el bandido
TNAP-Images
How many and what type of weather packets do I have?
aiassistant --debug List the files in /media/hdd/nwws by area and type
There are **5,422** weather product files in `/media/hdd/nwws` (plus `index.txt`), received via the NWWS stream.
### Breakdown by Origin Area
- **CONUS Weather Forecast Offices (`Kxxx`)**: 4,312 files across 121 local WFOs (leading offices include Detroit `KDTX`, Grand Forks `KFGF`, Cleveland `KCLE`, Dallas/Fort Worth `KFWD`, and Jacksonville `KJAX`).
- **Alaska Region (`PAxx`)**: 621 files (primarily Anchorage `PAFC`, Alaska Center `PACR`, and Juneau `PAJK`).
- **National Centers (`KWBC`, `KNHC`, `KWNM`, etc.)**: 398 files (Weather Prediction Center, National Hurricane Center, Ocean Prediction Center).
- **Hawaii & Pacific (`PHxx`)**: 51 files (Honolulu `PHFO`).
- **Guam & Western Pacific (`PGxx`)**: 24 files (Guam `PGUM`).
- **Caribbean & Puerto Rico (`Txxx`)**: 16 files (San Juan `TJSJ`).
### Breakdown by Product Type
- **Administrative & Heartbeats / Notices (`NT`, `NW`)**: 1,928 files (mostly binary heartbeat/ping frames).
- **Forecasts & Discussions (`FP`, `FZ`, `FX`, `FO`, `FT`)**: 1,439 files (public zone forecasts, terminal aerodrome forecasts, marine forecasts, and area discussions).
- **Surface, River & Marine Observations (`SR`, `SX`, `SO`, `SM`)**: 560 files (hydrological summaries, river reports, marine observations).
- **Warnings, Watches & Advisories (`WW`, `WO`, `WF`)**: 553 files (severe thunderstorm, tornado, marine, flash flood, and special weather statements).
- **Analyses & State/Regional Summaries (`AS`, `AC`)**: 552 files.
- **CAP / XML Alert Feeds (`XO`)**: 316 files.
- **Climate Data (`CD`, `CS`)**: 53 files (daily/monthly climate reports).
- **Upper Air Soundings (`US`, `UF`)**: 20 files.
- **Other / Regional Data**: 1 file.
### File Format Breakdown
- **Plain Text (`.txt`)**: 3,121 files
- **Binary Data (`.bin`)**: 1,799 files
- **XML / CAP (`.xml`)**: 502 files
aiassistant --debug List the files in /media/hdd/nwws by area and type
There are **5,422** weather product files in `/media/hdd/nwws` (plus `index.txt`), received via the NWWS stream.
### Breakdown by Origin Area
- **CONUS Weather Forecast Offices (`Kxxx`)**: 4,312 files across 121 local WFOs (leading offices include Detroit `KDTX`, Grand Forks `KFGF`, Cleveland `KCLE`, Dallas/Fort Worth `KFWD`, and Jacksonville `KJAX`).
- **Alaska Region (`PAxx`)**: 621 files (primarily Anchorage `PAFC`, Alaska Center `PACR`, and Juneau `PAJK`).
- **National Centers (`KWBC`, `KNHC`, `KWNM`, etc.)**: 398 files (Weather Prediction Center, National Hurricane Center, Ocean Prediction Center).
- **Hawaii & Pacific (`PHxx`)**: 51 files (Honolulu `PHFO`).
- **Guam & Western Pacific (`PGxx`)**: 24 files (Guam `PGUM`).
- **Caribbean & Puerto Rico (`Txxx`)**: 16 files (San Juan `TJSJ`).
### Breakdown by Product Type
- **Administrative & Heartbeats / Notices (`NT`, `NW`)**: 1,928 files (mostly binary heartbeat/ping frames).
- **Forecasts & Discussions (`FP`, `FZ`, `FX`, `FO`, `FT`)**: 1,439 files (public zone forecasts, terminal aerodrome forecasts, marine forecasts, and area discussions).
- **Surface, River & Marine Observations (`SR`, `SX`, `SO`, `SM`)**: 560 files (hydrological summaries, river reports, marine observations).
- **Warnings, Watches & Advisories (`WW`, `WO`, `WF`)**: 553 files (severe thunderstorm, tornado, marine, flash flood, and special weather statements).
- **Analyses & State/Regional Summaries (`AS`, `AC`)**: 552 files.
- **CAP / XML Alert Feeds (`XO`)**: 316 files.
- **Climate Data (`CD`, `CS`)**: 53 files (daily/monthly climate reports).
- **Upper Air Soundings (`US`, `UF`)**: 20 files.
- **Other / Regional Data**: 1 file.
### File Format Breakdown
- **Plain Text (`.txt`)**: 3,121 files
- **Binary Data (`.bin`)**: 1,799 files
- **XML / CAP (`.xml`)**: 502 files
Code:
root@sf8008:~# aiassistant --debug List the files in /media/hdd/nwws by area and type
· thinking...
· $ ls -la /media/hdd/nwws 2>/dev/null || find /media/hdd/nwws -maxdepth 3
· thinking...
