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release: v1

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Dustella 2025-02-13 15:47:11 +08:00
parent 35fe44339a
commit 239d052883
Signed by: Dustella
GPG Key ID: 35AA0AA3DC402D5C
3 changed files with 12 additions and 10 deletions
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19
colorbar.ipynb
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@ -2,7 +2,7 @@
"cells": [
{
"cell_type": "code",
"execution_count": 24,
"execution_count": 1,
"metadata": {},
"outputs": [
{
@ -17,7 +17,7 @@
" [0.940015, 0.975158, 0.131326, 1. ]], shape=(256, 4))"
]
},
"execution_count": 24,
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
@ -32,7 +32,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@ -42,31 +42,31 @@
},
{
"cell_type": "code",
"execution_count": 35,
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/tmp/ipykernel_4058693/3103424214.py:42: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
"/tmp/ipykernel_423824/3405611233.py:42: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
" ax.set_xticklabels([format_spec % val for val in ax.get_xticks()])\n"
]
},
{
"data": {
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"image/png": "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",
"text/plain": [
"<Figure size 500x80 with 1 Axes>"
]
},
"execution_count": 35,
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"image/png": "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",
"text/plain": [
"<Figure size 500x80 with 1 Axes>"
]
@ -129,6 +129,7 @@
" return fig\n",
"\n",
"def get_color(value):\n",
" value = value *0.4\n",
" if value == 0:\n",
" return (0, 0, 0, 0)\n",
" result = mpl.colormaps[\"hot\"](value)\n",
@ -137,7 +138,7 @@
" # return (r, g, b, a)\n",
" return (result[0], result[1], result[2], value*3)\n",
"\n",
"create_colorbar(get_color, vmax=0.3, width=5)"
"create_colorbar(get_color, vmax=0.8, width=5)"
]
}
],

1
src/plot.py
View File

@ -89,6 +89,7 @@ def create_heatmap(data,
raise ValueError('Invalid colormap name')
def get_color(value):
value = value * 0.4
if value == 0:
return (0, 0, 0, 0)
result = mapper(value)

2
src/process_data.py
View File

@ -25,7 +25,7 @@ def get_all_heat_points(data: np.ndarray, offset=(0, 0)) -> List[HeatPoint]:
if data[i, j] > 0:
if data[i, j] > 0:
points.append(
HeatPoint(lng=float(j+offset[1]+0.1), lat=float(i+offset[0]+0.1), count=int(data[i, j]*256+1)))
HeatPoint(lat=float(j+offset[1]+0.1), lng=float(i+offset[0]+0.1), count=int(data[i, j]*256+1)))
else:
continue
return list(filter(lambda d: d.count > 0, points))