Skip to content

Commit

Permalink
Merge pull request #116 from valeriupredoi/compare_linestyle
Browse files Browse the repository at this point in the history 
New feature: You can choose smoothing (ie 5 year smoothing window) and set custom plot labels for the legend.
  • Loading branch information
@valeriupredoi
valeriupredoi authored Nov 7, 2023
2 parents 836729d + 3966176 commit 95c5e01
Show file tree
Hide file tree
Showing 7 changed files with 141 additions and 31 deletions.
28 changes: 28 additions & 0 deletions README.md
View file
Original file line number Diff line number Diff line change
Expand Up @@ -229,6 +229,7 @@ strictFileCheck: <bool>
jobs:
<jobID1>:
description: <descrption of the first job>
label: 'Job info'
colour: red
thickness: 0.7
linestyle: '-'
Expand Down Expand Up @@ -271,6 +272,10 @@ These values are:
- The options are:
- `description`:
- A description of job, which helps people differentiate the jobs in the final report.
- `label`:
- A short description of the job which will appear in the plot legend.
- If you make it too long, it won't fit on the plot.
- Optional: Default behaviour is the jobID.
- `colour`:
- The colour of this jobs line in the report plots.
- Default colour is a randomly generated hex colour.
Expand Down Expand Up @@ -447,6 +452,8 @@ units: Units
dimensions: 1,2 or 3 # The number of dimensions after the calculations are performed
layers : Surface
regions : Global ignoreInlandSeas SouthernOcean ArcticOcean Equator10 Remainder NorthernSubpolarAtlantic NorthernSubpolarPacific
smoothings : DataOnly both5 both30 movingav30years 5and30 30and100
#paths:
modelFiles : $BASEDIR_MODEL/$JOBID/nemo_$JOBIDo_1y_*_grid-T.nc
Expand Down Expand Up @@ -500,6 +507,27 @@ basic functions such as multiply by or add to, or `noChange`, which can all be
called without providing the `path`, and which may have their own key word
arguments.

Some of the other options for each analysis include:
- `layers`:
- The z axis layers that you want to use.
- Typically include things like `Surface`, `500m`, `1000m` etc.
- Note that this is a pre-curated list, so you can't use it to select a specific depth (like `327m` or something)
- `regions`:
- A list of regions to investigate.
- This is a pre-curated list, and they are defined in so you can't.
- These regions are defined in the file `bgcval2/bgcvaltools/makeMask.py`.
- `smoothings`:
- This is the smoothing function (if any) to apply to the data before plotting it.
- The smoothing it added before plotting, but after saving the shelve file, so it doesn't impact the data.
- No smoothing is `DataOnly`, which is also the default behaviour.
- If several smoothing options are added here, bgcval2 will generate a comparison plot for each one.
- The smoothings are defined and performed in `bgcval2/timeseries/timeseriesPlots.py`
- Other modes exist:
- `movingav30years`: A 30 year moving average
- `both5`: Both no smoothing and a 5 year moving average
- `both30`: Both no smoothing and a 30 year moving average
- `5and30`: a 5 year moving average and a 30 year moving average.

Clearing the Cache
------------------

Expand Down
19 changes: 15 additions & 4 deletions bgcval2/analysis_compare.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -189,6 +189,7 @@ def timeseries_compare(jobs,
jobDescriptions={},
lineThicknesses=defaultdict(lambda: 1),
linestyles=defaultdict(lambda: '-'),
labels = {},
ensembles={},
config_user=None,
):
Expand Down Expand Up @@ -382,6 +383,7 @@ def timeseries_compare(jobs,
regions = av[name]['regions']
layers = av[name]['layers']
metrics = av[name]['metrics']
smoothings = av[name]['smoothings']
for region, layer, metric in itertools.product(regions, layers, metrics):
timesD = {}
arrD = {}
Expand All @@ -405,20 +407,22 @@ def timeseries_compare(jobs,
units = av[name]['modeldetails']['units']

ts = 'Together'
for ls in ['DataOnly', ]:
for smoothing in smoothings:
#or ls in ['DataOnly', ]:
tsp.multitimeseries(
timesD, # model times (in floats)
arrD, # model time series
data=-999, # in situ data distribution
title=title,
filename=bvt.folder(imageFolder) +
'_'.join([name, region, layer, ts, ls + '.png']),
'_'.join([name, region, layer, ts, smoothing + '.png']),
units=units,
plotStyle=ts,
lineStyle=ls,
smoothing=smoothing,
colours=colours,
thicknesses=lineThicknesses,
linestyles=linestyles,
labels=labels,
)

