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york-stsci committed Nov 8, 2023
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50 changes: 25 additions & 25 deletions jwql/instrument_monitors/common_monitors/edb_telemetry_monitor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -21,28 +21,28 @@
After filtering the data, the monitor calcualtes statistics. The monitor supports several different types
averaging. These include:
1. **daily\_means** - This is designed for mnemonics whose values do not change much over the course of a
1. **daily_means** - This is designed for mnemonics whose values do not change much over the course of a
day. In this case, mnemonic data is retrieved over a small amount of time each day (e.g. 12:00 - 12:15).
From these data, a daily mean is calculated. For all other types of telemetry, the EDB queries span
the full day.
2. **block\_means** - These are mnemonics where the user wishes to see mean values associated with each
2. **block_means** - These are mnemonics where the user wishes to see mean values associated with each
block of entries in the retrieved and filtered data. For example, you want to examine a voltage at times
when some other current is less than 0.25A. The script will read in all telemetry data, and filter out
data points for times where the current did not meet the criteria. It will then calculate the mean of
each remaining block of continuous good data. So if the data were good from 2:00 to 2:30, then bad until
3:00, and good again from 3:00-4:00, then the monitor will calculate a mean value for the 2:00-2:30
period, and a mean from the 3:00-4:00 period.
3. **time\_interval** - Mnemonics in this category have their data retrieved and filtered, and then averaged
3. **time_interval** - Mnemonics in this category have their data retrieved and filtered, and then averaged
over the requested time interval. For example, if the user sets a time interval of 5 minutes, then the
monitor caculates the mean value within each 5-minute block of the total time range of the data, and plots
the average values.
4. **every\_change** - This is the most complex case. Mnemonics in this category have their data filtered
and organized based on the value of a secondary mnemonic. For example, the IMIR\_HK\_GW14\_POS\_RATIO returns
4. **every_change** - This is the most complex case. Mnemonics in this category have their data filtered
and organized based on the value of a secondary mnemonic. For example, the IMIR_HK_GW14_POS_RATIO returns
a measure of the position of MIRI's grating wheel. We can plot this position as a function of the commanded
location of the grating wheel, which is provided by IMIR\_HK\_GW14\_CUR\_POS. In this case, the monitor will
location of the grating wheel, which is provided by IMIR_HK_GW14_CUR_POS. In this case, the monitor will
loop over the commanded positions and for each, gather the measured position information. The measured
positions associated with each commanded position will then be plotted separately. Note that this use
of "every change" is separate from the idea of every-change telemetry, in which telemetry points are
Expand All @@ -52,15 +52,15 @@
5. **all** - In this case, no averaging is done. (Although filtering is still done) All filtered data
are kept as they are retrived from the EDB, and plotted without any modification.
6. **all+daily\_means** - This is a combination of the "all" and "daily\_means" cases above. All data points
6. **all+daily_means** - This is a combination of the "all" and "daily_means" cases above. All data points
are retrieved from the EDB and optionally filtered by dependencies. Then daily means are calculated.
Both the full set of data and the daily means are plotted, along with deviations from the mean.
7. **all+block\_means** - This is a combination of the "all" and "block\_means" cases above. All data points
7. **all+block_means** - This is a combination of the "all" and "block_means" cases above. All data points
