Add code for 1D detector

[ivan-usov]

@JakHolzer , I created a pull request will all changes grouped on the same branch. This way it will be easier to introduce changes, that might affect all files at the same time. You should be able to push to that branch (det1d) too.

[JakHolzer]

I think it should be fine. The ccl files does not have the headers and there is only one data column, rest is numbers or is calculated, not read. So I think its alright, important think is that you know what is what :)

út 15. 9. 2020 v 16:46 odesílatel Ivan Usov notifications@github.com napsal:

@ivan-usov commented on this pull request.

In pyzebra/load_1D.py https://github.com/paulscherrerinstitute/pyzebra/pull/11#discussion_r488727539 :

• variable = variable.strip()
• if variable in META_VARS_FLOAT:
• det_variables["meta"][variable] = float(value)
• elif variable in META_VARS_STR:
• det_variables["meta"][variable] = str(value)[:-1].strip()
• elif variable in META_UB_MATRIX:
• det_variables["meta"][variable] = re.findall(r"[-+]?\d*.\d+|\d+", str(value))
• if "#data" in line:

• this is the end of metadata and the start of data section

• break

• read data

• if det_variables["file_type"] == "ccl":
• decimal = list()
• data = infile.readlines()

Btw, that commit also renames "omega" into "om", and "counts" into "Counts", that is consistent with the column names in dat files. I hope it doesn't break you other code, otherwise let me know what names you prefer, it will be easy to fix, e.g. with

row_names = next(infile).split()

row_names = ["NP", "omega", "counts", ...]

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[JakHolzer]

Well, the structure of the files seems very uniform and aligned, so I wanted to keep that. I have never worked with the software that uses it, so I'm not sure how it reads the data. My suggestion is to leave it like this and we can ask Oksana to try load one file with padding and second with tabs to see if it works.

st 16. 9. 2020 v 11:27 odesílatel Ivan Usov notifications@github.com napsal:

@ivan-usov commented on this pull request.

In pyzebra/comm_export.py https://github.com/paulscherrerinstitute/pyzebra/pull/11#discussion_r489296559 :

• corr_value = np.abs(value np.sin(ang1) np.cos(ang2))
• return corr_value

• +def export_comm(data, path, lorentz=False):

• """exports data in the *.comm format
• :param lorentz: perform Lorentz correction
• :param path: path to file + name
• :arg data - data to export, is dict after peak fitting
• """
• align = ">"
• if data["meta"]["indices"] == "hkl":
• extension = ".comm"
• padding = [6, 4, 10, 8]

These paddings look artificial to me. Do you think it was just tabs between numbers, and the different padding sizes are because of different lengths of those written numbers?

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[ivan-usov]

@JakHolzer , I would suggest not to extend this PR into parametric study, and merge it rather sooner than later. Do you have changes that are not related to parametric study still to be added here?

[JakHolzer]

None that I can think of at the moment. I think that parametric study is now complete (hopefully), maybe some minor additions such as saving the data if we agree how to do it. Nonetheless Romain would like to fit more peaks, which was not what the ccl code was written for, so there might come some changes into fit2 again if I find a way how to make it generic.

st 14. 10. 2020 v 15:52 odesílatel Ivan Usov notifications@github.com napsal:

@JakHolzer https://github.com/JakHolzer , I would suggest not to extend this PR into parametric study, and merge it rather sooner than later. Do you have changes that are not related to parametric study still to be added here?

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[JakHolzer]

Oh I see, I apologise. Thank you for so throughout correction :)

po 19. 10. 2020 v 15:44 odesílatel Ivan Usov notifications@github.com napsal:

@ivan-usov commented on this pull request.

In pyzebra/load_1D.py https://github.com/paulscherrerinstitute/pyzebra/pull/11#discussion_r507738757 :

@@ -165,7 +165,15 @@ def parse_1D(fileobj, data_type): for name, val in zip(col_names, line.split()): data_cols[name].append(float(val))

• measurements = dict(data_cols)
• data_cols['h_index'] = float(metadata['title'].split()[-3])
• data_cols['k_index'] = float(metadata['title'].split()[-2])
• data_cols['l_index'] = float(metadata['title'].split()[-1])
• data_cols['temperature'] = metadata['temp']
• data_cols['mag_field'] = metadata['mf']
• data_cols['omega_angle'] = metadata['omega']
• data_cols['number_of_measurements'] = len(data_cols['om'])
• data_cols['monitor'] = data_cols['Monitor1'][0]
• measurements["1"] = dict(data_cols)

The key should be just an integer 1, e.g. measurements[1] = dict(data_cols)

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[JakHolzer]

Well, its not necessary for dat files which actually carry the om array, in ccl its simplified to omega centre, step and number of points, so this is only way of getting it.

po 19. 10. 2020 v 16:12 odesílatel Ivan Usov notifications@github.com napsal:

@ivan-usov commented on this pull request.

In pyzebra/load_1D.py https://github.com/paulscherrerinstitute/pyzebra/pull/11#discussion_r507781946 :

• first line

• measurement_number, *params = line.split()
• for param, (param_name, param_type) in zip(params, ccl_first_line):
• d[param_name] = param_type(param)
• decimal.append(bool(Decimal(d["h_index"]) % 1 == 0))
• decimal.append(bool(Decimal(d["k_index"]) % 1 == 0))
• decimal.append(bool(Decimal(d["l_index"]) % 1 == 0))

• second line

• next_line = next(fileobj)
• params = next_line.split()
• for param, (param_name, param_type) in zip(params, ccl_second_line):
• d[param_name] = param_type(param)
• d["om"] = np.linspace(

OK, I didn't know how this "om" array should be constructed at all. This is clearly dependent on how daq works, so if you know that this array is calculated this way, then it's all good.

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