Merge pull request #15 from jennyfothergill/feat/plot-with-units

Add plot function to diffractometer class with units along axis

.gitignore

@@ -3,7 +3,7 @@ __pycache__/
 *.gsd
 *.txt
 *.png
-to_delete
+scratch
 
 # Compiled python modules.
 *.pyc

README.md

@@ -35,6 +35,10 @@ cd GIXStapose
 conda env create -f environment.yml;
 conda activate gixstapose
 ```
+3. With the environment active, install this package
+```
+pip install .
+```
 
 ### Usage
 To run a simple cubic example:

examples/Figure_Example.ipynb

@@ -24,9 +24,8 @@
     "import PIL\n",
     "import matplotlib.pyplot as plt\n",
     "from fresnel import camera, pathtrace\n",
-    "import mbuild as mb\n",
     "\n",
-    "from gixstapose.draw_scene import get_scene\n",
+    "from gixstapose.draw_scene import get_scene, get_info\n",
     "from gixstapose.diffractometer import Diffractometer"
    ]
   },
@@ -45,9 +44,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "scene, [N, types, typeids, positions, N_bonds, bonds, box] = get_scene(\n",
-    "    \"../gixstapose/data/sc10.pdb\"\n",
-    ")"
+    "scene, info = get_scene(\"../gixstapose/data/sc10.pdb\")"
    ]
   },
   {
@@ -121,13 +118,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d = Diffractometer()\n",
-    "d.load(positions, box)\n",
-    "dp = d.diffract_from_camera(cam)\n",
-    "fig,ax = plt.subplots(figsize=(10,10))\n",
-    "ax.axis(\"off\")\n",
-    "ax.set_axis_off()\n",
-    "ax.imshow(dp, cmap=\"jet\");"
+    "d = Diffractometer(length_scale=1.0)\n",
+    "d.load(info[\"positions\"], info[\"box\"])\n",
+    "d.diffract_from_camera(cam)\n",
+    "fig, ax = d.plot()"
    ]
   },
   {
@@ -150,9 +144,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Finally let's look at a messy example using real simulation data. This xml file was taken from [https://doi.org/10.18122/cme_lab/4/boisestate](https://doi.org/10.18122/cme_lab/4/boisestate).\n",
+    "Finally let's look at a messy example using real simulation data. The original xml file was taken from [https://doi.org/10.18122/cme_lab/4/boisestate](https://doi.org/10.18122/cme_lab/4/boisestate).\n",
     "\n",
-    "The original file can be found in p3ht_data/1000_oligomers/crystallinities/crystalline.xml. (The file was converted from an xml to a mol2 and reduced to contain only the backbone carbons (type CA) so it would load more quickly.) This is from one of our labs united-atom simulations of organic photovoltaic polymers, specifically [P3HT](http://gisaxs.com/index.php/Material:P3HT). Even with these simplifications, it is still a large file, so the following cells may be a little slow."
+    "The original file can be found in p3ht_data/1000_oligomers/crystallinities/crystalline.xml. The file was converted from an xml to a gsd using [cmeutils](https://github.com/cmelab/cmeutils) [xml_to_gsd](https://github.com/cmelab/cmeutils/blob/8e33350630891cde89c9ba82a9adc682407c320f/cmeutils/gsd_utils.py#L192) function, and reduced to contain only the backbone carbons (type CA) using [snap_delete_types](https://github.com/cmelab/cmeutils/blob/8e33350630891cde89c9ba82a9adc682407c320f/cmeutils/gsd_utils.py#L141) so it would load more quickly. \n",
+    "\n",
+    "This is from one of our united-atom simulations of organic photovoltaic polymers, specifically [P3HT](http://gisaxs.com/index.php/Material:P3HT). Even with these simplifications, it still contains 60,000 particles, so the following cells may be a little slow."
    ]
   },
   {
@@ -161,7 +157,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "p3ht_file = \"../gixstapose/data/crystalline_CAonly.mol2\""
+    "p3ht_file = \"../gixstapose/data/crystalline_CAonly.gsd\""
    ]
   },
   {
@@ -181,7 +177,7 @@
     "   position = [2.892, 5.120, -0.830],\n",
     "   look_at =  [-0.797, 0.604, 0.862],\n",
     "   up =       [-0.733, 0.390, -0.557],\n",
-    "   height =   7.307\n",
+    "   height =   170\n",
     ")"
    ]
   },
@@ -198,9 +194,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "p3ht_scene, [N, types, typeids, positions, N_bonds, bonds, box] = get_scene(\n",
-    "    p3ht_file, color={\"CA\": \"gray\"}\n",
-    ")\n",
+    "p3ht_scene, info = get_scene(p3ht_file, color={\"CA\": \"gray\"}, scale=4)\n",
     "p3ht_scene.camera = cam\n",
     "p3ht_output = pathtrace(p3ht_scene, light_samples=40, w=600, h=600)\n",
     "p3ht_output"
