lab template added

marimo/app.py

@@ -1,7 +1,8 @@
 from typing import Annotated, Callable, Coroutine
-from fastapi.responses import HTMLResponse, RedirectResponse, FileResponse
 import marimo
+from fastapi.responses import HTMLResponse, RedirectResponse, FileResponse
 from fastapi import FastAPI, Form, Request, Response
+import mimetypes
 import os
 from pathlib import Path
 
@@ -24,19 +25,38 @@ marimo_server = (
     .with_app(path="/surface", root="./surface_adsorption.py")
 )
 
-# Create a route to download a file
-@app.get("/download-pdf/{pdf_name}")
-async def download_pdf(pdf_name: str):
-    # Assuming PDFs are stored in a 'pdfs' directory
-    pdf_path = f"./pdfs/{pdf_name}"
+def get_media_type(file_name: str):
+    mime_type, _ = mimetypes.guess_type(file_name)
+    return mime_type or 'application/octet-stream'  # Default to binary if unknown
 
-    if os.path.exists(pdf_path):
+@app.get("/download-file/{file_name}")
+async def download_file(file_name: str):
+    file_path = f"./docs/{file_name}"
+    if os.path.exists(file_path):
+        # For example:
+        # For "document.pdf" - media_type would be "application/pdf"
+        # For "document.docx" - media_type would be "application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+        # For "document.doc" - media_type would be "application/msword"
         return FileResponse(
-            pdf_path,
-            media_type='application/pdf',
-            filename=pdf_name
+            file_path,
+            media_type=get_media_type(file_name),
+            filename=file_name
         )
-    return {"error": f"PDF not found [./pdfs/{pdf_name}]"}, 404
+    raise HTTPException(status_code=404, detail=f"Document not found [./docs/{file_name}]")
+
+## Create a route to download a file
+#@app.get("/download-file/{file_name}")
+#async def download_file(file_name: str):
+#    # Assuming files are stored in a 'docs' directory
+#    file_path = f"./docs/{file_name}"
+#
+#    if os.path.exists(file_path):
+#        return FileResponse(
+#            file_path,
+#            media_type='application/pdf',
+#            filename=file_name
+#        )
+#    return {"error": f"Document not found [./docs/{file_name}]"}, 404
 
 @app.get("/calendar", response_class=HTMLResponse)
 async def read_root():

marimo/docs/LabReportTemplate.docx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ee4b0b57305656bf251e9233d34294184a905515533ec86e17700d9b4db4948
+size 17371

marimo/{pdfs → docs}/NotesOfStatistics-1.pdf

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:dda949acff5354afefd6f9e7f466ad7754aba215160d9070e686387fe083c067
-size 2606853
+oid sha256:5a06c43e66bd1169ceace01a9a18890e2173f4bec4913c4cb604a40d58bf96c3
+size 2606862

marimo/index.html

@@ -90,22 +90,26 @@
 
     <!-- <h2>Labs</h2> -->
     <div class="lab-grid">
-        <a href="/download-pdf/LabManualCHEM2000-1.pdf" class="lab-card">
+        <a href="/calendar" class="lab-card" >
+            <h3 style="text-align: center">Calendar</h3>
+            <p style="text-align: center">Lab schedule and deadlines<br>(2025)</p>
+        </a>
+        <a href="/download-file/LabManualCHEM2000-1.pdf" class="lab-card">
             <h3 style="text-align: center">Lab Manual<br>(Download PDF)</h3>
             <p style="text-align: center">Version 1 - 20/02/2025</p>
         </a>
-        <a href="/download-pdf/NotesOfStatistics-1.pdf" class="lab-card">
+        <a href="/download-file/LabReportTemplate.docx" class="lab-card">
+            <h3 style="text-align: center">Lab Report Template<br>(Download DOCX)</h3>
+            <p style="text-align: center">Version 1 - 28/02/2025</p>
+        </a>
+        <a href="/download-file/NotesOfStatistics-1.pdf" class="lab-card">
             <h3 style="text-align: center">Notes of Statistics<br>(Download PDF)</h3>
             <p style="text-align: center">Version 1 - 20/02/2025</p>
         </a>
-        <a href="/download-pdf/python_primer.pdf" class="lab-card">
+        <a href="/download-file/python_primer.pdf" class="lab-card">
             <h3 style="text-align: center">A Python Primer<br>(Download PDF)</h3>
             <p style="text-align: center">Version 1 - 20/02/2025</p>
         </a>
-        <a href="/calendar" class="lab-card" >
-            <h3 style="text-align: center">Calendar</h3>
-            <p style="text-align: center">Lab schedule and deadlines<br>(2025)</p>
-        </a>
     </div>
     <hr style="width: 80%; height: 2px; background-color: #333; border: none;">
     <div class="lab-grid">

marimo/statistics_lab.py

@@ -43,7 +43,7 @@ def _(mo):
 
 
 @app.cell
-def _(lab, mo):
+def _(cek, lab, mo):
     def set_ID(value):
         return cek.set_ID(mo, lab, value)
 

src/pycek_public/cek_labs.py

@@ -7,6 +7,18 @@ from collections import OrderedDict
 from abc import ABC, abstractmethod
 
 
+def set_ID(mo, lab, value):
+    try:
+        student_number = int(value.strip())
+        if student_number <= 0:
+            error = f"### Invalid Student ID: {value}"
+            print(mo.md(error))
+            raise ValueError(error)
+        print(mo.md(f"Valid Student ID: {student_number}"))
+        lab.set_student_ID(int(value))
+    except ValueError:
+        print(mo.md(f"### Invalid Student ID: {value}"))
+
 class cek_labs(ABC):
     def __init__(self, **kwargs):
         self.token = None

src/pycek_public/plotting.py

@@ -3,67 +3,79 @@ import numpy as np
 from scipy import stats
 
 class plotting():
-    def quick_plot(self, scatter=None, line=None, columns=["X","Y"], output=None, hline=None):
-        """
-        Plot the data along with the best fit line and its associated confidence band.
 
