** from the top menu, then **NOMAD Remote Tools Hub**.
+In the appearing list you'll find the `xps` container, click on it and click **LAUNCH**.
+After a few moments a new tab will open which displays a jupyter environment providing the required analysis tools.
+To find the examples navigate to uploads inside the jupyter hub browser and select the folder with your noted upload id.
+There you'll find the example `ipynb` notebook.
+Double-clicking the notebook will open the example in the jupyter main window.
+
+# Where to go from here?
+
+If you're interested in using this pipeline and NOMAD in general you'll find support at [FAIRmat](https://www.fairmat-nfdi.eu/fairmat/consortium).
+
+For questions regarding the experiment or this specific example contact [Rubel Mozumder](https://www.fairmat-nfdi.eu/fairmat/fairmat_/fairmatteam) or [Florian Dobener](https://www.fairmat-nfdi.eu/fairmat/fairmat_/fairmatteam).
diff --git a/docker/xps/example/XPS workflow and fitting example.ipynb b/docker/xps/example/XPS workflow and fitting example.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ad8b62b1d98c57b7543273285d38920330a609c9
--- /dev/null
+++ b/docker/xps/example/XPS workflow and fitting example.ipynb
@@ -0,0 +1,368 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "38f1916e-2d0f-4f81-9b00-fedc615a96aa",
+ "metadata": {},
+ "source": [
+ "# XPS workflow and fitting example\n",
+ "\n",
+ "In this notebook a XPS measurement file from a SPECS detector is read and converted into the [NXmpes](https://manual.nexusformat.org/classes/contributed_definitions/NXmpes.html#nxmpes) NeXus standard. Additionally, a fit of a C1s spectrum is done."
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dc099289-78f5-4357-b7d3-715dd20179da",
+ "metadata": {},
+ "source": [
+ "## Create a NeXus file from measurement data\n",
+ "\n",
+ "To convert the available files to the NeXus format we use the convert function readily supplied by nexusutils."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "7d2c1df4-1d96-4255-a18e-e323c69d32b4",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from nexusutils.dataconverter.convert import convert, logger\n",
+ "import logging\n",
+ "logger.setLevel(logging.ERROR)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "bf441135-d4c5-4310-ae6e-39c87da1b726",
+ "metadata": {},
+ "source": [
+ "The input parameters are defined as follows\n",
+ "\n",
+ "**reader**: The specific reader which gets called inside the nexusparser. This is supplied in the nexusparser python code. If you create a specific reader for your measurement file it gets selecetd here. For the XPS SPECS reader it is called `xps`.\n",
+ "\n",
+ "**nxdl**: The specific nxdl file which to use. For XPS this should be `NXmpes` or one of its subdefinitions of the form `NXmpes_<name>`.\n",
+ " \n",
+ "**output**: The output filename of the NeXus file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8e8a76ff-918e-483d-9ed1-5417613710e1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "convert(input_file=[\"In-situ PBTTT XPS (SPECS).xml\"],\n",
+ " reader='xps',\n",
+ " nxdl='NXmpes',\n",
+ " output='PBTTT.nxs')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "fa5fe119-0a72-4744-a84d-4d1258f55031",
+ "metadata": {},
+ "source": [
+ "## View the data with H5Web\n",
+ "\n",
+ "H5Web is a tool for visualizing any data in the h5 data format. Since the NeXus format builds opon h5 it can be used to view this data as well. We just import the package and call H5Web with the output filename from the convert command above.\n",
+ "\n",
+ "You can also view this data with the H5Viewer or other tools from your local filesystem."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d4ff822a-4552-49b8-ba17-9b86fd8c2ac1",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from jupyterlab_h5web import H5Web"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "f62097ae-eb0f-4572-b8d9-bebc7266b43a",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "H5Web(\"PBTTT.nxs\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "359770eb-964f-48fd-97da-84038af10193",
+ "metadata": {},
+ "source": [
+ "## Analyze data\n",
+ "\n",
+ "First, we need to import the necessarry packages. We use h5py for reading the NeXus file, lmfit for fitting and the class XPSRegion from the provided `xps_region.py` file."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "ed892838-0f76-47a8-89b6-ba8bba4f9048",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import h5py\n",
+ "from xps_region import XPSRegion\n",
+ "\n",
