Merge branch 'master' into feat/coupling-expansion

.devcontainer/devcontainer.json

@@ -0,0 +1,33 @@
+{
+  "name": "Python 3",
+  // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
+  "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
+  "customizations": {
+    "codespaces": {
+      "openFiles": [
+        "README.md",
+        "view/streamlit_app.py"
+      ]
+    },
+    "vscode": {
+      "settings": {},
+      "extensions": [
+        "ms-python.python",
+        "ms-python.vscode-pylance"
+      ]
+    }
+  },
+  "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
+  "postAttachCommand": {
+    "server": "streamlit run view/streamlit_app.py --server.enableCORS false --server.enableXsrfProtection false"
+  },
+  "portsAttributes": {
+    "8501": {
+      "label": "Application",
+      "onAutoForward": "openPreview"
+    }
+  },
+  "forwardPorts": [
+    8501
+  ]
+}

.dockerignore

@@ -13,7 +13,8 @@ __pycache__/
 .ipynb_checkpoints/
 .vscode/
 .idea/
-assets/
 articles/
 examples/
 docs/
+!assets/icon-larger.png
+!view/assets/icon-larger.png

cmtj/utils/linear.py

@@ -78,8 +78,9 @@ def amplitude_scan(
         Hz = ct * Hspan
         if back:
             forward = np.vstack((Hx, Hy, Hz)).T
-            back = forward[::-1]
-            return np.concatenate((Hspan, Hspan[::-1]), axis=0), np.concatenate((forward, back), axis=0)
+            return np.concatenate((Hspan[:-1], Hspan[::-1]),
+                                  axis=0), np.concatenate(
+                                      (forward[:-1], forward[::-1]), axis=0)
         return Hspan, np.vstack((Hx, Hy, Hz)).T
 
     @staticmethod

docker/Dockerfile.app

@@ -3,10 +3,11 @@ EXPOSE 8501
 
 WORKDIR /app
 COPY view/ /app/
+COPY assets/ /app/
 RUN ls
 RUN python3 -m pip install streamlit && \
     git clone https://github.com/LemurPwned/cmtj.git && \
     cd cmtj && \
     python3 -m pip install .[utils]
 
