Contribute to SPCC Database
---------------------------

.. warning::
   Please note that we do not include narrowband filters. These highly specific
   filters are synthesized in Siril, ensuring precision, which applies to
   duonarrowband filters as well.

SPCC-Database JSON File Creator
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This tool helps you create SPCC-compatible JSON files from CSV data extracted using tools like https://apps.automeris.io/wpd4/.

.. raw:: html

   <div style="border: 1px solid #ccc; padding: 20px; margin: 20px 0; border-radius: 5px;">
       <h3>📄 JSON File Creator</h3>
       
       <div style="margin-bottom: 20px;">
           <label style="display: block; margin-bottom: 10px; font-weight: bold;">Device Type:</label>
           <select id="device-type" style="width: 100%; padding: 8px; margin-bottom: 15px; border: 1px solid #ccc; border-radius: 4px;">
               <option value="">Select device type...</option>
               <option value="sensor">Sensor</option>
               <option value="filter">Filter</option>
           </select>
           
           <div id="sensor-type-section" style="display: none;">
               <label style="display: block; margin-bottom: 10px; font-weight: bold;">Sensor Type:</label>
               <select id="sensor-color-type" style="width: 100%; padding: 8px; margin-bottom: 15px; border: 1px solid #ccc; border-radius: 4px;" disabled>
                   <option value="">Select sensor type...</option>
                   <option value="mono">Mono</option>
                   <option value="osc">Color</option>
               </select>
           </div>
           
           <div id="filter-type-section" style="display: none;">
               <label style="display: block; margin-bottom: 10px; font-weight: bold;">Filter Type:</label>
               <select id="filter-color-type" style="width: 100%; padding: 8px; margin-bottom: 15px; border: 1px solid #ccc; border-radius: 4px;" disabled>
                   <option value="">Select filter type...</option>
                   <option value="mono">Mono</option>
                   <option value="osc">Color</option>
               </select>
           </div>
       </div>
       
       <div id="input-fields" style="display: none;">
           <div style="display: grid; grid-template-columns: 1fr 1fr; gap: 15px; margin-bottom: 20px;">
               <div>
                   <label style="display: block; margin-bottom: 5px; font-weight: bold;">Manufacturer:</label>
                   <input type="text" id="manufacturer" placeholder="e.g., Sony" style="width: 100%; padding: 8px; border: 1px solid #ccc; border-radius: 4px;">
               </div>
               <div>
                   <label style="display: block; margin-bottom: 5px; font-weight: bold;">Model:</label>
                   <input type="text" id="model" placeholder="e.g., IMX585" style="width: 100%; padding: 8px; border: 1px solid #ccc; border-radius: 4px;">
               </div>
           </div>
           
           <div style="margin-bottom: 20px;">
               <label style="display: block; margin-bottom: 5px; font-weight: bold;">Data Source URL:</label>
               <input type="text" id="dataSource" placeholder="https://..." style="width: 100%; padding: 8px; border: 1px solid #ccc; border-radius: 4px;">
           </div>
           
           <div style="margin-bottom: 20px;">
               <label style="display: block; margin-bottom: 5px; font-weight: bold;">CSV File:</label>
               <input type="file" id="csv-input" accept=".csv" style="width: 100%; padding: 8px; border: 1px solid #ccc; border-radius: 4px;">
               <small style="color: #666; display: block; margin-top: 5px;">Upload CSV file with headers removed (as exported from wpd4)</small>
           </div>
           
           <button id="generate-btn" onclick="generateJSON()" disabled style="background-color: #28a745; color: white; padding: 12px 24px; border: none; border-radius: 5px; cursor: pointer; font-size: 16px;">🔧 Generate JSON</button>
       </div>
       
       <div id="json-output" style="margin-top: 20px; display: none;">
           <h4>Generated JSON:</h4>
           <div style="position: relative;">
               <pre id="json-content" style="background-color: #f8f9fa; padding: 15px; border-radius: 5px; overflow-x: auto; white-space: pre-wrap; font-family: 'Courier New', monospace; border: 1px solid #ddd; max-height: 400px; overflow-y: auto;"></pre>
               <button onclick="downloadGeneratedJSON()" style="position: absolute; top: 10px; right: 10px; background-color: #007bff; color: white; border: none; border-radius: 4px; padding: 8px 12px; cursor: pointer;">💾 Download</button>
           </div>
       </div>
   </div>

