From 5a86220f7367a969409d9fb99c2562ea30aa45c5 Mon Sep 17 00:00:00 2001
From: pcain-usgs <pcain@usgs.gov>
Date: Thu, 15 Oct 2020 14:47:18 -0600
Subject: [PATCH] Add elements in steps for product stream information
---
geomagio/algorithm/FilterAlgorithm.py | 30 +++++++++++++++++++++++++++
1 file changed, 30 insertions(+)
diff --git a/geomagio/algorithm/FilterAlgorithm.py b/geomagio/algorithm/FilterAlgorithm.py
index 49353c0cd..84b0f9234 100644
--- a/geomagio/algorithm/FilterAlgorithm.py
+++ b/geomagio/algorithm/FilterAlgorithm.py
@@ -14,31 +14,51 @@ from .. import TimeseriesUtility
STEPS = [
{ # 10 Hz to one second filter
"name": "10Hz",
+ "data_interval": "second",
+ "data_interval_type": "Average 1-Second",
"input_sample_period": 0.1,
"output_sample_period": 1.0,
"window": sps.firwin(123, 0.25, window="blackman", fs=10.0),
"type": "firfilter",
+ "filter_comments": [
+ "Vector 1-second values are computed from 0.1-second values using the INTERMAGNET blackman filter centered on the second. Scalar 1-second values are computed from 0.1-second values using the INTERMAGNET blackman filter centered on the second. "
+ ],
},
{ # one second to one minute filter
"name": "Intermagnet One Minute",
+ "data_interval": "minute",
+ "data_interval_type": "filtered 1-minute (00:15-01:45)",
"input_sample_period": 1.0,
"output_sample_period": 60.0,
"window": sps.get_window(window=("gaussian", 15.8734), Nx=91),
"type": "firfilter",
+ "filter_comments": [
+ "Vector 1-minute values are computed from 1-second values using the INTERMAGNET gaussian filter centered on the minute. Scalar 1-minute values are computed from 1-second values using the INTERMAGNET gaussian filter centered on the minute. "
+ ],
},
{ # one minute to one hour filter
"name": "One Hour",
+ "data_interval": "hour",
+ "data_interval_type": "filtered 1-hour",
"input_sample_period": 60.0,
"output_sample_period": 3600.0,
"window": sps.windows.boxcar(60),
"type": "average",
+ "filter_comments": [
+ "Vector 1-hour values are computed from 1-minute values using a boxcar filter centered on the nearest half-hour. Scalar 1-hour values are computed from 1-minute values using a boxcar filter centered on the nearest half-hour. "
+ ],
},
{ # one minute to one hour filter
"name": "One Day",
+ "data_interval": "day",
+ "data_interval_type": "filtered 1-Day",
"input_sample_period": 60.0,
"output_sample_period": 86400,
"window": sps.windows.boxcar(1440),
"type": "average",
+ "filter_comments": [
+ "Vector 1-day values are computed from 1-minute values using a boxcar filter centered on the nearest half-day. Scalar 1-day values are computed from 1-minute values using a boxcar filter centered on the nearest half-day. "
+ ],
},
]
@@ -129,6 +149,13 @@ class FilterAlgorithm(Algorithm):
self.steps = [
{
"name": "name" in data and data["name"] or "custom",
+ "data_interval": TimeseriesUtility.get_interval_from_delta(
+ self.output_sample_period
+ ),
+ "data_interval_type": "filtered custom interval",
+ "filter_comments": [
+ "Data produced by filter utilizing custom coefficients and intervals."
+ ],
"input_sample_period": self.input_sample_period,
"output_sample_period": self.output_sample_period,
"window": data["window"],
@@ -268,6 +295,9 @@ class FilterAlgorithm(Algorithm):
filtered = self.firfilter(data, window, decimation)
stats = Stats(trace.stats)
stats.delta = output_sample_period
+ stats.data_interval = step["data_interval"]
+ stats.data_interval_type = step["data_interval_type"]
+ stats.filter_comments = step["filter_comments"]
stats.starttime = starttime
stats.npts = len(filtered)
trace_out = self.create_trace(stats.channel, stats, filtered)
--
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