+ "from lmfit.models import GaussianModel"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "a30f1ecb-d9f9-44eb-b9d3-f61709109d6a",
+ "metadata": {},
+ "source": [
+ "### Load data and plot\n",
+ "\n",
+ "We want to load the C1s spectrum for PBTTT from our measurement file. Feel free to adapt to different regions in the file by changing the `MEASUREMENT` variable."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "400205d5",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "MEASUREMENT = \"specs__PBTTT__C1s\"\n",
+ "\n",
+ "with h5py.File(\"PBTTT.nxs\", \"r\") as xps_file:\n",
+ " binding_energy = xps_file[f\"/{MEASUREMENT}/data/BE\"][:]\n",
+ " cps = xps_file[f\"/{MEASUREMENT}/data/data\"][:]\n",
+ " cps_err = xps_file[f\"/{MEASUREMENT}/data/data_errors\"][:]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f6ffc43-6e23-4f54-8a8a-0192a21368ca",
+ "metadata": {},
+ "source": [
+ "With the loaded data we create the `c1s` `XPSRegion` containing the measurement data."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "129db648-3c47-4f81-9ee8-69788c96660e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "c1s = XPSRegion(binding_energy=binding_energy, counts=cps, counts_err=cps_err)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "1c500a40-b915-44ba-a8c4-a9c37f3f7f4f",
+ "metadata": {},
+ "source": [
+ "`XPSRegion` provides us a function to visualize the loaded data with"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "211918d9-8974-4ae2-97d7-51c6920fe6cb",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "c1s.plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cf8cb26e-df35-4ef3-bea3-e9ddcc452c2e",
+ "metadata": {},
+ "source": [
+ "### Fit data\n",
+ "\n",
+ "From the preview plot we can distinguish two peaks typically associated with the carbon C-O and C-C bonds. We construct two Gaussian peaks with the lmfit [GaussianModel](https://lmfit.github.io/lmfit-py/builtin_models.html#gaussianmodel) and initialize them with appropriate start values. Here we are just using initial good guesses for the start values. These, however, can eventually be deduced by data inside NOMAD as soon as enough data is available, e.g. similar to a peak detection in other XPS analysis programs. There are different peak shapes available in lmfit, such as Lorentz, Voigt, PseudoVoigt or skewed models. Please refer to the packages [documentation](https://lmfit.github.io/lmfit-py/builtin_models.html) for further details on these models and on [how to use them](https://lmfit.github.io/lmfit-py/model.html)."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "6401d0af-7830-4093-ae2e-d1c4a719ff61",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "co = GaussianModel(prefix=\"C1s_CO_\")\n",
+ "co.set_param_hint(\"C1s_CO_amplitude\", value=1900)\n",
+ "co.set_param_hint(\"C1s_CO_sigma\", value=.8)\n",
+ "co.set_param_hint(\"C1s_CO_center\", value=285.4)\n",
+ "\n",
+ "cc = GaussianModel(prefix=\"C1s_CC_\")\n",
+ "cc.set_param_hint(\"C1s_CC_amplitude\", value=280)\n",
+ "cc.set_param_hint(\"C1s_CC_center\", value=284.1)\n",
+ "cc.set_param_hint(\"C1s_CC_sigma\", value=0.7)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "25c2b42c-3e74-435e-88c6-64e5f04a2244",
+ "metadata": {},
+ "source": [
+ "We can simply add the two models together to create a composite model"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "62d6293b-f562-425d-b39a-22c8b3874676",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "comp = cc + co"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2aaf4749-62d6-4cec-af67-8daced4d3ed9",
+ "metadata": {},
+ "source": [
+ "In the next step we select a region in which we want to fit the data with `fit_region(...)`, calculate a shirley baseline with `calc_baseline()`, set the fit model (`.fit_model(comp)`) and perform a fit (`.fit()`). All of this functions can also be used independently. The fit function does not take the measurement uncertainties into account.\n",
+ "\n",
+ "Finally, the model is plotted with the previously used `plot()` method. Since we performed a fit the plot is now extended by the baseline and fits."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "c48fbeb7-1e46-4c0b-897c-3907654d033b",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "c1s.fit_region(281, 289).calc_baseline().fit_model(comp).fit().plot()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "97a42467-5bbb-4c9a-a97a-5d6ed573f5f0",
+ "metadata": {},
+ "source": [
