Update inverse design Space contents

.gitattributes

@@ -36,6 +36,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 material_res3.png filter=lfs diff=lfs merge=lfs -text
 figures/material_res3.png filter=lfs diff=lfs merge=lfs -text
 figures/forming_angle.png filter=lfs diff=lfs merge=lfs -text
+metadata.json filter=lfs diff=lfs merge=lfs -text
 data_generation/processed_dataset/config_1/metadata.json filter=lfs diff=lfs merge=lfs -text
 hybrid_diffusion_material_generation/checkpoints/config_1_continuous_100_epoch/exp_20260114_164540/best_model.pt filter=lfs diff=lfs merge=lfs -text
 inverse_design_demo/model_checkpoint.pth filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,27 +1,12 @@
 ---
-title: Thermoforming inverse design
+title: Inverse Design Demo
+emoji: 📚
+colorFrom: green
+colorTo: blue
 sdk: streamlit
-emoji: 🚀
-colorFrom: red
-colorTo: red
-short_description: Thermoforming inverse design
+app_file: app.py
+python_version: "3.10"
+pinned: false
 ---
-# 🏂 US Population Dashboard
 
-A dashboard web app template built in Python using Streamlit.
-
-## Demo App
-
-[![Streamlit App](https://static.streamlit.io/badges/streamlit_badge_black_white.svg)](https://population-dashboard.streamlit.app/)
-
-## Colab notebook
-[![Colab Notebook](https://colab.research.google.com/assets/colab-badge.svg)](https://github.com/dataprofessor/population-dashboard/blob/master/US_Population.ipynb)
-
-## Prerequisite libraries
-Here are the Python libraries used in the creation of this dashboard app
-
-## Data source
-US Population data spanning the duration of 2010-2019 was obtained from the [U.S. Census Bureau](https://www.census.gov/data/datasets/time-series/demo/popest/2010s-state-total.html).
-
-## Reference
-A talk entitled [_Crafting a Dashboard App in Python using Streamlit_](https://budapestbi.hu/2023/hu/program/speakers/chanin-nantasenamat/) showing how to build this app is given at the [Budapest BI Forum (Data Visualization track)](https://budapestbi.hu/2023/hu/en/program-data-visualization-track/) on November 22, 2023.
+Thermoplastic composite inverse design demo.

app.py

@@ -1,450 +1,14 @@
-#######################
-# Import libraries
-import streamlit as st
-import pandas as pd
-import altair as alt
-# import plotly.express as px
-from PIL import Image # Used to open and handle image files
-import matplotlib
-import matplotlib.pyplot as plt
-import numpy as np
-
-from model_inverse import inverse_design
-
-#######################
-# Page configuration
-st.set_page_config(
-    page_title="Inverse Design of Thermoplastic Composites for Thermoforming",
-#    page_icon="🏂",
-    layout="wide",
-    initial_sidebar_state="collapsed")
-
-alt.themes.enable('default')
-
-#######################
-# CSS styling
-
-st.markdown("""
-<style>
-/* Target the input element within its container (adjust class name as needed via browser inspection) */
-.stTextInput input {
-    border: 1px solid #333333; /* Set border width, style, and color */
-    border-radius: 6px; /* Optional: adds rounded corners */
-    padding: 10px; /* Optional: adds inner spacing */
-}
-</style>
-""", unsafe_allow_html=True)
-
-st.markdown("""
-<style>
-
-[data-testid="block-container"] {
-    padding-left: 2rem;
-    padding-right: 2rem;
-    padding-top: 1rem;
-    padding-bottom: 0rem;
-    margin-bottom: -7rem;
-}
-
-[data-testid="stVerticalBlock"] {
-    padding-left: 0rem;
-    padding-right: 0rem;
-}
-
-[data-testid="stMetric"] {
-    background-color: #393939;
-    text-align: center;
-    padding: 15px 0;
-}
-
-[data-testid="stMetricLabel"] {
-  display: flex;
-  justify-content: center;
-  align-items: center;
-}
-
-[data-testid="stMetricDeltaIcon-Up"] {
-    position: relative;
-    left: 38%;
-    -webkit-transform: translateX(-50%);
-    -ms-transform: translateX(-50%);
-    transform: translateX(-50%);
-}
-
-[data-testid="stMetricDeltaIcon-Down"] {
-    position: relative;
-    left: 38%;
-    -webkit-transform: translateX(-50%);
-    -ms-transform: translateX(-50%);
-    transform: translateX(-50%);
-}
-
-/* Main app + sidebar, target the label element itself */
-[data-testid="stAppViewContainer"] label,
-[data-testid="stWidgetLabel"] label {
-font-size: 18px !important;
-font-weight: 600 !important;   /* optional */
-color: #444 !important;         /* optional */
-}
-
-/* Some versions wrap label text inside a <div><p> */
-[data-testid="stAppViewContainer"] label > div > p,
-[data-testid="stWidgetLabel"] label > div > p {
-font-size: 18px !important;
-font-weight: 600 !important;
-}
-</style>
-""", unsafe_allow_html=True)
-
-st.markdown("""
-<style>
-div.stButton > button:first-child {
-    background-color: #ee7700; /* background */
-    color: white; /* White text */
-    font-size: 20px;
-    border-radius: 10px;
-    # display: block; # this line and the next center the button horizontally 
-    # margin: 0 auto;
-}
-</style>
-""", unsafe_allow_html=True)
-
-st.markdown("""
-<style>
-div[data-testid="stVirtualDropdown"] > div {
-    max-height: 10px !important; /* Adjust this value as needed */
-    overflow-y: auto;
-}
-</style>
-""", unsafe_allow_html=True)
-
-
-st.set_page_config(initial_sidebar_state="collapsed")
-
-st.markdown(
-    """
-<style>
-    [data-testid="collapsedControl"] {
-        display: none
-    }
-</style>
-""",
-    unsafe_allow_html=True,
-)
-
-#######################
-font = {'size'   : 18}
-
-matplotlib.rc('font', **font)
-
-#######################
-if 'input_changed' not in st.session_state:
-    st.session_state.input_changed= False
-def input_typed_in():
-    st.session_state.input_changed= True
-  
-if 'forming_input_changed' not in st.session_state:
-    st.session_state.forming_input_changed= False
-def forming_typed_in():
-    st.session_state.forming_input_changed= True
-    
-
-
-if 'input_curve_button_clicked' not in st.session_state:
-    st.session_state.input_curve_button_clicked= False
-def input_curve_click():
-    st.session_state.input_curve_button_clicked = True
-    
-if 'material_design_button_clicked' not in st.session_state:
-    st.session_state.material_design_button_clicked= False
-def material_design_click():
-    st.session_state.material_design_button_clicked = True
-    
-if 'forming_input_button_clicked' not in st.session_state:
-    st.session_state.forming_input_button_clicked= False
-def forming_input_click():
-    st.session_state.forming_input_button_clicked = True
-    
-if 'forming_design_button_clicked' not in st.session_state:
-    st.session_state.forming_design_button_clicked= False
-def forming_design_click():
-    st.session_state.forming_design_button_clicked = True
-      
-
-
-#######################
-# Load data
-#df_reshaped = pd.read_csv('data/us-population-2010-2019-reshaped.csv')
-
-######## Initialize data #############
-E1aV=0    # initial longitudinal stiffness
-E1bV=0    # 10% strain longitudinal stiffness
-G12aV=0    # initial longitudinal stiffness
-G12bV=0    # 10% strain longitudinal stiffness
-nlayers=4
-vf=0.6 
-angle=30
-
-#######################
-# Main Panel
-
-data_materials={
-        'Matrix':['ABS','Polyurethane','Nylon 6','Nylon 6','Nylon 66','PE','PP'],
-        'Filler':['Carbon Black','Glass Fiber','Glass Fiber','Carbon Fiber','Glass Fiber','Carbon Fiber','Glass Fiber'],
-        'VF':['15%','20%','20%','40%','30%','20%','30%'],
-        'Feature':['Blend','Extruded','Molded','Molded','Molded','Molded','Molded']
-        }
-
-data_physical = {
-        'Forming T (C)': ['180', '185', '190'],
-        'Punch V (m/s)': ['1.05', '1.8','1.67'],
-        'Cooling time (s)': ['45','80','120'],
-        'Holding force (kN)': ['23','24','25']
-        }
-
-st.title("Inverse Design of Thermoplastic Composites for Thermoforming")
-st.write("")
-st.write("")
-st.write("")
-st.write(r"$\textsf{\textbf{\Large Material Design Requirements}}$")
-#st.text_input(r"$\textsf{\textbf{\Large Material Design Requirements}}$")
-
-# First row with 5 columns
-col1_row1, col2_row1, col3_row1, col4_row1, col5_row1= st.columns([0.25,0.25,0.25,0.25,0.25])
-with col1_row1:
-    with st.container(border=False): # Container with a border
-        E1aV= st.number_input("Initial x-stiffness (MPa):", value=2000.00, format="%.2f", width=250, key="E1a", on_change=input_typed_in)
-        E1bV= st.number_input("10% strain x-stiffness (MPa):", value=1000.00, format="%.2f", width=250, key="E1b", on_change=input_typed_in)
-        S1bV= st.number_input("10% strain x-stress (MPa):", value=1000.00, format="%.2f", width=250, key="S1b", on_change=input_typed_in)
-        
-with col2_row1:
-    with st.container(border=False): # Container with a border
-        E2aV= st.number_input("Initial y-stiffness (MPa):", value=2000.00, format="%.2f", width=250, key="E2a", on_change=input_typed_in)
-        E2bV= st.number_input("10% strain y-stiffness (MPa):", value=1000.00, format="%.2f", width=250, key="E2b", on_change=input_typed_in)
-        S2bV= st.number_input("10% strain y-stress (MPa):", value=1000.00, format="%.2f", width=250, key="S2b", on_change=input_typed_in)
-        
-
-with col3_row1:
-    with st.container(border=False): # Container with a border
-        G12aV= st.number_input("Initial shear stiffness (MPa):", value=1800.00, format="%.2f", width=250, key="G12a", on_change=input_typed_in)
-        G12bV= st.number_input("0.1 shear strain stiffness (MPa):", value=1000.00, format="%.2f", width=250, key="G12b", on_change=input_typed_in)
-        S12bV= st.number_input("10% strain shear stress (MPa):", value=1000.00, format="%.2f", width=250, key="S12b", on_change=input_typed_in)
-        
-with col4_row1:
-    with st.container(border=False): # Container with a border
-        v12aV= st.number_input("Initial Poisson's ratio (vxy):", value=0.3, format="%.2f", width=250, key="v12a", on_change=input_typed_in)
-        v12bV= st.number_input("0.1 strain Poisson's ratio (vxy):", value=0.4, format="%.2f", width=250, key="v12b", on_change=input_typed_in)
-        
-        
-with col5_row1:
-    with st.container(border=False): # Container with a border
-        v21aV= st.number_input("Initial Poisson's ratio (vyx):", value=0.3, format="%.2f", width=250, key="v21a", on_change=input_typed_in)
-        v21bV= st.number_input("0.1 strain Poisson's ratio (vyx):", value=0.4, format="%.2f", width=250, key="v21b", on_change=input_typed_in)
-
-
-        
-st.write("")
-if st.session_state.input_changed == True:
-    st.session_state.input_curve_button_clicked = False
-    st.session_state.material_design_button_clicked = False
-    st.session_state.forming_input_button_clicked = False
-    st.session_state.forming_design_button_clicked = False
-    st.session_state.input_changed = False
-    
-st.button("Generate required stress-strain curves", use_container_width=True, on_click=input_curve_click)
- 
-if  st.session_state.input_curve_button_clicked == True:
-    #st.write(E1aV)
-    #st.write(E1bV)
-    x = np.linspace(0, 0.1, 20)
-    A = np.array([[0.2, 0.03], [0.01, 0.001]])
-    b = np.array([E1bV-E1aV, S1bV-E1aV*0.1])
-    a = np.linalg.solve(A, b)
-    y1= E1aV*x + a[0]*x**2 + a[1]*x**3
-    b = np.array([E2bV-E2aV, S2bV-E2aV*0.1])
-    a = np.linalg.solve(A, b)
-    y2= E2aV*x + a[0]*x**2 + a[1]*x**3
-    b = np.array([G12bV-G12aV, S12bV-G12aV*0.1])
-    a = np.linalg.solve(A, b)
-    y3= G12aV*x + a[0]*x**2 + a[1]*x**3
-    
-    y4 = v12aV*x + (v12bV - v12aV)/0.2*x*x
-    y5 = v21aV*x + (v21bV - v21aV)/0.2*x*x
-
- 
-    #ylimit=np.max([np.max(y1),np.max(y2), np.max(y3)])
-    # 2nd row with 3 columns
-    col1_row2, col2_row2, col3_row2, col4_row2, col5_row2= st.columns([0.2,0.2,0.2,0.2,0.2])
-    with col1_row2:
-        with st.container(border=False): # Container with a border
-            fig, ax = plt.subplots()
-            ax.plot(x, y1)
-            #ax.set_ylim([0, ylimit])
-            ax.set_ylabel('Stress (MPa)')
-            ax.set_xlabel('Strain')
-            ax.set_title('Longitudinal stress-strain (xx)')
-            st.pyplot(fig)
-    with col2_row2:
-        with st.container(border=False): # Container with a border
-            fig, ax = plt.subplots()
-            ax.plot(x, y2)
-            #ax.set_ylim([0, ylimit])
-            ax.set_ylabel('Stress (MPa)')
-            ax.set_xlabel('Strain')
-            ax.set_title('Transverse stress-strain (yy)')
-            st.pyplot(fig)
-    with col3_row2:
-        with st.container(border=False): # Container with a border
-            fig, ax = plt.subplots()
-            ax.plot(x, y3)
-            #ax.set_ylim([0, ylimit])
-            ax.set_ylabel('Stress (MPa)')
-            ax.set_xlabel('Strain')
-            ax.set_title('Shear stress-strain (xy')
-            st.pyplot(fig)
-    with col4_row2:
-        with st.container(border=False): # Container with a border
-            fig, ax = plt.subplots()
-            ax.plot(x, -y4)
-            #ax.set_ylim([-0.05, 0])
-            ax.set_xlabel('Strain xx')
-            ax.set_ylabel('Strain yy')
-            ax.set_title('Strain ratio with stress xx')
-            st.pyplot(fig)
-    with col5_row2:
-        with st.container(border=False): # Container with a border
-            fig, ax = plt.subplots()
-            ax.plot(x, -y5)
-            #ax.set_ylim([-0.05, 0])
-            ax.set_xlabel('Strain yy')
-            ax.set_ylabel('Strain xx')
-            ax.set_title('Strain ratio with stress yy')
-            st.pyplot(fig)
-           
-    st.write("")
-    st.button("Material Inverse Design", use_container_width=True, on_click=material_design_click)
-    if  st.session_state.material_design_button_clicked == True:
-        #st.write("")
-        
-        # 3rd row with 3 columns
-        col1_row3, col2_row3, col3_row3, col4_row3, col5_row3= st.columns([0.15,0.15,0.23,0.23,0.23])
-        with col1_row3:
-            with st.container(border=False): # Container with a border
-                st.write("Matrix material = ", "PEEK")
-                st.write("Fiber material = ", "Carbon")
-                
-        with col2_row3:
-            with st.container(border=False): # Container with a border
-                st.write("Number of layers =", nlayers)
-                st.write("Volume fraction =", vf)
-        
-        with col3_row3:
-            with st.container(border=False): # Container with a border  
-                df = pd.DataFrame({'Ply': [], 'Orientation': []})
-                plies = np.array([[1,90], [2,45], [3,-45], [4,-90]])
-                plies_df=pd.DataFrame(plies, columns=df.columns)
-                df = pd.concat([df, plies_df], ignore_index=True)
-                st.dataframe(df, hide_index=True)
-               
-
-        # 3.5rd row with 3 columns
-        col1_row35, col2_row35, col3_row35, col4_row35, col5_row35= st.columns([0.2,0.2,0.2,0.2,0.2])
-        with col1_row35:
-            with st.container(border=False): # Container with a border
-                image = Image.open('figures/material_res3.png')
-                new_image = image.resize((250, 200))
-                st.image(new_image, caption='')
-                      
-        with col2_row35:
-            with st.container(border=False): # Container with a border  
-                image = Image.open('figures/material_res3.png')
-                new_image = image.resize((250, 200))
-                st.image(new_image, caption='') 
-               
-        with col3_row35:
-            with st.container(border=False): # Container with a border  
-                image = Image.open('figures/material_res3.png')
-                new_image = image.resize((250, 200))
-                st.image(new_image, caption='')
-                      
-        with col4_row35:
-            with st.container(border=False): # Container with a border  
-                image = Image.open('figures/material_res3.png')
-                new_image = image.resize((250, 200))
-                st.image(new_image, caption='')
- 
-               
-        with col5_row35:
-            with st.container(border=False): # Container with a border  
-                image = Image.open('figures/material_res3.png')
-                new_image = image.resize((250, 200))
-                st.image(new_image, caption='')
-
-               
-        st.write("")
-        st.button("Thermoforming Requirements", use_container_width=True, on_click=forming_input_click)
-        if  st.session_state.forming_input_button_clicked == True:
-        #st.write("")
-        # 4th row with 3 columns
-            col1_row4, col2_row4, col3_row4, col4_row4, col5_row4 = st.columns([0.16,0.16,0.2,0.24,0.24])
-            with col1_row4:
-                with st.container(border=False): # Container with a border
-                    st.write("Matrix material", "PEEK")
-                    st.write("Fiber material=", "Carbon")
-                    st.write("Number of layers=", nlayers)
-                    st.write("Volume fraction=", vf)
-            with col2_row4:
-                with st.container(border=False): # Container with a border
-                    df = pd.DataFrame({'Ply': [], 'Orientation': []})
-                    plies = np.array([[1,90], [2,45], [3,-45], [4,-90]])
-                    plies_df=pd.DataFrame(plies, columns=df.columns)
-                    df = pd.concat([df, plies_df], ignore_index=True)
-                    st.dataframe(df, hide_index=True)
-                    
-            with col3_row4:
-                with st.container(border=False): # Container with a border
-                    image = Image.open('figures/forming_angle.png')
-                    new_image = image.resize((250, 200))
-                    st.image(new_image, caption='')
-                    
-            with col4_row4:
-                with st.container(border=False): # Container with a border
-                    angleA= st.number_input("Maximum warpage angle A (degree):", format="%.2f", width=300, key="A", on_change=forming_typed_in)
-                    angleB= st.number_input("Maximum warpage angle B (degree):", format="%.2f", width=300, key="B", on_change=forming_typed_in)
-                    
-                    
-            with col5_row4:
-                with st.container(border=False): # Container with a border
-                    angleC= st.number_input("Maximum warpage angle C (degree):", format="%.2f", width=300, key="C", on_change=forming_typed_in)
-                    max_stress= st.number_input("Maximum residual stress (MPa):", format="%.2f", width=300, key="max_stress", on_change=forming_typed_in)
-                   
-
-            st.write("")
-            if st.session_state.forming_input_changed == True:
-                st.session_state.forming_design_button_clicked = False
-                st.session_state.forming_input_changed = False
-            st.button("Thermoforming process design", use_container_width=True, on_click=forming_design_click)
-            if  st.session_state.forming_design_button_clicked == True:
-                best = inverse_design(ply_number=nlayers, 
-                        fiber_vf=vf, 
-                        y_target=[angleA, angleB, angleC, max_stress], 
-                        n_restarts=5,
-                        epochs=100)
-                # 5th row with 3 columns
-                col1_row5, col2_row5,col3_row5 = st.columns([0.25,0.25,0.25])
-                with col1_row5:
-                    with st.container(border=False): # Container with a border
-                        st.write("Forming temperature (C)=", best["input"][0])
-                with col2_row5:
-                    with st.container(border=False): # Container with a border
-                        st.write("Punching velocity (mm/s)=", best["input"][1])
-                with col3_row5:
-                    with st.container(border=False): # Container with a border
-                        st.write("Cooling time (s)=", best["input"][2])
-                     
-
-
-
+import os
+import runpy
+import sys
 
