Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -8,6 +8,8 @@ from PIL import Image
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import time
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import io
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import sys
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# Set page config with wider layout
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st.set_page_config(
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@@ -65,6 +67,18 @@ st.markdown("""
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padding: 10px;
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margin: 10px 0;
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}
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</style>
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""", unsafe_allow_html=True)
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@@ -76,48 +90,14 @@ current_dir = os.getcwd()
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output_dir = os.path.join(current_dir, "output")
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os.makedirs(output_dir, exist_ok=True)
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#
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cpp_file = os.path.join(current_dir, "app.cpp")
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executable = os.path.join(current_dir, "eigen_analysis")
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if not os.path.exists(cpp_file):
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st.error(f"C++ source file not found at: {cpp_file}")
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st.stop()
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# Compile the C++ code with the right OpenCV libraries
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if not os.path.exists(executable) or st.sidebar.button("Recompile C++ Code"):
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with st.sidebar:
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with st.spinner("Compiling C++ code..."):
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compile_commands = [
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f"g++ -o {executable} {cpp_file} `pkg-config --cflags --libs opencv4` -std=c++11",
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f"g++ -o {executable} {cpp_file} `pkg-config --cflags --libs opencv` -std=c++11",
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f"g++ -o {executable} {cpp_file} -I/usr/include/opencv4 -lopencv_core -lopencv_imgproc -std=c++11"
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]
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compiled = False
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for cmd in compile_commands:
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compile_result = subprocess.run(
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cmd,
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shell=True,
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capture_output=True,
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text=True
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)
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if compile_result.returncode == 0:
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compiled = True
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break
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if not compiled:
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st.error("All compilation attempts failed. Please check the system requirements.")
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st.stop()
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# Make sure the executable is executable
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os.chmod(executable, 0o755)
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st.success("C++ code compiled successfully")
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# Helper function for running commands with better debugging
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def run_command(cmd, show_output=True):
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cmd_str = " ".join(cmd)
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if show_output:
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st.code(f"Running command: {cmd_str}", language="bash")
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@@ -129,29 +109,91 @@ def run_command(cmd, show_output=True):
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True,
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check=
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)
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if
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return False, e.stdout, e.stderr
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except Exception as e:
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if show_output:
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st.error(f"Error executing command: {str(e)}")
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return False, "", str(e)
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# Create tabs for different analyses
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tab1, tab2 = st.tabs(["Eigenvalue Analysis", "Im(s) vs z Analysis"])
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# Debug mode
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debug_mode = st.checkbox("Debug Mode", value=False, key="eig_debug")
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# Generate button
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eig_generate_button = st.button("Generate Eigenvalue Analysis",
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status_text.text("Running eigenvalue analysis...")
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if debug_mode:
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success, stdout, stderr = run_command(cmd, True)
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else:
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# Start the process with pipe for stdout to read progress
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process = subprocess.Popen(
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cmd,
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stdout=subprocess.PIPE,
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stderr=subprocess.PIPE,
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text=True
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)
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# Track progress from stdout
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success = True
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while True:
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line = process.stdout.readline()
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if not line and process.poll() is not None:
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break
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if line
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# Get the return code and stderr
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returncode = process.poll()
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stderr = process.stderr.read()
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if returncode != 0:
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success = False
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st.error(f"Error executing the analysis: {stderr}")
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if success:
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progress_bar.progress(1.0)
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st.error(f"Output file not created: {data_file}")
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st.stop()
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yaxis={
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'title': 'Eigenvalues',
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'titlefont': {'size': 18, 'color': '#424242'},
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'tickfont': {'size': 14},
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'gridcolor': 'rgba(220, 220, 220, 0.5)',
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'showgrid': True
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},
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plot_bgcolor='rgba(240, 240, 240, 0.8)',
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paper_bgcolor='rgba(249, 249, 249, 0.8)',
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hovermode='closest',
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legend={
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'font': {'size': 14},
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'bgcolor': 'rgba(255, 255, 255, 0.9)',
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'bordercolor': 'rgba(200, 200, 200, 0.5)',
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'borderwidth': 1
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},
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margin={'l': 60, 'r': 30, 't': 100, 'b': 60},
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height=600,
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annotations=[
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{
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'text': f"Max Function: max{{k ∈ (0,∞)}} [yβ(a-1)k + (ak+1)((y-1)k-1)]/[(ak+1)(k²+k)]",
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'xref': 'paper', 'yref': 'paper',
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'x': 0.02, 'y': 0.02,
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'showarrow': False,
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'font': {'size': 12, 'color': 'rgb(30, 180, 30)'},
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'bgcolor': 'rgba(255, 255, 255, 0.9)',
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'bordercolor': 'rgb(30, 180, 30)',
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'borderwidth': 1,
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'borderpad': 4
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},
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'bgcolor': 'rgba(255, 255, 255, 0.9)',
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'borderwidth': 1
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}
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except Exception as e:
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st.error(f"An error occurred: {str(e)}")
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# Debug mode
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cubic_debug_mode = st.checkbox("Debug Mode", value=False, key="cubic_debug")
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# Show cubic equation
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st.markdown('<div class="math-box">', unsafe_allow_html=True)
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st.markdown("### Cubic Equation")
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status_text.text("Calculating Im(s) vs z values...")
