Merge pull request #369 from sartography/dmn-from-spreadsheet-395

Dmn from spreadsheet #395

crc/api.yml

@@ -1030,6 +1030,37 @@ paths:
                 type: string
                 format: binary
                 example: '<?xml version="1.0" encoding="UTF-8"?><bpmn:definitions></bpmn:definitions>'
+  /dmn_from_ss:
+#    parameters:
+#      - name: workflow_spec_id
+#        in: query
+#        required: true
+#        description: The unique id of a workflow specification
+#        schema:
+#          type: string
+    post:
+      operationId: crc.api.file.dmn_from_ss
+      summary: Create a DMN table from a spreadsheet
+      tags:
+        - Files
+      requestBody:
+        content:
+          multipart/form-data:
+            schema:
+              type: object
+              properties:
+                file:
+                  type: string
+                  format: binary
+      responses:
+        '200':
+          description: DMN file for workflow spec
+          content:
+            application/octet-stream:
+              schema:
+                type: string
+                format: binary
+                example: '<?xml version="1.0" encoding="UTF-8"?><bpmn:definitions></bpmn:definitions>'
   /task_events:
     parameters:
       - name: action

crc/api/file.py

@@ -1,4 +1,5 @@
 import io
+from datetime import datetime
 from typing import List
 
 import connexion
@@ -171,3 +172,15 @@ def update_file_info(file_id, body):
 
 def delete_file(file_id):
     FileService.delete_file(file_id)
+
+
+def dmn_from_ss():
+    file = connexion.request.files['file']
+    result = FileService.dmn_from_spreadsheet(file)
+    return send_file(
+        io.BytesIO(result),
+        attachment_filename='temp_dmn.dmn',
+        mimetype='text/xml',
+        cache_timeout=-1,  # Don't cache these files on the browser.
+        last_modified=datetime.now()
+    )

crc/services/file_service.py

@@ -1,7 +1,10 @@
 import hashlib
+import io
 import json
 import os
 from datetime import datetime
+import random
+import string
 
 import pandas as pd
 from github import Github, GithubObject, UnknownObjectException
@@ -436,3 +439,109 @@ class FileService(object):
                     branch=target_branch
                 )
                 return {'updated': True}
+
+    @staticmethod
+    def dmn_from_spreadsheet(ss_data):
+
+        def _get_random_string(length):
+            return ''.join(
+                [random.choice(string.ascii_letters + string.digits) for n in range(length)])
+
+        def _row_has_value(values):
+            for value_item in values:
+                if not pd.isnull(value_item):
+                    return True
+            return False
+
+        df = pd.read_excel(io.BytesIO(ss_data.read()), header=None)
+
+        xml_ns = "https://www.omg.org/spec/DMN/20191111/MODEL/"
+        dmndi_ns = "https://www.omg.org/spec/DMN/20191111/DMNDI/"
+        dc_ns = "http://www.omg.org/spec/DMN/20180521/DC/"
+        dmndi = "{%s}" % dmndi_ns
+        dc = "{%s}" % dc_ns
+        nsmap = {None: xml_ns, 'dmndi': dmndi_ns, 'dc': dc_ns}
+
+        root = etree.Element("definitions",
+                             id="Definitions",
+                             name="DRD",
+                             namespace="http://camunda.org/schema/1.0/dmn",
+                             nsmap=nsmap,
+                             )
+
+        decision_name = df.iat[0, 1]
+        decision_id = df.iat[1, 1]
+        decision = etree.SubElement(root, "decision",
+                                    id=decision_id,
+                                    name=decision_name
+                                    )
+        decision_table = etree.SubElement(decision, 'decisionTable', id='decisionTable_1')
+        input_output = df.iloc[2][1:]
+        count = 1
+        input_count = 1
+        output_count = 1
+        for item in input_output:
+            if item == 'Input':
+                label = df.iloc[3, count]
+                input_ = etree.SubElement(decision_table, 'input', id=f'input_{input_count}', label=label)
+                type_ref = df.iloc[5, count]
+                input_expression = etree.SubElement(input_, 'inputExpression', id=f'inputExpression_{input_count}',
+                                                    typeRef=type_ref)
+                expression = df.iloc[4, count]
+                expression_text = etree.SubElement(input_expression, 'text')
+                expression_text.text = expression
+
+                input_count += 1
+            elif item == 'Output':
+                label = df.iloc[3, count]
+                name = df.iloc[4, count]
+                type_ref = df.iloc[5, count]
+                decision_table.append(etree.Element('output', id=f'output_{output_count}',
+                                                    label=label, name=name, typeRef=type_ref))
+                output_count += 1
+            elif item == 'Annotation':
+                break
+            count += 1
+
+        row = 6
+        column_count = count
+        while row < df.shape[0]:
+            column = 1
+            row_values = df.iloc[row].values[1:column_count]
+            if _row_has_value(row_values):
+                rando = _get_random_string(7).lower()
+                rule = etree.SubElement(decision_table, 'rule', id=f'DecisionRule_{rando}')
+
+                i = 1
+                while i < input_count:
+                    input_entry = etree.SubElement(rule, 'inputEntry', id=f'UnaryTests_{_get_random_string(7)}')
+                    text_element = etree.SubElement(input_entry, 'text')
+                    text_element.text = str(df.iloc[row, column]) if not pd.isnull(df.iloc[row, column]) else ''
+                    i += 1
+                    column += 1
+                i = 1
+                while i < output_count:
+                    output_entry = etree.SubElement(rule, 'outputEntry', id=f'LiteralExpression_{_get_random_string(7)}')
+                    text_element = etree.SubElement(output_entry, 'text')
+                    text_element.text = str(df.iloc[row, column]) if not pd.isnull(df.iloc[row, column]) else ''
+                    i += 1
+                    column += 1
+
+                description = etree.SubElement(rule, 'description')
+                text = df.iloc[row, column] if not pd.isnull(df.iloc[row, column]) else ''
+                description.text = text
+
+            row += 1
+
+        dmndi_root = etree.SubElement(root, dmndi + "DMNDI")
+        dmndi_diagram = etree.SubElement(dmndi_root, dmndi + "DMNDiagram")
+        # rando = _get_random_string(7).lower()
+        dmndi_shape = etree.SubElement(dmndi_diagram, dmndi + "DMNShape",
+                                       dmnElementRef=decision_id)
+        bounds = etree.SubElement(dmndi_shape, dc + "Bounds",
+                                  height='80', width='180', x='100', y='100')
+
+        prefix = b'<?xml version="1.0" encoding="UTF-8"?>'
+        dmn_file = prefix + etree.tostring(root)
+
+        return dmn_file

tests/test_dmn_from_spreadsheet.py

@@ -0,0 +1,27 @@
+from tests.base_test import BaseTest
+
+from crc import app
+
+import io
+from lxml import etree
+import os
+
+
+class TestDMNFromSS(BaseTest):
+
+    def test_dmn_from_ss(self):
+
+        filepath = os.path.join(app.root_path, '..', 'tests', 'data',
+                                'dmn_from_spreadsheet', 'large_test_spreadsheet.xlsx')
+        f_handle = open(filepath, 'br')
+        ss_data = f_handle.read()
+
+        data = {'file': (io.BytesIO(ss_data), 'test.xlsx')}
+        rv = self.app.post('/v1.0/dmn_from_ss', data=data, follow_redirects=True,
+                           content_type='multipart/form-data', headers=self.logged_in_headers())
+
+        dmn_xml = rv.stream.response.data
+        root = etree.fromstring(dmn_xml)
+        children = root.getchildren()
+        self.assertEqual('{https://www.omg.org/spec/DMN/20191111/MODEL/}decision', children[0].tag)
+        self.assertEqual('{https://www.omg.org/spec/DMN/20191111/DMNDI/}DMNDI', children[1].tag)