Merge branch 'release-0.3.0'

.github/workflows/publish-to-testpypi.yml

@@ -18,20 +18,13 @@ jobs:
           python-version: 3.12
 
       - name: Install pypa/build
-        run: >-
-          python -m
-          pip install
-          build
-          --user
+        run:
+          python -m pip install build toml --user
 
       - name: Build a binary wheel and a source tarball
-        run: >-
-          python -m
-          build
-          --sdist
-          --wheel
-          --outdir dist/
-          .
+        run: |
+          python dev.py
+          python -m build --sdist --wheel --outdir dist/
 
       - name: Publish distribution 📦 to Test PyPI
         uses: pypa/gh-action-pypi-publish@master

README.md

@@ -79,7 +79,7 @@ NOTICE: Three arguments are required to specify the position and width of scale
 
 Default colors of elemental mapppings are corresponding to the following list in sequnce (*Matplotlib* default colors):
 
-<font color=#1f77b4>1f77b4</font>, <font color=#ff7f0e>ff7f0e</font>, <font color=#2ca02c>2ca02c</font>, <font color=#d62728>d62728</font>, <font color=#9467bd>9467bd</font>, <font color=#8c564b>8c564b</font>, <font color=#e377c2>e377c2</font>, <font color=7f7f7f>7f7f7f</font>', <font color=#bcbd22>bcbd22</font>, <font color=#17becf>17becf</font>
+<font color=#1f77b4>1f77b4</font>, <font color=#ff7f0e>ff7f0e</font>, <font color=#2ca02c>2ca02c</font>, <font color=#d62728>d62728</font>, <font color=#9467bd>9467bd</font>, <font color=#8c564b>8c564b</font>, <font color=#e377c2>e377c2</font>, <font color=7f7f7f>7f7f7f</font>, <font color=#bcbd22>bcbd22</font>, <font color=#17becf>17becf</font>
 
 Convert-EMD provides `-e`/`--eds` option for users to customize the color of elemental mappings.
 

dev.py

@@ -0,0 +1,13 @@
+developing = False
+
+if developing:
+    import datetime
+    import toml
+    time_now = datetime.datetime.now().strftime("%y%m%d%H%M%S")
+    f = open("pyproject.toml", "r")
+    data = toml.load(f)
+    data["project"]["version"] = data["project"]["version"] + ".dev" + time_now
+    f.close()
+    new_f = open("pyproject.toml", "w")
+    new_f.write(toml.dumps(data))
+    new_f.close()

pyproject.toml

@@ -1,11 +1,25 @@
 [build-system]
-requires = ["setuptools>=61.0"]
+requires = ["setuptools>=61"]
 build-backend = "setuptools.build_meta"
 
 [project]
 name = "convert-emd"
-version = "0.2.5"
-dependencies = ["rosettasciio[all]>=0.6"]
+version = "0.3.0"
+keywords = [
+    "electron microscopy",
+    "python",
+    "eds",
+    "emd",
+    "img"
+]
+dependencies = [
+  "matplotlib>=3.7",
+  "h5py>=3.7",
+  "numba>=0.52",
+  "sparse>=0.15",
+  "scikit-image>=0.21",
+  "rosettasciio>=0.6"
+]
 authors = [
   { name="Bing-Liang Leng", email="leng.bl@sjtu.edu.cn" },
 ]
@@ -16,6 +30,9 @@ classifiers = [
     "Programming Language :: Python :: 3",
     "License :: OSI Approved :: MIT License",
     "Operating System :: OS Independent",
+    "Topic :: Scientific/Engineering",
+    "Topic :: Scientific/Engineering :: Physics",
+    "Topic :: Scientific/Engineering :: Chemistry"
 ]
 