 
+APP_DIR = os.path.dirname(os.path.abspath(__file__))
+DEMO_DIR = os.path.join(APP_DIR, "inverse_design_demo")
+DEMO_APP = os.path.join(DEMO_DIR, "app.py")
 
+# Execute the original Streamlit script in this process.
+if DEMO_DIR not in sys.path:
+    sys.path.insert(0, DEMO_DIR)
+os.chdir(DEMO_DIR)
+runpy.run_path(DEMO_APP, run_name="__main__")

data_generation/hf_space_generation_ro/README.md

@@ -0,0 +1,23 @@
+# Hybrid Diffusion Material Generation (RO) — Hugging Face Space
+
+This folder is intended to be uploaded as a standalone Hugging Face Space.
+
+## What it does
+- Provides **5 groups of coefficient sliders** (RO params, 5×3) with min/max ranges loaded from `metadata_ro.json`.
+- Shows **real-time curve previews** derived from the current coefficients.
+- On **Generate**, runs diffusion sampling from a trained checkpoint and then runs **N simulations (1–5)** (instances `1..N`) and overlays the curves.
+
+## Expected inputs (paths)
+By default the app reads paths from environment variables (recommended for Spaces):
+- `MG_CHECKPOINT_DIR`: directory containing `training_config.json` and a model checkpoint (`best_model.pt`).
+- `MG_DATA_DIR`: processed data directory containing `metadata_ro.json` (for coefficient ranges and normalization stats).
+- `MG_CURVE_DIR`: directory containing curve files like `CPP_0.0924_1_11.txt`, etc.
+
+You can also edit these paths directly in the UI.
+
+## Run locally
+```bash
+pip install -r requirements.txt
+python app.py
+```
+

data_generation/hf_space_generation_ro/app.py

@@ -0,0 +1,474 @@
+from __future__ import annotations
+
+import os
+from functools import lru_cache
+from typing import List
+
+import gradio as gr
+import numpy as np
+import torch
+
+from space_lib.infer import (
+    load_model_bundle,
+    postprocess_sample,
+    sample,
+)
+from space_lib.metadata import (
+    load_metadata_ro,
+    normalize_ro13,
+    ro13_from_ro15,
+    ro15_from_groups,
+    ro_groups_from_ro15,
+)
+from space_lib.plots import plot_condition_and_simulations
+from space_lib.ro_curves import plot_ro_lateral_relation, plot_ro_stress_relation
+from space_lib.simulate import default_lam_dir, simulate_instances
+
+
+DEFAULT_CHECKPOINT_DIR = os.environ.get(
+    "MG_CHECKPOINT_DIR",
+    "/project/luofeng/feiyang/MaterialGeneration/hybrid_diffusion_material_generation_ro_fitting/checkpoints/config_1_continuous_100_epoch/exp_20260108_180412",
+)
+DEFAULT_DATA_DIR = os.environ.get(
+    "MG_DATA_DIR",
+    "/project/luofeng/feiyang/MaterialGeneration/data_generation/processed_dataset_ro",
+)
+DEFAULT_CURVE_DIR = os.environ.get(
+    "MG_CURVE_DIR",
+    "/project/luofeng/feiyang/MaterialGeneration/data_generation/shahriar_modified_2025_12/RVE_Datasets",
+)
+
+# UI simplification: hard-code these for the Space (no controls exposed)
+CHECKPOINT_DIR = DEFAULT_CHECKPOINT_DIR
+DATA_DIR = DEFAULT_DATA_DIR
+CURVE_DIR = DEFAULT_CURVE_DIR
+LAM_DIR = default_lam_dir()
+
+NORMALIZATION_METHOD = "zscore"
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+REMASK_PROB = 0.1
+ANGLE_RESOLUTION = 1.0
+X_MAX = 0.1  # fixed
+PLOT_HEIGHT = 260  # px: match ~3 stacked sliders visually
+
+
+@lru_cache(maxsize=4)
+def _get_meta(data_dir: str):
+    return load_metadata_ro(data_dir)
+
+
+_MODEL_CACHE = {}
+
+
+def _get_model(checkpoint_dir: str, device: str, angle_resolution: float):
+    key = (checkpoint_dir, device, float(angle_resolution))
+    if key not in _MODEL_CACHE:
+        _MODEL_CACHE[key] = load_model_bundle(checkpoint_dir, device=device, angle_resolution=angle_resolution)
+    return _MODEL_CACHE[key]
+
+
+def _angle_categories_from_meta(meta, angle_resolution: float) -> np.ndarray:
+    angle_min_raw = float(meta.raw.get("angle_min", 0.0))
+    angle_max_raw = float(meta.raw.get("angle_max", 90.0))
+    res = float(angle_resolution)
+    ang_min = np.floor(angle_min_raw / res) * res
+    ang_max = np.ceil(angle_max_raw / res) * res
+    num = int((ang_max - ang_min) / res) + 1
+    return np.linspace(ang_min, ang_max, num, dtype=np.float32)
+
+
+def _default_ro15_values(data_dir: str) -> np.ndarray:
+    meta = _get_meta(data_dir)
+    if meta.ro_mean_full is not None and meta.ro_mean_full.size == 15:
+        return meta.ro_mean_full.astype(np.float32)
+    return ((meta.ro_min_full + meta.ro_max_full) * 0.5).astype(np.float32)
+
+
+_RO_FLAT_IDXS_SHOWN = (0, 1, 2, 3, 4, 6, 7, 8, 9, 10, 12, 13, 14)  # hide c for groups 2 & 4
+
+
+def _load_test_ro15(data_dir: str, idx: int) -> np.ndarray:
+    """
+    Load RO coefficients from held-out test set (processed_dataset_ro/test_data_ro.npz).
+    Returns flattened (15,) corresponding to [5,3] row-major.
+    """
+    npz_path = os.path.join(data_dir, "test_data_ro.npz")
+    if not os.path.exists(npz_path):
+        raise FileNotFoundError(f"Missing test file: {npz_path}")
+    data = np.load(npz_path)
+    if "ramberg_osgood_params" not in data:
+        raise KeyError(f"'ramberg_osgood_params' not found in {npz_path}. Keys: {list(data.keys())}")
+    arr = data["ramberg_osgood_params"]  # (N,5,3)
+    n = int(arr.shape[0])
+    if n <= 0:
+        raise ValueError(f"No samples in {npz_path}")
+    idx = int(idx) % n
+    ro_5x3 = np.asarray(arr[idx], dtype=np.float32)
+    if ro_5x3.shape != (5, 3):
+        raise ValueError(f"Unexpected shape for ramberg_osgood_params[{idx}]: {ro_5x3.shape}")
+    return ro_5x3.reshape(-1)  # (15,)
+
+
+def _ro15_to_ui13(ro15: np.ndarray) -> list[float]:
+    ro15 = np.asarray(ro15, dtype=np.float32).reshape(-1)
+    if ro15.size != 15:
+        raise ValueError(f"Expected 15 values, got {ro15.size}")
+    return [round(float(ro15[i]), 3) for i in _RO_FLAT_IDXS_SHOWN]
+
+
+def _build_groups_from_slider_values(vals: List[float]) -> np.ndarray:
+    """
+    UI exposes only 13 coeffs:
+      - Group 1: a,b,c
+      - Group 2: a,b (c hidden/unused)
+      - Group 3: a,b,c
+      - Group 4: a,b (c hidden/unused)
+      - Group 5: a,b,c
+
+    Internally: return (5,3) with c for groups 2&4 forced to 0.
+    """
+    if len(vals) != 13:
+        raise ValueError(f"Expected 13 slider values, got {len(vals)}")
+    ro15 = np.zeros(15, dtype=np.float32)
+    for i, flat_idx in enumerate(_RO_FLAT_IDXS_SHOWN):
+        ro15[int(flat_idx)] = float(vals[i])
+    ro15[5] = 0.0
+    ro15[11] = 0.0
+    return ro15.reshape(5, 3)
+
+
+def ui_preview_11_stress(a: float, b: float, c: float):
+    meta = _get_meta(DATA_DIR)
+    fig = plot_ro_stress_relation(
+        a=a, b=b, c=c, x_scale=meta.eps_11_scale, x_max=X_MAX, title="Mode 11: σ11(ε11)"
+    )
+    return fig
+
+
+def ui_preview_11_lat(a: float, b: float):
+    meta = _get_meta(DATA_DIR)
+    fig = plot_ro_lateral_relation(
+        a=a, b=b, x_scale=meta.eps_11_scale, y_scale=meta.eps_22_scale, x_max=X_MAX, title="Mode 11: ε22(ε11)"
+    )
+    return fig
+
+
+def ui_preview_22_stress(a: float, b: float, c: float):
+    meta = _get_meta(DATA_DIR)
+    fig = plot_ro_stress_relation(
+        a=a, b=b, c=c, x_scale=meta.eps_22_scale, x_max=X_MAX, title="Mode 22: σ22(ε22)"
+    )
+    return fig
+
+
+def ui_preview_22_lat(a: float, b: float):
+    meta = _get_meta(DATA_DIR)
+    fig = plot_ro_lateral_relation(
+        a=a, b=b, x_scale=meta.eps_22_scale, y_scale=meta.eps_11_scale, x_max=X_MAX, title="Mode 22: ε11(ε22)"
+    )
+    return fig
+
+
+def ui_preview_12_stress(a: float, b: float, c: float):
+    meta = _get_meta(DATA_DIR)
+    fig = plot_ro_stress_relation(
+        a=a, b=b, c=c, x_scale=meta.eps_12_scale, x_max=X_MAX, title="Mode 12: σ12(ε12)"
+    )
+    return fig
+
+
+def ui_generate(
+    n_simulations: int,
+    *slider_vals: float,
+):
+    # Streaming generator: yields (plot, info, status_log) updates
+    meta = _get_meta(DATA_DIR)
+    log_lines: list[str] = []
+    def _push(msg: str):
+        log_lines.append(str(msg))
+        # keep log reasonably bounded
+        if len(log_lines) > 200:
+            log_lines[:] = log_lines[-200:]
+        return "\n".join(log_lines)
+
+    yield gr.update(), gr.update(), _push("Generating (diffusion sampling)...")
+    groups = _build_groups_from_slider_values(list(slider_vals))
+    ro15 = ro15_from_groups(groups)
+    ro13_raw = ro13_from_ro15(ro15)
+    cond_norm = normalize_ro13(ro13_raw, meta, NORMALIZATION_METHOD)
+
+    bundle = _get_model(CHECKPOINT_DIR, device=DEVICE, angle_resolution=ANGLE_RESOLUTION)
+    use_discrete_angles = bundle.use_discrete_angles
+    angle_categories = _angle_categories_from_meta(meta, ANGLE_RESOLUTION) if use_discrete_angles else None
+
+    cond_t = torch.tensor(cond_norm, dtype=torch.float32, device=DEVICE).view(1, -1)
+    out = sample(
+        model=bundle.model,
+        disc_diff_mat=bundle.disc_diff_mat,
+        disc_diff_vf_category=bundle.disc_diff_vf_category,
+        disc_diff_layer=bundle.disc_diff_layer,
+        disc_diff_angle=bundle.disc_diff_angle,
+        cont_diff=bundle.cont_diff,
+        cond=cond_t,
+        mask_ids=bundle.mask_ids,
+        device=DEVICE,
+        remask_prob=float(REMASK_PROB),
+        use_discrete_angles=use_discrete_angles,
+    )
+
+    mat_type, vf, upper_angles = postprocess_sample(out, use_discrete_angles=use_discrete_angles, angle_categories_deg=angle_categories)
+    # Continuous-angle model is constrained to (0, pi/2) in-model, but keep a defensive clip to metadata range.
+    ang_min = float(meta.raw.get("angle_min", 0.0))
+    ang_max = float(meta.raw.get("angle_max", 90.0))
+    upper_angles = [min(max(float(a), ang_min), ang_max) for a in upper_angles]
+    _push(f"Generation done.")
+    _push(f"Generated material: {mat_type}")
+    _push(f"Generated vf: {vf:.4f}")
+    _push(f"Generated upper angles (deg): {upper_angles}")
+    _push(f"Simulating {int(n_simulations)} instance(s)...")
+
+    # Show condition-only plot immediately
+    fig0 = plot_condition_and_simulations(
+        groups,
+        (meta.eps_11_scale, meta.eps_22_scale, meta.eps_12_scale),
+        {},
+    )
+    info0 = (
+        f"Generated material: {mat_type}\n"
+        f"Generated vf: {vf:.4f}\n"
+        f"Generated upper angles (deg): {upper_angles}\n"
+        f"Simulated instances: []"
+    )
+    yield fig0, info0, "\n".join(log_lines)
+
+    # Simulate sequentially so we can update status per instance.
+    sim_by_instance = {}
+    instances = list(range(1, int(n_simulations) + 1))
+    for inst in instances:
+        _push(f"Simulating instance {inst}/{len(instances)}...")
+        try:
+            sim_part = simulate_instances(
+                curve_dir=CURVE_DIR,
+                lam_dir=LAM_DIR,
+                mat_type=mat_type,
+                vf=vf,
+                upper_angles=upper_angles,
+                instances=[inst],
+                num_output_points=10,
+            )
+            sim_by_instance.update(sim_part)
+            _push(f"Instance {inst} done.")
+        except Exception as e:
+            _push(f"Instance {inst} failed: {type(e).__name__}: {e}")
+
+        # Update plot incrementally after each instance attempt
+        fig_i = plot_condition_and_simulations(
+            groups,
+            (meta.eps_11_scale, meta.eps_22_scale, meta.eps_12_scale),
+            sim_by_instance,
+        )
+        info_i = (
+            f"Generated material: {mat_type}\n"
+            f"Generated vf: {vf:.4f}\n"
+            f"Generated upper angles (deg): {upper_angles}\n"
+            f"Simulated instances: {sorted(sim_by_instance.keys())}"
+        )
+        yield fig_i, info_i, "\n".join(log_lines)
+
+    _push("Done.")
+    yield fig_i, info_i, "\n".join(log_lines)
+
+
+def build_app():
+    meta = _get_meta(DATA_DIR)
+    # Default initialization uses dataset-wide mean (or min/max midpoint).
+    # You can optionally load a held-out test condition via the UI accordion.
+    ro15_init = _default_ro15_values(DATA_DIR)
+    ro_min = meta.ro_min_full
+    ro_max = meta.ro_max_full
+
+    with gr.Blocks(
+        css="""
+        /* Make slider numeric input boxes consistent width */
+        .gradio-container .gr-slider input[type="number"],
+        .gradio-container .gr-number input[type="number"],
+        .gradio-container input[type="number"] {
+          width: 78px !important;
+          min-width: 78px !important;
+        }
+
+        /* --- Layout: make plot columns match the 3-slider column height; keep 2-slider column top-aligned --- */
+        #mode11_row, #mode22_row, #mode12_row {
+          align-items: stretch !important; /* row height = tallest column (3 sliders) */
+        }
+        #mode11_col3, #mode22_col3, #mode12_col3 {
+          align-self: flex-start !important; /* 2-slider column should NOT stretch */
+        }
+        #mode11_col2, #mode11_col4, #mode22_col2, #mode22_col4, #mode12_col2, #mode12_col4 {
+          display: flex !important;
+          flex-direction: column !important;
+        }
+        #mode11_stress_plot, #mode11_lat_plot, #mode22_stress_plot, #mode22_lat_plot, #mode12_stress_plot {
+          flex: 1 1 auto !important;
+          height: 100% !important;
+          min-height: 0 !important;
+        }
+        #mode11_stress_plot img, #mode11_lat_plot img, #mode22_stress_plot img, #mode22_lat_plot img, #mode12_stress_plot img {
+          height: 100% !important;
+          width: 100% !important;
+          object-fit: contain;
+        }
+        """
+    ) as demo:
+        gr.Markdown("## Hybrid Diffusion Material Generation (RO)")
+        n_simulations = gr.Slider(1, 5, value=3, step=1, label="Number of simulations (instances 1..N)")
+
+        with gr.Accordion("Init from test set (optional)", open=False):
+            gr.Markdown(
+                "Loads coefficients from `processed_dataset_ro/test_data_ro.npz` → `ramberg_osgood_params[idx]`.\n"
+                "This is useful to sanity-check performance on held-out conditions."
+            )
+            test_idx = gr.Number(value=0, precision=0, label="test_data_ro.npz index (int)")
+            load_test_btn = gr.Button("Load this test index into sliders")
+            rand_test_btn = gr.Button("Load a random test index into sliders")
+            test_status = gr.Markdown("")
+
+        # --- Mode 11 ---
+        gr.Markdown(
+            "### Mode 11\n"
+            "**Stress relationship:**\n"
+            "$$\\sigma_{11} = a\\,(\\varepsilon_{11}/s_{11}) + b\\,(\\varepsilon_{11}/s_{11})^{c}$$\n"
+            "**Lateral relationship:**\n"
+            "$$\\varepsilon_{22} = a\\,|\\varepsilon_{11}/s_{11}|^{b}\\,s_{22}$$\n"
+        )
+        with gr.Row(elem_id="mode11_row"):
+            # Column 1: three sliders (subrows)
+            with gr.Column(scale=1, elem_id="mode11_col1"):
+                g11_a = gr.Slider(float(ro_min[0]), float(ro_max[0]), value=round(float(ro15_init[0]), 3), step=0.001, label="ε11→σ11:\u00A0a")
+                g11_b = gr.Slider(float(ro_min[1]), float(ro_max[1]), value=round(float(ro15_init[1]), 3), step=0.001, label="ε11→σ11:\u00A0b")
+                g11_c = gr.Slider(float(ro_min[2]), float(ro_max[2]), value=round(float(ro15_init[2]), 3), step=0.001, label="ε11→σ11:\u00A0c")
+            # Column 2: plot
+            with gr.Column(scale=1, elem_id="mode11_col2"):
+                _fig = ui_preview_11_stress(float(ro15_init[0]), float(ro15_init[1]), float(ro15_init[2]))
+                mode11_stress_plot = gr.Plot(value=_fig, elem_id="mode11_stress_plot")
+            # Column 3: two sliders (subrows)
+            with gr.Column(scale=1, elem_id="mode11_col3"):
+                g11_lat_a = gr.Slider(float(ro_min[3]), float(ro_max[3]), value=round(float(ro15_init[3]), 3), step=0.001, label="ε11→ε22:\u00A0a")
+                g11_lat_b = gr.Slider(float(ro_min[4]), float(ro_max[4]), value=round(float(ro15_init[4]), 3), step=0.001, label="ε11→ε22:\u00A0b")
+            # Column 4: plot
+            with gr.Column(scale=1, elem_id="mode11_col4"):
+                _fig = ui_preview_11_lat(float(ro15_init[3]), float(ro15_init[4]))
+                mode11_lat_plot = gr.Plot(value=_fig, elem_id="mode11_lat_plot")
+
+        # --- Mode 22 ---
+        gr.Markdown(
+            "### Mode 22\n"
+            "**Stress relationship:**\n"
+            "$$\\sigma_{22} = a\\,(\\varepsilon_{22}/s_{22}) + b\\,(\\varepsilon_{22}/s_{22})^{c}$$\n"
+            "**Lateral relationship:**\n"
+            "$$\\varepsilon_{11} = a\\,|\\varepsilon_{22}/s_{22}|^{b}\\,s_{11}$$\n"
+        )
+        with gr.Row(elem_id="mode22_row"):
+            # Column 1: three sliders (subrows)
+            with gr.Column(scale=1, elem_id="mode22_col1"):
+                g22_a = gr.Slider(float(ro_min[6]), float(ro_max[6]), value=round(float(ro15_init[6]), 3), step=0.001, label="ε22→σ22:\u00A0a")
+                g22_b = gr.Slider(float(ro_min[7]), float(ro_max[7]), value=round(float(ro15_init[7]), 3), step=0.001, label="ε22→σ22:\u00A0b")
+                g22_c = gr.Slider(float(ro_min[8]), float(ro_max[8]), value=round(float(ro15_init[8]), 3), step=0.001, label="ε22→σ22:\u00A0c")
+            # Column 2: plot
+            with gr.Column(scale=1, elem_id="mode22_col2"):
+                _fig = ui_preview_22_stress(float(ro15_init[6]), float(ro15_init[7]), float(ro15_init[8]))
+                mode22_stress_plot = gr.Plot(value=_fig, elem_id="mode22_stress_plot")
+            # Column 3: two sliders (subrows)
+            with gr.Column(scale=1, elem_id="mode22_col3"):
+                g22_lat_a = gr.Slider(float(ro_min[9]), float(ro_max[9]), value=round(float(ro15_init[9]), 3), step=0.001, label="ε22→ε11:\u00A0a")
+                g22_lat_b = gr.Slider(float(ro_min[10]), float(ro_max[10]), value=round(float(ro15_init[10]), 3), step=0.001, label="ε22→ε11:\u00A0b")
+            # Column 4: plot
+            with gr.Column(scale=1, elem_id="mode22_col4"):
+                _fig = ui_preview_22_lat(float(ro15_init[9]), float(ro15_init[10]))
+                mode22_lat_plot = gr.Plot(value=_fig, elem_id="mode22_lat_plot")
+
+        # --- Mode 12 ---
+        gr.Markdown(
+            "### Mode 12\n"
+            "**Stress relationship:**\n"
+            "$$\\sigma_{12} = a\\,(\\varepsilon_{12}/s_{12}) + b\\,(\\varepsilon_{12}/s_{12})^{c}$$\n"
+        )
+        with gr.Row(elem_id="mode12_row"):
+            # Column 1: three sliders (subrows)
+            with gr.Column(scale=1, elem_id="mode12_col1"):
+                g12_a = gr.Slider(float(ro_min[12]), float(ro_max[12]), value=round(float(ro15_init[12]), 3), step=0.001, label="ε12→σ12:\u00A0a")
+                g12_b = gr.Slider(float(ro_min[13]), float(ro_max[13]), value=round(float(ro15_init[13]), 3), step=0.001, label="ε12→σ12:\u00A0b")
+                g12_c = gr.Slider(float(ro_min[14]), float(ro_max[14]), value=round(float(ro15_init[14]), 3), step=0.001, label="ε12→σ12:\u00A0c")
+            # Column 2: plot
+            with gr.Column(scale=1, elem_id="mode12_col2"):
+                _fig = ui_preview_12_stress(float(ro15_init[12]), float(ro15_init[13]), float(ro15_init[14]))
+                mode12_stress_plot = gr.Plot(value=_fig, elem_id="mode12_stress_plot")
+            # Column 3+4: placeholders (keeps layout consistent)
+            with gr.Column(scale=1, elem_id="mode12_col3"):
+                gr.Markdown("")
+            with gr.Column(scale=1, elem_id="mode12_col4"):
+                gr.Markdown("")
+
+        # For generation we still need a flat list of the 13 UI coefficients, in this order:
+        sliders = [g11_a, g11_b, g11_c, g11_lat_a, g11_lat_b, g22_a, g22_b, g22_c, g22_lat_a, g22_lat_b, g12_a, g12_b, g12_c]
+
+        def _load_idx_into_sliders(idx: int):
+            ro15 = _load_test_ro15(DATA_DIR, int(idx))
+            return _ro15_to_ui13(ro15), f"Loaded test index **{int(idx)}** into sliders."
+
+        def _load_random_into_sliders():
+            # pick a random valid index (deterministic enough for interactive usage)
+            npz_path = os.path.join(DATA_DIR, "test_data_ro.npz")
+            data = np.load(npz_path)
+            n = int(data["ramberg_osgood_params"].shape[0])
+            idx = int(np.random.randint(0, max(n, 1)))
+            ro15 = np.asarray(data["ramberg_osgood_params"][idx], dtype=np.float32).reshape(-1)
+            return _ro15_to_ui13(ro15), f"Loaded random test index **{idx}** into sliders."
+
+        # Wire test-set loaders
+        load_test_btn.click(
+            fn=lambda idx: (*_load_idx_into_sliders(idx)[0], _load_idx_into_sliders(idx)[1]),
+            inputs=[test_idx],
+            outputs=sliders + [test_status],
+            show_progress=False,
+        )
+        rand_test_btn.click(
+            fn=lambda: (*_load_random_into_sliders()[0], _load_random_into_sliders()[1]),
+            inputs=[],
+            outputs=sliders + [test_status],
+            show_progress=False,
+        )
+
+        gr.Markdown("### Generate + simulate")
+        gen_btn = gr.Button("Generate")
+        gen_plot = gr.Plot(label="Condition + simulated curves (overlaid)")
+        gen_info = gr.Textbox(label="Generated parameters", lines=6)
+        gen_status = gr.Textbox(label="Run log", lines=8, value="Idle.", interactive=False)
+
+        # Wire live updates
+        for s in (g11_a, g11_b, g11_c):
+            s.change(ui_preview_11_stress, inputs=[g11_a, g11_b, g11_c], outputs=[mode11_stress_plot], show_progress=False)
+        for s in (g11_lat_a, g11_lat_b):
+            s.change(ui_preview_11_lat, inputs=[g11_lat_a, g11_lat_b], outputs=[mode11_lat_plot], show_progress=False)
+
+        for s in (g22_a, g22_b, g22_c):
+            s.change(ui_preview_22_stress, inputs=[g22_a, g22_b, g22_c], outputs=[mode22_stress_plot], show_progress=False)
+        for s in (g22_lat_a, g22_lat_b):
+            s.change(ui_preview_22_lat, inputs=[g22_lat_a, g22_lat_b], outputs=[mode22_lat_plot], show_progress=False)
+
+        for s in (g12_a, g12_b, g12_c):
+            s.change(ui_preview_12_stress, inputs=[g12_a, g12_b, g12_c], outputs=[mode12_stress_plot], show_progress=False)
+
+        # Generate
+        gen_btn.click(
+            ui_generate,
+            inputs=[n_simulations] + sliders,
+            outputs=[gen_plot, gen_info, gen_status],
+        )
+
+    return demo
+
+
+if __name__ == "__main__":
+    build_app().launch(server_name="0.0.0.0", server_port=int(os.environ.get("PORT", "7860")))
+