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if cubic_debug_mode:
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success, stdout, stderr = run_command(cmd, True)
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else:
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# Run the command with our helper function
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success = result.returncode == 0
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if not success:
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st.error(f"Error executing cubic analysis: {
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if success:
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status_text.text("Calculations complete! Generating visualization...")
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st.error(f"Output file not created: {data_file}")
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st.stop()
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margin={'l': 60, 'r': 30, 't': 100, 'b': 60},
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height=600,
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annotations=[
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'text': f"Cubic Equation: {cubic_a}zs³ + [{cubic_a+1}z+{cubic_a}(1-{cubic_y})]s² + [z+{cubic_a+1}-{cubic_y}-{cubic_y*cubic_beta}({cubic_a-1})]s + 1 = 0",
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'xref': 'paper', 'yref': 'paper',
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'bgcolor': 'rgba(255, 255, 255, 0.9)',
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except Exception as e:
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st.error(f"An error occurred: {str(e)}")
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# Show placeholder
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st.info("👈 Set parameters and click 'Generate Im(s) vs z Analysis' to create a visualization.")
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st.markdown('</div>', unsafe_allow_html=True)
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import time
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import io
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import sys
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import tempfile
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import platform
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13 |
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# Set page config with wider layout
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15 |
st.set_page_config(
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padding: 10px;
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margin: 10px 0;
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}
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.stWarning {
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background-color: #fff3cd;
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padding: 10px;
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border-left: 3px solid #ffc107;
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margin: 10px 0;
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}
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.stSuccess {
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background-color: #d4edda;
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padding: 10px;
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border-left: 3px solid #28a745;
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margin: 10px 0;
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}
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</style>
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""", unsafe_allow_html=True)
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output_dir = os.path.join(current_dir, "output")
|
91 |
os.makedirs(output_dir, exist_ok=True)
|
92 |
|
93 |
+
# Path to the C++ source file and executable
|
94 |
cpp_file = os.path.join(current_dir, "app.cpp")
|
95 |
executable = os.path.join(current_dir, "eigen_analysis")
|
96 |
+
if platform.system() == "Windows":
|
97 |
+
executable += ".exe"
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
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|
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|
|
98 |
|
99 |
# Helper function for running commands with better debugging
|
100 |
+
def run_command(cmd, show_output=True, timeout=None):
|
101 |
cmd_str = " ".join(cmd)
|
102 |
if show_output:
|
103 |
st.code(f"Running command: {cmd_str}", language="bash")
|
|
|
109 |
stdout=subprocess.PIPE,
|
110 |
stderr=subprocess.PIPE,
|
111 |
text=True,
|
112 |
+
check=False,
|
113 |
+
timeout=timeout
|
114 |
)
|
115 |
|
116 |
+
if result.returncode == 0:
|
117 |
+
if show_output:
|
118 |
+
st.success("Command completed successfully.")
|
119 |
+
if result.stdout and show_output:
|
120 |
+
with st.expander("Command Output"):
|
121 |
+
st.code(result.stdout)
|
122 |
+
return True, result.stdout, result.stderr
|
123 |
+
else:
|
124 |
+
if show_output:
|
125 |
+
st.error(f"Command failed with return code {result.returncode}")
|
126 |
+
st.error(f"Command: {cmd_str}")
|
127 |
+
st.error(f"Error output: {result.stderr}")
|
128 |
+
return False, result.stdout, result.stderr
|
|
|
129 |
|
130 |
+
except subprocess.TimeoutExpired:
|
131 |
+
if show_output:
|
132 |
+
st.error(f"Command timed out after {timeout} seconds")
|
133 |
+
return False, "", f"Command timed out after {timeout} seconds"
|
134 |
except Exception as e:
|
135 |
if show_output:
|
136 |
st.error(f"Error executing command: {str(e)}")
|
137 |
return False, "", str(e)
|
138 |
|
139 |
+
# Check if C++ source file exists
|
140 |
+
if not os.path.exists(cpp_file):
|
141 |
+
with open(cpp_file, "w") as f:
|
142 |
+
st.warning(f"C++ source file not found at: {cpp_file}")
|
143 |
+
st.info("Creating an empty file. Please paste the C++ code into this file and recompile.")
