Cristallina/cristallina_analysis_package
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Addded run number from scan and scan from run number functions

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vonka_j
2023-02-08 12:07:57 +01:00
parent 2d0f076809
commit a42cb07cf5
3 changed files with 81 additions and 10 deletions
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5
src/cristallina/__init__.py
View File

@@ -14,3 +14,8 @@ except PackageNotFoundError: # pragma: no cover
__version__ = "unknown"
finally:
del version, PackageNotFoundError
from . import utils
from . import plot
from . import analysis

46
src/cristallina/analysis.py
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@@ -176,9 +176,10 @@ def get_contrast_images(
)
def fit_2d_gaussian(image, roi: Optional[ROI] = None):
def fit_2d_gaussian(image, roi: Optional[ROI] = None, plot=False):
"""
2D Gaussian fit using LMFit for a given image and an optional region of interest.
2D Gaussian fit using LMFit for a given image and an optional region of interest.
plot=True as optional argument plots the fit results.
Returns the x, y coordinates of the center and the results object which contains
further fit statistics.
@@ -218,5 +219,46 @@ def fit_2d_gaussian(image, roi: Optional[ROI] = None):
else:
center_x = result.params["centerx"].value
center_y = result.params["centery"].value
if plot == True:
X, Y = np.meshgrid(np.arange(len_y), np.arange(len_x))
Z = griddata((x, y), z, (X, Y), method='linear', fill_value=0)
fig, axs = plt.subplots(2, 2, figsize=(10, 10))
# vmax = np.nanpercentile(Z, 99.9)
vmax = np.max(Z)
ax = axs[0, 0]
art = ax.pcolor(X, Y, Z, vmin=0, vmax=vmax, shading='auto')
plt.colorbar(art, ax=ax, label='z')
ax.set_title('Data')
ax = axs[0, 1]
fit = model.func(X, Y, **result.best_values)
art = ax.pcolor(X, Y, fit, vmin=0, vmax=vmax, shading='auto')
plt.colorbar(art, ax=ax, label='z')
ax.set_title('Fit')
ax = axs[1, 0]
fit = model.func(X, Y, **result.best_values)
art = ax.pcolor(X, Y, Z-fit, vmin=0, shading='auto')
plt.colorbar(art, ax=ax, label='z')
ax.set_title('Data - Fit')
ax = axs[1, 1]
fit = model.func(X, Y, **result.best_values)
art = ax.pcolor(X, Y, fit, vmin=0, vmax=vmax, shading='auto')
ax.contour(X, Y, fit, 8, colors='r',alpha=0.4)
plt.colorbar(art, ax=ax, label='z')
ax.set_title('Data & Fit')
for ax in axs.ravel():
ax.set_xlabel('x')
ax.set_ylabel('y')
plt.suptitle('2D Gaussian fit results')
plt.tight_layout()
plt.show()
return center_x, center_y, result

40
src/cristallina/utils.py
View File

@@ -24,6 +24,24 @@ def scan_info(run_number, base_path=None, small_data=True):
return scan
def get_scan_from_run_number_or_scan(run_number_or_scan,small_data=True):
"""Returns SFScanInfo object from run number or SFScanInfo object (then just passes that to output)"""
if type(run_number_or_scan) == SFScanInfo:
scan = run_number_or_scan
else:
scan = scan_info(run_number_or_scan,small_data=small_data)
return scan
def get_run_number_from_run_number_or_scan(run_number_or_scan,small_data=True):
"""Returns run number from run number or SFScanInfo object"""
if type(run_number_or_scan) == int:
rn = run_number_or_scan
elif type(run_number_or_scan) == SFScanInfo:
rn = int(str(run_number_or_scan.fs)[-19:-15])
else:
raise ValueError("Input must be an int or SFScanInfo object")
return rn
def channel_names(run_number,verbose=False):
"""Prints channel names for a given run_number or scan object"""
if type(run_number) == SFScanInfo:
@@ -79,16 +97,22 @@ def print_run_info(
break
def process_run(run_number, rois,detector='JF16T03V01', calculate =None, only_shots=slice(None), n_jobs=cpu_count()):
def process_run(run_number, rois,detector='JF16T03V01', calculate =None, only_shots=slice(None), n_jobs=cpu_count()-2):
"""Process rois for a given detector. Save the results small data in the res/small_data/run...
By default only sum of rois is calculated, [mean,std,img] can be added to the "calculate" optional parameter.
"""
# Load scan object with SFScanInfo
scan = scan_info(run_number,small_data=False)
rn = get_run_number_from_run_number_or_scan(run_number)
# Load scan object with SFScanInfo
scan = scan_info(rn,small_data=False)
# Make the small data folder if it doesn't exist
if not os.path.exists( heuristic_extract_smalldata_path() ):
os.mkdir( heuristic_extract_smalldata_path() )
# Set the path for later small data saving
path_with_run_folder = heuristic_extract_smalldata_path()+'run'+str(run_number).zfill(4)
# Make the small data run folder if it doesn't exist
if not os.path.exists( path_with_run_folder ):
os.mkdir( path_with_run_folder )
@@ -117,8 +141,8 @@ def process_run(run_number, rois,detector='JF16T03V01', calculate =None, only_sh
det_pids = data[detector].pids
sd[roi.name] = det_pids[only_shots], data[detector][only_shots, bottom:top,left:right].sum(axis=(1, 2))
if calculate:
if 'mean' in calculate:
sd[roi.name+"_mean"] = (det_pids[only_shots], data[detector][only_shots, bottom:top,left:right].mean(axis=(1, 2)))
if 'means' in calculate:
sd[roi.name+"_means"] = (det_pids[only_shots], data[detector][only_shots, bottom:top,left:right].mean(axis=(1, 2)))
if 'std' in calculate:
sd[roi.name+"_std"] = (det_pids[only_shots], data[detector][only_shots, bottom:top,left:right].std(axis=(1, 2)))
if 'img' in calculate:
@@ -129,8 +153,8 @@ def process_run(run_number, rois,detector='JF16T03V01', calculate =None, only_sh
# These channels have only one dataset per step of the scan, so we take the first pulseID
sd[roi.name + "_info"] =([det_pids[0]], [f"roi {roi.name}: {left},{right}; {bottom},{top} (left, right, bottom, top)"])
sd[roi.name + "_mean_img"] = ([det_pids[0]], [data[detector][:, bottom:top,left:right].mean(axis=(0))] )
sd[roi.name + "_mean_img"] = ([det_pids[0]], [data[detector][only_shots, bottom:top,left:right].mean(axis=(0))] )
sd[roi.name + "_step_sum"] = ([det_pids[0]], [data[detector][only_shots, bottom:top,left:right].sum()] )
Parallel(n_jobs=n_jobs,verbose=10)(delayed(process_step)(i) for i in range(len(scan)))
class ROI: