| Name | parsefire JSON |
| Version |
2024.9.1
JSON |
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| home_page | None |
| Summary | Python Package for consistent data mining from unstructured text documents. |
| upload_time | 2024-09-16 19:00:04 |
| maintainer | None |
| docs_url | None |
| author | Michel Fodje |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
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| VCS |
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| bugtrack_url |
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| requirements |
No requirements were recorded.
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# `parsefire`
## Overview
The `parsefire` package is a python package for consistent data mining from unstructured text documents.
It provides tools for parsing text files based on specified lexicons. It supports parsing simple fields, nested sections,
tables, and custom regex patterns. The parsing specifications are defined in a lexicon, which is a dictionary of
containing fields. Fields are specified using a simple syntax that includes the field name, type and optionally field
size and even custom regular expressions. The package the uses the specification to generate regular expressions
for parsing the text and converting the parsed data into a dictionary of the specified type.
Specifications can be written into YAML files and loaded for parsing by the `TextParser` class.
For example, the following lexicon specifies two fields, `age` and `name`, which are parsed from the text `Age: 25\nName: John Doe`.
As fields in a YAML file:
```yaml
fields:
- "Age: <int:age>"
- "Name: <str:name>"
```
As a python dictionary:
```python
specs = {
'fields': [
"Age: <int:age>",
"Name: <str:name>"
]
}
```
## Modules
### `parsefire.parser`
#### Functions
- **`parse_file(data_file: Union[str, Path], specs: dict, size: int = -1) -> dict`**
Parses a text file and returns a dictionary of matched key-value pairs.
**Parameters:**
- `data_file`: The file path or name.
- `specs`: A nested dictionary of specifications.
- `size`: Maximum size of the file to parse in bytes (default is -1, which means no limit).
**Returns:**
- A nested dictionary of key-value pairs.
- **`parse_text(specs: dict, text: str) -> dict`**
Parses the given text using the provided specifications.
**Parameters:**
- `specs`: A dictionary of specifications.
- `text`: The text to parse.
**Returns:**
- A dictionary of parsed key-value pairs.
#### Classes
- **`TextParser`**
A class for parsing text files using a lexicon.
**Class Methods:**
- `parse(cls, filename: str, silent=False) -> dict`
- Parses the provided file and returns a dictionary.
- **Parameters:**
- `filename`: The text file to parse.
- `silent`: If `True`, returns an empty dictionary instead of throwing exceptions.
- **Returns:**
- A dictionary of parsed key-value pairs.
- `parse_text(cls, text: str, lexicon: dict) -> Any`
- Parses the given text using the lexicon dictionary.
- **Parameters:**
- `text`: The text to parse.
- `lexicon`: The lexicon specification dictionary.
- **Returns:**
- Parsed data.
- `get_lexicon(cls, filename) -> dict`
- Returns the lexicon specified for a given file.
- **Parameters:**
- `filename`: The name of the file.
- **Returns:**
- A dictionary of lexicon specifications.
- **`MissingLexicon`**
Exception raised when a lexicon is missing.
- **`FilesMissing`**
Exception raised when files are missing.
## Usage Examples
### Parsing Simple Fields
```python
from parsefire.parser import parse_text
specs = {
'fields': [
"Age: <int:age>",
"Name: <str:name>"
]
}
text = "Age: 25\nName: John Doe"
result = parse_text(specs, text)
print(result) # Output: {'age': 25, 'name': 'John Doe'}
```
### Parsing Nested Sections
```python
from parsefire.parser import parse_text
specs = {
'sections': {
'person': {
'fields': [
"Age: <int:age>",
"Name: <str:name>"
]
}
}
}
text = "Age: 25\nName: John Doe"
result = parse_text(specs, text)
print(result) # Output: {'person': {'age': 25, 'name': 'John Doe'}}
```
### Parsing Tables
```python
from parsefire.parser import parse_text
specs = {
'table': "Row: <int:id> <str:name>"
}
text = "Row: 1 John\nRow: 2 Jane"
result = parse_text(specs, text)
print(result) # Output: [{'id': 1, 'name': 'John'}, {'id': 2, 'name': 'Jane'}]
```
### Parsing with Custom Regex
```python
from parsefire.parser import parse_text
specs = {
'fields': [
r"Code: <str:code:([A-Z]{3}-\d{3})>"
]
}
text = "Code: ABC-123"
result = parse_text(specs, text)
print(result) # Output: {'code': 'ABC-123'}
```
### Parsing with Domains
A domain is a section of text that is matched by a regular expression. The domain is used to extract the text that will be parsed.