-CMD streamlit run streamlit_app.py
+CMD streamlit run nav.py

view/domain.py

@@ -0,0 +1,231 @@
+from threading import RLock
+
+import matplotlib.pyplot as plt
+import numpy as np
+import streamlit as st
+from helpers import read_mh_data
+from simulation_fns import create_single_domain, get_axis_angles
+from utils import GENERIC_BOUNDS, GENERIC_UNITS
+
+from cmtj import AxialDriver, Junction
+from cmtj.utils import FieldScan
+
+_lock = RLock()
+
+with st.container():
+    st.write("# Domain fitting")
+
+    st.write(
+        "Fit M(H) to multidomain model. "
+        "Upload your file with experimental data: with columns H, mx, my, mz.\n"
+        "First column is always H in A/m, the rest are the components of the magnetisation in range (-1, 1)."
+        "If you upload just two columns, the script will assume that the data is in the form H, mx."
+    )
+
+    progress_bar = st.progress(0)
+
+
+def simulate_domains():
+    htheta, hphi = get_axis_angles(st.session_state.H_axis)
+    Hscan, Hvecs = FieldScan.amplitude_scan(
+        st.session_state.Hmin * 1e3,
+        st.session_state.Hmax * 1e3,
+        st.session_state.Hsteps,
+        htheta,
+        hphi,
+        back=st.session_state.back_to_back_scan,
+    )
+
+    domains = []
+    for i in range(st.session_state.N):
+        dom = create_single_domain(i)
+        domains.append(dom)
+    j = Junction(layers=domains)
+    maxlen = len(Hvecs)
+    # plot data
+    weights = [st.session_state[f"area{i}"] * 1e-18 for i in range(st.session_state.N)]
+    wsum = sum(weights)
+    weights = [w / wsum for w in weights]
+    Mmixed = np.zeros((len(Hscan), 3))
+    for i, H in enumerate(Hvecs):
+        j.clearLog()
+        j.setLayerExternalFieldDriver("all", AxialDriver(*H))
+        j.runSimulation(
+            st.session_state.sim_time * 1e-9,
+            st.session_state.int_step,
+            st.session_state.int_step,
+        )
+        log = j.getLog()
+        for l in range(st.session_state.N):
+            mx = np.mean(log[f"domain_{l}_mx"][-st.session_state.last_N :])
+            my = np.mean(log[f"domain_{l}_my"][-st.session_state.last_N :])
+            mz = np.mean(log[f"domain_{l}_mz"][-st.session_state.last_N :])
+            Mmixed[i] += np.array([mx, my, mz]) * weights[l]
+        # for each layer take last N values
+        progress_bar.progress(int((i / maxlen) * 100))
+    st.session_state["Mmixed"] = Mmixed
+    st.session_state["Hscan"] = Hscan
+
+
+with st.sidebar:
+    experimental_file = st.file_uploader(
+        "Upload your experimental data here",
+        help="Upload your data here. "
+        "Must be `\t` separated values and have H and m headers. "
+        "Express H in A/m and m in units",
+        type=["txt", "dat", "tsv"],
+        accept_multiple_files=False,
+        key="upload",
+    )
+    with st.expander("Simulation parameters", expanded=True):
+        st.selectbox(
+            "H axis", options=["x", "y", "z", "xy", "xz", "yz"], key="H_axis", index=0
+        )
+        st.number_input(
+            "Hmin (kA/m)", min_value=-1000.0, max_value=1000.0, value=-400.0, key="Hmin"
+        )
+        st.number_input(
+            "Hmax (kA/m)", min_value=0.0, max_value=1000.0, value=400.0, key="Hmax"
+        )
+        st.number_input(
+            "H steps", min_value=1, max_value=1000, value=50, key="Hsteps", format="%d"
+        )
+        # boolean if to back to back scan
+        st.checkbox("Back to back scan", key="back_to_back_scan", value=True)
+        st.number_input(
+            "int_step",
+            min_value=1e-14,
+            max_value=1e-12,
+            value=1e-12,
+            key="int_step",
+            format="%.1e",
+        )
+        st.number_input(
+            "sim_time (ns)",
+            min_value=1,
+            max_value=500,
+            value=16,
+            key="sim_time",
+            format="%d",
+        )
+        st.number_input(
+            "last N values to consider",
+            min_value=1,
+            max_value=1000,
+            value=100,
+            key="last_N",
+        )
+
+    shared_params = st.expander("Shared parameters", expanded=True)
+    with shared_params:
+        st.slider(
+            f"Ms ({GENERIC_UNITS['Ms']})",
+            min_value=GENERIC_BOUNDS["Ms"][0],
+            max_value=GENERIC_BOUNDS["Ms"][1],
+            value=1.8,
+            step=0.01,
+            key="Ms_shared",
+        )
+        st.number_input(
+            "thickness (nm)",
+            min_value=1.0,
+            max_value=10.0,
+            value=1.0,
+            key="thickness_shared",
+        )
+        st.number_input(
+            "alpha",
+            min_value=1e-3,
+            max_value=0.1,
+            value=1e-3,
+            key="alpha_shared",
+            format="%.3f",
+        )
+    domain_params = st.expander("Domain parameters", expanded=True)
+    with domain_params:
+        N = st.number_input(
+            "Number of domains", min_value=1, max_value=10, value=1, key="N"
+        )
+        for i in range(N):
+            st.markdown(f"#### Domain {i+1}")
+            unit_k = GENERIC_UNITS["K"]
+            st.number_input(
+                f"K ({i+1}) " r" ($\mathrm{" f"{unit_k}" "}$)",
+                min_value=GENERIC_BOUNDS["K"][0],
+                max_value=GENERIC_BOUNDS["K"][1],
+                value=1.2e3,
+                step=10.0,
+                key=f"K{i}",
+            )
+
+            st.number_input(
+                f"area ({i+1}) " r"($\mathrm{nm^2}$)",
+                min_value=1.0,
+                max_value=500.0,
+                value=10.0,
+                key=f"area{i}",
+            )
+            st.radio(
+                f"anisotropy axis ({i+1})",
+                options=["x", "y", "z"],
+                key=f"anisotropy_axis{i}",
+                index=2,
+            )
+            st.markdown("-----\n")
+
+
+def render(Hscan, Mmixed):
+    if len(Hscan) <= 0 or len(Mmixed) <= 0:
+        return
+    with _lock:
+        with plt.style.context(["dark_background"]):
+            w, h = plt.figaspect(1 / 3)
+            fig, ax = plt.subplots(
+                1, 3, dpi=400, figsize=(w, h), sharex=True, sharey=True
+            )
+            if experimental_file is not None:
+                fields, mh = read_mh_data()
+                render_from_exp(ax, fields=fields, mh=mh)
+            render_from_sim(ax, Hscan, Mmixed)
+
+            st.pyplot(fig)
+
+
+def render_from_exp(ax, fields, mh):
+    if len(fields) <= 0 or len(mh) <= 0:
+        st.toast("No data to render")
+
+    m = np.asarray(mh)
+    for i, l in zip(range(m.shape[1]), "xyz"):
+        ax[i].plot(
+            fields,
+            m[:, i],
+            label=f"$m_{l}$",
+            color="black",
+            marker="x",
+            alpha=0.5,
+            linestyle="--",
+        )
+
+
+def render_from_sim(ax, Hscan, Mmixed):
+    ax[0].plot(Hscan / 1e3, Mmixed[:, 0], label="$m_x$", color="crimson")
+    ax[1].plot(Hscan / 1e3, Mmixed[:, 1], label="$m_y$", color="forestgreen")
+    ax[2].plot(Hscan / 1e3, Mmixed[:, 2], label="$m_z$", color="royalblue")
+
+    ax[0].set_title(r"$m_x$")
+    ax[1].set_title(r"$m_y$")
+    ax[2].set_title(r"$m_z$")
+    ax[0].set_xlabel("H (kA/m)")
+    ax[1].set_xlabel("H (kA/m)")
+    ax[2].set_xlabel("H (kA/m)")
+
+
+simulate_btn = st.button(
+    "Simulate",
+    key="simulate",
+    on_click=simulate_domains,
+    type="primary",
+)
+
+render(st.session_state.get("Hscan", []), st.session_state.get("Mmixed", []))