data_generation/hf_space_generation_ro/lam.py

@@ -0,0 +1,707 @@
+
+
+
+
+# --------------------- python packages ---------------------
+
+# all these packages should be present in the system 
+import numpy as np
+import matplotlib.pyplot as plt
+from dataclasses import dataclass
+from pathlib import Path
+import warnings
+from numpy.lib._iotools import ConversionWarning
+from itertools import combinations_with_replacement
+from math import comb
+
+# --------------------- dataset parameter grids ---------------------
+
+#MATERIAL_TYPES = ["CPP", "GPP", "CHDPE", "GHDPE"]                 # e.g. ["CPP", "GFPP", ...]
+MATERIAL_TYPES = ["GHDPE", ]
+VOL_FRACTIONS  = ["0.0924", "0.2155", "0.3079", "0.4002", "0.4926"]             # e.g. ["0.0924", "0.1500", ...]
+INSTANCES      = [1]                     # e.g. [1, 2, 3, 4]
+
+
+# Number of plies in the *upper* half of the symmetric laminate
+UPPER_LAYER_COUNTS = [5,6,7]    # example: [1, 2, 3]
+
+# Candidate angles (in degrees) each upper-half ply can take
+CANDIDATE_ANGLES = [10,20,35,40,65,82]   
+
+# --------------------- input directory ---------------------
+
+# Folder that contains RVE input files(3*100)(!!!update the path accordingly)
+CURVE_DIR = Path(r"RVE_Datasets")
+
+
+
+# --------------------- output directory ---------------------
+
+# Folder that contains the laminate output files(!!!update the path accordingly)
+OUT_DIR_ALL = Path(r"Output_directory")
+
+
+# --------------------- user-set Poisson ratios ---------------------
+NU12 = 0.36
+NU13 = 0.36
+NU23 = 0.86
+
+COND_MAX = 1e12 #maximum allowable condition number of the stiffness matrix
+
+# ===============================================================
+@dataclass
+class Curve1D:
+    eps: np.ndarray
+    sig: np.ndarray  # Pa
+
+    def __post_init__(self):
+        idx = np.argsort(self.eps)
+        self.eps = np.asarray(self.eps, float)[idx]
+        self.sig = np.asarray(self.sig, float)[idx]
+
+    def stress(self, e):
+        ea = np.clip(abs(e), self.eps[0], self.eps[-1])
+        sa = np.interp(ea, self.eps, self.sig)
+        return np.sign(e) * sa
+
+    def tangent(self, e):
+        ea = np.clip(abs(e), self.eps[0], self.eps[-1])
+        i  = np.searchsorted(self.eps, ea) - 1
+        i  = np.clip(i, 0, len(self.eps)-2)
+        de = self.eps[i+1] - self.eps[i]
+        ds = self.sig[i+1] - self.sig[i]
+        return (ds/de)
+
+def read_instance_metadata(prefix: str):
+    """
+    Read volume fraction and fiber centers from the 11-file for this instance.
+    Example prefix: 'CHDPE_0.0924_1' -> CHDPE_0.0924_1_11.txt
+    """
+    meta_file = CURVE_DIR / f"{prefix}_11.txt"
+    vol_frac = None
+    centers_str = None
+
+    with open(meta_file, "r") as f:
+        for line in f:
+            line = line.strip()
+            if line.startswith("volume fraction="):
+                # split at '=', take right-hand side, convert to float
+                vol_frac = float(line.split("=", 1)[1])
+            elif line.startswith("fiber_centers_YZ="):
+                # take everything after '=' as a raw string
+                centers_str = line.split("=", 1)[1].strip()
+
+    if vol_frac is None:
+        raise ValueError(f"volume fraction not found in {meta_file}")
+    if centers_str is None:
+        raise ValueError(f"fiber_centers_YZ not found in {meta_file}")
+
+    
+    n_fibers = centers_str.count("(")
+
+    return vol_frac, centers_str, n_fibers
+
+
+
+
+
+def load_curve_from_file(filename, strain_col=1, stress_col=2, skiprows=1, is_shear=False):
+    """
+    Load a 2-col curve file.
+    If is_shear=True, strain column is assumed 'tensorial' and converted to engineering γ by *2.0*.
+    
+    """
+    p = Path(filename)
+    if not p.exists():
+        raise FileNotFoundError(f"Missing curve file: {p.resolve()}")
+
+    
+    with warnings.catch_warnings():
+        warnings.simplefilter("ignore", ConversionWarning)
+        arr = np.genfromtxt(
+            p,
+            dtype=float,
+            delimiter=None,
+            skip_header=skiprows,
+            invalid_raise=False  # bad lines (like "volume fraction= ...") are skipped
+        )
+    
+    
+    eps = arr[:, strain_col]
+    if is_shear:
+        eps = 2.0 * eps                      # tensorial -> engineering γ
+    sig = 1e6 * arr[:, stress_col]          # MPa -> Pa
+    return Curve1D(eps, sig)
+    
+    
+def load_ud_material_from_files(prefix: str) -> "PlyMaterial":
+    """
+    Build a PlyMaterial from three curve files with a common prefix, e.g.
+    prefix='CPP_0.0924_1' → CPP_0.0924_1_11.txt, CPP_0.0924_1_22.txt, CPP_0.0924_1_12.txt
+    """
+    # 11 curve: Strain_11, Stress_11, Strain_22, Strain_33
+    c11 = load_curve_from_file(
+        CURVE_DIR / f"{prefix}_11.txt",
+        strain_col=0,   # Strain_11
+        stress_col=1,   # Stress_11
+        skiprows=1
+    )
+
+    # 22 curve: Strain_22, Stress_22, Strain_11, Strain_33
+    c22 = load_curve_from_file(
+        CURVE_DIR / f"{prefix}_22.txt",
+        strain_col=0,   # Strain_22
+        stress_col=1,   # Stress_22
+        skiprows=1
+    )
+
+    # Reuse 22 curve for 33
+    c33 = c22
+
+    # 12 shear curve: Strain_12, Stress_12
+    c12 = load_curve_from_file(
+        CURVE_DIR / f"{prefix}_12.txt",
+        strain_col=0,   # Strain_12
+        stress_col=1,   # Stress_12
+        skiprows=1,
+        is_shear=True   
+    )
+
+    # Reuse 12 curve for 13 and 23
+    c13 = c12
+    c23 = c12
+
+    return PlyMaterial(
+        E1_curve=c11, E2_curve=c22, E3_curve=c33,
+        G12_curve=c12, G13_curve=c13, G23_curve=c23,
+        nu12=NU12, nu13=NU13, nu23=NU23
+    )
+    
+
+# ===============================================================
+#  3D orthotropic C' (Voigt 11,22,33,23,13,12)
+# (engineering shear convention: C66=G12, etc.)
+# ===============================================================
+def orthotropic_C_prime(E1,E2,E3,G12,G13,G23, nu12,nu13,nu23):
+    V = (1.0
+         - (E3/E2)*(nu23**2)
+         - (E3/E1)*(nu13**2)
+         - (E2/E1)*(nu12**2)
+         - 2*(E3/E1)*(nu12*nu13*nu23))
+
+    C11 = ((1.0 - (E3/E2)*nu23**2) * E1) / V
+    C22 = ((1.0 - (E3/E1)*nu13**2) * E2) / V
+    C33 = ((1.0 - (E2/E1)*nu12**2) * E3) / V
+    C12 = ((nu12 + (E3/E2)*nu13*nu23) * E2) / V
+    C13 = ((nu13 + nu12*nu23) * E3) / V
+    C23 = ((nu23 + (E2/E1)*nu12*nu13) * E3) / V
+
+    C = np.zeros((6,6))
+    C[0,0] = C11; C[1,1] = C22; C[2,2] = C33
+    C[0,1] = C12; C[1,0] = C12
+    C[0,2] = C13; C[2,0] = C13
+    C[1,2] = C23; C[2,1] = C23
+    C[3,3] = G23; C[4,4] = G13; C[5,5] = G12
+    return C
+
+# ===============================================================
+# Transformations (engineering shear)
+# ===============================================================
+def T_sigma(theta_deg):
+    th = np.radians(theta_deg); m, n = np.cos(th), np.sin(th)
+    return np.array([
+        [ m*m,  n*n, 0, 0, 0,  2*m*n],
+        [ n*n,  m*m, 0, 0, 0, -2*m*n],
+        [   0,    0, 1, 0, 0,      0],
+        [   0,    0, 0, m,-n,      0],
+        [   0,    0, 0, n, m,      0],
+        [-m*n,  m*n, 0, 0, 0, m*m-n*n]
+    ], float)
+
+def T_eps(theta_deg):
+    th = np.radians(theta_deg); m, n = np.cos(th), np.sin(th)
+    return np.array([
+        [ m*m,  n*n, 0, 0, 0,   m*n],
+        [ n*n,  m*m, 0, 0, 0,  -m*n],
+        [   0,    0, 1, 0, 0,     0],
+        [   0,    0, 0, m,-n,     0],
+        [   0,    0, 0, n, m,     0],
+        [-2*m*n,2*m*n,0, 0, 0, m*m-n*n]
+    ], float)
+
+
+def T(theta_deg):
+    th = np.radians(theta_deg); m, n = np.cos(th), np.sin(th)
+    return np.array([
+        [ m*m,  n*n, 0, 0, 0,  2*m*n],
+        [ n*n,  m*m, 0, 0, 0,  -2*m*n],
+        [   0,    0, 1, 0, 0,     0],
+        [   0,    0, 0, m,-n,     0],
+        [   0,    0, 0, n, m,     0],
+        [-1*m*n,1*m*n,0, 0, 0, m*m-n*n]
+    ], float)    
+    
+
+# ===============================================================
+# Ply material & ply
+# ===============================================================
+@dataclass
+class PlyMaterial:
+    E1_curve:  Curve1D
+    E2_curve:  Curve1D
+    E3_curve:  Curve1D
+    G12_curve: Curve1D
+    G13_curve: Curve1D
+    G23_curve: Curve1D
+    nu12: float
+    nu13: float
+    nu23: float
+
+    def tangents_from_local_strain(self, e_local):
+        e11,e22,e33,g23,g13,g12 = e_local
+        floor = -1e12
+        E1  = max(self.E1_curve.tangent(e11),  floor)
+        E2  = max(self.E2_curve.tangent(e22),  floor)
+        E3  = max(self.E3_curve.tangent(e33),  floor)
+        G12 = max(self.G12_curve.tangent(g12), floor)
+        G13 = max(self.G13_curve.tangent(g13), floor)
+        G23 = max(self.G23_curve.tangent(g23), floor)
+        return E1,E2,E3,G12,G13,G23
+
+
+@dataclass
+class Ply:
+    theta_deg: float           # initial/reference fibre angle
+    thickness: float
+    mat: PlyMaterial
+    e_prev: np.ndarray = None
+    s_prev: np.ndarray = None
+    theta_curr_deg: float = None   # running (current) fibre angle
+
+    def init_state(self):
+        self.e_prev = np.zeros(6)
+        self.s_prev = np.zeros(6)
+        self.theta_curr_deg = float(self.theta_deg)
+
+
+class Laminate:
+    def __init__(self, plies, tol=1e-12):
+        self.plies = plies
+        for p in self.plies:
+            p.init_state()
+        t0 = self.plies[0].thickness
+        for i,p in enumerate(self.plies, 1):
+            if abs(p.thickness - t0) > tol:
+                raise ValueError(f"Equal-thickness assumption violated at ply {i}: {p.thickness} vs {t0}")
+        self.N = len(self.plies)
+        self.t = t0
+        self.total_t = self.N * self.t
+
+    # ---------- Incremental affine update using strain increments (engineering shear) ----------
+    def update_fiber_angles_incremental(self, d_ex, d_ey, d_gxy):
+        """
+        a_{n+1} = ΔF a_n / ||ΔF a_n||   with ΔF = [[1+Δex, Δgxy/2],[Δgxy/2, 1+Δey]]
+        Updates each ply's theta_curr_deg in-place.
+        """
+        Fd = np.array([[1.0 + d_ex,     0.5*d_gxy],
+                       [0.5*d_gxy,      1.0 + d_ey]], dtype=float)   #valid for small strain increments
+
+        for p in self.plies:
+            th = np.radians(p.theta_curr_deg)
+            a  = np.array([np.cos(th), np.sin(th)])
+            a_new = Fd @ a
+            nrm = np.linalg.norm(a_new)
+            if nrm > 1e-14:
+                a_new /= nrm
+                p.theta_curr_deg = np.degrees(np.arctan2(a_new[1], a_new[0]))
+
+    # ---------- Build effective laminate C from PREVIOUS global strains ----------
+    def effective_C_from_previous_strains(self, ex_prev, ey_prev, ezz_prev, g23_prev, g13_prev, gxy_prev):
+        """
+        Thickness-average of per-ply global tangential stiffness matrices,
+        each built from tangents evaluated at the previous-step local strains.
+        """
+        e_prev_global = np.array([ex_prev, ey_prev, ezz_prev, g23_prev, g13_prev, gxy_prev], float)
+        Csum = np.zeros((6,6), float)
+        for p in self.plies:
+            theta = p.theta_curr_deg
+            T_e = T_eps(theta)
+            T_e_inv = np.linalg.inv(T_e)
+            T_s = T_sigma(theta)
+
+            e_local_prev = T_e @ e_prev_global
+            
+ 
+                #print(f"theta={theta}")
+            
+            E1,E2,E3,G12,G13,G23 = p.mat.tangents_from_local_strain(e_local_prev)
+            Cprime = orthotropic_C_prime(E1,E2,E3,G12,G13,G23, p.mat.nu12, p.mat.nu13, p.mat.nu23)
+            
+              
+
+            Tt=T(theta)
+            Cglob  = np.linalg.inv(Tt) @ Cprime @ np.linalg.inv(Tt).T   
+            
+            Csum  += Cglob
+        return Csum / self.N
+
+
+# ===============================================================
+# Helpers for symmetric stacking from upper-half definition
+# ===============================================================
+
+def build_full_symmetric_stack(upper_angles):
+    """
+    Given a list of angles for the *upper* half of the laminate,
+    build the full symmetric stack with opposite angles in the lower half.
+    Example: [0, 45, -45] -> [0, 45, -45, +45, -45, 0]
+    """
+    upper = list(upper_angles)
+    lower = [-a for a in reversed(upper)]
+    return upper + lower
+
+def _format_angle_for_label(a):
+    """
+    Format a ply angle for use in filenames:
+    - integer degrees
+    - explicit sign (+ or -)
+    Examples:
+      45.0  -> '+45'
+      -30.0 -> '-30'
+      0.0   -> '+0'
+    """
+    a_int = int(round(float(a)))
+    sign = "+" if a_int >= 0 else "-"