```python
from parsefire.parser import parse_text
specs = {
'domain': r"Inside:(.*?)(?=Outside|$)",
'fields': [
"Age: <int:age>",
"Name: <str:name>"
]
}
text = (
"Inside:\n"
"Age: 25\n"
"Name: John Doe\n"
"Outside:\n"
"Age: 30\n"
"Name: Jane Doe"
)
result = parse_text(specs, text)
print(result) # Output: {'age': 25, 'name': 'John Doe'}
```
### A more complex example
Here is an example of how to parse a text file with multiple sections and tables.
```yaml
root:
sections:
quality:
domain: "REFINEMENT OF DIFFRACTION PARAMETERS USING ALL IMAGES(.+?)THE DATA COLLECTION STATISTICS REPORTED BELOW ASSUMES"
fields:
- " STANDARD DEVIATION OF SPOT POSITION (PIXELS) <float:pixel_error>"
- " STANDARD DEVIATION OF SPINDLE POSITION (DEGREES) <float:angle_error>"
- " CRYSTAL MOSAICITY (DEGREES) <float:mosaicity>"
statistics:
domain: "STATISTICS OF SAVED DATA SET .*? WITH SIGNAL/NOISE >= -3.0(.+?)NUMBER OF REFLECTIONS IN SELECTED"
table: " <float:shell> <int:observed> <int:unique> <int:possible> <float:completeness>% <float:r_obs>% <float:r_exp>% <int:compared> <float:i_sigma> <float:r_meas>% <float:cc_half><char:signif> <int:cor_ano><char:asignif> <float:sig_ano> <int:Nano>"
summary:
domain: "STATISTICS OF SAVED DATA SET .*? WITH SIGNAL/NOISE >= -3.0(.+?)WILSON STATISTICS OF DATA SET"
fields:
- " total <int:observed> <int:unique> <int:possible> <float:completeness>% <float:r_obs>% <float:r_exp>% <int:compared> <float:i_sigma> <float:r_meas>% <float:cc_half><char:signif> <int:cor_ano><char:asignif> <float:sig_ano> <int:Nano>"
- " NUMBER OF REFLECTIONS IN SELECTED SUBSET OF IMAGES <int:reflections>"
- " NUMBER OF SYSTEMATIC ABSENT REFLECTIONS <int:absent>"
- " NUMBER OF REJECTED MISFITS <int:misfits>"
```
And here is a snippet of the corresponding text file:
```
******************************************************************************
MEAN DISCREPANCIES BETWEEN OBSERVED AND CALCULATED SPOT LOCATIONS
******************************************************************************
The discrepancies in X- and Y-coordinates of the spots are depicted in the
two images DX-CORRECTIONS.cbf and DY-CORRECTIONS.cbf for inspection with
the XDS-Viewer.
******************************************************************************
REFINEMENT OF DIFFRACTION PARAMETERS USING ALL IMAGES
******************************************************************************
REFINED VALUES OF DIFFRACTION PARAMETERS DERIVED FROM 50627 INDEXED SPOTS
REFINED PARAMETERS: POSITION BEAM AXIS ORIENTATION CELL
STANDARD DEVIATION OF SPOT POSITION (PIXELS) 1.99
STANDARD DEVIATION OF SPINDLE POSITION (DEGREES) 0.43
SPACE GROUP NUMBER 1
UNIT CELL PARAMETERS 57.578 58.086 148.620 89.360 89.832 89.615
E.S.D. OF CELL PARAMETERS 1.3E-01 1.6E-01 3.5E-01 1.7E-01 6.1E-02 9.4E-02
REC. CELL PARAMETERS 0.017368 0.017217 0.006729 90.639 90.164 90.383
COORDINATES OF UNIT CELL A-AXIS -12.315 34.160 44.684
COORDINATES OF UNIT CELL B-AXIS -37.590 -39.382 20.249
COORDINATES OF UNIT CELL C-AXIS 107.768 -64.427 79.517
CRYSTAL MOSAICITY (DEGREES) 0.301
LAB COORDINATES OF ROTATION AXIS 0.999999 0.001263 -0.000442
DIRECT BEAM COORDINATES (REC. ANGSTROEM) 0.001820 0.001247 0.806579
DETECTOR COORDINATES (PIXELS) OF DIRECT BEAM 1279.44 1261.60
DETECTOR ORIGIN (PIXELS) AT 1276.19 1259.38
CRYSTAL TO DETECTOR DISTANCE (mm) 247.52
LAB COORDINATES OF DETECTOR X-AXIS 1.000000 0.000000 0.000000
LAB COORDINATES OF DETECTOR Y-AXIS 0.000000 1.000000 0.000000
...