are retrieved from the EDB and optionally filtered by dependencies. Then means for each block of good data
are calculated. Both the full set of data and the means are plotted, along with deviations from the mean.
8. **all+time\_interval** - This is a combination of the "all" and "time\_interval" cases above. All data points
8. **all+time_interval** - This is a combination of the "all" and "time_interval" cases above. All data points
are retrieved from the EDB and optionally filtered by dependencies. Then means are calculated for each block
of time lasting the duration of the time interval. Both the full set of data and the means are plotted, along
with deviations from the mean.
Expand All @@ -72,24 +72,24 @@
The entry for each mnemonic has several pieces of information, described below.
- **name**: Name of the mnemonic as it appears in the EDB.
- **database\_id** Optional name to use in the plot title for this mnemonic. Any averaged data saved to the JWQL database will be saved under this name if it is present.
- **database_id** Optional name to use in the plot title for this mnemonic. Any averaged data saved to the JWQL database will be saved under this name if it is present.
- **description**: Summary describing the data contained in the mnemonic. Placed in the plot title.
- **dependency**: This is a list of mnemonics and conditions that will be used to filter the data
- **plot\_data**: Description of how the data are to be plotted. There are two options: "nominal", in which case
- **plot_data**: Description of how the data are to be plotted. There are two options: "nominal", in which case
the mnemonic data are plotted as-is, and "*<mnem>" where <mnem> is the name of another mnemonic. In this case, the
data for this second mnemonic are retrieved using the same dependencies as the primary mnemonic. The primary mnemonic
and this second mnemonic are then multiplied together and plotted. This option was designed around plotting power as
the product of current and voltage.
A further option for the **"plot\_data"** field is the addition of a comma-separated list of statistics to be overplotted.
A further option for the **"plot_data"** field is the addition of a comma-separated list of statistics to be overplotted.
Options are: "mean", "median", "max", and "min". Note that this is a little confusing, because in many cases the menmonic's
data will already contain the median value of the data (and the original data as returned from the EDB will not be
available). The monitor realized this though, so if you specify "mean" for a mnemonic in the "daily\_mean" list, it will simply
available). The monitor realized this though, so if you specify "mean" for a mnemonic in the "daily_mean" list, it will simply
plot the same data twice, on top of itself.
As an example, in order to plot the daily mean and maximum values of the product of SE\_ZIMIRICEA and SE\_ZBUSVLT, the plot\_data
entry would be: "*SE\_ZBUSVLT,max". If you also wanted to plot the minimum daily value, the entry would be: "*SE\_ZBUSVLT,max,min".
And similarly, to plot SE\_ZIMIRICEA on its own (not as a product), the plot\_data entries shown above would become: "nominal,max"
As an example, in order to plot the daily mean and maximum values of the product of SE_ZIMIRICEA and SE_ZBUSVLT, the plot_data
entry would be: "*SE_ZBUSVLT,max". If you also wanted to plot the minimum daily value, the entry would be: "*SE_ZBUSVLT,max,min".
And similarly, to plot SE_ZIMIRICEA on its own (not as a product), the plot_data entries shown above would become: "nominal,max"
and "nominal,max,min".
* **nominal_value**: Optional. The "expected" value for this mnemonic. If provided, a horizontal dashed line will be added at this value.
Expand Down Expand Up @@ -991,14 +991,14 @@ def get_dependency_data(self, dependency, starttime, endtime):
if dependency["name"] in self.query_results:

# We need the full time to be covered
if ((self.query_results[dependency["name"]].requested_start_time <= starttime)
and (self.query_results[dependency["name"]].requested_end_time >= endtime)):
if ((self.query_results[dependency["name"]].requested_start_time <= starttime) and
(self.query_results[dependency["name"]].requested_end_time >= endtime)):

logging.info(f'Dependency {dependency["name"]} is already present in self.query_results.')

# Extract data for the requested time range
matching_times = np.where((self.query_results[dependency["name"]].data["dates"] >= starttime)
& (self.query_results[dependency["name"]].data["dates"] <= endtime))
matching_times = np.where((self.query_results[dependency["name"]].data["dates"] >= starttime) &
(self.query_results[dependency["name"]].data["dates"] <= endtime))
dep_mnemonic = {"dates": self.query_results[dependency["name"]].data["dates"][matching_times],
"euvalues": self.query_results[dependency["name"]].data["euvalues"][matching_times]}

Expand Down Expand Up @@ -1138,16 +1138,16 @@ def get_history_every_change(self, mnemonic, start_date, end_date):
devs = []

# Keep only data that fall at least partially within the plot range
if (((np.min(row.time) > self._plot_start) & (np.min(row.time) < self._plot_end)) \
| ((np.max(row.time) > self._plot_start) & (np.max(row.time) < self._plot_end))):
if (((np.min(row.time) > self._plot_start) & (np.min(row.time) < self._plot_end))
| ((np.max(row.time) > self._plot_start) & (np.max(row.time) < self._plot_end))):
times.extend(row.time)
values.extend(row.mnemonic_value)
medians.append(row.median)
devs.append(row.stdev)
hist[row.dependency_value] = (times, values, medians, devs)
else:
if (((np.min(row.time) > self._plot_start) & (np.min(row.time) < self._plot_end)) \
| ((np.max(row.time) > self._plot_start) & (np.max(row.time) < self._plot_end))):
if (((np.min(row.time) > self._plot_start) & (np.min(row.time) < self._plot_end))
| ((np.max(row.time) > self._plot_start) & (np.max(row.time) < self._plot_end))):
hist[row.dependency_value] = (row.time, row.mnemonic_value, row.median, row.stdev)

return hist
Expand Down Expand Up @@ -1854,7 +1854,7 @@ def calculate_statistics(mnemonic_instance, telemetry_type):
mnemonic_instance.block_stats()
elif telemetry_type == "every_change":
mnemonic_instance.block_stats_filter_positions()
#mnemonic_instance.block_stats(ignore_vals=[0.], ignore_edges=True, every_change=True)
# mnemonic_instance.block_stats(ignore_vals=[0.], ignore_edges=True, every_change=True)
elif telemetry_type == "time_interval":
mnemonic_instance.timed_stats()
elif telemetry_type == "all":
Expand Down
4 changes: 2 additions & 2 deletions jwql/instrument_monitors/common_monitors/readnoise_monitor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -413,7 +413,7 @@ def process(self, file_list):
processed_file = file.replace("uncal", "refpix")
if not os.path.isfile(processed_file):
files_to_calibrate.append(file)

# Run the files through the necessary pipeline steps
outputs = run_parallel_pipeline(files_to_calibrate, "uncal", "refpix", self.instrument)

Expand All @@ -422,7 +422,7 @@ def process(self, file_list):

# Get relevant header information for this file
self.get_metadata(filename)

if filename in outputs:
processed_file = outputs[filename]
else:
Expand Down
32 changes: 16 additions & 16 deletions jwql/utils/constants.py
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Original file line number Diff line number Diff line change
Expand Up @@ -330,23 +330,23 @@
# to filter out bad values. Tuple values are the expected value and
# the standard deviation associated with the value
MIRI_POS_RATIO_VALUES = {'FW': {'FND': (-164.8728073, 0.204655346),
'OPAQUE': (380.6122145, 0.078856646),
'F1000W': (-24.15638797, 0.182865887),
'F1130W': (137.8245397, 0.24910941),
'F1280W': (-298.7062532, 0.229963508),
'P750L': (12.39439777, 0.246932037),
'F1500W': (-377.9888235, 0.263432415),
'F1800W': (435.9046314, 0.27885876),
'F2100W': (-126.5991201, 0.197193968),
'F560W': (218.0010353, 0.282554884),
'FLENS': (-212.7978283, 0.409300208),
'F2300C': (306.0488778, 0.265448583),
'F770W': (-62.48455213, 0.340861733),
'F1550C': (188.7366748, 0.291288105),
'F2550W': (-324.2364737, 0.176262309),
'F1140C': (82.81057729, 0.169772457),
'OPAQUE': (380.6122145, 0.078856646),
'F1000W': (-24.15638797, 0.182865887),
'F1130W': (137.8245397, 0.24910941),
'F1280W': (-298.7062532, 0.229963508),
'P750L': (12.39439777, 0.246932037),
'F1500W': (-377.9888235, 0.263432415),
'F1800W': (435.9046314, 0.27885876),
'F2100W': (-126.5991201, 0.197193968),
'F560W': (218.0010353, 0.282554884),
'FLENS': (-212.7978283, 0.409300208),
'F2300C': (306.0488778, 0.265448583),
'F770W': (-62.48455213, 0.340861733),
'F1550C': (188.7366748, 0.291288105),
'F2550W': (-324.2364737, 0.176262309),
'F1140C': (82.81057729, 0.169772457),
'F2550WR': (-255.5816917, 0.251581688),
'F1065C': (261.4486618, 0.16177981),
'F1065C': (261.4486618, 0.16177981),
},
'CCC': {'CLOSED': (398.0376386, 0.173703628),
'OPEN': (504.0482685, 0.328112274)
Expand Down

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