Mahmudm commited on
Commit
f0bfd2d
·
verified ·
1 Parent(s): 32837db

Upload 162 files

Browse files
This view is limited to 50 files because it contains too many changes.   See raw diff
Files changed (50) hide show
  1. .gitattributes +5 -0
  2. NameModel_app.zip +3 -0
  3. Taal_273070_20200112_scenario_yizhou.zip +3 -0
  4. app.py +298 -122
  5. ash_animator/__init__.py +12 -0
  6. ash_animator/__pycache__/__init__.cpython-312.pyc +0 -0
  7. ash_animator/__pycache__/animation_all.cpython-312.pyc +0 -0
  8. ash_animator/__pycache__/animation_single.cpython-312.pyc +0 -0
  9. ash_animator/__pycache__/animation_vertical.cpython-312.pyc +0 -0
  10. ash_animator/__pycache__/basemaps.cpython-312.pyc +0 -0
  11. ash_animator/__pycache__/converter.cpython-312.pyc +0 -0
  12. ash_animator/__pycache__/export.cpython-312.pyc +0 -0
  13. ash_animator/__pycache__/interpolation.cpython-312.pyc +0 -0
  14. ash_animator/__pycache__/plot_3dfield_data.cpython-312.pyc +0 -0
  15. ash_animator/__pycache__/plot_horizontal_data.cpython-312.pyc +0 -0
  16. ash_animator/__pycache__/utils.cpython-312.pyc +0 -0
  17. ash_animator/animation_all.py +516 -0
  18. ash_animator/animation_single.py +147 -0
  19. ash_animator/animation_vertical.py +360 -0
  20. ash_animator/basemaps.py +131 -0
  21. ash_animator/converter.py +414 -0
  22. ash_animator/export.py +119 -0
  23. ash_animator/interpolation.py +14 -0
  24. ash_animator/plot_3dfield_data.py +465 -0
  25. ash_animator/plot_horizontal_data.py +564 -0
  26. ash_animator/utils.py +23 -0
  27. media/2D/2d_fields/air_concentration/air_concentration.gif +3 -0
  28. media/2D/frames/air_concentration/frame_0001.jpg +3 -0
  29. media/2D/frames/air_concentration/frame_0008.jpg +3 -0
  30. media/2D/frames/air_concentration/frame_0009.jpg +3 -0
  31. media/2D/frames/air_concentration/frame_0010.jpg +3 -0
  32. media/Taal_273070_20200112_scenario_yizhou.zip +3 -0
  33. media/last_run.txt +1 -0
  34. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z1.txt +97 -0
  35. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z10.txt +37 -0
  36. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z11.txt +37 -0
  37. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z12.txt +37 -0
  38. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z2.txt +112 -0
  39. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z3.txt +98 -0
  40. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z4.txt +120 -0
  41. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z5.txt +129 -0
  42. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z6.txt +194 -0
  43. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z7.txt +297 -0
  44. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z8.txt +208 -0
  45. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z9.txt +39 -0
  46. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z1.txt +37 -0
  47. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z10.txt +37 -0
  48. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z11.txt +37 -0
  49. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z12.txt +37 -0
  50. unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z2.txt +37 -0
.gitattributes CHANGED
@@ -32,3 +32,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
32
  *.zip filter=lfs diff=lfs merge=lfs -text
33
  *.zst filter=lfs diff=lfs merge=lfs -text
34
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
 
 
 
 
32
  *.zip filter=lfs diff=lfs merge=lfs -text
33
  *.zst filter=lfs diff=lfs merge=lfs -text
34
  *tfevents* filter=lfs diff=lfs merge=lfs -text
35
+ media/2D/2d_fields/air_concentration/air_concentration.gif filter=lfs diff=lfs merge=lfs -text
36
+ media/2D/frames/air_concentration/frame_0001.jpg filter=lfs diff=lfs merge=lfs -text
37
+ media/2D/frames/air_concentration/frame_0008.jpg filter=lfs diff=lfs merge=lfs -text
38
+ media/2D/frames/air_concentration/frame_0009.jpg filter=lfs diff=lfs merge=lfs -text
39
+ media/2D/frames/air_concentration/frame_0010.jpg filter=lfs diff=lfs merge=lfs -text
NameModel_app.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:58ecfedfead9237ae58b93ed6351f6492703145dfae1349591f9d9ac489a8867
3
+ size 741076
Taal_273070_20200112_scenario_yizhou.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9bb340a75132c3008a149557ff85f8bc05b4a46e70eee027503e30b9573fdd39
3
+ size 181349
app.py CHANGED
@@ -1,147 +1,323 @@
 
 
 
1
  import io
2
- import random
3
- from typing import List, Tuple
4
-
5
- import aiohttp
6
  import panel as pn
7
- from PIL import Image
8
- from transformers import CLIPModel, CLIPProcessor
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
9
 
10
- pn.extension(design="bootstrap", sizing_mode="stretch_width")
 
 
11
 
12
- ICON_URLS = {
13
- "brand-github": "https://github.com/holoviz/panel",
14
- "brand-twitter": "https://twitter.com/Panel_Org",
15
- "brand-linkedin": "https://www.linkedin.com/company/panel-org",
16
- "message-circle": "https://discourse.holoviz.org/",
17
- "brand-discord": "https://discord.gg/AXRHnJU6sP",
18
- }
19
 
 
 
 
 
 
20
 
21
- async def random_url(_):
22
- pet = random.choice(["cat", "dog"])
23
- api_url = f"https://api.the{pet}api.com/v1/images/search"
24
- async with aiohttp.ClientSession() as session:
25
- async with session.get(api_url) as resp:
26
- return (await resp.json())[0]["url"]
 
 
 
27
 
 
 
 
 
 
 
 
 
28
 
29
- @pn.cache
30
- def load_processor_model(
31
- processor_name: str, model_name: str
32
- ) -> Tuple[CLIPProcessor, CLIPModel]:
33
- processor = CLIPProcessor.from_pretrained(processor_name)
34
- model = CLIPModel.from_pretrained(model_name)
35
- return processor, model
36
 
 
 
 
 
37
 
38
- async def open_image_url(image_url: str) -> Image:
39
- async with aiohttp.ClientSession() as session:
40
- async with session.get(image_url) as resp:
41
- return Image.open(io.BytesIO(await resp.read()))
 
42
 
 
 
43
 
44
- def get_similarity_scores(class_items: List[str], image: Image) -> List[float]:
45
- processor, model = load_processor_model(
46
- "openai/clip-vit-base-patch32", "openai/clip-vit-base-patch32"
 
 
 
 
 
 
 
 
 
47
  )
48
- inputs = processor(
49
- text=class_items,
50
- images=[image],
51
- return_tensors="pt", # pytorch tensors
 
 
 
 
 
 
52
  )
53
- outputs = model(**inputs)
54
- logits_per_image = outputs.logits_per_image
55
- class_likelihoods = logits_per_image.softmax(dim=1).detach().numpy()
56
- return class_likelihoods[0]
57
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
58
 
59
- async def process_inputs(class_names: List[str], image_url: str):
60
- """
61
- High level function that takes in the user inputs and returns the
62
- classification results as panel objects.
63
- """
64
  try:
65
- main.disabled = True
66
- if not image_url:
67
- yield "##### ⚠️ Provide an image URL"
68
- return
69
-
70
- yield "##### ⚙ Fetching image and running model..."
71
- try:
72
- pil_img = await open_image_url(image_url)
73
- img = pn.pane.Image(pil_img, height=400, align="center")
74
- except Exception as e:
75
- yield f"##### 😔 Something went wrong, please try a different URL!"
76
- return
77
-
78
- class_items = class_names.split(",")
79
- class_likelihoods = get_similarity_scores(class_items, pil_img)
80
-
81
- # build the results column
82
- results = pn.Column("##### 🎉 Here are the results!", img)
83
-
84
- for class_item, class_likelihood in zip(class_items, class_likelihoods):
85
- row_label = pn.widgets.StaticText(
86
- name=class_item.strip(), value=f"{class_likelihood:.2%}", align="center"
87
- )
88
- row_bar = pn.indicators.Progress(
89
- value=int(class_likelihood * 100),
90
- sizing_mode="stretch_width",
91
- bar_color="secondary",
92
- margin=(0, 10),
93
- design=pn.theme.Material,
94
- )
95
- results.append(pn.Column(row_label, row_bar))
96
- yield results
97
- finally:
98
- main.disabled = False
99
-
100
-
101
- # create widgets
102
- randomize_url = pn.widgets.Button(name="Randomize URL", align="end")
103
-
104
- image_url = pn.widgets.TextInput(
105
- name="Image URL to classify",
106
- value=pn.bind(random_url, randomize_url),
107
- )
108
- class_names = pn.widgets.TextInput(
109
- name="Comma separated class names",
110
- placeholder="Enter possible class names, e.g. cat, dog",
111
- value="cat, dog, parrot",
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
112
  )
113
 
114
- input_widgets = pn.Column(
115
- "##### 😊 Click randomize or paste a URL to start classifying!",
116
- pn.Row(image_url, randomize_url),
117
- class_names,
 
 
118
  )
119
 
120
- # add interactivity
121
- interactive_result = pn.panel(
122
- pn.bind(process_inputs, image_url=image_url, class_names=class_names),
123
- height=600,
 
124
  )
125
 
126
- # add footer
127
- footer_row = pn.Row(pn.Spacer(), align="center")
128
- for icon, url in ICON_URLS.items():
129
- href_button = pn.widgets.Button(icon=icon, width=35, height=35)
130
- href_button.js_on_click(code=f"window.open('{url}')")
131
- footer_row.append(href_button)
132
- footer_row.append(pn.Spacer())
133
-
134
- # create dashboard
135
- main = pn.WidgetBox(
136
- input_widgets,
137
- interactive_result,
138
- footer_row,
139
  )
140
 
141
- title = "Panel Demo - Image Classification"
142
- pn.template.BootstrapTemplate(
143
- title=title,
144
- main=main,
145
- main_max_width="min(50%, 698px)",
146
- header_background="#F08080",
147
- ).servable(title=title)
 
 
 
1
+ import os
2
+ import glob
3
+ import shutil
4
  import io
5
+ import logging
 
 
 
6
  import panel as pn
7
+ import xarray as xr
8
+ import numpy as np
9
+ from datetime import datetime
10
+ from types import SimpleNamespace
11
+ from collections import defaultdict
12
+ from ash_animator.converter import NAMEDataProcessor
13
+ from ash_animator.plot_3dfield_data import Plot_3DField_Data
14
+ from ash_animator.plot_horizontal_data import Plot_Horizontal_Data
15
+ from ash_animator import create_grid
16
+
17
+ pn.extension()
18
+
19
+ MEDIA_DIR = "media"
20
+ os.makedirs(MEDIA_DIR, exist_ok=True)
21
+
22
+ # Logging setup
23
+ LOG_FILE = os.path.join(MEDIA_DIR, "app_errors.log")
24
+ logging.basicConfig(filename=LOG_FILE, level=logging.ERROR,
25
+ format="%(asctime)s - %(levelname)s - %(message)s")
26
+
27
+ animator_obj = {}
28
+
29
+ # ---------------- Widgets ----------------
30
+ file_input = pn.widgets.FileInput(accept=".zip")
31
+ process_button = pn.widgets.Button(name="📦 Process ZIP", button_type="primary")
32
+ reset_button = pn.widgets.Button(name="🔄 Reset App", button_type="danger")
33
+ status = pn.pane.Markdown("### Upload a NAME Model ZIP to begin")
34
+
35
+ download_button = pn.widgets.FileDownload(
36
+ label="⬇️ Download All Exports",
37
+ filename="all_exports.zip",
38
+ button_type="success",
39
+ callback=lambda: io.BytesIO(
40
+ open(shutil.make_archive(
41
+ os.path.join(MEDIA_DIR, "all_exports").replace(".zip", ""),
42
+ "zip", MEDIA_DIR
43
+ ), 'rb').read()
44
+ )
45
+ )
46
+
47
+ log_link = pn.widgets.FileDownload(
48
+ label="🪵 View Error Log", file=LOG_FILE,
49
+ filename="app_errors.log", button_type="warning"
50
+ )
51
+
52
+ threshold_slider_3d = pn.widgets.FloatSlider(name='3D Threshold', start=0.0, end=1.0, step=0.05, value=0.1)
53
+ zoom_slider_3d = pn.widgets.IntSlider(name='3D Zoom Level', start=1, end=20, value=19)
54
+ cmap_select_3d = pn.widgets.Select(name='3D Colormap', options=["rainbow", "viridis", "plasma"])
55
+ fps_slider_3d = pn.widgets.IntSlider(name='3D FPS', start=1, end=10, value=2)
56
+ Altitude_slider = pn.widgets.IntSlider(name='Define Ash Altitude', start=1, end=15, value=1)
57
+
58
+ threshold_slider_2d = pn.widgets.FloatSlider(name='2D Threshold', start=0.0, end=1.0, step=0.01, value=0.005)
59
+ zoom_slider_2d = pn.widgets.IntSlider(name='2D Zoom Level', start=1, end=20, value=19)
60
+ fps_slider_2d = pn.widgets.IntSlider(name='2D FPS', start=1, end=10, value=2)
61
+ cmap_select_2d = pn.widgets.Select(name='2D Colormap', options=["rainbow", "viridis", "plasma"])
62
+
63
+ # ---------------- Core Functions ----------------
64
+ def process_zip(event=None):
65
+ if file_input.value:
66
+ zip_path = os.path.join(MEDIA_DIR, file_input.filename)
67
+ with open(zip_path, "wb") as f:
68
+ f.write(file_input.value)
69
+ status.object = "✅ ZIP uploaded and saved."
70
+ else:
71
+ zip_path = os.path.join(MEDIA_DIR, "default_model.zip")
72
+ if not os.path.exists(zip_path):
73
+ status.object = "❌ No ZIP uploaded and default_model.zip not found."
74
+ return
75
+ status.object = "📦 Using default_model.zip"
76
+
77
+ output_dir = os.path.join("./", "ash_output")
78
+ shutil.rmtree(output_dir, ignore_errors=True)
79
+ os.makedirs(output_dir, exist_ok=True)
80
+
81
+ try:
82
+ processor = NAMEDataProcessor(output_root=output_dir)
83
+ processor.batch_process_zip(zip_path)
84
+
85
+ # animator_obj["3d"] = [xr.open_dataset(fp).load()
86
+ # for fp in sorted(glob.glob(os.path.join(output_dir, "3D", "*.nc")))]
87
+
88
+ animator_obj["3d"] = []
89
+ for fp in sorted(glob.glob(os.path.join(output_dir, "3D", "*.nc"))):
90
+ with xr.open_dataset(fp) as ds:
91
+ animator_obj["3d"].append(ds.load())
92
+
93
+ animator_obj["2d"] = []
94
+ for fp in sorted(glob.glob(os.path.join(output_dir, "horizontal", "*.nc"))):
95
+ with xr.open_dataset(fp) as ds:
96
+ animator_obj["2d"].append(ds.load())
97
 
98
+
99
+ # animator_obj["2d"] = [xr.open_dataset(fp).load()
100
+ # for fp in sorted(glob.glob(os.path.join(output_dir, "horizontal", "*.nc")))]
101
 
102
+ with open(os.path.join(MEDIA_DIR, "last_run.txt"), "w") as f:
103
+ f.write(zip_path)
 
 
 
 
 
104
 
105
+ status.object += f" | ✅ Loaded 3D: {len(animator_obj['3d'])} & 2D: {len(animator_obj['2d'])}"
106
+ update_media_tabs()
107
+ except Exception as e:
108
+ logging.exception("Error during ZIP processing")
109
+ status.object = f"❌ Processing failed: {e}"
110
 
111
+ def reset_app(event=None):
112
+ animator_obj.clear()
113
+ file_input.value = None
114
+ status.object = "🔄 App has been reset."
115
+ for folder in ["ash_output", "2D", "3D"]:
116
+ shutil.rmtree(os.path.join(MEDIA_DIR, folder), ignore_errors=True)
117
+ if os.path.exists(os.path.join(MEDIA_DIR, "last_run.txt")):
118
+ os.remove(os.path.join(MEDIA_DIR, "last_run.txt"))
119
+ update_media_tabs()
120
 
121
+ def restore_previous_session():
122
+ try:
123
+ state_file = os.path.join(MEDIA_DIR, "last_run.txt")
124
+ if os.path.exists(state_file):
125
+ with open(state_file) as f:
126
+ zip_path = f.read().strip()
127
+ if os.path.exists(zip_path):
128
+ output_dir = os.path.join("./", "ash_output")
129
 
130
+ animator_obj["3d"] = []
131
+ for fp in sorted(glob.glob(os.path.join(output_dir, "3D", "*.nc"))):
132
+ with xr.open_dataset(fp) as ds:
133
+ animator_obj["3d"].append(ds.load())
 
 
 
134
 
135
+ animator_obj["2d"] = []
136
+ for fp in sorted(glob.glob(os.path.join(output_dir, "horizontal", "*.nc"))):
137
+ with xr.open_dataset(fp) as ds:
138
+ animator_obj["2d"].append(ds.load())
139
 
140
+ status.object = f"🔁 Restored previous session from {os.path.basename(zip_path)}"
141
+ update_media_tabs()
142
+ except Exception as e:
143
+ logging.exception("Error restoring previous session")
144
+ status.object = f"⚠️ Could not restore previous session: {e}"
145
 
146
+ process_button.on_click(process_zip)
147
+ reset_button.on_click(reset_app)
148
 
149
+ # ---------------- Animator Builders ----------------
150
+ def build_animator_3d():
151
+ ds = animator_obj["3d"]
152
+ attrs = ds[0].attrs
153
+ lons, lats, grid = create_grid(attrs)
154
+ return SimpleNamespace(
155
+ datasets=ds,
156
+ levels=ds[0].altitude.values,
157
+ lons=lons,
158
+ lats=lats,
159
+ lon_grid=grid[0],
160
+ lat_grid=grid[1],
161
  )
162
+
163
+ def build_animator_2d():
164
+ ds = animator_obj["2d"]
165
+ lat_grid, lon_grid = xr.broadcast(ds[0]["latitude"], ds[0]["longitude"])
166
+ return SimpleNamespace(
167
+ datasets=ds,
168
+ lats=ds[0]["latitude"].values,
169
+ lons=ds[0]["longitude"].values,
170
+ lat_grid=lat_grid.values,
171
+ lon_grid=lon_grid.values,
172
  )
 
 
 
 
173
 
174
+ # ---------------- Plot Functions ----------------
175
+ def plot_z_level():
176
+ try:
177
+ animator = build_animator_3d()
178
+ out = os.path.join(MEDIA_DIR, "3D")
179
+ os.makedirs(out, exist_ok=True)
180
+ Plot_3DField_Data(animator, out, cmap_select_3d.value,
181
+ threshold_slider_3d.value, zoom_slider_3d.value,
182
+ fps_slider_3d.value).plot_single_z_level(
183
+ Altitude_slider.value, f"ash_altitude{Altitude_slider.value}km_runTimes.gif")
184
+ update_media_tabs()
185
+ status.object = "✅ Z-Level animation created."
186
+ except Exception as e:
187
+ logging.exception("Error in plot_z_level")
188
+ status.object = f"❌ Error in Z-Level animation: {e}"
189
 
190
+ def plot_vertical_profile():
 
 
 
 
191
  try:
192
+ animator = build_animator_3d()
193
+ out = os.path.join(MEDIA_DIR, "3D")
194
+ os.makedirs(out, exist_ok=True)
195
+ plotter = Plot_3DField_Data(animator, out, cmap_select_3d.value, fps_slider_3d.value,
196
+ threshold_slider_3d.value, zoom_level=zoom_slider_3d.value,
197
+ basemap_type='basemap')
198
+ plotter.plot_vertical_profile_at_time(Altitude_slider.value - 1,
199
+ filename=f"T{Altitude_slider.value - 1}_profile.gif")
200
+ update_media_tabs()
201
+ status.object = "✅ Vertical profile animation created."
202
+ except Exception as e:
203
+ logging.exception("Error in plot_vertical_profile")
204
+ status.object = f"❌ Error in vertical profile animation: {e}"
205
+
206
+ def animate_all_altitude_profiles():
207
+ try:
208
+ animator = build_animator_3d()
209
+ out = os.path.join(MEDIA_DIR, "3D")
210
+ Plot_3DField_Data(animator, out, cmap_select_3d.value,
211
+ threshold_slider_3d.value, zoom_slider_3d.value).animate_all_altitude_profiles()
212
+ update_media_tabs()
213
+ status.object = " All altitude profile animations created."
214
+ except Exception as e:
215
+ logging.exception("Error in animate_all_altitude_profiles")
216
+ status.object = f"❌ Error animating all altitude profiles: {e}"
217
+
218
+ def export_jpg_frames():
219
+ try:
220
+ animator = build_animator_3d()
221
+ out = os.path.join(MEDIA_DIR, "3D")
222
+ Plot_3DField_Data(animator, out, cmap_select_3d.value,
223
+ threshold_slider_3d.value, zoom_slider_3d.value).export_frames_as_jpgs(include_metadata=True)
224
+ update_media_tabs()
225
+ status.object = "✅ JPG frames exported."
226
+ except Exception as e:
227
+ logging.exception("Error exporting JPG frames")
228
+ status.object = f"❌ Error exporting JPG frames: {e}"
229
+
230
+ def plot_2d_field(field):
231
+ try:
232
+ animator = build_animator_2d()
233
+ out = os.path.join(MEDIA_DIR, "2D")
234
+ Plot_Horizontal_Data(animator, out, cmap_select_2d.value, fps_slider_2d.value,
235
+ include_metadata=True, threshold=threshold_slider_2d.value,
236
+ zoom_width_deg=6.0, zoom_height_deg=6.0,
237
+ zoom_level=zoom_slider_2d.value,
238
+ static_frame_export=True).plot_single_field_over_time(field, f"{field}.gif")
239
+ update_media_tabs()
240
+ status.object = f"✅ 2D field `{field}` animation created."
241
+ except Exception as e:
242
+ logging.exception(f"Error in plot_2d_field: {field}")
243
+ status.object = f"❌ Error in 2D field `{field}` animation: {e}"
244
+
245
+ # ---------------- Layout ----------------
246
+ def human_readable_size(size):
247
+ for unit in ['B', 'KB', 'MB', 'GB']:
248
+ if size < 1024: return f"{size:.1f} {unit}"
249
+ size /= 1024
250
+ return f"{size:.1f} TB"
251
+
252
+ def generate_output_gallery(base_folder):
253
+ grouped = defaultdict(lambda: defaultdict(list))
254
+ for root, _, files in os.walk(os.path.join(MEDIA_DIR, base_folder)):
255
+ for file in files:
256
+ ext = os.path.splitext(file)[1].lower()
257
+ subfolder = os.path.relpath(root, MEDIA_DIR)
258
+ grouped[subfolder][ext].append(os.path.join(root, file))
259
+
260
+ folder_tabs = []
261
+ for subfolder, ext_files in sorted(grouped.items()):
262
+ type_tabs = []
263
+ for ext, paths in sorted(ext_files.items()):
264
+ previews = []
265
+ for path in sorted(paths, key=os.path.getmtime, reverse=True):
266
+ size = human_readable_size(os.path.getsize(path))
267
+ mod = datetime.fromtimestamp(os.path.getmtime(path)).strftime("%Y-%m-%d %H:%M")
268
+ title = f"**{os.path.basename(path)}**\\n_{size}, {mod}_"
269
+ download = pn.widgets.FileDownload(label="⬇", file=path, filename=os.path.basename(path), width=60)
270
+ if ext in [".gif", ".png", ".jpg", ".jpeg"]:
271
+ preview = pn.pane.Image(path, width=320)
272
+ else:
273
+ with open(path, "r", errors="ignore") as f:
274
+ content = f.read(2048)
275
+ preview = pn.pane.PreText(content, width=320)
276
+ card = pn.Card(pn.pane.Markdown(title), preview, pn.Row(download), width=360)
277
+ previews.append(card)
278
+ type_tabs.append((ext.upper(), pn.GridBox(*previews, ncols=2)))
279
+ folder_tabs.append((subfolder, pn.Tabs(*type_tabs)))
280
+ return pn.Tabs(*folder_tabs)
281
+
282
+ def update_media_tabs():
283
+ media_tab_2d.objects[:] = [generate_output_gallery("2D")]
284
+ media_tab_3d.objects[:] = [generate_output_gallery("3D")]
285
+
286
+ media_tab_2d = pn.Column(generate_output_gallery("2D"))
287
+ media_tab_3d = pn.Column(generate_output_gallery("3D"))
288
+
289
+ media_tab = pn.Tabs(
290
+ ("2D Outputs", media_tab_2d),
291
+ ("3D Outputs", media_tab_3d)
292
  )
293
 
294
+ tab3d = pn.Column(
295
+ threshold_slider_3d, zoom_slider_3d, fps_slider_3d, Altitude_slider, cmap_select_3d,
296
+ pn.widgets.Button(name="🎞 Generate animation at selected altitude level", button_type="primary", on_click=lambda e: tab3d.append(plot_z_level())),
297
+ pn.widgets.Button(name="📈 Generate vertical profile animation at time index", button_type="primary", on_click=lambda e: tab3d.append(plot_vertical_profile())),
298
+ pn.widgets.Button(name="📊 Generate all altitude level animations", button_type="primary", on_click=lambda e: tab3d.append(animate_all_altitude_profiles())),
299
+ pn.widgets.Button(name="🖼 Export all animation frames as JPG", button_type="primary", on_click=lambda e: tab3d.append(export_jpg_frames())),
300
  )
301
 
302
+ tab2d = pn.Column(
303
+ threshold_slider_2d, zoom_slider_2d, fps_slider_2d, cmap_select_2d,
304
+ pn.widgets.Button(name="🌫 Animate Air Concentration", button_type="primary", on_click=lambda e: tab2d.append(plot_2d_field("air_concentration"))),
305
+ pn.widgets.Button(name="🌧 Animate Dry Deposition Rate", button_type="primary", on_click=lambda e: tab2d.append(plot_2d_field("dry_deposition_rate"))),
306
+ pn.widgets.Button(name="💧 Animate Wet Deposition Rate", button_type="primary", on_click=lambda e: tab2d.append(plot_2d_field("wet_deposition_rate"))),
307
  )
308
 
309
+ tabs = pn.Tabs(
310
+ ("🧱 3D Field", tab3d),
311
+ ("🌍 2D Field", tab2d),
312
+ ("📁 Media Viewer", media_tab)
 
 
 
 
 
 
 
 
 