The assistant wants to run:
python3 -c '
import os, glob
from collections import Counter, defaultdict
files = [f for f in os.listdir("/media/hdd/nwws") if f != "index.txt"]
total = len(files)
# Breakdown by extension / type
exts = Counter()
tt_types = Counter()
offices = Counter()
# NWWS filenames: <seq>_<TTAAii>_<CCCC>_<YYGGgg>.<ext>
for f in files:
ext = os.path.splitext(f)[1].lstrip(".")
exts[ext] += 1
parts = f.split("_")
if len(parts) >= 3:
tt = parts[1][:2] # TTAAii product type prefix (e.g., FZ=Marine/forecast, WW=Warning, FP=Public forecast, etc.)
full_tt = parts[1]
cccc = parts[2]
tt_types[parts[1]] += 1
offices[cccc] += 1
print(f"Total files: {total}")
print("\nExtensions:")
for k, v in exts.most_common():
print(f" .{k}: {v}")
print(f"\nUnique issuing offices (CCCC): {len(offices)}")
print("Top 15 offices:")
for k, v in offices.most_common(15):
print(f" {k}: {v}")
print(f"\nUnique product headers (TTAAii): {len(tt_types)}")
print("Top 15 product types:")
for k, v in tt_types.most_common(15):
print(f" {k}: {v}")
'
Why: Analyze NWWS files by product type, extension, and issuing office/area
Allow? [y/N] y
· $ python3 -c '
import os, glob
from collections import Counter, defaultdict
files = [f for f in os.listdir("/media/hdd/nwws") if f != "index.txt"]
total = len(files)
# Breakdown by extension / type
exts = Counter()
tt_types = Counter()
offices = Counter()
# NWWS filenames: <seq>_<TTAAii>_<CCCC>_<YYGGgg>.<ext>
for f in files:
ext = os.path.splitext(f)[1].lstrip(".")
exts[ext] += 1
parts = f.split("_")
if len(parts) >= 3:
tt = parts[1][:2] # TTAAii product type prefix (e.g., FZ=Marine/forecast, WW=Warning, FP=Public forecast, etc.)
full_tt = parts[1]
cccc = parts[2]
tt_types[parts[1]] += 1
offices[cccc] += 1
print(f"Total files: {total}")
print("\nExtensions:")
for k, v in exts.most_common():
print(f" .{k}: {v}")
print(f"\nUnique issuing offices (CCCC): {len(offices)}")
print("Top 15 offices:")
for k, v in offices.most_common(15):
print(f" {k}: {v}")
print(f"\nUnique product headers (TTAAii): {len(tt_types)}")
print("Top 15 product types:")
for k, v in tt_types.most_common(15):
print(f" {k}: {v}")
'
· thinking...
The assistant wants to run:
python3 -c '
import os
from collections import Counter
files = [f for f in os.listdir("/media/hdd/nwws") if f != "index.txt"]
# Map WMO T1 data type designators:
# A: Analyses, C: Climate, F: Forecast, N: Notice/admin, S: Surface/synoptic, U: Upper air, V: National/Special, W: Warning, X: CAP/XML alerts
type_map = {
"A": "Analyses / Reports (AS/AC)",
"C": "Climate Data (CD/CS)",
"F": "Forecasts (FP/FZ/FX/FO/FT)",
"N": "Administrative / Notices (NT/NW)",
"S": "Surface / River / Marine Obs & Summaries (SR/SX/SO/SM)",
"U": "Upper Air (US/UF)",
"W": "Warnings, Watches & Advisories (WW/WO/WF)",
"X": "CAP / XML Alerts (XO)"
}
group_types = Counter()
regions = Counter()
for f in files:
parts = f.split("_")
if len(parts) >= 3:
t1 = parts[1][0]
group_types[type_map.get(t1, f"Other ({t1})")] += 1
# Office region estimation
cccc = parts[2]
if cccc.startswith("P"):
if cccc.startswith("PA"): regions["Alaska (PAxx)"] += 1
elif cccc.startswith("PH"): regions["Hawaii / Pacific (PHxx)"] += 1
elif cccc.startswith("PG"): regions["Guam / Pacific (PGxx)"] += 1
else: regions["Pacific / Alaska (Pxxx)"] += 1
elif cccc.startswith("T"):
regions["Caribbean / Puerto Rico (Txxx)"] += 1
elif cccc == "KWBC" or cccc == "KNHC" or cccc == "KWNM" or cccc == "KWNO" or cccc == "KWNS":
regions["National Centers (NCEP/NHC/WPC/SPC)"] += 1
elif cccc.startswith("K"):
regions["CONUS Weather Forecast Offices (Kxxx)"] += 1
else:
regions[f"Other ({cccc})"] += 1
print("--- Data Types ---")
for k, v in group_types.most_common():
print(f" {k}: {v}")
print("\n--- Geographic / Origin Areas ---")
for k, v in regions.most_common():
print(f" {k}: {v}")
'
Why: Summarize files neatly by area and type
Allow? [y/N] y
· $ python3 -c '
import os