# Generate a list of comparison images:
Expand Down Expand Up @@ -515,7 +519,8 @@ def load_comparison_yml(master_compare_yml_fn):
descriptions = {}
shifttimes = {} # number of years to shift time axis.
timeranges = {}

labels = {}

for jobID, job_dict in details['jobs'].items():
if job_dict.get('colour', False):
colours[jobID] = job_dict['colour']
Expand All @@ -530,13 +535,16 @@ def load_comparison_yml(master_compare_yml_fn):
timeranges[jobID] = job_dict.get('timerange', None)
suites[jobID] = job_dict.get('suite', default_suite)
auto_download_dict[jobID] = job_dict.get('auto_download', auto_download_dict[jobID])
labels[jobID] = job_dict.get('label', jobID)


details['colours'] = colours
details['descriptions'] = descriptions
details['thicknesses'] = thicknesses
details['linestyles'] = linestyles
details['shifttimes'] = shifttimes
details['timeranges'] = timeranges
details['labels'] = labels
details['suites'] = suites
details['auto_download'] = auto_download_dict
return details
Expand Down Expand Up @@ -567,6 +575,7 @@ def load_yml_and_run(compare_yml, config_user, skip_timeseries):
descriptions = details['descriptions']
shifttimes = details['shifttimes']
timeranges = details['timeranges']
labels = details['labels']
suites = details['suites']
auto_download = details['auto_download']
strictFileCheck = details.get('strictFileCheck', True)
Expand All @@ -578,6 +587,7 @@ def load_yml_and_run(compare_yml, config_user, skip_timeseries):
print(jobID, 'colour:',colours[jobID])
print(jobID, 'line thickness & style:',thicknesses[jobID], linestyles[jobID])
print(jobID, 'Shift time by', shifttimes[jobID])
print(jobID, 'Label: ', labels[jobID])
print(jobID, 'Time range (None means all):', timeranges.get(jobID, None))
print(jobID, 'suite:', suites[jobID])
print(jobID, 'auto_download', auto_download[jobID])
Expand Down Expand Up @@ -620,6 +630,7 @@ def load_yml_and_run(compare_yml, config_user, skip_timeseries):
analysisname=analysis_name,
lineThicknesses=thicknesses,
linestyles=linestyles,
labels=labels,
config_user=config_user,
)

Expand Down
5 changes: 5 additions & 0 deletions bgcval2/analysis_timeseries.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -297,6 +297,11 @@ def load_key_file(key, paths, jobID, strictFileCheck=True):
output_dict['metrics'] = key_dict.get('metrics', metricList)
output_dict['metrics'] = parse_list_from_string(output_dict['metrics'])

# Smoothings:
default_smoothings = ['DataOnly', ]
output_dict['smoothings'] = key_dict.get('smoothings', default_smoothings)
output_dict['smoothings'] = parse_list_from_string(output_dict['smoothings'])

# Load Grid:
gridFile = key_dict.get('gridFile', paths.orcaGridfn)
output_dict['gridFile'] = list_input_files(gridFile, key_dict, paths)[0]
Expand Down
10 changes: 8 additions & 2 deletions bgcval2/download_from_mass.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -503,8 +503,14 @@ def download_from_mass(
shared_file_path = os.path.join(paths.shared_mass_scripts, os.path.basename(download_script_path))
print('writing file in shared path', shared_file_path)

# check destination permissions:
i_can_write_this = os.access(shared_file_path, os.W_OK)
# check destination existance and permissions
if os.path.exists(shared_file_path):
# check destination permissions
i_can_write_this = os.access(shared_file_path, os.W_OK)
else:
# File doesn't exist!
i_can_write_this = True

if i_can_write_this:
# I created this file and I own it:
# make local copy group writable:
Expand Down
107 changes: 82 additions & 25 deletions bgcval2/timeseries/timeseriesPlots.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -702,9 +702,10 @@ def multitimeseries(
'lime',
],
plotStyle='Together',
lineStyle='',
smoothing='',
thicknesses=defaultdict(lambda: 1),
linestyles=defaultdict(lambda: '-'),
labels={},
):

