diff --git a/examples/PySDM_examples/expansion_chamber/aerosol.py b/examples/PySDM_examples/expansion_chamber/aerosol.py
index 8943ebe0b..d7fd58304 100644
--- a/examples/PySDM_examples/expansion_chamber/aerosol.py
+++ b/examples/PySDM_examples/expansion_chamber/aerosol.py
@@ -38,13 +38,10 @@ def __init__(
mass_fractions=mode1,
water_molar_volume=water_molar_volume,
),
- "spectrum": spectra.Lognormal(
+ "spectrum": spectra.Gaussian(
norm_factor=N,
- m_mode=158 * si.nm,
- s_geom=2,
+ loc=316 / 2 * si.nm,
+ scale=257 / 2 * si.nm,
),
},
)
- # mean diameter 316nm, standard deviation 257nm
- # not sure how to interpret the standard deviation given in the paper
- # because it looks like it's a lognormal distribution in Fig 2
diff --git a/examples/PySDM_examples/expansion_chamber/expansion_experiment.ipynb b/examples/PySDM_examples/expansion_chamber/expansion_experiment.ipynb
index 04865e7e6..f324cb3a3 100644
--- a/examples/PySDM_examples/expansion_chamber/expansion_experiment.ipynb
+++ b/examples/PySDM_examples/expansion_chamber/expansion_experiment.ipynb
@@ -31,7 +31,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
@@ -48,2950 +48,12 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
+ "image/svg+xml": "\n\n\n",
"text/plain": [
""
]
@@ -3004,7 +66,7 @@
{
"data": {
"application/vnd.jupyter.widget-view+json": {
- "model_id": "6b144d9af1a64fdb9f201e5e46f961d6",
+ "model_id": "2b728273fc454d1fa2daaeeec91d0a6e",
"version_major": 2,
"version_minor": 0
},
@@ -3051,7 +113,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {},
"outputs": [
{
@@ -3069,18 +131,7 @@
"8 45000.0\n",
"9 50000.0\n",
"10 55000.0\n",
- "11 60000.0\n",
- "[ 100. 10000. 15000. 20000. 25000. 30000. 35000. 40000. 45000. 50000.\n",
- " 55000. 60000.]\n",
- "[ 22.75197349 14.02952422 9.3313343 4.90250394 1.3754211\n",
- " -2.22650374 -6.40524781 -11.31013493 -17.10985754 -23.96731697\n",
- " -32.10461863 -41.61700796]\n",
- "[ 22.75198932 14.0290065 9.32445132 4.22824895 -1.160582\n",
- " -6.84208189 -13.01262823 -19.67291953 -26.92166646 -34.8575308\n",
- " -43.67694679 -53.47821023]\n",
- "[ 22.75239684 14.06569688 9.38070725 4.30527883 -1.06298974\n",
- " -6.72410419 -12.87328365 -19.51053965 -26.73349356 -34.63977293\n",
- " -43.42462343 -53.1856958 ]\n"
+ "11 60000.0\n"
]
}
],
@@ -3122,7 +173,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
@@ -3156,1539 +207,12 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": null,
"metadata": {},
"outputs": [
{
"data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
+ "image/svg+xml": "\n\n\n",
"text/plain": [
""
]
@@ -4714,1805 +238,7 @@
},
{
"data": {
- "image/svg+xml": [
- "\n",
- "\n",
- "\n"
- ],
+ "image/svg+xml": "\n\n\n",
"text/plain": [
""
]
@@ -6599,7 +325,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.9.2"
+ "version": "3.10.9"
},
"vscode": {
"interpreter": {