|
144 |
+
f.write("// Paste the C++ code here and recompile\n")
|
145 |
+
|
146 |
+
# Compile the C++ code with the right OpenCV libraries
|
147 |
+
st.sidebar.title("Compiler Settings")
|
148 |
+
need_compile = not os.path.exists(executable) or st.sidebar.button("Recompile C++ Code")
|
149 |
+
|
150 |
+
if need_compile:
|
151 |
+
with st.sidebar:
|
152 |
+
with st.spinner("Compiling C++ code..."):
|
153 |
+
# Try to detect the OpenCV installation
|
154 |
+
opencv_detection_cmd = ["pkg-config", "--cflags", "--libs", "opencv4"]
|
155 |
+
opencv_found, opencv_flags, _ = run_command(opencv_detection_cmd, show_output=False)
|
156 |
+
|
157 |
+
compile_commands = []
|
158 |
+
|
159 |
+
if opencv_found:
|
160 |
+
compile_commands.append(
|
161 |
+
f"g++ -o {executable} {cpp_file} {opencv_flags.strip()} -std=c++11"
|
162 |
+
)
|
163 |
+
else:
|
164 |
+
# Try different OpenCV configurations
|
165 |
+
compile_commands = [
|
166 |
+
f"g++ -o {executable} {cpp_file} `pkg-config --cflags --libs opencv4` -std=c++11",
|
167 |
+
f"g++ -o {executable} {cpp_file} `pkg-config --cflags --libs opencv` -std=c++11",
|
168 |
+
f"g++ -o {executable} {cpp_file} -I/usr/include/opencv4 -lopencv_core -lopencv_imgproc -std=c++11",
|
169 |
+
f"g++ -o {executable} {cpp_file} -I/usr/local/include/opencv4 -lopencv_core -lopencv_imgproc -std=c++11"
|
170 |
+
]
|
171 |
+
|
172 |
+
compiled = False
|
173 |
+
compile_output = ""
|
174 |
+
|
175 |
+
for cmd in compile_commands:
|
176 |
+
st.text(f"Trying: {cmd}")
|
177 |
+
success, stdout, stderr = run_command(cmd.split(), show_output=False)
|
178 |
+
compile_output += f"Command: {cmd}\nOutput: {stdout}\nError: {stderr}\n\n"
|
179 |
+
|
180 |
+
if success:
|
181 |
+
compiled = True
|
182 |
+
st.success(f"Successfully compiled with: {cmd}")
|
183 |
+
break
|
184 |
+
|
185 |
+
if not compiled:
|
186 |
+
st.error("All compilation attempts failed.")
|
187 |
+
with st.expander("Compilation Details"):
|
188 |
+
st.code(compile_output)
|
189 |
+
st.stop()
|
190 |
+
|
191 |
+
# Make sure the executable is executable
|
192 |
+
if platform.system() != "Windows":
|
193 |
+
os.chmod(executable, 0o755)
|
194 |
+
|
195 |
+
st.success("C++ code compiled successfully!")
|
196 |
+
|
197 |
# Create tabs for different analyses
|
198 |
tab1, tab2 = st.tabs(["Eigenvalue Analysis", "Im(s) vs z Analysis"])
|
199 |
|
|
|
254 |
|
255 |
# Debug mode
|
256 |
debug_mode = st.checkbox("Debug Mode", value=False, key="eig_debug")
|
257 |
+
|
258 |
+
# Timeout setting
|
259 |
+
timeout_seconds = st.number_input(
|
260 |
+
"Computation timeout (seconds)",
|
261 |
+
min_value=30,
|
262 |
+
max_value=3600,
|
263 |
+
value=300,
|
264 |
+
help="Maximum time allowed for computation before timeout",
|
265 |
+
key="eig_timeout"
|
266 |
+
)
|
267 |
|
268 |
# Generate button
|
269 |
eig_generate_button = st.button("Generate Eigenvalue Analysis",
|
|
|
315 |
status_text.text("Running eigenvalue analysis...")