313
  )
314
 
315
+ sidebar = pn.Column("## 🌋 NAME Ash Visualizer", file_input, process_button, reset_button, download_button, log_link, status)
316
+
317
+ restore_previous_session()
318
+
319
+ pn.template.FastListTemplate(
320
+ title="NAME Visualizer Dashboard",
321
+ sidebar=sidebar,
322
+ main=[tabs],
323
+ ).servable()
ash_animator/__init__.py ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ # Auto-generated __init__.py to import all modules
3
+ from .basemaps import *
4
+ from .converter import *
5
+ from .interpolation import *
6
+ from .plot_3dfield_data import *
7
+ from .plot_horizontal_data import *
8
+ from .utils import *
9
+ from .animation_all import *
10
+ from .animation_single import *
11
+ from .animation_vertical import *
12
+ from .export import *
ash_animator/__pycache__/__init__.cpython-312.pyc ADDED
Binary file (522 Bytes). View file
 
ash_animator/__pycache__/animation_all.cpython-312.pyc ADDED
Binary file (12.2 kB). View file
 
ash_animator/__pycache__/animation_single.cpython-312.pyc ADDED
Binary file (11.5 kB). View file
 
ash_animator/__pycache__/animation_vertical.cpython-312.pyc ADDED
Binary file (14.3 kB). View file
 
ash_animator/__pycache__/basemaps.cpython-312.pyc ADDED
Binary file (5.01 kB). View file
 
ash_animator/__pycache__/converter.cpython-312.pyc ADDED
Binary file (14.2 kB). View file
 
ash_animator/__pycache__/export.cpython-312.pyc ADDED
Binary file (9.3 kB). View file
 
ash_animator/__pycache__/interpolation.cpython-312.pyc ADDED
Binary file (1.03 kB). View file
 
ash_animator/__pycache__/plot_3dfield_data.cpython-312.pyc ADDED
Binary file (33.5 kB). View file
 
ash_animator/__pycache__/plot_horizontal_data.cpython-312.pyc ADDED
Binary file (30.8 kB). View file
 
ash_animator/__pycache__/utils.cpython-312.pyc ADDED
Binary file (1.61 kB). View file
 
