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feat: ✨ CSV output near complete

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Jake Pullen
2025-11-10 21:18:32 +00:00
parent 88146c08b1
commit a4ce73b28a
7 changed files with 178 additions and 75 deletions
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.gitignore
+1
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@@ -11,4 +11,5 @@ wheels/
dat_files/* dat_files/*
asc_files/* asc_files/*
csv_files/*
*.tar.gz *.tar.gz
NIMROD_timeseries_cleaned.py
+66 -28
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@@ -1,29 +1,38 @@
from __future__ import division, print_function from __future__ import division, print_function
import numpy as np import numpy as np
import glob import glob
import pandas as pd
from datetime import datetime
# Configuration # Configuration
asc_path = "asc_files/" asc_path = "asc_files/"
asc_wildcard_file = "*.asc" asc_wildcard_file = "*.asc"
asc_mult_source = asc_path + asc_wildcard_file asc_mult_source = asc_path + asc_wildcard_file
five_min_rainfall_spatial_timeseries_name = 'timeseries_data.txt' def read_ascii_header(ascii_raster_file: str) -> list:
"""Reads header information from an ASCII DEM
things = [ Args:
# loc name, loc id, x loc, y loc, resolution ascii_raster_file (str): Path to the ASCII raster file
[1725 , 2175 , 608500 , 216500 , 1000 , -1 ], # 'BRICSC', 'TM0816'
[1725 , 2175 , 568500 , 342500 , 1000 , -1 ], # 'HEACSC', 'TF6842'
[1725.0, 2175.0, -404500.0, -624500.0, 1000.0, -1.0]# example
]
def read_ascii_header(ascii_raster_file): Returns:
"""Reads header information from an ASCII DEM""" list: Header data as a list of floats
"""
with open(ascii_raster_file) as f: with open(ascii_raster_file) as f:
header_data = [float(f.__next__().split()[1]) for x in range(6)] header_data = [float(f.__next__().split()[1]) for x in range(6)]
return header_data return header_data
def calculate_crop_coords(basin_header, radar_header):
"""Calculate crop coordinates based on header data""" def calculate_crop_coords(basin_header: list, radar_header: list) -> tuple:
"""Calculate crop coordinates based on header data
Args:
basin_header (list): Basin header data
radar_header (list): Radar header data
Returns:
tuple: (start_col, start_row, end_col, end_row) as integers
"""
y0_radar = radar_header[3] y0_radar = radar_header[3]
x0_radar = radar_header[2] x0_radar = radar_header[2]
@@ -32,8 +41,8 @@ def calculate_crop_coords(basin_header, radar_header):
nrows_radar = radar_header[1] nrows_radar = radar_header[1]
nrows_basin = basin_header[1] nrows_basin = 2 # hardcoded, we always expect 2 rows
ncols_basin = basin_header[0] ncols_basin = 2 # hardcoded, we always expect 2 columns
cellres_radar = radar_header[4] cellres_radar = radar_header[4]
cellres_basin = basin_header[4] cellres_basin = basin_header[4]
@@ -50,40 +59,69 @@ def calculate_crop_coords(basin_header, radar_header):
start_row = np.floor(nrows_radar - ((yp + ypp) / cellres_radar)) start_row = np.floor(nrows_radar - ((yp + ypp) / cellres_radar))
end_row = np.ceil(nrows_radar - (yp / cellres_radar)) end_row = np.ceil(nrows_radar - (yp / cellres_radar))
print(start_col, start_row, end_col, end_row) #print(start_col, start_row, end_col, end_row)
return int(start_col), int(start_row), int(end_col), int(end_row) return int(start_col), int(start_row), int(end_col), int(end_row)
def extract_cropped_rain_data(): def extract_cropped_rain_data(location):
"""Extract cropped rain data and create rainfall timeseries""" """Extract cropped rain data and create rainfall timeseries
Returns:
None
"""
rainfile = [] rainfile = []
#basin_header = read_ascii_header(basinsource)
basin_header = things[0] # just BRICSC for now
# Create datetime list
datetime_list = []
print(location)
for f in glob.iglob(asc_mult_source): for f in glob.iglob(asc_mult_source):
print(f) # print(f)
radar_header = read_ascii_header(f) radar_header = read_ascii_header(f)
# print(radar_header) start_col, start_row, end_col, end_row = calculate_crop_coords(
location, radar_header
start_col, start_row, end_col, end_row = calculate_crop_coords(basin_header, radar_header) )