   <script>
       let csvData = null;
       let generatedJSON = null;

       document.getElementById('device-type').addEventListener('change', function() {
           const sensorColorType = document.getElementById('sensor-color-type');
           const filterColorType = document.getElementById('filter-color-type');
           const sensorTypeSection = document.getElementById('sensor-type-section');
           const filterTypeSection = document.getElementById('filter-type-section');
           const inputFields = document.getElementById('input-fields');
           
           if (this.value === 'sensor') {
               sensorTypeSection.style.display = 'block';
               filterTypeSection.style.display = 'none';
               sensorColorType.disabled = false;
               filterColorType.disabled = true;
               sensorColorType.value = '';
               filterColorType.value = '';
               inputFields.style.display = 'none';
           } else if (this.value === 'filter') {
               sensorTypeSection.style.display = 'none';
               filterTypeSection.style.display = 'block';
               sensorColorType.disabled = true;
               filterColorType.disabled = false;
               sensorColorType.value = '';
               filterColorType.value = '';
               inputFields.style.display = 'none';
           } else {
               sensorTypeSection.style.display = 'none';
               filterTypeSection.style.display = 'none';
               sensorColorType.disabled = true;
               filterColorType.disabled = true;
               sensorColorType.value = '';
               filterColorType.value = '';
               inputFields.style.display = 'none';
           }
           updateGenerateButton();
       });

       document.getElementById('sensor-color-type').addEventListener('change', function() {
           const inputFields = document.getElementById('input-fields');
           
           if (this.value) {
               inputFields.style.display = 'block';
           } else {
               inputFields.style.display = 'none';
           }
           updateGenerateButton();
       });

       document.getElementById('filter-color-type').addEventListener('change', function() {
           const inputFields = document.getElementById('input-fields');
           
           if (this.value) {
               inputFields.style.display = 'block';
           } else {
               inputFields.style.display = 'none';
           }
           updateGenerateButton();
       });

       document.getElementById('csv-input').addEventListener('change', function(event) {
           const file = event.target.files[0];
           if (file) {
               const reader = new FileReader();
               reader.onload = function(e) {
                   csvData = e.target.result;
                   updateGenerateButton();
               };
               reader.readAsText(file);
           } else {
               csvData = null;
               updateGenerateButton();
           }
       });

       ['manufacturer', 'model', 'dataSource'].forEach(id => {
           document.getElementById(id).addEventListener('input', updateGenerateButton);
       });

       function updateGenerateButton() {
           const deviceType = document.getElementById('device-type').value;
           const sensorColorType = document.getElementById('sensor-color-type').value;
           const filterColorType = document.getElementById('filter-color-type').value;
           const manufacturer = document.getElementById('manufacturer').value.trim();
           const model = document.getElementById('model').value.trim();
           const dataSource = document.getElementById('dataSource').value.trim();
           
           // Détermine le type de couleur actuel selon le device type
           const colorType = deviceType === 'sensor' ? sensorColorType : filterColorType;
           
           const generateBtn = document.getElementById('generate-btn');
           generateBtn.disabled = !(deviceType && colorType && manufacturer && model && dataSource && csvData);
       }

       function getCurrentColorType() {
           const deviceType = document.getElementById('device-type').value;
           return deviceType === 'sensor' ? 
               document.getElementById('sensor-color-type').value : 
               document.getElementById('filter-color-type').value;
       }

       function parseCSVColor(csvText) {
           const lines = csvText.split('\n').filter(line => line.trim());
           const data = {RED: [], GREEN: [], BLUE: []};
           
           lines.forEach(line => {
               const cols = line.split(',').map(col => col.trim());
               if (cols.length >= 6) {
                   if (cols[0] && cols[1] && !isNaN(cols[0]) && !isNaN(cols[1])) {
                       data.RED.push([parseFloat(cols[0]), parseFloat(cols[1])]);
                   }
                   if (cols[2] && cols[3] && !isNaN(cols[2]) && !isNaN(cols[3])) {
                       data.GREEN.push([parseFloat(cols[2]), parseFloat(cols[3])]);
                   }
                   if (cols[4] && cols[5] && !isNaN(cols[4]) && !isNaN(cols[5])) {
                       data.BLUE.push([parseFloat(cols[4]), parseFloat(cols[5])]);
                   }
               }
           });
           
           data.RED.sort((a, b) => a[0] - b[0]);
           data.GREEN.sort((a, b) => a[0] - b[0]);
           data.BLUE.sort((a, b) => a[0] - b[0]);
           