+ "The fit result gets stored inside the `fit_result` parameter and is displayed to extract, e.g., the peak central energies. Please note that the fitting does not take the measurement uncertainties into account and the errors are simple fitting errors."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "9345ab8f-b65f-49f2-a260-bf1c7805e4a0",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "c1s.fit_result.params"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "22d85cef-be55-402e-9083-c40a245fadb1",
+ "metadata": {},
+ "source": [
+ "We can also extract a fitting parameter shared accross different peaks, e.g. the peak central energies. This refers to the text behind the model paramters prefix, so we select `center` here to get the central energies."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "4b7b17c0-d781-4b62-8274-c82b00f3c267",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "c1s.peak_property('center')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "16cbedf6-0e82-43ca-909a-e826fd8df7e7",
+ "metadata": {},
+ "source": [
+ "Typically, we are also interested in the peak areas which can be calculated with `peak_areas()`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "8aa0759b-a920-4491-97ca-54381a85d3a8",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "(areas := c1s.peak_areas())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "f2b57a15-e95b-4dba-a14e-63031ffa3408",
+ "metadata": {},
+ "source": [
+ "and their ratios"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "d55e8ea6-3fc9-4049-be1b-9fcec5f27ffd",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "areas / areas.max()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "b909f5bc-446b-4c11-b5b8-1eeec79c894d",
+ "metadata": {},
+ "source": [
+ "To asses the quality of the fit the fit residual can be viewed with `plot_residual()`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "cfa5fe97",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "c1s.plot_residual()"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "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": "3.10.8"
+ },
+ "vscode": {
+ "interpreter": {
+ "hash": "0cbdf5d5ef28617c8bf3753ff15cd1b7b5539de5aaa68a35c3d38ca27e1ab0fa"
+ }
+ },
+ "widgets": {
+ "application/vnd.jupyter.widget-state+json": {
+ "state": {},
+ "version_major": 2,
+ "version_minor": 0
+ }
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/docker/xps/example/xps_region.py b/docker/xps/example/xps_region.py
new file mode 100644
index 0000000000000000000000000000000000000000..b1eac21ecaa65da5cf60f214ebe83795b3d969df
--- /dev/null
+++ b/docker/xps/example/xps_region.py
@@ -0,0 +1,326 @@
+"""Fitting functions for XPS spectra"""
+from typing import Optional
+from dataclasses import dataclass
+import numpy as np
+from numpy.linalg import norm
+import plotly.graph_objects as go
+import pandas as pd
+from lmfit import Model, CompositeModel
+
+
+@dataclass
+class XPSRegion:
+ """An XPS region representation"""
+
+ binding_energy: np.ndarray
+ counts: np.ndarray
+ counts_err: np.ndarray
+ baseline: Optional[np.ndarray] = None
+ _fit_region: slice = slice(None, None)
+ _fit_mod: Optional[Model] = None
+ fit_result: Optional[Model] = None
+
+ def fit_region(self, start: int, stop: int) -> "XPSRegion":
+ """Select a fit region within this XPSregion by x-axis value.
+ The fit region is always selected between start and stop, regardless of their order.
+ Both points are included in the region,
+ hence the actual selected region may be a little larger.
+
+ Args:
+ start (int): The start ot the region.
+ stop (int): The end of the region.
+
+ Returns:
+ XPSRegion: This class
+ """
+ region = np.argwhere(
+ (self.binding_energy >= start) & (self.binding_energy <= stop)
+ )
+
+ self._fit_region = slice(region[0, 0], region[-1, 0], 1)
+ return self
+
+ def fit_model(self, model: Model) -> "XPSRegion":
+ """Supply a fit model to fit this xps region.
+
+ Args:
+ model (lmfit.Model): The lmfit model to use.
+
+ Returns:
+ XPSRegion: This class
+ """
+ self._fit_mod = model
+ return self
+
+ def fit(self, *args, **kwargs) -> "XPSRegion":
+ """Perform a fit of the data. You need to define a fit_model first and
+ execute a baseline correction before using this method.
+
+ Raises:
+ ValueError: If no fit model is provided or the baseline has not been corrected.
+
+ Returns:
+ XPSRegion: This class
+ """
+ if self._fit_mod is None:
+ raise ValueError("You need to provide a fit model before performing a fit.")
+
+ if self.baseline is None:
+ raise ValueError(
+ "You need to perform a baseline correction before using this method."
