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gobbo_a
2024-06-10 10:44:16 +02:00
parent 9256184430
commit 3093557c99
79 changed files with 2747 additions and 189 deletions
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script/cpython/Ophyd.py
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from ophyd.sim import det1, det2, det3, det4, det, motor, motor1, motor2, motor3,img, sig, direct_img, pseudo1x3
from ophyd import Signal
from ophyd.signal import EpicsSignal
def read(name):
return globals()[name].read()
script/cpython/TestData.py
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###################################################################################################
#Data Manipulation: Using the data access API to generate and retrieve data
###################################################################################################
#Creating a 1D dataset from an array
path="group/data1"
data1d = [1.0, 2.0, 3.0, 4.0, 5.0]
save_dataset(path, data1d)
#Reading ii back
read =load_data(path)
print (list(read))
assert data1d==list(read)
plot(read)
#Creating a 2D dataset from an array with some attributes
data2d = [ [1.0, 2.0, 3.0, 4.0, 5.0], [2.0, 3.0, 4.0, 5.0, 6.0, ], [3.0, 4.0, 5.0, 6.0, 7.0]]
path="group/data2"
save_dataset(path, data2d)
set_attribute(path, "AttrString", "Value")
set_attribute(path, "AttrInteger", 1)
set_attribute(path, "AttrDouble", 2.0)
set_attribute(path, "AttrBoolean", True)
#Reading it back
read =load_data(path)
print ([list(a) for a in read])
plot(read)
#Creating a 3D dataset from an array
data3d = [ [ [1,2,3,4,5], [2,3,4,5,6], [3,4,5,6,7]], [ [3,2,3,4,5], [4,3,4,5,6], [5,4,5,6,7]]]
path="group/data3"
save_dataset(path, data3d)
#Reading it back
read =load_data(path,0)
print ([list(a) for a in read])
read =load_data(path,1)
print ([list(a) for a in read])
#Creating a INT dataset adding elements one by one
path = "group/data4"
create_dataset(path, 'i')
for i in range(10):
append_dataset(path,i)
#Creating a 2D data FLOAT dataset adding lines one by one
path = "group/data5"
create_dataset(path, 'd', False, (0,0))
for row in data2d:
append_dataset(path, row)
#Creating a Table (compund type)
path = "group/data6"
names = ["a", "b", "c", "d"]
table_types = ["d", "d", "d", "[d"]
lenghts = [0,0,0,5]
table = [ [1,2,3,[0,1,2,3,4]],
[2,3,4,[3,4,5,6,7]],
[3,4,5,[6,7,8,9,4]] ]
create_table(path, names, table_types, lenghts)
for row in table:
append_table(path, row)
flush_data()
#Read it back
read =load_data(path)
print (read)
#Writing scalars (datasets with rank 0)
save_dataset("group/val1", 1)
save_dataset("group/val2", 3.14)
save_dataset("group/val3", "test")
print (load_data("group/val1"))
print (load_data("group/val2"))
print (load_data("group/val3"))
script/cpython/TestInterrupt.py
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import threading
from threading import Thread
from _thread import interrupt_main
import sys
# task executed in a new thread
def task():
# block for a moment
sleep(3)
# interrupt the main thread
print('Interrupting main thread now')
interrupt_main()
# start the new thread
thread = Thread(target=task)
thread.start()
print(threading.current_thread() == threading.main_thread())
# handle being interrupted
try:
# wait around
while True:
print('Main thread waiting...')
sleep(0.5)
except KeyboardInterrupt:
# terminate main thread
print('Main interrupted! Exiting.')