|
316 |
|
317 |
if debug_mode:
|
318 |
+
success, stdout, stderr = run_command(cmd, True, timeout=timeout_seconds)
|
319 |
+
# Process stdout for progress updates
|
320 |
+
if success:
|
321 |
+
progress_bar.progress(1.0)
|
322 |
else:
|
323 |
# Start the process with pipe for stdout to read progress
|
324 |
process = subprocess.Popen(
|
325 |
cmd,
|
326 |
stdout=subprocess.PIPE,
|
327 |
stderr=subprocess.PIPE,
|
328 |
+
text=True,
|
329 |
+
bufsize=1,
|
330 |
+
universal_newlines=True
|
331 |
)
|
332 |
|
333 |
# Track progress from stdout
|
334 |
success = True
|
335 |
+
stdout_lines = []
|
336 |
+
|
337 |
+
start_time = time.time()
|
338 |
while True:
|
339 |
+
# Check for timeout
|
340 |
+
if time.time() - start_time > timeout_seconds:
|
341 |
+
process.kill()
|
342 |
+
status_text.error(f"Computation timed out after {timeout_seconds} seconds")
|
343 |
+
success = False
|
344 |
+
break
|
345 |
+
|
346 |
+
# Try to read a line (non-blocking)
|
347 |
line = process.stdout.readline()
|
348 |
if not line and process.poll() is not None:
|
349 |
break
|
350 |
|
351 |
+
if line:
|
352 |
+
stdout_lines.append(line)
|
353 |
+
if line.startswith("PROGRESS:"):
|
354 |
+
try:
|
355 |
+
# Update progress bar
|
356 |
+
progress_value = float(line.split(":")[1].strip())
|
357 |
+
progress_bar.progress(progress_value)
|
358 |
+
status_text.text(f"Calculating... {int(progress_value * 100)}% complete")
|
359 |
+
except:
|
360 |
+
pass
|
361 |
+
elif line:
|
362 |
+
status_text.text(line.strip())
|
363 |
+
|
364 |
# Get the return code and stderr
|
365 |
returncode = process.poll()
|
366 |
stderr = process.stderr.read()
|
|
|
368 |
if returncode != 0:
|
369 |
success = False
|
370 |
st.error(f"Error executing the analysis: {stderr}")
|
371 |
+
with st.expander("Error Details"):
|
372 |
+
st.code(stderr)
|
373 |
|
374 |
if success:
|
375 |
progress_bar.progress(1.0)
|
|
|
380 |
st.error(f"Output file not created: {data_file}")
|
381 |
st.stop()
|
382 |
|
383 |
+
try:
|
384 |
+
# Load the results from the JSON file
|
385 |
+
with open(data_file, 'r') as f:
|
386 |
+
data = json.load(f)
|
387 |
+
|
388 |
+
# Extract data
|
389 |
+
beta_values = np.array(data['beta_values'])
|
390 |
+
max_eigenvalues = np.array(data['max_eigenvalues'])
|
391 |
+
min_eigenvalues = np.array(data['min_eigenvalues'])
|
392 |
+
theoretical_max = np.array(data['theoretical_max'])
|
393 |
+
theoretical_min = np.array(data['theoretical_min'])
|
394 |
+
|
395 |
+
# Create an interactive plot using Plotly
|
396 |
+
fig = go.Figure()
|
397 |
+
|
398 |
+
# Add traces for each line
|
399 |
+
fig.add_trace(go.Scatter(
|
400 |
+
x=beta_values,
|
401 |
+
y=max_eigenvalues,
|
402 |
+
mode='lines+markers',
|
403 |
+
name='Empirical Max Eigenvalue',
|
404 |
+
line=dict(color='rgb(220, 60, 60)', width=3),
|
405 |
+
marker=dict(
|
406 |
+
symbol='circle',
|
407 |
+
size=8,
|
408 |
+
color='rgb(220, 60, 60)',
|
409 |
+
line=dict(color='white', width=1)
|
410 |
+
),
|
411 |
+
hovertemplate='β: %{x:.3f}<br>Value: %{y:.6f}<extra>Empirical Max</extra>'
|
412 |
+
))
|
413 |
+
|
414 |
+
fig.add_trace(go.Scatter(
|
415 |
+
x=beta_values,
|
416 |
+
y=min_eigenvalues,
|
417 |
+
mode='lines+markers',
|
418 |
+
name='Empirical Min Eigenvalue',
|
419 |
+
line=dict(color='rgb(60, 60, 220)', width=3),
|
420 |
+
marker=dict(
|
421 |
+
symbol='circle',
|
422 |
+
size=8,
|
423 |
+
color='rgb(60, 60, 220)',
|
424 |
+
line=dict(color='white', width=1)
|
425 |
+
),
|
426 |
+
hovertemplate='β: %{x:.3f}<br>Value: %{y:.6f}<extra>Empirical Min</extra>'
|
427 |
+
))
|
428 |
+
|
429 |
+
fig.add_trace(go.Scatter(
|
430 |
+
x=beta_values,
|
431 |
+
y=theoretical_max,
|
432 |
+
mode='lines+markers',
|
433 |
+
name='Theoretical Max Function',
|
434 |
+
line=dict(color='rgb(30, 180, 30)', width=3),
|
435 |
+
marker=dict(
|
436 |
+
symbol='diamond',
|
437 |
+
size=8,
|
438 |
+
color='rgb(30, 180, 30)',
|
439 |
+
line=dict(color='white', width=1)
|
440 |
+
),
|
441 |
+
hovertemplate='β: %{x:.3f}<br>Value: %{y:.6f}<extra>Theoretical Max</extra>'
|
442 |
+
))
|
443 |
+
|
444 |
+
fig.add_trace(go.Scatter(
|
445 |
+
x=beta_values,
|
446 |
+
y=theoretical_min,
|
447 |
+
mode='lines+markers',
|
448 |
+
name='Theoretical Min Function',
|
449 |
+
line=dict(color='rgb(180, 30, 180)', width=3),
|
450 |
+
marker=dict(
|
451 |
+
symbol='diamond',
|
452 |
+
size=8,
|
453 |
+
color='rgb(180, 30, 180)',
|
454 |
+
line=dict(color='white', width=1)
|
455 |
+
),
|
456 |
+
hovertemplate='β: %{x:.3f}<br>Value: %{y:.6f}<extra>Theoretical Min</extra>'
|
457 |
+
))
|
458 |
+
|
459 |
+
# Configure layout for better appearance
|
460 |
+
fig.update_layout(
|
461 |
+
title={
|
462 |
+
'text': f'Eigenvalue Analysis: n={n}, p={p}, a={a}, y={y:.4f}',
|
463 |
+
'font': {'size': 24, 'color': '#1E88E5'},
|
464 |
+
'y': 0.95,
|
465 |
+
'x': 0.5,
|
466 |
+
'xanchor': 'center',
|
467 |
+
'yanchor': 'top'
|
468 |
+
},
|
469 |
+
xaxis={
|
470 |
+
'title': 'β Parameter',
|
471 |
+
'titlefont': {'size': 18, 'color': '#424242'},
|
472 |
+
'tickfont': {'size': 14},
|
473 |
+
'gridcolor': 'rgba(220, 220, 220, 0.5)',
|
474 |
+
'showgrid': True
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
475 |
},
|
476 |
+
yaxis={
|
477 |
+
'title': 'Eigenvalues',
|
478 |
+
'titlefont': {'size': 18, 'color': '#424242'},
|
479 |
+
'tickfont': {'size': 14},
|
480 |
+
'gridcolor': 'rgba(220, 220, 220, 0.5)',
|
481 |
+
'showgrid': True
|
482 |
+
},
|
483 |
+
plot_bgcolor='rgba(240, 240, 240, 0.8)',
|
484 |
+
paper_bgcolor='rgba(249, 249, 249, 0.8)',
|
485 |
+
hovermode='closest',
|
486 |
+
legend={
|
487 |
+
'font': {'size': 14},
|
488 |
'bgcolor': 'rgba(255, 255, 255, 0.9)',
|
489 |
+
'bordercolor': 'rgba(200, 200, 200, 0.5)',
|
490 |
+
'borderwidth': 1
|
491 |
+
},
|
492 |
+
margin={'l': 60, 'r': 30, 't': 100, 'b': 60},
|
493 |
+
height=600,
|
494 |
+
annotations=[
|
495 |
+
{
|
496 |
+
'text': f"Max Function: max{{k ∈ (0,∞)}} [yβ(a-1)k + (ak+1)((y-1)k-1)]/[(ak+1)(k²+k)]",
|
497 |
+
'xref': 'paper', 'yref': 'paper',
|
498 |
+
'x': 0.02, 'y': 0.02,
|
499 |
+
'showarrow': False,
|
500 |
+
'font': {'size': 12, 'color': 'rgb(30, 180, 30)'},
|
501 |
+
'bgcolor': 'rgba(255, 255, 255, 0.9)',
|
502 |
+
'bordercolor': 'rgb(30, 180, 30)',
