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Merge branch 'master' into interaction
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‎README.md

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@@ -6,13 +6,13 @@ A fast plotting library built using the `pygfx` render engine that can use [Vulk
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Checkout pygfx!
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https://github.com/pygfx/pygfx
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`fastplotlib` is in the alpha stage and experimental but you're welcome to try it out or contribute!
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`fastplotlib` is in the alpha stage and experimental, but you're welcome to try it out or contribute!
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Questions, ideas? Chat on gitter: https://gitter.im/fastplotlib/community?utm_source=share-link&utm_medium=link&utm_campaign=share-link
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# Examples
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**See the examples directory. Start out with `simple.ipynb` which uses the high level API.**
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**See the examples directory. Start out with `simple.ipynb`.**
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### Simple image plot
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```python
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plot = Plot()
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data = np.random.rand(512, 512) * 255
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plot.image(data=data, vmin=0, vmax=255, cmap='viridis')
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data = np.random.rand(512, 512)
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plot.image(data=data)
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plot.show()
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```
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plot = Plot()
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data = np.random.rand(512, 512) * 255
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image = plot.image(data=data, vmin=0, vmax=255, cmap='viridis')
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data = np.random.rand(512, 512)
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image = plot.image(data=data)
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def update_data():
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new_data = np.random.rand(512, 512) * 255
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new_data = np.random.rand(512, 512)
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image.update_data(new_data)
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plot.add_animations([update_data])
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plot.add_animations(update_data)
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plot.show()
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```
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Note: do not download the version that is currently on PYPI (i.e. don't just do `pip install fastplotlib`, it is outdated (we're waiting for the next release of `pygfx`)
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**Installing `simplejpeg` is recommended for faster plotting in notebooks using rfb. You will need C compilers setup on your computer to install it:**
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```bash
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pip install simplejpeg
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```
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Clone or download the repo to try the examples
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```bash
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For more information see: https://github.com/pygfx/wgpu-py#platform-requirements
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### Windows:
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Apparently Vulkan should be installed by default on Windows 11.
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Vulkan should be installed by default on Windows 11.
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### Linux:
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Debian based distros:

‎examples/gridplot.ipynb

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‎examples/gridplot_simple.ipynb

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‎examples/histogram.ipynb

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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "bfd22d77d24549128dd925e4114c929e",
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"model_id": "27e7e540a7be408997b143ac274b2b8e",
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"version_major": 2,
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"version_minor": 0
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},
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{
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"data": {
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src='data:image/png;base64,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' style='width:500.0px;height:300.0px;' /><div style='position: absolute; top:0; left:0; padding:1px 3px; background: #777; color:#fff; font-size: 90%; font-family:sans-serif; '>initial snapshot</div></div>"
6161
],
6262
"text/plain": [
6363
"<jupyter_rfb._utils.Snapshot object>"
@@ -69,7 +69,7 @@
6969
{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
72-
"model_id": "36e4fff0aa7a4fc993c70f7cfc2aaf4a",
72+
"model_id": "ff05c3706cfd401eb97f58b6e12e6d2a",
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"version_major": 2,
7474
"version_minor": 0
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},
@@ -83,7 +83,7 @@
8383
}
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],
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"source": [
86-
"plot.histogram(data=data, bins=100)\n",
86+
"plot.add_histogram(data=data, bins=100)\n",
8787
"\n",
8888
"plot.set_axes_visibility(True)\n",
8989
"plot.show()"

‎examples/image_widget.ipynb

Lines changed: 17 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -38,7 +38,7 @@
3838
{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
41-
"model_id": "6d575ba7671047ca88c36606344714fa",
41+
"model_id": "e0a67d7965234e9a9bec72f2195177fb",
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"version_major": 2,
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"version_minor": 0
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},
@@ -68,7 +68,7 @@
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
71-
"model_id": "8de187407b7746168c8d20a428d8712e",
71+
"model_id": "23e3f9b31e9a4f4ca2f93a046a82a699",
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"version_major": 2,
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"version_minor": 0
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},
@@ -148,7 +148,7 @@
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},
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{
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"cell_type": "code",
151-
"execution_count": 10,
151+
"execution_count": 9,
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"id": "882162eb-c873-42df-a945-d5e05ad141c9",
153153
"metadata": {},
154154
"outputs": [],
@@ -159,14 +159,14 @@
159159
},
160160
{
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"cell_type": "code",
162-
"execution_count": 12,
162+
"execution_count": 10,
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"id": "bf9f92b6-38ad-4d78-b88c-a32d473b6462",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
169-
"model_id": "005bcbc7755748cfaf0644e28beb3b0e",
169+
"model_id": "305e7bd04a4c42d18ccbdbb42bafc421",
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"version_major": 2,
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"version_minor": 0
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},
@@ -200,14 +200,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"execution_count": 11,
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"id": "403dde31-981a-46fb-b005-1bcef19c4f2c",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
210-
"model_id": "2b0a10be5d5b43b5a08f51a9d8f9b1dc",
210+
"model_id": "8c629365b4a744d799d18bc8364759f5",
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"version_major": 2,
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"version_minor": 0
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},
@@ -233,20 +233,20 @@
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},
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{
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"cell_type": "code",
236-
"execution_count": 14,
236+
"execution_count": 12,
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"id": "b59d95e2-9092-4915-beef-01661d164781",
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"metadata": {},
239239
"outputs": [
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{
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"data": {
242242
"text/plain": [
243-
"two: Subplot @ 0x7f91486a7a00\n",
243+
"two: Subplot @ 0x7fb6093796c0\n",
244244
" parent: None\n",
245245
" Graphics:\n",
246-
"\tfastplotlib.ImageGraphic @ 0x7f914881ceb0"
246+
"\tfastplotlib.ImageGraphic @ 0x7fb5c1935d50"
247247
]
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},
249-
"execution_count": 14,
249+
"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
@@ -265,7 +265,7 @@
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},
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{
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"cell_type": "code",
268-
"execution_count": 15,
268+
"execution_count": 13,
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"id": "a8f070db-da11-4062-95aa-f19b96351ee8",
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"metadata": {},
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"outputs": [],
@@ -283,14 +283,14 @@
283283
},
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{
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"cell_type": "code",
286-
"execution_count": 16,
286+
"execution_count": 14,
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"id": "b1587410-a08e-484c-8795-195a413d6374",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
293-
"model_id": "a2e4d723405345e0a7bd7b005330d018",
293+
"model_id": "27ced610a06c43c8b4a11de135ede09e",
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"version_major": 2,
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"version_minor": 0
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},
@@ -319,14 +319,14 @@
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"execution_count": 15,
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"id": "3ccea6c6-9580-4720-bce8-a5507cf867a3",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
329-
"model_id": "78a4ed0f59734124a7f3ee23e373e64a",
329+
"model_id": "41e6fb74806a4843a86f46c22d2426df",
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"version_major": 2,
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"version_minor": 0
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},
@@ -352,7 +352,7 @@
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},
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{
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"cell_type": "code",
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"execution_count": 18,
355+
"execution_count": 16,
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"id": "fd4433a9-2add-417c-a618-5891371efae0",
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"metadata": {},
358358
"outputs": [],

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