+    return f"{sign}{abs(a_int)}"
+
+
+
+
+
+def stack_label_from_upper(upper_angles):
+    """
+    Return a human-readable description of the *upper-half* stacking sequence,
+    with comma separation, e.g. [0, 45, -45] -> '0, 45, -45'.
+    """
+    parts = []
+    for a in upper_angles:
+        # show integer degrees without .0
+        if float(a).is_integer():
+            parts.append(f"{int(a)}")
+        else:
+            parts.append(f"{a}")
+    return ", ".join(parts)
+
+# ===============================================================
+# 5D uniaxial test driver (σy=τxy=σz=τyz=τxz=0), incremental: ds = Ceff_prev @ de
+# ===============================================================
+
+
+
+def run_uniaxial_test_from_files_5d(prefix: str, stack_angles, mode="11"):
+
+    # ---- Material from files ----
+
+    tply = 0.05 #dummy value, doesn't effect the result
+
+
+
+
+    # ---- Layup from full stacking sequence ----
+    plies = [Ply(angle_deg, tply, mat) for angle_deg in stack_angles]
+    lam = Laminate(plies)
+
+
+
+
+    # ---- Choose loading mode and strain path ----
+    if mode == "11":
+        main_index = 0   # ε11
+        eps_max = 0.10
+    elif mode == "22":
+        main_index = 1   # ε22
+        eps_max = 0.10
+    elif mode == "12":
+        main_index = 5   # γ12 (engineering shear internally)
+        eps_max = 0.20   # ⇒ tensorial E12 = γ12/2 goes to 0.10
+    else:
+        raise ValueError("mode must be '11', '22' or '12'")
+
+    main_steps = np.linspace(0.0, eps_max, 1500)  #1500 strain increments are chosen for a converged result
+
+
+
+
+    # ---- Histories (totals) ----
+    ex_hist, sx_hist = [], []      # main component strain & stress
+    ey_hist, gxy_hist = [], []
+    ezz_hist, g23_hist, g13_hist = [], [], []
+    e11_hist = []                  # always store total ε11
+
+
+
+    # ---- Previous-step totals ----
+    ex_prev  = 0.0
+    ey_prev  = 0.0
+    gxy_prev = 0.0
+    ezz_prev = 0.0
+    g23_prev = 0.0
+    g13_prev = 0.0
+    s1_prev  = 0.0
+
+    for i in range(1, len(main_steps)):
+        main_target = main_steps[i]
+
+        # Previous value of the driven strain component
+        if main_index == 0:
+            main_prev = ex_prev
+        elif main_index == 1:
+            main_prev = ey_prev
+        else:  # main_index == 5  (γ12)
+            main_prev = gxy_prev
+
+        # Increment in prescribed "main" strain (ε11 / ε22 / γ12)
+        dmain = main_target - main_prev
+        
+        #  Tangent Ceff at previous state from laminate
+        Ceff_prev = lam.effective_C_from_previous_strains(
+            ex_prev, ey_prev, ezz_prev, g23_prev, g13_prev, gxy_prev
+        )
+
+        # ---- Safeguard: check conditioning of Ceff_prev ----
+        cond = np.linalg.cond(Ceff_prev)
+        if not np.isfinite(cond) or cond > COND_MAX:
+            raise np.linalg.LinAlgError(
+                f"Effective C is ill-conditioned (cond={cond:.3e}) at step {i}"
+            )
+
+        #  Compute ONLY the column of the compliance needed via solve,
+        #  instead of inverting the full 6x6 matrix.
+        e_j = np.zeros(6)
+        e_j[main_index] = 1.0
+
+        try:
+            # S_col satisfies: Ceff_prev @ S_col = e_j
+            S_col = np.linalg.solve(Ceff_prev, e_j)
+        except np.linalg.LinAlgError as err:
+            raise np.linalg.LinAlgError(
+                f"Failed to solve for compliance column at step {i}: {err}"
+            )
+
+        Sjj = S_col[main_index]
+        if abs(Sjj) < 1e-20:
+            raise ZeroDivisionError(
+                f"Sjj is zero or too small at step {i} (Sjj={Sjj:.3e})."
+            )
+
+
+        ds1 = dmain / Sjj
+        de_vec = S_col * ds1
+        # enforce exactly the prescribed main increment
+        de_vec[main_index] = dmain
+        
+        
+        
+
+ 
+
+        de1, de2, de3, de4, de5, de6 = de_vec
+        de4 = 0.0
+        de5 = 0.0
+
+        # (4) Update totals
+        s1 = s1_prev + ds1
+        ex = ex_prev + de1
+        ey = ey_prev + de2
+        ezz = ezz_prev + de3
+        g23 = g23_prev + de4
+        g13 = g13_prev + de5
+        gxy = gxy_prev + de6
+
+        # (5) Update fibre angles from INCREMENTS (Δex, Δey, Δγxy)
+        lam.update_fiber_angles_incremental(de1, de2, de6)
+
+        # (6) Commit for next step
+        ex_prev, ey_prev, ezz_prev = ex, ey, ezz
+        g23_prev, g13_prev, gxy_prev = g23, g13, gxy
+        s1_prev = s1
+
+
+
+        # (7) Save totals for output
+        # ex_hist  stores the *driven* component: ε11, ε22, or *tensorial* E12 = γ12/2
+        if main_index == 0:
+            main_strain = ex
+        elif main_index == 1:
+            main_strain = ey
+        else:  # shear
+            main_strain = 0.5 * gxy   # convert engineering γ12 -> tensorial E12
+
+
+        ex_hist.append(main_strain)
+        sx_hist.append(s1)
+        ey_hist.append(ey); gxy_hist.append(gxy)
+        ezz_hist.append(ezz); g23_hist.append(g23); g13_hist.append(g13)
+        e11_hist.append(ex)   # store true ε11 regardless of mode
+
+
+
+
+        angles_str = ", ".join(f"{p.theta_curr_deg:.2f}°" for p in lam.plies)
+        
+
+
+    return (np.array(ex_hist), np.array(sx_hist),
+            np.array(ey_hist), np.array(gxy_hist),
+            np.array(ezz_hist), np.array(g23_hist), np.array(g13_hist),
+            np.array(e11_hist))
+        
+
+
+
+if __name__ == "__main__":
+
+    # ---- estimate total number of laminate output files ----
+    n_mat   = len(MATERIAL_TYPES)
+    n_vf    = len(VOL_FRACTIONS)
+    n_inst  = len(INSTANCES)
+    n_ang   = len(CANDIDATE_ANGLES)
+
+    # combinations-with-replacement count for each upper-half ply count
+    n_stacks_per_instance = sum(
+        comb(n_ang + n_layers - 1, n_layers) for n_layers in UPPER_LAYER_COUNTS
+    )
+
+    expected_files = n_mat * n_vf * n_inst * n_stacks_per_instance
+    print(f"Expected number of laminate output files: {expected_files}")
+   
+    file_count = 0
+    for mat_type in MATERIAL_TYPES:
+        for vf in VOL_FRACTIONS:
+            vf_str = vf  
+
+            for inst in INSTANCES:
+                prefix = f"{mat_type}_{vf_str}_{inst}"
+                vf_meta, centers_meta, n_fibers = read_instance_metadata(prefix)
+                mat = load_ud_material_from_files(prefix)
+
+                # ---- loop over upper-half stacking sequences ----
+                for n_layers in UPPER_LAYER_COUNTS:
+                    for upper_angles in combinations_with_replacement(CANDIDATE_ANGLES, n_layers):
+                        upper_angles = list(upper_angles)
+                        full_angles = build_full_symmetric_stack(upper_angles)
+                        
+                        # human-readable label for this stacking (upper half only)
+                        stack_label_human = stack_label_from_upper(upper_angles)  # e.g. "0, 30, -45"
+                        stack_label_file = "_".join(s.strip().replace("+", "p").replace("-", "m")
+                            for s in stack_label_human.split(","))
+                        
+                        combined_blocks = {}
+                
+
+                        for mode in ("11", "22", "12"):
+
+
+                            try:
+                                # main strain, stress, ε22, γ12, ε33, γ23, γ13, ε11
+                                ex, sx, ey, gxy, ezz, g23, g13, e11 = run_uniaxial_test_from_files_5d(
+                                    prefix,
+                                    full_angles,
+                                    mode=mode,
+                                )
+                            except Exception as e:
+                                print(f"Skipping {prefix}, stack={stack_label_human} due to error: {e}")
+                                continue
+
+
+                            # 10 equally spaced output points in the driven (main) strain
+                            N = 10
+                            x_out = np.linspace(ex[0], ex[-1], N)
+                            sx_out_MPa = np.interp(x_out, ex, sx/1e6)
+
+                            #  Pick Voigt-notation headers and lateral strain based on mode
+                            if mode == "11":
+                                strain_label   = "eps_11"
+                                stress_label   = "sig_11"
+                                lateral_label  = "eps_22"
+                                lateral_series = ey          # ε22 is lateral in 11-test
+                            elif mode == "22":
+                                strain_label   = "eps_22"
+                                stress_label   = "sig_22"
+                                lateral_label  = "eps_11"
+                                lateral_series = e11         # ε11 is lateral in 22-test
+                            elif mode == "12":
+                                strain_label   = "eps_12"
+                                stress_label   = "sig_12"
+                                lateral_label  = None
+                                lateral_series = None
+                            else:
+                                raise ValueError(f"Unknown mode {mode}")
+
+
+
+                            #  Build lateral output (if any) on the same main-strain grid
+                            if lateral_series is not None:
+                                lateral_out = np.interp(x_out, ex, lateral_series)
+                            else:
+                                lateral_out = None
+
+                            #  Build header line and numeric rows (strings) for this mode
+                            if lateral_label is None:
+                                # shear case (12): two columns
+                                header_line = f"{strain_label:<8}  {stress_label:<8}"
+                            else:
+                                # tensile 11 or 22: three columns
+                                header_line = f"{strain_label:<8}  {stress_label:<8}  {lateral_label:<8}"
+
+                            rows = []
+                            if lateral_label is None:
+                                # two columns: eps, sig
+                                for eps_val, sig_val in zip(x_out, sx_out_MPa):
+                                    line = f"{eps_val:8.6f}  {sig_val:8.3f}"
+                                    rows.append(line)
+                            else:
+                                # three columns: eps, sig, lateral_eps
+                                for eps_val, sig_val, lat_val in zip(x_out, sx_out_MPa, lateral_out):
+                                    line = f"{eps_val:8.6f}  {sig_val:8.3f}  {lat_val:8.6f}"
+                                    rows.append(line)
+
+                            # store this block for the combined file
+                            combined_blocks[mode] = (header_line, rows)
+
+
+                        # ---- write combined file: 11, then 22, then 12, then common metadata ----
+                        OUT_DIR_ALL.mkdir(parents=True, exist_ok=True)
+                        combined_file = OUT_DIR_ALL / (
+                            f"{mat_type}_{vf_str}_{inst}_{stack_label_file}.txt"
+                        )
+
+                        with open(combined_file, "w") as fc:
+                            # write 11, then 22, then 12 in order
+                            for m in ("11", "22", "12"):
+                                header_line, rows = combined_blocks[m]
+                                fc.write(header_line + "\n")
+                                for line in rows:
+                                    fc.write(line + "\n")
+                                fc.write("\n")
+
+                            # one common metadata block at the end
+                            fc.write(f"volume fraction= {vf_meta:.6f}\n")
+                            fc.write(f"material type= {mat_type}\n")
+                            fc.write("loading modes= 11, 22, 12\n")
+                            fc.write(f"stacking sequence= {stack_label_human}\n")
+                            fc.write(f"instance= {inst}\n")
+                            fc.write(f"number of fibers= {n_fibers}\n")
+                            fc.write(f"fiber_centers_YZ={centers_meta}\n")
+
+
+                        file_count += 1
+                        if file_count % 500 == 0 or file_count == expected_files:
+                            print(f"Generated {file_count}/{expected_files} files")                              
+
+
+
+
+
+
+