|
503 |
+
'borderwidth': 1,
|
504 |
+
'borderpad': 4
|
505 |
+
},
|
506 |
+
{
|
507 |
+
'text': f"Min Function: min{{t ∈ (-1/a,0)}} [yβ(a-1)t + (at+1)((y-1)t-1)]/[(at+1)(t²+t)]",
|
508 |
+
'xref': 'paper', 'yref': 'paper',
|
509 |
+
'x': 0.55, 'y': 0.02,
|
510 |
+
'showarrow': False,
|
511 |
+
'font': {'size': 12, 'color': 'rgb(180, 30, 180)'},
|
512 |
+
'bgcolor': 'rgba(255, 255, 255, 0.9)',
|
513 |
+
'bordercolor': 'rgb(180, 30, 180)',
|
514 |
+
'borderwidth': 1,
|
515 |
+
'borderpad': 4
|
516 |
+
}
|
517 |
+
]
|
518 |
+
)
|
519 |
+
|
520 |
+
# Add custom modebar buttons
|
521 |
+
fig.update_layout(
|
522 |
+
modebar_add=[
|
523 |
+
'drawline', 'drawopenpath', 'drawclosedpath',
|
524 |
+
'drawcircle', 'drawrect', 'eraseshape'
|
525 |
+
],
|
526 |
+
modebar_remove=['lasso2d', 'select2d'],
|
527 |
+
dragmode='zoom'
|
528 |
+
)
|
529 |
+
|
530 |
+
# Clear progress container
|
531 |
+
progress_container.empty()
|
532 |
+
|
533 |
+
# Display the interactive plot in Streamlit
|
534 |
+
st.plotly_chart(fig, use_container_width=True)
|
535 |
+
|
536 |
+
# Display statistics
|
537 |
+
with st.expander("Statistics"):
|
538 |
+
col1, col2 = st.columns(2)
|
539 |
+
with col1:
|
540 |
+
st.write("### Eigenvalue Statistics")
|
541 |
+
st.write(f"Max empirical value: {max_eigenvalues.max():.6f}")
|
542 |
+
st.write(f"Min empirical value: {min_eigenvalues.min():.6f}")
|
543 |
+
with col2:
|
544 |
+
st.write("### Theoretical Values")
|
545 |
+
st.write(f"Max theoretical value: {theoretical_max.max():.6f}")
|
546 |
+
st.write(f"Min theoretical value: {theoretical_min.min():.6f}")
|
547 |
+
|
548 |
+
except json.JSONDecodeError as e:
|
549 |
+
st.error(f"Error parsing JSON results: {str(e)}")
|
550 |
+
if os.path.exists(data_file):
|
551 |
+
with open(data_file, 'r') as f:
|
552 |
+
content = f.read()
|
553 |
+
st.code(content[:1000] + "..." if len(content) > 1000 else content)
|
554 |
|
555 |
except Exception as e:
|
556 |
st.error(f"An error occurred: {str(e)}")
|
|
|
717 |
# Debug mode
|
718 |
cubic_debug_mode = st.checkbox("Debug Mode", value=False, key="cubic_debug")
|
719 |
|
720 |
+
# Timeout setting
|
721 |
+
cubic_timeout = st.number_input(
|
722 |
+
"Computation timeout (seconds)",
|
723 |
+
min_value=10,
|
724 |
+
max_value=600,
|
725 |
+
value=60,
|
726 |
+
help="Maximum time allowed for computation before timeout",
|
727 |
+
key="cubic_timeout"
|
728 |
+
)
|
729 |
+
|
730 |
# Show cubic equation
|
731 |
st.markdown('<div class="math-box">', unsafe_allow_html=True)
|
732 |
st.markdown("### Cubic Equation")
|
|
|
780 |
status_text.text("Calculating Im(s) vs z values...")
|
781 |
|
782 |
if cubic_debug_mode:
|
783 |
+
success, stdout, stderr = run_command(cmd, True, timeout=cubic_timeout)
|
784 |
else:
|
785 |
# Run the command with our helper function
|
786 |
+
success, stdout, stderr = run_command(cmd, False, timeout=cubic_timeout)
|
|
|
787 |
if not success:
|
788 |
+
st.error(f"Error executing cubic analysis: {stderr}")
|
789 |
|
790 |
if success:
|
791 |
status_text.text("Calculations complete! Generating visualization...")
