Features of version 1.2.0 (#198)

* feat: storing processing time into tempfile (#194)

* feat: update to handle shift data for cv layout (#173)

* feat: read mw, mp, d, mn values for sec (#192)

chem_spectra/controller/transform_api.py

@@ -212,8 +212,10 @@ def combine_images():
         list_files.append(file_container)
 
     params = extract_params(request)
+    extras = request.form.get('extras', default=None)
+
     transform_model = TraModel(None, params=params, multiple_files=list_files)
-    tf_combine = transform_model.tf_combine(list_file_names=params['list_file_names'])
+    tf_combine = transform_model.tf_combine(list_file_names=params['list_file_names'], extraParams=extras)
     if (not tf_combine):
         abort(400)
 

chem_spectra/lib/composer/ms.py

@@ -60,13 +60,22 @@ def __gen_config(self):
 
     def __gen_ms_spectra(self):
         msspcs = []
+        ms_tempfile = tempfile.TemporaryFile()
         for idx, dt in enumerate(self.core.datatables):
             msspc = [
                 '##PAGE={}\n'.format(idx + 1),
                 '##NPOINTS={}\n'.format(dt['pts']),
                 TEXT_MS_DATA_TABLE,
             ]
-            msspcs = msspcs + msspc + dt['dt']
+            my_content = msspc + dt['dt']
+            file_content = ''.join(my_content)
+            ms_tempfile.write(file_content.encode('utf-8'))
+
+        ms_tempfile.seek(0)
+        lines = ms_tempfile.readlines()
+        decoded_lines = [line.decode('utf-8').strip() for line in lines]
+        msspcs = '\n'.join(decoded_lines)
+        ms_tempfile.close()
         return msspcs
 
     def __compose(self):

chem_spectra/lib/composer/ni.py

@@ -130,6 +130,16 @@ def __gen_header_cyclic_voltammetry(self):
             '$$ === CHEMSPECTRA CYCLIC VOLTAMMETRY ===\n',
         ]
 
+    def __gen_header_sec(self):
+        core_dic = self.core.dic
+        sec_data_key = ['MN', 'MW', 'MP', 'D']
+        result = []
+        for key in sec_data_key:
+            dic_value = core_dic.get(key, [])
+            key_str = f"##{key}={dic_value[0]}\n" if len(dic_value) > 0 else f'##{key}=\n'
+            result.append(key_str)
+        return result
+
     def __get_xy_of_peak(self, peak):
         if peak is None:
             return '', ''
@@ -201,6 +211,8 @@ def __compose(self):
         meta.extend(self.gen_headers_root())
 
         meta.extend(self.__gen_headers_spectrum_orig())
+        if self.core.is_sec:
+            meta.extend(self.__gen_header_sec())
         meta.extend(self.gen_spectrum_orig())
         meta.extend(self.__gen_headers_im())
         meta.extend(self.__gen_headers_integration())
@@ -285,6 +297,12 @@ def tf_img(self):
         ]
         codes, verts = zip(*path_data)
         marker = mpath.Path(verts, codes)
+
+        circle = mpath.Path.unit_circle()
+        cirle_verts = np.concatenate([circle.vertices, verts])
+        cirle_codes = np.concatenate([circle.codes, codes])
+        cut_star_marker = mpath.Path(cirle_verts, cirle_codes)
+
         x_peaks = []
         y_peaks = []
         if self.core.edit_peaks:
@@ -296,6 +314,7 @@ def tf_img(self):
 
         x_peckers = []
         y_peckers = []
+        x_peaks_ref, y_peaks_ref = [], []
         if self.core.is_cyclic_volta:
             x_peaks = []
             y_peaks = []
@@ -328,8 +347,13 @@ def tf_img(self):
                     x_peaks.extend([x_max_peak])
                     y_peaks.extend([y_max_peak])
                 else:
-                    x_peaks.extend([x_max_peak, x_min_peak])
-                    y_peaks.extend([y_max_peak, y_min_peak])
+                    is_ref = peak.get('isRef', False) if 'isRef' in peak else False
+                    if is_ref:
+                        x_peaks_ref.extend([x_max_peak, x_min_peak])
+                        y_peaks_ref.extend([y_max_peak, y_min_peak])
+                    else:
+                        x_peaks.extend([x_max_peak, x_min_peak])
+                        y_peaks.extend([y_max_peak, y_min_peak])
 