ash_animator/animation_all.py ADDED
@@ -0,0 +1,516 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ # import os
3
+ # import numpy as np
4
+ # import matplotlib.pyplot as plt
5
+ # import matplotlib.animation as animation
6
+ # import matplotlib.ticker as mticker
7
+ # import cartopy.crs as ccrs
8
+ # import cartopy.feature as cfeature
9
+ # from adjustText import adjust_text
10
+ # import cartopy.io.shapereader as shpreader
11
+ # from .interpolation import interpolate_grid
12
+ # from .basemaps import draw_etopo_basemap
13
+
14
+ # def animate_all_z_levels(animator, output_folder: str, fps: int = 2, threshold: float = 0.1):
15
+ # os.makedirs(output_folder, exist_ok=True)
16
+
17
+ # countries_shp = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries')
18
+ # reader = shpreader.Reader(countries_shp)
19
+ # country_geoms = list(reader.records())
20
+
21
+ # for z_index, z_val in enumerate(animator.levels):
22
+ # fig = plt.figure(figsize=(16, 7))
23
+ # proj = ccrs.PlateCarree()
24
+ # ax1 = fig.add_subplot(1, 2, 1, projection=proj)
25
+ # ax2 = fig.add_subplot(1, 2, 2, projection=proj)
26
+
27
+ # valid_mask = np.stack([
28
+ # ds['ash_concentration'].values[z_index] for ds in animator.datasets
29
+ # ]).max(axis=0) > 0
30
+ # y_idx, x_idx = np.where(valid_mask)
31
+
32
+ # if y_idx.size == 0 or x_idx.size == 0:
33
+ # print(f"Z level {z_val} km has no valid data. Skipping...")
34
+ # plt.close()
35
+ # continue
36
+
37
+ # y_min, y_max = y_idx.min(), y_idx.max()
38
+ # x_min, x_max = x_idx.min(), x_idx.max()
39
+
40
+ # buffer_y = int((y_max - y_min) * 0.5)
41
+ # buffer_x = int((x_max - x_min) * 0.5)
42
+
43
+ # y_start = max(0, y_min - buffer_y)
44
+ # y_end = min(animator.lat_grid.shape[0], y_max + buffer_y + 1)
45
+ # x_start = max(0, x_min - buffer_x)
46
+ # x_end = min(animator.lon_grid.shape[1], x_max + buffer_x + 1)
47
+
48
+ # lat_zoom = animator.lats[y_start:y_end]
49
+ # lon_zoom = animator.lons[x_start:x_end]
50
+ # lon_zoom_grid, lat_zoom_grid = np.meshgrid(lon_zoom, lat_zoom)
51
+
52
+ # valid_frames = []
53
+ # for t in range(len(animator.datasets)):
54
+ # data = animator.datasets[t]['ash_concentration'].values[z_index]
55
+ # interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
56
+ # interp = np.where(interp < 0, np.nan, interp)
57
+ # if np.isfinite(interp).sum() > 0:
58
+ # valid_frames.append(t)
59
+
60
+ # if not valid_frames:
61
+ # print(f"No valid frames for Z={z_val} km. Skipping animation.")
62
+ # plt.close()
63
+ # continue
64
+
65
+ # def update(t):
66
+ # ax1.clear()
67
+ # ax2.clear()
68
+
69
+ # data = animator.datasets[t]['ash_concentration'].values[z_index]
70
+ # interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
71
+ # interp = np.where(interp < 0, np.nan, interp)
72
+ # zoom_plot = interp[y_start:y_end, x_start:x_end]
73
+
74
+ # valid_vals = interp[np.isfinite(interp)]
75
+ # if valid_vals.size == 0:
76
+ # return []
77
+
78
+ # min_val = np.nanmin(valid_vals)
79
+ # max_val = np.nanmax(valid_vals)
80
+ # log_cutoff = 1e-3
81
+ # log_ratio = max_val / (min_val + 1e-6)
82
+ # use_log = min_val > log_cutoff and log_ratio > 100
83
+
84
+ # if use_log:
85
+ # data_for_plot = np.where(interp > log_cutoff, interp, np.nan)
86
+ # levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20)
87
+ # scale_label = "Hybrid Log"
88
+ # else:
89
+ # data_for_plot = interp
90
+ # levels = np.linspace(0, max_val, 20)
91
+ # scale_label = "Linear"
92
+
93
+ # draw_etopo_basemap(ax1, mode='stock')
94
+ # draw_etopo_basemap(ax2, mode='stock')
95
+
96
+ # c1 = ax1.contourf(animator.lons, animator.lats, data_for_plot, levels=levels,
97
+ # cmap="rainbow", alpha=0.6, transform=proj)
98
+ # ax1.contour(animator.lons, animator.lats, data_for_plot, levels=levels,
99
+ # colors='black', linewidths=0.5, transform=proj)
100
+ # ax1.set_title(f"T{t+1} | Alt: {z_val} km (Full - {scale_label})")
101
+ # ax1.set_extent([animator.lons.min(), animator.lons.max(), animator.lats.min(), animator.lats.max()])
102
+ # ax1.coastlines()
103
+ # ax1.add_feature(cfeature.BORDERS, linestyle=':')
104
+ # ax1.add_feature(cfeature.LAND)
105
+ # ax1.add_feature(cfeature.OCEAN)
106
+
107
+ # c2 = ax2.contourf(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=levels,
108
+ # cmap="rainbow", alpha=0.4, transform=proj)
109
+ # ax2.contour(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=levels,
110
+ # colors='black', linewidths=0.5, transform=proj)
111
+ # ax2.set_title(f"T{t+1} | Alt: {z_val} km (Zoom - {scale_label})")
112
+ # ax2.set_extent([lon_zoom.min(), lon_zoom.max(), lat_zoom.min(), lat_zoom.max()])
113
+ # ax2.coastlines()
114
+ # ax2.add_feature(cfeature.BORDERS, linestyle=':')
115
+ # ax2.add_feature(cfeature.LAND)
116
+ # ax2.add_feature(cfeature.OCEAN)
117
+
118
+ # ax2.text(animator.lons[0], animator.lats[0], animator.country_label, fontsize=9, color='white',
119
+ # transform=proj, bbox=dict(facecolor='black', alpha=0.5))
120
+
121
+ # texts_ax1, texts_ax2 = [], []
122
+ # for country in country_geoms:
123
+ # name = country.attributes['NAME_LONG']
124
+ # geom = country.geometry
125
+ # try:
126
+ # lon, lat = geom.centroid.x, geom.centroid.y
127
+ # if (lon_zoom.min() <= lon <= lon_zoom.max()) and (lat_zoom.min() <= lat <= lat_zoom.max()):
128
+ # text = ax2.text(lon, lat, name, fontsize=6, transform=proj,
129
+ # ha='center', va='center', color='white',
130
+ # bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
131
+ # texts_ax2.append(text)
132
+
133
+ # if (animator.lons.min() <= lon <= animator.lons.max()) and (animator.lats.min() <= lat <= animator.lats.max()):
134
+ # text = ax1.text(lon, lat, name, fontsize=6, transform=proj,
135
+ # ha='center', va='center', color='white',
136
+ # bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
137
+ # texts_ax1.append(text)
138
+ # except:
139
+ # continue
140
+
141
+ # adjust_text(texts_ax1, ax=ax1, only_move={'points': 'y', 'text': 'y'},
142
+ # arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
143
+ # adjust_text(texts_ax2, ax=ax2, only_move={'points': 'y', 'text': 'y'},
144
+ # arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
145
+
146
+ # if np.nanmax(valid_vals) > threshold:
147
+ # alert_text = f"⚠ Exceeds {threshold} g/m³!"
148
+ # for ax in [ax1, ax2]:
149
+ # ax.text(0.99, 0.01, alert_text, transform=ax.transAxes,
150
+ # ha='right', va='bottom', fontsize=10, color='red',
151
+ # bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
152
+ # ax1.contour(animator.lons, animator.lats, interp, levels=[threshold], colors='red', linewidths=2, transform=proj)
153
+ # ax2.contour(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=[threshold], colors='red', linewidths=2, transform=proj)
154
+
155
+ # if not hasattr(update, "colorbar"):
156
+ # update.colorbar = fig.colorbar(c1, ax=[ax1, ax2], orientation='vertical',
157
+ # label="Ash concentration (g/m³)")
158
+ # formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
159
+ # update.colorbar.ax.yaxis.set_major_formatter(formatter)
160
+ # if use_log:
161
+ # update.colorbar.ax.text(1.05, 1.02, "log scale", transform=update.colorbar.ax.transAxes,
162
+ # fontsize=9, color='gray', rotation=90, ha='left', va='bottom')
163
+
164
+ # return []
165
+
166
+ # ani = animation.FuncAnimation(fig, update, frames=valid_frames, blit=False)
167
+ # gif_path = os.path.join(output_folder, f"ash_T1-Tn_Z{z_index+1}.gif")
168
+ # ani.save(gif_path, writer='pillow', fps=fps)
169
+ # plt.close()
170
+ # print(f"✅ Saved animation for Z={z_val} km to {gif_path}")
171
+ ###################################################################################################################
172
+ # import os
173
+ # import numpy as np
174
+ # import matplotlib.pyplot as plt
175
+ # import matplotlib.animation as animation
176
+ # import matplotlib.ticker as mticker
177
+ # import cartopy.crs as ccrs
178
+ # import cartopy.feature as cfeature
179
+ # from adjustText import adjust_text
180
+ # import cartopy.io.shapereader as shpreader
181
+ # from .interpolation import interpolate_grid
182
+ # from .basemaps import draw_etopo_basemap
183
+
184
+ # def animate_all_z_levels(animator, output_folder: str, fps: int = 2, threshold: float = 0.1):
185
+ # os.makedirs(output_folder, exist_ok=True)
186
+
187
+ # countries_shp = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries')
188
+ # reader = shpreader.Reader(countries_shp)
189
+ # country_geoms = list(reader.records())
190
+
191
+ # # Compute consistent zoom window across all z-levels and time frames
192
+ # valid_mask_all = np.zeros_like(animator.datasets[0]['ash_concentration'].values[0], dtype=bool)
193
+ # for ds in animator.datasets:
194
+ # for z in range(len(animator.levels)):
195
+ # valid_mask_all |= ds['ash_concentration'].values[z] > 0
196
+
197
+ # y_idx_all, x_idx_all = np.where(valid_mask_all)
198
+ # if y_idx_all.size == 0 or x_idx_all.size == 0:
199
+ # raise ValueError("No valid data found across any Z level or frame.")
200
+
201
+ # y_min, y_max = y_idx_all.min(), y_idx_all.max()
202
+ # x_min, x_max = x_idx_all.min(), x_idx_all.max()
203
+ # buffer_y = int((y_max - y_min) * 0.5)
204
+ # buffer_x = int((x_max - x_min) * 0.5)
205
+
206
+ # y_start = max(0, y_min - buffer_y)
207
+ # y_end = min(animator.lat_grid.shape[0], y_max + buffer_y + 1)
208
+ # x_start = max(0, x_min - buffer_x)
209
+ # x_end = min(animator.lon_grid.shape[1], x_max + buffer_x + 1)
210
+
211
+ # lat_zoom = animator.lats[y_start:y_end]
212
+ # lon_zoom = animator.lons[x_start:x_end]
213
+ # lon_zoom_grid, lat_zoom_grid = np.meshgrid(lon_zoom, lat_zoom)
214
+
215
+ # for z_index, z_val in enumerate(animator.levels):
216
+ # fig = plt.figure(figsize=(16, 7))
217
+ # proj = ccrs.PlateCarree()
218
+ # ax1 = fig.add_subplot(1, 2, 1, projection=proj)
219
+ # ax2 = fig.add_subplot(1, 2, 2, projection=proj)
220
+
221
+ # valid_frames = []
222
+ # for t in range(len(animator.datasets)):
223
+ # data = animator.datasets[t]['ash_concentration'].values[z_index]
224
+ # interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
225
+ # interp = np.where(interp < 0, np.nan, interp)
226
+ # if np.isfinite(interp).sum() > 0:
227
+ # valid_frames.append(t)
228
+
229
+ # if not valid_frames:
230
+ # print(f"No valid frames for Z={z_val} km. Skipping animation.")
231
+ # plt.close()
232
+ # continue
233
+
234
+ # def update(t):
235
+ # ax1.clear()
236
+ # ax2.clear()
237
+
238
+ # data = animator.datasets[t]['ash_concentration'].values[z_index]
239
+ # interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
240
+ # interp = np.where(interp < 0, np.nan, interp)
241
+ # zoom_plot = interp[y_start:y_end, x_start:x_end]
242
+
243
+ # valid_vals = interp[np.isfinite(interp)]
244
+ # if valid_vals.size == 0:
245
+ # return []
246
+
247
+ # min_val = np.nanmin(valid_vals)
248
+ # max_val = np.nanmax(valid_vals)
249
+ # log_cutoff = 1e-3
250
+ # log_ratio = max_val / (min_val + 1e-6)
251
+ # use_log = min_val > log_cutoff and log_ratio > 100
252
+
253
+ # if use_log:
254
+ # data_for_plot = np.where(interp > log_cutoff, interp, np.nan)
255
+ # levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20)
256
+ # scale_label = "Hybrid Log"
257
+ # else:
258
+ # data_for_plot = interp
259
+ # levels = np.linspace(0, max_val, 20)
260
+ # scale_label = "Linear"
261
+
262
+ # draw_etopo_basemap(ax1, mode='stock')
263
+ # draw_etopo_basemap(ax2, mode='stock')
264
+
265
+ # c1 = ax1.contourf(animator.lons, animator.lats, data_for_plot, levels=levels,
266
+ # cmap="rainbow", alpha=0.6, transform=proj)
267
+ # ax1.contour(animator.lons, animator.lats, data_for_plot, levels=levels,
268
+ # colors='black', linewidths=0.5, transform=proj)
269
+ # ax1.set_title(f"T{t+1} | Alt: {z_val} km (Full - {scale_label})")
270
+ # ax1.set_extent([animator.lons.min(), animator.lons.max(), animator.lats.min(), animator.lats.max()])
271
+ # ax1.coastlines()
272
+ # ax1.add_feature(cfeature.BORDERS, linestyle=':')
273
+ # ax1.add_feature(cfeature.LAND)
274
+ # ax1.add_feature(cfeature.OCEAN)
275
+
276
+ # c2 = ax2.contourf(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=levels,
277
+ # cmap="rainbow", alpha=0.4, transform=proj)
278
+ # ax2.contour(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=levels,
279
+ # colors='black', linewidths=0.5, transform=proj)
280
+ # ax2.set_title(f"T{t+1} | Alt: {z_val} km (Zoom - {scale_label})")
281
+ # ax2.set_extent([lon_zoom.min(), lon_zoom.max(), lat_zoom.min(), lat_zoom.max()])
282
+ # ax2.coastlines()
283
+ # ax2.add_feature(cfeature.BORDERS, linestyle=':')
284
+ # ax2.add_feature(cfeature.LAND)
285
+ # ax2.add_feature(cfeature.OCEAN)
286
+
287
+ # ax2.text(animator.lons[0], animator.lats[0], animator.country_label, fontsize=9, color='white',
288
+ # transform=proj, bbox=dict(facecolor='black', alpha=0.5))
289
+
290
+ # texts_ax1, texts_ax2 = [], []
291
+ # for country in country_geoms:
292
+ # name = country.attributes['NAME_LONG']
293
+ # geom = country.geometry
294
+ # try:
295
+ # lon, lat = geom.centroid.x, geom.centroid.y
296
+ # if (lon_zoom.min() <= lon <= lon_zoom.max()) and (lat_zoom.min() <= lat <= lat_zoom.max()):
297
+ # text = ax2.text(lon, lat, name, fontsize=6, transform=proj,
298
+ # ha='center', va='center', color='white',
299
+ # bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
300
+ # texts_ax2.append(text)
301
+
302
+ # if (animator.lons.min() <= lon <= animator.lons.max()) and (animator.lats.min() <= lat <= animator.lats.max()):
303
+ # text = ax1.text(lon, lat, name, fontsize=6, transform=proj,
304
+ # ha='center', va='center', color='white',
305
+ # bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
306
+ # texts_ax1.append(text)
307
+ # except:
308
+ # continue
309
+
310
+ # adjust_text(texts_ax1, ax=ax1, only_move={'points': 'y', 'text': 'y'},
311
+ # arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
312
+ # adjust_text(texts_ax2, ax=ax2, only_move={'points': 'y', 'text': 'y'},
313
+ # arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
314
+
315
+ # if np.nanmax(valid_vals) > threshold:
316
+ # alert_text = f"⚠ Exceeds {threshold} g/m³!"
317
+ # for ax in [ax1, ax2]:
318
+ # ax.text(0.99, 0.01, alert_text, transform=ax.transAxes,
319
+ # ha='right', va='bottom', fontsize=10, color='red',
320
+ # bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
321
+ # ax1.contour(animator.lons, animator.lats, interp, levels=[threshold], colors='red', linewidths=2, transform=proj)
322
+ # ax2.contour(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=[threshold], colors='red', linewidths=2, transform=proj)
323
+
324
+ # if not hasattr(update, "colorbar"):
325
+ # update.colorbar = fig.colorbar(c1, ax=[ax1, ax2], orientation='vertical',
326
+ # label="Ash concentration (g/m³)")
327
+ # formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
328
+ # update.colorbar.ax.yaxis.set_major_formatter(formatter)
329
+ # if use_log:
330
+ # update.colorbar.ax.text(1.05, 1.02, "log scale", transform=update.colorbar.ax.transAxes,
331
+ # fontsize=9, color='gray', rotation=90, ha='left', va='bottom')
332
+
333
+ # return []
334
+
335
+ # ani = animation.FuncAnimation(fig, update, frames=valid_frames, blit=False)
336
+ # gif_path = os.path.join(output_folder, f"ash_T1-Tn_Z{z_index+1}.gif")
337
+ # ani.save(gif_path, writer='pillow', fps=fps)
338
+ # plt.close()
339
+ # print(f"✅ Saved animation for Z={z_val} km to {gif_path}")
340
+
341
+
342
+ import os
343
+ import numpy as np
344
+ import matplotlib.pyplot as plt
345
+ import matplotlib.animation as animation
346
+ import matplotlib.ticker as mticker
347
+ import cartopy.crs as ccrs
348
+ import cartopy.feature as cfeature
349
+ from adjustText import adjust_text
350
+ import cartopy.io.shapereader as shpreader
351
+ from .interpolation import interpolate_grid
352
+ from .basemaps import draw_etopo_basemap
353
+
354
+ def animate_all_z_levels(animator, output_folder: str, fps: int = 2, threshold: float = 0.1,
355
+ zoom_width_deg: float = 6.0, zoom_height_deg: float = 6.0):
356
+ os.makedirs(output_folder, exist_ok=True)
357
+
358
+ countries_shp = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries')
359
+ reader = shpreader.Reader(countries_shp)
360
+ country_geoms = list(reader.records())
361
+
362
+ # Find the most active region (max concentration point)
363
+ max_conc = -np.inf
364
+ center_lat = center_lon = None
365
+ for ds in animator.datasets:
366
+ for z in range(len(animator.levels)):
367
+ data = ds['ash_concentration'].values[z]
368
+ if np.max(data) > max_conc:
369
+ max_conc = np.max(data)
370
+ max_idx = np.unravel_index(np.argmax(data), data.shape)
371
+ center_lat = animator.lat_grid[max_idx]
372
+ center_lon = animator.lon_grid[max_idx]
373
+
374
+ if center_lat is None or center_lon is None:
375
+ raise ValueError("No valid concentration found to determine zoom center.")
376
+
377
+ # Compute fixed zoom extents in lat/lon degrees
378
+ lon_zoom_min = center_lon - zoom_width_deg / 2
379
+ lon_zoom_max = center_lon + zoom_width_deg / 2
380
+ lat_zoom_min = center_lat - zoom_height_deg / 2
381
+ lat_zoom_max = center_lat + zoom_height_deg / 2
382
+
383
+ # Create zoom grids for plotting
384
+ lat_zoom = animator.lats[(animator.lats >= lat_zoom_min) & (animator.lats <= lat_zoom_max)]
385
+ lon_zoom = animator.lons[(animator.lons >= lon_zoom_min) & (animator.lons <= lon_zoom_max)]
386
+ lon_zoom_grid, lat_zoom_grid = np.meshgrid(lon_zoom, lat_zoom)
387
+
388
+ for z_index, z_val in enumerate(animator.levels):
389
+ fig = plt.figure(figsize=(16, 7))
390
+ proj = ccrs.PlateCarree()
391
+ ax1 = fig.add_subplot(1, 2, 1, projection=proj)
392
+ ax2 = fig.add_subplot(1, 2, 2, projection=proj)
393
+
394
+ valid_frames = []
395
+ for t in range(len(animator.datasets)):
396
+ data = animator.datasets[t]['ash_concentration'].values[z_index]
397
+ interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
398
+ interp = np.where(interp < 0, np.nan, interp)
399
+ if np.isfinite(interp).sum() > 0:
400
+ valid_frames.append(t)
401
+
402
+ if not valid_frames:
403
+ print(f"No valid frames for Z={z_val} km. Skipping animation.")
404
+ plt.close()
405
+ continue
406
+
407
+ def update(t):
408
+ ax1.clear()
409
+ ax2.clear()
410
+
411
+ data = animator.datasets[t]['ash_concentration'].values[z_index]
412
+ interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
413
+ interp = np.where(interp < 0, np.nan, interp)
414
+
415
+ # Extract zoom window from interpolated data
416
+ lat_idx = np.where((animator.lats >= lat_zoom_min) & (animator.lats <= lat_zoom_max))[0]
417
+ lon_idx = np.where((animator.lons >= lon_zoom_min) & (animator.lons <= lon_zoom_max))[0]
418
+ zoom_plot = interp[np.ix_(lat_idx, lon_idx)]
419
+
420
+ valid_vals = interp[np.isfinite(interp)]
421
+ if valid_vals.size == 0:
422
+ return []
423
+
424
+ min_val = np.nanmin(valid_vals)
425
+ max_val = np.nanmax(valid_vals)
426
+ log_cutoff = 1e-3
427
+ log_ratio = max_val / (min_val + 1e-6)
428
+ use_log = min_val > log_cutoff and log_ratio > 100
429
+
430
+ if use_log:
431
+ data_for_plot = np.where(interp > log_cutoff, interp, np.nan)
432
+ levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20)
433
+ scale_label = "Hybrid Log"
434
+ else:
435
+ data_for_plot = interp
436
+ levels = np.linspace(0, max_val, 20)
437
+ scale_label = "Linear"
438
+
439
+ draw_etopo_basemap(ax1, mode='stock')
440
+ draw_etopo_basemap(ax2, mode='stock')
441
+
442
+ c1 = ax1.contourf(animator.lons, animator.lats, data_for_plot, levels=levels,
443
+ cmap="rainbow", alpha=0.6, transform=proj)
444
+ ax1.contour(animator.lons, animator.lats, data_for_plot, levels=levels,
445
+ colors='black', linewidths=0.5, transform=proj)
446
+ ax1.set_title(f"T{t+1} | Alt: {z_val} km (Full - {scale_label})")
447
+ ax1.set_extent([animator.lons.min(), animator.lons.max(), animator.lats.min(), animator.lats.max()])
448
+ ax1.coastlines()
449
+ ax1.add_feature(cfeature.BORDERS, linestyle=':')
450
+ ax1.add_feature(cfeature.LAND)
451
+ ax1.add_feature(cfeature.OCEAN)
452
+
453
+ c2 = ax2.contourf(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=levels,
454
+ cmap="rainbow", alpha=0.4, transform=proj)
455
+ ax2.contour(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=levels,
456
+ colors='black', linewidths=0.5, transform=proj)
457
+ ax2.set_title(f"T{t+1} | Alt: {z_val} km (Zoom - {scale_label})")
458
+ ax2.set_extent([lon_zoom_min, lon_zoom_max, lat_zoom_min, lat_zoom_max])
459
+ ax2.coastlines()
460
+ ax2.add_feature(cfeature.BORDERS, linestyle=':')
461
+ ax2.add_feature(cfeature.LAND)
462
+ ax2.add_feature(cfeature.OCEAN)
463
+
464
+ ax2.text(animator.lons[0], animator.lats[0], animator.country_label, fontsize=9, color='white',
465
+ transform=proj, bbox=dict(facecolor='black', alpha=0.5))
466
+
467
+ texts_ax1, texts_ax2 = [], []
468
+ for country in country_geoms:
469
+ name = country.attributes['NAME_LONG']
470
+ geom = country.geometry
471
+ try:
472
+ lon, lat = geom.centroid.x, geom.centroid.y
473
+ if (lon_zoom_min <= lon <= lon_zoom_max) and (lat_zoom_min <= lat <= lat_zoom_max):
474
+ text = ax2.text(lon, lat, name, fontsize=6, transform=proj,
475
+ ha='center', va='center', color='white',
476
+ bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
477
+ texts_ax2.append(text)
478
+
479
+ if (animator.lons.min() <= lon <= animator.lons.max()) and (animator.lats.min() <= lat <= animator.lats.max()):
480
+ text = ax1.text(lon, lat, name, fontsize=6, transform=proj,
481
+ ha='center', va='center', color='white',
482
+ bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
483
+ texts_ax1.append(text)
484
+ except:
485
+ continue
486
+
487
+ adjust_text(texts_ax1, ax=ax1, only_move={'points': 'y', 'text': 'y'},
488
+ arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
489
+ adjust_text(texts_ax2, ax=ax2, only_move={'points': 'y', 'text': 'y'},
490
+ arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
491
+
492
+ if np.nanmax(valid_vals) > threshold:
493
+ alert_text = f"⚠ Exceeds {threshold} g/m³!"
494
+ for ax in [ax1, ax2]:
495
+ ax.text(0.99, 0.01, alert_text, transform=ax.transAxes,
496
+ ha='right', va='bottom', fontsize=10, color='red',
497
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
498
+ ax1.contour(animator.lons, animator.lats, interp, levels=[threshold], colors='red', linewidths=2, transform=proj)
499
+ ax2.contour(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=[threshold], colors='red', linewidths=2, transform=proj)
500
+
501
+ if not hasattr(update, "colorbar"):
502
+ update.colorbar = fig.colorbar(c1, ax=[ax1, ax2], orientation='vertical',
503
+ label="Ash concentration (g/m³)")
504
+ formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
505
+ update.colorbar.ax.yaxis.set_major_formatter(formatter)
506
+ if use_log:
507
+ update.colorbar.ax.text(1.05, 1.02, "log scale", transform=update.colorbar.ax.transAxes,
508
+ fontsize=9, color='gray', rotation=90, ha='left', va='bottom')
509
+
510
+ return []
511
+
512
+ ani = animation.FuncAnimation(fig, update, frames=valid_frames, blit=False)
513
+ gif_path = os.path.join(output_folder, f"ash_T1-Tn_Z{z_index+1}.gif")
514
+ ani.save(gif_path, writer='pillow', fps=fps)
515
+ plt.close()
516
+ print(f"✅ Saved animation for Z={z_val} km to {gif_path}")
ash_animator/animation_single.py ADDED
@@ -0,0 +1,147 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import os
3
+ import numpy as np
4
+ import matplotlib.pyplot as plt
5
+ import matplotlib.animation as animation
6
+ import matplotlib.ticker as mticker
7
+ import cartopy.crs as ccrs
8
+ import cartopy.feature as cfeature
9
+ from .interpolation import interpolate_grid
10
+ from .basemaps import draw_etopo_basemap
11
+
12
+ def animate_single_z_level(animator, z_km: float, output_path: str, fps: int = 2, include_metadata: bool = True, threshold: float = 0.1):
13
+ if z_km not in animator.levels:
14
+ print(f"Z level {z_km} km not found in dataset.")
15
+ return
16
+
17
+ z_index = np.where(animator.levels == z_km)[0][0]
18
+ fig = plt.figure(figsize=(16, 7))
19
+ proj = ccrs.PlateCarree()
20
+ ax1 = fig.add_subplot(1, 2, 1, projection=proj)
21
+ ax2 = fig.add_subplot(1, 2, 2, projection=proj)
22
+
23
+ meta = animator.datasets[0].attrs
24
+ legend_text = (
25
+ f"Run name: {meta.get('run_name', 'N/A')}\n"
26
+ f"Run time: {meta.get('run_time', 'N/A')}\n"
27
+ f"Met data: {meta.get('met_data', 'N/A')}\n"
28
+ f"Start release: {meta.get('start_of_release', 'N/A')}\n"
29
+ f"End release: {meta.get('end_of_release', 'N/A')}\n"
30
+ f"Source strength: {meta.get('source_strength', 'N/A')} g/s\n"
31
+ f"Release loc: {meta.get('release_location', 'N/A')}\n"
32
+ f"Release height: {meta.get('release_height', 'N/A')} m asl\n"
33
+ f"Run duration: {meta.get('run_duration', 'N/A')}"
34
+ )
35
+
36
+ valid_mask = np.stack([
37
+ ds['ash_concentration'].values[z_index] for ds in animator.datasets
38
+ ]).max(axis=0) > 0
39
+ y_idx, x_idx = np.where(valid_mask)
40
+
41
+ if y_idx.size == 0 or x_idx.size == 0:
42
+ print(f"Z level {z_km} km has no valid data. Skipping...")
43
+ plt.close()
44
+ return
45
+
46
+ y_min, y_max = y_idx.min(), y_idx.max()
47
+ x_min, x_max = x_idx.min(), x_idx.max()
48
+ buffer_y = int((y_max - y_min) * 0.5)
49
+ buffer_x = int((x_max - x_min) * 0.5)
50
+ y_start = max(0, y_min - buffer_y)
51
+ y_end = min(animator.lat_grid.shape[0], y_max + buffer_y + 1)
52
+ x_start = max(0, x_min - buffer_x)
53
+ x_end = min(animator.lon_grid.shape[1], x_max + buffer_x + 1)
54
+
55
+ lat_zoom = animator.lats[y_start:y_end]
56
+ lon_zoom = animator.lons[x_start:x_end]
57
+ lon_zoom_grid, lat_zoom_grid = np.meshgrid(lon_zoom, lat_zoom)
58
+
59
+ valid_frames = []
60
+ for t in range(len(animator.datasets)):
61
+ interp = interpolate_grid(animator.datasets[t]['ash_concentration'].values[z_index],
62
+ animator.lon_grid, animator.lat_grid)
63
+ if np.isfinite(interp).sum() > 0:
64
+ valid_frames.append(t)
65
+
66
+ if not valid_frames:
67
+ print(f"No valid frames for Z={z_km} km. Skipping animation.")
68
+ plt.close()
69
+ return
70
+
71
+ def update(t):
72
+ ax1.clear()
73
+ ax2.clear()
74
+
75
+ data = animator.datasets[t]['ash_concentration'].values[z_index]
76
+ interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
77
+ interp = np.where(interp < 0, np.nan, interp)
78
+ zoom_plot = interp[y_start:y_end, x_start:x_end]
79
+
80
+ valid_vals = interp[np.isfinite(interp)]
81
+ if valid_vals.size == 0:
82
+ return []
83
+
84
+ min_val = np.nanmin(valid_vals)
85
+ max_val = np.nanmax(valid_vals)
86
+ log_cutoff = 1e-3
87
+ use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
88
+
89
+ levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
90
+ data_for_plot = np.where(interp > log_cutoff, interp, 0) if use_log else interp
91
+ scale_label = "Log" if use_log else "Linear"
92
+
93
+ draw_etopo_basemap(ax1, mode='stock')
94
+ draw_etopo_basemap(ax2, mode='stock')
95
+
96
+ c1 = ax1.contourf(animator.lons, animator.lats, data_for_plot, levels=levels,
97
+ cmap="rainbow", alpha=0.6, transform=proj)
98
+ ax1.set_title(f"T{t+1} | Alt: {z_km} km (Full - {scale_label})")
99
+ ax1.set_extent([animator.lons.min(), animator.lons.max(), animator.lats.min(), animator.lats.max()])
100
+ ax1.coastlines(); ax1.add_feature(cfeature.BORDERS); ax1.add_feature(cfeature.LAND); ax1.add_feature(cfeature.OCEAN)
101
+
102
+ c2 = ax2.contourf(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=levels,
103
+ cmap="rainbow", alpha=0.6, transform=proj)
104
+ ax2.set_title(f"T{t+1} | Alt: {z_km} km (Zoom - {scale_label})")
105
+ ax2.set_extent([lon_zoom.min(), lon_zoom.max(), lat_zoom.min(), lat_zoom.max()])
106
+ ax2.coastlines(); ax2.add_feature(cfeature.BORDERS); ax2.add_feature(cfeature.LAND); ax2.add_feature(cfeature.OCEAN)
107
+
108
+ if not hasattr(update, "colorbar"):
109
+ update.colorbar = fig.colorbar(c1, ax=[ax1, ax2], orientation='vertical',
110
+ label="Ash concentration (g/m³)")
111
+ formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
112
+ update.colorbar.ax.yaxis.set_major_formatter(formatter)
113
+ if use_log:
114
+ update.colorbar.ax.text(1.05, 1.02, "log scale", transform=update.colorbar.ax.transAxes,
115
+ fontsize=9, color='gray', rotation=90, ha='left', va='bottom')
116
+
117
+ if include_metadata:
118
+ ax1.annotate(legend_text, xy=(0.75, 0.99), xycoords='axes fraction',
119
+ fontsize=8, ha='left', va='top',
120
+ bbox=dict(boxstyle="round", facecolor="white", edgecolor="gray"))
121
+ for ax in [ax1, ax2]:
122
+ ax.text(0.01, 0.01,
123
+ f"Source: NAME\nRes: {animator.x_res:.2f}°\n{meta.get('run_name', 'N/A')}",
124
+ transform=ax.transAxes, fontsize=8, color='white',
125
+ bbox=dict(facecolor='black', alpha=0.5))
126
+
127
+ for ax in [ax1, ax2]:
128
+ ax.text(0.01, 0.98, f"Time step T{t+1}", transform=ax.transAxes,
129
+ fontsize=9, color='white', va='top', ha='left',
130
+ bbox=dict(facecolor='black', alpha=0.4, boxstyle='round'))
131
+
132
+ if np.nanmax(valid_vals) > threshold:
133
+ alert_text = f"⚠ Exceeds {threshold} g/m³!"
134
+ for ax in [ax1, ax2]:
135
+ ax.text(0.99, 0.01, alert_text, transform=ax.transAxes,
136
+ ha='right', va='bottom', fontsize=10, color='red',
137
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
138
