Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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Download water level data from USGS using the dataretrieval package. Use when accessing real-time or historical streamflow data, downloading gage height or discharge measurements, or working with USGS station IDs.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
This guide covers downloading water level data from USGS using the dataretrieval Python package. USGS maintains thousands of stream gages across the United States that record water levels at 15-minute intervals.
pip install dataretrieval
The NWIS module is reliable and straightforward for accessing gage height data.
from dataretrieval import nwis
# Get instantaneous values (15-min intervals)
df, meta = nwis.get_iv(
sites='<station_id>',
start='<start_date>',
end='<end_date>',
parameterCd='00065'
)
# Get daily values
df, meta = nwis.get_dv(
sites='<station_id>',
start='<start_date>',
end='<end_date>',
parameterCd='00060'
)
# Get site information
info, meta = nwis.get_info(sites='<station_id>')
| Code | Parameter | Unit | Description |
|---|---|---|---|
00065 | Gage height | feet | Water level above datum |
00060 | Discharge | cfs | Streamflow volume |
| Function | Description | Data Frequency |
|---|---|---|
nwis.get_iv() | Instantaneous values | ~15 minutes |
nwis.get_dv() | Daily values | Daily |
nwis.get_info() | Site information | N/A |
nwis.get_stats() | Statistical summaries | N/A |
nwis.get_peaks() | Annual peak discharge | Annual |
The DataFrame has a datetime index and these columns:
| Column | Description |
|---|---|
site_no | Station ID |
00065 | Water level value |
00065_cd | Quality code (can ignore) |
from dataretrieval import nwis
station_ids = ['<id_1>', '<id_2>', '<id_3>']
all_data = {}
for site_id in station_ids:
try:
df, meta = nwis.get_iv(
sites=site_id,
start='<start_date>',
end='<end_date>',
parameterCd='00065'
)
if len(df) > 0:
all_data[site_id] = df
except Exception as e:
print(f"Failed to download {site_id}: {e}")
print(f"Successfully downloaded: {len(all_data)} stations")
# Find the gage height column (excludes quality code column)
gage_col = [c for c in df.columns if '00065' in str(c) and '_cd' not in str(c)]
if gage_col:
water_levels = df[gage_col[0]]
print(water_levels.head())
| Issue | Cause | Solution |
|---|---|---|
| Empty DataFrame | Station has no data for date range | Try different dates or use get_iv() |
get_dv() returns empty | No daily gage height data | Use get_iv() and aggregate |
| Connection error | Network issue | Wrap in try/except, retry |
| Rate limited | Too many requests | Add delays between requests |
len(df) > 0 before processingget_iv() for gage height, as daily data is often unavailable_cd)还没有评价。欢迎分享你的使用感受。
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Perform various data analysis on SEC 13-F and obtain some insights of fund activities such as number of holdings, AUM, and change of holdings between two quarters.
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AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching `acopf-math-model.md` and MATPOWER branch fields. Use when computing branch flows in either direction, aggregating bus injections for nodal balance, checking MVA (rateA) limits, computing branch loading %, or debugging sign/units issues in AC power flow.
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Redact text from PDF documents for blind review anonymization
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Use when checking simplified ADA-derived plan-view bathroom accessibility constraints such as turning space, door clear width, toilet centerline, grab bars, and lavatory knee/toe clearance.
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Analyze failed GitHub Action jobs for a pull request.
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Use when extracting plan-view architectural geometry from DXF files with semantic CAD layers, especially when outputs must normalize rooms, doors, fixtures, clearances, and grab bars into machine-checkable JSON.
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