data_generation/hf_space_generation_ro/models.py

@@ -0,0 +1,17 @@
+"""
+Compatibility shim for torch checkpoint loading.
+
+Some training checkpoints were saved with objects pickled from a top-level module
+named `models` (e.g. `models.ModelConfig`, `models.MaterialHybridDenoiser`).
+
+In the Hugging Face Space we keep the implementation in `space_lib/models.py`,
+but we also provide this top-level module so `torch.load()` can unpickle.
+"""
+
+from space_lib.models import (  # noqa: F401
+    ModelConfig,
+    MaterialHybridDenoiser,
+    SelfCrossAttnBlock,
+    timestep_embedding,
+)
+

data_generation/hf_space_generation_ro/requirements.txt

@@ -0,0 +1,6 @@
+gradio==4.44.1
+numpy==1.26.4
+matplotlib==3.8.4
+torch==2.2.2
+tqdm==4.66.4
+pyyaml==6.0.2

data_generation/hf_space_generation_ro/space_lib/__init__.py

@@ -0,0 +1,2 @@
+"""Space-local library for RO-conditioned hybrid diffusion material generation."""
+

data_generation/hf_space_generation_ro/space_lib/infer.py

@@ -0,0 +1,262 @@
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import dataclass
+from typing import Dict, Optional, Tuple
+
+import numpy as np
+import pickle
+import torch
+import torch.nn.functional as F
+
+from .models import MaterialHybridDenoiser, ModelConfig
+
+
+VF_CATEGORIES = [0.0924, 0.2155, 0.3079, 0.4002, 0.4926]
+MATERIAL_NAMES = ["CPP", "CHDPE", "GPP", "GHDPE"]
+
+
+@dataclass(frozen=True)
+class DiscreteMaskDiffusion:
+    T: int
+
+
+class GaussianDiffusion:
+    def __init__(self, T: int, beta_start: float = 1e-4, beta_end: float = 2e-2, device: str = "cpu"):
+        self.T = int(T)
+        betas = torch.linspace(beta_start, beta_end, self.T, device=device)
+        alphas = 1.0 - betas
+        alpha_bar = torch.cumprod(alphas, dim=0)
+        self.betas = betas
+        self.alphas = alphas
+        self.sqrt_alpha_bar = torch.sqrt(alpha_bar)
+        self.sqrt_one_minus_alpha_bar = torch.sqrt(1.0 - alpha_bar)
+        self.sqrt_recip_alphas = torch.sqrt(1.0 / alphas)
+
+    @torch.no_grad()
+    def p_sample_step(self, x_t: torch.Tensor, t: torch.Tensor, eps_pred: torch.Tensor) -> torch.Tensor:
+        beta_t = self.betas[t].view(-1, 1, 1)
+        sqrt_recip_alpha_t = self.sqrt_recip_alphas[t].view(-1, 1, 1)
+        sqrt_one_minus_a_bar = self.sqrt_one_minus_alpha_bar[t].view(-1, 1, 1)
+        mu = sqrt_recip_alpha_t * (x_t - (beta_t / sqrt_one_minus_a_bar.clamp_min(1e-8)) * eps_pred)
+        noise = torch.randn_like(x_t)
+        nonzero_mask = (t != 0).float().view(-1, 1, 1)
+        return mu + nonzero_mask * torch.sqrt(beta_t) * noise
+
+
+@torch.no_grad()
+def sample(
+    model: MaterialHybridDenoiser,
+    disc_diff_mat: DiscreteMaskDiffusion,
+    disc_diff_vf_category: DiscreteMaskDiffusion,
+    disc_diff_layer: DiscreteMaskDiffusion,
+    disc_diff_angle: Optional[DiscreteMaskDiffusion] = None,
+    cont_diff: Optional[GaussianDiffusion] = None,
+    cond: torch.Tensor = None,
+    mask_ids: Dict[str, int] = None,
+    device: str = "cpu",
+    remask_prob: float = 0.1,
+    use_discrete_angles: bool = True,
+) -> Dict[str, torch.Tensor]:
+    model.eval()
+    B = cond.shape[0]
+    L = model.cfg.n_max_layer
+
+    x_material_t = torch.full((B,), mask_ids["material"], dtype=torch.long, device=device)
+    x_vf_category_t = torch.full((B,), mask_ids["vf_category"], dtype=torch.long, device=device)
+    x_layer_t = torch.full((B, L), mask_ids["layer"], dtype=torch.long, device=device)
+
+    if use_discrete_angles:
+        if disc_diff_angle is None:
+            raise ValueError("disc_diff_angle is required when use_discrete_angles=True")
+        if "angle" not in mask_ids:
+            raise ValueError("mask_ids must include 'angle' when use_discrete_angles=True")
+        x_angle_t = torch.full((B, L), mask_ids["angle"], dtype=torch.long, device=device)
+        T = disc_diff_angle.T
+    else:
+        if cont_diff is None:
+            raise ValueError("cont_diff is required when use_discrete_angles=False")
+        x_angle_t = torch.randn(B, L, 1, device=device)
+        T = cont_diff.T
+
+    for t_int in reversed(range(T)):
+        t = torch.full((B,), t_int, dtype=torch.long, device=device)
+        outputs = model(x_material_t, x_vf_category_t, x_layer_t, x_angle_t, cond, t)
+
+        probs_mat = F.softmax(outputs["material_logits"], dim=-1)
+        remask_mat = (x_material_t == mask_ids["material"]) | (torch.rand(B, device=device) < remask_prob)
+        if remask_mat.any():
+            new_material = torch.multinomial(probs_mat[remask_mat], 1).squeeze(-1)
+            x_material_t = x_material_t.clone()
+            x_material_t[remask_mat] = new_material
+
+        probs_vf = F.softmax(outputs["vf_category_logits"], dim=-1)
+        remask_vf = (x_vf_category_t == mask_ids["vf_category"]) | (torch.rand(B, device=device) < remask_prob)
+        if remask_vf.any():
+            new_vf = torch.multinomial(probs_vf[remask_vf], 1).squeeze(-1)
+            x_vf_category_t = x_vf_category_t.clone()
+            x_vf_category_t[remask_vf] = new_vf
+
+        if use_discrete_angles:
+            probs_angle = F.softmax(outputs["angle_logits"], dim=-1)  # (B,L,K+1)
+            remask_angle = (torch.rand(B, L, device=device) < remask_prob)
+            masked = (x_angle_t == mask_ids["angle"]) | remask_angle
+            if masked.any():
+                flat_probs = probs_angle.view(-1, probs_angle.size(-1))[masked.view(-1)]
+                new_angle = torch.multinomial(flat_probs, 1).squeeze(-1)
+                x_angle_t = x_angle_t.clone()
+                x_angle_t[masked] = new_angle
+
+            dead_category = probs_angle.size(-1) - 1
+            x_layer_t = (x_angle_t != dead_category).long()
+        else:
+            probs_layer = F.softmax(outputs["layer_logits"], dim=-1)  # (B,L,2)
+            remask_layer = (torch.rand(B, L, device=device) < remask_prob)
+            masked = (x_layer_t == mask_ids["layer"]) | remask_layer
+            if masked.any():
+                flat_probs = probs_layer.view(-1, 2)[masked.view(-1)]
+                new_layer = torch.multinomial(flat_probs, 1).squeeze(-1)
+                x_layer_t = x_layer_t.clone()
+                x_layer_t[masked] = new_layer
+
+            angle_pred = outputs["angle"]
+            sqrt_alpha_bar_t = cont_diff.sqrt_alpha_bar[t].view(-1, 1, 1)
+            sqrt_one_minus_alpha_bar_t = cont_diff.sqrt_one_minus_alpha_bar[t].view(-1, 1, 1)
+            eps_pred = (x_angle_t - sqrt_alpha_bar_t * angle_pred) / sqrt_one_minus_alpha_bar_t.clamp_min(1e-8)
+            x_angle_t = cont_diff.p_sample_step(x_angle_t, t, eps_pred)
+
+    return {"material_t": x_material_t, "vf_category_t": x_vf_category_t, "layer_t": x_layer_t, "angle_t": x_angle_t}
+
+
+@dataclass
+class ModelBundle:
+    model: MaterialHybridDenoiser
+    disc_diff_mat: DiscreteMaskDiffusion
+    disc_diff_vf_category: DiscreteMaskDiffusion
+    disc_diff_layer: DiscreteMaskDiffusion
+    disc_diff_angle: Optional[DiscreteMaskDiffusion]
+    cont_diff: Optional[GaussianDiffusion]
+    mask_ids: Dict[str, int]
+    use_discrete_angles: bool
+    T: int
+    beta_start: float
+    beta_end: float
+    angle_resolution: float
+
+
+def _load_state_dict_safely(obj) -> Dict[str, torch.Tensor]:
+    if isinstance(obj, dict):
+        for k in ("model_state_dict", "state_dict", "model"):
+            if k in obj and isinstance(obj[k], dict):
+                return obj[k]
+        # If it already looks like a state_dict
+        if all(isinstance(v, torch.Tensor) for v in obj.values()):
+            return obj  # type: ignore[return-value]
+    raise ValueError("Unrecognized checkpoint format (expected dict with model state_dict)")
+
+
+def load_model_bundle(checkpoint_dir: str, device: Optional[str] = None, angle_resolution: float = 1.0) -> ModelBundle:
+    cfg_path = os.path.join(checkpoint_dir, "training_config.json")
+    with open(cfg_path, "r") as f:
+        train_cfg = json.load(f)
+
+    use_discrete_angles = bool(train_cfg.get("use_discrete_angles", True))
+    model_cfg = ModelConfig(**train_cfg["model_config"])
+
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+
+    mask_ids = dict(train_cfg.get("mask_ids", {}))
+    # Angle category count only matters for discrete mode. We default to 7 if not present.
+    n_angle_categories = int(train_cfg.get("n_angle_categories", 7))
+    # IMPORTANT: match training model signature exactly (cfg, mask_ids, use_discrete_angles, n_angle_categories)
+    model = MaterialHybridDenoiser(
+        model_cfg,
+        mask_ids=mask_ids,
+        use_discrete_angles=use_discrete_angles,
+        n_angle_categories=n_angle_categories,
+    ).to(device)
+
+    # Load checkpoint (prefer best_model.pt)
+    ckpt_path = os.path.join(checkpoint_dir, "best_model.pt")
+    if not os.path.exists(ckpt_path):
+        ckpt_path = os.path.join(checkpoint_dir, "checkpoint_epoch_1.pt")
+    # Some checkpoints may contain pickled objects referencing the original training module
+    # layout (e.g. top-level `models`). We ship a compatibility `models.py` in the Space.
+    # Also prefer weights-only loading when supported to avoid unpickling non-tensor objects.
+    try:
+        ckpt = torch.load(ckpt_path, map_location=device, weights_only=True)  # type: ignore[call-arg]
+    except TypeError:
+        ckpt = torch.load(ckpt_path, map_location=device)
+    except pickle.UnpicklingError:
+        # PyTorch raised because weights-only loader encountered non-tensor objects
+        # (e.g. models.ModelConfig). We trust this checkpoint (bundled by us), so fall back.
+        ckpt = torch.load(ckpt_path, map_location=device, weights_only=False)  # type: ignore[call-arg]
+    state_dict = _load_state_dict_safely(ckpt)
+    # Enforce exact match with checkpoint; otherwise generation can look arbitrarily bad.
+    model.load_state_dict(state_dict, strict=True)
+
+    T = int(train_cfg.get("T", 100))
+    beta_start = float(train_cfg.get("beta_start", 1e-4))
+    beta_end = float(train_cfg.get("beta_end", 2e-2))
+
+    disc = DiscreteMaskDiffusion(T=T)
+    disc_angle = DiscreteMaskDiffusion(T=T) if use_discrete_angles else None
+    cont = GaussianDiffusion(T=T, beta_start=beta_start, beta_end=beta_end, device=device) if not use_discrete_angles else None
+
+    # Some training configs omit angle mask when continuous; for discrete, define a reasonable default if missing.
+    if use_discrete_angles and "angle" not in mask_ids:
+        # categories: n_angle_categories + dead (1) -> +1 ; then mask id is last index
+        mask_ids["angle"] = n_angle_categories + 1
+
+    return ModelBundle(
+        model=model,
+        disc_diff_mat=disc,
+        disc_diff_vf_category=disc,
+        disc_diff_layer=disc,
+        disc_diff_angle=disc_angle,
+        cont_diff=cont,
+        mask_ids=mask_ids,
+        use_discrete_angles=use_discrete_angles,
+        T=T,
+        beta_start=beta_start,
+        beta_end=beta_end,
+        angle_resolution=float(angle_resolution),
+    )
+
+
+def vf_category_to_volume_fraction(vf_category: int) -> float:
+    vf_category = int(vf_category)
+    if 0 <= vf_category < len(VF_CATEGORIES):
+        return float(VF_CATEGORIES[vf_category])
+    return float(VF_CATEGORIES[0])
+
+
+def postprocess_sample(
+    out: Dict[str, torch.Tensor],
+    use_discrete_angles: bool,
+    angle_categories_deg: Optional[np.ndarray],
+) -> Tuple[str, float, list[float]]:
+    mat_idx = int(out["material_t"].item())
+    vf_idx = int(out["vf_category_t"].item())
+    mat = MATERIAL_NAMES[mat_idx] if 0 <= mat_idx < len(MATERIAL_NAMES) else f"MAT{mat_idx}"
+    vf = vf_category_to_volume_fraction(vf_idx)
+
+    layer = out["layer_t"].detach().cpu().numpy()[0]
+    angle = out["angle_t"].detach().cpu().numpy()[0]
+
+    if use_discrete_angles:
+        if angle_categories_deg is None:
+            raise ValueError("angle_categories_deg is required for discrete angles")
+        dead_category = int(len(angle_categories_deg))
+        alive_mask = angle != dead_category
+        angle_cats = angle[alive_mask].astype(int)
+        angles_deg = [float(angle_categories_deg[c]) for c in angle_cats]
+    else:
+        alive_mask = layer == 1
+        vals = angle[alive_mask, 0] if angle.ndim > 1 else angle[alive_mask]
+        angles_deg = np.rad2deg(vals).astype(np.float32).tolist()
+
+    return mat, vf, sorted([float(a) for a in angles_deg])
+