+ )
+
+ self.fit_result = self._fit_mod.fit(
+ (
+ self.counts[self._fit_region] - self.baseline
+ if self._fit_region
+ else self.counts[self._fit_region]
+ ),
+ *args,
+ x=self.binding_energy[self._fit_region],
+ **kwargs,
+ )
+
+ return self
+
+ def calc_baseline(self, bg_type: str = "shirley") -> "XPSRegion":
+ """Calculate the baseline for this xps spectrum in the given region.
+
+ Args:
+ bg_type (str, optional): The background type. Defaults to "shirley".
+
+ Raises:
+ ValueError: If the bg_type is unsupported.
+
+ Returns:
+ XPSRegion: This class
+ """
+ baselines = {"shirley": shirley_baseline}
+ if bg_type not in baselines:
+ raise ValueError(f"Unsupported baseline type {bg_type}.")
+
+ self.baseline = baselines[bg_type](
+ self.binding_energy[self._fit_region], self.counts[self._fit_region]
+ )
+
+ return self
+
+ def peak_property(self, prop: str) -> pd.DataFrame:
+ """Generates a dataframe with values for a property `prop` of a fitting model.
+
+ Args:
+ prop (str):
+ The name of the property to deduce for the peaks,
+ e.g. `center` for the peak center for gaussian or lorentzian shapes.
+
+ Raises:
+ ValueError: Thrown if no prior fit is performed.
+
+ Returns:
+ pd.DataFrame: A pandas DataFrame containing the peak property for each peak.
+ """
+ if not self.fit_result:
+ raise ValueError("You need to perform a fit first.")
+
+ props = pd.DataFrame()
+ for prefix in map(lambda x: x.prefix, self.fit_result.components):
+ if f"{prefix}{prop}" not in self.fit_result.params:
+ continue
+
+ props = pd.concat(
+ [
+ props,
+ pd.DataFrame(
+ {prop: self.fit_result.params.get(f"{prefix}{prop}").value},
+ index=[prefix.rstrip("_")],
+ ),
+ ]
+ )
+
+ return props
+
+ def peak_areas(self, region_only=False) -> pd.DataFrame:
+ """Calculates the peak areas of the given fit models peaks.
+
+ Args:
+ region_only (bool, optional):
+ Set true if only the area inside the set region should be consider.
+ Defaults to False.
+
+ Raises:
+ ValueError: Thrown if no prior fit is performed.
+
+ Returns:
+ pandas.DataFrame: A pandas DataFrame containing the peak areas.
+ """
+ if not self.fit_result:
+ raise ValueError("You need to perform a fit first.")
+
+ areas = pd.DataFrame()
+ if region_only:
+ peaks = self.fit_result.eval_components(
+ x=self.binding_energy[self._fit_region]
+ )
+ else:
+ peaks = self.fit_result.eval_components(x=self.binding_energy)
+ for prefix in peaks:
+ areas = pd.concat(
+ [
+ areas,
+ pd.DataFrame(
+ {"Area": sum(peaks[prefix])},
+ index=[prefix.rstrip("_")],
+ ),
+ ]
+ )
+ return areas
+
+ def plot_residual(self):
+ """Plot the fit residual"""
+ if not self.fit_result:
+ raise ValueError("You need to perform a fit first.")
+
+ fig = go.Figure(
+ data=go.Scatter(
+ name="Residual",
+ x=self.binding_energy[self._fit_region],
+ y=self.fit_result.residual,
+ )
+ )
+ fig.update_xaxes(title="Binding energy (eV)")
+ fig.update_yaxes(title="Residual")
+ fig.layout.xaxis.autorange = "reversed"
+
+ fig.show()
+
+ def plot(self):
+ """Plot the xps region"""
+ fig = go.Figure(
+ data=go.Scatter(
+ name="Measurement",
+ x=self.binding_energy,
+ y=self.counts,
+ error_y=dict(
+ type="data", # value of error bar given in data coordinates
+ array=self.counts_err,
+ visible=True,
+ ),
+ )
+ )
+ if self._fit_region.start is not None:
+ fig.add_vline(
+ self.binding_energy[self._fit_region.start], line_color="lightgrey"
+ )
+ if self._fit_region.stop is not None:
+ fig.add_vline(
+ self.binding_energy[self._fit_region.stop], line_color="lightgrey"
+ )
+ if self.baseline is not None:
+ fig.add_trace(
+ go.Scatter(
+ name="Baseline",
+ x=self.binding_energy[self._fit_region],
+ y=self.baseline,
+ ),
+ )
+ if self.fit_result is not None:
+ fig.add_trace(
+ go.Scatter(
+ name="Fit",
+ x=self.binding_energy[self._fit_region],
+ y=self.fit_result.best_fit + self.baseline,
+ ),
+ )
+ if isinstance(self._fit_mod, CompositeModel):
+ peaks = self.fit_result.eval_components(
+ x=self.binding_energy[self._fit_region]
+ )
+ for prefix in peaks:
+ fig.add_trace(
+ go.Scatter(
+ name=prefix.rstrip("_"),
+ x=self.binding_energy[self._fit_region],
+ y=peaks[prefix] + self.baseline,
+ )
+ )
+
+ fig.update_xaxes(title="Binding energy (eV)")
+ fig.update_yaxes(title="CPS")
+ fig.layout.xaxis.autorange = "reversed"
+
+ fig.show()
+
+
+# pylint: disable=invalid-name
+def shirley_baseline(
+ x: np.ndarray, y: np.ndarray, tol: float = 1e-5, maxit: int = 10
+) -> np.ndarray:
+ """Calculate the shirley background according to the Sherwood method.