 [project.urls]

src/convert_emd/drawing.py

@@ -1,15 +1,17 @@
 import os
+import re
 import matplotlib.pyplot as plt
 import convert_emd.function as emdfun
 
 def draw_scale_bar(frame, size_x, size_y, sb_x_start, sb_y_start, width_factor, sb_color):
     sb_lst = [0.1,0.2,0.5,1,2,5,10,20,50,100,200,500,1000,2000,5000]
     scale, unit = emdfun.get_scale(frame)
+    if " / " in unit: unit = re.sub(r" / ", "_", unit)
     sb_len_float = size_x * scale / 6
     sb_len = sorted(sb_lst, key=lambda a: abs(a - sb_len_float))[0]
     sb_len_px = sb_len / scale
     sb_start_x, sb_start_y, sb_width = (size_x * sb_x_start , size_y * sb_y_start, size_y / width_factor)
-    return [plt.Rectangle((sb_start_x, sb_start_y), sb_len_px, sb_width, color=sb_color, fill=True), "_" + str(sb_len) + unit]
+    return [plt.Rectangle((sb_start_x, sb_start_y), sb_len_px, sb_width, color=sb_color, fill=True), "_" + str(sb_len) + "(" + unit + ")"]
 
 def convert_emd(file_name, data, output_type, scale_bar, sb_color, sb_x_start, sb_y_start, sb_width_factor, stretch, overlay_alpha, sub_alpha, eds_color, mapping_overlay, overlay):
     output_dir = file_name + "/"
@@ -29,12 +31,12 @@ def convert_emd(file_name, data, output_type, scale_bar, sb_color, sb_x_start, s
         if dim ==1:
             save_file = open(output_name + title + "_" + str(i) + ".txt", "w", encoding = "utf-8")
             save_file.write(frame["axes"][0]["name"] + "(" + frame["axes"][0]["units"] + ")" + "\t" +"Intensity(a.u.)" + "\n")
-            signal_data = emdfun.signal1d_data(frame).transpose()
+            signal_data = emdfun.signal1d_data(frame)
             emdfun.write_signal1d(save_file, signal_data)
             save_file.close()
 
         if dim == 2:
-            frame["data"] = emdfun.contrast_stretch(frame, stretch)
+            frame["data"] = emdfun.contrast_stretch(frame["data"], stretch)
             cmp = "gray"
             if overlay:
                 if title in mapping_overlay: mapping_frame.append(i)
@@ -49,7 +51,7 @@ def convert_emd(file_name, data, output_type, scale_bar, sb_color, sb_x_start, s
                     ele += 1
                     if ele > 9: ele = 0
 
-            size_x, size_y = (frame["axes"][1]["size"], frame["axes"][0]["size"])
+            size_x, size_y = emdfun.get_size(frame)
             plt.figure(figsize=(size_x/100, size_y/100), facecolor="black")
             ax = plt.gca()
             plt.imshow(frame["data"], cmap=cmp)
@@ -68,12 +70,37 @@ def convert_emd(file_name, data, output_type, scale_bar, sb_color, sb_x_start, s
                 plt.savefig(output_name + title + "_" + str(i) + output_type)
             plt.close()
 
-        if dim == 3 and emdfun.is_eds_spectrum(frame):
-            save_file = open(output_name + title + "_" + str(i) + ".txt", "w", encoding = "utf-8")
-            save_file.write(frame["axes"][2]["name"] + "(" + frame["axes"][2]["units"] + ")" + "\t" +"Intensity(a.u.)" + "\n")
-            signal_data = emdfun.signal3d_to_1d_data(frame).transpose()
-            emdfun.write_signal1d(save_file, signal_data)
-            save_file.close()
+        if dim == 3:
+            if emdfun.is_eds_spectrum(frame):
+                save_file = open(output_name + title + "_" + str(i) + ".txt", "w", encoding = "utf-8")
+                save_file.write(frame["axes"][2]["name"] + "(" + frame["axes"][2]["units"] + ")" + "\t" +"Intensity(a.u.)" + "\n")
+                signal_data = emdfun.signal3d_to_1d_data(frame)
+                emdfun.write_signal1d(save_file, signal_data)
+                save_file.close()
+            else:
+                cmp = "gray"
+                size_x, size_y = emdfun.get_size(frame)
+                split_frame = emdfun.series_images(frame)
+                split_unit = frame["axes"][0]["units"]
+                for i in range(frame["axes"][0]["size"]):
+                    frame["data"][i] = emdfun.contrast_stretch(frame["data"][i], stretch)
+                    plt.figure(figsize=(size_x/100, size_y/100), facecolor="black")
+                    ax = plt.gca()
+                    plt.imshow(frame["data"][i], cmap=cmp)
+
+                    if scale_bar == True:
+                        bar = draw_scale_bar(frame, size_x, size_y, sb_x_start, sb_y_start, sb_width_factor, sb_color)
+                        ax.add_patch(bar[0])
+                        sb_text = bar[1]
+
+                    plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
+                    plt.margins = (0, 0)
+                    plt.axis("off")
+                    if scale_bar == True:
+                        plt.savefig(output_name + title + "_" + str(split_frame[i])[:5] + split_unit + "_" + sb_text + output_type)
+                    else:
+                        plt.savefig(output_name + title + "_" + str(split_frame[i])[:5] + split_unit + output_type)
+                    plt.close()
 