data_generation/hf_space_generation_ro/space_lib/metadata.py

@@ -0,0 +1,127 @@
+from __future__ import annotations
+
+import json
+import os
+from dataclasses import dataclass
+from typing import Any, Dict, Tuple
+
+import numpy as np
+
+
+RO_DROP_FLAT_IDXS = (5, 11)  # padded zeros in flattened [5,3]
+
+
+@dataclass(frozen=True)
+class ROMetadata:
+    raw: Dict[str, Any]
+    ro_min_full: np.ndarray  # (15,)
+    ro_max_full: np.ndarray  # (15,)
+    ro_mean_full: np.ndarray | None  # (15,) or None
+    ro_std_full: np.ndarray | None  # (15,) or None
+    eps_11_scale: float
+    eps_22_scale: float
+    eps_12_scale: float
+
+    @property
+    def ro_min_13(self) -> np.ndarray:
+        keep = _keep_mask_15()
+        return self.ro_min_full[keep]
+
+    @property
+    def ro_max_13(self) -> np.ndarray:
+        keep = _keep_mask_15()
+        return self.ro_max_full[keep]
+
+    @property
+    def ro_mean_13(self) -> np.ndarray | None:
+        if self.ro_mean_full is None:
+            return None
+        keep = _keep_mask_15()
+        return self.ro_mean_full[keep]
+
+    @property
+    def ro_std_13(self) -> np.ndarray | None:
+        if self.ro_std_full is None:
+            return None
+        keep = _keep_mask_15()
+        return self.ro_std_full[keep]
+
+
+def _keep_mask_15() -> np.ndarray:
+    keep = np.ones(15, dtype=bool)
+    keep[list(RO_DROP_FLAT_IDXS)] = False
+    return keep
+
+
+def load_metadata_ro(data_dir: str) -> ROMetadata:
+    path = os.path.join(data_dir, "metadata_ro.json")
+    with open(path, "r") as f:
+        raw = json.load(f)
+
+    ro_min_full = np.asarray(raw["ramberg_osgood_param_min"], dtype=np.float32)
+    ro_max_full = np.asarray(raw["ramberg_osgood_param_max"], dtype=np.float32)
+    ro_mean_full = raw.get("ramberg_osgood_param_mean", None)
+    ro_std_full = raw.get("ramberg_osgood_param_std", None)
+
+    ro_mean_arr = np.asarray(ro_mean_full, dtype=np.float32) if ro_mean_full is not None else None
+    ro_std_arr = np.asarray(ro_std_full, dtype=np.float32) if ro_std_full is not None else None
+
+    return ROMetadata(
+        raw=raw,
+        ro_min_full=ro_min_full,
+        ro_max_full=ro_max_full,
+        ro_mean_full=ro_mean_arr,
+        ro_std_full=ro_std_arr,
+        eps_11_scale=float(raw.get("eps_11_scale", 0.1)),
+        eps_22_scale=float(raw.get("eps_22_scale", 0.1)),
+        eps_12_scale=float(raw.get("eps_12_scale", 0.1)),
+    )
+
+
+def ro15_from_groups(groups_5x3: np.ndarray) -> np.ndarray:
+    arr = np.asarray(groups_5x3, dtype=np.float32)
+    if arr.shape != (5, 3):
+        raise ValueError(f"Expected shape (5,3), got {arr.shape}")
+    return arr.flatten()  # (15,)
+
+
+def ro13_from_ro15(ro15: np.ndarray) -> np.ndarray:
+    ro15 = np.asarray(ro15, dtype=np.float32).flatten()
+    if ro15.size != 15:
+        raise ValueError(f"Expected 15 values, got {ro15.size}")
+    keep = _keep_mask_15()
+    return ro15[keep]  # (13,)
+
+
+def normalize_ro13(ro13_raw: np.ndarray, meta: ROMetadata, method: str) -> np.ndarray:
+    x = np.asarray(ro13_raw, dtype=np.float32).flatten()
+    if x.size != 13:
+        raise ValueError(f"Expected 13 values, got {x.size}")
+
+    method = (method or "minmax").lower()
+    if method == "minmax":
+        mn = meta.ro_min_13
+        mx = meta.ro_max_13
+        rng = mx - mn
+        rng = np.where(rng == 0, 1.0, rng)
+        return ((x - mn) / rng).astype(np.float32)
+    if method == "zscore":
+        if meta.ro_mean_13 is None or meta.ro_std_13 is None:
+            raise ValueError("metadata_ro.json missing ramberg_osgood_param_mean/std for zscore")
+        mu = meta.ro_mean_13
+        sd = np.where(meta.ro_std_13 == 0, 1.0, meta.ro_std_13)
+        return ((x - mu) / sd).astype(np.float32)
+
+    raise ValueError(f"Unknown normalization method: {method}")
+
+
+def ro_groups_from_ro15(ro15: np.ndarray) -> np.ndarray:
+    ro15 = np.asarray(ro15, dtype=np.float32).flatten()
+    if ro15.size != 15:
+        raise ValueError(f"Expected 15 values, got {ro15.size}")
+    return ro15.reshape(5, 3)
+
+
+def ro15_minmax(meta: ROMetadata) -> Tuple[np.ndarray, np.ndarray]:
+    return meta.ro_min_full.copy(), meta.ro_max_full.copy()
+

data_generation/hf_space_generation_ro/space_lib/models.py

@@ -0,0 +1,238 @@
+import torch
+import torch.nn as nn
+from typing import Dict, Tuple
+from dataclasses import dataclass
+import math
+
+
+# =========================
+# Config
+# =========================
+
+@dataclass
+class ModelConfig:
+    # problem sizes
+    n_conditions: int = 8
+    n_materials: int = 10
+    n_vf_categories: int = 5  # Volume fraction categories: 0.0924, 0.2155, 0.3079, 0.4002, 0.4926
+    n_max_layer: int = 24
+
+    # model architecture
+    d_model: int = 256
+    n_heads: int = 4
+    n_layers: int = 6
+    dropout: float = 0.0
+
+    # angle is in radians, limited to (-pi/2, pi/2)
+
+
+# =========================
+# Model
+# =========================
+
+def timestep_embedding(t: torch.Tensor, dim: int) -> torch.Tensor:
+    """
+    Sinusoidal timestep embedding. t: (B,)
+    """
+    half = dim // 2
+    freqs = torch.exp(-math.log(10000) * torch.arange(0, half, device=t.device) / half)
+    args = t.float().unsqueeze(1) * freqs.unsqueeze(0)
+    emb = torch.cat([torch.cos(args), torch.sin(args)], dim=1)
+    if dim % 2 == 1:
+        emb = torch.cat([emb, torch.zeros_like(emb[:, :1])], dim=1)
+    return emb  # (B, dim)
+
+
+class SelfCrossAttnBlock(nn.Module):
+    def __init__(self, d_model, n_heads, dropout=0.0):
+        super().__init__()
+        self.self_attn = nn.MultiheadAttention(
+            d_model, n_heads, dropout=dropout, batch_first=True
+        )
+        self.cross_attn = nn.MultiheadAttention(
+            d_model, n_heads, dropout=dropout, batch_first=True
+        )
+        self.ff = nn.Sequential(
+            nn.Linear(d_model, 4 * d_model),
+            nn.SiLU(),
+            nn.Linear(4 * d_model, d_model),
+        )
+
+        self.ln1 = nn.LayerNorm(d_model)
+        self.ln2 = nn.LayerNorm(d_model)
+        self.ln3 = nn.LayerNorm(d_model)
+
+    def forward(self, x, cond_tokens, key_padding_mask=None):
+        """
+        x:          (B, N, d)   ← material + nfiber + angle tokens
+        cond_tokens:(B, M, d)   ← condition tokens
+        key_padding_mask: (B, N) optional padding mask (True = mask out, False = keep)
+        """
+        # self-attention (within tokens)
+        x = self.ln1(x + self.self_attn(x, x, x, key_padding_mask=key_padding_mask)[0])
+
+        # cross-attention (tokens attend to conditions)
+        x = self.ln2(x + self.cross_attn(x, cond_tokens, cond_tokens)[0])
+
+        # feed-forward
+        x = self.ln3(x + self.ff(x))
+        return x
+
+
+class MaterialHybridDenoiser(nn.Module):
+    """
+    Inputs:
+      material_t: (B,)  in [0..n_materials-1] or MASK
+      vf_category_t: (B,)  in [0..4] volume fraction category or MASK
+      layer_t:    (B,L) in {0,1} or MASK
+                  Note: When use_discrete_angles=True, layer_t is redundant (derived from angle_t,
+                  where angle_t==n_angle_categories means dead layer). The model ignores layer_emb
+                  in this case and only uses angle_emb.
+      angle_t:    (B,L) discrete category indices [0..n_angle_categories-1] or MASK (if use_discrete_angles)
+                  OR (B,L,1) continuous (if not use_discrete_angles)
+                  When discrete: category n_angle_categories = dead layer, n_angle_categories+1 = MASK
+      cond:       (B,C) continuous
+      t:          (B,) timestep
+
+    Outputs:
+      material logits: (B, n_materials)
+      vf_category_logits: (B, 5)  # 5 volume fraction categories
+      layer logits:    (B,L,2)              # alive/dead (only if not use_discrete_angles)
+      angle_logits:    (B,L,n_angle_categories+1)  # discrete angle categories + dead (if use_discrete_angles)
+                  OR angle: (B,L,1)        # angle in radians, range (0, pi/2) (if not use_discrete_angles)
+    """
+    def __init__(self, cfg: ModelConfig, mask_ids: Dict[str, int], use_discrete_angles: bool = True, n_angle_categories: int = 7):
+        super().__init__()
+        self.cfg = cfg
+        self.L = cfg.n_max_layer
+        d = cfg.d_model
+        self.mask_ids = mask_ids
+        self.use_discrete_angles = use_discrete_angles
+        self.n_angle_categories = n_angle_categories  # 7 categories: 0, 15, 30, 45, 60, 75, 90 degrees
+
+        # +1 to include mask token for material
+        self.material_emb = nn.Embedding(cfg.n_materials + 1, d)
+        # vf_category: 5 categories (0-4) plus mask; we allocate 6
+        self.vf_category_emb = nn.Embedding(cfg.n_vf_categories + 1, d)
+
+        if use_discrete_angles:
+            # Angle categories: 0..n_angle_categories-1
+            # Category n_angle_categories: dead layer
+            # Category n_angle_categories+1: mask token
+            self.angle_emb = nn.Embedding(n_angle_categories + 2, d)
+            self.layer_emb = None  # Not needed when using discrete angles
+        else:
+            # layer token: {MASK, 0, 1} => 3 (only needed for continuous angles)
+            self.layer_emb = nn.Embedding(3, d)
+            self.angle_in = nn.Linear(1, d)
+
+        # Condition projection (TRAINED).
+        # Input cond can be either:
+        # - (B, C): raw condition vector (preferred; gets projected here)
+        # - (B, C, d): already-projected condition tokens (backward-compatible)
+        self.cond_proj = nn.ModuleList([
+            nn.Linear(1, d) for _ in range(cfg.n_conditions)
+        ])
+
+        self.blocks = nn.ModuleList([
+            SelfCrossAttnBlock(d, cfg.n_heads, cfg.dropout)
+            for _ in range(cfg.n_layers)
+        ])
+
+        # Positional embeddings for entire sequence: material (pos 0) + vf_category (pos 1) + layers (pos 2..)
+        self.pos_emb = nn.Embedding(2 + cfg.n_max_layer, d)
+
+        self.t_proj = nn.Linear(d, d)
+
+        enc_layer = nn.TransformerEncoderLayer(
+            d_model=d,
+            nhead=cfg.n_heads,
+            dropout=cfg.dropout,
+            batch_first=True,
+        )
+        self.encoder = nn.TransformerEncoder(enc_layer, num_layers=cfg.n_layers)
+        self.ln = nn.LayerNorm(d)
+
+        self.material_head = nn.Linear(d, cfg.n_materials)
+        self.vf_category_head = nn.Linear(d, cfg.n_vf_categories)  # 5 volume fraction categories
+        if use_discrete_angles:
+            self.angle_head = nn.Linear(d, n_angle_categories + 1)  # angles + dead
+            self.layer_head = None
+        else:
+            self.layer_head = nn.Linear(d, 2)  # alive/dead for continuous mode
+            self.angle_head = nn.Linear(d, 1)
+
+    def forward(self, material_t, vf_category_t, layer_t, angle_t, cond, t):
+        B, L = layer_t.shape
+        assert L == self.L
+
+        # Project conditions if provided as raw scalars (B, C)
+        if cond.dim() == 2:
+            cond_list = []
+            for i in range(cond.shape[1]):
+                cond_list.append(self.cond_proj[i](cond[:, i:i+1].unsqueeze(-1)))  # (B, 1, d)
+            cond = torch.cat(cond_list, dim=1)  # (B, C, d)
+
+        # global tokens
+        g_mat = self.material_emb(material_t).unsqueeze(1)  # (B,1,d)
+        g_vf = self.vf_category_emb(vf_category_t).unsqueeze(1)  # (B,1,d)
+
+        # per-layer tokens
+        if self.use_discrete_angles:
+            layer_h = self.angle_emb(angle_t)  # (B, L, d)
+        else:
+            layer_h = self.layer_emb(layer_t) + self.angle_in(angle_t)  # (B,L,d)
+
+        h = torch.cat([g_mat, g_vf, layer_h], dim=1)  # (B, 2+L, d)
+
+        # positional embeddings
+        pos_indices = torch.arange(2 + self.L, device=h.device)  # (2+L,)
+        h = h + self.pos_emb(pos_indices).unsqueeze(0)  # (B, 2+L, d)
+
+        # timestep
+        t_emb = timestep_embedding(t, h.size(-1))  # (B,d)
+        h = h + self.t_proj(t_emb).unsqueeze(1)
+
+        # key padding mask for discrete angle dead tokens
+        key_padding_mask = None
+        if self.use_discrete_angles:
+            dead_category = self.n_angle_categories
+            is_dead = (angle_t == dead_category)  # (B, L)
+            first_dead_pos = torch.zeros(B, dtype=torch.long, device=angle_t.device)
+            for b in range(B):
+                dead_positions = torch.where(is_dead[b])[0]
+                if len(dead_positions) > 0:
+                    first_dead_pos[b] = dead_positions[0].item() + 2
+                else:
+                    first_dead_pos[b] = 2 + L
+            N = 2 + L
+            key_padding_mask = torch.zeros(B, N, dtype=torch.bool, device=h.device)
+            for b in range(B):
+                first_invalid = first_dead_pos[b].item()
+                if first_invalid < 2 + L:
+                    key_padding_mask[b, first_invalid:] = True
+                    key_padding_mask[b, :2] = False
+
+        for block in self.blocks:
+            h = block(h, cond, key_padding_mask=key_padding_mask)
+
+        h = self.ln(h)
+
+        if self.use_discrete_angles:
+            angle_logits = self.angle_head(h[:, 2:])  # (B, L, n_angle_categories + 1)
+            out = {
+                "material_logits": self.material_head(h[:, 0]),
+                "vf_category_logits": self.vf_category_head(h[:, 1]),
+                "angle_logits": angle_logits,
+            }
+        else:
+            angle_raw = self.angle_head(h[:, 2:])  # (B,L,1)
+            angle = torch.sigmoid(angle_raw) * (math.pi / 2)  # (B,L,1)
+            out = {
+                "material_logits": self.material_head(h[:, 0]),
+                "vf_category_logits": self.vf_category_head(h[:, 1]),
+                "layer_logits": self.layer_head(h[:, 2:]),
+                "angle": angle,
+            }
+        return out
+

data_generation/hf_space_generation_ro/space_lib/plots.py

@@ -0,0 +1,205 @@
+from __future__ import annotations
+
+from typing import Dict, Tuple
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+from .ro_curves import ro_stress, ro_strain_strain
+
+
+def _fit_ro_stress_curve(strain: np.ndarray, stress: np.ndarray, x_scale: float) -> tuple[float, float, float] | None:
+    """
+    Fit y = a*xn + b*xn^c where xn = strain/x_scale.
+    Lightweight grid-search over c; solve a,b by least squares.
+    Returns (a,b,c) for ro_stress().
+    """
+    x = np.asarray(strain, dtype=np.float32).reshape(-1)
+    y = np.asarray(stress, dtype=np.float32).reshape(-1)
+    if x.size < 4 or y.size != x.size:
+        return None
+    x_scale = float(x_scale) if x_scale and x_scale > 0 else 1.0
+    xn = np.clip(x / x_scale, 1e-6, None)
+
+    c_grid = np.concatenate(
+        [
+            np.linspace(0.5, 6.0, 56, dtype=np.float32),
+            np.linspace(6.5, 20.0, 28, dtype=np.float32),
+        ]
+    )
+    best: tuple[float, float, float] | None = None
+    best_mse = float("inf")
+    for c in c_grid:
+        phi1 = xn
+        phi2 = np.power(xn, float(c))
+        A = np.stack([phi1, phi2], axis=1)
+        try:
+            coef, *_ = np.linalg.lstsq(A, y, rcond=None)
+        except Exception:
+            continue
+        a, b = float(coef[0]), float(coef[1])
+        yhat = a * phi1 + b * phi2
+        mse = float(np.mean((yhat - y) ** 2))
+        if np.isfinite(mse) and mse < best_mse:
+            best_mse = mse
+            best = (a, b, float(c))
+    return best
+
+
+def _fit_ro_lateral_curve(strain: np.ndarray, lateral: np.ndarray, x_scale: float, y_scale: float) -> tuple[float, float] | None:
+    """
+    Fit y_norm = a*|xn|^b where xn=strain/x_scale and y_norm=lateral/y_scale.
+    Grid-search over b; solve a by least squares (allows negative a).
+    Returns (a,b) for ro_strain_strain().
+    """
+    x = np.asarray(strain, dtype=np.float32).reshape(-1)
+    y = np.asarray(lateral, dtype=np.float32).reshape(-1)
+    if x.size < 4 or y.size != x.size:
+        return None
+    x_scale = float(x_scale) if x_scale and x_scale > 0 else 1.0
+    y_scale = float(y_scale) if y_scale and y_scale > 0 else 1.0
+    xn = np.clip(np.abs(x / x_scale), 1e-6, None)
+    yn = y / y_scale
+
+    b_grid = np.concatenate(
+        [
+            np.linspace(0.2, 6.0, 60, dtype=np.float32),
+            np.linspace(6.5, 20.0, 28, dtype=np.float32),
+        ]
+    )
+    best: tuple[float, float] | None = None
+    best_mse = float("inf")
+    for b in b_grid:
+        phi = np.power(xn, float(b))
+        denom = float(phi @ phi)
+        if denom <= 1e-12:
+            continue
+        a = float((phi @ yn) / denom)
+        yhat = a * phi
+        mse = float(np.mean((yhat - yn) ** 2))
+        if np.isfinite(mse) and mse < best_mse:
+            best_mse = mse
+            best = (a, float(b))
+    return best
+
+
+def plot_condition_and_simulations(
+    ro_groups_5x3: np.ndarray,
+    eps_scales: Tuple[float, float, float],
+    simulations_by_instance: Dict[int, Dict[str, Dict[str, np.ndarray]]],
+) -> plt.Figure:
+    ro = np.asarray(ro_groups_5x3, dtype=np.float32).reshape(5, 3)
+    eps_11_scale, eps_22_scale, eps_12_scale = map(float, eps_scales)
+
+    fig, axes = plt.subplots(2, 3, figsize=(12, 7))
+    modes = ["11", "22", "12"]
+    colors = ["b", "c", "m", "g", "r"]
+
+    for col, mode in enumerate(modes):
+        # Determine x-range from simulation if possible
+        x_min, x_max = None, None
+        for inst, sim in simulations_by_instance.items():
+            if mode in sim:
+                x = sim[mode]["strain"]
+                x_min = float(np.min(x))
+                x_max = float(np.max(x))
+                break
+
+        if x_min is None or x_max is None or x_max <= x_min:
+            # fallback stable range
+            if mode == "11":
+                x_min, x_max = 0.0, eps_11_scale
+            elif mode == "22":
+                x_min, x_max = 0.0, eps_22_scale
+            else:
+                x_min, x_max = 0.0, eps_12_scale
+
+        x_fit = np.linspace(float(x_min), float(x_max), 250, dtype=np.float32)
+
+        # Condition curve (black)
+        if mode == "11":
+            a0, b0, c0 = ro[0]
+            y = ro_stress(x_fit, float(a0), float(b0), float(c0), eps_11_scale)
+            axes[0, col].plot(x_fit, y, color="k", linewidth=2, label="Cond input")
+
+            a1, b1, _ = ro[1]
+            y_lat = ro_strain_strain(x_fit, float(a1), float(b1), eps_11_scale, eps_22_scale)
+            axes[1, col].plot(x_fit, y_lat, color="k", linewidth=2, label="Cond input")
+            axes[0, col].set_title("Mode 11")
+            axes[1, col].set_title("Mode 11 lateral")
+
+        elif mode == "22":
+            a0, b0, c0 = ro[2]
+            y = ro_stress(x_fit, float(a0), float(b0), float(c0), eps_22_scale)
+            axes[0, col].plot(x_fit, y, color="k", linewidth=2, label="Cond input")
+
+            a1, b1, _ = ro[3]
+            y_lat = ro_strain_strain(x_fit, float(a1), float(b1), eps_22_scale, eps_11_scale)
+            axes[1, col].plot(x_fit, y_lat, color="k", linewidth=2, label="Cond input")
+            axes[0, col].set_title("Mode 22")
+            axes[1, col].set_title("Mode 22 lateral")
+
+        else:
+            a0, b0, c0 = ro[4]
+            y = ro_stress(x_fit, float(a0), float(b0), float(c0), eps_12_scale)
+            axes[0, col].plot(x_fit, y, color="k", linewidth=2, label="Cond input")
+            axes[0, col].set_title("Mode 12")
+            axes[1, col].axis("off")
+
+        # Simulations overlay: dots + fitted RO curves (like train.py on_the_fly_validation)
+        for idx, (inst, sim) in enumerate(sorted(simulations_by_instance.items(), key=lambda kv: kv[0])):
+            if mode not in sim:
+                continue
+            d = sim[mode]
+            c = colors[idx % len(colors)]
+            axes[0, col].plot([], [], color=c, linewidth=2, label=f"Sim inst{inst}")
+            axes[0, col].scatter(d["strain"], d["stress"], color=c, s=22, alpha=0.85, label="_nolegend_")
+
+            if mode == "11":
+                x_scale = eps_11_scale
+            elif mode == "22":
+                x_scale = eps_22_scale
+            else:
+                x_scale = eps_12_scale
+
+            fit = _fit_ro_stress_curve(d["strain"], d["stress"], x_scale=x_scale)
+            if fit is not None and len(d["strain"]) >= 2:
+                fa, fb, fc = fit
+                x_line = np.linspace(float(np.min(d["strain"])), float(np.max(d["strain"])), 200, dtype=np.float32)
+                y_line = ro_stress(x_line, fa, fb, fc, x_scale)
+                axes[0, col].plot(x_line, y_line, color=c, linewidth=2, alpha=0.9, label="_nolegend_")
+
+            if mode in ("11", "22") and d.get("lateral") is not None:
+                axes[1, col].plot([], [], color=c, linewidth=2, label=f"Sim inst{inst}")
+                axes[1, col].scatter(d["strain"], d["lateral"], color=c, s=22, alpha=0.85, label="_nolegend_")
+
+                if mode == "11":
+                    x_scale_lat, y_scale_lat = eps_11_scale, eps_22_scale
+                else:
+                    x_scale_lat, y_scale_lat = eps_22_scale, eps_11_scale
+                fit_lat = _fit_ro_lateral_curve(d["strain"], d["lateral"], x_scale=x_scale_lat, y_scale=y_scale_lat)
+                if fit_lat is not None and len(d["strain"]) >= 2:
+                    fa, fb = fit_lat
+                    x_line = np.linspace(float(np.min(d["strain"])), float(np.max(d["strain"])), 200, dtype=np.float32)
+                    y_line = ro_strain_strain(x_line, fa, fb, x_scale_lat, y_scale_lat)
+                    axes[1, col].plot(x_line, y_line, color=c, linewidth=2, alpha=0.9, label="_nolegend_")
+
+        axes[0, col].set_xlabel("strain")
+        axes[0, col].set_ylabel("stress (MPa)")
+        axes[0, col].grid(True, alpha=0.25)
+        if mode in ("11", "22"):
+            axes[1, col].set_xlabel("strain")
+            axes[1, col].set_ylabel("lateral strain")
+            axes[1, col].grid(True, alpha=0.25)
+
+        # Legends
+        axes[0, col].legend(fontsize=8)
+        if mode in ("11", "22"):
+            axes[1, col].legend(fontsize=8)
+
+    fig.tight_layout()
+    return fig
+