+
+ Args:
+ x (np.ndarray): The x-axis on which to calculate the shirley baseline.
+ y (np.ndarray): The y-axis on which to calculate the shirley baseline.
+ tol (float, optional):
+ The convergence tolerance at which to stop the iteration.
+ Defaults to 1e-5.
+ maxit (int, optional):
+ The maximum iteration after which to stop the iteration.
+ Defaults to 10.
+
+ Raises:
+ ValueError:
+ Is thrown when the arrays have different dimensions,
+ are not numpy arrays or are empty.
+ Is also thrown when the fit does not converge.
+
+ Returns:
+ np.ndarray: The shirley baseline for the x, y dataset.
+ """
+
+ if not isinstance(x, np.ndarray) or not isinstance(y, np.ndarray):
+ raise ValueError(
+ f"Parameters x and y must be of type numpy array, not {type(x)} and {type(y)}"
+ )
+
+ if len(x) != len(y):
+ raise ValueError("x and y arrays have different dimensions.")
+
+ if not x.any():
+ raise ValueError("x-array is empty.")
+
+ if len(x.shape) > 1:
+ raise ValueError(
+ f"Data arrays must be one-dimensional. Found dimension {x.shape}."
+ )
+
+ is_reversed = False
+ if x[0] < x[-1]:
+ is_reversed = True
+ x = x[::-1]
+ y = y[::-1]
+
+ background = np.zeros(x.shape)
+ background_next = background.copy()
+
+ iters = 0
+ while True:
+ k = (y[0] - y[-1]) / np.trapz(y - background, x=x)
+
+ for energy in range(len(x)):
+ background_next[energy] = k * np.trapz(
+ y[energy:] - background[energy:], x=x[energy:]
+ )
+
+ background = background_next.copy()
+ if norm(background_next - background) < tol:
+ break
+
+ iters += 1
+ if iters == maxit:
+ raise ValueError(
+ "Maximum number of iterations exceeded before convergence."
+ )
+
+ if is_reversed:
+ return (y[-1] + background)[::-1]
+ return y[-1] + background
diff --git a/docker/xps/requirements.in b/docker/xps/requirements.in
new file mode 100644
index 0000000000000000000000000000000000000000..93ceafdcde83e1f2af73e1660ee77dbc450656dd
--- /dev/null
+++ b/docker/xps/requirements.in
@@ -0,0 +1,9 @@
+h5glance==0.8
+h5grove==1.1.0
+jupyterlab[full]==3.2.9
+jupyterlab_h5web[full]==6.0.0
+punx==0.2.5
+lmfit
+plotly
+ipywidgets
+nodejs
\ No newline at end of file
diff --git a/docker/xps/requirements.txt b/docker/xps/requirements.txt
new file mode 100755
index 0000000000000000000000000000000000000000..984f13ecb642f2c4c0fb08a0d40fe22a00fa4ba7
--- /dev/null
+++ b/docker/xps/requirements.txt
@@ -0,0 +1,320 @@
+#
+# This file is autogenerated by pip-compile with Python 3.10
+# by the following command:
+#
+# pip-compile
+#
+anyio==3.6.2
+ # via jupyter-server
+argon2-cffi==21.3.0
+ # via
+ # jupyter-server
+ # nbclassic
+argon2-cffi-bindings==21.2.0
+ # via argon2-cffi
+asteval==0.9.28
+ # via lmfit
+asttokens==2.2.1
+ # via stack-data
+attrs==22.1.0
+ # via jsonschema
+babel==2.11.0
+ # via jupyterlab-server
+backcall==0.2.0
+ # via ipython
+beautifulsoup4==4.11.1
+ # via nbconvert