     if overlay:
         element = ""

src/convert_emd/function.py

@@ -1,10 +1,25 @@
 import matplotlib.colors as mcolors
 import numpy as np
 from skimage import exposure
-from rsciio.emd import file_reader
+from rsciio.emd._emd_velox import FeiEMDReader
+import h5py as h5
 
 def get_data(file_name):
-    return file_reader(file_name + ".emd")
+    file = h5.File(file_name + ".emd","r")
+    emd_reader = FeiEMDReader(
+        lazy = False,
+        select_type = None,
+        first_frame = 0,
+        last_frame = None,
+        sum_frames = True,
+        sum_EDS_detectors = True,
+        rebin_energy = 1,
+        SI_dtype = None,
+        load_SI_image_stack = False,
+    )
+    emd_reader.read_file(file)
+    data = emd_reader.dictionaries
+    return data
 
 def data_signal_type(frame):
     return frame["metadata"]["Signal"]["signal_type"]
@@ -26,29 +41,35 @@ def eds_elements(data):
     return element
 
 def get_scale(frame):
-    return (frame["axes"][1]["scale"], frame["axes"][1]["units"])
+    return (frame["axes"][-1]["scale"], frame["axes"][-1]["units"])
 
 def get_title(frame):
     return frame["metadata"]["General"]["title"]
 
 def get_size(frame):
-    return (frame["axes"][1]["size"], frame["axes"][0]["size"])
+    return (frame["axes"][-1]["size"], frame["axes"][-2]["size"])
 
 def signal1d_data(frame):
     offset = frame["axes"][0]["offset"]
     scale = frame["axes"][0]["scale"]
     size = frame["axes"][0]["size"]
     x_data = np.arange(offset, scale*size+offset, scale)
     y_data = frame["data"]
-    return np.asarray([x_data, y_data])
+    return np.asarray([x_data, y_data]).transpose()
 
 def signal3d_to_1d_data(frame):
     offset = frame["axes"][2]["offset"]
     scale = frame["axes"][2]["scale"]
     size = frame["axes"][2]["size"]
     x_data = np.arange(offset, scale*size+offset, scale)
     y_data = frame["data"].sum(axis=(0, 1))
-    return np.asarray([x_data, y_data])
+    return np.asarray([x_data, y_data]).transpose()
+
+def series_images(frame):
+    offset = frame["axes"][0]["offset"]
+    scale = frame["axes"][0]["scale"]
+    size = frame["axes"][0]["size"]
+    return np.arange(offset, scale*size+offset, scale)
 
 def write_signal1d(file, data):
     return np.savetxt(file, data, delimiter="\t")
@@ -61,6 +82,6 @@ def create_cmp(color):
 def default_colors():
     return list(mcolors.TABLEAU_COLORS.values())
 
-def contrast_stretch(frame, stretch):
-    low_constrain, high_constrain = np.percentile(frame["data"], (stretch[0], stretch[1]))
-    return exposure.rescale_intensity(frame["data"], in_range=(low_constrain, high_constrain))
+def contrast_stretch(data, stretch):
+    low_constrain, high_constrain = np.percentile(data, (stretch[0], stretch[1]))
+    return exposure.rescale_intensity(data, in_range=(low_constrain, high_constrain))