data_generation/hf_space_generation_ro/space_lib/ro_curves.py

@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+from typing import Tuple
+
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+
+
+def ro_stress(x: np.ndarray, a: float, b: float, c: float, x_scale: float) -> np.ndarray:
+    x = np.asarray(x, dtype=np.float32)
+    x_norm = x / x_scale if x_scale and x_scale > 0 else x
+    return a * x_norm + b * np.power(x_norm, c)
+
+
+def ro_strain_strain(x: np.ndarray, a: float, b: float, x_scale: float, y_scale: float) -> np.ndarray:
+    x = np.asarray(x, dtype=np.float32)
+    x_norm = x / x_scale if x_scale and x_scale > 0 else x
+    y_norm = a * np.power(np.abs(x_norm), b)
+    return y_norm * y_scale if y_scale and y_scale > 0 else y_norm
+
+
+def plot_ro_stress_relation(
+    *,
+    a: float,
+    b: float,
+    c: float,
+    x_scale: float,
+    x_max: float,
+    title: str,
+) -> plt.Figure:
+    x = np.linspace(0.0, float(x_max), 250, dtype=np.float32)
+    y = ro_stress(x, float(a), float(b), float(c), float(x_scale))
+    fig, ax = plt.subplots(1, 1, figsize=(6, 3.5))
+    ax.plot(x, y, color="k", linewidth=2)
+    ax.set_title(title)
+    ax.set_xlabel("strain")
+    ax.set_ylabel("stress (MPa)")
+    ax.grid(True, alpha=0.25)
+    fig.tight_layout()
+    return fig
+
+
+def plot_ro_lateral_relation(
+    *,
+    a: float,
+    b: float,
+    x_scale: float,
+    y_scale: float,
+    x_max: float,
+    title: str,
+) -> plt.Figure:
+    x = np.linspace(0.0, float(x_max), 250, dtype=np.float32)
+    y = ro_strain_strain(x, float(a), float(b), float(x_scale), float(y_scale))
+    fig, ax = plt.subplots(1, 1, figsize=(6, 3.5))
+    ax.plot(x, y, color="k", linewidth=2)
+    ax.set_title(title)
+    ax.set_xlabel("strain")
+    ax.set_ylabel("lateral strain")
+    ax.grid(True, alpha=0.25)
+    fig.tight_layout()
+    return fig
+
+
+def plot_condition_preview(
+    ro_groups_5x3: np.ndarray,
+    eps_scales: Tuple[float, float, float],
+    x_max: float | None = None,
+) -> plt.Figure:
+    """
+    Preview only the conditioned RO curves implied by the sliders.
+    Layout matches validation plots: 2 rows (stress, lateral) x 3 cols (11,22,12).
+    """
+    ro = np.asarray(ro_groups_5x3, dtype=np.float32).reshape(5, 3)
+    eps_11_scale, eps_22_scale, eps_12_scale = (float(eps_scales[0]), float(eps_scales[1]), float(eps_scales[2]))
+
+    # Use a strain range that is stable for UI preview.
+    # Default to 0..scale for each mode unless user overrides x_max.
+    def _x_grid(scale: float) -> np.ndarray:
+        xm = float(x_max) if x_max is not None else float(scale)
+        xm = max(xm, 1e-6)
+        return np.linspace(0.0, xm, 250, dtype=np.float32)
+
+    fig, axes = plt.subplots(2, 3, figsize=(12, 7))
+    modes = ["11", "22", "12"]
+    for col, mode in enumerate(modes):
+        if mode == "11":
+            x = _x_grid(eps_11_scale)
+            a0, b0, c0 = ro[0]
+            y_stress = ro_stress(x, float(a0), float(b0), float(c0), eps_11_scale)
+            axes[0, col].plot(x, y_stress, color="k", linewidth=2)
+
+            a1, b1, _ = ro[1]
+            y_lat = ro_strain_strain(x, float(a1), float(b1), eps_11_scale, eps_22_scale)
+            axes[1, col].plot(x, y_lat, color="k", linewidth=2)
+
+            axes[0, col].set_title("Mode 11: σ11(ε11)")
+            axes[1, col].set_title("Mode 11: ε22(ε11)")
+
+        elif mode == "22":
+            x = _x_grid(eps_22_scale)
+            a0, b0, c0 = ro[2]
+            y_stress = ro_stress(x, float(a0), float(b0), float(c0), eps_22_scale)
+            axes[0, col].plot(x, y_stress, color="k", linewidth=2)
+
+            a1, b1, _ = ro[3]
+            y_lat = ro_strain_strain(x, float(a1), float(b1), eps_22_scale, eps_11_scale)
+            axes[1, col].plot(x, y_lat, color="k", linewidth=2)
+
+            axes[0, col].set_title("Mode 22: σ22(ε22)")
+            axes[1, col].set_title("Mode 22: ε11(ε22)")
+
+        else:  # 12
+            x = _x_grid(eps_12_scale)
+            a0, b0, c0 = ro[4]
+            y_stress = ro_stress(x, float(a0), float(b0), float(c0), eps_12_scale)
+            axes[0, col].plot(x, y_stress, color="k", linewidth=2)
+            axes[0, col].set_title("Mode 12: σ12(ε12)")
+            axes[1, col].axis("off")
+
+        axes[0, col].set_xlabel("strain")
+        axes[0, col].set_ylabel("stress (MPa)")
+        if mode in ("11", "22"):
+            axes[1, col].set_xlabel("strain")
+            axes[1, col].set_ylabel("lateral strain")
+
+        axes[0, col].grid(True, alpha=0.25)
+        if mode in ("11", "22"):
+            axes[1, col].grid(True, alpha=0.25)
+
+    fig.tight_layout()
+    return fig
+

data_generation/hf_space_generation_ro/space_lib/simulate.py

@@ -0,0 +1,242 @@
+from __future__ import annotations
+
+import importlib.util
+import os
+from pathlib import Path
+from typing import Dict, List, Tuple
+
+import numpy as np
+
+
+def format_vol_fraction(vf: float) -> str:
+    return f"{float(vf):.4f}"
+
+
+def _import_lam(lam_dir: str):
+    lam_dir_p = Path(lam_dir).resolve()
+    lam_py = lam_dir_p / "lam.py"
+    if not lam_py.exists():
+        raise FileNotFoundError(f"lam.py not found in {lam_dir_p}")
+    spec = importlib.util.spec_from_file_location("lam", lam_py)
+    lam = importlib.util.module_from_spec(spec)
+    assert spec and spec.loader
+    spec.loader.exec_module(lam)
+    return lam
+
+
+def run_simulation_with_mat(lam, prefix: str, full_angles: List[float], mode: str, mat):
+    """
+    Copy of data_generation/generate_data_mp.py::run_simulation_with_mat.
+    Returns: ex, sx, ey, gxy, ezz, g23, g13, e11
+    """
+    tply = 0.05
+    plies = [lam.Ply(float(angle_deg), tply, mat) for angle_deg in full_angles]
+    laminate = lam.Laminate(plies)
+
+    if mode == "11":
+        main_index = 0
+        eps_max = 0.10
+    elif mode == "22":
+        main_index = 1
+        eps_max = 0.10
+    elif mode == "12":
+        main_index = 5
+        eps_max = 0.20
+    else:
+        raise ValueError("mode must be '11', '22' or '12'")
+
+    main_steps = np.linspace(0.0, eps_max, 1500)
+
+    ex_hist, sx_hist = [], []
+    ey_hist, gxy_hist = [], []
+    ezz_hist, g23_hist, g13_hist = [], [], []
+    e11_hist = []
+
+    ex_prev = 0.0
+    ey_prev = 0.0
+    gxy_prev = 0.0
+    ezz_prev = 0.0
+    g23_prev = 0.0
+    g13_prev = 0.0
+    s1_prev = 0.0
+
+    for i in range(1, len(main_steps)):
+        main_target = main_steps[i]
+
+        if main_index == 0:
+            main_prev = ex_prev
+        elif main_index == 1:
+            main_prev = ey_prev
+        else:
+            main_prev = gxy_prev
+
+        dmain = main_target - main_prev
+
+        Ceff_prev = laminate.effective_C_from_previous_strains(
+            ex_prev, ey_prev, ezz_prev, g23_prev, g13_prev, gxy_prev
+        )
+
+        cond = np.linalg.cond(Ceff_prev)
+        if not np.isfinite(cond) or cond > lam.COND_MAX:
+            raise np.linalg.LinAlgError(
+                f"Effective C is ill-conditioned (cond={cond:.3e}) at step {i}"
+            )
+
+        e_j = np.zeros(6)
+        e_j[main_index] = 1.0
+
+        try:
+            S_col = np.linalg.solve(Ceff_prev, e_j)
+        except np.linalg.LinAlgError as err:
+            raise np.linalg.LinAlgError(
+                f"Failed to solve for compliance column at step {i}: {err}"
+            )
+
+        Sjj = S_col[main_index]
+        if abs(Sjj) < 1e-20:
+            raise ZeroDivisionError(
+                f"Sjj is zero or too small at step {i} (Sjj={Sjj:.3e})."
+            )
+
+        ds1 = dmain / Sjj
+        de_vec = S_col * ds1
+        de_vec[main_index] = dmain
+
+        de1, de2, de3, de4, de5, de6 = de_vec
+        de4 = 0.0
+        de5 = 0.0
+
+        s1 = s1_prev + ds1
+        ex = ex_prev + de1
+        ey = ey_prev + de2
+        ezz = ezz_prev + de3
+        g23 = g23_prev + de4
+        g13 = g13_prev + de5
+        gxy = gxy_prev + de6
+
+        laminate.update_fiber_angles_incremental(de1, de2, de6)
+
+        ex_prev, ey_prev, ezz_prev = ex, ey, ezz
+        g23_prev, g13_prev, gxy_prev = g23, g13, gxy
+        s1_prev = s1
+
+        if main_index == 0:
+            main_strain = ex
+        elif main_index == 1:
+            main_strain = ey
+        else:
+            main_strain = 0.5 * gxy
+
+        ex_hist.append(main_strain)
+        sx_hist.append(s1)
+        ey_hist.append(ey)
+        gxy_hist.append(gxy)
+        ezz_hist.append(ezz)
+        g23_hist.append(g23)
+        g13_hist.append(g13)
+        e11_hist.append(ex)
+
+    result = (
+        np.array(ex_hist),
+        np.array(sx_hist),
+        np.array(ey_hist),
+        np.array(gxy_hist),
+        np.array(ezz_hist),
+        np.array(g23_hist),
+        np.array(g13_hist),
+        np.array(e11_hist),
+    )
+    if len(result) != 8:
+        raise ValueError(f"Internal error: expected 8 return values, got {len(result)}")
+    return result
+
+
+def simulate_instances(
+    *,
+    curve_dir: str,
+    lam_dir: str,
+    mat_type: str,
+    vf: float,
+    upper_angles: List[float],
+    instances: List[int],
+    num_output_points: int = 10,
+) -> Dict[int, Dict[str, Dict[str, np.ndarray]]]:
+    """
+    Returns:
+      {instance: {"11": {"strain","stress","lateral"}, "22": {...}, "12": {...}}}
+    """
+    lam = _import_lam(lam_dir)
+
+    curve_dir_path = Path(curve_dir).resolve()
+    if not curve_dir_path.exists():
+        raise FileNotFoundError(f"curve_dir does not exist: {curve_dir_path}")
+
+    original_curve_dir = lam.CURVE_DIR
+    lam.CURVE_DIR = curve_dir_path
+    try:
+        vf_str = format_vol_fraction(vf)
+        out: Dict[int, Dict[str, Dict[str, np.ndarray]]] = {}
+        errors: Dict[int, List[str]] = {}
+
+        for inst in instances:
+            prefix = f"{mat_type}_{vf_str}_{int(inst)}"
+            # Fail fast if required curve files are missing.
+            for mode in ("11", "22", "12"):
+                p = curve_dir_path / f"{prefix}_{mode}.txt"
+                if not p.exists():
+                    raise FileNotFoundError(f"Missing curve file: {p} (needed for simulation)")
+            vf_meta, centers_meta, n_fibers = lam.read_instance_metadata(prefix)
+            mat = lam.load_ud_material_from_files(prefix)
+            full_angles = lam.build_full_symmetric_stack(sorted([float(a) for a in upper_angles]))
+
+            sim_modes: Dict[str, Dict[str, np.ndarray]] = {}
+            for mode in ("11", "22", "12"):
+                try:
+                    ex, sx, ey, gxy, ezz, g23, g13, e11 = run_simulation_with_mat(lam, prefix, full_angles, mode, mat)
+                    if len(ex) < 2:
+                        continue
+                    x_out = np.linspace(float(ex[0]), float(ex[-1]), int(num_output_points))
+                    sx_out_mpa = np.interp(x_out, ex, sx / 1e6)
+                    if mode == "11":
+                        lat_out = np.interp(x_out, ex, ey)
+                    elif mode == "22":
+                        lat_out = np.interp(x_out, ex, e11)
+                    else:
+                        lat_out = None
+                    sim_modes[mode] = {
+                        "strain": x_out.astype(np.float32),
+                        "stress": sx_out_mpa.astype(np.float32),
+                        "lateral": None if lat_out is None else lat_out.astype(np.float32),
+                    }
+                except Exception as e:
+                    errors.setdefault(int(inst), []).append(f"{mode} failed: {type(e).__name__}: {e}")
+                    continue
+
+            if sim_modes:
+                out[int(inst)] = sim_modes
+            else:
+                errors.setdefault(int(inst), []).append("all modes failed")
+
+        if not out:
+            msg = f"No simulations succeeded for {mat_type} vf={vf_str} angles={upper_angles}."
+            if errors:
+                msg += " Errors: " + "; ".join(f"inst{inst}: {errs}" for inst, errs in errors.items())
+            raise RuntimeError(msg)
+
+        return out
+    finally:
+        lam.CURVE_DIR = original_curve_dir
+
+
+def default_lam_dir() -> str:
+    """
+    Default to the Space directory that contains a vendored `lam.py`.
+    Can be overridden via env MG_LAM_DIR.
+    """
+    env = os.environ.get("MG_LAM_DIR")
+    if env:
+        return env
+    # Space layout: data_generation/hf_space_generation_ro/lam.py
+    here = Path(__file__).resolve()
+    return str(here.parent.parent)
+

data_generation/processed_dataset/config_1/metadata.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1445e1ac3bf051c811c1c55520b1551d6298c467c276e2d65546d9bdb989d3d4
+size 237926037

