I have training my model to detect normal vs pneumonia chest x-ray classes. This is my dataset as listed below:
train_batch= ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input)\
.flow_from_directory(directory=train_path, target_size=(224,224), classes=['NORMAL', 'PNEUMONIA'],
batch_size=32,class_mode='categorical')
val_batch= ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
.flow_from_directory(directory=val_path, target_size=(224,224), classes=['NORMAL', 'PNEUMONIA'], batch_size=32, class_mode='categorical')
test_batch= ImageDataGenerator(preprocessing_function=tf.keras.applications.vgg16.preprocess_input) \
.flow_from_directory(directory=test_path, target_size=(224,224), classes=['NORMAL', 'PNEUMONIA'], batch_size=16,class_mode='categorical', shuffle=False)
Found 3616 images belonging to 2 classes. #training
Found 1616 images belonging to 2 classes. #validation
Found 624 images belonging to 2 classes. #test
my model consist of 5 CNN layers where image w,h = (224* 224,3) with 16 feature map as first layer and then 32, 64, 128,256. Batch normalization , max pooling and dropout is added to every cnn layer, but last dense layer is as follow
model.add(Dense(units=2 , activation='softmax'))
optim = Adam( lr=0.001 )
model.compile(optimizer=optim , loss= 'categorical_crossentropy' , metrics= ['accuracy'])
history=model.fit_generator(train_batch,
steps_per_epoch= 113, #3616/32=113
epochs = 25,
validation_data = val_batch,
validation_steps = 51 #1616/32=51
#verbose=2
#callbacks=callbacks #remove to chk
)
as it can be seen in the graph that my training and validation accuracy and loss is good but when I plot confusion matrix it dose not seems good why??
prediction = model.predict_generator(test_batch,steps= stepss) #, verbose=0)
prediction1 = np.argmax(prediction, axis=1)
cm = confusion_matrix (test_batch.classes, prediction1)
print(cm)
this is my confusion matrix as below
as you can see my graph which is as below
after that I did fine tuning of my model with VGG!6 by replacing last dense layer with my own dense layer with two outputs and here is the graph and confusion matrix:
I do not understand why my testing in not going good even with vgg16 model as you can see the results so please give me your valuable suggestions THANKS
I'm trying to learn more about pyhf and my understanding of what the goals are might be limited. I would love to fit my HEP data outside of ROOT, but I could be imposing expectations on pyhf which are not what the authors intended for it's use.
I'd like to write myself a hello-world example, but I might just not know what I'm doing. My misunderstanding could also be gaps in my statistical knowledge.
With that preface, let me explain what I'm trying to explore.
I have some observed set of events for which I calculate some observable and make a binned histogram of that data. I hypothesize that there are two contributing physics processes, which I call signal and background. I generate some Monte Carlo samples for these processes and the theorized total number of events is close to, but not exactly what I observe.
I would like to:
Fit the data to this two process hypothesis
Get from the fit the optimal values for the number of events for each process
Get the uncertainties on these fitted values
If appropriate, calculate an upper limit on the number of signal events.
My starter code is below, where all I'm doing is an ML fit but I'm not sure where to go. I know it's not set up to do what I want, but I'm getting lost in the examples I find on RTD. I'm sure it's me, this is not a criticism of the documentation.
import pyhf
import numpy as np
import matplotlib.pyplot as plt
nbins = 15
# Generate a background and signal MC sample`
MC_signal_events = np.random.normal(5,1.0,200)
MC_background_events = 10*np.random.random(1000)
signal_data = np.histogram(MC_signal_events,bins=nbins)[0]
bkg_data = np.histogram(MC_background_events,bins=nbins)[0]
# Generate an observed dataset with a slightly different
# number of events
signal_events = np.random.normal(5,1.0,180)
background_events = 10*np.random.random(1050)
observed_events = np.array(signal_events.tolist() + background_events.tolist())
observed_sample = np.histogram(observed_events,bins=nbins)[0]
# Plot these samples, if you like
plt.figure(figsize=(12,4))
plt.subplot(1,3,1)
plt.hist(observed_events,bins=nbins,label='Observations')
plt.legend()
plt.subplot(1,3,2)
plt.hist(MC_signal_events,bins=nbins,label='MC signal')
plt.legend()
plt.subplot(1,3,3)
plt.hist(MC_background_events,bins=nbins,label='MC background')
plt.legend()
# Use a very naive estimate of the background
# uncertainties
bkg_uncerts = np.sqrt(bkg_data)
print("Defining the PDF.......")