|
|
|
795 |
st.error(f"Output file not created: {data_file}")
|
796 |
st.stop()
|
797 |
|
798 |
+
try:
|
799 |
+
# Load the results from the JSON file
|
800 |
+
with open(data_file, 'r') as f:
|
801 |
+
data = json.load(f)
|
802 |
+
|
803 |
+
# Extract data
|
804 |
+
z_values = np.array(data['z_values'])
|
805 |
+
ims_values1 = np.array(data['ims_values1'])
|
806 |
+
ims_values2 = np.array(data['ims_values2'])
|
807 |
+
ims_values3 = np.array(data['ims_values3'])
|
808 |
+
|
809 |
+
# Create an interactive plot using Plotly
|
810 |
+
fig = go.Figure()
|
811 |
+
|
812 |
+
# Add traces for each root's imaginary part
|
813 |
+
fig.add_trace(go.Scatter(
|
814 |
+
x=z_values,
|
815 |
+
y=ims_values1,
|
816 |
+
mode='lines',
|
817 |
+
name='Im(s₁)',
|
818 |
+
line=dict(color='rgb(220, 60, 60)', width=3),
|
819 |
+
hovertemplate='z: %{x:.3f}<br>Im(s₁): %{y:.6f}<extra>Root 1</extra>'
|
820 |
+
))
|
821 |
+
|
822 |
+
fig.add_trace(go.Scatter(
|
823 |
+
x=z_values,
|
824 |
+
y=ims_values2,
|
825 |
+
mode='lines',
|
826 |
+
name='Im(s₂)',
|
827 |
+
line=dict(color='rgb(60, 60, 220)', width=3),
|
828 |
+
hovertemplate='z: %{x:.3f}<br>Im(s₂): %{y:.6f}<extra>Root 2</extra>'
|
829 |
+
))
|
830 |
+
|
831 |
+
fig.add_trace(go.Scatter(
|
832 |
+
x=z_values,
|
833 |
+
y=ims_values3,
|
834 |
+
mode='lines',
|
835 |
+
name='Im(s₃)',
|
836 |
+
line=dict(color='rgb(30, 180, 30)', width=3),
|
837 |
+
hovertemplate='z: %{x:.3f}<br>Im(s₃): %{y:.6f}<extra>Root 3</extra>'
|
838 |
+
))
|
839 |
+
|
840 |
+
# Configure layout for better appearance
|
841 |
+
fig.update_layout(
|
842 |
+
title={
|
843 |
+
'text': f'Im(s) vs z Analysis: a={cubic_a}, y={cubic_y}, β={cubic_beta}',
|
844 |
+
'font': {'size': 24, 'color': '#1E88E5'},
|
845 |
+
'y': 0.95,
|
846 |
+
'x': 0.5,
|
847 |
+
'xanchor': 'center',
|
848 |
+
'yanchor': 'top'
|
849 |
+
},
|
850 |
+
xaxis={
|
851 |
+
'title': 'z (logarithmic scale)',
|
852 |
+
'titlefont': {'size': 18, 'color': '#424242'},
|
853 |
+
'tickfont': {'size': 14},
|
854 |
+
'gridcolor': 'rgba(220, 220, 220, 0.5)',
|
855 |
+
'showgrid': True,
|
856 |
+
'type': 'log' # Use logarithmic scale for better visualization
|
857 |
+
},
|
858 |
+
yaxis={
|
859 |
+
'title': 'Im(s)',
|
860 |
+
'titlefont': {'size': 18, 'color': '#424242'},
|
861 |
+
'tickfont': {'size': 14},
|
862 |
+
'gridcolor': 'rgba(220, 220, 220, 0.5)',
|
863 |
+
'showgrid': True
|
864 |
+
},
|
865 |
+
plot_bgcolor='rgba(240, 240, 240, 0.8)',
|
866 |
+
paper_bgcolor='rgba(249, 249, 249, 0.8)',
|
867 |
+
hovermode='closest',
|
868 |
+
legend={
|
869 |
+
'font': {'size': 14},
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
870 |
'bgcolor': 'rgba(255, 255, 255, 0.9)',
|
871 |
+
'bordercolor': 'rgba(200, 200, 200, 0.5)',
|
872 |
+
'borderwidth': 1
|
873 |
+
},
|
874 |
+
margin={'l': 60, 'r': 30, 't': 100, 'b': 60},
|
875 |
+
height=600,
|
876 |
+
annotations=[
|
877 |
+
{
|
878 |
+
'text': f"Cubic Equation: {cubic_a}zs³ + [{cubic_a+1}z+{cubic_a}(1-{cubic_y})]s² + [z+{cubic_a+1}-{cubic_y}-{cubic_y*cubic_beta}({cubic_a-1})]s + 1 = 0",