                 if 'pecker' in peak and peak['pecker'] is not None:
                     pecker = peak['pecker']
@@ -346,6 +370,15 @@ def tf_img(self):
                 peak_label = 'x: {x}\ny: {y}'.format(x=x_float, y=y_float)
                 plt.text(x_pos, y_pos, peak_label)
 
+            # display x value of ref peak for cyclic voltammetry
+            for i in range(len(x_peaks_ref)):
+                x_pos = x_peaks_ref[i]
+                y_pos = y_peaks_ref[i] + h * 0.1
+                x_float = '{:.2e}'.format(x_pos)
+                y_float = '{:.2e}'.format(y_peaks_ref[i])
+                peak_label = 'x: {x}\ny: {y}'.format(x=x_float, y=y_float)
+                plt.text(x_pos, y_pos, peak_label)
+
         plt.plot(
             x_peaks,
             y_peaks,
@@ -364,6 +397,15 @@ def tf_img(self):
             markersize=50,
         )
 
+        plt.plot(
+            x_peaks_ref,
+            y_peaks_ref,
+            'r',
+            ls='',
+            marker=cut_star_marker,
+            markersize=50,
+        )
+
         # ----- PLOT integration -----
         refShift, refArea = self.refShift, self.refArea
         itg_h = y_max + h * 0.1
@@ -479,6 +521,9 @@ def tf_img(self):
             plt.ylabel("Y ({})".format(self.core.label['y']), fontsize=18)
         plt.locator_params(nbins=self.__plt_nbins())
         plt.grid(False)
+
+        self.__generate_info_box(plt)
+
         # Save
         tf_img = tempfile.NamedTemporaryFile(suffix='.png')
         plt.savefig(tf_img, format='png')
@@ -487,6 +532,25 @@ def tf_img(self):
         plt.cla()
         return tf_img
 
+
+    def __generate_info_box(self, plotlib):
+        if not self.core.is_sec:
+            return
+        core_dic = self.core.dic
+        sec_data_key = ['MN', 'MW', 'MP', 'D']
+        result = []
+        for key in sec_data_key:
+            dic_value = core_dic.get(key, [])
+            key_str = f"{key}={dic_value[0]}" if len(dic_value) > 0 else None
+            if key_str is not None:
+                result.append(key_str)
+
+        info_str = '\n'.join(result)
+        props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)
+        ax = plotlib.gca()
+        ax.text(0.05, 0.95, info_str, fontsize=14, verticalalignment='top', bbox=props, transform=ax.transAxes)
+
+
     def __prepare_metadata_info_for_csv(self, csv_writer: csv.DictWriter):
         csv_writer.writerow({
             'Anodic E(V)': 'Measurement type',

chem_spectra/lib/converter/share.py

@@ -33,23 +33,35 @@ def parse_params(params):
     peaks_str = params.get('peaks_str', None)
     delta = 0.0
     mass = params.get('mass', 0)
+    mass = mass if mass else 0
     scan = params.get('scan', None)
     thres = params.get('thres', None)
     clear = params.get('clear', False)
+    clear = clear if clear else False
     integration = params.get('integration')
     integration = json.loads(integration) if integration else default_itg
     multiplicity = params.get('multiplicity')
     multiplicity = json.loads(multiplicity) if multiplicity else default_mpy
     ext = params.get('ext', '')
+    ext = ext if ext else ''
     fname = params.get('fname', '').split('.')
     fname = fname[:-2] if (len(fname) > 2 and (fname[-2] in ['edit', 'peak'])) else fname[:-1]
     fname = '.'.join(fname)
     waveLength = params.get('waveLength')
     waveLength = json.loads(waveLength) if waveLength else default_wavelength
-    axesUnits = params.get('axesUnits')
-    axesUnits = json.loads(axesUnits) if axesUnits else None
 