+bleach==5.0.1
+ # via nbconvert
+certifi==2022.12.7
+ # via requests
+cffi==1.15.1
+ # via
+ # argon2-cffi-bindings
+ # pynacl
+charset-normalizer==2.1.1
+ # via requests
+comm==0.1.2
+ # via ipykernel
+debugpy==1.6.4
+ # via ipykernel
+decorator==5.1.1
+ # via ipython
+defusedxml==0.7.1
+ # via nbconvert
+deprecated==1.2.13
+ # via pygithub
+entrypoints==0.4
+ # via jupyter-client
+executing==1.2.0
+ # via stack-data
+fastjsonschema==2.16.2
+ # via nbformat
+future==0.18.2
+ # via uncertainties
+h5glance==0.8
+ # via -r requirements.in
+h5grove==1.1.0
+ # via
+ # -r requirements.in
+ # jupyterlab-h5web
+h5py==3.7.0
+ # via
+ # h5glance
+ # h5grove
+ # hdf5plugin
+ # punx
+hdf5plugin==4.0.1
+ # via jupyterlab-h5web
+htmlgen==2.0.0
+ # via h5glance
+idna==3.4
+ # via
+ # anyio
+ # requests
+ipykernel==6.19.2
+ # via
+ # ipywidgets
+ # nbclassic
+ipython==8.7.0
+ # via
+ # ipykernel
+ # ipywidgets
+ # jupyterlab
+ipython-genutils==0.2.0
+ # via nbclassic
+ipywidgets==8.0.3
+ # via -r requirements.in
+jedi==0.18.2
+ # via ipython
+jinja2==3.1.2
+ # via
+ # jupyter-server
+ # jupyterlab
+ # jupyterlab-server
+ # nbclassic
+ # nbconvert
+json5==0.9.10
+ # via jupyterlab-server
+jsonschema==4.17.3
+ # via
+ # jupyterlab-server
+ # nbformat
+jupyter-client==7.4.8
+ # via
+ # ipykernel
+ # jupyter-server
+ # nbclassic
+ # nbclient
+jupyter-core==5.1.0
+ # via
+ # jupyter-client
+ # jupyter-server
+ # jupyterlab
+ # nbclassic
+ # nbclient
+ # nbconvert
+ # nbformat
+jupyter-server==1.23.3
+ # via
+ # jupyterlab
+ # jupyterlab-h5web
+ # jupyterlab-server
+ # nbclassic
+ # notebook-shim
+jupyterlab[full]==3.2.9
+ # via -r requirements.in
+jupyterlab-h5web[full]==6.0.0
+ # via -r requirements.in
+jupyterlab-pygments==0.2.2
+ # via nbconvert
+jupyterlab-server==2.16.5
+ # via jupyterlab
+jupyterlab-widgets==3.0.4
+ # via ipywidgets
+lmfit==1.1.0
+ # via -r requirements.in
+lxml==4.9.1
+ # via
+ # punx
+ # pyresttable
+markupsafe==2.1.1
+ # via
+ # jinja2
+ # nbconvert
+matplotlib-inline==0.1.6
+ # via
+ # ipykernel
+ # ipython
+mistune==2.0.4
+ # via nbconvert
+nbclassic==0.4.8
+ # via jupyterlab
+nbclient==0.7.2
+ # via nbconvert
+nbconvert==7.2.6
+ # via
+ # jupyter-server
+ # nbclassic
+nbformat==5.7.0
+ # via
+ # jupyter-server
+ # nbclassic
+ # nbclient
+ # nbconvert
+nest-asyncio==1.5.6
+ # via
+ # ipykernel
+ # jupyter-client
+ # nbclassic
+nodejs==0.1.1
+ # via -r requirements.in
+notebook-shim==0.2.2
+ # via nbclassic
+numpy==1.23.5
+ # via
+ # h5grove
+ # h5py
+ # lmfit
+ # punx
+ # pyresttable
+ # scipy
+ # tifffile
+optional-django==0.1.0
+ # via nodejs
+orjson==3.8.3
+ # via h5grove
+packaging==22.0
+ # via
+ # ipykernel
+ # jupyter-server
+ # jupyterlab
+ # jupyterlab-server
+ # nbconvert
+pandocfilters==1.5.0
+ # via nbconvert
+parso==0.8.3
+ # via jedi
+pexpect==4.8.0
+ # via ipython
+pickleshare==0.7.5