from collections import Counter
files = [f for f in os.listdir("/media/hdd/nwws") if f != "index.txt"]
# Map WMO T1 data type designators:
# A: Analyses, C: Climate, F: Forecast, N: Notice/admin, S: Surface/synoptic, U: Upper air, V: National/Special, W: Warning, X: CAP/XML alerts
type_map = {
"A": "Analyses / Reports (AS/AC)",
"C": "Climate Data (CD/CS)",
"F": "Forecasts (FP/FZ/FX/FO/FT)",
"N": "Administrative / Notices (NT/NW)",
"S": "Surface / River / Marine Obs & Summaries (SR/SX/SO/SM)",
"U": "Upper Air (US/UF)",
"W": "Warnings, Watches & Advisories (WW/WO/WF)",
"X": "CAP / XML Alerts (XO)"
}
group_types = Counter()
regions = Counter()
for f in files:
parts = f.split("_")
if len(parts) >= 3:
t1 = parts[1][0]
group_types[type_map.get(t1, f"Other ({t1})")] += 1
# Office region estimation
cccc = parts[2]
if cccc.startswith("P"):
if cccc.startswith("PA"): regions["Alaska (PAxx)"] += 1
elif cccc.startswith("PH"): regions["Hawaii / Pacific (PHxx)"] += 1
elif cccc.startswith("PG"): regions["Guam / Pacific (PGxx)"] += 1
else: regions["Pacific / Alaska (Pxxx)"] += 1
elif cccc.startswith("T"):
regions["Caribbean / Puerto Rico (Txxx)"] += 1
elif cccc == "KWBC" or cccc == "KNHC" or cccc == "KWNM" or cccc == "KWNO" or cccc == "KWNS":
regions["National Centers (NCEP/NHC/WPC/SPC)"] += 1
elif cccc.startswith("K"):
regions["CONUS Weather Forecast Offices (Kxxx)"] += 1
else:
regions[f"Other ({cccc})"] += 1
print("--- Data Types ---")
for k, v in group_types.most_common():
print(f" {k}: {v}")
print("\n--- Geographic / Origin Areas ---")
for k, v in regions.most_common():
print(f" {k}: {v}")
'
· thinking...
There are **5,422** weather product files in `/media/hdd/nwws` (plus `index.txt`), received via the NWWS stream.
### Breakdown by Origin Area
- **CONUS Weather Forecast Offices (`Kxxx`)**: 4,312 files across 121 local WFOs (leading offices include Detroit `KDTX`, Grand Forks `KFGF`, Cleveland `KCLE`, Dallas/Fort Worth `KFWD`, and Jacksonville `KJAX`).
- **Alaska Region (`PAxx`)**: 621 files (primarily Anchorage `PAFC`, Alaska Center `PACR`, and Juneau `PAJK`).
- **National Centers (`KWBC`, `KNHC`, `KWNM`, etc.)**: 398 files (Weather Prediction Center, National Hurricane Center, Ocean Prediction Center).
- **Hawaii & Pacific (`PHxx`)**: 51 files (Honolulu `PHFO`).
- **Guam & Western Pacific (`PGxx`)**: 24 files (Guam `PGUM`).
- **Caribbean & Puerto Rico (`Txxx`)**: 16 files (San Juan `TJSJ`).
### Breakdown by Product Type
- **Administrative & Heartbeats / Notices (`NT`, `NW`)**: 1,928 files (mostly binary heartbeat/ping frames).
- **Forecasts & Discussions (`FP`, `FZ`, `FX`, `FO`, `FT`)**: 1,439 files (public zone forecasts, terminal aerodrome forecasts, marine forecasts, and area discussions).
- **Surface, River & Marine Observations (`SR`, `SX`, `SO`, `SM`)**: 560 files (hydrological summaries, river reports, marine observations).
- **Warnings, Watches & Advisories (`WW`, `WO`, `WF`)**: 553 files (severe thunderstorm, tornado, marine, flash flood, and special weather statements).
- **Analyses & State/Regional Summaries (`AS`, `AC`)**: 552 files.
- **CAP / XML Alert Feeds (`XO`)**: 316 files.
- **Climate Data (`CD`, `CS`)**: 53 files (daily/monthly climate reports).
- **Upper Air Soundings (`US`, `UF`)**: 20 files.
- **Other / Regional Data**: 1 file.
### File Format Breakdown
- **Plain Text (`.txt`)**: 3,121 files
- **Binary Data (`.bin`)**: 1,799 files
- **XML / CAP (`.xml`)**: 502 files
[core] /usr/lib/enigma2/python/Plugins/Extensions/AIAssistant
[step 1] finish=STOP candidatesTokenCount=66, promptTokenCount=3915, totalTokenCount=3981
[step 2] finish=STOP candidatesTokenCount=436, promptTokenCount=7457, totalTokenCount=7893
[step 3] finish=STOP candidatesTokenCount=662, promptTokenCount=8278, totalTokenCount=8940
[step 4] finish=STOP candidatesTokenCount=565, promptTokenCount=9187, totalTokenCount=9752
root@sf8008:~#
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