+ ax1.contour(animator.lons, animator.lats, interp, levels=[threshold], colors='red', linewidths=2, transform=proj)
139
+ ax2.contour(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=[threshold], colors='red', linewidths=2, transform=proj)
140
+
141
+ return []
142
+
143
+ ani = animation.FuncAnimation(fig, update, frames=valid_frames, blit=False)
144
+ os.makedirs(os.path.dirname(output_path), exist_ok=True)
145
+ ani.save(output_path, writer='pillow', fps=fps)
146
+ plt.close()
147
+ print(f"✅ Saved animation for Z={z_km} km to {output_path}")
ash_animator/animation_vertical.py ADDED
@@ -0,0 +1,360 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import os
3
+ import numpy as np
4
+ import matplotlib.pyplot as plt
5
+ import matplotlib.animation as animation
6
+ import matplotlib.ticker as mticker
7
+ import cartopy.crs as ccrs
8
+ import cartopy.feature as cfeature
9
+ import cartopy.io.shapereader as shpreader
10
+ from .interpolation import interpolate_grid
11
+ from .basemaps import draw_etopo_basemap
12
+
13
+ # def animate_vertical_profile(animator, t_index: int, output_path: str, fps: int = 2, include_metadata: bool = True, threshold: float = 0.1):
14
+ # if not (0 <= t_index < len(animator.datasets)):
15
+ # print(f"Invalid time index {t_index}. Must be between 0 and {len(animator.datasets) - 1}.")
16
+ # return
17
+
18
+ # ds = animator.datasets[t_index]
19
+ # fig = plt.figure(figsize=(16, 7))
20
+ # proj = ccrs.PlateCarree()
21
+ # ax1 = fig.add_subplot(1, 2, 1, projection=proj)
22
+ # ax2 = fig.add_subplot(1, 2, 2, projection=proj)
23
+
24
+ # meta = ds.attrs
25
+ # legend_text = (
26
+ # f"Run name: {meta.get('run_name', 'N/A')}\n"
27
+ # f"Run time: {meta.get('run_time', 'N/A')}\n"
28
+ # f"Met data: {meta.get('met_data', 'N/A')}\n"
29
+ # f"Start release: {meta.get('start_of_release', 'N/A')}\n"
30
+ # f"End release: {meta.get('end_of_release', 'N/A')}\n"
31
+ # f"Source strength: {meta.get('source_strength', 'N/A')} g/s\n"
32
+ # f"Release loc: {meta.get('release_location', 'N/A')}\n"
33
+ # f"Release height: {meta.get('release_height', 'N/A')} m asl\n"
34
+ # f"Run duration: {meta.get('run_duration', 'N/A')}"
35
+ # )
36
+
37
+ # valid_mask = np.stack([ds['ash_concentration'].values[z] for z in range(len(animator.levels))]).max(axis=0) > 0
38
+ # y_idx, x_idx = np.where(valid_mask)
39
+
40
+ # if y_idx.size == 0 or x_idx.size == 0:
41
+ # print(f"No valid data found for time T{t_index+1}. Skipping...")
42
+ # plt.close()
43
+ # return
44
+
45
+ # y_min, y_max = y_idx.min(), y_idx.max()
46
+ # x_min, x_max = x_idx.min(), x_idx.max()
47
+ # buffer_y = int((y_max - y_min) * 0.1)
48
+ # buffer_x = int((x_max - x_min) * 0.1)
49
+ # y_start = max(0, y_min - buffer_y)
50
+ # y_end = min(animator.lat_grid.shape[0], y_max + buffer_y + 1)
51
+ # x_start = max(0, x_min - buffer_x)
52
+ # x_end = min(animator.lon_grid.shape[1], x_max + buffer_x + 1)
53
+
54
+ # lat_zoom = animator.lats[y_start:y_end]
55
+ # lon_zoom = animator.lons[x_start:x_end]
56
+ # lon_zoom_grid, lat_zoom_grid = np.meshgrid(lon_zoom, lat_zoom)
57
+
58
+ # z_indices_with_data = []
59
+ # for z_index in range(len(animator.levels)):
60
+ # data = ds['ash_concentration'].values[z_index]
61
+ # interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
62
+ # if np.isfinite(interp).sum() > 0:
63
+ # z_indices_with_data.append(z_index)
64
+
65
+ # if not z_indices_with_data:
66
+ # print(f"No valid Z-levels at time T{t_index+1}.")
67
+ # plt.close()
68
+ # return
69
+
70
+ # def update(z_index):
71
+ # ax1.clear()
72
+ # ax2.clear()
73
+
74
+ # data = ds['ash_concentration'].values[z_index]
75
+ # interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
76
+ # interp = np.where(interp < 0, np.nan, interp)
77
+ # zoom_plot = interp[y_start:y_end, x_start:x_end]
78
+
79
+ # valid_vals = interp[np.isfinite(interp)]
80
+ # if valid_vals.size == 0:
81
+ # return []
82
+
83
+ # min_val = np.nanmin(valid_vals)
84
+ # max_val = np.nanmax(valid_vals)
85
+ # log_cutoff = 1e-3
86
+ # use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
87
+
88
+ # levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
89
+ # data_for_plot = np.where(interp > log_cutoff, interp, 0) if use_log else interp
90
+ # scale_label = "Log" if use_log else "Linear"
91
+
92
+ # draw_etopo_basemap(ax1, mode='stock')
93
+ # draw_etopo_basemap(ax2, mode='stock')
94
+
95
+ # c1 = ax1.contourf(animator.lons, animator.lats, data_for_plot, levels=levels,
96
+ # cmap="rainbow", alpha=0.6, transform=proj)
97
+ # ax1.set_title(f"T{t_index+1} | Alt: {animator.levels[z_index]} km (Full - {scale_label})")
98
+ # ax1.set_extent([animator.lons.min(), animator.lons.max(), animator.lats.min(), animator.lats.max()])
99
+ # ax1.coastlines(); ax1.add_feature(cfeature.BORDERS, linestyle=':')
100
+ # ax1.add_feature(cfeature.LAND); ax1.add_feature(cfeature.OCEAN)
101
+
102
+ # c2 = ax2.contourf(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=levels,
103
+ # cmap="rainbow", alpha=0.6, transform=proj)
104
+ # ax2.set_title(f"T{t_index+1} | Alt: {animator.levels[z_index]} km (Zoom - {scale_label})")
105
+ # ax2.set_extent([lon_zoom.min(), lon_zoom.max(), lat_zoom.min(), lat_zoom.max()])
106
+ # ax2.coastlines(); ax2.add_feature(cfeature.BORDERS, linestyle=':')
107
+ # ax2.add_feature(cfeature.LAND); ax2.add_feature(cfeature.OCEAN)
108
+
109
+ # for ax in [ax1, ax2]:
110
+ # ax.text(0.01, 0.98, f"Altitude: {animator.levels[z_index]:.2f} km", transform=ax.transAxes,
111
+ # fontsize=9, color='white', va='top', ha='left',
112
+ # bbox=dict(facecolor='black', alpha=0.4, boxstyle='round'))
113
+
114
+ # if include_metadata:
115
+ # ax.text(0.01, 0.01,
116
+ # f"Source: NAME\nRes: {animator.x_res:.2f}°\n{meta.get('run_name', 'N/A')}",
117
+ # transform=ax.transAxes, fontsize=8, color='white',
118
+ # bbox=dict(facecolor='black', alpha=0.5))
119
+
120
+ # if np.nanmax(valid_vals) > threshold:
121
+ # for ax in [ax1, ax2]:
122
+ # ax.text(0.99, 0.01, f"⚠ Exceeds {threshold} g/m³!", transform=ax.transAxes,
123
+ # ha='right', va='bottom', fontsize=10, color='red',
124
+ # bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
125
+ # ax1.contour(animator.lons, animator.lats, interp, levels=[threshold], colors='red', linewidths=2, transform=proj)
126
+ # ax2.contour(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=[threshold], colors='red', linewidths=2, transform=proj)
127
+
128
+ # if include_metadata and not hasattr(update, "legend_text"):
129
+ # ax1.annotate(legend_text, xy=(0.75, 0.99), xycoords='axes fraction',
130
+ # fontsize=8, ha='left', va='top',
131
+ # bbox=dict(boxstyle="round", facecolor="white", edgecolor="gray"))
132
+
133
+ # if not hasattr(update, "colorbar"):
134
+ # update.colorbar = fig.colorbar(c1, ax=[ax1, ax2], orientation='vertical',
135
+ # label="Ash concentration (g/m³)")
136
+ # formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
137
+ # update.colorbar.ax.yaxis.set_major_formatter(formatter)
138
+
139
+ # if use_log:
140
+ # update.colorbar.ax.text(1.05, 1.02, "log scale", transform=update.colorbar.ax.transAxes,
141
+ # fontsize=9, color='gray', rotation=90, ha='left', va='bottom')
142
+
143
+ # return []
144
+
145
+ # os.makedirs(os.path.dirname(output_path), exist_ok=True)
146
+ # ani = animation.FuncAnimation(fig, update, frames=z_indices_with_data, blit=False)
147
+ # ani.save(output_path, writer='pillow', fps=fps)
148
+ # plt.close()
149
+ # print(f"✅ Saved vertical profile animation for T{t_index+1} to {output_path}")
150
+
151
+ # def animate_all_vertical_profiles(animator, output_folder: str, fps: int = 2, include_metadata: bool = True, threshold: float = 0.1):
152
+ # os.makedirs(output_folder, exist_ok=True)
153
+ # for t_index in range(len(animator.datasets)):
154
+ # output_path = os.path.join(output_folder, f"vertical_T{t_index+1:02d}.gif")
155
+ # print(f"🔄 Generating vertical profile animation for T{t_index+1}...")
156
+ # animate_vertical_profile(animator, t_index, output_path, fps, include_metadata, threshold)
157
+
158
+ import os
159
+ import numpy as np
160
+ import matplotlib.pyplot as plt
161
+ import matplotlib.animation as animation
162
+ import matplotlib.ticker as mticker
163
+ import cartopy.crs as ccrs
164
+ import cartopy.feature as cfeature
165
+ import cartopy.io.shapereader as shpreader
166
+ from .interpolation import interpolate_grid
167
+ from .basemaps import draw_etopo_basemap
168
+ from adjustText import adjust_text
169
+
170
+ def animate_vertical_profile(animator, t_index: int, output_path: str, fps: int = 2,
171
+ include_metadata: bool = True, threshold: float = 0.1,
172
+ zoom_width_deg: float = 6.0, zoom_height_deg: float = 6.0):
173
+ if not (0 <= t_index < len(animator.datasets)):
174
+ print(f"Invalid time index {t_index}. Must be between 0 and {len(animator.datasets) - 1}.")
175
+ return
176
+
177
+ countries_shp = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries')
178
+ reader = shpreader.Reader(countries_shp)
179
+ country_geoms = list(reader.records())
180
+
181
+ ds = animator.datasets[t_index]
182
+ fig = plt.figure(figsize=(18, 7)) # Wider for metadata outside
183
+ proj = ccrs.PlateCarree()
184
+ ax1 = fig.add_subplot(1, 2, 1, projection=proj)
185
+ ax2 = fig.add_subplot(1, 2, 2, projection=proj)
186
+
187
+ meta = ds.attrs
188
+ legend_text = (
189
+ f"Run name: {meta.get('run_name', 'N/A')}\n"
190
+ f"Run time: {meta.get('run_time', 'N/A')}\n"
191
+ f"Met data: {meta.get('met_data', 'N/A')}\n"
192
+ f"Start release: {meta.get('start_of_release', 'N/A')}\n"
193
+ f"End release: {meta.get('end_of_release', 'N/A')}\n"
194
+ f"Source strength: {meta.get('source_strength', 'N/A')} g/s\n"
195
+ f"Release loc: {meta.get('release_location', 'N/A')}\n"
196
+ f"Release height: {meta.get('release_height', 'N/A')} m asl\n"
197
+ f"Run duration: {meta.get('run_duration', 'N/A')}"
198
+ )
199
+
200
+ # 🔍 Find most active point at this time step
201
+ max_conc = -np.inf
202
+ center_lat = center_lon = None
203
+ for z in range(len(animator.levels)):
204
+ data = ds['ash_concentration'].values[z]
205
+ if np.max(data) > max_conc:
206
+ max_conc = np.max(data)
207
+ max_idx = np.unravel_index(np.argmax(data), data.shape)
208
+ center_lat = animator.lat_grid[max_idx]
209
+ center_lon = animator.lon_grid[max_idx]
210
+
211
+ if center_lat is None or center_lon is None:
212
+ print(f"No valid data found for time T{t_index+1}. Skipping...")
213
+ plt.close()
214
+ return
215
+
216
+ # 🌍 Define fixed zoom extents
217
+ lon_zoom_min = center_lon - zoom_width_deg / 2
218
+ lon_zoom_max = center_lon + zoom_width_deg / 2
219
+ lat_zoom_min = center_lat - zoom_height_deg / 2
220
+ lat_zoom_max = center_lat + zoom_height_deg / 2
221
+
222
+ lat_zoom = animator.lats[(animator.lats >= lat_zoom_min) & (animator.lats <= lat_zoom_max)]
223
+ lon_zoom = animator.lons[(animator.lons >= lon_zoom_min) & (animator.lons <= lon_zoom_max)]
224
+ lon_zoom_grid, lat_zoom_grid = np.meshgrid(lon_zoom, lat_zoom)
225
+
226
+ z_indices_with_data = []
227
+ for z_index in range(len(animator.levels)):
228
+ data = ds['ash_concentration'].values[z_index]
229
+ interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
230
+ if np.isfinite(interp).sum() > 0:
231
+ z_indices_with_data.append(z_index)
232
+
233
+ if not z_indices_with_data:
234
+ print(f"No valid Z-levels at time T{t_index+1}.")
235
+ plt.close()
236
+ return
237
+
238
+ def update(z_index):
239
+ ax1.clear()
240
+ ax2.clear()
241
+
242
+ data = ds['ash_concentration'].values[z_index]
243
+ interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
244
+ interp = np.where(interp < 0, np.nan, interp)
245
+
246
+ lat_idx = np.where((animator.lats >= lat_zoom_min) & (animator.lats <= lat_zoom_max))[0]
247
+ lon_idx = np.where((animator.lons >= lon_zoom_min) & (animator.lons <= lon_zoom_max))[0]
248
+ zoom_plot = interp[np.ix_(lat_idx, lon_idx)]
249
+
250
+ valid_vals = interp[np.isfinite(interp)]
251
+ if valid_vals.size == 0:
252
+ return []
253
+
254
+ min_val = np.nanmin(valid_vals)
255
+ max_val = np.nanmax(valid_vals)
256
+ log_cutoff = 1e-3
257
+ use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
258
+
259
+ levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
260
+ data_for_plot = np.where(interp > log_cutoff, interp, 0) if use_log else interp
261
+ scale_label = "Log" if use_log else "Linear"
262
+
263
+ draw_etopo_basemap(ax1, mode='stock')
264
+ draw_etopo_basemap(ax2, mode='stock')
265
+
266
+ c1 = ax1.contourf(animator.lons, animator.lats, data_for_plot, levels=levels,
267
+ cmap="rainbow", alpha=0.6, transform=proj)
268
+ ax1.set_title(f"T{t_index+1} | Alt: {animator.levels[z_index]} km (Full - {scale_label})")
269
+ ax1.set_extent([animator.lons.min(), animator.lons.max(), animator.lats.min(), animator.lats.max()])
270
+ ax1.coastlines(); ax1.add_feature(cfeature.BORDERS, linestyle=':')
271
+ ax1.add_feature(cfeature.LAND); ax1.add_feature(cfeature.OCEAN)
272
+
273
+ c2 = ax2.contourf(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=levels,
274
+ cmap="rainbow", alpha=0.6, transform=proj)
275
+ ax2.set_title(f"T{t_index+1} | Alt: {animator.levels[z_index]} km (Zoom - {scale_label})")
276
+ ax2.set_extent([lon_zoom_min, lon_zoom_max, lat_zoom_min, lat_zoom_max])
277
+ ax2.coastlines(); ax2.add_feature(cfeature.BORDERS, linestyle=':')
278
+ ax2.add_feature(cfeature.LAND); ax2.add_feature(cfeature.OCEAN)
279
+
280
+ for ax in [ax1, ax2]:
281
+ ax.text(0.01, 0.98, f"Altitude: {animator.levels[z_index]:.2f} km", transform=ax.transAxes,
282
+ fontsize=9, color='white', va='top', ha='left',
283
+ bbox=dict(facecolor='black', alpha=0.4, boxstyle='round'))
284
+
285
+ if include_metadata:
286
+ fig.text(0.50, 0.1, legend_text, va='center', ha='left', fontsize=8,
287
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'),
288
+ transform=fig.transFigure)
289
+
290
+ if np.nanmax(valid_vals) > threshold:
291
+ for ax in [ax1, ax2]:
292
+ ax.text(0.99, 0.01, f"⚠ Exceeds {threshold} g/m³!", transform=ax.transAxes,
293
+ ha='right', va='bottom', fontsize=10, color='red',
294
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
295
+ ax1.contour(animator.lons, animator.lats, interp, levels=[threshold], colors='red', linewidths=2, transform=proj)
296
+ ax2.contour(lon_zoom_grid, lat_zoom_grid, zoom_plot, levels=[threshold], colors='red', linewidths=2, transform=proj)
297
+
298
+ if not hasattr(update, "colorbar"):
299
+ update.colorbar = fig.colorbar(c1, ax=[ax1, ax2], orientation='vertical',
300
+ label="Ash concentration (g/m³)", shrink=0.75)
301
+ formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
302
+ update.colorbar.ax.yaxis.set_major_formatter(formatter)
303
+
304
+ if use_log:
305
+ update.colorbar.ax.text(1.05, 1.02, "log scale", transform=update.colorbar.ax.transAxes,
306
+ fontsize=9, color='gray', rotation=90, ha='left', va='bottom')
307
+
308
+ ######################3
309
+
310
+
311
+ texts_ax1, texts_ax2 = [], []
312
+ for country in country_geoms:
313
+ name = country.attributes['NAME_LONG']
314
+ geom = country.geometry
315
+ try:
316
+ lon, lat = geom.centroid.x, geom.centroid.y
317
+ if (lon_zoom_min <= lon <= lon_zoom_max) and (lat_zoom_min <= lat <= lat_zoom_max):
318
+ text = ax2.text(lon, lat, name, fontsize=6, transform=proj,
319
+ ha='center', va='center', color='white',
320
+ bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
321
+ texts_ax2.append(text)
322
+
323
+ if (animator.lons.min() <= lon <= animator.lons.max()) and (animator.lats.min() <= lat <= animator.lats.max()):
324
+ text = ax1.text(lon, lat, name, fontsize=6, transform=proj,
325
+ ha='center', va='center', color='white',
326
+ bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
327
+ texts_ax1.append(text)
328
+ except:
329
+ continue
330
+
331
+ adjust_text(texts_ax1, ax=ax1, only_move={'points': 'y', 'text': 'y'},
332
+ arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
333
+ adjust_text(texts_ax2, ax=ax2, only_move={'points': 'y', 'text': 'y'},
334
+ arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
335
+
336
+
337
+ ############################################
338
+
339
+
340
+
341
+
342
+ return []
343
+
344
+ os.makedirs(os.path.dirname(output_path), exist_ok=True)
345
+ ani = animation.FuncAnimation(fig, update, frames=z_indices_with_data, blit=False)
346
+ ani.save(output_path, writer='pillow', fps=fps)
347
+ plt.close()
348
+ print(f"✅ Saved vertical profile animation for T{t_index+1} to {output_path}")
349
+
350
+
351
+ def animate_all_vertical_profiles(animator, output_folder: str, fps: int = 2,
352
+ include_metadata: bool = True, threshold: float = 0.1,
353
+ zoom_width_deg: float = 10.0, zoom_height_deg: float = 6.0):
354
+ os.makedirs(output_folder, exist_ok=True)
355
+ for t_index in range(len(animator.datasets)):
356
+ output_path = os.path.join(output_folder, f"vertical_T{t_index+1:02d}.gif")
357
+ print(f"🔄 Generating vertical profile animation for T{t_index+1}...")
358
+ animate_vertical_profile(animator, t_index, output_path, fps,
359
+ include_metadata, threshold,
360
+ zoom_width_deg, zoom_height_deg)
ash_animator/basemaps.py ADDED
@@ -0,0 +1,131 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ # import contextily as ctx
3
+ # from mpl_toolkits.basemap import Basemap
4
+ # import cartopy.crs as ccrs
5
+ # import cartopy.feature as cfeature
6
+
7
+ # def draw_etopo_basemap(ax, mode="basemap", zoom=7):
8
+ # try:
9
+ # if mode == "stock":
10
+ # ax.stock_img()
11
+ # elif mode == "contextily":
12
+ # extent = ax.get_extent(ccrs.PlateCarree())
13
+ # ax.set_extent(extent, crs=ccrs.PlateCarree())
14
+ # ctx.add_basemap(ax, crs=ccrs.PlateCarree(), source=ctx.providers.CartoDB.Voyager, zoom=zoom)
15
+ # elif mode == "basemap":
16
+ # extent = ax.get_extent(ccrs.PlateCarree())
17
+ # m = Basemap(projection='cyl',
18
+ # llcrnrlon=extent[0], urcrnrlon=extent[1],
19
+ # llcrnrlat=extent[2], urcrnrlat=extent[3],
20
+ # resolution='h', ax=ax)
21
+ # m.shadedrelief()
22
+ # m.drawcoastlines(linewidth=0.5)
23
+ # m.drawcountries(linewidth=0.7)
24
+ # m.drawmapboundary()
25
+ # else:
26
+ # raise ValueError(f"Unsupported basemap mode: {mode}")
27
+ # except Exception as e:
28
+ # print(f"[Relief Error - {mode} mode]:", e)
29
+ # ax.add_feature(cfeature.LAND)
30
+ # ax.add_feature(cfeature.OCEAN)
31
+
32
+ import os
33
+ import hashlib
34
+ import contextily as ctx
35
+ from mpl_toolkits.basemap import Basemap
36
+ import cartopy.crs as ccrs
37
+ import cartopy.feature as cfeature
38
+ from PIL import Image
39
+ import matplotlib.pyplot as plt
40
+
41
+ # Define cache directories
42
+ # Optional: Set tile cache directory (must be done before contextily downloads tiles)
43
+ os.environ["XDG_CACHE_HOME"] = os.path.expanduser("~/.contextily_cache")
44
+
45
+ CTX_TILE_CACHE_DIR = os.path.expanduser("~/.contextily_cache")
46
+ BASEMAP_TILE_CACHE_DIR = os.path.expanduser("~/.basemap_cache")
47
+
48
+ os.makedirs(CTX_TILE_CACHE_DIR, exist_ok=True)
49
+ os.makedirs(BASEMAP_TILE_CACHE_DIR, exist_ok=True)
50
+
51
+ def draw_etopo_basemap(ax, mode="basemap", zoom=11):
52
+ """
53
+ Draws a high-resolution basemap background on the provided Cartopy GeoAxes.
54
+
55
+ Parameters
56
+ ----------
57
+ ax : matplotlib.axes._subplots.AxesSubplot
58
+ The matplotlib Axes object (with Cartopy projection) to draw the map background on.
59
+
60
+ mode : str, optional
61
+ The basemap mode to use:
62
+ - "stock": Default stock image from Cartopy.
63
+ - "contextily": Web tile background (CartoDB Voyager), with caching.
64
+ - "basemap": High-resolution shaded relief using Basemap, with caching.
65
+ Default is "basemap".
66
+
67
+ zoom : int, optional
68
+ Tile zoom level (only for "contextily"). Higher = more detail. Default is 7.
69
+
70
+ Notes
71
+ -----
72
+ - Uses high resolution for Basemap (resolution='h') and saves figure at 300 DPI.
73
+ - Cached images are reused using extent-based hashing to avoid re-rendering.
74
+ - Basemap is deprecated; Cartopy with web tiles is recommended for new projects.
75
+ """
76
+ try:
77
+ if mode == "stock":
78
+ ax.stock_img()
79
+
80
+ elif mode == "contextily":
81
+ extent = ax.get_extent(crs=ccrs.PlateCarree())
82
+ ax.set_extent(extent, crs=ccrs.PlateCarree())
83
+ ctx.add_basemap(
84
+ ax,
85
+ crs=ccrs.PlateCarree(),
86
+ source=ctx.providers.CartoDB.Voyager,
87
+ zoom=zoom
88
+ )
89
+
90
+ elif mode == "basemap":
91
+ extent = ax.get_extent(crs=ccrs.PlateCarree())
92
+
93
+ # Create a hash key for this extent
94
+ extent_str = f"{extent[0]:.4f}_{extent[1]:.4f}_{extent[2]:.4f}_{extent[3]:.4f}"
95
+ cache_key = hashlib.md5(extent_str.encode()).hexdigest()
96
+ cache_file = os.path.join(BASEMAP_TILE_CACHE_DIR, f"{cache_key}_highres.png")
97
+
98
+ if os.path.exists(cache_file):
99
+ img = Image.open(cache_file)
100
+ ax.imshow(img, extent=extent, transform=ccrs.PlateCarree())
101
+ else:
102
+ # Create a high-resolution temporary figure
103
+ temp_fig, temp_ax = plt.subplots(figsize=(12, 9),
104
+ subplot_kw={'projection': ccrs.PlateCarree()})
105
+ temp_ax.set_extent(extent, crs=ccrs.PlateCarree())
106
+
107
+ m = Basemap(projection='cyl',
108
+ llcrnrlon=extent[0], urcrnrlon=extent[1],
109
+ llcrnrlat=extent[2], urcrnrlat=extent[3],
110
+ resolution='f', ax=temp_ax) # 'h' = high resolution
111
+
112
+ m.shadedrelief()
113
+ # m.drawcoastlines(linewidth=0.1)
114
+ # m.drawcountries(linewidth=0.1)
115
+ # m.drawmapboundary()
116
+
117
+ # Save high-DPI figure for clarity
118
+ temp_fig.savefig(cache_file, dpi=300, bbox_inches='tight', pad_inches=0)
119
+ plt.close(temp_fig)
120
+
121
+ # Load and display the cached image
122
+ img = Image.open(cache_file)
123
+ ax.imshow(img, extent=extent, transform=ccrs.PlateCarree())
124
+
125
+ else:
126
+ raise ValueError(f"Unsupported basemap mode: {mode}")
127
+
128
+ except Exception as e:
129
+ print(f"[Relief Error - {mode} mode]:", e)
130
+ ax.add_feature(cfeature.LAND)
131
+ ax.add_feature(cfeature.OCEAN)
ash_animator/converter.py ADDED
@@ -0,0 +1,414 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # # Full updated and corrected version of NAMEDataConverter with sanitized metadata keys
2
+
3
+ # import os
4
+ # import re
5
+ # import zipfile
6
+ # import shutil
7
+ # import numpy as np
8
+ # import xarray as xr
9
+ # import matplotlib.pyplot as plt
10
+ # import matplotlib.animation as animation
11
+ # from typing import List, Tuple
12
+
13
+ # class NAMEDataConverter:
14
+ # def __init__(self, output_dir: str):
15
+ # self.output_dir = output_dir
16
+ # os.makedirs(self.output_dir, exist_ok=True)
17
+
18
+ # def _sanitize_key(self, key: str) -> str:
19
+ # # Replace non-alphanumeric characters with underscores, and ensure it starts with a letter
20
+ # key = re.sub(r'\W+', '_', key)
21
+ # if not key[0].isalpha():
22
+ # key = f"attr_{key}"
23
+ # return key
24
+
25
+ # def _parse_metadata(self, lines: List[str]) -> dict:
26
+ # metadata = {}
27
+ # for line in lines:
28
+ # if ":" in line:
29
+ # key, value = line.split(":", 1)
30
+ # clean_key = self._sanitize_key(key.strip().lower())
31
+ # metadata[clean_key] = value.strip()
32
+
33
+ # try:
34
+ # metadata.update({
35
+ # "x_origin": float(metadata["x_grid_origin"]),
36
+ # "y_origin": float(metadata["y_grid_origin"]),
37
+ # "x_size": int(metadata["x_grid_size"]),
38
+ # "y_size": int(metadata["y_grid_size"]),
39
+ # "x_res": float(metadata["x_grid_resolution"]),
40
+ # "y_res": float(metadata["y_grid_resolution"]),
41
+ # "prelim_cols": int(metadata["number_of_preliminary_cols"]),
42
+ # "n_fields": int(metadata["number_of_field_cols"]),
43
+ # })
44
+ # except KeyError as e:
45
+ # raise ValueError(f"Missing required metadata field: {e}")
46
+ # except ValueError as e:
47
+ # raise ValueError(f"Invalid value in metadata: {e}")
48
+
49
+ # if metadata["x_res"] == 0 or metadata["y_res"] == 0:
50
+ # raise ZeroDivisionError("Grid resolution cannot be zero.")
51
+
52
+ # return metadata
53
+
54
+ # def _get_data_lines(self, lines: List[str]) -> List[str]:
55
+ # idx = next(i for i, l in enumerate(lines) if l.strip() == "Fields:")
56
+ # return lines[idx + 1:]
57
+
58
+ # def convert_3d_group(self, group: List[Tuple[int, str]], output_filename: str) -> str:
59
+ # first_file_path = group[0][1]
60
+ # with open(first_file_path, 'r') as f:
61
+ # lines = f.readlines()
62
+ # meta = self._parse_metadata(lines)
63
+
64
+ # lons = np.round(np.arange(meta["x_origin"], meta["x_origin"] + meta["x_size"] * meta["x_res"], meta["x_res"]), 6)
65
+ # lats = np.round(np.arange(meta["y_origin"], meta["y_origin"] + meta["y_size"] * meta["y_res"], meta["y_res"]), 6)
66
+
67
+ # z_levels = []
68
+ # z_coords = []
69
+
70
+ # for z_idx, filepath in group:
71
+ # with open(filepath, 'r') as f:
72
+ # lines = f.readlines()
73
+ # data_lines = self._get_data_lines(lines)
74
+ # grid = np.zeros((meta["y_size"], meta["x_size"]), dtype=np.float32)
75
+
76
+ # for line in data_lines:
77
+ # parts = [p.strip().strip(',') for p in line.strip().split(',') if p.strip()]
78
+ # if len(parts) >= 5 and parts[0].isdigit() and parts[1].isdigit():
79
+ # try:
80
+ # x = int(parts[0]) - 1
81
+ # y = int(parts[1]) - 1
82
+ # val = float(parts[4])
83
+ # if 0 <= x < meta["x_size"] and 0 <= y < meta["y_size"]:
84
+ # grid[y, x] = val
85
+ # except Exception:
86
+ # continue
87
+ # z_levels.append(grid)
88
+ # z_coords.append(z_idx)
89
+
90
+ # z_cube = np.stack(z_levels, axis=0)
91
+ # ds = xr.Dataset(
92
+ # {
93
+ # "ash_concentration": (['altitude', 'latitude', 'longitude'], z_cube)
94
+ # },
95
+ # coords={
96
+ # "altitude": np.array(z_coords, dtype=np.float32),
97
+ # "latitude": lats,
98
+ # "longitude": lons
99
+ # },
100
+ # attrs={
101
+ # "title": "Volcanic Ash Concentration",
102
+ # "source": "NAME model output processed to NetCDF",
103
+ # **{k: str(v) for k, v in meta.items()} # Ensure all attrs are strings
104
+ # }
105
+ # )
106
+ # ds["ash_concentration"].attrs.update({
107
+ # "units": "g/m^3",
108
+ # "long_name": "Volcanic ash concentration"
109
+ # })
110
+ # ds["altitude"].attrs["units"] = "kilometers above sea level"
111
+ # ds["latitude"].attrs["units"] = "degrees_north"
112
+ # ds["longitude"].attrs["units"] = "degrees_east"
113
+
114
+ # out_path = os.path.join(self.output_dir, output_filename)
115
+ # ds.to_netcdf(out_path)
116
+ # return out_path
117
+
118
+ # def batch_process_zip(self, zip_path: str) -> List[str]:
119
+ # extract_dir = os.path.join(self.output_dir, "unzipped")
120
+ # os.makedirs(extract_dir, exist_ok=True)
121
+
122
+ # with zipfile.ZipFile(zip_path, 'r') as zip_ref:
123
+ # zip_ref.extractall(extract_dir)
124
+
125
+ # txt_files = []
126
+ # for root, _, files in os.walk(extract_dir):
127
+ # for file in files:
128
+ # if file.endswith(".txt"):
129
+ # txt_files.append(os.path.join(root, file))
130
+
131
+ # pattern = re.compile(r"_T(\d+)_.*_Z(\d+)\.txt$")
132
+ # grouped = {}
133
+ # for f in txt_files:
134
+ # match = pattern.search(f)
135
+ # if match:
136
+ # t = int(match.group(1))
137
+ # z = int(match.group(2))
138
+ # grouped.setdefault(t, []).append((z, f))
139
+
140
+ # nc_files = []
141
+ # for t_key in sorted(grouped):
142
+ # group = sorted(grouped[t_key])
143
+ # out_nc = self.convert_3d_group(group, f"T{t_key}.nc")
144
+ # nc_files.append(out_nc)
145
+ # return nc_files
146
+
147
+ # Re-defining the integrated class first
148
+ import os
149
+ import re
150
+ import zipfile
151
+ import numpy as np
152
+ import xarray as xr
153
+ from typing import List, Tuple
154
+ import shutil
155
+
156
+
157
+ class NAMEDataProcessor:
158
+ def __init__(self, output_root: str):
159
+ self.output_root = output_root
160
+ self.output_3d = os.path.join(self.output_root, "3D")
161
+ self.output_horizontal = os.path.join(self.output_root, "horizontal")
162
+ os.makedirs(self.output_3d, exist_ok=True)
163
+ os.makedirs(self.output_horizontal, exist_ok=True)
164
+
165
+ def _sanitize_key(self, key: str) -> str:
166
+ key = re.sub(r'\W+', '_', key)
167
+ if not key[0].isalpha():
168
+ key = f"attr_{key}"
169
+ return key
170
+
171
+ def _parse_metadata(self, lines: List[str]) -> dict:
172
+ metadata = {}
173
+ for line in lines:
174
+ if ":" in line:
175
+ key, value = line.split(":", 1)
176
+ clean_key = self._sanitize_key(key.strip().lower())
177
+ metadata[clean_key] = value.strip()
178
+
179
+ try:
180
+ metadata.update({
181
+ "x_origin": float(metadata["x_grid_origin"]),
182
+ "y_origin": float(metadata["y_grid_origin"]),
183
+ "x_size": int(metadata["x_grid_size"]),
184
+ "y_size": int(metadata["y_grid_size"]),
185
+ "x_res": float(metadata["x_grid_resolution"]),
186
+ "y_res": float(metadata["y_grid_resolution"]),
187
+ })
188
+ except KeyError as e:
189
+ raise ValueError(f"Missing required metadata field: {e}")
190
+ except ValueError as e:
191
+ raise ValueError(f"Invalid value in metadata: {e}")
192
+
193
+ if metadata["x_res"] == 0 or metadata["y_res"] == 0:
194
+ raise ZeroDivisionError("Grid resolution cannot be zero.")
195
+
196
+ return metadata
197
+
198
+ def _get_data_lines(self, lines: List[str]) -> List[str]:
199
+ idx = next(i for i, l in enumerate(lines) if l.strip() == "Fields:")
200
+ return lines[idx + 1:]
201
+
202
+ def _is_horizontal_file(self, filename: str) -> bool:
203
+ return "HorizontalField" in filename