+ # via ipython
+platformdirs==2.6.0
+ # via jupyter-core
+plotly==5.11.0
+ # via -r requirements.in
+prometheus-client==0.15.0
+ # via
+ # jupyter-server
+ # nbclassic
+prompt-toolkit==3.0.36
+ # via ipython
+psutil==5.9.4
+ # via ipykernel
+ptyprocess==0.7.0
+ # via
+ # pexpect
+ # terminado
+punx==0.2.5
+ # via -r requirements.in
+pure-eval==0.2.2
+ # via stack-data
+pycparser==2.21
+ # via cffi
+pygithub==1.57
+ # via punx
+pygments==2.13.0
+ # via
+ # ipython
+ # nbconvert
+pyjwt==2.6.0
+ # via pygithub
+pynacl==1.5.0
+ # via pygithub
+pyresttable==2020.0.6
+ # via punx
+pyrsistent==0.19.2
+ # via jsonschema
+python-dateutil==2.8.2
+ # via jupyter-client
+pytz==2022.6
+ # via babel
+pyzmq==24.0.1
+ # via
+ # ipykernel
+ # jupyter-client
+ # jupyter-server
+ # nbclassic
+requests==2.28.1
+ # via
+ # jupyterlab-server
+ # punx
+ # pygithub
+scipy==1.9.3
+ # via lmfit
+send2trash==1.8.0
+ # via
+ # jupyter-server
+ # nbclassic
+six==1.16.0
+ # via
+ # asttokens
+ # bleach
+ # python-dateutil
+sniffio==1.3.0
+ # via anyio
+soupsieve==2.3.2.post1
+ # via beautifulsoup4
+stack-data==0.6.2
+ # via ipython
+tenacity==8.1.0
+ # via plotly
+terminado==0.17.1
+ # via
+ # jupyter-server
+ # nbclassic
+tifffile==2022.10.10
+ # via h5grove
+tinycss2==1.2.1
+ # via nbconvert
+tornado==6.2
+ # via
+ # ipykernel
+ # jupyter-client
+ # jupyter-server
+ # jupyterlab
+ # nbclassic
+ # terminado
+traitlets==5.7.1
+ # via
+ # comm
+ # ipykernel
+ # ipython
+ # ipywidgets
+ # jupyter-client
+ # jupyter-core
+ # jupyter-server
+ # matplotlib-inline
+ # nbclassic
+ # nbclient
+ # nbconvert
+ # nbformat
+uncertainties==3.1.7
+ # via lmfit
+urllib3==1.26.13
+ # via requests
+wcwidth==0.2.5
+ # via prompt-toolkit
+webencodings==0.5.1
+ # via
+ # bleach
+ # tinycss2
+websocket-client==1.4.2
+ # via jupyter-server
+widgetsnbextension==4.0.4
+ # via ipywidgets
+wrapt==1.14.1
+ # via deprecated
+
+# The following packages are considered to be unsafe in a requirements file:
+# pip
diff --git a/tools.json b/tools.json
index 44aff6b961b708de23ce8dbc34684ccadff88ab8..0aec6b86bf3753c4ca01d6d9f8b51f8c2f6a5e07 100644
--- a/tools.json
+++ b/tools.json
@@ -93,6 +93,24 @@
}
]
},
+ "xps": {
+ "short_description": "An example for analyzing XPS data.",
+ "description": "Includes tools for analyzing XPS spectra and converting SPECS xml files into NeXus.",
+ "image": "gitlab-registry.mpcdf.mpg.de/nomad-lab/nomad-remote-tools-hub/xps-jupyter",
+ "mount_path": "/home/jovyan",
+ "file_extensions": [
+ "ipynb",
+ "nxs",
+ "h5",
+ "hdf5"
+ ],
+ "maintainer": [
+ {
+ "name": "Florian Dobener",
+ "email": "florian.dobener@physik.hu-berlin.de"
+ }
+ ]
+ },
"webtop": {
"description": "Baseline webtop image for test",
"image": "gitlab-registry.mpcdf.mpg.de/nomad-lab/nomad-remote-tools-hub/webtop",
@@ -156,4 +174,4 @@
}
]
}
-}
+}
\ No newline at end of file