******************************************************************************
SUMMARY OF DATA SET STATISTICS FOR IMAGE DATA_RANGE= 1 201
******************************************************************************
COMPLETENESS AND QUALITY OF DATA SET
------------------------------------
R-FACTOR
observed = (SUM(ABS(I(h,i)-I(h))))/(SUM(I(h,i)))
expected = expected R-FACTOR derived from Sigma(I)
COMPARED = number of reflections used for calculating R-FACTOR
I/SIGMA = mean of intensity/Sigma(I) of unique reflections
(after merging symmetry-related observations)
Sigma(I) = standard deviation of reflection intensity I
estimated from sample statistics
R-meas = redundancy independent R-factor (intensities)
Diederichs & Karplus (1997), Nature Struct. Biol. 4, 269-275.
CC(1/2) = percentage of correlation between intensities from
random half-datasets. Correlation significant at
the 0.1% level is marked by an asterisk.
Karplus & Diederichs (2012), Science 336, 1030-33
Anomal = percentage of correlation between random half-sets
Corr of anomalous intensity differences. Correlation
significant at the 0.1% level is marked.
SigAno = mean anomalous difference in units of its estimated
standard deviation (|F(+)-F(-)|/Sigma). F(+), F(-)
are structure factor estimates obtained from the
merged intensity observations in each parity class.
Nano = Number of unique reflections used to calculate
Anomal_Corr & SigAno. At least two observations
for each (+ and -) parity are required.
SUBSET OF INTENSITY DATA WITH SIGNAL/NOISE >= -3.0 AS FUNCTION OF RESOLUTION
RESOLUTION NUMBER OF REFLECTIONS COMPLETENESS R-FACTOR R-FACTOR COMPARED I/SIGMA R-meas CC(1/2) Anomal SigAno Nano
LIMIT OBSERVED UNIQUE POSSIBLE OF DATA observed expected Corr
4.62 4309 3073 10593 29.0% 463.8% 1726.2% 2472 0.14 655.9% 2.1 0 0.000 0
3.27 7484 6163 19118 32.2% 126.4% 169.2% 2642 0.29 178.8% -9.3 0 0.000 0
2.67 9554 8499 24772 34.3% 120.0% 159.2% 2110 0.29 169.7% -0.5 0 0.000 0
2.32 11329 10542 29236 36.1% 126.2% 180.5% 1574 0.23 178.5% -7.2 0 0.000 0
2.07 12693 12240 33223 36.8% 125.2% 169.5% 906 0.21 177.1% -9.3 0 0.000 0
1.89 13718 13520 36735 36.8% 159.8% 393.6% 396 0.16 226.0% -6.2 0 0.000 0
1.75 15033 14992 39873 37.6% 216.9% 846.8% 82 0.14 306.8% 5.2 0 0.000 0
1.64 9949 9947 42876 23.2% 92.6% 93.6% 4 0.05 130.9% 0.0 0 0.000 0
1.55 3810 3810 45658 8.3% -99.9% -99.9% 0 0.00 -99.9% 0.0 0 0.000 0
total 87879 82786 282084 29.3% 148.8% 283.9% 10186 0.17 210.4% -0.2 0 0.000 0
NUMBER OF REFLECTIONS IN SELECTED SUBSET OF IMAGES 87879
NUMBER OF REJECTED MISFITS 0
NUMBER OF SYSTEMATIC ABSENT REFLECTIONS 0
NUMBER OF ACCEPTED OBSERVATIONS 87879
NUMBER OF UNIQUE ACCEPTED REFLECTIONS 82786
******************************************************************************
SUMMARY OF DATA SET STATISTICS FOR IMAGE DATA_RANGE= 1 401
******************************************************************************
```
Raw data
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"description": "# `parsefire`\n\n## Overview\n\nThe `parsefire` package is a python package for consistent data mining from unstructured text documents. \nIt provides tools for parsing text files based on specified lexicons. It supports parsing simple fields, nested sections, \ntables, and custom regex patterns. The parsing specifications are defined in a lexicon, which is a dictionary of \ncontaining fields. Fields are specified using a simple syntax that includes the field name, type and optionally field\nsize and even custom regular expressions. The package the uses the specification to generate regular expressions\nfor parsing the text and converting the parsed data into a dictionary of the specified type.