204
+
205
+ def _convert_horizontal(self, filepath: str, output_filename: str) -> str:
206
+ with open(filepath, 'r') as f:
207
+ lines = f.readlines()
208
+
209
+ meta = self._parse_metadata(lines)
210
+ data_lines = self._get_data_lines(lines)
211
+
212
+ lons = np.round(np.arange(meta["x_origin"], meta["x_origin"] + meta["x_size"] * meta["x_res"], meta["x_res"]), 6)
213
+ lats = np.round(np.arange(meta["y_origin"], meta["y_origin"] + meta["y_size"] * meta["y_res"], meta["y_res"]), 6)
214
+
215
+ air_conc = np.zeros((meta["y_size"], meta["x_size"]), dtype=np.float32)
216
+ dry_depo = np.zeros((meta["y_size"], meta["x_size"]), dtype=np.float32)
217
+ wet_depo = np.zeros((meta["y_size"], meta["x_size"]), dtype=np.float32)
218
+
219
+ for line in data_lines:
220
+ parts = [p.strip().strip(',') for p in line.strip().split(',') if p.strip()]
221
+ if len(parts) >= 7 and parts[0].isdigit() and parts[1].isdigit():
222
+ try:
223
+ x = int(parts[0]) - 1
224
+ y = int(parts[1]) - 1
225
+ air_val = float(parts[4])
226
+ dry_val = float(parts[5])
227
+ wet_val = float(parts[6])
228
+ if 0 <= x < meta["x_size"] and 0 <= y < meta["y_size"]:
229
+ air_conc[y, x] = air_val
230
+ dry_depo[y, x] = dry_val
231
+ wet_depo[y, x] = wet_val
232
+ except Exception:
233
+ continue
234
+
235
+ ds = xr.Dataset(
236
+ {
237
+ "air_concentration": (['latitude', 'longitude'], air_conc),
238
+ "dry_deposition_rate": (['latitude', 'longitude'], dry_depo),
239
+ "wet_deposition_rate": (['latitude', 'longitude'], wet_depo)
240
+ },
241
+ coords={
242
+ "latitude": lats,
243
+ "longitude": lons
244
+ },
245
+ attrs={
246
+ "title": "Volcanic Ash Horizontal Output (Multiple Fields)",
247
+ "source": "NAME model output processed to NetCDF (horizontal multi-field)",
248
+ **{k: str(v) for k, v in meta.items()}
249
+ }
250
+ )
251
+
252
+ ds["air_concentration"].attrs.update({
253
+ "units": "g/m^3",
254
+ "long_name": "Boundary Layer Average Air Concentration"
255
+ })
256
+ ds["dry_deposition_rate"].attrs.update({
257
+ "units": "g/m^2/s",
258
+ "long_name": "Dry Deposition Rate"
259
+ })
260
+ ds["wet_deposition_rate"].attrs.update({
261
+ "units": "g/m^2/s",
262
+ "long_name": "Wet Deposition Rate"
263
+ })
264
+ ds["latitude"].attrs["units"] = "degrees_north"
265
+ ds["longitude"].attrs["units"] = "degrees_east"
266
+
267
+ out_path = os.path.join(self.output_horizontal, output_filename)
268
+ ds.to_netcdf(out_path, engine="netcdf4")
269
+
270
+ return out_path
271
+
272
+
273
+ def _convert_3d_group(self, group: List[Tuple[int, str]], output_filename: str) -> str:
274
+ first_file_path = group[0][1]
275
+ with open(first_file_path, 'r') as f:
276
+ lines = f.readlines()
277
+ meta = self._parse_metadata(lines)
278
+
279
+ lons = np.round(np.arange(meta["x_origin"], meta["x_origin"] + meta["x_size"] * meta["x_res"], meta["x_res"]), 6)
280
+ lats = np.round(np.arange(meta["y_origin"], meta["y_origin"] + meta["y_size"] * meta["y_res"], meta["y_res"]), 6)
281
+
282
+ z_levels = []
283
+ z_coords = []
284
+
285
+ for z_idx, filepath in group:
286
+ with open(filepath, 'r') as f:
287
+ lines = f.readlines()
288
+ data_lines = self._get_data_lines(lines)
289
+ grid = np.zeros((meta["y_size"], meta["x_size"]), dtype=np.float32)
290
+
291
+ for line in data_lines:
292
+ parts = [p.strip().strip(',') for p in line.strip().split(',') if p.strip()]
293
+ if len(parts) >= 5 and parts[0].isdigit() and parts[1].isdigit():
294
+ try:
295
+ x = int(parts[0]) - 1
296
+ y = int(parts[1]) - 1
297
+ val = float(parts[4])
298
+ if 0 <= x < meta["x_size"] and 0 <= y < meta["y_size"]:
299
+ grid[y, x] = val
300
+ except Exception:
301
+ continue
302
+ z_levels.append(grid)
303
+ z_coords.append(z_idx)
304
+
305
+ z_cube = np.stack(z_levels, axis=0)
306
+ ds = xr.Dataset(
307
+ {
308
+ "ash_concentration": (['altitude', 'latitude', 'longitude'], z_cube)
309
+ },
310
+ coords={
311
+ "altitude": np.array(z_coords, dtype=np.float32),
312
+ "latitude": lats,
313
+ "longitude": lons
314
+ },
315
+ attrs={
316
+ "title": "Volcanic Ash Concentration (3D)",
317
+ "source": "NAME model output processed to NetCDF (3D fields)",
318
+ **{k: str(v) for k, v in meta.items()}
319
+ }
320
+ )
321
+
322
+ out_path = os.path.join(self.output_3d, output_filename)
323
+
324
+ # 🔥 Check if file exists, delete it first
325
+ # if os.path.exists(out_path):
326
+ # os.remove(out_path)
327
+
328
+ # 🔥 Save NetCDF safely using netCDF4
329
+ ds.to_netcdf(out_path, engine="netcdf4")
330
+
331
+ return out_path
332
+
333
+
334
+ def batch_process_zip(self, zip_path: str) -> List[str]:
335
+ extract_dir = os.path.abspath("unzipped")
336
+
337
+ os.makedirs(extract_dir, exist_ok=True)
338
+
339
+ ###
340
+
341
+
342
+ # Function to empty folder contents
343
+ def empty_folder(folder_path):
344
+ import os
345
+ import glob
346
+ files = glob.glob(os.path.join(folder_path, '*'))
347
+ for f in files:
348
+ try:
349
+ os.remove(f)
350
+ except IsADirectoryError:
351
+ shutil.rmtree(f)
352
+
353
+ # 🛠 Clear cached open files and garbage collect before deleting
354
+
355
+ # 🔥 Empty previous outputs, do not delete folders
356
+ if os.path.exists(self.output_3d):
357
+ empty_folder(self.output_3d)
358
+ else:
359
+ os.makedirs(self.output_3d, exist_ok=True)
360
+
361
+ # if os.path.exists(self.output_horizontal):
362
+ # empty_folder(self.output_horizontal)
363
+ # else:
364
+ # os.makedirs(self.output_horizontal, exist_ok=True)
365
+
366
+ # if os.path.exists(extract_dir):
367
+ # shutil.rmtree(extract_dir)
368
+ # os.makedirs(extract_dir, exist_ok=True)
369
+
370
+
371
+
372
+
373
+
374
+ #####
375
+
376
+ with zipfile.ZipFile(zip_path, 'r') as zip_ref:
377
+ zip_ref.extractall(extract_dir)
378
+
379
+ txt_files = []
380
+ for root, _, files in os.walk(extract_dir):
381
+ for file in files:
382
+ if file.endswith(".txt"):
383
+ txt_files.append(os.path.join(root, file))
384
+
385
+ horizontal_files = []
386
+ grouped_3d = {}
387
+
388
+ pattern = re.compile(r"_T(\d+)_.*_Z(\d+)\.txt$")
389
+
390
+ for f in txt_files:
391
+ if self._is_horizontal_file(f):
392
+ horizontal_files.append(f)
393
+ else:
394
+ match = pattern.search(f)
395
+ if match:
396
+ t = int(match.group(1))
397
+ z = int(match.group(2))
398
+ grouped_3d.setdefault(t, []).append((z, f))
399
+
400
+ nc_files = []
401
+
402
+ # Process horizontal
403
+ for f in sorted(horizontal_files):
404
+ base_name = os.path.splitext(os.path.basename(f))[0]
405
+ out_nc = self._convert_horizontal(f, f"{base_name}.nc")
406
+ nc_files.append(out_nc)
407
+
408
+ # Process 3D
409
+ for t_key in sorted(grouped_3d):
410
+ group = sorted(grouped_3d[t_key])
411
+ out_nc = self._convert_3d_group(group, f"T{t_key}.nc")
412
+ nc_files.append(out_nc)
413
+
414
+ return nc_files
ash_animator/export.py ADDED
@@ -0,0 +1,119 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import os
3
+ import numpy as np
4
+ import matplotlib.pyplot as plt
5
+ import matplotlib.ticker as mticker
6
+ import cartopy.crs as ccrs
7
+ import cartopy.feature as cfeature
8
+ from .interpolation import interpolate_grid
9
+ from .basemaps import draw_etopo_basemap
10
+
11
+ def export_frames_as_jpgs(animator, output_folder: str, include_metadata: bool = True):
12
+ os.makedirs(output_folder, exist_ok=True)
13
+
14
+ meta = animator.datasets[0].attrs
15
+ legend_text = (
16
+ f"Run name: {meta.get('run_name', 'N/A')}\n"
17
+ f"Run time: {meta.get('run_time', 'N/A')}\n"
18
+ f"Met data: {meta.get('met_data', 'N/A')}\n"
19
+ f"Start of release: {meta.get('start_of_release', 'N/A')}\n"
20
+ f"End of release: {meta.get('end_of_release', 'N/A')}\n"
21
+ f"Source strength: {meta.get('source_strength', 'N/A')} g / s\n"
22
+ f"Release location: {meta.get('release_location', 'N/A')}\n"
23
+ f"Release height: {meta.get('release_height', 'N/A')} m asl\n"
24
+ f"Run duration: {meta.get('run_duration', 'N/A')}"
25
+ )
26
+
27
+ for z_index, z_val in enumerate(animator.levels):
28
+ z_dir = os.path.join(output_folder, f"ash_T1-Tn_Z{z_index+1}")
29
+ os.makedirs(z_dir, exist_ok=True)
30
+
31
+ valid_mask = np.stack([
32
+ ds['ash_concentration'].values[z_index] for ds in animator.datasets
33
+ ]).max(axis=0) > 0
34
+ y_idx, x_idx = np.where(valid_mask)
35
+
36
+ if y_idx.size == 0 or x_idx.size == 0:
37
+ print(f"Z level {z_val} km has no valid data. Skipping...")
38
+ continue
39
+
40
+ y_min, y_max = y_idx.min(), y_idx.max()
41
+ x_min, x_max = x_idx.min(), x_idx.max()
42
+
43
+ for t in range(len(animator.datasets)):
44
+ data = animator.datasets[t]['ash_concentration'].values[z_index]
45
+ interp = interpolate_grid(data, animator.lon_grid, animator.lat_grid)
46
+ if np.isfinite(interp).sum() == 0:
47
+ continue
48
+
49
+ fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18, 8), subplot_kw={'projection': ccrs.PlateCarree()})
50
+ valid_vals = interp[np.isfinite(interp)]
51
+ min_val = np.nanmin(valid_vals)
52
+ max_val = np.nanmax(valid_vals)
53
+ log_cutoff = 1e-3
54
+ log_ratio = max_val / (min_val + 1e-6)
55
+ use_log = min_val > log_cutoff and log_ratio > 100
56
+
57
+ levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
58
+ data_for_plot = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
59
+ scale_label = "Hybrid Log" if use_log else "Linear"
60
+
61
+ # Plot full
62
+ c1 = ax1.contourf(animator.lons, animator.lats, data_for_plot, levels=levels,
63
+ cmap="rainbow", alpha=0.6, transform=ccrs.PlateCarree())
64
+ draw_etopo_basemap(ax1, mode='stock')
65
+ ax1.set_extent([animator.lons.min(), animator.lons.max(), animator.lats.min(), animator.lats.max()])
66
+ ax1.set_title(f"T{t+1} | Alt: {z_val} km (Full - {scale_label})")
67
+ ax1.coastlines(); ax1.add_feature(cfeature.BORDERS)
68
+ ax1.add_feature(cfeature.LAND); ax1.add_feature(cfeature.OCEAN)
69
+
70
+ # Zoom region
71
+ buffer_y = int((y_max - y_min) * 0.5)
72
+ buffer_x = int((x_max - x_min) * 0.5)
73
+
74
+ y_start = max(0, y_min - buffer_y)
75
+ y_end = min(data_for_plot.shape[0], y_max + buffer_y + 1)
76
+ x_start = max(0, x_min - buffer_x)
77
+ x_end = min(data_for_plot.shape[1], x_max + buffer_x + 1)
78
+
79
+ zoom = data_for_plot[y_start:y_end, x_start:x_end]
80
+ lon_zoom = animator.lons[x_start:x_end]
81
+ lat_zoom = animator.lats[y_start:y_end]
82
+
83
+ c2 = ax2.contourf(lon_zoom, lat_zoom, zoom, levels=levels,
84
+ cmap="rainbow", alpha=0.6, transform=ccrs.PlateCarree())
85
+ draw_etopo_basemap(ax2, mode='stock')
86
+ ax2.set_extent([lon_zoom.min(), lon_zoom.max(), lat_zoom.min(), lat_zoom.max()])
87
+ ax2.set_title(f"T{t+1} | Alt: {z_val} km (Zoom - {scale_label})")
88
+ ax2.coastlines(); ax2.add_feature(cfeature.BORDERS)
89
+ ax2.add_feature(cfeature.LAND); ax2.add_feature(cfeature.OCEAN)
90
+
91
+ for ax in [ax1, ax2]:
92
+ ax.text(0.01, 0.98, f"Time step T{t+1}", transform=ax.transAxes,
93
+ fontsize=9, color='white', va='top', ha='left',
94
+ bbox=dict(facecolor='black', alpha=0.4, boxstyle='round'))
95
+
96
+ if include_metadata:
97
+ for ax in [ax1, ax2]:
98
+ ax.text(0.01, 0.01,
99
+ f"Source: NAME model\nRes: {animator.x_res:.2f}°\n{meta.get('run_name', 'N/A')}",
100
+ transform=ax.transAxes, fontsize=8, color='white',
101
+ bbox=dict(facecolor='black', alpha=0.5))
102
+ ax1.annotate(legend_text, xy=(0.75, 0.99), xycoords='axes fraction',
103
+ fontsize=8, ha='left', va='top',
104
+ bbox=dict(boxstyle="round", facecolor="white", edgecolor="gray"),
105
+ annotation_clip=False)
106
+
107
+ cbar = fig.colorbar(c1, ax=[ax1, ax2], orientation='vertical', shrink=0.75, pad=0.03)
108
+ cbar.set_label("Ash concentration (g/m³)")
109
+ formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
110
+ cbar.ax.yaxis.set_major_formatter(formatter)
111
+
112
+ if use_log:
113
+ cbar.ax.text(1.1, 1.02, "log scale", transform=cbar.ax.transAxes,
114
+ fontsize=9, color='gray', rotation=90, ha='left', va='bottom')
115
+
116
+ frame_path = os.path.join(z_dir, f"frame_{t+1:04d}.jpg")
117
+ plt.savefig(frame_path, dpi=150, bbox_inches='tight')
118
+ plt.close(fig)
119
+ print(f"Saved {frame_path}")
ash_animator/interpolation.py ADDED
@@ -0,0 +1,14 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ import numpy as np
3
+ from scipy.interpolate import griddata
4
+
5
+ def interpolate_grid(data, lon_grid, lat_grid):
6
+ data = np.where(data < 0, np.nan, data)
7
+ mask = data > 0
8
+ if np.count_nonzero(mask) < 10:
9
+ return np.full_like(data, np.nan)
10
+
11
+ points = np.column_stack((lon_grid[mask], lat_grid[mask]))
12
+ values = data[mask]
13
+ grid_z = griddata(points, values, (lon_grid, lat_grid), method='cubic')
14
+ return np.where(grid_z < 0, 0, grid_z)
ash_animator/plot_3dfield_data.py ADDED
@@ -0,0 +1,465 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import os
2
+ import numpy as np
3
+ import matplotlib.pyplot as plt
4
+ import matplotlib.animation as animation
5
+ import matplotlib.ticker as mticker
6
+ import cartopy.crs as ccrs
7
+ import cartopy.feature as cfeature
8
+ import cartopy.io.shapereader as shpreader
9
+ from adjustText import adjust_text
10
+ from .interpolation import interpolate_grid
11
+ from .basemaps import draw_etopo_basemap
12
+ import imageio.v2 as imageio
13
+ import shutil
14
+
15
+ class Plot_3DField_Data:
16
+
17
+ """
18
+ A class for visualizing 3D spatiotemporal field data (e.g., ash concentration) across time and altitude levels.
19
+
20
+ This class uses matplotlib and cartopy to create:
21
+ - Animated GIFs of spatial fields at given altitudes
22
+ - Vertical profile animations over time
23
+ - Exported static frames with metadata annotations and zoomed views
24
+
25
+ Parameters
26
+ ----------
27
+ animator : object
28
+ A container holding the dataset, including:
29
+ - datasets: list of xarray-like DataArrays with 'ash_concentration'
30
+ - lons, lats: 1D longitude and latitude arrays
31
+ - lat_grid, lon_grid: 2D grid arrays for spatial mapping
32
+ - levels: 1D array of vertical altitude levels (e.g., in km)
33
+ output_dir : str
34
+ Base directory for saving all outputs. Defaults to "plots".
35
+ cmap : str
36
+ Matplotlib colormap name. Defaults to "rainbow".
37
+ fps : int
38
+ Frames per second for GIFs. Defaults to 2.
39
+ include_metadata : bool
40
+ Whether to annotate each figure with simulation metadata. Defaults to True.
41
+ threshold : float
42
+ Value threshold (e.g., in g/m³) to highlight exceedances. Defaults to 0.1.
43
+ zoom_width_deg : float
44
+ Width of the zoomed-in region in degrees. Defaults to 6.0.
45
+ zoom_height_deg : float
46
+ Height of the zoomed-in region in degrees. Defaults to 6.0.
47
+ zoom_level : int
48
+ Zoom level passed to basemap drawing. Defaults to 7.
49
+ basemap_type : str
50
+ Type of basemap to draw (passed to draw_etopo_basemap). Defaults to "basemap".
51
+
52
+ Methods
53
+ -------
54
+ plot_single_z_level(z_km, filename)
55
+ Generate animation over time at a specific altitude level.
56
+
57
+ plot_vertical_profile_at_time(t_index, filename=None)
58
+ Generate vertical profile GIF for a single timestep.
59
+
60
+ animate_altitude(t_index, output_path)
61
+ Animate altitude slices for one timestep.
62
+
63
+ animate_all_altitude_profiles(output_folder='altitude_profiles')
64
+ Generate vertical animations for all time steps.
65
+
66
+ export_frames_as_jpgs(include_metadata=True)
67
+ Export individual frames as static `.jpg` images with annotations.
68
+ """
69
+ def __init__(self, animator, output_dir="plots", cmap="rainbow", fps=2,
70
+ include_metadata=True, threshold=0.1,
71
+ zoom_width_deg=6.0, zoom_height_deg=6.0, zoom_level=7, basemap_type="basemap"):
72
+ self.animator = animator
73
+ self.output_dir = os.path.abspath(os.path.join(os.getcwd(), output_dir))
74
+ os.makedirs(self.output_dir, exist_ok=True)
75
+ self.cmap = cmap
76
+ self.fps = fps
77
+ self.include_metadata = include_metadata
78
+ self.threshold = threshold
79
+ self.zoom_width = zoom_width_deg
80
+ self.zoom_height = zoom_height_deg
81
+ shp = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries')
82
+ self.country_geoms = list(shpreader.Reader(shp).records())
83
+ self.zoom_level=zoom_level
84
+ self.basemap_type=basemap_type
85
+
86
+ #############3
87
+
88
+
89
+ # Load shapefile once
90
+ countries_shp = shpreader.natural_earth(
91
+ resolution='110m',
92
+ category='cultural',
93
+ name='admin_0_countries'
94
+ )
95
+ self.country_geoms = list(shpreader.Reader(countries_shp).records())
96
+
97
+ # Cache extent bounds
98
+ self.lon_min = np.min(self.animator.lons)
99
+ self.lon_max = np.max(self.animator.lons)
100
+ self.lat_min = np.min(self.animator.lats)
101
+ self.lat_max = np.max(self.animator.lats)
102
+
103
+ #####################3
104
+
105
+ def _make_dirs(self, path):
106
+ path = os.path.abspath(os.path.join(os.getcwd(), os.path.dirname(path)))
107
+ os.makedirs(path, exist_ok=True)
108
+
109
+ def _get_zoom_indices(self, center_lat, center_lon):
110
+ lon_min = center_lon - self.zoom_width / 2
111
+ lon_max = center_lon + self.zoom_width / 2
112
+ lat_min = center_lat - self.zoom_height / 2
113
+ lat_max = center_lat + self.zoom_height / 2
114
+ lat_idx = np.where((self.animator.lats >= lat_min) & (self.animator.lats <= lat_max))[0]
115
+ lon_idx = np.where((self.animator.lons >= lon_min) & (self.animator.lons <= lon_max))[0]
116
+ return lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max
117
+
118
+ def _get_max_concentration_location(self):
119
+ max_conc = -np.inf
120
+ center_lat = center_lon = None
121
+ for ds in self.animator.datasets:
122
+ for z in range(len(self.animator.levels)):
123
+ data = ds['ash_concentration'].values[z]
124
+ if np.max(data) > max_conc:
125
+ max_conc = np.max(data)
126
+ max_idx = np.unravel_index(np.argmax(data), data.shape)
127
+ center_lat = self.animator.lat_grid[max_idx]
128
+ center_lon = self.animator.lon_grid[max_idx]
129
+ return center_lat, center_lon
130
+
131
+ def _add_country_labels(self, ax, extent):
132
+ proj = ccrs.PlateCarree()
133
+ texts = []
134
+ for country in self.country_geoms:
135
+ name = country.attributes['NAME_LONG']
136
+ geom = country.geometry
137
+ try:
138
+ lon, lat = geom.centroid.x, geom.centroid.y
139
+ if extent[0] <= lon <= extent[1] and extent[2] <= lat <= extent[3]:
140
+ text = ax.text(lon, lat, name, fontsize=6, transform=proj,
141
+ ha='center', va='center', color='white',
142
+ bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
143
+ texts.append(text)
144
+ except:
145
+ continue
146
+ adjust_text(texts, ax=ax, only_move={'points': 'y', 'text': 'y'},
147
+ arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
148
+
149
+ def _plot_frame(self, ax, data, lons, lats, title, levels, scale_label, proj):
150
+ draw_etopo_basemap(ax, mode=self.basemap_type, zoom=self.zoom_level)
151
+ c = ax.contourf(lons, lats, data, levels=levels, cmap=self.cmap, alpha=0.6, transform=proj)
152
+ ax.contour(lons, lats, data, levels=levels, colors='black', linewidths=0.5, transform=proj)
153
+ ax.set_title(title)
154
+ ax.set_extent([lons.min(), lons.max(), lats.min(), lats.max()])
155
+ ax.coastlines()
156
+ ax.add_feature(cfeature.BORDERS, linestyle=':')
157
+ ax.add_feature(cfeature.LAND)
158
+ ax.add_feature(cfeature.OCEAN)
159
+ return c
160
+
161
+
162
+
163
+ # metadata placement function and usage
164
+
165
+ def _draw_metadata_sidebar(self, fig, meta_dict):
166
+ lines = [
167
+ f"Run name: {meta_dict.get('run_name', 'N/A')}",
168
+ f"Run time: {meta_dict.get('run_time', 'N/A')}",
169
+ f"Met data: {meta_dict.get('met_data', 'N/A')}",
170
+ f"Start release: {meta_dict.get('start_of_release', 'N/A')}",
171
+ f"End release: {meta_dict.get('end_of_release', 'N/A')}",
172
+ f"Source strength: {meta_dict.get('source_strength', 'N/A')} g/s",
173
+ f"Release loc: {meta_dict.get('release_location', 'N/A')}",
174
+ f"Release height: {meta_dict.get('release_height', 'N/A')} m asl",
175
+ f"Run duration: {meta_dict.get('run_duration', 'N/A')}"
176
+ ]
177
+ full_text = "\n".join(lines) # ✅ actual newlines
178
+ fig.text(0.1, 0.095, full_text, va='center', ha='left',
179
+ fontsize=9, family='monospace', color='black',
180
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
181
+
182
+
183
+
184
+ def plot_single_z_level(self, z_km, filename="z_level.gif"):
185
+
186
+ if z_km not in self.animator.levels:
187
+ print(f"Z level {z_km} km not found.")
188
+ return
189
+ z_index = np.where(self.animator.levels == z_km)[0][0]
190
+ output_path = os.path.join(self.output_dir, "z_levels", filename)
191
+ fig = plt.figure(figsize=(16, 8))
192
+ proj = ccrs.PlateCarree()
193
+ ax1 = fig.add_subplot(1, 2, 1, projection=proj)
194
+ ax2 = fig.add_subplot(1, 2, 2, projection=proj)
195
+
196
+ center_lat, center_lon = self._get_max_concentration_location()
197
+ lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
198
+ lat_zoom = self.animator.lats[lat_idx]
199
+ lon_zoom = self.animator.lons[lon_idx]
200
+ lon_zoom_grid, lat_zoom_grid = np.meshgrid(lon_zoom, lat_zoom)
201
+
202
+
203
+
204
+ meta = self.animator.datasets[0].attrs
205
+ valid_frames = []
206
+ for t in range(len(self.animator.datasets)):
207
+ interp = interpolate_grid(self.animator.datasets[t]['ash_concentration'].values[z_index],
208
+ self.animator.lon_grid, self.animator.lat_grid)
209
+ if np.isfinite(interp).sum() > 0:
210
+ valid_frames.append(t)
211
+ if not valid_frames:
212
+ print(f"No valid frames for Z={z_km} km.")
213
+ plt.close()
214
+ return
215
+
216
+ def update(t):
217
+ ax1.clear()
218
+ ax2.clear()
219
+
220
+ data = self.animator.datasets[t]['ash_concentration'].values[z_index]
221
+ interp = interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
222
+ interp = np.where(interp < 0, np.nan, interp)
223
+ zoom_plot = interp[np.ix_(lat_idx, lon_idx)]
224
+
225
+ valid_vals = interp[np.isfinite(interp)]
226
+ if valid_vals.size == 0:
227
+ return []
228
+
229
+ min_val, max_val = np.nanmin(valid_vals), np.nanmax(valid_vals)
230
+ log_cutoff = 1e-3
231
+ use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
232
+
233
+ levels = (
234
+ np.logspace(np.log10(log_cutoff), np.log10(max_val), 20)
235
+ if use_log else
236
+ np.linspace(0, max_val, 20)
237
+ )
238
+ data_for_plot = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
239
+ scale_label = "Log" if use_log else "Linear"
240
+
241
+ c = self._plot_frame(ax1, data_for_plot, self.animator.lons, self.animator.lats,
242
+ f"T{t+1} | Alt: {z_km} km (Full - {scale_label})", levels, scale_label, proj)
243
+ self._plot_frame(ax2, zoom_plot, lon_zoom, lat_zoom,
244
+ f"T{t} | Alt: {z_km} km (Zoom - {scale_label})", levels, scale_label, proj)
245
+
246
+ self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
247
+ self.animator.lats.min(), self.animator.lats.max()])
248
+ self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
249
+
250
+ if not hasattr(update, "colorbar"):
251
+ update.colorbar = fig.colorbar(c, ax=[ax1, ax2], orientation='vertical',
252
+ label="Ash concentration (g/m³)")
253
+ formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
254
+ update.colorbar.ax.yaxis.set_major_formatter(formatter)
255
+
256
+ # ✅ Draw threshold outline and label only if exceeded
257
+ if np.nanmax(valid_vals) > self.threshold:
258
+ ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
259
+ colors='red', linewidths=2, transform=proj)
260
+ ax2.contour(lon_zoom, lat_zoom, zoom_plot, levels=[self.threshold],
261
+ colors='red', linewidths=2, transform=proj)
262
+ ax2.text(0.99, 0.01, f"⚠ Max Thresold Exceed: {np.nanmax(valid_vals):.2f} > {self.threshold} g/m³",
263
+ transform=ax2.transAxes, ha='right', va='bottom',
264
+ fontsize=9, color='red',
265
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
266
+
267
+ return []
268
+
269
+
270
+
271
+
272
+ self._draw_metadata_sidebar(fig, meta)
273
+ self._make_dirs(output_path)
274
+ fig.tight_layout()
275
+ ani = animation.FuncAnimation(fig, update, frames=valid_frames, blit=False, cache_frame_data =False)
276
+ ani.save(output_path, writer='pillow', fps=self.fps, dpi=300)
277
+ plt.close()
278
+ print(f"✅ Saved Z-level animation to {output_path}")
279
+
280
+ def plot_vertical_profile_at_time(self, t_index, filename=None):
281
+ time_label = f"T{t_index+1}"
282
+ for z_index, z_val in enumerate(self.animator.levels):
283
+ filename = f"TimeSlices_Z{z_val:.1f}km.gif"
284
+ self.plot_single_z_level(z_val, filename=os.path.join("vertical_profiles_timeSlice", filename))
285
+
286
+
287
+ ################################################
288
+
289
+
290
+
291
+ def animate_altitude(self, t_index: int, output_path: str):
292
+ if not (0 <= t_index < len(self.animator.datasets)):
293
+ print(f"Invalid time index {t_index}. Must be between 0 and {len(self.animator.datasets) - 1}.")
294
+
295
+
296
+ ds = self.animator.datasets[t_index]
297
+ fig = plt.figure(figsize=(18, 7))
298
+ proj = ccrs.PlateCarree()
299
+ ax1 = fig.add_subplot(1, 2, 1, projection=proj)
300
+ ax2 = fig.add_subplot(1, 2, 2, projection=proj)
301
+
302
+ meta = ds.attrs
303
+ center_lat, center_lon = self._get_max_concentration_location()
304
+ if center_lat is None or center_lon is None:
305
+ print(f"No valid data found for time T{t_index + 1}. Skipping...")
306
+ plt.close()
307
+ return
308
+
309
+ lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
310
+ lat_zoom = self.animator.lats[lat_idx]
311
+ lon_zoom = self.animator.lons[lon_idx]
312
+ lon_zoom_grid, lat_zoom_grid = np.meshgrid(lon_zoom, lat_zoom)
313
+
314
+ z_indices_with_data = []
315
+ for z_index in range(len(self.animator.levels)):
316
+ data = ds['ash_concentration'].values[z_index]
317
+ interp = interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
318
+ if np.isfinite(interp).sum() > 0:
319
+ z_indices_with_data.append(z_index)
320
+
321
+ if not z_indices_with_data:
322
+ print(f"No valid Z-levels at time T{t_index + 1}.")
323
+ plt.close()
324
+ return
325
+
326
+ def update(z_index):
327
+ ax1.clear()
328
+ ax2.clear()
329
+
330
+ data = ds['ash_concentration'].values[z_index]
331
+ interp = interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
332
+ interp = np.where(interp < 0, np.nan, interp)
333
+ zoom_plot = interp[np.ix_(lat_idx, lon_idx)]
334
+
335
+ valid_vals = interp[np.isfinite(interp)]
336
+ if valid_vals.size == 0:
337
+ return []
338
+
339
+ min_val, max_val = np.nanmin(valid_vals), np.nanmax(valid_vals)
340
+ log_cutoff = 1e-3
341
+ use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
342
+
343
+ levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
344
+ data_for_plot = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
345
+ scale_label = "Log" if use_log else "Linear"
346
+
347
+ title1 = f"T{t_index + 1} | Alt: {self.animator.levels[z_index]} km (Full - {scale_label})"
348
+ title2 = f"T{t_index + 1} | Alt: {self.animator.levels[z_index]} km (Zoom - {scale_label})"
349
+
350
+ c1 = self._plot_frame(ax1, data_for_plot, self.animator.lons, self.animator.lats, title1, levels, scale_label, proj)
351
+ self._plot_frame(ax2, zoom_plot, lon_zoom, lat_zoom, title2, levels, scale_label, proj)
352
+
353
+ self._add_country_labels(ax1, [self.lon_min, self.lon_max, self.lat_min, self.lat_max])
354
+ self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
355
+
356
+ if self.include_metadata:
357
+ self._draw_metadata_sidebar(fig, meta)
358
+
359
+ if not hasattr(update, "colorbar"):
360
+ update.colorbar = fig.colorbar(c1, ax=[ax1, ax2], orientation='vertical',
361
+ label="Ash concentration (g/m³)", shrink=0.75)
362
+ formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
363
+ update.colorbar.ax.yaxis.set_major_formatter(formatter)
364
+
365
+ if np.nanmax(valid_vals) > self.threshold:
366
+ ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
367
+ colors='red', linewidths=2, transform=proj)
368
+ ax2.contour(lon_zoom, lat_zoom, zoom_plot, levels=[self.threshold],
369
+ colors='red', linewidths=2, transform=proj)
370
+
371
+
372
+ ax2.text(0.99, 0.01, f"⚠ Max Thresold Exceed: {np.nanmax(valid_vals):.2f} > {self.threshold} g/m³",
373
+ transform=ax2.transAxes, ha='right', va='bottom',
374
+ fontsize=9, color='red',
375
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
376
+ return []
377
+
378
+ os.makedirs(os.path.dirname(output_path), exist_ok=True)
379
+ #fig.set_size_inches(18, 7)
380
+ fig.tight_layout(rect=[0.02, 0.02, 0.98, 0.98])
381
+ ani = animation.FuncAnimation(fig, update, frames=z_indices_with_data, blit=False, cache_frame_data =False)
382
+ ani.save(output_path, writer='pillow', fps=self.fps, dpi=300)
383
+ plt.close()
384
+ print(f"✅ Saved vertical profile animation for T{t_index + 1} to {output_path}")
385
+
386
+
387
+
388
+ def animate_all_altitude_profiles(self, output_folder='altitude_profiles'):
389
+ output_folder = os.path.join(self.output_dir, "altitude_profiles")
390
+ os.makedirs(output_folder, exist_ok=True)
391
+ for t_index in range(len(self.animator.datasets)):
392
+ output_path = os.path.join(output_folder, f"vertical_T{t_index + 1:02d}.gif")
393
+ print(f"🔄 Generating vertical profile animation for T{t_index + 1}...")
394
+ self.animate_altitude(t_index, output_path)
395
+
396
+
397
+
398
+
399
+
400
+
401
+ def export_frames_as_jpgs(self, include_metadata: bool = True):
402
+ output_folder = os.path.join(self.output_dir, "frames")
403
+ os.makedirs(output_folder, exist_ok=True)