+    def quick_plot(self, scatter=None, line=None, columns=["X", "Y"], output=None, hline=None):
+        """
+        Plot the data along with the best fit line and its associated confidence band using
+        Matplotlib's object-oriented API to avoid race conditions.
+    
         Parameters:
-            data (list): List of data to plot
-            columns (list): Axes labels (default is "X","Y")
-
+            scatter (list or array): Data to plot as scatter points
+            line (list or array): Data to plot as lines
+            columns (list): Axes labels (default is ["X","Y"])
+            output (str): If provided, save figure to this path. If "marimo", return the figure object
+            hline (float): If provided, add a horizontal line at this y-value
+    
         Returns:
-            None: Displays a matplotlib plot with data, fit line, and confidence band
-
+            None or Figure: Displays a matplotlib plot or returns the figure object if output="marimo"
+    
         Example:
-            >>> quick_plot(x_data, y_data)
+            >>> quick_plot(scatter=x_data, line=y_data)
         """
-
+        # Input validation
         if scatter is None and line is None:
             raise ValueError("Either scatter or line should be provided")
-        
-        if not isinstance(scatter,list):
-            scatter = [scatter]
-        if not isinstance(line,list):
-            line = [line]
-
-        # Plot the observed data points as a scatter plot
+    
+        # Convert inputs to lists if they aren't already
+        if not isinstance(scatter, list):
+            scatter = [scatter] if scatter is not None else []
+        if not isinstance(line, list):
+            line = [line] if line is not None else []
+    
+        # Create figure and axes objects (OO approach)
+        fig, ax = plt.subplots(figsize=(6, 6))
+    
+        # Plot scatter data
         idx = 0
         for ds in scatter:
             if ds is not None:
-                plt.scatter(ds[:,0],ds[:,1],label="Data "+str(idx)),
+                ax.scatter(ds[:, 0], ds[:, 1], label=f"Data {idx}")
                 idx += 1
-            #     ymin = np.min(ds[:,1] - 0.1*np.abs(np.min(ds[:,1])))
-            #     ymax = np.max(ds[:,1] + 0.1*np.abs(np.max(ds[:,1])))
-            
-            # ax = plt.gca()        
-            # ax.set_ylim([ymin, ymax])
-
+    
+        # Plot line data
         for ds in line:
             if ds is not None:
-                plt.plot(ds[:,0],ds[:,1],color='red',label="Data "+str(idx)),
+                ax.plot(ds[:, 0], ds[:, 1], color='red', label=f"Data {idx}")
                 idx += 1
-
-        # Add labels and title to the plot
-        plt.xlabel(columns[0])
-        plt.ylabel(columns[1])
-        
+    
+        # Set labels
+        ax.set_xlabel(columns[0])
+        ax.set_ylabel(columns[1])
+    
+        # Add horizontal line if specified
         if hline is not None:
-            plt.axhline(hline, color='black', linestyle='--')
-
-        # Display the legend
-        plt.legend()
-
-        ax = plt.gca()
+            ax.axhline(hline, color='black', linestyle='--')
+    
+        # Add legend
+        ax.legend()
+    
+        # Add watermark
         ax.text(0.5, 0.5, 'TEMPLATE', transform=ax.transAxes,
-            fontsize=40, color='gray', alpha=0.5,
-            ha='center', va='center', rotation=30)
-
-        # Show the plot
+                fontsize=40, color='gray', alpha=0.5,
+                ha='center', va='center', rotation=30)
+    
+        # Handle output
         if output is None:
-            plt.show()
+            # Use this instead of plt.show() to avoid blocking behavior
+            fig.canvas.draw_idle()
+            plt.show(block=False)
         elif output == "marimo":
-            return plt.gcf()
+            return fig
         else:
-            plt.savefig(output)
-        plt.close()
+            # Save the figure to the specified path
+            fig.savefig(output)
+    
+        # Close the figure to free memory if not returning it
+        if output != "marimo":
+            plt.close(fig)
 

src/pycek_public/statistics_lab.py

@@ -27,7 +27,7 @@ class stats_lab(cek.cek_labs):
                 (1.0, 0.1),
                 (12., 2.0)
             ],
-            'exp_values' : (1.0,10.0),
+            'expected_value' : (1.0,10.0),
             "precision" : 3,
             }
         
@@ -51,7 +51,7 @@ class stats_lab(cek.cek_labs):
             "function" : lambda x,m,q: m*x + q, 
             "gen_values" : {'m':12.3 , 'q':1.0},
             "xrange" : [0.0 , 10.0],
-            "exp_values" : 11.3,
+            "expected_value" : (11.3,0.9),
             "precision" : 3,
             }
 
@@ -94,6 +94,9 @@ class stats_lab(cek.cek_labs):
             number_of_values = self.number_of_values,
             sample = self.sample,
         )
+
+        if "expected_value" in prm:
+            self.add_metadata( **{"expected_value": prm["expected_value"]} )
         
         if self.sample in ["Averages", 'Propagation of uncertainty', 'Comparison of averages']:
             data = self._generate_normal_random(self.number_of_values, prm['gen_values'])