pdf = pyhf.simplemodels.hepdata_like(signal_data=signal_data.tolist(), \
bkg_data=bkg_data.tolist(), \
bkg_uncerts=bkg_uncerts.tolist())
print("Fit.......")
data = pyhf.tensorlib.astensor(observed_sample.tolist() + pdf.config.auxdata)
bestfit_pars, twice_nll = pyhf.infer.mle.fit(data, pdf, return_fitted_val=True)
print(bestfit_pars)
print(twice_nll)
plt.show()
Note: this answer is based on pyhf v0.5.2.
Alright, so it looks like you've managed to figure most of the big pieces for sure. However, there's two different ways to do this depending on how you prefer to set things up. In both cases, I assume you want an unconstrained fit and you want to...
fit your signal+background model to observed data
fit your background model to observed data
First, let's discuss uncertainties briefly. At the moment, we default to numpy for the tensor background and scipy for the optimizer. See documentation:
numpy backend
scipy optimizer
However, one unfortunate drawback right now with the scipy optimizer is that it cannot return the uncertainties. What you need to do anywhere in your code before the fit (although we generally recommend as early as possible) is to use the minuit optimizer instead:
pyhf.set_backend('numpy', 'minuit')
This will get you the nice features of being able to get the correlation matrix, the uncertainties on the fitted parameters, and the hessian -- amongst other things. We're working to make this consistent for scipy as well, but this is not ready right now.
All optimizations go through our optimizer API which you can currently view through the mixin here in our documentation. Specifically, the signature is
minimize(
objective,
data,
pdf,
init_pars,
par_bounds,
fixed_vals=None,
return_fitted_val=False,
return_result_obj=False,
do_grad=None,
do_stitch=False,
**kwargs)
There are a lot of options here. Let's just focus on the fact that one of the keyword arguments we can pass through is return_uncertainties which will change the bestfit parameters by adding a column for the fitted parameter uncertainty which you want.
1. Signal+Background
In this case, we want to just use the default model
result, twice_nll = pyhf.infer.mle.fit(
data,
pdf,
return_uncertainties=True,
return_fitted_val=True
)
bestfit_pars, errors = result.T
2. Background-Only
In this case, we need to turn off the signal. The way we do this is by setting the parameter of interest (POI) fixed to 0.0. Then we can get the fitted parameters for the background-only model in a similar way, but using fixed_poi_fit instead of an unconstrained fit:
result, twice_nll = pyhf.infer.mle.fixed_poi_fit(
0.0,
data,
pdf,
return_uncertainties=True,
return_fitted_val=True
)
bestfit_pars, errors = result.T
Note that this is quite simply a quick way of doing the following unconstrained fit
bkg_params = pdf.config.suggested_init()
fixed_params = pdf.config.suggested_fixed()
bkg_params[pdf.config.poi_index] = 0.0
fixed_params[pdf.config.poi_index] = True
result, twice_nll = pyhf.infer.mle.fit(
data,
pdf,
init_pars=bkg_params,
fixed_params=fixed_params,
return_uncertainties=True,
return_fitted_val=True
)
bestfit_pars, errors = result.T
Hopefully that clarifies things up more!
Giordon's solution should answer all of your question, but I thought I'd also write out the code to basically address everything we can.
I also take the liberty of changing some of your values a bit so that the signal isn't so strong that the observed CLs value isn't far off to the right of the Brazil band (the results aren't wrong obviously, but it probably makes more sense to be talking about using the discovery test statistic at that point then setting limits. :))
Environment
For this example I'm going to setup a clean Python 3 virtual environment and then install the dependencies (here we're going to be using pyhf v0.5.2)
$ python3 -m venv "${HOME}/.venvs/question"
$ . "${HOME}/.venvs/question/bin/activate"
(question) $ cat requirements.txt
pyhf[minuit,contrib]~=0.5.2
black
(question) $ python -m pip install -r requirements.txt
Code
While we can't easily get the best fit value for both the number of signal events as well as the background events we definitely can do inference to get the best fit value for the signal strength.