@@ -210,7 +204,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "At this particular angle we'll draw your attention to parallel stripes of gray separated by black shadowy regions: These stripes are \"lamellae\" and are a periodic feature that should be measurable with the GIXStapose diffractometer. (Reminder! I used GIXStapose to find an orientation with measurable lamellar peaks and saved its camera orientation so we *could* return to this precise orientation!)"
+    "At this particular angle we'll draw your attention to parallel stripes of gray separated by black shadowy regions: These stripes are \"lamellae\" and are a periodic feature that should be measurable with the GIXStapose diffractometer. (Reminder! I used GIXStapose GUI to find an orientation with measurable lamellar peaks and saved its camera orientation so we *could* return to this precise orientation!)"
    ]
   },
   {
@@ -219,13 +213,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "d = Diffractometer()\n",
-    "d.load(positions, box)\n",
+    "d = Diffractometer(length_scale=1)\n",
+    "d.load(info[\"positions\"], info[\"box\"])\n",
     "dp = d.diffract_from_camera(cam)\n",
-    "fig,ax = plt.subplots(figsize=(10,10))\n",
-    "ax.axis(\"off\")\n",
-    "ax.set_axis_off()\n",
-    "ax.imshow(dp, cmap=\"jet\");"
+    "fig, ax = d.plot()"
    ]
   },
   {
@@ -235,6 +226,82 @@
     "Aw yeah, just look at that classic lamellar structure! The reflections of the (100) and (200) crystallographic planes are clearly visibile, with a hint of (300)! In this diffraction pattern, the wave vectors pointing to these yellow/green peak centers can be used to derive average lamellar spacings that would be a mess to deduce from the direct visualization. This example showcases how looking at chemical structures through the diffraction lens *first* can identify periodic features that may not be immediately visible to the eye. Once we know a periodic feature exists, we can pull up the chemical structure visualization and look for features at the expected length scales. We can also use this orientation as a reference point to generate a sequence of rotations that reveal other structures (e.g., for you P3HT experts, an orientation that more clearly reveals the spacing of the pi-pi stacking of the P3HT backbones)."
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Finally, if you want to get the reciprocal space values of the peaks in your diffraction pattern, we provide an interactive class called `PeakLabeller` you can use. Sometimes the interactive backends of matplotlib are a little tricky, so you may need to restart your kernel and reload some information."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib qt5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This cell will reload necessary information if needed\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "from fresnel import camera\n",
+    "\n",
+    "from gixstapose.draw_scene import get_info\n",
+    "from gixstapose.diffractometer import Diffractometer, PeakLabeller\n",
+    "\n",
+    "\n",
+    "p3ht_file = \"../gixstapose/data/crystalline_CAonly.gsd\"\n",
+    "info = get_info(p3ht_file)\n",
+    "\n",
+    "cam = camera.Orthographic(\n",
+    "   position = [2.892, 5.120, -0.830],\n",
+    "   look_at =  [-0.797, 0.604, 0.862],\n",
+    "   up =       [-0.733, 0.390, -0.557],\n",
+    "   height =   170\n",
+    ")\n",
+    "\n",
+    "d = Diffractometer(length_scale=1)\n",
+    "d.load(info[\"positions\"], info[\"box\"])\n",
+    "dp = d.diffract_from_camera(cam)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fig, ax = d.plot()\n",
+    "\n",
+    "PeakLabeller(ax)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "peaks = [0.057, 0.290]\n",
+    "for peak in peaks:\n",
+    "    print(f\"The peak at {peak} 1/Å corresponds to a periodic spacing of {1/peak:.2f} Å.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The figure below was created using `PeakLabeller`. We can see that these periodic distances are reasonable for the lamellar spacing (\\~17 Å) and pi-stacking (\\~4 Å) and agree with [previously reported values](https://doi.org/10.1016/j.orgel.2013.02.028).\n",
+    "![labelled peaks](../gixstapose/data/labelledpeaks.png)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,

examples/environment.yml

@@ -0,0 +1,16 @@
+name: gixstapose-ex
+channels:
+  - conda-forge
+dependencies:
+  - fresnel>=0.13
+  - gsd>=2.4
+  - jupyter
+  - matplotlib>=3.4
+  - mbuild>=0.13
+  - numpy>=1.21
+  - openbabel=3.1.1
+  - pillow>=8.3
+  - pip>=21
+  - py3Dmol>=1.7
+  - python=3.8
+  - rowan>=1.3