if 0 in [len(timesD), len(list(timesD.keys()))]: return
Expand Down Expand Up @@ -783,7 +784,8 @@ def multitimeseries(
if np.min(arr) < ylims[0]: ylims[0] = np.min(arr)
if np.max(arr) > ylims[1]: ylims[1] = np.max(arr)

if lineStyle.lower() in ['spline', 'all']:
label = labels.get(jobID, jobID)
if smoothing.lower() in ['spline', 'all']:
tnew = np.linspace(times[0], times[-1], 60)
arr_smooth = interpolate.spline(times, arr, tnew)
pyplot.plot(
Expand All @@ -792,10 +794,10 @@ def multitimeseries(
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID],
label=jobID + ' spline',
label=label + ' spline',
)

if lineStyle.lower() in ['movingaverage', 'both', 'all']:
if smoothing.lower() in ['movingaverage', 'both', 'all']:
if len(times) > 100.: window = 30
elif len(times) > 30.: window = 15
elif len(times) > 10.: window = 4
Expand All @@ -810,10 +812,10 @@ def multitimeseries(
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID],
label=jobID,
label=label,
)

if lineStyle.lower() in [
if smoothing.lower() in [
'movingaverage5',
]:
window = 5
Expand All @@ -827,11 +829,65 @@ def multitimeseries(
arr_new,
c=colours[jobID],
ls=linestyles[jobID],
label=jobID,
label=label,
lw=thicknesses[jobID],
)

if lineStyle.lower() in [
if smoothing.lower() == '5and30':
arr_new5 = movingaverage_DT(arr,
times,
window_len=5.,
window_units='years')

arr_new30 = movingaverage_DT(arr,
times,
window_len=30.,
window_units='years')

pyplot.plot(
times,
arr_new5,
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID]/2.,
)
pyplot.plot(
times,
arr_new30,
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID],
label=label,
)
if smoothing.lower() == '30and100':
arr_new5 = movingaverage_DT(arr,
times,
window_len=30.,
window_units='years')

arr_new30 = movingaverage_DT(arr,
times,
window_len=100.,
window_units='years')

pyplot.plot(
times,
arr_new5,
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID]/2.,
)
pyplot.plot(
times,
arr_new30,
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID],
label=label,
)


if smoothing.lower() in [
'movingav1year',
]:
arr_new = movingaverage_DT(arr,
Expand All @@ -844,10 +900,10 @@ def multitimeseries(
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID],
label=jobID,
label=label,
)
if lineStyle.lower() in [
'movingav5years',
if smoothing.lower() in [
'movingav5years', 'both5'
]:
arr_new = movingaverage_DT(arr,
times,
Expand All @@ -859,11 +915,11 @@ def multitimeseries(
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID],
label=jobID,
label=label,
)

if lineStyle.lower() in [
'movingav30years',
if smoothing.lower() in [
'movingav30years', 'both30'
]:
pyplot.plot(times,
arr,
Expand All @@ -881,11 +937,11 @@ def multitimeseries(
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID],
label=jobID,
label=label,
)

if lineStyle.lower() in [
'movingav100years',
if smoothing.lower() in [
'movingav100years', 'both100',
]:
pyplot.plot(times,
arr,
Expand All @@ -903,10 +959,10 @@ def multitimeseries(
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID],
label=jobID,
label=label,
)

if lineStyle.lower() in [
if smoothing.lower() in [
'movingaverage12',
]:
window = 12
Expand All @@ -921,10 +977,10 @@ def multitimeseries(
c=colours[jobID],
ls=linestyles[jobID],
lw=2.,
label=jobID,
label=label,
)

if lineStyle.lower() in [
if smoothing.lower() in [
'movingaverage60',
]:
window = 60
Expand All @@ -939,28 +995,29 @@ def multitimeseries(
c=colours[jobID],
ls=linestyles[jobID],
lw=2.,
label=jobID,
label=label,
)

if lineStyle.lower() in [
if smoothing.lower() in [
'',
'both',
'all',
'both5', 'both30', 'both100',
]:
pyplot.plot(times,
arr,
c=colours[jobID],
ls=linestyles[jobID],
lw=0.25)

if lineStyle.lower() in ['dataonly']:
if smoothing.lower() in ['dataonly']:
pyplot.plot(
times,
arr,
c=colours[jobID],
ls=linestyles[jobID],
lw=thicknesses[jobID],
label=jobID,
label=label,
)

if type(data) == type(10.):
Expand Down
Loading

0 comments on commit 95c5e01

Please sign in to comment.