The following chunk of code (which is long only because of the visualization) should address all of the points of your question.
# answer.py
import numpy as np
import pyhf
import matplotlib.pyplot as plt
import pyhf.contrib.viz.brazil
# Goals:
# - Fit the model to the observed data
# - Infer the best fit signal strength given the model
# - Get the uncertainties on the best fit signal strength
# - Calculate an 95% CL upper limit on the signal strength
def plot_hist(ax, bins, data, bottom=0, color=None, label=None):
bin_width = bins[1] - bins[0]
bin_leftedges = bins[:-1]
bin_centers = [edge + bin_width / 2.0 for edge in bin_leftedges]
ax.bar(
bin_centers, data, bin_width, bottom=bottom, alpha=0.5, color=color, label=label
)
def plot_data(ax, bins, data, label="Data"):
bin_width = bins[1] - bins[0]
bin_leftedges = bins[:-1]
bin_centers = [edge + bin_width / 2.0 for edge in bin_leftedges]
ax.scatter(bin_centers, data, color="black", label=label)
def invert_interval(test_mus, hypo_tests, test_size=0.05):
# This will be taken care of in v0.5.3
cls_obs = np.array([test[0] for test in hypo_tests]).flatten()
cls_exp = [
np.array([test[1][idx] for test in hypo_tests]).flatten() for idx in range(5)
]
crossing_test_stats = {"exp": [], "obs": None}
for cls_exp_sigma in cls_exp:
crossing_test_stats["exp"].append(
np.interp(
test_size, list(reversed(cls_exp_sigma)), list(reversed(test_mus))
)
)
crossing_test_stats["obs"] = np.interp(
test_size, list(reversed(cls_obs)), list(reversed(test_mus))
)
return crossing_test_stats
def main():
np.random.seed(0)
pyhf.set_backend("numpy", "minuit")
observable_range = [0.0, 10.0]
bin_width = 0.5
_bins = np.arange(observable_range[0], observable_range[1] + bin_width, bin_width)
n_bkg = 2000
n_signal = int(np.sqrt(n_bkg))
# Generate simulation
bkg_simulation = 10 * np.random.random(n_bkg)
signal_simulation = np.random.normal(5, 1.0, n_signal)
bkg_sample, _ = np.histogram(bkg_simulation, bins=_bins)
signal_sample, _ = np.histogram(signal_simulation, bins=_bins)
# Generate observations
signal_events = np.random.normal(5, 1.0, int(n_signal * 0.8))
bkg_events = 10 * np.random.random(int(n_bkg + np.sqrt(n_bkg)))
observed_events = np.array(signal_events.tolist() + bkg_events.tolist())
observed_sample, _ = np.histogram(observed_events, bins=_bins)
# Visualize the simulation and observations
fig, ax = plt.subplots()
fig.set_size_inches(7, 5)
plot_hist(ax, _bins, bkg_sample, label="Background")
plot_hist(ax, _bins, signal_sample, bottom=bkg_sample, label="Signal")
plot_data(ax, _bins, observed_sample)
ax.legend(loc="best")
ax.set_ylim(top=np.max(observed_sample) * 1.4)
ax.set_xlabel("Observable")
ax.set_ylabel("Count")
fig.savefig("components.png")
# Build the model
bkg_uncerts = np.sqrt(bkg_sample)
model = pyhf.simplemodels.hepdata_like(
signal_data=signal_sample.tolist(),
bkg_data=bkg_sample.tolist(),
bkg_uncerts=bkg_uncerts.tolist(),
)
data = pyhf.tensorlib.astensor(observed_sample.tolist() + model.config.auxdata)
# Perform inference
fit_result = pyhf.infer.mle.fit(data, model, return_uncertainties=True)
bestfit_pars, par_uncerts = fit_result.T
print(
f"best fit parameters:\
\n * signal strength: {bestfit_pars[0]} +/- {par_uncerts[0]}\
\n * nuisance parameters: {bestfit_pars[1:]}\
\n * nuisance parameter uncertainties: {par_uncerts[1:]}"
)
# Perform hypothesis test scan
_start = 0.0
_stop = 5
_step = 0.1
poi_tests = np.arange(_start, _stop + _step, _step)
print("\nPerforming hypothesis tests\n")
hypo_tests = [
pyhf.infer.hypotest(
mu_test,
data,
model,
return_expected_set=True,
return_test_statistics=True,
qtilde=True,
)
for mu_test in poi_tests
]
# Upper limits on signal strength
results = invert_interval(poi_tests, hypo_tests)
print(f"Observed Limit on µ: {results['obs']:.2f}")
print("-----")
for idx, n_sigma in enumerate(np.arange(-2, 3)):
print(
"Expected {}Limit on µ: {:.3f}".format(