|
879 |
+
'xref': 'paper', 'yref': 'paper',
|
880 |
+
'x': 0.5, 'y': 0.02,
|
881 |
+
'showarrow': False,
|
882 |
+
'font': {'size': 12, 'color': 'black'},
|
883 |
+
'bgcolor': 'rgba(255, 255, 255, 0.9)',
|
884 |
+
'bordercolor': 'rgba(0, 0, 0, 0.5)',
|
885 |
+
'borderwidth': 1,
|
886 |
+
'borderpad': 4,
|
887 |
+
'align': 'center'
|
888 |
+
}
|
889 |
+
]
|
890 |
+
)
|
891 |
+
|
892 |
+
# Add custom modebar buttons
|
893 |
+
fig.update_layout(
|
894 |
+
modebar_add=[
|
895 |
+
'drawline', 'drawopenpath', 'drawclosedpath',
|
896 |
+
'drawcircle', 'drawrect', 'eraseshape'
|
897 |
+
],
|
898 |
+
modebar_remove=['lasso2d', 'select2d'],
|
899 |
+
dragmode='zoom'
|
900 |
+
)
|
901 |
+
|
902 |
+
# Clear progress container
|
903 |
+
progress_container.empty()
|
904 |
+
|
905 |
+
# Display the interactive plot in Streamlit
|
906 |
+
st.plotly_chart(fig, use_container_width=True)
|
907 |
+
|
908 |
+
# Add explanation text
|
909 |
+
st.markdown("""
|
910 |
+
### Explanation of the Analysis
|
911 |
+
|
912 |
+
This plot shows the imaginary parts of the three roots (s₁, s₂, s₃) of the cubic equation as a function of z.
|
913 |
+
The cubic equation being solved is:
|
914 |
+
|
915 |
+
```
|
916 |
+
zas³ + [z(a+1)+a(1-y)]s² + [z+(a+1)-y-yβ(a-1)]s + 1 = 0
|
917 |
+
```
|
918 |
+
|
919 |
+
Where a, y, and β are parameters you can adjust in the control panel. The imaginary parts of the roots represent
|
920 |
+
oscillatory behavior in the system.
|
921 |
+
|
922 |
+
- When Im(s) = 0, the root is purely real
|
923 |
+
- When Im(s) ≠ 0, the root has an oscillatory component
|
924 |
+
""")
|
925 |
+
|
926 |
+
except json.JSONDecodeError as e:
|
927 |
+
st.error(f"Error parsing JSON results: {str(e)}")
|
928 |
+
if os.path.exists(data_file):
|
929 |
+
with open(data_file, 'r') as f:
|
930 |
+
content = f.read()
|
931 |
+
st.code(content[:1000] + "..." if len(content) > 1000 else content)
|
932 |
|
933 |
except Exception as e:
|
934 |
st.error(f"An error occurred: {str(e)}")
|
|
|
1028 |
# Show placeholder
|
1029 |
st.info("👈 Set parameters and click 'Generate Im(s) vs z Analysis' to create a visualization.")
|
1030 |
|
1031 |
+
st.markdown('</div>', unsafe_allow_html=True)
|
1032 |
+
|
1033 |
+
# Add footer with instructions
|
1034 |
+
st.markdown("""
|
1035 |
+
---
|
1036 |
+
### Instructions for Using the Dashboard
|
1037 |
+
|
1038 |
+
1. **Select a tab** at the top to choose between Eigenvalue Analysis and Im(s) vs z Analysis
|
1039 |
+
2. **Adjust parameters** in the left panel to configure your analysis
|
1040 |
+
3. **Click the Generate button** to run the analysis with the selected parameters
|
1041 |
+
4. **Explore the results** in the interactive plot
|
1042 |
+
5. For advanced users, you can enable **Debug Mode** to see detailed output
|
1043 |
+
|
1044 |
+
If you encounter any issues with compilation, try clicking the "Recompile C++ Code" button in the sidebar.
|
1045 |
+
""")
|