script/cpython/gfitoff.py
+7 -1
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@@ -27,4 +27,10 @@ def gfitoff(x, y, off=None, amp=None, com=None, sigma=None):
def gauss_fn(x, a, b, c, d):
return a + b * np.exp(-(np.power((x - c), 2) / (2 * np.power(d, 2))))
return a + b * np.exp(-(np.power((x - c), 2) / (2 * np.power(d, 2))))
"""
x=np.array([-200.30429237268825, -200.2650700434188, -200.22115208318002, -199.9457671375377, -199.86345548879072, -199.85213073174933, -199.35687977133284, -199.13811861090275, -197.97304970346386, -197.2952215624348, -195.09076092936948, -192.92276048970703, -191.96871876227698, -189.49577852322938, -187.9652790409825, -183.63756456925222, -180.04899765472996, -178.43839623242422, -174.07311671294445, -172.0410133577918, -165.90824309893102, -160.99771795989466, -159.30176653939253, -154.27688897558514, -152.0854103810786, -145.75652847587313, -140.80843828908465, -139.23982133191495, -134.27073891256106, -132.12649284133064, -125.95947209775511, -121.00309550337462, -119.26736932643232, -114.2706655484383, -112.07393889578914, -105.72295990367157, -100.8088439880125, -99.2034906238494, -94.30042325164636, -92.15010048151461, -85.92203653534293, -81.03913275494665, -79.27412793784428, -74.33487658582118, -72.06274362408762, -65.76562628131825, -60.91255356825276, -59.20334389560392, -54.33286972659312, -52.19387171350535, -45.94978737932291, -41.03014719193582, -39.301602568238906, -34.35572209014114, -32.04464301272608, -25.8221033382824, -20.922074315528747, -19.21590299233186, -14.31090212502093, -12.217203140101386, -5.9283722049240435, -0.9863587170369246, 0.7408048387279834, 5.71126832601389, 7.972628957879352, 14.204559894256546, 19.11839959633025, 20.8218087836657, 25.678748486941828, 27.822718344586864, 34.062659474970715, 38.9745656819391, 40.77409719734158, 45.72080631619803, 47.974156754056835, 54.23453768983539, 59.12020360609568, 60.77306570712026, 65.70734521458867, 67.8344660434617, 74.03187028154134, 78.96532114824849, 80.76070945985495, 85.74802197591286, 87.9140889204674, 94.18082276873524, 99.25790470037091, 100.68454787413205, 105.7213026221542, 107.79483801526698, 113.99555681638138, 119.0707052529143, 120.72715813056156, 125.77551384921307, 127.91257836719551, 134.2011330887875, 139.23043006997628, 140.71673537840158, 145.76288138835983, 147.80216629676042, 154.06420451405637, 159.0846626604798, 160.76183155710717, 165.73699067536242, 167.9265357747636, 173.96705069576544, 178.2522282751915, 179.9042617354548, 183.54586165856657, 185.23269803071796, 189.41678143751972, 191.87149157986588, 192.8741468985015, 195.0241934550453, 195.966634211846, 197.9821647518146, 198.99006812859284, 199.33202054855676, 199.91897441965887, 200.11536227958896, 200.22280936469997, 200.25181179127208])
y=np.array([11.0, 6.0, 8.0, 5.0, 11.0, 7.0, 18.0, 11.0, 12.0, 10.0, 8.0, 6.0, 16.0, 4.0, 12.0, 9.0, 15.0, 14.0, 8.0, 20.0, 15.0, 8.0, 9.0, 11.0, 13.0, 12.0, 13.0, 15.0, 13.0, 20.0, 10.0, 7.0, 17.0, 11.0, 20.0, 13.0, 13.0, 23.0, 14.0, 10.0, 17.0, 15.0, 20.0, 16.0, 14.0, 13.0, 18.0, 22.0, 9.0, 20.0, 12.0, 14.0, 17.0, 19.0, 14.0, 14.0, 23.0, 19.0, 15.0, 20.0, 20.0, 21.0, 20.0, 23.0, 22.0, 15.0, 10.0, 17.0, 21.0, 15.0, 23.0, 23.0, 25.0, 18.0, 16.0, 21.0, 22.0, 16.0, 16.0, 14.0, 19.0, 20.0, 18.0, 20.0, 23.0, 13.0, 16.0, 20.0, 25.0, 15.0, 15.0, 17.0, 22.0, 26.0, 19.0, 30.0, 25.0, 17.0, 17.0, 23.0, 16.0, 27.0, 21.0, 21.0, 26.0, 27.0, 21.0, 17.0, 20.0, 20.0, 21.0, 19.0, 25.0, 19.0, 13.0, 23.0, 20.0, 20.0, 18.0, 20.0, 19.0, 25.0])
[off, amp, com, sigma] = gfitoff(x, y, off=None, amp=None, com=None, sigma=None)
print ([off, amp, com, sigma])
"""
script/cpython/linfit.py
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@@ -19,14 +19,39 @@ def test():
return np.ones(5)
def test2(name, x=None, y=None):
def test2(name, x=None, y=None, **kwargs):
print (name,x,y)
ret = y*x
print (ret)
print(kwargs.get("z", 0.0))
return ret
def add(x,y,z):
return x+y+z
def read_dev(dev):
return dev.read()
return dev.read()
def print_dict(d):
for k in d.keys():
print (k, d[k])
ret = {}
ret.update(d)
return ret
def get_tuple():
return (1,2,3)
#x=np.array([0,1,2,3,4,5,6,7,8,9])
#y=np.array([1,2,3,6,9,6,3,2,1,0])