           return data;
       }

       function parseCSVMono(csvText) {
           const lines = csvText.split('\n').filter(line => line.trim());
           const data = [];
           
           lines.forEach(line => {
               const cols = line.split(',').map(col => col.trim());
               if (cols.length >= 2 && cols[0] && cols[1] && !isNaN(cols[0]) && !isNaN(cols[1])) {
                   data.push([parseFloat(cols[0]), parseFloat(cols[1])]);
               }
           });
           
           return data.sort((a, b) => a[0] - b[0]);
       }

       function generateJSON() {
           const deviceType = document.getElementById('device-type').value;
           const colorType = getCurrentColorType();
           const manufacturer = document.getElementById('manufacturer').value.trim();
           const model = document.getElementById('model').value.trim();
           const dataSource = document.getElementById('dataSource').value.trim();
           
           try {
               let jsonData;
               
               if (colorType === 'mono') {
                   const parsedData = parseCSVMono(csvData);
                   
                   jsonData = [{
                       model: `${manufacturer} ${model}`,
                       name: `${manufacturer} ${model}`,
                       type: deviceType === 'sensor' ? "MONO_SENSOR" : "MONO_FILTER",
                       dataQualityMarker: 2,
                       dataSource: dataSource,
                       manufacturer: manufacturer,
                       version: 1,
                       wavelength: {
                           value: parsedData.map(d => d[0]),
                           units: "nm"
                       },
                       values: {
                           value: parsedData.map(d => d[1]),
                           range: deviceType === 'sensor' ? 1 : 100
                       }
                   }];
               } else if (colorType === 'osc') {
                   if (deviceType === 'sensor') {
                       // Pour les capteurs OSC : 3 canaux (RED, GREEN, BLUE)
                       const parsedData = parseCSVColor(csvData);
                       
                       jsonData = [
                           {
                               model: `${manufacturer} ${model}`,
                               name: `${manufacturer} ${model} Red`,
                               type: "OSC_SENSOR",
                               dataQualityMarker: 2,
                               dataSource: dataSource,
                               manufacturer: manufacturer,
                               channel: "RED",
                               comment: "Covers all cameras using this chip",
                               version: 1,
                               wavelength: {
                                   value: parsedData.RED.map(d => d[0]),
                                   units: "nm"
                               },
                               values: {
                                   value: parsedData.RED.map(d => d[1]),
                                   range: 1
                               }
                           },
                           {
                               model: `${manufacturer} ${model}`,
                               name: `${manufacturer} ${model} Green`,
                               type: "OSC_SENSOR",
                               dataQualityMarker: 2,
                               dataSource: dataSource,
                               manufacturer: manufacturer,
                               channel: "GREEN",
                               comment: "Covers all cameras using this chip",
                               version: 1,
                               wavelength: {
                                   value: parsedData.GREEN.map(d => d[0]),
                                   units: "nm"
                               },
                               values: {
                                   value: parsedData.GREEN.map(d => d[1]),
                                   range: 1
                               }
                           },
                           {
                               model: `${manufacturer} ${model}`,
                               name: `${manufacturer} ${model} Blue`,
                               type: "OSC_SENSOR",
                               dataQualityMarker: 2,
                               dataSource: dataSource,
                               manufacturer: manufacturer,
                               channel: "BLUE",
                               comment: "Covers all cameras using this chip",
                               version: 1,
                               wavelength: {
                                   value: parsedData.BLUE.map(d => d[0]),
                                   units: "nm"
                               },
                               values: {
                                   value: parsedData.BLUE.map(d => d[1]),
                                   range: 1
                               }
                           }
                       ];
                   } else {
                       // Pour les filtres OSC : un seul canal avec les données du CSV 2 colonnes
                       const parsedData = parseCSVMono(csvData);
                       
                       jsonData = [{
                           model: `${manufacturer} ${model}`,
                           name: `${manufacturer} ${model}`,
                           type: "OSC_FILTER",
                           dataQualityMarker: 2,
                           dataSource: dataSource,
                           manufacturer: manufacturer,
                           version: 1,
                           wavelength: {
                               value: parsedData.map(d => d[0]),
                               units: "nm"
                           },
                           values: {
                               value: parsedData.map(d => d[1]),
                               range: 100
                           }
                       }];
                   }
               }
               
               generatedJSON = jsonData;
               
               document.getElementById('json-content').textContent = JSON.stringify(jsonData, null, 4);
               document.getElementById('json-output').style.display = 'block';
               
           } catch (error) {
               document.getElementById('json-content').textContent = `Error generating JSON: ${error.message}`;
               document.getElementById('json-output').style.display = 'block';
               console.error('Error:', error);
           }
       }

       function downloadGeneratedJSON() {
           if (!generatedJSON) return;
           
           const manufacturer = document.getElementById('manufacturer').value.trim();
           const model = document.getElementById('model').value.trim();
           
           const filename = `${manufacturer}_${model}.json`;
           
           const blob = new Blob([JSON.stringify(generatedJSON, null, 4)], { type: 'application/json' });
           const url = URL.createObjectURL(blob);
           
           const a = document.createElement('a');
           a.href = url;
           a.download = filename;
           document.body.appendChild(a);
           a.click();
           document.body.removeChild(a);
           
           URL.revokeObjectURL(url);
       }
   </script>

SPCC-Database JSON File Validator
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

This validator allows you to detect and remove duplicate wavelength values in your JSON files.

.. raw:: html

   <div style="border: 1px solid #ccc; padding: 20px; margin: 20px 0; border-radius: 5px;">
       <h3>🔍 JSON File Validator</h3>
       <input type="file" id="file-input" accept=".json" style="margin-bottom: 15px; width: 100%;" />
       <div style="margin-bottom: 15px;">
           <button id="remove-btn" onclick="removeDuplicates()" disabled style="background-color: #dc3545; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer; margin-right: 10px;">Remove duplicates</button>
           <button id="display-btn" onclick="displayData()" disabled style="background-color: #28a745; color: white; padding: 10px 20px; border: none; border-radius: 5px; cursor: pointer;">Display data</button>
       </div>
       <div id="result" style="margin-top: 20px; padding: 15px; border-radius: 5px; white-space: pre-wrap; font-family: 'Courier New', monospace;"></div>
   </div>

   <script src="https://cdn.jsdelivr.net/pyodide/v0.24.1/full/pyodide.js"></script>
   <script>
       let pyodide;
       let selectedFile = null;
       let fileContent = null;
       let correctedData = null;
       let plotImageData = null;

       async function initPyodide() {
           const resultDiv = document.getElementById('result');
           
           try {
               pyodide = await loadPyodide();
               await pyodide.loadPackage(['matplotlib']);
           } catch (error) {
               resultDiv.innerHTML = '<div style="background-color: #f8d7da; border: 1px solid #f5c6cb; color: #721c24; padding: 10px; border-radius: 5px;">Error loading Python: ' + error.message + '</div>';
           }
       }

       document.getElementById('file-input').addEventListener('change', async function(event) {
           selectedFile = event.target.files[0];
           const removeBtn = document.getElementById('remove-btn');
           const displayBtn = document.getElementById('display-btn');
           
           if (selectedFile) {
               fileContent = await selectedFile.text();
               removeBtn.disabled = false;
               displayBtn.disabled = false;
               correctedData = null;
               plotImageData = null;
               document.getElementById('result').innerHTML = '<div style="background-color: #d1ecf1; border: 1px solid #bee5eb; color: #0c5460; padding: 10px; border-radius: 5px;">File selected: ' + selectedFile.name + '</div>';
           } else {
               removeBtn.disabled = true;
               displayBtn.disabled = true;
               fileContent = null;
               correctedData = null;
               plotImageData = null;
           }
       });

       async function removeDuplicates() {
           if (!fileContent || !pyodide) return;

           const resultDiv = document.getElementById('result');
           resultDiv.innerHTML = '<div style="text-align: center; color: #666;">Removing duplicates...</div>';

           try {
               pyodide.globals.set('file_content', fileContent);
               
               const response = await fetch('./_static/remove_duplicates_web.py');
               if (!response.ok) {
                   throw new Error('Failed to load Python script: ' + response.statusText);
               }
               const pythonCode = await response.text();
               pyodide.runPython(pythonCode);
               
               const result = pyodide.globals.get('result').toJs({dict_converter: Object.fromEntries});

               if (result && result.error) {
                   resultDiv.innerHTML = '<div style="background-color: #f8d7da; border: 1px solid #f5c6cb; color: #721c24; padding: 10px; border-radius: 5px;">Error: ' + result.error + '</div>';
               } else if (result) {
                   // Simple: just store the original data with duplicates removed manually in JS
                   if (result.has_duplicates) {
                       const originalData = JSON.parse(fileContent);
                       correctedData = originalData.map(channel => {
                           const newChannel = {...channel};
                           const seen = new Set();
                           const newWavelengths = [];
                           const newValues = [];
                           
                           for (let i = 0; i < channel.wavelength.value.length; i++) {
                               const wl = channel.wavelength.value[i];
                               if (!seen.has(wl)) {
                                   seen.add(wl);
                                   newWavelengths.push(wl);
                                   newValues.push(channel.values.value[i]);
                               }
                           }
                           
                           newChannel.wavelength.value = newWavelengths;
                           newChannel.values.value = newValues;
                           return newChannel;
                       });
                   }
                   
                   let reportHtml = '<div style="background-color: ' + (result.has_duplicates ? '#fff3cd; border: 1px solid #ffeaa7; color: #856404;' : '#d4edda; border: 1px solid #c3e6cb; color: #155724;') + ' padding: 10px; border-radius: 5px;">';
                   reportHtml += '<div style="white-space: pre-wrap; font-family: \'Courier New\', monospace; margin-bottom: 15px;">';
                   reportHtml += '<strong>🔧 Duplicate Removal Report:</strong>\n\n';
                   reportHtml += 'Channels processed: ' + result.channels_processed + '\n';
                   reportHtml += 'Channels with duplicates: ' + result.channels_with_duplicates + '\n';
                   reportHtml += 'Total duplicates removed: ' + result.total_duplicates_removed + '\n\n';
                   
                   if (result.channel_reports && Array.isArray(result.channel_reports)) {
                       result.channel_reports.forEach(function(channel) {
                           reportHtml += 'Channel: ' + channel.channel_name + ' (' + channel.channel_type + ')\n';
                           reportHtml += '  Entries before: ' + channel.total_entries_before + '\n';
                           reportHtml += '  Entries after: ' + channel.total_entries_after + '\n';
                           reportHtml += '  Duplicates removed: ' + channel.duplicate_count + '\n';
                           if (channel.removed_wavelengths && channel.removed_wavelengths.length > 0) {
                               reportHtml += '  Removed wavelengths: ' + channel.removed_wavelengths.join(', ') + '\n';
                           }
                           reportHtml += '\n';
                       });
                   }
                   
                   if (result.has_duplicates) {
                       reportHtml += '✅ File has been cleaned and is ready for download.';
                   } else {
                       reportHtml += '✅ No duplicates found. File is already clean.';
                   }
                   
                   reportHtml += '</div>';
                   
                   if (result.has_duplicates) {
                       reportHtml += '<button id="download-cleaned-btn" style="background-color: #007bff; color: white; padding: 8px 16px; border: none; border-radius: 5px; cursor: pointer; font-family: Arial, sans-serif;">💾 Download cleaned file</button>';
                   }
                   
                   reportHtml += '</div>';
                   resultDiv.innerHTML = reportHtml;
                   
                   if (result.has_duplicates) {
                       setTimeout(() => {
                           const downloadBtn = document.getElementById('download-cleaned-btn');
                           if (downloadBtn) {
                               downloadBtn.addEventListener('click', downloadCorrectedFile);
                           }
                       }, 100);
                   }
               }

           } catch (error) {
               resultDiv.innerHTML = '<div style="background-color: #f8d7da; border: 1px solid #f5c6cb; color: #721c24; padding: 10px; border-radius: 5px;">Execution error: ' + error.message + '</div>';
           }
       }

       async function displayData() {
           if (!fileContent || !pyodide) return;

           const resultDiv = document.getElementById('result');
           resultDiv.innerHTML = '<div style="text-align: center; color: #666;">Generating plot...</div>';

           try {
               pyodide.globals.set('file_content', fileContent);
               
               const response = await fetch('./_static/visualize_web.py');
               if (!response.ok) {
                   throw new Error('Failed to load visualization script: ' + response.statusText);
               }
               const pythonCode = await response.text();
               pyodide.runPython(pythonCode);
               
               const result = pyodide.globals.get('result').toJs({dict_converter: Object.fromEntries});

               if (result && result.error) {
                   resultDiv.innerHTML = '<div style="background-color: #f8d7da; border: 1px solid #f5c6cb; color: #721c24; padding: 10px; border-radius: 5px;">Error: ' + result.error + '</div>';
               } else if (result && result.success) {
                   plotImageData = result.image;
                   
                   resultDiv.innerHTML = '<div style="background-color: #d4edda; border: 1px solid #c3e6cb; color: #155724; padding: 10px; border-radius: 5px; text-align: center;"><strong>📈 Spectral Response Plot:</strong><br><br><div style="position: relative; display: inline-block;"><img src="data:image/png;base64,' + result.image + '" style="max-width: 100%; height: auto; border: 1px solid #ddd; border-radius: 5px;"><button id="download-image-btn" style="position: absolute; top: 10px; right: 10px; background-color: rgba(0, 123, 255, 0.8); color: white; border: none; border-radius: 50%; width: 40px; height: 40px; cursor: pointer; font-size: 16px; display: flex; align-items: center; justify-content: center; box-shadow: 0 2px 4px rgba(0,0,0,0.3);" title="Download image">💾</button></div></div>';
                   
                   setTimeout(() => {
                       const imageBtn = document.getElementById('download-image-btn');
                       if (imageBtn) {
                           imageBtn.addEventListener('click', downloadImage);
                       }
                   }, 100);
               } else {
                   resultDiv.innerHTML = '<div style="background-color: #f8d7da; border: 1px solid #f5c6cb; color: #721c24; padding: 10px; border-radius: 5px;">No plot generated</div>';
               }

           } catch (error) {
               resultDiv.innerHTML = '<div style="background-color: #f8d7da; border: 1px solid #f5c6cb; color: #721c24; padding: 10px; border-radius: 5px;">Execution error: ' + error.message + '</div>';
           }
       }

       function downloadCorrectedFile() {
           if (!correctedData || !selectedFile) return;
           
           try {
               const correctedJson = JSON.stringify(correctedData, null, 4);
               const blob = new Blob([correctedJson], { type: 'application/json' });
               const url = URL.createObjectURL(blob);
               
               const a = document.createElement('a');
               a.href = url;
               a.download = selectedFile.name.replace('.json', '_cleaned.json');
               document.body.appendChild(a);
               a.click();
               document.body.removeChild(a);
               
               URL.revokeObjectURL(url);
           } catch (error) {
               console.error('Error downloading file:', error);
           }
       }

       function downloadImage() {
           if (!plotImageData || !selectedFile) return;
           
           try {
               const imageBytes = Uint8Array.from(atob(plotImageData), c => c.charCodeAt(0));
               const blob = new Blob([imageBytes], { type: 'image/png' });
               const url = URL.createObjectURL(blob);
               
               const a = document.createElement('a');
               a.href = url;
               a.download = selectedFile.name.replace('.json', '_spectral_plot.png');
               document.body.appendChild(a);
               a.click();
               document.body.removeChild(a);
               
               URL.revokeObjectURL(url);
           } catch (error) {
               console.error('Error downloading image:', error);
           }
       }

       initPyodide();
   </script>


Submitting Your Files
~~~~~~~~~~~~~~~~~~~~~

To add a new file to the database, we use the GitLab merge request (MR) process. Don't worry, it's simpler than it sounds! First, you'll need to create a `GitLab account <https://gitlab.com/users/sign_up>`_ if you don't have one yet. Then, follow this quick step-by-step guide for beginners.


1. **Fork the repository**
   
   Go to the `Siril SPCC Database GitLab page <https://gitlab.com/free-astro/siril-spcc-database>`_ and click the :guilabel:`Fork` button. This creates your own copy of the database that you can edit.

2. **Clone your fork**
   
   Once you've forked the repository, clone it to your computer using this command in your terminal::

    git clone https://gitlab.com/your-username/siril-spcc-database.git
   
   Replace ``your-username`` with your actual GitLab username.

3. **Add your file**
   
   Place your JSON file in the correct folder:
   
   - ``mono_sensors/`` for monochrome sensors
   - ``osc_sensors/`` for color sensors
   - ``mono_filters/`` for monochrome filters
   - ``osc_filters/`` for color filters

4. **Commit your changes**
   
   After adding your file, commit the changes with the following commands::

    git add .
    git commit -m "Added data for [Your Sensor/Filter Name]"

5. **Push your changes**
   
   Push the changes to your forked repository::

    git push origin main

6. **Create a Merge Request**
   
   Go back to the original repository on GitLab, and you'll see a button asking if you want to create a Merge Request. Click on it, review your changes, and submit the request. Our team will then review and merge your file into the main database!

.. note::
   Please open a Merge Request for each new dataset added to facilitate easier review and ensure seamless integration. Thank you for your contributions!

Video Tutorial
~~~~~~~~~~~~~~

.. raw:: html

   <iframe width="640" height="360" 
           src="https://www.youtube.com/embed/2Kso4GIf5Hw" 
           frameborder="0" allowfullscreen>
   </iframe>