404
+ meta = self.animator.datasets[0].attrs
405
+ legend_text = "\\n".join([
406
+ f"Run name: {meta.get('run_name', 'N/A')}",
407
+ f"Run time: {meta.get('run_time', 'N/A')}",
408
+ f"Met data: {meta.get('met_data', 'N/A')}",
409
+ f"Start release: {meta.get('start_of_release', 'N/A')}",
410
+ f"End release: {meta.get('end_of_release', 'N/A')}",
411
+ f"Strength: {meta.get('source_strength', 'N/A')} g/s",
412
+ f"Location: {meta.get('release_location', 'N/A')}",
413
+ f"Height: {meta.get('release_height', 'N/A')} m asl",
414
+ f"Duration: {meta.get('run_duration', 'N/A')}"
415
+ ])
416
+ for z_index, z_val in enumerate(self.animator.levels):
417
+ z_dir = os.path.join(output_folder, f"Z{z_val:.1f}km")
418
+ os.makedirs(z_dir, exist_ok=True)
419
+ for t in range(len(self.animator.datasets)):
420
+ data = self.animator.datasets[t]['ash_concentration'].values[z_index]
421
+ interp = interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
422
+ if not np.isfinite(interp).any():
423
+ continue
424
+ fig = plt.figure(figsize=(16, 8))
425
+ proj = ccrs.PlateCarree()
426
+ ax1 = fig.add_subplot(1, 2, 1, projection=proj)
427
+ ax2 = fig.add_subplot(1, 2, 2, projection=proj)
428
+ valid_vals = interp[np.isfinite(interp)]
429
+ min_val, max_val = np.nanmin(valid_vals), np.nanmax(valid_vals)
430
+ log_cutoff = 1e-3
431
+ use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
432
+ levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
433
+ data_for_plot = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
434
+ scale_label = "Log" if use_log else "Linear"
435
+ center_lat, center_lon = self._get_max_concentration_location()
436
+ lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
437
+ zoom_plot = interp[np.ix_(lat_idx, lon_idx)]
438
+ lon_zoom = self.animator.lons[lon_idx]
439
+ lat_zoom = self.animator.lats[lat_idx]
440
+ c1 = self._plot_frame(ax1, data_for_plot, self.animator.lons, self.animator.lats,
441
+ f"T{t+1} | Alt: {z_val} km (Full - {scale_label})", levels, scale_label, proj)
442
+ self._plot_frame(ax2, zoom_plot, lon_zoom, lat_zoom,
443
+ f"T{t+1} | Alt: {z_val} km (Zoom - {scale_label})", levels, scale_label, proj)
444
+ self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
445
+ self.animator.lats.min(), self.animator.lats.max()])
446
+ self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
447
+ if np.nanmax(valid_vals) > self.threshold:
448
+ ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
449
+ colors='red', linewidths=2, transform=proj)
450
+ ax2.contour(lon_zoom, lat_zoom, zoom_plot, levels=[self.threshold],
451
+ colors='red', linewidths=2, transform=proj)
452
+ ax2.text(0.99, 0.01, f"⚠ Max: {np.nanmax(valid_vals):.2f} > {self.threshold} g/m³",
453
+ transform=ax2.transAxes, ha='right', va='bottom',
454
+ fontsize=9, color='red',
455
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
456
+ if include_metadata:
457
+ self._draw_metadata_sidebar(fig, meta)
458
+ cbar = fig.colorbar(c1, ax=[ax1, ax2], orientation='vertical', shrink=0.75, pad=0.03)
459
+ cbar.set_label("Ash concentration (g/m³)")
460
+ formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
461
+ cbar.ax.yaxis.set_major_formatter(formatter)
462
+ frame_path = os.path.join(z_dir, f"frame_{t+1:04d}.jpg")
463
+ plt.savefig(frame_path, dpi=150, bbox_inches='tight')
464
+ plt.close(fig)
465
+ print(f"📸 Saved {frame_path}")
ash_animator/plot_horizontal_data.py ADDED
@@ -0,0 +1,564 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ''' import os
2
+ import numpy as np
3
+ import matplotlib.pyplot as plt
4
+ import matplotlib.animation as animation
5
+ import matplotlib.ticker as mticker
6
+ import cartopy.crs as ccrs
7
+ import cartopy.feature as cfeature
8
+ import cartopy.io.shapereader as shpreader
9
+ from adjustText import adjust_text
10
+ from ash_animator.interpolation import interpolate_grid
11
+ from ash_animator.basemaps import draw_etopo_basemap
12
+
13
+ class Plot_Horizontal_Data:
14
+ def __init__(self, animator, output_dir="plots", cmap="rainbow", fps=2,
15
+ include_metadata=True, threshold=0.1,
16
+ zoom_width_deg=6.0, zoom_height_deg=6.0, zoom_level=7, static_frame_export=False):
17
+ self.animator = animator
18
+ self.output_dir = os.path.abspath(os.path.join(os.getcwd(), output_dir))
19
+ os.makedirs(self.output_dir, exist_ok=True)
20
+ self.cmap = cmap
21
+ self.fps = fps
22
+ self.include_metadata = include_metadata
23
+ self.threshold = threshold
24
+ self.zoom_width = zoom_width_deg
25
+ self.zoom_height = zoom_height_deg
26
+ shp = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries')
27
+ self.country_geoms = list(shpreader.Reader(shp).records())
28
+ self.interpolate_grid= interpolate_grid
29
+ self._draw_etopo_basemap=draw_etopo_basemap
30
+ self.zoom_level=zoom_level
31
+ self.static_frame_export=static_frame_export
32
+
33
+ def _make_dirs(self, path):
34
+ os.makedirs(os.path.abspath(os.path.join(os.getcwd(), os.path.dirname(path))), exist_ok=True)
35
+
36
+ def _get_max_concentration_location(self, field):
37
+ max_val = -np.inf
38
+ lat = lon = None
39
+ for ds in self.animator.datasets:
40
+ data = ds[field].values
41
+ if np.max(data) > max_val:
42
+ max_val = np.max(data)
43
+ idx = np.unravel_index(np.argmax(data), data.shape)
44
+ lat = self.animator.lat_grid[idx]
45
+ lon = self.animator.lon_grid[idx]
46
+ return lat, lon
47
+
48
+ def _get_zoom_indices(self, center_lat, center_lon):
49
+ lon_min = center_lon - self.zoom_width / 2
50
+ lon_max = center_lon + self.zoom_width / 2
51
+ lat_min = center_lat - self.zoom_height / 2
52
+ lat_max = center_lat + self.zoom_height / 2
53
+ lat_idx = np.where((self.animator.lats >= lat_min) & (self.animator.lats <= lat_max))[0]
54
+ lon_idx = np.where((self.animator.lons >= lon_min) & (self.animator.lons <= lon_max))[0]
55
+ return lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max
56
+
57
+ def _add_country_labels(self, ax, extent):
58
+ proj = ccrs.PlateCarree()
59
+ texts = []
60
+ for country in self.country_geoms:
61
+ name = country.attributes['NAME_LONG']
62
+ geom = country.geometry
63
+ try:
64
+ lon, lat = geom.centroid.x, geom.centroid.y
65
+ if extent[0] <= lon <= extent[1] and extent[2] <= lat <= extent[3]:
66
+ text = ax.text(lon, lat, name, fontsize=6, transform=proj,
67
+ ha='center', va='center', color='white',
68
+ bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
69
+ texts.append(text)
70
+ except:
71
+ continue
72
+ adjust_text(texts, ax=ax, only_move={'points': 'y', 'text': 'y'},
73
+ arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
74
+
75
+ def _draw_metadata_sidebar(self, fig, meta_dict):
76
+ lines = [
77
+ f"Run name: {meta_dict.get('run_name', 'N/A')}",
78
+ f"Run time: {meta_dict.get('run_time', 'N/A')}",
79
+ f"Met data: {meta_dict.get('met_data', 'N/A')}",
80
+ f"Start release: {meta_dict.get('start_of_release', 'N/A')}",
81
+ f"End release: {meta_dict.get('end_of_release', 'N/A')}",
82
+ f"Source strength: {meta_dict.get('source_strength', 'N/A')} g/s",
83
+ f"Release loc: {meta_dict.get('release_location', 'N/A')}",
84
+ f"Release height: {meta_dict.get('release_height', 'N/A')} m asl",
85
+ f"Run duration: {meta_dict.get('run_duration', 'N/A')}"
86
+ ]
87
+
88
+ # Split into two columns
89
+ mid = len(lines) // 2 + len(lines) % 2
90
+ left_lines = lines[:mid]
91
+ right_lines = lines[mid:]
92
+
93
+ left_text = "\n".join(left_lines)
94
+ right_text = "\n".join(right_lines)
95
+
96
+ # right column
97
+ fig.text(0.05, 0.05, left_text, va='bottom', ha='left',
98
+ fontsize=9, family='monospace', color='black',
99
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
100
+
101
+ # left column
102
+ fig.text(0.3, 0.05, right_text, va='bottom', ha='left',
103
+ fontsize=9, family='monospace', color='black',
104
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
105
+
106
+
107
+
108
+
109
+
110
+ def _plot_frame(self, ax, data, lons, lats, title, levels, scale_label, proj):
111
+ self._draw_etopo_basemap(ax, mode='basemap', zoom=self.zoom_level)
112
+ c = ax.contourf(lons, lats, data, levels=levels, cmap=self.cmap, alpha=0.6, transform=proj)
113
+ ax.set_title(title)
114
+ ax.set_extent([lons.min(), lons.max(), lats.min(), lats.max()])
115
+ ax.coastlines()
116
+ ax.add_feature(cfeature.BORDERS, linestyle=':')
117
+ ax.add_feature(cfeature.LAND)
118
+ ax.add_feature(cfeature.OCEAN)
119
+ return c
120
+
121
+ def get_available_2d_fields(self):
122
+ ds = self.animator.datasets[0]
123
+ return [v for v in ds.data_vars if ds[v].ndim == 2]
124
+
125
+ def plot_single_field_over_time(self, field, filename="field.gif"):
126
+ output_path = os.path.join(self.output_dir, "2d_fields", field, filename)
127
+ meta = self.animator.datasets[0].attrs
128
+ center_lat, center_lon = self._get_max_concentration_location(field)
129
+ lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
130
+ lat_zoom = self.animator.lats[lat_idx]
131
+ lon_zoom = self.animator.lons[lon_idx]
132
+
133
+ valid_frames = []
134
+ for t in range(len(self.animator.datasets)):
135
+ data = self.animator.datasets[t][field].values
136
+ interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
137
+ if np.isfinite(interp).sum() > 0:
138
+ valid_frames.append(t)
139
+
140
+ if not valid_frames:
141
+ print(f"No valid frames to plot for field '{field}'.")
142
+ return
143
+
144
+ fig = plt.figure(figsize=(16, 8))
145
+ proj = ccrs.PlateCarree()
146
+ ax1 = fig.add_subplot(1, 2, 1, projection=proj)
147
+ ax2 = fig.add_subplot(1, 2, 2, projection=proj)
148
+
149
+ def update(t):
150
+ ax1.clear()
151
+ ax2.clear()
152
+ data = self.animator.datasets[t][field].values
153
+ interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
154
+ zoom = interp[np.ix_(lat_idx, lon_idx)]
155
+ valid = interp[np.isfinite(interp)]
156
+ if valid.size == 0:
157
+ return []
158
+
159
+ min_val, max_val = np.nanmin(valid), np.nanmax(valid)
160
+ log_cutoff = 1e-3
161
+ use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
162
+ levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
163
+ plot_data = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
164
+ scale_label = "Log" if use_log else "Linear"
165
+
166
+ c = self._plot_frame(ax1, plot_data, self.animator.lons, self.animator.lats,
167
+ f"T{t+1} | {field} (Full - {scale_label})", levels, scale_label, proj)
168
+ self._plot_frame(ax2, zoom, lon_zoom, lat_zoom,
169
+ f"T{t+1} | {field} (Zoom - {scale_label})", levels, scale_label, proj)
170
+
171
+ self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
172
+ self.animator.lats.min(), self.animator.lats.max()])
173
+ self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
174
+
175
+ # Inside update() function:
176
+ if not hasattr(update, "colorbar"):
177
+ unit_label = f"{field}:({self.animator.datasets[0][field].attrs.get("units", field)})" #self.animator.datasets[0][field].attrs.get("units", field)
178
+ update.colorbar = fig.colorbar(c, ax=[ax1, ax2], orientation='vertical', label=unit_label)
179
+ formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
180
+ update.colorbar.ax.yaxis.set_major_formatter(formatter)
181
+
182
+
183
+ if np.nanmax(valid) > self.threshold:
184
+ ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
185
+ colors='red', linewidths=2, transform=proj)
186
+ ax2.contour(lon_zoom, lat_zoom, zoom, levels=[self.threshold],
187
+ colors='red', linewidths=2, transform=proj)
188
+ ax2.text(0.99, 0.01, f"⚠ Max Thresold Exceed: {np.nanmax(valid):.2f} > {self.threshold}",
189
+ transform=ax2.transAxes, ha='right', va='bottom',
190
+ fontsize=9, color='red',
191
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
192
+
193
+ if self.static_frame_export:
194
+ frame_folder = os.path.join(self.output_dir, "frames", field)
195
+ os.makedirs(frame_folder, exist_ok=True)
196
+ frame_path = os.path.join(frame_folder, f"frame_{t+1:04d}.jpg")
197
+ plt.savefig(frame_path, dpi=300, bbox_inches='tight')
198
+ print(f"🖼️ Saved static frame: {frame_path}")
199
+
200
+ return []
201
+
202
+ if self.include_metadata:
203
+ self._draw_metadata_sidebar(fig, meta)
204
+
205
+ self._make_dirs(output_path)
206
+ fig.tight_layout()
207
+ ani = animation.FuncAnimation(fig, update, frames=valid_frames, blit=False, cache_frame_data =False)
208
+ ani.save(output_path, writer='pillow', fps=self.fps)
209
+ plt.close()
210
+ print(f"✅ Saved enhanced 2D animation for {field} to {output_path}")
211
+
212
+ # def export_frames_as_jpgs(self, fields=None, include_metadata=True):
213
+ # all_fields = self.get_available_2d_fields()
214
+ # if fields:
215
+ # fields = [f for f in fields if f in all_fields]
216
+ # else:
217
+ # fields = all_fields
218
+
219
+ # meta = self.animator.datasets[0].attrs
220
+
221
+ # for field in fields:
222
+ # print(f"📤 Exporting frames for field: {field}")
223
+ # output_folder = os.path.join(self.output_dir, "frames", field)
224
+ # os.makedirs(output_folder, exist_ok=True)
225
+
226
+ # center_lat, center_lon = self._get_max_concentration_location(field)
227
+ # lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
228
+ # lat_zoom = self.animator.lats[lat_idx]
229
+ # lon_zoom = self.animator.lons[lon_idx]
230
+
231
+ # for t, ds in enumerate(self.animator.datasets):
232
+ # data = ds[field].values
233
+ # interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
234
+ # if not np.isfinite(interp).any():
235
+ # continue
236
+
237
+ # fig = plt.figure(figsize=(16, 8))
238
+ # proj = ccrs.PlateCarree()
239
+ # ax1 = fig.add_subplot(1, 2, 1, projection=proj)
240
+ # ax2 = fig.add_subplot(1, 2, 2, projection=proj)
241
+ # zoom = interp[np.ix_(lat_idx, lon_idx)]
242
+ # valid = interp[np.isfinite(interp)]
243
+ # min_val, max_val = np.nanmin(valid), np.nanmax(valid)
244
+ # log_cutoff = 1e-3
245
+ # use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
246
+ # levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
247
+ # plot_data = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
248
+ # scale_label = "Log" if use_log else "Linear"
249
+
250
+ # c = self._plot_frame(ax1, plot_data, self.animator.lons, self.animator.lats,
251
+ # f"T{t+1} | {field} (Full - {scale_label})", levels, scale_label, proj)
252
+ # self._plot_frame(ax2, zoom, lon_zoom, lat_zoom,
253
+ # f"T{t+1} | {field} (Zoom - {scale_label})", levels, scale_label, proj)
254
+
255
+ # self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
256
+ # self.animator.lats.min(), self.animator.lats.max()])
257
+ # self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
258
+
259
+ # if include_metadata:
260
+ # self._draw_metadata_sidebar(fig, meta)
261
+
262
+ # cbar = fig.colorbar(c, ax=[ax1, ax2], orientation='vertical', shrink=0.75, pad=0.03)
263
+ # unit_label = f"{field}:({self.animator.datasets[0][field].attrs.get('units', field)})"
264
+ # cbar.set_label(unit_label)
265
+ # formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
266
+ # cbar.ax.yaxis.set_major_formatter(formatter)
267
+
268
+ # if np.nanmax(valid) > self.threshold:
269
+ # ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
270
+ # colors='red', linewidths=2, transform=proj)
271
+ # ax2.contour(lon_zoom, lat_zoom, zoom, levels=[self.threshold],
272
+ # colors='red', linewidths=2, transform=proj)
273
+ # ax2.text(0.99, 0.01, f"⚠ Max: {np.nanmax(valid):.2f} > {self.threshold}",
274
+ # transform=ax2.transAxes, ha='right', va='bottom',
275
+ # fontsize=9, color='red',
276
+ # bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
277
+
278
+ # frame_path = os.path.join(output_folder, f"frame_{t+1:04d}.jpg")
279
+ # plt.savefig(frame_path, dpi=150, bbox_inches='tight')
280
+ # plt.close(fig)
281
+ # print(f"📸 Saved {frame_path}")
282
+ '''
283
+
284
+ import os
285
+ import numpy as np
286
+ import matplotlib.pyplot as plt
287
+ import matplotlib.animation as animation
288
+ import matplotlib.ticker as mticker
289
+ import cartopy.crs as ccrs
290
+ import cartopy.feature as cfeature
291
+ import cartopy.io.shapereader as shpreader
292
+ from adjustText import adjust_text
293
+ from ash_animator.interpolation import interpolate_grid
294
+ from ash_animator.basemaps import draw_etopo_basemap
295
+
296
+ class Plot_Horizontal_Data:
297
+ def __init__(self, animator, output_dir="plots", cmap="rainbow", fps=2,
298
+ include_metadata=True, threshold=0.1,
299
+ zoom_width_deg=6.0, zoom_height_deg=6.0, zoom_level=7, static_frame_export=False):
300
+ self.animator = animator
301
+ self.output_dir = os.path.abspath(os.path.join(os.getcwd(), output_dir))
302
+ os.makedirs(self.output_dir, exist_ok=True)
303
+ self.cmap = cmap
304
+ self.fps = fps
305
+ self.include_metadata = include_metadata
306
+ self.threshold = threshold
307
+ self.zoom_width = zoom_width_deg
308
+ self.zoom_height = zoom_height_deg
309
+ shp = shpreader.natural_earth(resolution='110m', category='cultural', name='admin_0_countries')
310
+ self.country_geoms = list(shpreader.Reader(shp).records())
311
+ self.interpolate_grid= interpolate_grid
312
+ self._draw_etopo_basemap=draw_etopo_basemap
313
+ self.zoom_level=zoom_level
314
+ self.static_frame_export=static_frame_export
315
+
316
+ def _make_dirs(self, path):
317
+ os.makedirs(os.path.abspath(os.path.join(os.getcwd(), os.path.dirname(path))), exist_ok=True)
318
+
319
+ def _get_max_concentration_location(self, field):
320
+ max_val = -np.inf
321
+ lat = lon = None
322
+ for ds in self.animator.datasets:
323
+ data = ds[field].values
324
+ if np.max(data) > max_val:
325
+ max_val = np.max(data)
326
+ idx = np.unravel_index(np.argmax(data), data.shape)
327
+ lat = self.animator.lat_grid[idx]
328
+ lon = self.animator.lon_grid[idx]
329
+ return lat, lon
330
+
331
+ def _get_zoom_indices(self, center_lat, center_lon):
332
+ lon_min = center_lon - self.zoom_width / 2
333
+ lon_max = center_lon + self.zoom_width / 2
334
+ lat_min = center_lat - self.zoom_height / 2
335
+ lat_max = center_lat + self.zoom_height / 2
336
+ lat_idx = np.where((self.animator.lats >= lat_min) & (self.animator.lats <= lat_max))[0]
337
+ lon_idx = np.where((self.animator.lons >= lon_min) & (self.animator.lons <= lon_max))[0]
338
+ return lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max
339
+
340
+ def _add_country_labels(self, ax, extent):
341
+ proj = ccrs.PlateCarree()
342
+ texts = []
343
+ for country in self.country_geoms:
344
+ name = country.attributes['NAME_LONG']
345
+ geom = country.geometry
346
+ try:
347
+ lon, lat = geom.centroid.x, geom.centroid.y
348
+ if extent[0] <= lon <= extent[1] and extent[2] <= lat <= extent[3]:
349
+ text = ax.text(lon, lat, name, fontsize=6, transform=proj,
350
+ ha='center', va='center', color='white',
351
+ bbox=dict(facecolor='black', alpha=0.5, linewidth=0))
352
+ texts.append(text)
353
+ except:
354
+ continue
355
+ adjust_text(texts, ax=ax, only_move={'points': 'y', 'text': 'y'},
356
+ arrowprops=dict(arrowstyle="->", color='white', lw=0.5))
357
+
358
+ def _draw_metadata_sidebar(self, fig, meta_dict):
359
+ lines = [
360
+ f"Run name: {meta_dict.get('run_name', 'N/A')}",
361
+ f"Run time: {meta_dict.get('run_time', 'N/A')}",
362
+ f"Met data: {meta_dict.get('met_data', 'N/A')}",
363
+ f"Start release: {meta_dict.get('start_of_release', 'N/A')}",
364
+ f"End release: {meta_dict.get('end_of_release', 'N/A')}",
365
+ f"Source strength: {meta_dict.get('source_strength', 'N/A')} g/s",
366
+ f"Release loc: {meta_dict.get('release_location', 'N/A')}",
367
+ f"Release height: {meta_dict.get('release_height', 'N/A')} m asl",
368
+ f"Run duration: {meta_dict.get('run_duration', 'N/A')}"
369
+ ]
370
+
371
+ # Split into two columns
372
+ mid = len(lines) // 2 + len(lines) % 2
373
+ left_lines = lines[:mid]
374
+ right_lines = lines[mid:]
375
+
376
+ left_text = "\n".join(left_lines)
377
+ right_text = "\n".join(right_lines)
378
+
379
+ # right column
380
+ fig.text(0.05, 0.05, left_text, va='bottom', ha='left',
381
+ fontsize=9, family='monospace', color='black',
382
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
383
+
384
+ # left column
385
+ fig.text(0.3, 0.05, right_text, va='bottom', ha='left',
386
+ fontsize=9, family='monospace', color='black',
387
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='gray'))
388
+
389
+
390
+
391
+
392
+
393
+ def _plot_frame(self, ax, data, lons, lats, title, levels, scale_label, proj):
394
+ self._draw_etopo_basemap(ax, mode='basemap', zoom=self.zoom_level)
395
+ c = ax.contourf(lons, lats, data, levels=levels, cmap=self.cmap, alpha=0.6, transform=proj)
396
+ ax.set_title(title)
397
+ ax.set_extent([lons.min(), lons.max(), lats.min(), lats.max()])
398
+ ax.coastlines()
399
+ ax.add_feature(cfeature.BORDERS, linestyle=':')
400
+ ax.add_feature(cfeature.LAND)
401
+ ax.add_feature(cfeature.OCEAN)
402
+ return c
403
+
404
+ def get_available_2d_fields(self):
405
+ ds = self.animator.datasets[0]
406
+ return [v for v in ds.data_vars if ds[v].ndim == 2]
407
+
408
+ def plot_single_field_over_time(self, field, filename="field.gif"):
409
+ output_path = os.path.join(self.output_dir, "2d_fields", field, filename)
410
+ meta = self.animator.datasets[0].attrs
411
+ center_lat, center_lon = self._get_max_concentration_location(field)
412
+ lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
413
+ lat_zoom = self.animator.lats[lat_idx]
414
+ lon_zoom = self.animator.lons[lon_idx]
415
+
416
+ valid_frames = []
417
+ for t in range(len(self.animator.datasets)):
418
+ data = self.animator.datasets[t][field].values
419
+ interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
420
+ if np.isfinite(interp).sum() > 0:
421
+ valid_frames.append(t)
422
+
423
+ if not valid_frames:
424
+ print(f"No valid frames to plot for field '{field}'.")
425
+ return
426
+
427
+ fig = plt.figure(figsize=(16, 8))
428
+ proj = ccrs.PlateCarree()
429
+ ax1 = fig.add_subplot(1, 2, 1, projection=proj)
430
+ ax2 = fig.add_subplot(1, 2, 2, projection=proj)
431
+
432
+ def update(t):
433
+ ax1.clear()
434
+ ax2.clear()
435
+ data = self.animator.datasets[t][field].values
436
+ interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
437
+ zoom = interp[np.ix_(lat_idx, lon_idx)]
438
+ valid = interp[np.isfinite(interp)]
439
+ if valid.size == 0:
440
+ return []
441
+
442
+ min_val, max_val = np.nanmin(valid), np.nanmax(valid)
443
+ log_cutoff = 1e-3
444
+ use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
445
+ levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
446
+ plot_data = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
447
+ scale_label = "Log" if use_log else "Linear"
448
+
449
+ c = self._plot_frame(ax1, plot_data, self.animator.lons, self.animator.lats,
450
+ f"T{t+1} | {field} (Full - {scale_label})", levels, scale_label, proj)
451
+ self._plot_frame(ax2, zoom, lon_zoom, lat_zoom,
452
+ f"T{t+1} | {field} (Zoom - {scale_label})", levels, scale_label, proj)
453
+
454
+ self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
455
+ self.animator.lats.min(), self.animator.lats.max()])
456
+ self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
457
+
458
+ # Inside update() function:
459
+ if not hasattr(update, "colorbar"):
460
+ unit_label = f"{field}:({self.animator.datasets[0][field].attrs.get('units', field)})" #self.animator.datasets[0][field].attrs.get("units", field)
461
+ update.colorbar = fig.colorbar(c, ax=[ax1, ax2], orientation='vertical', label=unit_label)
462
+ formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
463
+ update.colorbar.ax.yaxis.set_major_formatter(formatter)
464
+
465
+
466
+ if np.nanmax(valid) > self.threshold:
467
+ ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
468
+ colors='red', linewidths=2, transform=proj)
469
+ ax2.contour(lon_zoom, lat_zoom, zoom, levels=[self.threshold],
470
+ colors='red', linewidths=2, transform=proj)
471
+ ax2.text(0.99, 0.01, f"⚠ Max Thresold Exceed: {np.nanmax(valid):.2f} > {self.threshold}",
472
+ transform=ax2.transAxes, ha='right', va='bottom',
473
+ fontsize=9, color='red',
474
+ bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
475
+
476
+ if self.static_frame_export:
477
+ frame_folder = os.path.join(self.output_dir, "frames", field)
478
+ os.makedirs(frame_folder, exist_ok=True)
479
+ frame_path = os.path.join(frame_folder, f"frame_{t+1:04d}.jpg")
480
+ plt.savefig(frame_path, dpi=300, bbox_inches='tight')
481
+ print(f"🖼️ Saved static frame: {frame_path}")
482
+
483
+ return []
484
+
485
+ if self.include_metadata:
486
+ self._draw_metadata_sidebar(fig, meta)
487
+
488
+ self._make_dirs(output_path)
489
+ fig.tight_layout()
490
+ ani = animation.FuncAnimation(fig, update, frames=valid_frames, blit=False, cache_frame_data =False)
491
+ ani.save(output_path, writer='pillow', fps=self.fps)
492
+ plt.close()
493
+ print(f"✅ Saved enhanced 2D animation for {field} to {output_path}")
494
+
495
+ # def export_frames_as_jpgs(self, fields=None, include_metadata=True):
496
+ # all_fields = self.get_available_2d_fields()
497
+ # if fields:
498
+ # fields = [f for f in fields if f in all_fields]
499
+ # else:
500
+ # fields = all_fields
501
+
502
+ # meta = self.animator.datasets[0].attrs
503
+
504
+ # for field in fields:
505
+ # print(f"📤 Exporting frames for field: {field}")
506
+ # output_folder = os.path.join(self.output_dir, "frames", field)
507
+ # os.makedirs(output_folder, exist_ok=True)
508
+
509
+ # center_lat, center_lon = self._get_max_concentration_location(field)
510
+ # lat_idx, lon_idx, lon_min, lon_max, lat_min, lat_max = self._get_zoom_indices(center_lat, center_lon)
511
+ # lat_zoom = self.animator.lats[lat_idx]
512
+ # lon_zoom = self.animator.lons[lon_idx]
513
+
514
+ # for t, ds in enumerate(self.animator.datasets):
515
+ # data = ds[field].values
516
+ # interp = self.interpolate_grid(data, self.animator.lon_grid, self.animator.lat_grid)
517
+ # if not np.isfinite(interp).any():
518
+ # continue
519
+
520
+ # fig = plt.figure(figsize=(16, 8))
521
+ # proj = ccrs.PlateCarree()
522
+ # ax1 = fig.add_subplot(1, 2, 1, projection=proj)
523
+ # ax2 = fig.add_subplot(1, 2, 2, projection=proj)
524
+ # zoom = interp[np.ix_(lat_idx, lon_idx)]
525
+ # valid = interp[np.isfinite(interp)]
526
+ # min_val, max_val = np.nanmin(valid), np.nanmax(valid)
527
+ # log_cutoff = 1e-3
528
+ # use_log = min_val > log_cutoff and (max_val / (min_val + 1e-6)) > 100
529
+ # levels = np.logspace(np.log10(log_cutoff), np.log10(max_val), 20) if use_log else np.linspace(0, max_val, 20)
530
+ # plot_data = np.where(interp > log_cutoff, interp, np.nan) if use_log else interp
531
+ # scale_label = "Log" if use_log else "Linear"
532
+
533
+ # c = self._plot_frame(ax1, plot_data, self.animator.lons, self.animator.lats,
534
+ # f"T{t+1} | {field} (Full - {scale_label})", levels, scale_label, proj)
535
+ # self._plot_frame(ax2, zoom, lon_zoom, lat_zoom,
536
+ # f"T{t+1} | {field} (Zoom - {scale_label})", levels, scale_label, proj)
537
+
538
+ # self._add_country_labels(ax1, [self.animator.lons.min(), self.animator.lons.max(),
539
+ # self.animator.lats.min(), self.animator.lats.max()])
540
+ # self._add_country_labels(ax2, [lon_min, lon_max, lat_min, lat_max])
541
+
542
+ # if include_metadata:
543
+ # self._draw_metadata_sidebar(fig, meta)
544
+
545
+ # cbar = fig.colorbar(c, ax=[ax1, ax2], orientation='vertical', shrink=0.75, pad=0.03)
546
+ # unit_label = f"{field}:({self.animator.datasets[0][field].attrs.get('units', field)})"
547
+ # cbar.set_label(unit_label)
548
+ # formatter = mticker.FuncFormatter(lambda x, _: f'{x:.2g}')
549
+ # cbar.ax.yaxis.set_major_formatter(formatter)
550
+
551
+ # if np.nanmax(valid) > self.threshold:
552
+ # ax1.contour(self.animator.lons, self.animator.lats, interp, levels=[self.threshold],
553
+ # colors='red', linewidths=2, transform=proj)
554
+ # ax2.contour(lon_zoom, lat_zoom, zoom, levels=[self.threshold],
555
+ # colors='red', linewidths=2, transform=proj)
556
+ # ax2.text(0.99, 0.01, f"⚠ Max: {np.nanmax(valid):.2f} > {self.threshold}",
557
+ # transform=ax2.transAxes, ha='right', va='bottom',
558
+ # fontsize=9, color='red',
559
+ # bbox=dict(facecolor='white', alpha=0.8, edgecolor='red'))
560
+
561
+ # frame_path = os.path.join(output_folder, f"frame_{t+1:04d}.jpg")
562
+ # plt.savefig(frame_path, dpi=150, bbox_inches='tight')
563
+ # plt.close(fig)
564
+ # print(f"📸 Saved {frame_path}")
ash_animator/utils.py ADDED
@@ -0,0 +1,23 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+
2
+ from geopy.geocoders import Nominatim
3
+ import numpy as np
4
+
5
+ def create_grid(attrs):
6
+ x_origin = float(attrs["x_origin"])
7
+ y_origin = float(attrs["y_origin"])
8
+ x_res = float(attrs["x_res"])
9
+ y_res = float(attrs["y_res"])
10
+ x_grid_size = int(attrs["x_grid_size"])
11
+ y_grid_size = int(attrs["y_grid_size"])
12
+
13
+ lons = np.round(np.linspace(x_origin, x_origin + (x_grid_size - 1) * x_res, x_grid_size), 6)
14
+ lats = np.round(np.linspace(y_origin, y_origin + (y_grid_size - 1) * y_res, y_grid_size), 6)
15
+ return lons, lats, np.meshgrid(lons, lats)
16
+
17
+ def get_country_label(lat, lon):
18
+ geolocator = Nominatim(user_agent="ash_animator")
19
+ try:
20
+ location = geolocator.reverse((lat, lon), language='en')
21
+ return location.raw['address'].get('country', 'Unknown')
22
+ except:
23
+ return "Unknown"
media/2D/2d_fields/air_concentration/air_concentration.gif ADDED