\n\nSpecifications can be written into YAML files and loaded for parsing by the `TextParser` class.\n\nFor example, the following lexicon specifies two fields, `age` and `name`, which are parsed from the text `Age: 25\\nName: John Doe`.\nAs fields in a YAML file:\n\n```yaml\nfields:\n - \"Age: <int:age>\"\n - \"Name: <str:name>\"\n```\n\nAs a python dictionary:\n\n```python\nspecs = {\n 'fields': [\n \"Age: <int:age>\",\n \"Name: <str:name>\"\n ]\n}\n```\n\n\n## Modules\n\n### `parsefire.parser`\n\n#### Functions\n\n- **`parse_file(data_file: Union[str, Path], specs: dict, size: int = -1) -> dict`**\n\n Parses a text file and returns a dictionary of matched key-value pairs.\n\n **Parameters:**\n - `data_file`: The file path or name.\n - `specs`: A nested dictionary of specifications.\n - `size`: Maximum size of the file to parse in bytes (default is -1, which means no limit).\n\n **Returns:**\n - A nested dictionary of key-value pairs.\n\n- **`parse_text(specs: dict, text: str) -> dict`**\n\n Parses the given text using the provided specifications.\n\n **Parameters:**\n - `specs`: A dictionary of specifications.\n - `text`: The text to parse.\n\n **Returns:**\n - A dictionary of parsed key-value pairs.\n\n#### Classes\n\n- **`TextParser`**\n\n A class for parsing text files using a lexicon.\n\n **Class Methods:**\n - `parse(cls, filename: str, silent=False) -> dict`\n - Parses the provided file and returns a dictionary.\n - **Parameters:**\n - `filename`: The text file to parse.\n - `silent`: If `True`, returns an empty dictionary instead of throwing exceptions.\n - **Returns:**\n - A dictionary of parsed key-value pairs.\n\n - `parse_text(cls, text: str, lexicon: dict) -> Any`\n - Parses the given text using the lexicon dictionary.\n - **Parameters:**\n - `text`: The text to parse.\n - `lexicon`: The lexicon specification dictionary.\n - **Returns:**\n - Parsed data.\n\n - `get_lexicon(cls, filename) -> dict`\n - Returns the lexicon specified for a given file.\n - **Parameters:**\n - `filename`: The name of the file.\n - **Returns:**\n - A dictionary of lexicon specifications.\n\n- **`MissingLexicon`**\n\n Exception raised when a lexicon is missing.\n\n- **`FilesMissing`**\n\n Exception raised when files are missing.\n\n## Usage Examples\n\n### Parsing Simple Fields\n\n```python\nfrom parsefire.parser import parse_text\n\nspecs = {\n 'fields': [\n \"Age: <int:age>\",\n \"Name: <str:name>\"\n ]\n}\ntext = \"Age: 25\\nName: John Doe\"\nresult = parse_text(specs, text)\nprint(result) # Output: {'age': 25, 'name': 'John Doe'}\n```\n\n### Parsing Nested Sections\n\n```python\nfrom parsefire.parser import parse_text\n\nspecs = {\n 'sections': {\n 'person': {\n 'fields': [\n \"Age: <int:age>\",\n \"Name: <str:name>\"\n ]\n }\n }\n}\ntext = \"Age: 25\\nName: John Doe\"\nresult = parse_text(specs, text)\nprint(result) # Output: {'person': {'age': 25, 'name': 'John Doe'}}\n```\n\n### Parsing Tables\n\n```python\nfrom parsefire.parser import parse_text\n\nspecs = {\n 'table': \"Row: <int:id> <str:name>\"\n}\ntext = \"Row: 1 John\\nRow: 2 Jane\"\nresult = parse_text(specs, text)\nprint(result) # Output: [{'id': 1, 'name': 'John'}, {'id': 2, 'name': 'Jane'}]\n```\n\n### Parsing with Custom Regex\n\n```python\nfrom parsefire.parser import parse_text\n\nspecs = {\n 'fields': [\n r\"Code: <str:code:([A-Z]{3}-\\d{3})>\"\n ]\n}\ntext = \"Code: ABC-123\"\nresult = parse_text(specs, text)\nprint(result) # Output: {'code': 'ABC-123'}\n```\n\n### Parsing with Domains\nA domain is a section of text that is matched by a regular expression. The domain is used to extract the text that will be parsed.