#print( linfit(x,y) )
#x=(0,1,2,3,4,5,6,7,8,9)
#y=(1,2,3,6,9,6,3,2,1,0)
#print( linfit(x,y) )
#print_dict({"a":1, "b":2})
script/cpython/test2.py
+14 -9
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@@ -1,14 +1,19 @@
from jeputils import import_py
import matplotlib
matplotlib.use('TkAgg')
import_py("cpython/gfitoff", "gfitoff")
import matplotlib.pyplot as plt
from mathutils import Gaussian
x=to_array([-200.30429237268825, -200.2650700434188, -200.22115208318002, -199.9457671375377, -199.86345548879072, -199.85213073174933, -199.35687977133284, -199.13811861090275, -197.97304970346386, -197.2952215624348, -195.09076092936948, -192.92276048970703, -191.96871876227698, -189.49577852322938, -187.9652790409825, -183.63756456925222, -180.04899765472996, -178.43839623242422, -174.07311671294445, -172.0410133577918, -165.90824309893102, -160.99771795989466, -159.30176653939253, -154.27688897558514, -152.0854103810786, -145.75652847587313, -140.80843828908465, -139.23982133191495, -134.27073891256106, -132.12649284133064, -125.95947209775511, -121.00309550337462, -119.26736932643232, -114.2706655484383, -112.07393889578914, -105.72295990367157, -100.8088439880125, -99.2034906238494, -94.30042325164636, -92.15010048151461, -85.92203653534293, -81.03913275494665, -79.27412793784428, -74.33487658582118, -72.06274362408762, -65.76562628131825, -60.91255356825276, -59.20334389560392, -54.33286972659312, -52.19387171350535, -45.94978737932291, -41.03014719193582, -39.301602568238906, -34.35572209014114, -32.04464301272608, -25.8221033382824, -20.922074315528747, -19.21590299233186, -14.31090212502093, -12.217203140101386, -5.9283722049240435, -0.9863587170369246, 0.7408048387279834, 5.71126832601389, 7.972628957879352, 14.204559894256546, 19.11839959633025, 20.8218087836657, 25.678748486941828, 27.822718344586864, 34.062659474970715, 38.9745656819391, 40.77409719734158, 45.72080631619803, 47.974156754056835, 54.23453768983539, 59.12020360609568, 60.77306570712026, 65.70734521458867, 67.8344660434617, 74.03187028154134, 78.96532114824849, 80.76070945985495, 85.74802197591286, 87.9140889204674, 94.18082276873524, 99.25790470037091, 100.68454787413205, 105.7213026221542, 107.79483801526698, 113.99555681638138, 119.0707052529143, 120.72715813056156, 125.77551384921307, 127.91257836719551, 134.2011330887875, 139.23043006997628, 140.71673537840158, 145.76288138835983, 147.80216629676042, 154.06420451405637, 159.0846626604798, 160.76183155710717, 165.73699067536242, 167.9265357747636, 173.96705069576544, 178.2522282751915, 179.9042617354548, 183.54586165856657, 185.23269803071796, 189.41678143751972, 191.87149157986588, 192.8741468985015, 195.0241934550453, 195.966634211846, 197.9821647518146, 198.99006812859284, 199.33202054855676, 199.91897441965887, 200.11536227958896, 200.22280936469997, 200.25181179127208],'d')
y=to_array([11.0, 6.0, 8.0, 5.0, 11.0, 7.0, 18.0, 11.0, 12.0, 10.0, 8.0, 6.0, 16.0, 4.0, 12.0, 9.0, 15.0, 14.0, 8.0, 20.0, 15.0, 8.0, 9.0, 11.0, 13.0, 12.0, 13.0, 15.0, 13.0, 20.0, 10.0, 7.0, 17.0, 11.0, 20.0, 13.0, 13.0, 23.0, 14.0, 10.0, 17.0, 15.0, 20.0, 16.0, 14.0, 13.0, 18.0, 22.0, 9.0, 20.0, 12.0, 14.0, 17.0, 19.0, 14.0, 14.0, 23.0, 19.0, 15.0, 20.0, 20.0, 21.0, 20.0, 23.0, 22.0, 15.0, 10.0, 17.0, 21.0, 15.0, 23.0, 23.0, 25.0, 18.0, 16.0, 21.0, 22.0, 16.0, 16.0, 14.0, 19.0, 20.0, 18.0, 20.0, 23.0, 13.0, 16.0, 20.0, 25.0, 15.0, 15.0, 17.0, 22.0, 26.0, 19.0, 30.0, 25.0, 17.0, 17.0, 23.0, 16.0, 27.0, 21.0, 21.0, 26.0, 27.0, 21.0, 17.0, 20.0, 20.0, 21.0, 19.0, 25.0, 19.0, 13.0, 23.0, 20.0, 20.0, 18.0, 20.0, 19.0, 25.0],'d')
[off, amp, com, sigma] = gfitoff(x, y, off=None, amp=None, com=None, sigma=None)
print "Fit: ", [off, amp, com, sigma]
g = Gaussian(amp, com, sigma)
plot([y, [g.value(i)+off for i in x]], ["data", "fit"], xdata = x)
fig, ax = plt.subplots()
fruits = ['apple', 'blueberry', 'cherry', 'orange']
counts = [40, 100, 30, 55]
bar_labels = ['red', 'blue', '_red', 'orange']
bar_colors = ['tab:red', 'tab:blue', 'tab:red', 'tab:orange']
ax.bar(fruits, counts, label=bar_labels, color=bar_colors)
ax.set_ylabel('fruit supply')
ax.set_title('Fruit supply by kind and color')
ax.legend(title='Fruit color')
plt.show()