     jcamp_idx = params.get('jcamp_idx', 0)
+    jcamp_idx = jcamp_idx if jcamp_idx else 0
+    axesUnitsJson = params.get('axesUnits')
+    axesUnitsDic = json.loads(axesUnitsJson) if axesUnitsJson else None
+    axesUnits = None
+    if axesUnitsDic != None and 'axes' in axesUnitsDic:
+        axes = axesUnitsDic.get('axes', [{'xUnit': '', 'yUnit': ''}])
+        try:
+            axesUnits = axes[jcamp_idx]
+        except:
+            pass
+
     cyclicvolta = params.get('cyclic_volta')
     cyclicvolta = json.loads(cyclicvolta) if cyclicvolta else None
     listMaxMinPeaks = None

chem_spectra/lib/shared/calc.py

@@ -149,11 +149,9 @@ def cal_cyclic_volta_shift_prev_offset_at_index(cyclic_data, index=0):
       return 0.0
 
     spectra = spectra_list[index]
-    analysed_data = spectra['list']
-    arr_has_ref_value = list(filter(lambda x: ('isRef' in x and x['isRef'] == True),  analysed_data))
-    if len(arr_has_ref_value) > 0:
-        shift = spectra['shift']
-        if 'prevValue' in shift:
-            offset = shift['prevValue']
+    hasRefPeak = spectra.get('hasRefPeak', False) == True
+    shift = spectra['shift']
+    if 'prevValue' in shift:
+        offset = shift['prevValue'] if hasRefPeak else -shift['prevValue']
 
     return offset

chem_spectra/model/transformer.py

@@ -19,6 +19,8 @@
 from chem_spectra.lib.composer.base import BaseComposer     # noqa: F401
 from chem_spectra.lib.converter.nmrium.base import NMRiumDataConverter
 import matplotlib.pyplot as plt  # noqa: E402
+import matplotlib.path as mpath  # noqa: E402
+import numpy as np  # noqa: E402
 
 from chem_spectra.model.concern.property import decorate_sim_property
 
@@ -268,13 +270,45 @@ def tf_nmrium(self):
         nicp = NIComposer(converter)
         tf_jcamp = nicp.tf_jcamp()
         return tf_jcamp
-
-    def tf_combine(self, list_file_names=None):
+
+    def __get_cyclic_volta_ref_peaks(self, curve_idx, extraParams):
+        x_peaks, y_peaks = [], []
+        try:
+            extras_dict = json.loads(extraParams) if extraParams else None
+            cyclic_volta_str = extras_dict['cyclicvolta'] if extras_dict else None
+            cyclic_volta = json.loads(cyclic_volta_str) if cyclic_volta_str else None
+            spectra_list = cyclic_volta['spectraList'] if cyclic_volta else None
+            spectra_extra = spectra_list[curve_idx] if spectra_list and curve_idx < len(spectra_list) else None
+            list_peaks = spectra_extra['list'] if spectra_extra else []
+            x_peaks, y_peaks = [], []
+            for peak in list_peaks:
+                min_peak, max_peak, isRef = peak['min'], peak['max'], peak['isRef']
+                if isRef == True:
+                    x_peaks.extend([min_peak['x'], max_peak['x']])
+                    y_peaks.extend([min_peak['y'], max_peak['y']])
+        except:
+            pass
+
+        return x_peaks, y_peaks
+
+    def tf_combine(self, list_file_names=None, extraParams=None):
         if not self.multiple_files:
             return False
+
+        path_data = [
+            (mpath.Path.MOVETO, (0, 5)),
+            (mpath.Path.LINETO, (0, 20)),
+        ]
+        codes, verts = zip(*path_data)
+
+        circle = mpath.Path.unit_circle()
+        cirle_verts = np.concatenate([circle.vertices, verts])
+        cirle_codes = np.concatenate([circle.codes, codes])
+        cut_star_marker = mpath.Path(cirle_verts, cirle_codes)
 
         plt.rcParams['figure.figsize'] = [16, 9]
         plt.rcParams['font.size'] = 14
+        plt.rcParams['legend.loc'] = 'upper left'
         curve_idx = self.params.get('jcamp_idx', 0)
 
         xlabel, ylabel = '', ''
@@ -315,6 +349,17 @@ def tf_combine(self, list_file_names=None):
                 core_label_x = nicp.core.label['x']
                 core_label_y = nicp.core.label['y']
                 if nicp.core.is_cyclic_volta:
+                    x_peaks, y_peaks = self.__get_cyclic_volta_ref_peaks(curve_idx, extraParams)
+
+                    plt.plot(
+                        x_peaks,
+                        y_peaks,
+                        'r',
+                        ls='',
+                        marker=cut_star_marker,
+                        markersize=50,
+                    )
+
                     if core_label_x not in dic_x_label:
                         xlabel_set.append(core_label_x)
                         dic_x_label[core_label_x] = 1

setup.py

@@ -6,7 +6,7 @@
 
 setup(
     name='chem-spectra-app',
-    version='1.1.2',
+    version='1.2.0',
     packages=find_packages(),
     include_package_data=True,
     zip_safe=False,

tests/lib/composer/test_ni_composer.py

@@ -1,15 +1,19 @@
 import json
 import pytest
 import numpy as np
+import tempfile
+import zipfile
 from chem_spectra.lib.converter.jcamp.base import JcampBaseConverter
 from chem_spectra.lib.converter.jcamp.ni import JcampNIConverter
 from chem_spectra.lib.composer.ni import NIComposer
 from chem_spectra.controller.helper.file_container import FileContainer
+from chem_spectra.lib.converter.bagit.base import BagItBaseConverter as BagItConveter
 
 source_nmr = './tests/fixtures/source/1H.dx'
 source_nmr_edit = './tests/fixtures/source/1H.edit.jdx'
 source_ir = './tests/fixtures/source/IR.dx'
 source_dir_molfile = './tests/fixtures/source/molfile/c60h57fn4.mol'
+source_sec =  './tests/fixtures/source/sec/SEC-ExFile.zip'
 
 @pytest.fixture
 def data_schema():
@@ -37,6 +41,10 @@ def molfile_data():
     molfile_str = molfile.read()
     return molfile_str
 
+@pytest.fixture
+def bagit_file_sec():
+    return source_sec
+
 def test_init_ni_composer_failed():
     with pytest.raises(Exception) as error:
         _ = NIComposer(None)
@@ -250,3 +258,19 @@ def test_ni_composer_generate_nmrium_with_molfile(jcamp_file_1h, molfile_data):
         assert 'id' in molecule_data.keys()
         assert molecule_data['label'] == 'P1'
         assert molecule_data['molfile'] == molfile_data
+
+def test_ni_composer_generate_jcamp(bagit_file_sec):
+    with tempfile.TemporaryDirectory() as td:
+        with zipfile.ZipFile(bagit_file_sec, 'r') as z:
+            z.extractall(td)
+
+        converter = BagItConveter(td)
+        for jcamp_file in converter.data:
+            base_converter = JcampBaseConverter(jcamp_file.name)
+            ni_converter = JcampNIConverter(base=base_converter)
+            ni_composer = NIComposer(core=ni_converter)
+            headers = ni_composer._NIComposer__gen_header_sec()
+            assert headers in [
+                ['##MN=1.287E+3\n', '##MW=1.465E+3\n', '##MP=1.345E+3\n', '##D=1.139E+0\n'],
+                ['##MN=\n', '##MW=\n', '##MP=\n', '##D=\n']
+            ]