\n\n```python\nfrom parsefire.parser import parse_text\n\nspecs = {\n 'domain': r\"Inside:(.*?)(?=Outside|$)\",\n 'fields': [\n \"Age: <int:age>\",\n \"Name: <str:name>\"\n ]\n}\ntext = (\n \"Inside:\\n\"\n \"Age: 25\\n\"\n \"Name: John Doe\\n\"\n \"Outside:\\n\"\n \"Age: 30\\n\"\n \"Name: Jane Doe\"\n)\nresult = parse_text(specs, text)\nprint(result) # Output: {'age': 25, 'name': 'John Doe'}\n```\n\n### A more complex example\n\nHere is an example of how to parse a text file with multiple sections and tables.\n\n```yaml\nroot:\n sections:\n quality:\n domain: \"REFINEMENT OF DIFFRACTION PARAMETERS USING ALL IMAGES(.+?)THE DATA COLLECTION STATISTICS REPORTED BELOW ASSUMES\"\n fields:\n - \" STANDARD DEVIATION OF SPOT POSITION (PIXELS) <float:pixel_error>\"\n - \" STANDARD DEVIATION OF SPINDLE POSITION (DEGREES) <float:angle_error>\"\n - \" CRYSTAL MOSAICITY (DEGREES) <float:mosaicity>\"\n\n statistics:\n domain: \"STATISTICS OF SAVED DATA SET .*? WITH SIGNAL/NOISE >= -3.0(.+?)NUMBER OF REFLECTIONS IN SELECTED\"\n table: \" <float:shell> <int:observed> <int:unique> <int:possible> <float:completeness>% <float:r_obs>% <float:r_exp>% <int:compared> <float:i_sigma> <float:r_meas>% <float:cc_half><char:signif> <int:cor_ano><char:asignif> <float:sig_ano> <int:Nano>\"\n\n summary:\n domain: \"STATISTICS OF SAVED DATA SET .*? WITH SIGNAL/NOISE >= -3.0(.+?)WILSON STATISTICS OF DATA SET\"\n fields:\n - \" total <int:observed> <int:unique> <int:possible> <float:completeness>% <float:r_obs>% <float:r_exp>% <int:compared> <float:i_sigma> <float:r_meas>% <float:cc_half><char:signif> <int:cor_ano><char:asignif> <float:sig_ano> <int:Nano>\"\n - \" NUMBER OF REFLECTIONS IN SELECTED SUBSET OF IMAGES <int:reflections>\"\n - \" NUMBER OF SYSTEMATIC ABSENT REFLECTIONS <int:absent>\"\n - \" NUMBER OF REJECTED MISFITS <int:misfits>\"\n```\n\nAnd here is a snippet of the corresponding text file:\n\n```\n ******************************************************************************\n MEAN DISCREPANCIES BETWEEN OBSERVED AND CALCULATED SPOT LOCATIONS\n ******************************************************************************\n\n The discrepancies in X- and Y-coordinates of the spots are depicted in the\n two images DX-CORRECTIONS.cbf and DY-CORRECTIONS.cbf for inspection with\n the XDS-Viewer.\n\n\n\n ******************************************************************************\n REFINEMENT OF DIFFRACTION PARAMETERS USING ALL IMAGES\n ******************************************************************************\n\n\n REFINED VALUES OF DIFFRACTION PARAMETERS DERIVED FROM 50627 INDEXED SPOTS\n REFINED PARAMETERS: POSITION BEAM AXIS ORIENTATION CELL\n STANDARD DEVIATION OF SPOT POSITION (PIXELS) 1.99\n STANDARD DEVIATION OF SPINDLE POSITION (DEGREES) 0.43\n SPACE GROUP NUMBER 1\n UNIT CELL PARAMETERS 57.578 58.086 148.620 89.360 89.832 89.615\n E.S.D. OF CELL PARAMETERS 1.3E-01 1.6E-01 3.5E-01 1.7E-01 6.1E-02 9.4E-02\n REC. CELL PARAMETERS 0.017368 0.017217 0.006729 90.639 90.164 90.383\n COORDINATES OF UNIT CELL A-AXIS -12.315 34.160 44.684\n COORDINATES OF UNIT CELL B-AXIS -37.590 -39.382 20.249\n COORDINATES OF UNIT CELL C-AXIS 107.768 -64.427 79.517\n CRYSTAL MOSAICITY (DEGREES) 0.301\n LAB COORDINATES OF ROTATION AXIS 0.999999 0.001263 -0.000442\n DIRECT BEAM COORDINATES (REC. ANGSTROEM) 0.001820 0.001247 0.806579\n DETECTOR COORDINATES (PIXELS) OF DIRECT BEAM 1279.44 1261.60\n DETECTOR ORIGIN (PIXELS) AT 1276.19 1259.38\n CRYSTAL TO DETECTOR DISTANCE (mm) 247.52\n LAB COORDINATES OF DETECTOR X-AXIS 1.000000 0.000000 0.000000\n LAB COORDINATES OF DETECTOR Y-AXIS 0.000000 1.000000 0.000000\n\n...