Git LFS Details

  • SHA256: ed93902f14b5a4840d721b554fb79047b3e11c7e07ce480a37f58a3be9fbb76c
  • Pointer size: 131 Bytes
  • Size of remote file: 635 kB
media/2D/frames/air_concentration/frame_0001.jpg ADDED

Git LFS Details

  • SHA256: 22fc9d2422ad47b5841b65db04d3242caa68f20a29f94810690654456b83cbd8
  • Pointer size: 131 Bytes
  • Size of remote file: 749 kB
media/2D/frames/air_concentration/frame_0008.jpg ADDED

Git LFS Details

  • SHA256: c487f0f046ae15349c482ab8b622d739465a7ce3360bb949e5860089acde72e9
  • Pointer size: 131 Bytes
  • Size of remote file: 741 kB
media/2D/frames/air_concentration/frame_0009.jpg ADDED

Git LFS Details

  • SHA256: fb758323a52cb444302ad4f79d2dacbe8367f9102e5f46d244e4f3630e17fa73
  • Pointer size: 131 Bytes
  • Size of remote file: 743 kB
media/2D/frames/air_concentration/frame_0010.jpg ADDED

Git LFS Details

  • SHA256: 9f62c92518851a39a96b8496618ad3bd60d0a61f3b8af2bbc5ea8526f2c24bd8
  • Pointer size: 131 Bytes
  • Size of remote file: 745 kB
media/Taal_273070_20200112_scenario_yizhou.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:9bb340a75132c3008a149557ff85f8bc05b4a46e70eee027503e30b9573fdd39
3
+ size 181349
media/last_run.txt ADDED
@@ -0,0 +1 @@
 