start_col = int(round(start_col)) start_col = int(round(start_col))
start_row = int(round(start_row)) start_row = int(round(start_row))
end_col = int(round(end_col)) end_col = int(round(end_col))
end_row = int(round(end_row)) end_row = int(round(end_row))
cur_rawgrid = np.genfromtxt(f, skip_header=6, filling_values=0.0, loose=True, invalid_raise=False) cur_rawgrid = np.genfromtxt(
f, skip_header=6, filling_values=0.0, loose=True, invalid_raise=False
)
cur_croppedrain = cur_rawgrid[start_row:end_row, start_col:end_col] cur_croppedrain = cur_rawgrid[start_row:end_row, start_col:end_col]
print(cur_croppedrain)
# Flatten the cropped rain data into a 1D array # Flatten the cropped rain data into a 1D array
cur_rainrow = cur_croppedrain.flatten() cur_rainrow = cur_croppedrain.flatten()
print(cur_rainrow)
rainfile.append(cur_rainrow) rainfile.append(cur_rainrow)
# Extract datetime from filename
filename = f.split("/")[-1] # Get just the filename
# 20240929 0015
date_str = filename[:8] # YYYYMMDD
time_str = filename[8:12] # HHMM
# Parse datetime
parsed_date = datetime.strptime(f"{date_str}{time_str}", "%Y%m%d%H%M")
datetime_list.append(parsed_date)
rainfile_arr = np.vstack(rainfile) rainfile_arr = np.vstack(rainfile)
np.savetxt(five_min_rainfall_spatial_timeseries_name, rainfile_arr, delimiter=' ', fmt='%1.1f')
# Create DataFrame with datetime index
df = pd.DataFrame(rainfile_arr, index=datetime_list)
# sort the dataframe into date order
sorted_df = df.sort_index()
# add headers
header_row = ['rainfall_1', 'rainfall_2', 'rainfall_3', 'rainfall_4']
file_name = f"csv_files/{location[0]}_timeseries_data.csv"
sorted_df.to_csv(file_name, sep=",", float_format="%1.4f", header=header_row, index_label='datetime')
if __name__ == '__main__': if __name__ == "__main__":
extract_cropped_rain_data() locations = [
# loc name, loc id, x loc, y loc, resolution
["BRICSC", "TM0816", 608500, 216500, 1000],
["HEACSC", "TF6842", 568500, 342500, 1000],
]
for place in locations:
extract_cropped_rain_data(place)
batch_nimrod.py
+1
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@@ -20,6 +20,7 @@ IN_TOP_FOLDER = "./dat_files"
OUT_TOP_FOLDER = "./asc_files" OUT_TOP_FOLDER = "./asc_files"
CONFIG_FILE = "config.yaml" CONFIG_FILE = "config.yaml"
def get_datetime(file_name: str) -> str: def get_datetime(file_name: str) -> str:
""" """
Extract datetime from a filename using regex pattern matching. Extract datetime from a filename using regex pattern matching.
pyproject.toml
+1
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@@ -6,6 +6,7 @@ readme = "README.md"
requires-python = ">=3.12" requires-python = ">=3.12"
dependencies = [ dependencies = [
"numpy>=2.3.4", "numpy>=2.3.4",
"pandas>=2.3.3",
"pyyaml>=6.0.3", "pyyaml>=6.0.3",
"ruff>=0.14.3", "ruff>=0.14.3",
] ]
rainfile_5min.txt
-13
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@@ -1,13 +0,0 @@
timeseries_data.txt
-13
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@@ -1,13 +0,0 @@
uv.lock
Generated
+88
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@@ -8,6 +8,7 @@ version = "1.0.0"
source = { virtual = "." } source = { virtual = "." }
dependencies = [ dependencies = [
{ name = "numpy" }, { name = "numpy" },
{ name = "pandas" },
{ name = "pyyaml" }, { name = "pyyaml" },
{ name = "ruff" }, { name = "ruff" },
] ]
@@ -15,6 +16,7 @@ dependencies = [
[package.metadata] [package.metadata]
requires-dist = [ requires-dist = [
{ name = "numpy", specifier = ">=2.3.4" }, { name = "numpy", specifier = ">=2.3.4" },
{ name = "pandas", specifier = ">=2.3.3" },
{ name = "pyyaml", specifier = ">=6.0.3" }, { name = "pyyaml", specifier = ">=6.0.3" },
{ name = "ruff", specifier = ">=0.14.3" }, { name = "ruff", specifier = ">=0.14.3" },
] ]
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] ]
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name = "pandas"
version = "2.3.3"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "numpy" },
{ name = "python-dateutil" },
{ name = "pytz" },
{ name = "tzdata" },
]
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