" " if n_sigma == 0 else "({} σ) ".format(n_sigma),
results["exp"][idx],
)
)
# Visualize the "Brazil band"
fig, ax = plt.subplots()
fig.set_size_inches(7, 5)
ax.set_title("Hypothesis Tests")
ax.set_ylabel(r"$\mathrm{CL}_{s}$")
ax.set_xlabel(r"$\mu$")
pyhf.contrib.viz.brazil.plot_results(ax, poi_tests, hypo_tests)
fig.savefig("brazil_band.png")
if __name__ == "__main__":
main()
which when run gives
(question) $ python answer.py
best fit parameters:
* signal strength: 1.5884737977889158 +/- 0.7803435235862329
* nuisance parameters: [0.99020988 1.06040191 0.90488207 1.03531383 1.09093327 1.00942088
1.07789316 1.01125627 1.06202964 0.95780043 0.94990993 1.04893286
1.0560711 0.9758487 0.93692481 1.04683181 1.05785515 0.92381263
0.93812855 0.96751869]
* nuisance parameter uncertainties: [0.06966439 0.07632218 0.0611428 0.07230328 0.07872258 0.06899675
0.07472849 0.07403246 0.07613661 0.08606657 0.08002775 0.08655314
0.07564512 0.07308117 0.06743479 0.07383134 0.07460864 0.06632003
0.06683251 0.06270965]
Performing hypothesis tests
/home/stackoverflow/.venvs/question/lib/python3.7/site-packages/pyhf/infer/calculators.py:229: RuntimeWarning: invalid value encountered in double_scalars
teststat = (qmu - qmu_A) / (2 * self.sqrtqmuA_v)
Observed Limit on µ: 2.89
-----
Expected (-2 σ) Limit on µ: 0.829
Expected (-1 σ) Limit on µ: 1.110
Expected Limit on µ: 1.542
Expected (1 σ) Limit on µ: 2.147
Expected (2 σ) Limit on µ: 2.882
Let us know if you have any further questions!
I am trying to replicate the resnet18 paper. Before running this on the full Image Net dataset on disk, I'm doing some evaluation runs with the publicly available imagenette/320px dataset from TFDS (much much smaller subset of imagenet with 10 classes, already in .tfrecord format._
Note: the full notebook to do training and tracing is available here: resnet18_baseline.ipynb Just switch to a GPU runtime and run all the cells. It's already set-up with tensorboard profiling on the second batch. (You can use TPU as well, but some keras.layers.experimental.preprocessing layers do not support TPU ops yet and you have to enable soft device placement. Please use a GPU).
Input Operations
read images from the input dataset. These images usually have got different dimensions and we need some crop function because input tensors can not have different dimensions for batching. Therefore, for training I use random crop and for testing/validation datasets a center crop.
random_crop_layer = keras.layers.experimental.preprocessing.RandomCrop(224, 224)
center_crop_layer = keras.layers.experimental.preprocessing.CenterCrop(224, 224)
#tf.function(experimental_relax_shapes=True) # avoid retracing
def train_crop_fn(x, y):
return random_crop_layer(x), y
#tf.function(experimental_relax_shapes=True)
def eval_crop_fn(x, y):
return center_crop_layer(x), y
Perform some simple preprocessing/augmentations to the input data. These include rescaling to 0-1 and also scaling based on mean and stdev of the rgb colours on imagenet. Also, random
rescaling_layer = keras.layers.experimental.preprocessing.Rescaling(1./255)
train_preproc = keras.Sequential([
rescaling_layer
])
# from https://github.com/tensorflow/models/blob/master/official/vision/image_classification/preprocessing.py
# Calculated from the ImageNet training set
MEAN_RGB = (0.485 , 0.456, 0.406)
STDDEV_RGB = (0.229, 0.224, 0.225)
#tf.function
def z_score_scale(x):
return (x - MEAN_RGB) / STDDEV_RGB
#tf.function
def train_preproc_fn(x, y):
return z_score_scale(train_preproc(x)), y
#tf.function
def eval_preproc_fn(x, y):
return z_score_scale(eval_preproc(x)), y
Input Pipeline
def get_input_pipeline(input_ds, bs, crop_fn, augmentation_fn):
ret_ds = (
input_ds
.batch(1) # pre-crop are different dimensions and can't be batched
.map(crop_fn,
num_parallel_calls=tf.data.experimental.AUTOTUNE)
.unbatch()
.batch(bs)
.map(augmentation_fn, # augmentations can be batched though.
