Revision and some bug fixes

FAIRChemistry · Dec 30, 2022 · 8159d52 · 8159d52
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diff --git a/SAS-workflows/Giess_2022/M1_AnIML.ipynb b/SAS-workflows/Giess_2022/M1_AnIML.ipynb
@@ -102,7 +102,7 @@
     "from lxml import etree\n",
     "from pyaniml import AnIMLDocument, Sample, Series, ExperimentStep, Device, IndividualValueSet, SeriesSet, Category, Parameter, Unit, SIUnit\n",
     "from pyaniml.core.method import Author, Software\n",
-    "from sastools.readers import PDHReader\n",
+    "from sastools.readers import PDHReader, infer_type\n",
     "print (\"Done.\")"
    ]
   },
@@ -136,7 +136,7 @@
    "source": [
     "#### **Conversion of PDH to AnIML** <a class=\"anchor\" name=\"conversion\"></a>\n",
     "\n",
-    "With this section of the notebook, a new AnIML document can be created in memory and later be serialized to XML. The presented code cells provide the most important API calles for building an AnIML document with pyAnIML.  \n",
+    "With this section of the notebook, a new AnIML document can be created in memory and later be serialized to XML. The presented code cells provide the most important API calls for building an AnIML document with pyAnIML.  \n",
     "Running a code cell more than once will add the contents of this cell to the AnIML document accordingly more than once. This can be used to add multiple samples, experiments, series, ... to the same document by simple changing the respective code cell before serializing it."
    ]
   },
@@ -202,7 +202,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pdh_dir = PDHReader(path_to_datasets / \"raw/OTAC_measurement_data/OTAC_097wtp_T\")\n",
+    "pdh_dir = PDHReader(path_to_datasets / \"raw/OTAC_measurement_data/OTAC_065wtp_T\")\n",
     "dict_of_files = pdh_dir.enumerate_available_files()\n",
     "for index, file in dict_of_files.items():\n",
     "    print(f\"{index}: {file}\")"
@@ -223,7 +223,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pdh_file = dict_of_files[65]\n",
+    "pdh_file = dict_of_files[0]\n",
     "raw_dataframe = pdh_dir.extract_data(pdh_file)\n",
     "raw_metadata = pdh_dir.extract_metadata(pdh_file)\n",
     "print(raw_dataframe)"
@@ -358,7 +358,7 @@
    "id": "3b271c79-6191-4701-95a5-8dbe1276a5a1",
    "metadata": {},
    "source": [
-    "7. If applicable, create the device with its various settings and/or software on which the sample(s) were measured/analysed and add them to the experiment step object:"
+    "7. If applicable, create the device with its various settings and/or software on which the sample(s) were measured/analyzed and add them to the experiment step object:"
    ]
   },
   {
@@ -484,7 +484,7 @@
    "id": "cccc81e3-826f-4bfa-b507-f2459d8e1017",
    "metadata": {},
    "source": [
-    "8. Create a series for every dimension of data present in the dataframe. The data from the dataframe goes into an IndividualValueSet in form of a list, which is then added to the actual series object, together with unit information (from PDH metadata) a name, ID, the data type, the dependency, and plot scale:"
+    "8. Create a series for every dimension of data present in the dataframe. The data from the dataframe goes into an IndividualValueSet in form of a list, which is then added to the actual series object, together with unit information (from PDH metadata) a name, ID, data type, dependency, and plot scale:"
    ]
   },
   {
@@ -770,7 +770,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "path_to_AnIML_file = path_to_datasets / f\"processed/fairsaxs_220502.animl\""
+    "path_to_AnIML_file = path_to_datasets / f\"processed/fairsaxs_220512.animl\""
    ]
   },
   {
@@ -879,7 +879,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "path_to_AnIML_file = path_to_datasets / f\"processed/fairsaxs_220502.animl\""
+    "path_to_AnIML_file = path_to_datasets / f\"processed/fairsaxs_220512.animl\""
    ]
   },
   {
@@ -952,9 +952,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:fairsaxs]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-fairsaxs-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -966,7 +966,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.4"
   },
   "vscode": {
    "interpreter": {

diff --git a/SAS-workflows/Giess_2022/M2-1_Curve_fitting.ipynb b/SAS-workflows/Giess_2022/M2-1_Curve_fitting.ipynb
@@ -144,7 +144,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "path_to_AnIML_file = path_to_datasets / f\"download/fairsaxs_220512/fairsaxs_220512.animl\""
+    "path_to_AnIML_file = path_to_datasets / f\"processed/fairsaxs_220512.animl\""
    ]
   },
   {
@@ -185,8 +185,8 @@
    "outputs": [],
    "source": [
     "list_of_IDs = reader.available_seriesIDs\n",
-    "for series_ID in list_of_IDs:\n",
-    "    print(series_ID)"
+    "for index, series_ID in enumerate(list_of_IDs):\n",
+    "    print(index, series_ID)"
    ]
   },
   {
@@ -330,7 +330,7 @@
    "id": "c114d047",
    "metadata": {},
    "source": [
-    "Automatically setting up the specifications for the fitting algorithm. Parameters are as following:\n",
+    "Automatically setting up the specifications for the fitting algorithm. Parameters are as follows:\n",
     "- model_type: The type of model ('GaussianModel', 'LorentzianModel' or 'VoigtModel')\n",
     "- tolerance: Tolerated variance of the peak location between model parameters given by the automatic_peak_finding and the final fit parameters (default is 0.5)"
    ]
@@ -344,7 +344,7 @@
    "source": [
     "curvefitting.set_specifications_automatically(\n",
     "    model_type = 'LorentzianModel',\n",
-    "    tolerance = 0.01\n",
+    "    tolerance = 0.5\n",
     ")"
    ]
   },
@@ -449,7 +449,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "curvefitting.save_list_of_peak_centers()\n"
+    "curvefitting.save_list_of_peak_centers()"
    ]
   },
   {
@@ -491,9 +491,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:fairsaxs]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-fairsaxs-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -505,7 +505,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.4"
   },
   "vscode": {
    "interpreter": {

diff --git a/SAS-workflows/Giess_2022/M2-1_Origin.ipynb b/SAS-workflows/Giess_2022/M2-1_Origin.ipynb
@@ -150,7 +150,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "path_to_AnIML_file = path_to_datasets / f\"download/fairsaxs_220512/fairsaxs_220512.animl\""
+    "path_to_AnIML_file = path_to_datasets / f\"processed/fairsaxs_220512.animl\""
    ]
   },
   {
@@ -269,9 +269,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "Python [conda env:fairsaxs]",
    "language": "python",
-   "name": "python3"
+   "name": "conda-env-fairsaxs-py"
   },
   "language_info": {
    "codemirror_mode": {
@@ -283,7 +283,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.8"
+   "version": "3.10.4"
   },
   "vscode": {
    "interpreter": {