view/helpers.py

@@ -3,6 +3,22 @@
 import streamlit as st
 from simulation_fns import get_pimm_data, get_vsd_data
 
+from cmtj.utils import Filters
+
+
+def read_mh_data():
+    filedata = st.session_state.upload.read().decode("utf-8")
+    lines = filedata.split("\n")
+    fields, mag = [], []
+    for line in lines[1:]:
+        if line.startswith("#"):
+            continue
+        vals = line.split()
+        if len(vals):
+            fields.append(float(vals[0]))
+            mag.append([float(val) for val in vals[1:]])
+    return fields, mag
+
 
 def read_data():
     filedata = st.session_state.upload.read().decode("utf-8")
@@ -104,8 +120,9 @@ def simulate_vsd():
             fstep=st.session_state.fstep * 1e9,
             Rtype=st.session_state.res_type,
             sim_time=st.session_state.sim_time * 1e-9,
-            Hoex_mag=st.session_state.Hoex_mag * 1e3,
+            Hoex_mag=st.session_state.Hoex_mag,
         )
+        spec = Filters.detrend_axis(spec, axis=0)
     plot_data(Hscan, freqs, spec, title="VSD spectrum")
 
 

view/nav.py

@@ -0,0 +1,27 @@
+import streamlit as st
+from PIL import Image
+
+app_title = "CMTJ UI"
+im = Image.open("assets/icon-larger.png")
+st.logo(im)
+domain_page = st.Page(
+    "domain.py",
+    title="Domain fitting",
+    url_path="domains",
+    icon=":material/bubble_chart:",
+)
+spectrum_page = st.Page(
+    "streamlit_app.py",
+    title="Spectrum fitting",
+    url_path="spectrum",
+    icon=":material/bar_chart:",
+)
+
+pg = st.navigation([domain_page, spectrum_page])
+st.set_page_config(
+    page_title=app_title,
+    page_icon="🦈",
+    layout="wide",
+)
+pg.run()
+# > streamlit run ./view/streamlit_app.py --server.runOnSave true

view/requirements.txt

@@ -1,9 +1,10 @@
 git+https://github.com/huawei-noah/HEBO.git#subdirectory=HEBO
-git+https://github.com/lemurpwned/cmtj.git@cmtj-updates
+git+https://github.com/lemurpwned/cmtj.git@streamlit/ui
 matplotlib
 networkx
 numba
 numpy
 scipy
+setuptools
 streamlit
 tqdm