num_parallel_calls=tf.data.experimental.AUTOTUNE)
)
return ret_ds
# dataset loading
def load_imagenette():
train_ds, ds_info = tfds.load('imagenette/320px', split='train', as_supervised=True, with_info=True)
valid_ds = tfds.load('imagenette/320px', split='validation', as_supervised=True)
return train_ds, valid_ds, valid_ds, ds_info.features['label'].num_classes
# pipeline construction
train_ds, valid_ds, test_ds, num_classes = load_imagenette()
# datasets used for training (notice that I use prefetch here)
train_samples = get_input_pipeline(train_ds, BS, train_crop_fn, train_preproc_fn).prefetch(tf.data.experimental.AUTOTUNE)
valid_samples = get_input_pipeline(valid_ds, BS, eval_crop_fn, eval_preproc_fn).prefetch(tf.data.experimental.AUTOTUNE)
test_samples = get_input_pipeline(test_ds, BS, eval_crop_fn, eval_preproc_fn).prefetch(tf.data.experimental.AUTOTUNE)
Training and Profiling
I use tensorboard profiler to check the second batch size and I get a warning that this is highly input bound, with about 40% of processing wasted on inputs.
For a classic resnet18 model, you can drive the batch size up to 768 without getting a OOM error, which is what I use. A single step with bs 256 takes about 2-3 seconds.
I also get a warning that on_train_batch_size_end is slow, at around ~1.5 seconds, compared to the 1s batch time.
The model training code is very simple keras:
model.fit(
train_samples,
validation_data=valid_samples,
epochs=100,
batch_size=BS,
use_multiprocessing=True
callbacks=[tensorboard_callback, model_checkpoint_callback, early_stop_callback, reduce_lr_callback]
)
and the callbacks are specified as:
log_dir = os.path.join(os.getcwd(), 'logs')
tensorboard_callback = TensorBoard(log_dir=log_dir, update_freq="epoch", profile_batch=2)
reduce_lr_callback = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=5, min_lr=0.001, verbose=1)
model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath='model.{epoch:02d}-{val_loss:.4f}.h5',
monitor='val_loss',
verbose=1,
save_best_only=True)
early_stop_callback = keras.callbacks.EarlyStopping(monitor='val_loss', patience=15)
Lastly, here are some sample tensorboard profiling screenshots. I can't figure out how to make this run faster:
So heres my question, I have a big 3dim array which is 100GB in size as a #zarr file (the array is more than twice the size). I have tried using the histogram from #Dask to calculate but I get an error saying that it cant do it because the file has tuples within tuples. Im guessing thats the zarr file formate rather than anything else?
any thoughts?
edit:
yes the bigger computer thing wouldnt actually work...
Im running a dask client on a single machine, it runsthe calculation but just gets stuck somewhere.