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_1_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
+5.000000e-03	9.534925e+01	-2.310667e-03	-2.307645e-03
+1.000000e-02	1.905271e+02	-4.604358e-03	-4.598334e-03
+1.500000e-02	2.853640e+02	-6.886136e-03	-6.877142e-03
+2.000000e-02	3.792480e+02	-9.173770e-03	-9.161830e-03
+2.500000e-02	4.720984e+02	-1.146994e-02	-1.145497e-02
+3.000000e-02	5.644633e+02	-1.375957e-02	-1.374152e-02
+3.500000e-02	6.565959e+02	-1.603539e-02	-1.601427e-02
+4.000000e-02	7.485469e+02	-1.829625e-02	-1.827210e-02
+4.500000e-02	8.403284e+02	-2.054202e-02	-2.051485e-02
+5.000000e-02	9.319449e+02	-2.277277e-02	-2.274262e-02
+5.500000e-02	1.023402e+03	-2.498851e-02	-2.495542e-02
+6.000000e-02	1.114698e+03	-2.718959e-02	-2.715359e-02
+6.500000e-02	1.205837e+03	-2.937602e-02	-2.933714e-02
+7.000000e-02	1.296823e+03	-3.154793e-02	-3.150620e-02
+7.500000e-02	1.387657e+03	-3.370544e-02	-3.366089e-02
+8.000000e-02	1.478341e+03	-3.584868e-02	-3.580135e-02
+8.500000e-02	1.568879e+03	-3.797777e-02	-3.792769e-02
+9.000000e-02	1.659271e+03	-4.009285e-02	-4.004004e-02
+9.500000e-02	1.749521e+03	-4.219405e-02	-4.213854e-02
+1.000000e-01	1.839629e+03	-4.428148e-02	-4.422331e-02
+volume fraction= 0.092363
+fiber_centers_YZ= (0.017275,0.022793) (0.002167,0.005617) (0.009285,0.034121) (0.042275,0.022793) (0.027167,0.005617) (0.034285,0.034121) (0.052167,0.005617)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_1_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
+0.000000e+00	0.000000e+00
+5.000000e-03	2.839216e+00
+1.000000e-02	5.588398e+00
+1.500000e-02	7.680908e+00
+2.000000e-02	8.895033e+00
+2.500000e-02	9.728212e+00
+3.000000e-02	1.044392e+01
+3.500000e-02	1.109897e+01
+4.000000e-02	1.170674e+01
+4.500000e-02	1.227277e+01
+5.000000e-02	1.280090e+01
+5.500000e-02	1.329438e+01
+6.000000e-02	1.375612e+01
+6.500000e-02	1.418882e+01
+7.000000e-02	1.459495e+01
+7.500000e-02	1.497678e+01
+8.000000e-02	1.533224e+01
+8.500000e-02	1.567142e+01
+9.000000e-02	1.599245e+01
+9.500000e-02	1.629687e+01
+1.000000e-01	1.658625e+01
+volume fraction= 0.092363
+fiber_centers_YZ= (0.017275,0.022793) (0.002167,0.005617) (0.009285,0.034121) (0.042275,0.022793) (0.027167,0.005617) (0.034285,0.034121) (0.052167,0.005617)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_1_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
+5.000000e-03	5.396585e+00	-1.306340e-04	-4.456201e-03
+1.000000e-02	1.065990e+01	-2.572005e-04	-8.881963e-03
+1.500000e-02	1.482475e+01	-3.587396e-04	-1.336267e-02
+2.000000e-02	1.732569e+01	-4.229332e-04	-1.795116e-02
+2.500000e-02	1.903323e+01	-4.683321e-04	-2.257021e-02
+3.000000e-02	2.046323e+01	-5.063235e-04	-2.717229e-02
+3.500000e-02	2.175922e+01	-5.402466e-04	-3.174410e-02
+4.000000e-02	2.295725e+01	-5.710798e-04	-3.628243e-02
+4.500000e-02	2.407041e+01	-5.992676e-04	-4.078629e-02
+5.000000e-02	2.510145e+01	-6.248446e-04	-4.525610e-02
+5.500000e-02	2.606607e+01	-6.485539e-04	-4.968996e-02
+6.000000e-02	2.696554e+01	-6.703592e-04	-5.408849e-02
+6.500000e-02	2.780489e+01	-6.904354e-04	-5.845164e-02
+7.000000e-02	2.858871e+01	-7.089376e-04	-6.277939e-02
+7.500000e-02	2.932103e+01	-7.259987e-04	-6.707179e-02
+8.000000e-02	3.000571e+01	-7.417412e-04	-7.132894e-02
+8.500000e-02	3.064632e+01	-7.562750e-04	-7.555094e-02
+9.000000e-02	3.124631e+01	-7.697046e-04	-7.973792e-02
+9.500000e-02	3.180916e+01	-7.821311e-04	-8.389001e-02
+1.000000e-01	3.233775e+01	-7.936391e-04	-8.800741e-02
+volume fraction= 0.092363
+fiber_centers_YZ= (0.017275,0.022793) (0.002167,0.005617) (0.009285,0.034121) (0.042275,0.022793) (0.027167,0.005617) (0.034285,0.034121) (0.052167,0.005617)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_2_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
+5.000000e-03	9.536606e+01	-2.301440e-03	-2.316797e-03
+1.000000e-02	1.905607e+02	-4.585958e-03	-4.616585e-03
+1.500000e-02	2.854143e+02	-6.858610e-03	-6.904451e-03
+2.000000e-02	3.793148e+02	-9.136979e-03	-9.198358e-03
+2.500000e-02	4.721818e+02	-1.142342e-02	-1.150120e-02
+3.000000e-02	5.645633e+02	-1.370305e-02	-1.379772e-02
+3.500000e-02	6.567123e+02	-1.596880e-02	-1.608052e-02
+4.000000e-02	7.486799e+02	-1.821957e-02	-1.834841e-02
+4.500000e-02	8.404779e+02	-2.045524e-02	-2.060124e-02
+5.000000e-02	9.321136e+02	-2.267578e-02	-2.283897e-02
+5.500000e-02	1.023585e+03	-2.488153e-02	-2.506197e-02
+6.000000e-02	1.114897e+03	-2.707250e-02	-2.727023e-02
+6.500000e-02	1.206052e+03	-2.924882e-02	-2.946387e-02
+7.000000e-02	1.297054e+03	-3.141061e-02	-3.164303e-02
+7.500000e-02	1.387905e+03	-3.355801e-02	-3.380782e-02
+8.000000e-02	1.478606e+03	-3.569113e-02	-3.595838e-02
+8.500000e-02	1.569159e+03	-3.781010e-02	-3.809483e-02
+9.000000e-02	1.659568e+03	-3.991505e-02	-4.021730e-02
+9.500000e-02	1.749834e+03	-4.200612e-02	-4.232591e-02
+1.000000e-01	1.839958e+03	-4.408342e-02	-4.442080e-02
+volume fraction= 0.092363
+fiber_centers_YZ= (0.020136,0.014168) (0.010174,0.043883) (0.008863,0.020511) (0.045136,0.014168) (0.035174,0.043883) (0.033863,0.020511)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_2_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
+0.000000e+00	0.000000e+00
+5.000000e-03	2.874010e+00
+1.000000e-02	5.624720e+00
+1.500000e-02	7.697166e+00
+2.000000e-02	8.929249e+00
+2.500000e-02	9.775334e+00
+3.000000e-02	1.049814e+01
+3.500000e-02	1.115800e+01
+4.000000e-02	1.176904e+01
+4.500000e-02	1.233750e+01
+5.000000e-02	1.286757e+01
+5.500000e-02	1.336269e+01
+6.000000e-02	1.382596e+01
+6.500000e-02	1.426022e+01
+7.000000e-02	1.466802e+01
+7.500000e-02	1.505176e+01
+8.000000e-02	1.541368e+01
+8.500000e-02	1.575582e+01
+9.000000e-02	1.608007e+01
+9.500000e-02	1.638818e+01
+1.000000e-01	1.668206e+01
+volume fraction= 0.092363
+fiber_centers_YZ= (0.020136,0.014168) (0.010174,0.043883) (0.008863,0.020511) (0.045136,0.014168) (0.035174,0.043883) (0.033863,0.020511)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_2_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
+5.000000e-03	5.497978e+00	-1.325568e-04	-4.451303e-03
+1.000000e-02	1.083645e+01	-2.604151e-04	-8.874794e-03
+1.500000e-02	1.501241e+01	-3.616524e-04	-1.335973e-02
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_3_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
+5.000000e-03	9.537730e+01	-2.300143e-03	-2.318003e-03
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+1.000000e-01	1.840176e+03	-4.404062e-02	-4.446332e-02
+volume fraction= 0.092363
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_3_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
+0.000000e+00	0.000000e+00
+5.000000e-03	2.876095e+00
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_3_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_4_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_4_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
+0.000000e+00	0.000000e+00
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_4_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_5_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_5_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.0924_5_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_1_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
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+1.000000e-01	4.242610e+03	-4.152411e-02	-4.253925e-02
+volume fraction= 0.215513
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_1_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
+0.000000e+00	0.000000e+00
+5.000000e-03	3.826899e+00
+1.000000e-02	7.119306e+00
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+volume fraction= 0.215513
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_1_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_2_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_2_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_2_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_3_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_3_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_3_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
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+1.000000e-01	4.329286e+01	-4.445155e-04	-8.789071e-02
+volume fraction= 0.215513
+fiber_centers_YZ= (0.011831,0.005758) (0.007619,0.028165) (0.004083,0.039896) (0.000104,0.015820) (0.010460,0.017838) (0.020118,0.024590) (0.015527,0.040871) (0.036831,0.005758) (0.032619,0.028165) (0.029083,0.039896) (0.025104,0.015820) (0.035460,0.017838) (0.045118,0.024590) (0.040527,0.040871) (0.050104,0.015820)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_4_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
+5.000000e-03	2.178472e+02	-2.199787e-03	-2.190225e-03
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+1.500000e-02	6.519534e+02	-6.555993e-03	-6.527475e-03
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+2.500000e-02	1.081982e+03	-1.091732e-02	-1.086909e-02
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+3.500000e-02	1.508925e+03	-1.525954e-02	-1.519063e-02
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+1.000000e-01	4.242508e+03	-4.213368e-02	-4.193063e-02
+volume fraction= 0.215513
+fiber_centers_YZ= (0.015753,0.009729) (0.007691,0.016919) (0.007308,0.006878) (0.010610,0.037217) (0.012315,0.026873) (0.000615,0.030461) (0.020472,0.045532) (0.040753,0.009729) (0.032691,0.016919) (0.032308,0.006878) (0.035610,0.037217) (0.037315,0.026873) (0.025615,0.030461) (0.045472,0.045532) (0.050615,0.030461)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_4_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
+0.000000e+00	0.000000e+00
+5.000000e-03	3.687761e+00
+1.000000e-02	6.972763e+00
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+5.500000e-02	1.540594e+01
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+volume fraction= 0.215513
+fiber_centers_YZ= (0.015753,0.009729) (0.007691,0.016919) (0.007308,0.006878) (0.010610,0.037217) (0.012315,0.026873) (0.000615,0.030461) (0.020472,0.045532) (0.040753,0.009729) (0.032691,0.016919) (0.032308,0.006878) (0.035610,0.037217) (0.037315,0.026873) (0.025615,0.030461) (0.045472,0.045532) (0.050615,0.030461)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_4_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
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+1.000000e-01	4.339807e+01	-4.446428e-04	-8.789499e-02
+volume fraction= 0.215513
+fiber_centers_YZ= (0.015753,0.009729) (0.007691,0.016919) (0.007308,0.006878) (0.010610,0.037217) (0.012315,0.026873) (0.000615,0.030461) (0.020472,0.045532) (0.040753,0.009729) (0.032691,0.016919) (0.032308,0.006878) (0.035610,0.037217) (0.037315,0.026873) (0.025615,0.030461) (0.045472,0.045532) (0.050615,0.030461)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_5_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
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+1.000000e-01	4.242692e+03	-4.242045e-02	-4.164258e-02
+volume fraction= 0.215513
+fiber_centers_YZ= (0.018802,0.037413) (0.006169,0.028326) (0.015373,0.007545) (0.017987,0.026778) (0.002253,0.006858) (0.008082,0.042031) (0.016899,0.017719) (0.043802,0.037413) (0.031169,0.028326) (0.040373,0.007545) (0.042987,0.026778) (0.027253,0.006858) (0.033082,0.042031) (0.041899,0.017719) (0.052253,0.006858)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_5_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.2155_5_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.3079_1_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.3079_1_12.txt

@@ -0,0 +1,24 @@
+Strain_12	Stress_12
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data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.3079_1_22.txt

@@ -0,0 +1,24 @@
+Strain_22	Stress_22	Strain_11	Strain_33
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+6.000000e-02	5.080212e+01	-3.616799e-04	-5.338515e-02
+6.500000e-02	5.256817e+01	-3.745301e-04	-5.771461e-02
+7.000000e-02	5.423133e+01	-3.866242e-04	-6.201059e-02
+7.500000e-02	5.580521e+01	-3.980618e-04	-6.627272e-02
+8.000000e-02	5.730090e+01	-4.089247e-04	-7.050072e-02
+8.500000e-02	5.872809e+01	-4.192840e-04	-7.469444e-02
+9.000000e-02	6.009515e+01	-4.292008e-04	-7.885379e-02
+9.500000e-02	6.140867e+01	-4.387449e-04	-8.297920e-02
+1.000000e-01	6.267566e+01	-4.479225e-04	-8.706988e-02
+volume fraction= 0.307876
+fiber_centers_YZ= (0.016091,0.007156) (0.007983,0.030747) (0.015540,0.032727) (0.005918,0.043270) (0.004559,0.015036) (0.016730,0.015471) (0.019107,0.043869) (0.002578,0.007493) (0.012864,0.022411) (0.000522,0.034035) (0.041091,0.007156) (0.032983,0.030747) (0.040540,0.032727) (0.030918,0.043270) (0.029559,0.015036) (0.041730,0.015471) (0.044107,0.043869) (0.027578,0.007493) (0.037864,0.022411) (0.025522,0.034035) (0.052578,0.007493) (0.050522,0.034035)

data_generation/shahriar_modified_2025_12/RVE_Datasets/CHDPE_0.3079_2_11.txt

@@ -0,0 +1,24 @@
+Strain_11	Stress_11	Strain_22	Strain_33
+0.000000e+00	0.000000e+00	0.000000e+00	0.000000e+00
+5.000000e-03	3.096753e+02	-2.107945e-03	-2.110143e-03
+1.000000e-02	6.185627e+02	-4.200729e-03	-4.205056e-03
+1.500000e-02	9.265392e+02	-6.282791e-03	-6.288871e-03
+2.000000e-02	1.233167e+03	-8.369273e-03	-8.375856e-03
+2.500000e-02	1.538428e+03	-1.046183e-02	-1.046781e-02
+3.000000e-02	1.842740e+03	-1.254801e-02	-1.255285e-02
+3.500000e-02	2.146292e+03	-1.462174e-02	-1.462507e-02
+4.000000e-02	2.449127e+03	-1.668215e-02	-1.668364e-02
+4.500000e-02	2.751255e+03	-1.872916e-02	-1.872850e-02
+5.000000e-02	3.052684e+03	-2.076269e-02	-2.075960e-02
+5.500000e-02	3.353429e+03	-2.278276e-02	-2.277696e-02
+6.000000e-02	3.653474e+03	-2.479063e-02	-2.478178e-02
+6.500000e-02	3.952851e+03	-2.678415e-02	-2.677212e-02
+7.000000e-02	4.251547e+03	-2.876523e-02	-2.874975e-02
+7.500000e-02	4.549584e+03	-3.073328e-02	-3.071409e-02
+8.000000e-02	4.846948e+03	-3.268883e-02	-3.266574e-02
+8.500000e-02	5.143656e+03	-3.463208e-02	-3.460481e-02
+9.000000e-02	5.439713e+03	-3.656241e-02	-3.653079e-02
+9.500000e-02	5.735121e+03	-3.848027e-02	-3.844412e-02
+1.000000e-01	6.029886e+03	-4.038580e-02	-4.034495e-02
+volume fraction= 0.307876
+fiber_centers_YZ= (0.011407,0.012536) (0.012042,0.025734) (0.001826,0.010511) (0.000075,0.030859) (0.000460,0.020979) (0.014482,0.041024) (0.003143,0.039885) (0.019847,0.005768) (0.016297,0.018670) (0.011150,0.004606) (0.036407,0.012536) (0.037042,0.025734) (0.026826,0.010511) (0.025075,0.030859) (0.025460,0.020979) (0.039482,0.041024) (0.028143,0.039885) (0.044847,0.005768) (0.041297,0.018670) (0.036150,0.004606) (0.051826,0.010511) (0.050075,0.030859) (0.050460,0.020979) (0.053143,0.039885)