 
1
+ media\default_model.zip
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z1.txt ADDED
@@ -0,0 +1,97 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 1000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
38
+ 120, 114, 119.75, 13.25, 7.345203E-05,
39
+ 120, 115, 119.75, 13.5, 5.622633E-05,
40
+ 120, 116, 119.75, 13.75, 2.600824E-05,
41
+ 121, 113, 120, 13, 0.0001420568,
42
+ 121, 114, 120, 13.25, 0.001363128,
43
+ 121, 115, 120, 13.5, 0.003970164,
44
+ 121, 116, 120, 13.75, 0.00291151,
45
+ 121, 117, 120, 14, 9.206738E-05,
46
+ 121, 118, 120, 14.25, 1.456495E-05,
47
+ 122, 113, 120.25, 13, 0.001457789,
48
+ 122, 114, 120.25, 13.25, 0.01133905,
49
+ 122, 115, 120.25, 13.5, 0.03271282,
50
+ 122, 116, 120.25, 13.75, 0.03133794,
51
+ 122, 117, 120.25, 14, 0.003700373,
52
+ 123, 112, 120.5, 12.75, 3.815254E-05,
53
+ 123, 113, 120.5, 13, 0.001681373,
54
+ 123, 114, 120.5, 13.25, 0.03288734,
55
+ 123, 115, 120.5, 13.5, 0.07128608,
56
+ 123, 116, 120.5, 13.75, 0.1296768,
57
+ 123, 117, 120.5, 14, 0.04034004,
58
+ 123, 118, 120.5, 14.25, 5.493374E-05,
59
+ 124, 113, 120.75, 13, 0.0002777606,
60
+ 124, 114, 120.75, 13.25, 0.01638796,
61
+ 124, 115, 120.75, 13.5, 0.03514561,
62
+ 124, 116, 120.75, 13.75, 0.1086908,
63
+ 124, 117, 120.75, 14, 0.1927676,
64
+ 124, 118, 120.75, 14.25, 0.0002998679,
65
+ 124, 119, 120.75, 14.5, 1.071569E-10,
66
+ 125, 113, 121, 13, 0.000159862,
67
+ 125, 114, 121, 13.25, 0.002424539,
68
+ 125, 115, 121, 13.5, 0.002680934,
69
+ 125, 116, 121, 13.75, 0.003586116,
70
+ 125, 117, 121, 14, 0.142053,
71
+ 125, 118, 121, 14.25, 0.003128162,
72
+ 125, 119, 121, 14.5, 6.248734E-05,
73
+ 126, 113, 121.25, 13, 1.786754E-05,
74
+ 126, 115, 121.25, 13.5, 1.350236E-05,
75
+ 126, 116, 121.25, 13.75, 1.28469E-05,
76
+ 126, 117, 121.25, 14, 0.0001430601,
77
+ 126, 118, 121.25, 14.25, 0.001556279,
78
+ 126, 119, 121.25, 14.5, 0.001372935,
79
+ 126, 120, 121.25, 14.75, 0.0002339001,
80
+ 126, 121, 121.25, 15, 3.106254E-05,
81
+ 127, 118, 121.5, 14.25, 0.0002478267,
82
+ 127, 119, 121.5, 14.5, 0.001072011,
83
+ 127, 120, 121.5, 14.75, 0.0005181016,
84
+ 127, 121, 121.5, 15, 6.368926E-05,
85
+ 128, 119, 121.75, 14.5, 1.124057E-05,
86
+ 128, 120, 121.75, 14.75, 0.0001651402,
87
+ 128, 121, 121.75, 15, 0.000116237,
88
+ 128, 122, 121.75, 15.25, 0.000122752,
89
+ 128, 123, 121.75, 15.5, 3.474941E-05,
90
+ 128, 124, 121.75, 15.75, 1.624418E-05,
91
+ 128, 125, 121.75, 16, 4.155787E-06,
92
+ 129, 121, 122, 15, 2.920656E-05,
93
+ 129, 122, 122, 15.25, 5.199026E-05,
94
+ 129, 123, 122, 15.5, 3.1246E-05,
95
+ 129, 124, 122, 15.75, 1.447262E-06,
96
+ 130, 123, 122.25, 15.5, 6.670825E-06,
97
+ 130, 124, 122.25, 15.75, 1.719066E-05,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z10.txt ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 19000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z11.txt ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 22500 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z12.txt ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 27500 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z2.txt ADDED
@@ -0,0 +1,112 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 3000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
38
+ 120, 113, 119.75, 13, 3.151353E-05,
39
+ 120, 114, 119.75, 13.25, 2.210932E-05,
40
+ 120, 115, 119.75, 13.5, 1.178464E-05,
41
+ 121, 113, 120, 13, 0.0001126801,
42
+ 121, 114, 120, 13.25, 0.0005374243,
43
+ 121, 115, 120, 13.5, 0.0002840176,
44
+ 121, 116, 120, 13.75, 4.521007E-05,
45
+ 122, 112, 120.25, 12.75, 4.670541E-05,
46
+ 122, 113, 120.25, 13, 0.001202281,
47
+ 122, 114, 120.25, 13.25, 0.003869232,
48
+ 122, 115, 120.25, 13.5, 0.001962312,
49
+ 122, 116, 120.25, 13.75, 0.0001883732,
50
+ 123, 112, 120.5, 12.75, 2.581522E-05,
51
+ 123, 113, 120.5, 13, 0.002264641,
52
+ 123, 114, 120.5, 13.25, 0.01127036,
53
+ 123, 115, 120.5, 13.5, 0.004743796,
54
+ 123, 116, 120.5, 13.75, 0.004018477,
55
+ 123, 117, 120.5, 14, 9.492574E-05,
56
+ 124, 113, 120.75, 13, 0.0008278259,
57
+ 124, 114, 120.75, 13.25, 0.007400203,
58
+ 124, 115, 120.75, 13.5, 0.0391963,
59
+ 124, 116, 120.75, 13.75, 0.07150318,
60
+ 124, 117, 120.75, 14, 0.008536345,
61
+ 125, 114, 121, 13.25, 0.001417874,
62
+ 125, 115, 121, 13.5, 0.0306792,
63
+ 125, 116, 121, 13.75, 0.1119654,
64
+ 125, 117, 121, 14, 0.2439914,
65
+ 125, 118, 121, 14.25, 0.02892056,
66
+ 125, 119, 121, 14.5, 0.001362004,
67
+ 125, 120, 121, 14.75, 0.0002818799,
68
+ 125, 121, 121, 15, 8.935516E-05,
69
+ 126, 113, 121.25, 13, 9.283876E-06,
70
+ 126, 114, 121.25, 13.25, 4.714714E-05,
71
+ 126, 115, 121.25, 13.5, 0.002435901,
72
+ 126, 116, 121.25, 13.75, 0.02259675,
73
+ 126, 117, 121.25, 14, 0.06305796,
74
+ 126, 118, 121.25, 14.25, 0.06351896,
75
+ 126, 119, 121.25, 14.5, 0.02769424,
76
+ 126, 120, 121.25, 14.75, 0.0255998,
77
+ 126, 121, 121.25, 15, 0.01439965,
78
+ 126, 122, 121.25, 15.25, 0.002417035,
79
+ 126, 123, 121.25, 15.5, 0.0002603215,
80
+ 126, 124, 121.25, 15.75, 6.742119E-05,
81
+ 127, 116, 121.5, 13.75, 0.0009422529,
82
+ 127, 117, 121.5, 14, 0.007668528,
83
+ 127, 118, 121.5, 14.25, 0.0222777,
84
+ 127, 119, 121.5, 14.5, 0.02496219,
85
+ 127, 120, 121.5, 14.75, 0.02905941,
86
+ 127, 121, 121.5, 15, 0.01414512,
87
+ 127, 122, 121.5, 15.25, 0.002081324,
88
+ 127, 123, 121.5, 15.5, 0.0002854613,
89
+ 127, 124, 121.5, 15.75, 4.66012E-05,
90
+ 128, 116, 121.75, 13.75, 1.17622E-05,
91
+ 128, 117, 121.75, 14, 0.0001454951,
92
+ 128, 118, 121.75, 14.25, 0.001557329,
93
+ 128, 119, 121.75, 14.5, 0.003246944,
94
+ 128, 120, 121.75, 14.75, 0.003654109,
95
+ 128, 121, 121.75, 15, 0.002385811,
96
+ 128, 122, 121.75, 15.25, 0.0004549951,
97
+ 128, 123, 121.75, 15.5, 8.048277E-05,
98
+ 128, 125, 121.75, 16, 1.627556E-05,
99
+ 129, 119, 122, 14.5, 3.451304E-05,
100
+ 129, 120, 122, 14.75, 0.000101785,
101
+ 129, 121, 122, 15, 0.0001553481,
102
+ 129, 122, 122, 15.25, 0.0002352085,
103
+ 129, 123, 122, 15.5, 0.0002999381,
104
+ 129, 124, 122, 15.75, 0.000136597,
105
+ 129, 125, 122, 16, 6.32211E-05,
106
+ 129, 126, 122, 16.25, 7.975153E-06,
107
+ 130, 122, 122.25, 15.25, 7.096558E-06,
108
+ 130, 123, 122.25, 15.5, 2.498302E-05,
109
+ 130, 124, 122.25, 15.75, 0.0001287205,
110
+ 130, 125, 122.25, 16, 6.148266E-05,
111
+ 130, 126, 122.25, 16.25, 4.595343E-05,
112
+ 131, 126, 122.5, 16.25, 5.254344E-06,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z3.txt ADDED
@@ -0,0 +1,98 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 5000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
38
+ 124, 120, 120.75, 14.75, 1.832862E-05,
39
+ 125, 117, 121, 14, 0.1071744,
40
+ 125, 118, 121, 14.25, 0.1898806,
41
+ 125, 119, 121, 14.5, 0.1062204,
42
+ 125, 120, 121, 14.75, 0.04167035,
43
+ 125, 121, 121, 15, 0.009898688,
44
+ 125, 122, 121, 15.25, 0.001467171,
45
+ 125, 123, 121, 15.5, 5.674901E-05,
46
+ 126, 117, 121.25, 14, 0.0002987186,
47
+ 126, 118, 121.25, 14.25, 0.02782325,
48
+ 126, 119, 121.25, 14.5, 0.09352003,
49
+ 126, 120, 121.25, 14.75, 0.1199462,
50
+ 126, 121, 121.25, 15, 0.1002778,
51
+ 126, 122, 121.25, 15.25, 0.04825792,
52
+ 126, 123, 121.25, 15.5, 0.009866159,
53
+ 126, 124, 121.25, 15.75, 0.0004914963,
54
+ 127, 118, 121.5, 14.25, 0.0008210147,
55
+ 127, 119, 121.5, 14.5, 0.01132668,
56
+ 127, 120, 121.5, 14.75, 0.02896771,
57
+ 127, 121, 121.5, 15, 0.03513165,
58
+ 127, 122, 121.5, 15.25, 0.04076821,
59
+ 127, 123, 121.5, 15.5, 0.01694665,
60
+ 127, 124, 121.5, 15.75, 0.002089425,
61
+ 127, 125, 121.5, 16, 9.950028E-06,
62
+ 128, 118, 121.75, 14.25, 1.539078E-05,
63
+ 128, 119, 121.75, 14.5, 0.0008586162,
64
+ 128, 120, 121.75, 14.75, 0.003469678,
65
+ 128, 121, 121.75, 15, 0.005778265,
66
+ 128, 122, 121.75, 15.25, 0.004883057,
67
+ 128, 123, 121.75, 15.5, 0.002288275,
68
+ 128, 124, 121.75, 15.75, 0.0004972389,
69
+ 128, 125, 121.75, 16, 1.796154E-05,
70
+ 128, 126, 121.75, 16.25, 1.441912E-05,
71
+ 128, 127, 121.75, 16.5, 3.396637E-06,
72
+ 129, 120, 122, 14.75, 0.0003278529,
73
+ 129, 121, 122, 15, 0.000854575,
74
+ 129, 122, 122, 15.25, 0.0009055724,
75
+ 129, 123, 122, 15.5, 0.0007337024,
76
+ 129, 124, 122, 15.75, 0.0003872091,
77
+ 129, 125, 122, 16, 0.0002032383,
78
+ 129, 126, 122, 16.25, 2.154827E-05,
79
+ 129, 127, 122, 16.5, 1.780571E-06,
80
+ 130, 121, 122.25, 15, 0.0001244367,
81
+ 130, 122, 122.25, 15.25, 0.0002427659,
82
+ 130, 123, 122.25, 15.5, 0.0004279363,
83
+ 130, 124, 122.25, 15.75, 0.0002095186,
84
+ 130, 125, 122.25, 16, 0.0001560012,
85
+ 130, 126, 122.25, 16.25, 7.090886E-05,
86
+ 130, 127, 122.25, 16.5, 4.815324E-05,
87
+ 130, 128, 122.25, 16.75, 1.677327E-05,
88
+ 131, 122, 122.5, 15.25, 1.008342E-05,
89
+ 131, 123, 122.5, 15.5, 5.903353E-05,
90
+ 131, 124, 122.5, 15.75, 5.071654E-05,
91
+ 131, 125, 122.5, 16, 4.200674E-05,
92
+ 131, 126, 122.5, 16.25, 2.104882E-05,
93
+ 131, 127, 122.5, 16.5, 1.786964E-05,
94
+ 131, 128, 122.5, 16.75, 1.045481E-05,
95
+ 131, 129, 122.5, 17, 3.892807E-06,
96
+ 132, 125, 122.75, 16, 1.948814E-05,
97
+ 132, 126, 122.75, 16.25, 2.796266E-05,
98
+ 132, 129, 122.75, 17, 3.03299E-06,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z4.txt ADDED
@@ -0,0 +1,120 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 7000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
38
+ 125, 117, 121, 14, 0.1234117,
39
+ 125, 118, 121, 14.25, 0.01902798,
40
+ 125, 119, 121, 14.5, 0.005364289,
41
+ 125, 120, 121, 14.75, 0.000805156,
42
+ 125, 121, 121, 15, 5.191433E-05,
43
+ 126, 117, 121.25, 14, 0.06572944,
44
+ 126, 118, 121.25, 14.25, 0.1743895,
45
+ 126, 119, 121.25, 14.5, 0.04336735,
46
+ 126, 120, 121.25, 14.75, 0.00239888,
47
+ 126, 121, 121.25, 15, 1.830715E-05,
48
+ 127, 117, 121.5, 14, 0.000526721,
49
+ 127, 118, 121.5, 14.25, 0.1083765,
50
+ 127, 119, 121.5, 14.5, 0.08831672,
51
+ 127, 120, 121.5, 14.75, 0.01520912,
52
+ 127, 121, 121.5, 15, 0.002765159,
53
+ 127, 122, 121.5, 15.25, 0.001005686,
54
+ 127, 123, 121.5, 15.5, 9.233864E-05,
55
+ 128, 118, 121.75, 14.25, 0.01266538,
56
+ 128, 119, 121.75, 14.5, 0.0860876,
57
+ 128, 120, 121.75, 14.75, 0.04378112,
58
+ 128, 121, 121.75, 15, 0.009834636,
59
+ 128, 122, 121.75, 15.25, 0.001592182,
60
+ 128, 123, 121.75, 15.5, 0.0002373249,
61
+ 128, 125, 121.75, 16, 1.53453E-05,
62
+ 128, 126, 121.75, 16.25, 1.303039E-05,
63
+ 129, 118, 122, 14.25, 0.0004872618,
64
+ 129, 119, 122, 14.5, 0.02244982,
65
+ 129, 120, 122, 14.75, 0.05446454,
66
+ 129, 121, 122, 15, 0.02280708,
67
+ 129, 122, 122, 15.25, 0.004015724,
68
+ 129, 123, 122, 15.5, 0.0006667624,
69
+ 129, 124, 122, 15.75, 0.0001274908,
70
+ 129, 125, 122, 16, 0.0001149381,
71
+ 129, 126, 122, 16.25, 2.609871E-05,
72
+ 129, 127, 122, 16.5, 4.272111E-05,
73
+ 129, 128, 122, 16.75, 7.941143E-06,
74
+ 130, 119, 122.25, 14.5, 0.00229462,
75
+ 130, 120, 122.25, 14.75, 0.02620347,
76
+ 130, 121, 122.25, 15, 0.03568861,
77
+ 130, 122, 122.25, 15.25, 0.01130614,
78
+ 130, 123, 122.25, 15.5, 0.002651222,
79
+ 130, 124, 122.25, 15.75, 0.0005461422,
80
+ 130, 125, 122.25, 16, 0.0002027196,
81
+ 130, 126, 122.25, 16.25, 0.000123466,
82
+ 130, 127, 122.25, 16.5, 0.0001030938,
83
+ 130, 128, 122.25, 16.75, 2.181705E-05,
84
+ 131, 119, 122.5, 14.5, 0.0001928969,
85
+ 131, 120, 122.5, 14.75, 0.004697453,
86
+ 131, 121, 122.5, 15, 0.0152602,
87
+ 131, 122, 122.5, 15.25, 0.01426645,
88
+ 131, 123, 122.5, 15.5, 0.006414582,
89
+ 131, 124, 122.5, 15.75, 0.001753196,
90
+ 131, 125, 122.5, 16, 0.0005218849,
91
+ 131, 126, 122.5, 16.25, 0.0001514535,
92
+ 131, 127, 122.5, 16.5, 4.750156E-05,
93
+ 131, 128, 122.5, 16.75, 3.249941E-05,
94
+ 131, 129, 122.5, 17, 3.10239E-05,
95
+ 132, 119, 122.75, 14.5, 2.428079E-05,
96
+ 132, 120, 122.75, 14.75, 0.0003906027,
97
+ 132, 121, 122.75, 15, 0.002045316,
98
+ 132, 122, 122.75, 15.25, 0.003867209,
99
+ 132, 123, 122.75, 15.5, 0.003004558,
100
+ 132, 124, 122.75, 15.75, 0.001598635,
101
+ 132, 125, 122.75, 16, 0.0004885108,
102
+ 132, 126, 122.75, 16.25, 0.0001285777,
103
+ 132, 127, 122.75, 16.5, 6.977042E-05,
104
+ 132, 128, 122.75, 16.75, 1.965358E-05,
105
+ 132, 129, 122.75, 17, 4.237151E-05,
106
+ 132, 130, 122.75, 17.25, 2.409082E-05,
107
+ 133, 120, 123, 14.75, 4.00559E-05,
108
+ 133, 121, 123, 15, 2.602069E-05,
109
+ 133, 122, 123, 15.25, 0.0002770847,
110
+ 133, 123, 123, 15.5, 0.0001807743,
111
+ 133, 124, 123, 15.75, 8.627385E-05,
112
+ 133, 125, 123, 16, 5.220342E-05,
113
+ 133, 126, 123, 16.25, 1.164414E-05,
114
+ 133, 127, 123, 16.5, 3.527872E-05,
115
+ 133, 128, 123, 16.75, 4.068049E-05,
116
+ 133, 129, 123, 17, 1.243638E-05,
117
+ 133, 130, 123, 17.25, 2.116325E-05,
118
+ 133, 131, 123, 17.5, 1.55598E-05,
119
+ 133, 132, 123, 17.75, 6.152012E-06,
120
+ 134, 128, 123.25, 16.75, 9.220726E-06,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z5.txt ADDED
@@ -0,0 +1,129 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 9000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
38
+ 125, 117, 121, 14, 0.06898191,
39
+ 125, 118, 121, 14.25, 0.03289511,
40
+ 126, 117, 121.25, 14, 0.00224039,
41
+ 126, 118, 121.25, 14.25, 0.09919951,
42
+ 126, 119, 121.25, 14.5, 0.06965803,
43
+ 126, 120, 121.25, 14.75, 0.01552968,
44
+ 126, 121, 121.25, 15, 0.0006118558,
45
+ 126, 122, 121.25, 15.25, 8.398663E-05,
46
+ 127, 118, 121.5, 14.25, 0.007041673,
47
+ 127, 119, 121.5, 14.5, 0.03958223,
48
+ 127, 120, 121.5, 14.75, 0.08249489,
49
+ 127, 121, 121.5, 15, 0.04705694,
50
+ 127, 122, 121.5, 15.25, 0.006514756,
51
+ 127, 123, 121.5, 15.5, 0.0002950616,
52
+ 127, 124, 121.5, 15.75, 4.134601E-05,
53
+ 127, 125, 121.5, 16, 8.057184E-05,
54
+ 128, 118, 121.75, 14.25, 2.034633E-05,
55
+ 128, 119, 121.75, 14.5, 0.006565935,
56
+ 128, 120, 121.75, 14.75, 0.030691,
57
+ 128, 121, 121.75, 15, 0.04808839,
58
+ 128, 122, 121.75, 15.25, 0.04375883,
59
+ 128, 123, 121.75, 15.5, 0.01544368,
60
+ 128, 124, 121.75, 15.75, 0.00178602,
61
+ 128, 125, 121.75, 16, 0.0003033282,
62
+ 128, 126, 121.75, 16.25, 0.0002123726,
63
+ 128, 127, 121.75, 16.5, 1.205982E-05,
64
+ 129, 119, 122, 14.5, 0.0003113352,
65
+ 129, 120, 122, 14.75, 0.005724736,
66
+ 129, 121, 122, 15, 0.01940874,
67
+ 129, 122, 122, 15.25, 0.03175062,
68
+ 129, 123, 122, 15.5, 0.03573182,
69
+ 129, 124, 122, 15.75, 0.02254891,
70
+ 129, 125, 122, 16, 0.005114635,
71
+ 129, 126, 122, 16.25, 0.0004420028,
72
+ 129, 127, 122, 16.5, 0.0001147449,
73
+ 129, 128, 122, 16.75, 5.181966E-05,
74
+ 129, 129, 122, 17, 6.466838E-05,
75
+ 129, 130, 122, 17.25, 5.45986E-05,
76
+ 129, 131, 122, 17.5, 1.571672E-05,
77
+ 129, 132, 122, 17.75, 1.559137E-05,
78
+ 130, 120, 122.25, 14.75, 0.0002433661,
79
+ 130, 121, 122.25, 15, 0.004868901,
80
+ 130, 122, 122.25, 15.25, 0.01413328,
81
+ 130, 123, 122.25, 15.5, 0.02152275,
82
+ 130, 124, 122.25, 15.75, 0.02527227,
83
+ 130, 125, 122.25, 16, 0.02232271,
84
+ 130, 126, 122.25, 16.25, 0.007527761,
85
+ 130, 127, 122.25, 16.5, 0.0005829639,
86
+ 130, 128, 122.25, 16.75, 0.0002166384,
87
+ 130, 129, 122.25, 17, 5.924731E-05,
88
+ 130, 130, 122.25, 17.25, 7.314311E-05,
89
+ 130, 131, 122.25, 17.5, 4.50703E-05,
90
+ 130, 132, 122.25, 17.75, 9.010204E-06,
91
+ 131, 120, 122.5, 14.75, 4.411209E-05,
92
+ 131, 121, 122.5, 15, 0.0002816019,
93
+ 131, 122, 122.5, 15.25, 0.003049549,
94
+ 131, 123, 122.5, 15.5, 0.009221563,
95
+ 131, 124, 122.5, 15.75, 0.01291798,
96
+ 131, 125, 122.5, 16, 0.01417599,
97
+ 131, 126, 122.5, 16.25, 0.01471124,
98
+ 131, 127, 122.5, 16.5, 0.006280135,
99
+ 131, 128, 122.5, 16.75, 0.0003908131,
100
+ 131, 129, 122.5, 17, 4.026011E-05,
101
+ 131, 130, 122.5, 17.25, 3.994577E-05,
102
+ 131, 131, 122.5, 17.5, 5.481579E-05,
103
+ 131, 133, 122.5, 18, 1.347075E-05,
104
+ 132, 122, 122.75, 15.25, 0.0002512288,
105
+ 132, 123, 122.75, 15.5, 0.001575867,
106
+ 132, 124, 122.75, 15.75, 0.003163365,
107
+ 132, 125, 122.75, 16, 0.00385646,
108
+ 132, 126, 122.75, 16.25, 0.004527654,
109
+ 132, 127, 122.75, 16.5, 0.004513398,
110
+ 132, 128, 122.75, 16.75, 0.001677582,
111
+ 132, 129, 122.75, 17, 0.0001914731,
112
+ 132, 130, 122.75, 17.25, 3.960486E-05,
113
+ 132, 131, 122.75, 17.5, 3.67114E-05,
114
+ 132, 132, 122.75, 17.75, 2.488445E-05,
115
+ 132, 133, 122.75, 18, 5.25589E-06,
116
+ 132, 135, 122.75, 18.5, 7.131451E-06,
117
+ 133, 122, 123, 15.25, 1.427139E-05,
118
+ 133, 123, 123, 15.5, 0.00012912,
119
+ 133, 124, 123, 15.75, 0.0001626896,
120
+ 133, 125, 123, 16, 0.0002683933,
121
+ 133, 126, 123, 16.25, 0.0003018458,
122
+ 133, 127, 123, 16.5, 0.0004253907,
123
+ 133, 128, 123, 16.75, 0.0004501775,
124
+ 133, 129, 123, 17, 4.029605E-05,
125
+ 133, 130, 123, 17.25, 3.655336E-05,
126
+ 133, 131, 123, 17.5, 1.555869E-05,
127
+ 134, 125, 123.25, 16, 1.443723E-05,
128
+ 134, 127, 123.25, 16.5, 1.185754E-05,
129
+ 134, 129, 123.25, 17, 1.642355E-05,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z6.txt ADDED
@@ -0,0 +1,194 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 11000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
38
+ 124, 128, 120.75, 16.75, 6.221109E-05,
39
+ 124, 129, 120.75, 17, 4.036166E-05,
40
+ 124, 130, 120.75, 17.25, 4.119542E-05,
41
+ 125, 117, 121, 14, 0.0622182,
42
+ 125, 118, 121, 14.25, 0.01277359,
43
+ 125, 119, 121, 14.5, 2.138454E-05,
44
+ 125, 120, 121, 14.75, 8.514526E-05,
45
+ 125, 121, 121, 15, 4.234628E-05,
46
+ 125, 122, 121, 15.25, 2.128574E-05,
47
+ 125, 127, 121, 16.5, 4.163329E-05,
48
+ 125, 128, 121, 16.75, 0.0001412808,
49
+ 125, 129, 121, 17, 7.697552E-05,
50
+ 125, 130, 121, 17.25, 0.0001729393,
51
+ 125, 131, 121, 17.5, 0.0002793811,
52
+ 125, 132, 121, 17.75, 5.478346E-05,
53
+ 126, 117, 121.25, 14, 0.0008748188,
54
+ 126, 118, 121.25, 14.25, 0.1041974,
55
+ 126, 119, 121.25, 14.5, 0.03328305,
56
+ 126, 120, 121.25, 14.75, 0.001406505,
57
+ 126, 121, 121.25, 15, 0.0005954904,
58
+ 126, 122, 121.25, 15.25, 0.0001699915,
59
+ 126, 123, 121.25, 15.5, 0.0002946049,
60
+ 126, 124, 121.25, 15.75, 4.199668E-05,
61
+ 126, 125, 121.25, 16, 4.211965E-05,
62
+ 126, 126, 121.25, 16.25, 8.194144E-05,
63
+ 126, 127, 121.25, 16.5, 7.915337E-05,
64
+ 126, 128, 121.25, 16.75, 3.97684E-05,
65
+ 126, 129, 121.25, 17, 9.260629E-05,
66
+ 126, 130, 121.25, 17.25, 0.0001086774,
67
+ 126, 131, 121.25, 17.5, 0.0001386326,
68
+ 126, 132, 121.25, 17.75, 0.0001915632,
69
+ 127, 118, 121.5, 14.25, 0.005296591,
70
+ 127, 119, 121.5, 14.5, 0.07859905,
71
+ 127, 120, 121.5, 14.75, 0.03961777,
72
+ 127, 121, 121.5, 15, 0.006926836,
73
+ 127, 122, 121.5, 15.25, 0.001831552,
74
+ 127, 123, 121.5, 15.5, 0.0003300227,
75
+ 127, 124, 121.5, 15.75, 0.0003337682,
76
+ 127, 125, 121.5, 16, 0.0003885732,
77
+ 127, 126, 121.5, 16.25, 0.0002186491,
78
+ 127, 127, 121.5, 16.5, 0.000131449,
79
+ 127, 128, 121.5, 16.75, 9.575831E-05,
80
+ 127, 129, 121.5, 17, 1.964502E-05,
81
+ 127, 130, 121.5, 17.25, 3.632389E-05,
82
+ 127, 133, 121.5, 18, 1.512874E-05,
83
+ 128, 119, 121.75, 14.5, 0.006370666,
84
+ 128, 120, 121.75, 14.75, 0.05709197,
85
+ 128, 121, 121.75, 15, 0.04001419,
86
+ 128, 122, 121.75, 15.25, 0.02055781,
87
+ 128, 123, 121.75, 15.5, 0.007529017,
88
+ 128, 124, 121.75, 15.75, 0.001310615,
89
+ 128, 125, 121.75, 16, 0.0003127966,
90
+ 128, 126, 121.75, 16.25, 0.0003177128,
91
+ 128, 127, 121.75, 16.5, 0.0002474229,
92
+ 128, 128, 121.75, 16.75, 0.0001769759,
93
+ 128, 129, 121.75, 17, 0.000161892,
94
+ 128, 130, 121.75, 17.25, 3.615832E-05,
95
+ 128, 131, 121.75, 17.5, 5.506534E-05,
96
+ 128, 132, 121.75, 17.75, 1.75275E-05,
97
+ 128, 133, 121.75, 18, 2.89427E-05,
98
+ 129, 120, 122, 14.75, 0.004740195,
99
+ 129, 121, 122, 15, 0.04091293,
100
+ 129, 122, 122, 15.25, 0.04057813,
101
+ 129, 123, 122, 15.5, 0.02420357,
102
+ 129, 124, 122, 15.75, 0.01466465,
103
+ 129, 125, 122, 16, 0.004439455,
104
+ 129, 126, 122, 16.25, 0.0005244176,
105
+ 129, 127, 122, 16.5, 0.0007242206,
106
+ 129, 128, 122, 16.75, 0.0002385112,
107
+ 129, 129, 122, 17, 9.866249E-05,
108
+ 129, 130, 122, 17.25, 0.0002000228,
109
+ 129, 131, 122, 17.5, 0.0004704146,
110
+ 129, 132, 122, 17.75, 0.0002639875,
111
+ 129, 133, 122, 18, 9.922256E-05,
112
+ 129, 134, 122, 18.25, 0.0001151264,
113
+ 129, 135, 122, 18.5, 2.804919E-05,
114
+ 130, 121, 122.25, 15, 0.003280296,
115
+ 130, 122, 122.25, 15.25, 0.02504453,
116
+ 130, 123, 122.25, 15.5, 0.03768711,
117
+ 130, 124, 122.25, 15.75, 0.02522154,
118
+ 130, 125, 122.25, 16, 0.02000674,
119
+ 130, 126, 122.25, 16.25, 0.00986207,
120
+ 130, 127, 122.25, 16.5, 0.002313988,
121
+ 130, 128, 122.25, 16.75, 0.0008495215,
122
+ 130, 129, 122.25, 17, 0.0005191759,
123
+ 130, 130, 122.25, 17.25, 0.0006682072,
124
+ 130, 131, 122.25, 17.5, 0.0005532792,
125
+ 130, 132, 122.25, 17.75, 0.0002793283,
126
+ 130, 133, 122.25, 18, 0.0001133961,
127
+ 130, 134, 122.25, 18.25, 0.0001818698,
128
+ 130, 135, 122.25, 18.5, 5.843351E-05,
129
+ 130, 136, 122.25, 18.75, 9.999425E-06,
130
+ 131, 121, 122.5, 15, 1.987706E-05,
131
+ 131, 122, 122.5, 15.25, 0.001202978,
132
+ 131, 123, 122.5, 15.5, 0.01218185,
133
+ 131, 124, 122.5, 15.75, 0.02681912,
134
+ 131, 125, 122.5, 16, 0.02582716,
135
+ 131, 126, 122.5, 16.25, 0.02219759,
136
+ 131, 127, 122.5, 16.5, 0.01519257,
137
+ 131, 128, 122.5, 16.75, 0.003614416,
138
+ 131, 129, 122.5, 17, 0.0007025765,
139
+ 131, 130, 122.5, 17.25, 0.0005188695,
140
+ 131, 131, 122.5, 17.5, 0.0005340927,
141
+ 131, 132, 122.5, 17.75, 0.0002755789,
142
+ 131, 133, 122.5, 18, 0.0002350702,
143
+ 131, 134, 122.5, 18.25, 0.000112796,
144
+ 131, 135, 122.5, 18.5, 0.000131118,
145
+ 131, 136, 122.5, 18.75, 6.583513E-05,
146
+ 131, 138, 122.5, 19.25, 1.433079E-05,
147
+ 132, 123, 122.75, 15.5, 0.0002521633,
148
+ 132, 124, 122.75, 15.75, 0.004151984,
149
+ 132, 125, 122.75, 16, 0.01522794,
150
+ 132, 126, 122.75, 16.25, 0.02182925,
151
+ 132, 127, 122.75, 16.5, 0.01839288,
152
+ 132, 128, 122.75, 16.75, 0.008324588,
153
+ 132, 129, 122.75, 17, 0.001951292,
154
+ 132, 130, 122.75, 17.25, 0.0006910883,
155
+ 132, 131, 122.75, 17.5, 0.0007270788,
156
+ 132, 132, 122.75, 17.75, 0.0005217993,
157
+ 132, 133, 122.75, 18, 0.0003192426,
158
+ 132, 134, 122.75, 18.25, 0.000137487,
159
+ 132, 135, 122.75, 18.5, 0.0001577495,
160
+ 132, 136, 122.75, 18.75, 0.0001022264,
161
+ 132, 137, 122.75, 19, 6.035015E-05,
162
+ 132, 138, 122.75, 19.25, 3.065452E-05,
163
+ 132, 139, 122.75, 19.5, 1.765224E-05,
164
+ 133, 124, 123, 15.75, 5.781758E-05,
165
+ 133, 125, 123, 16, 0.0008737573,
166
+ 133, 126, 123, 16.25, 0.004744236,
167
+ 133, 127, 123, 16.5, 0.008771173,
168
+ 133, 128, 123, 16.75, 0.005445542,
169
+ 133, 129, 123, 17, 0.001555368,
170
+ 133, 130, 123, 17.25, 0.0006647338,
171
+ 133, 131, 123, 17.5, 0.0004687287,
172
+ 133, 132, 123, 17.75, 0.0003556611,
173
+ 133, 133, 123, 18, 0.0002114093,
174
+ 133, 134, 123, 18.25, 0.0001475303,
175
+ 133, 135, 123, 18.5, 0.00012588,
176
+ 133, 136, 123, 18.75, 1.820795E-05,
177
+ 133, 137, 123, 19, 3.518468E-05,
178
+ 133, 138, 123, 19.25, 1.161282E-05,
179
+ 133, 139, 123, 19.5, 1.765224E-05,
180
+ 134, 126, 123.25, 16.25, 0.0001508185,
181
+ 134, 127, 123.25, 16.5, 0.0006174166,
182
+ 134, 128, 123.25, 16.75, 0.001200111,
183
+ 134, 129, 123.25, 17, 0.0004430999,
184
+ 134, 130, 123.25, 17.25, 0.0001755207,
185
+ 134, 131, 123.25, 17.5, 8.36772E-05,
186
+ 134, 132, 123.25, 17.75, 2.520311E-05,
187
+ 134, 133, 123.25, 18, 1.265662E-05,
188
+ 134, 134, 123.25, 18.25, 3.630448E-05,
189
+ 134, 135, 123.25, 18.5, 3.370708E-05,
190
+ 134, 136, 123.25, 18.75, 2.21591E-05,
191
+ 135, 128, 123.5, 16.75, 1.772547E-05,
192
+ 135, 129, 123.5, 17, 6.980081E-05,
193
+ 135, 130, 123.5, 17.25, 1.49036E-05,
194
+ 135, 135, 123.5, 18.5, 1.310325E-05,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z7.txt ADDED
@@ -0,0 +1,297 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 13000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
38
+ 123, 126, 120.5, 16.25, 0.0003881654,
39
+ 123, 127, 120.5, 16.5, 0.00505147,
40
+ 123, 128, 120.5, 16.75, 0.004085907,
41
+ 123, 129, 120.5, 17, 0.002943351,
42
+ 123, 130, 120.5, 17.25, 0.00121261,
43
+ 123, 131, 120.5, 17.5, 0.0003039129,
44
+ 123, 132, 120.5, 17.75, 6.521434E-05,
45
+ 123, 133, 120.5, 18, 0.000152381,
46
+ 124, 119, 120.75, 14.5, 2.138454E-05,
47
+ 124, 120, 120.75, 14.75, 0.0001494056,
48
+ 124, 121, 120.75, 15, 0.0001712197,
49
+ 124, 122, 120.75, 15.25, 0.0003645199,
50
+ 124, 123, 120.75, 15.5, 0.0001932626,
51
+ 124, 124, 120.75, 15.75, 0.0005376139,
52
+ 124, 125, 120.75, 16, 0.001420918,
53
+ 124, 126, 120.75, 16.25, 0.005272218,
54
+ 124, 127, 120.75, 16.5, 0.007810672,
55
+ 124, 128, 120.75, 16.75, 0.008227875,
56
+ 124, 129, 120.75, 17, 0.00767334,
57
+ 124, 130, 120.75, 17.25, 0.006461089,
58
+ 124, 131, 120.75, 17.5, 0.005390836,
59
+ 124, 132, 120.75, 17.75, 0.002931942,
60
+ 124, 133, 120.75, 18, 0.003352241,
61
+ 124, 134, 120.75, 18.25, 0.00150418,
62
+ 124, 135, 120.75, 18.5, 0.001200728,
63
+ 124, 136, 120.75, 18.75, 0.0001749084,
64
+ 125, 117, 121, 14, 0.03247498,
65
+ 125, 118, 121, 14.25, 0.05699011,
66
+ 125, 119, 121, 14.5, 0.02690178,
67
+ 125, 120, 121, 14.75, 0.01589066,
68
+ 125, 121, 121, 15, 0.01131044,
69
+ 125, 122, 121, 15.25, 0.00895768,
70
+ 125, 123, 121, 15.5, 0.007230231,
71
+ 125, 124, 121, 15.75, 0.006018548,
72
+ 125, 125, 121, 16, 0.006342597,
73
+ 125, 126, 121, 16.25, 0.00572445,
74
+ 125, 127, 121, 16.5, 0.004627066,
75
+ 125, 128, 121, 16.75, 0.004009823,
76
+ 125, 129, 121, 17, 0.004563737,
77
+ 125, 130, 121, 17.25, 0.006406643,
78
+ 125, 131, 121, 17.5, 0.006806058,
79
+ 125, 132, 121, 17.75, 0.007142992,
80
+ 125, 133, 121, 18, 0.006005429,
81
+ 125, 134, 121, 18.25, 0.005190082,
82
+ 125, 135, 121, 18.5, 0.004997081,
83
+ 125, 136, 121, 18.75, 0.00166163,
84
+ 125, 137, 121, 19, 0.0002624267,
85
+ 126, 118, 121.25, 14.25, 0.02274891,
86
+ 126, 119, 121.25, 14.5, 0.03958242,
87
+ 126, 120, 121.25, 14.75, 0.02480255,
88
+ 126, 121, 121.25, 15, 0.01712777,
89
+ 126, 122, 121.25, 15.25, 0.01427649,
90
+ 126, 123, 121.25, 15.5, 0.01022712,
91
+ 126, 124, 121.25, 15.75, 0.009394526,
92
+ 126, 125, 121.25, 16, 0.007164803,
93
+ 126, 126, 121.25, 16.25, 0.006263139,
94
+ 126, 127, 121.25, 16.5, 0.006382365,
95
+ 126, 128, 121.25, 16.75, 0.005470656,
96
+ 126, 129, 121.25, 17, 0.004426755,
97
+ 126, 130, 121.25, 17.25, 0.004062616,
98
+ 126, 131, 121.25, 17.5, 0.004195487,
99
+ 126, 132, 121.25, 17.75, 0.004955451,
100
+ 126, 133, 121.25, 18, 0.007160906,
101
+ 126, 134, 121.25, 18.25, 0.005708982,
102
+ 126, 135, 121.25, 18.5, 0.004431257,
103
+ 126, 136, 121.25, 18.75, 0.002080839,
104
+ 126, 137, 121.25, 19, 0.00135515,
105
+ 126, 138, 121.25, 19.25, 0.0005042375,
106
+ 126, 139, 121.25, 19.5, 0.0001098152,
107
+ 127, 118, 121.5, 14.25, 6.408151E-05,
108
+ 127, 119, 121.5, 14.5, 0.006733222,
109
+ 127, 120, 121.5, 14.75, 0.010721,
110
+ 127, 121, 121.5, 15, 0.01588823,
111
+ 127, 122, 121.5, 15.25, 0.01183864,
112
+ 127, 123, 121.5, 15.5, 0.008447092,
113
+ 127, 124, 121.5, 15.75, 0.006401335,
114
+ 127, 125, 121.5, 16, 0.005647928,
115
+ 127, 126, 121.5, 16.25, 0.005179127,
116
+ 127, 127, 121.5, 16.5, 0.005061267,
117
+ 127, 128, 121.5, 16.75, 0.005116696,
118
+ 127, 129, 121.5, 17, 0.005707907,
119
+ 127, 130, 121.5, 17.25, 0.005642754,
120
+ 127, 131, 121.5, 17.5, 0.00470066,
121
+ 127, 132, 121.5, 17.75, 0.003818045,
122
+ 127, 133, 121.5, 18, 0.004331268,
123
+ 127, 134, 121.5, 18.25, 0.005695854,
124
+ 127, 135, 121.5, 18.5, 0.004896687,
125
+ 127, 136, 121.5, 18.75, 0.003482735,
126
+ 127, 137, 121.5, 19, 0.00286123,
127
+ 127, 138, 121.5, 19.25, 0.001635549,
128
+ 127, 139, 121.5, 19.5, 0.001053703,
129
+ 127, 140, 121.5, 19.75, 0.0003519544,
130
+ 128, 119, 121.75, 14.5, 6.309758E-05,
131
+ 128, 120, 121.75, 14.75, 0.005846873,
132
+ 128, 121, 121.75, 15, 0.02048152,
133
+ 128, 122, 121.75, 15.25, 0.01427897,
134
+ 128, 123, 121.75, 15.5, 0.01028029,
135
+ 128, 124, 121.75, 15.75, 0.008981369,
136
+ 128, 125, 121.75, 16, 0.006682611,
137
+ 128, 126, 121.75, 16.25, 0.005272868,
138
+ 128, 127, 121.75, 16.5, 0.004901115,
139
+ 128, 128, 121.75, 16.75, 0.003646349,
140
+ 128, 129, 121.75, 17, 0.004524055,
141
+ 128, 130, 121.75, 17.25, 0.004674797,
142
+ 128, 131, 121.75, 17.5, 0.005083202,
143
+ 128, 132, 121.75, 17.75, 0.005147811,
144
+ 128, 133, 121.75, 18, 0.004718282,
145
+ 128, 134, 121.75, 18.25, 0.004913406,
146
+ 128, 135, 121.75, 18.5, 0.005286446,
147
+ 128, 136, 121.75, 18.75, 0.00563042,
148
+ 128, 137, 121.75, 19, 0.004719276,
149
+ 128, 138, 121.75, 19.25, 0.003617305,
150
+ 128, 139, 121.75, 19.5, 0.002486874,
151
+ 128, 140, 121.75, 19.75, 0.001274224,
152
+ 128, 141, 121.75, 20, 0.0007270561,
153
+ 128, 142, 121.75, 20.25, 0.0001765383,
154
+ 128, 143, 121.75, 20.5, 2.210301E-05,
155
+ 129, 120, 122, 14.75, 0.0001022483,
156
+ 129, 121, 122, 15, 0.004356414,
157
+ 129, 122, 122, 15.25, 0.01384246,
158
+ 129, 123, 122, 15.5, 0.01279092,
159
+ 129, 124, 122, 15.75, 0.01033123,
160
+ 129, 125, 122, 16, 0.008920626,
161
+ 129, 126, 122, 16.25, 0.006815142,
162
+ 129, 127, 122, 16.5, 0.004981393,
163
+ 129, 128, 122, 16.75, 0.00398336,
164
+ 129, 129, 122, 17, 0.00337705,
165
+ 129, 130, 122, 17.25, 0.003798835,
166
+ 129, 131, 122, 17.5, 0.003652796,
167
+ 129, 132, 122, 17.75, 0.003649437,
168
+ 129, 133, 122, 18, 0.004836492,
169
+ 129, 134, 122, 18.25, 0.004978335,
170
+ 129, 135, 122, 18.5, 0.005616009,
171
+ 129, 136, 122, 18.75, 0.005613013,
172
+ 129, 137, 122, 19, 0.005793108,
173
+ 129, 138, 122, 19.25, 0.005114507,
174
+ 129, 139, 122, 19.5, 0.003745043,
175
+ 129, 140, 122, 19.75, 0.002754437,
176
+ 129, 141, 122, 20, 0.002090641,
177
+ 129, 142, 122, 20.25, 0.001301664,
178
+ 129, 143, 122, 20.5, 0.0005746784,
179
+ 129, 144, 122, 20.75, 0.0001106967,
180
+ 130, 121, 122.25, 15, 0.000100791,
181
+ 130, 122, 122.25, 15.25, 0.001986217,
182
+ 130, 123, 122.25, 15.5, 0.007703658,
183
+ 130, 124, 122.25, 15.75, 0.01019076,
184
+ 130, 125, 122.25, 16, 0.009386936,
185
+ 130, 126, 122.25, 16.25, 0.008047561,
186
+ 130, 127, 122.25, 16.5, 0.006762965,
187
+ 130, 128, 122.25, 16.75, 0.005123602,
188
+ 130, 129, 122.25, 17, 0.004430163,
189
+ 130, 130, 122.25, 17.25, 0.003683947,
190
+ 130, 131, 122.25, 17.5, 0.003290506,
191
+ 130, 132, 122.25, 17.75, 0.002935608,
192
+ 130, 133, 122.25, 18, 0.003057464,
193
+ 130, 134, 122.25, 18.25, 0.003267714,
194
+ 130, 135, 122.25, 18.5, 0.004716324,
195
+ 130, 136, 122.25, 18.75, 0.004913819,
196
+ 130, 137, 122.25, 19, 0.005429264,
197
+ 130, 138, 122.25, 19.25, 0.005292431,
198
+ 130, 139, 122.25, 19.5, 0.004847661,
199
+ 130, 140, 122.25, 19.75, 0.003948504,
200
+ 130, 141, 122.25, 20, 0.002281324,
201
+ 130, 142, 122.25, 20.25, 0.002270003,
202
+ 130, 143, 122.25, 20.5, 0.001502727,
203
+ 130, 144, 122.25, 20.75, 0.0008191555,
204
+ 130, 145, 122.25, 21, 0.0001995864,
205
+ 130, 146, 122.25, 21.25, 4.442696E-05,
206
+ 130, 147, 122.25, 21.5, 2.225164E-05,
207
+ 131, 123, 122.5, 15.5, 0.0005285646,
208
+ 131, 124, 122.5, 15.75, 0.002373407,
209
+ 131, 125, 122.5, 16, 0.005884645,
210
+ 131, 126, 122.5, 16.25, 0.007631014,
211
+ 131, 127, 122.5, 16.5, 0.00852002,
212
+ 131, 128, 122.5, 16.75, 0.007380202,
213
+ 131, 129, 122.5, 17, 0.0061512,
214
+ 131, 130, 122.5, 17.25, 0.005145038,
215
+ 131, 131, 122.5, 17.5, 0.004492051,
216
+ 131, 132, 122.5, 17.75, 0.003933463,
217
+ 131, 133, 122.5, 18, 0.003020111,
218
+ 131, 134, 122.5, 18.25, 0.002440456,
219
+ 131, 135, 122.5, 18.5, 0.002173362,
220
+ 131, 136, 122.5, 18.75, 0.002705903,
221
+ 131, 137, 122.5, 19, 0.00383835,
222
+ 131, 138, 122.5, 19.25, 0.00377879,
223
+ 131, 139, 122.5, 19.5, 0.003623716,
224
+ 131, 140, 122.5, 19.75, 0.002984216,
225
+ 131, 141, 122.5, 20, 0.002591837,
226
+ 131, 142, 122.5, 20.25, 0.001667034,
227
+ 131, 143, 122.5, 20.5, 0.001258502,
228
+ 131, 144, 122.5, 20.75, 0.001173217,
229
+ 131, 145, 122.5, 21, 0.0006873632,
230
+ 131, 146, 122.5, 21.25, 0.0001999213,
231
+ 131, 147, 122.5, 21.5, 0.0001112582,
232
+ 131, 148, 122.5, 21.75, 4.458052E-05,
233
+ 132, 124, 122.75, 15.75, 7.719002E-05,
234
+ 132, 125, 122.75, 16, 0.000570726,
235
+ 132, 126, 122.75, 16.25, 0.002881713,
236
+ 132, 127, 122.75, 16.5, 0.005001624,
237
+ 132, 128, 122.75, 16.75, 0.006472536,
238
+ 132, 129, 122.75, 17, 0.005827757,
239
+ 132, 130, 122.75, 17.25, 0.005286319,
240
+ 132, 131, 122.75, 17.5, 0.003968958,
241
+ 132, 132, 122.75, 17.75, 0.003610264,
242
+ 132, 133, 122.75, 18, 0.002853944,
243
+ 132, 134, 122.75, 18.25, 0.002301368,
244
+ 132, 135, 122.75, 18.5, 0.001625787,
245
+ 132, 136, 122.75, 18.75, 0.001204883,
246
+ 132, 137, 122.75, 19, 0.001390813,
247
+ 132, 138, 122.75, 19.25, 0.001797552,
248
+ 132, 139, 122.75, 19.5, 0.001998109,
249
+ 132, 140, 122.75, 19.75, 0.001786098,
250
+ 132, 141, 122.75, 20, 0.001406355,
251
+ 132, 142, 122.75, 20.25, 0.001065322,
252
+ 132, 143, 122.75, 20.5, 0.0005294664,
253
+ 132, 144, 122.75, 20.75, 0.0007522101,
254
+ 132, 145, 122.75, 21, 0.0005319772,
255
+ 132, 146, 122.75, 21.25, 0.0004663819,
256
+ 132, 147, 122.75, 21.5, 8.900656E-05,
257
+ 132, 148, 122.75, 21.75, 4.458052E-05,
258
+ 132, 149, 122.75, 22, 2.232918E-05,
259
+ 133, 125, 123, 16, 1.875221E-05,
260
+ 133, 126, 123, 16.25, 5.408812E-05,
261
+ 133, 127, 123, 16.5, 0.0008191921,
262
+ 133, 128, 123, 16.75, 0.001746536,
263
+ 133, 129, 123, 17, 0.002542191,
264
+ 133, 130, 123, 17.25, 0.002469992,
265
+ 133, 131, 123, 17.5, 0.002293412,
266
+ 133, 132, 123, 17.75, 0.001636158,
267
+ 133, 133, 123, 18, 0.001516576,
268
+ 133, 134, 123, 18.25, 0.001340334,
269
+ 133, 135, 123, 18.5, 0.001080037,
270
+ 133, 136, 123, 18.75, 0.0006584466,
271
+ 133, 137, 123, 19, 0.000354185,
272
+ 133, 138, 123, 19.25, 0.0003647237,
273
+ 133, 139, 123, 19.5, 0.0003446671,
274
+ 133, 140, 123, 19.75, 0.0003778531,
275
+ 133, 141, 123, 20, 0.0003135069,
276
+ 133, 142, 123, 20.25, 0.0002992589,
277
+ 133, 143, 123, 20.5, 0.0001755153,
278
+ 133, 144, 123, 20.75, 6.641801E-05,
279
+ 133, 145, 123, 21, 8.870506E-05,
280
+ 133, 146, 123, 21.25, 0.0001110674,
281
+ 133, 147, 123, 21.5, 4.450328E-05,
282
+ 134, 127, 123.25, 16.5, 1.861714E-05,
283
+ 134, 128, 123.25, 16.75, 4.981032E-05,
284
+ 134, 129, 123.25, 17, 0.0002279336,
285
+ 134, 130, 123.25, 17.25, 0.0003933623,
286
+ 134, 131, 123.25, 17.5, 0.0003516745,
287
+ 134, 132, 123.25, 17.75, 0.0004597068,
288
+ 134, 133, 123.25, 18, 0.0001899822,
289
+ 134, 134, 123.25, 18.25, 0.0001674486,
290
+ 134, 135, 123.25, 18.5, 0.0001811963,
291
+ 134, 136, 123.25, 18.75, 0.0001925258,
292
+ 134, 137, 123.25, 19, 8.227721E-05,
293
+ 134, 138, 123.25, 19.25, 4.711351E-05,
294
+ 134, 139, 123.25, 19.5, 1.162471E-05,
295
+ 134, 140, 123.25, 19.75, 5.894935E-05,
296
+ 135, 131, 123.5, 17.5, 1.45547E-05,
297
+ 135, 134, 123.5, 18.25, 3.725484E-05,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z8.txt ADDED
@@ -0,0 +1,208 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 15000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
38
+ 122, 126, 120.25, 16.25, 4.312949E-05,
39
+ 122, 127, 120.25, 16.5, 6.47772E-05,
40
+ 122, 128, 120.25, 16.75, 2.162074E-05,
41
+ 122, 129, 120.25, 17, 0.0001082458,
42
+ 122, 130, 120.25, 17.25, 6.503524E-05,
43
+ 122, 131, 120.25, 17.5, 2.170807E-05,
44
+ 122, 133, 120.25, 18, 2.176871E-05,
45
+ 123, 121, 120.5, 15, 2.143361E-05,
46
+ 123, 122, 120.5, 15.25, 0.0007725167,
47
+ 123, 123, 120.5, 15.5, 0.003115276,
48
+ 123, 124, 120.5, 15.75, 0.006732906,
49
+ 123, 125, 120.5, 16, 0.009541231,
50
+ 123, 126, 120.5, 16.25, 0.01004918,
51
+ 123, 127, 120.5, 16.5, 0.004836697,
52
+ 123, 128, 120.5, 16.75, 0.005318701,
53
+ 123, 129, 120.5, 17, 0.00569373,
54
+ 123, 130, 120.5, 17.25, 0.005267852,
55
+ 123, 131, 120.5, 17.5, 0.003560125,
56
+ 123, 132, 120.5, 17.75, 0.002130335,
57
+ 123, 133, 120.5, 18, 0.0009578232,
58
+ 123, 134, 120.5, 18.25, 0.000305196,
59
+ 123, 135, 120.5, 18.5, 6.549429E-05,
60
+ 123, 136, 120.5, 18.75, 2.186355E-05,
61
+ 124, 118, 120.75, 14.25, 4.272101E-05,
62
+ 124, 119, 120.75, 14.5, 0.009024283,
63
+ 124, 120, 120.75, 14.75, 0.02042397,
64
+ 124, 121, 120.75, 15, 0.0222695,
65
+ 124, 122, 120.75, 15.25, 0.02193085,
66
+ 124, 123, 120.75, 15.5, 0.01959402,
67
+ 124, 124, 120.75, 15.75, 0.01396059,
68
+ 124, 125, 120.75, 16, 0.01012275,
69
+ 124, 126, 120.75, 16.25, 0.004334517,
70
+ 124, 127, 120.75, 16.5, 0.0004966251,
71
+ 124, 128, 120.75, 16.75, 0.0003459318,
72
+ 124, 129, 120.75, 17, 0.0008443168,
73
+ 124, 130, 120.75, 17.25, 0.002601409,
74
+ 124, 131, 120.75, 17.5, 0.004623821,
75
+ 124, 132, 120.75, 17.75, 0.0062171,
76
+ 124, 133, 120.75, 18, 0.003896601,
77
+ 124, 134, 120.75, 18.25, 0.004752336,
78
+ 124, 135, 120.75, 18.5, 0.002510614,
79
+ 124, 136, 120.75, 18.75, 0.001377404,
80
+ 124, 137, 120.75, 19, 0.0003065496,
81
+ 124, 138, 120.75, 19.25, 8.771741E-05,
82
+ 124, 139, 120.75, 19.5, 2.196304E-05,
83
+ 125, 117, 121, 14, 0.01482926,
84
+ 125, 118, 121, 14.25, 0.02488494,
85
+ 125, 119, 121, 14.5, 0.02037949,
86
+ 125, 120, 121, 14.75, 0.009141549,
87
+ 125, 121, 121, 15, 0.00613001,
88
+ 125, 122, 121, 15.25, 0.005214489,
89
+ 125, 123, 121, 15.5, 0.003781301,
90
+ 125, 124, 121, 15.75, 0.002624234,
91
+ 125, 125, 121, 16, 0.0006461334,
92
+ 125, 130, 121, 17.25, 2.167841E-05,
93
+ 125, 131, 121, 17.5, 8.683227E-05,
94
+ 125, 132, 121, 17.75, 0.000391286,
95
+ 125, 133, 121, 18, 0.0005006804,
96
+ 125, 134, 121, 18.25, 0.0009591873,
97
+ 125, 135, 121, 18.5, 0.001506368,
98
+ 125, 136, 121, 18.75, 0.005925017,
99
+ 125, 137, 121, 19, 0.00437928,
100
+ 125, 138, 121, 19.25, 0.001951713,
101
+ 125, 139, 121, 19.5, 0.0005051498,
102
+ 125, 140, 121, 19.75, 0.0001099857,
103
+ 126, 131, 121.25, 17.5, 2.16268E-05,
104
+ 126, 132, 121.25, 17.75, 2.162661E-05,
105
+ 126, 133, 121.25, 18, 0.0004561418,
106
+ 126, 134, 121.25, 18.25, 0.0005431389,
107
+ 126, 135, 121.25, 18.5, 0.001329941,
108
+ 126, 136, 121.25, 18.75, 0.00290523,
109
+ 126, 137, 121.25, 19, 0.004356808,
110
+ 126, 138, 121.25, 19.25, 0.005679703,
111
+ 126, 139, 121.25, 19.5, 0.004744015,
112
+ 126, 140, 121.25, 19.75, 0.002089729,
113
+ 126, 141, 121.25, 20, 0.0005948641,
114
+ 126, 142, 121.25, 20.25, 6.620187E-05,
115
+ 126, 143, 121.25, 20.5, 2.210301E-05,
116
+ 127, 130, 121.5, 17.25, 2.150614E-05,
117
+ 127, 131, 121.5, 17.5, 0.0002146032,
118
+ 127, 132, 121.5, 17.75, 0.0009032274,
119
+ 127, 133, 121.5, 18, 0.0006452485,
120
+ 127, 134, 121.5, 18.25, 0.001077636,
121
+ 127, 135, 121.5, 18.5, 0.00238579,
122
+ 127, 136, 121.5, 18.75, 0.002894883,
123
+ 127, 137, 121.5, 19, 0.002816882,
124
+ 127, 138, 121.5, 19.25, 0.003132672,
125
+ 127, 139, 121.5, 19.5, 0.00404057,
126
+ 127, 140, 121.5, 19.75, 0.00453126,
127
+ 127, 141, 121.5, 20, 0.004362336,
128
+ 127, 142, 121.5, 20.25, 0.001963989,
129
+ 127, 143, 121.5, 20.5, 0.0003315452,
130
+ 127, 144, 121.5, 20.75, 6.641801E-05,
131
+ 127, 145, 121.5, 21, 2.217627E-05,
132
+ 128, 131, 121.75, 17.5, 6.369563E-05,
133
+ 128, 132, 121.75, 17.75, 0.0001280711,
134
+ 128, 133, 121.75, 18, 0.0003877443,
135
+ 128, 134, 121.75, 18.25, 0.000580451,
136
+ 128, 135, 121.75, 18.5, 0.001031155,
137
+ 128, 136, 121.75, 18.75, 0.001320112,
138
+ 128, 137, 121.75, 19, 0.001933896,
139
+ 128, 138, 121.75, 19.25, 0.002359719,
140
+ 128, 139, 121.75, 19.5, 0.002501227,
141
+ 128, 140, 121.75, 19.75, 0.003518625,
142
+ 128, 141, 121.75, 20, 0.003877133,
143
+ 128, 142, 121.75, 20.25, 0.004281054,
144
+ 128, 143, 121.75, 20.5, 0.003271247,
145
+ 128, 144, 121.75, 20.75, 0.0009519915,
146
+ 128, 145, 121.75, 21, 8.870506E-05,
147
+ 128, 146, 121.75, 21.25, 4.442696E-05,
148
+ 129, 134, 122, 18.25, 2.145771E-05,
149
+ 129, 135, 122, 18.5, 0.0002791832,
150
+ 129, 136, 122, 18.75, 0.0005564889,
151
+ 129, 137, 122, 19, 0.0005789094,
152
+ 129, 138, 122, 19.25, 0.001084929,
153
+ 129, 139, 122, 19.5, 0.001726047,
154
+ 129, 140, 122, 19.75, 0.00153522,
155
+ 129, 141, 122, 20, 0.002533294,
156
+ 129, 142, 122, 20.25, 0.002868436,
157
+ 129, 143, 122, 20.5, 0.003735309,
158
+ 129, 144, 122, 20.75, 0.003320901,
159
+ 129, 145, 122, 21, 0.00166322,
160
+ 129, 146, 122, 21.25, 0.0002887753,
161
+ 129, 147, 122, 21.5, 4.450328E-05,
162
+ 130, 136, 122.25, 18.75, 4.212198E-05,
163
+ 130, 137, 122.25, 19, 0.0001492352,
164
+ 130, 138, 122.25, 19.25, 0.000213492,
165
+ 130, 139, 122.25, 19.5, 0.0005180273,
166
+ 130, 140, 122.25, 19.75, 0.0008296919,
167
+ 130, 141, 122.25, 20, 0.001141144,
168
+ 130, 142, 122.25, 20.25, 0.001543421,
169
+ 130, 143, 122.25, 20.5, 0.001790149,
170
+ 130, 144, 122.25, 20.75, 0.002634481,
171
+ 130, 145, 122.25, 21, 0.002772033,
172
+ 130, 146, 122.25, 21.25, 0.001643798,
173
+ 130, 147, 122.25, 21.5, 0.0004672845,
174
+ 130, 148, 122.25, 21.75, 6.687077E-05,
175
+ 131, 131, 122.5, 17.5, 1.600035E-05,
176
+ 131, 132, 122.5, 17.75, 1.602249E-05,
177
+ 131, 133, 122.5, 18, 1.604505E-05,
178
+ 131, 136, 122.5, 18.75, 3.99442E-05,
179
+ 131, 137, 122.5, 19, 4.186171E-05,
180
+ 131, 138, 122.5, 19.25, 8.493321E-05,
181
+ 131, 139, 122.5, 19.5, 6.34996E-05,
182
+ 131, 140, 122.5, 19.75, 0.0002810433,
183
+ 131, 141, 122.5, 20, 0.0002182945,
184
+ 131, 142, 122.5, 20.25, 0.000462839,
185
+ 131, 143, 122.5, 20.5, 0.0006629468,
186
+ 131, 144, 122.5, 20.75, 0.0008855736,
187
+ 131, 145, 122.5, 21, 0.001397105,
188
+ 131, 146, 122.5, 21.25, 0.001732652,
189
+ 131, 147, 122.5, 21.5, 0.001134834,
190
+ 131, 148, 122.5, 21.75, 0.0005349661,
191
+ 131, 149, 122.5, 22, 0.0001339751,
192
+ 132, 141, 122.75, 20, 0.0001077375,
193
+ 132, 142, 122.75, 20.25, 4.39075E-05,
194
+ 132, 143, 122.75, 20.5, 0.0001546217,
195
+ 132, 144, 122.75, 20.75, 0.0001548315,
196
+ 132, 145, 122.75, 21, 0.0002881474,
197
+ 132, 146, 122.75, 21.25, 0.0004886966,
198
+ 132, 147, 122.75, 21.5, 0.000623046,
199
+ 132, 148, 122.75, 21.75, 0.0005349661,
200
+ 132, 149, 122.75, 22, 0.0002456209,
201
+ 132, 150, 122.75, 22.25, 8.947555E-05,
202
+ 133, 145, 123, 21, 2.217627E-05,
203
+ 133, 146, 123, 21.25, 2.221348E-05,
204
+ 133, 147, 123, 21.5, 6.675492E-05,
205
+ 133, 148, 123, 21.75, 6.687077E-05,
206
+ 133, 149, 123, 22, 6.698753E-05,
207
+ 133, 150, 123, 22.25, 8.947555E-05,
208
+ 133, 151, 123, 22.5, 2.240891E-05,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T10_202001121400_Z9.txt ADDED
@@ -0,0 +1,39 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 14:00 UTC,
35
+ , , , , Z = 17000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
38
+ 128, 141, 121.75, 20, 2.2032E-05,
39
+ 129, 142, 122, 20.25, 2.206729E-05,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z1.txt ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 05:00 UTC,
35
+ , , , , Z = 1000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z10.txt ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 05:00 UTC,
35
+ , , , , Z = 19000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z11.txt ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 05:00 UTC,
35
+ , , , , Z = 22500 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z12.txt ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 05:00 UTC,
35
+ , , , , Z = 27500 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,
unzipped/Taal_273070_20200112_scenario_yizhou/Taal_273070_20200112_0500_20250107041508.174287/AQOutput_3DField_C1_T1_202001120500_Z2.txt ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ NAME III (version 8.2)
2
+ Run name: Taal_273070_20250107041508.174287
3
+ Run time: 07/01/2025 04:15:10.918 UTC
4
+ Met data: NWP Flow.EC HRES Flow
5
+ Start of release: 12/01/2020 06:57 UTC
6
+ End of release: 12/01/2020 21:10 UTC
7
+ Source strength: 5.333333E+08 g / s
8
+ Release location: 120.9930E 14.0020N
9
+ Release height: 311.000 to 15311.000m asl
10
+ Run duration: 7day 0hr 0min
11
+ X grid origin: 90.00000
12
+ Y grid origin: -15.00000
13
+ X grid size: 281
14
+ Y grid size: 221
15
+ X grid resolution: 0.2500000
16
+ Y grid resolution: 0.2500000
17
+ Number of preliminary cols: 4
18
+ Number of field cols: 1
19
+
20
+ Fields:
21
+ , , , , VOLCANIC,
22
+ , , , , AirConc Ash,
23
+ , , , , Air Concentration,
24
+ , , , , VOLCANIC_ASH,
25
+ , , , , g / m^3,
26
+ , , , , All sources,
27
+ , , , , No ensemble averaging,
28
+ , , , , 1hr 0min average,
29
+ , , , , No horizontal averaging,
30
+ , , , , No vertical averaging,
31
+ , , , , ,
32
+ , , , , ,
33
+ , , , , ,
34
+ , , , , 12/01/2020 05:00 UTC,
35
+ , , , , Z = 3000 m agl,
36
+ , , , , ,
37
+ X Index, Y Index, X (Lat-Long), Y (Lat-Long), ,