\n ******************************************************************************\n SUMMARY OF DATA SET STATISTICS FOR IMAGE DATA_RANGE= 1 201\n ******************************************************************************\n\n\n COMPLETENESS AND QUALITY OF DATA SET\n ------------------------------------\n\n R-FACTOR\n observed = (SUM(ABS(I(h,i)-I(h))))/(SUM(I(h,i)))\n expected = expected R-FACTOR derived from Sigma(I)\n\n COMPARED = number of reflections used for calculating R-FACTOR\n I/SIGMA = mean of intensity/Sigma(I) of unique reflections\n (after merging symmetry-related observations)\n Sigma(I) = standard deviation of reflection intensity I\n estimated from sample statistics\n\n R-meas = redundancy independent R-factor (intensities)\n Diederichs & Karplus (1997), Nature Struct. Biol. 4, 269-275.\n\n CC(1/2) = percentage of correlation between intensities from\n random half-datasets. Correlation significant at\n the 0.1% level is marked by an asterisk.\n Karplus & Diederichs (2012), Science 336, 1030-33\n Anomal = percentage of correlation between random half-sets\n Corr of anomalous intensity differences. Correlation\n significant at the 0.1% level is marked.\n SigAno = mean anomalous difference in units of its estimated\n standard deviation (|F(+)-F(-)|/Sigma). F(+), F(-)\n are structure factor estimates obtained from the\n merged intensity observations in each parity class.\n Nano = Number of unique reflections used to calculate\n Anomal_Corr & SigAno. At least two observations\n for each (+ and -) parity are required.\n\n\n SUBSET OF INTENSITY DATA WITH SIGNAL/NOISE >= -3.0 AS FUNCTION OF RESOLUTION\n RESOLUTION NUMBER OF REFLECTIONS COMPLETENESS R-FACTOR R-FACTOR COMPARED I/SIGMA R-meas CC(1/2) Anomal SigAno Nano\n LIMIT OBSERVED UNIQUE POSSIBLE OF DATA observed expected Corr\n\n 4.62 4309 3073 10593 29.0% 463.8% 1726.2% 2472 0.14 655.9% 2.1 0 0.000 0\n 3.27 7484 6163 19118 32.2% 126.4% 169.2% 2642 0.29 178.8% -9.3 0 0.000 0\n 2.67 9554 8499 24772 34.3% 120.0% 159.2% 2110 0.29 169.7% -0.5 0 0.000 0\n 2.32 11329 10542 29236 36.1% 126.2% 180.5% 1574 0.23 178.5% -7.2 0 0.000 0\n 2.07 12693 12240 33223 36.8% 125.2% 169.5% 906 0.21 177.1% -9.3 0 0.000 0\n 1.89 13718 13520 36735 36.8% 159.8% 393.6% 396 0.16 226.0% -6.2 0 0.000 0\n 1.75 15033 14992 39873 37.6% 216.9% 846.8% 82 0.14 306.8% 5.2 0 0.000 0\n 1.64 9949 9947 42876 23.2% 92.6% 93.6% 4 0.05 130.9% 0.0 0 0.000 0\n 1.55 3810 3810 45658 8.3% -99.9% -99.9% 0 0.00 -99.9% 0.0 0 0.000 0\n total 87879 82786 282084 29.3% 148.8% 283.9% 10186 0.17 210.4% -0.2 0 0.000 0\n\n\n NUMBER OF REFLECTIONS IN SELECTED SUBSET OF IMAGES 87879\n NUMBER OF REJECTED MISFITS 0\n NUMBER OF SYSTEMATIC ABSENT REFLECTIONS 0\n NUMBER OF ACCEPTED OBSERVATIONS 87879\n NUMBER OF UNIQUE ACCEPTED REFLECTIONS 82786\n\n\n\n ******************************************************************************\n SUMMARY OF DATA SET STATISTICS FOR IMAGE DATA_RANGE= 1 401\n ******************************************************************************\n\n```\n",
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