I just tried dask.map function across the file but when I plot it out I get something like this:
ValueError: setting an array element with a sequence.
heres a version of the script:
def histo(img):
return da.histogram(img, bins=255, range=[0, 255])
histo_1 = da.map_blocks(histo, fimg)
I am actually going to try and use it out side of the map function. I wonder rather than the map funtion, does the windowing from map blocks, actually cause the issue. well, ill let you know if it is or now....
edit 2
So I tried to remove the map blocks function as suggested and this was my result:
[in] h, bins =da.histogram(fused_crop, bins=255, range=[0, 255])
[in] bins
[out] array([ 0., 1., 2., 3., 4., 5., 6., 7., 8., 9., 10.,
11., 12., 13., 14., 15., 16., 17., 18., 19., 20., 21.,
22., 23., 24., 25., 26., 27., 28., 29., 30., 31., 32.,
33., 34., 35., 36., 37., 38., 39., 40., 41., 42., 43.,
44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54.,
55., 56., 57., 58., 59., 60., 61., 62., 63., 64., 65.,
66., 67., 68., 69., 70., 71., 72., 73., 74., 75., 76.,
77., 78., 79., 80., 81., 82., 83., 84., 85., 86., 87.,
88., 89., 90., 91., 92., 93., 94., 95., 96., 97., 98.,
99., 100., 101., 102., 103., 104., 105., 106., 107., 108., 109.,
110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.,
121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,
132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142.,
143., 144., 145., 146., 147., 148., 149., 150., 151., 152., 153.,
154., 155., 156., 157., 158., 159., 160., 161., 162., 163., 164.,
165., 166., 167., 168., 169., 170., 171., 172., 173., 174., 175.,
176., 177., 178., 179., 180., 181., 182., 183., 184., 185., 186.,
187., 188., 189., 190., 191., 192., 193., 194., 195., 196., 197.,
198., 199., 200., 201., 202., 203., 204., 205., 206., 207., 208.,
209., 210., 211., 212., 213., 214., 215., 216., 217., 218., 219.,
220., 221., 222., 223., 224., 225., 226., 227., 228., 229., 230.,
231., 232., 233., 234., 235., 236., 237., 238., 239., 240., 241.,
242., 243., 244., 245., 246., 247., 248., 249., 250., 251., 252.,
253., 254., 255.])
[in] h.compute
[out] <bound method DaskMethodsMixin.compute of dask.array<sum-aggregate, shape=(255,), dtype=int64, chunksize=(255,), chunktype=numpy.ndarray>>
im going to try in another notebook and see if it still occurs.
edit 3
its the stranges thing, but if I just declare the variable h, it comes out as one small element from the dask array?
edit
Strange, if i call the xarray.hist or the da.hist function, they both fall over. If I use the skimage.exposure.histogram it works but it appears that the zarr file is unpacked before the histogram is a calculated. Which is a bit of a problem...
Update 7th June 2020 (with a solution for not big but annoyingly medium data) see below for answer.
You probably want to use dask's function for this rather than map_blocks. For the latter, Dask expects the output of each call to be the same size as the input block, or a shape derived from the input block, instead of the one-dimensional fixed-size output of histogram.
h, bins =da.histogram(fused_crop, bins=255, range=[0, 255])
h.compute()
Update 7th June 2020 (with a solution for not big but annoyingly medium data):
So unfortunately I got a bit ill around this time and it took a while for me to feel a bit better. Then the pandemic happened and I was on full childcare duty. I tried lots of different option and what ultimately, this looked like was that the following:
1) if just using x.compute, the memory would very quickly fill up.
2) Using distributed would fill the hard drive with spill to disk and take hours but would hang and crash and not do anything because...it would compute (im guessing here but based on the graph and dask api) it would create a sub histogram array for every chunk... that would all need to be merged at some point.
3) The chunking of my data was sub optimal so the amount of tasks was massive but even then I couldn't compute a histogram when i improved the chunking.
In the end I looked for a dynamic way of updating the histogram data. So I used Zarr to do it, by computing to it. Since it allows conccurrent reads and writing functions. As a reminder : my data is a zarr array in 3 dims x,y,z and uncompressed 300GB but compressed it's about 100GB. On my 4 yr old laptop with 16GB of ram using the following worked (I should have said my data was 16 bit unsigned:
imgs = da.from_zarr(.....)
imgs2 = imgs.rechunk((a,b,c)) ## individual chunk dim per dim
h, bins = da.histogram(imgs2, bins = 255, range=[0, 65535]) # binning to 256
h_out = da.to_zarr(h, "histogram.zarr")
I ran the progress bar alongside the process and to get a histogram from file took :
[########################################] | 100% Completed | 18min 47.3s
Which I dont think is too bad for a 300GB array. Hopefully this helps someone else as well, thanks for the help earlier in the year #mdurant.