Clean up broken links, standardize file naming, update lucidoc config

docs/eido/README.md

@@ -35,8 +35,8 @@ An eido schema is written using the JSON Schema vocabulary, plus a few additiona
 
 ## How to use eido
 
-- [Use eido to validate data from the command line](cli.md)
-- [Use eido to validate data from Python](demo.md)
+- [Use eido to validate data from the command line](code/cli.md)
+- [Use eido to validate data from Python](code/demo.md)
 - [Write your own schema](writing-a-schema.md)
 
 ---

docs/eido/plugin_api_docs.md → docs/eido/code/plugin-api-docs.md

@@ -1,4 +1,3 @@
-Final targets: basic_pep_filter, csv_pep_filter, yaml_pep_filter, yaml_samples_pep_filter
 <script>
 document.addEventListener('DOMContentLoaded', (event) => {
   document.querySelectorAll('h3 code').forEach((block) => {
@@ -93,4 +92,4 @@ This filter can save the YAML to file, if kwargs include `path`.
 
 
 
-*Version Information: `eido` v0.2.2-dev, generated by `lucidoc` v0.4.4*
+*Version Information: `eido` v0.2.1, generated by `lucidoc` v0.4.3*

docs/eido/code/python-api.md

@@ -153,63 +153,6 @@ Convert a `peppy.Project` object to a selected format
 
 
 
-```python
-def basic_pep_filter(p, **kwargs) -> Dict[str, str]
-```
-
-Basic PEP filter, that does not convert the Project object.
-
-This filter can save the PEP representation to file, if kwargs include `path`.
-#### Parameters:
-
-- `p` (`peppy.Project`):  a Project to run filter on
-
-
-
-
-```python
-def yaml_pep_filter(p, **kwargs) -> Dict[str, str]
-```
-
-YAML PEP filter, that returns Project object representation.
-
-This filter can save the YAML to file, if kwargs include `path`.
-#### Parameters:
-
-- `p` (`peppy.Project`):  a Project to run filter on
-
-
-
-
-```python
-def csv_pep_filter(p, **kwargs) -> Dict[str, str]
-```
-
-CSV PEP filter, that returns Sample object representations
-
-This filter can save the CSVs to files, if kwargs include
-`sample_table_path` and/or `subsample_table_path`.
-#### Parameters:
-
-- `p` (`peppy.Project`):  a Project to run filter on
-
-
-
-
-```python
-def yaml_samples_pep_filter(p, **kwargs) -> Dict[str, str]
-```
-
-YAML samples PEP filter, that returns only Sample object representations.
-
-This filter can save the YAML to file, if kwargs include `path`.
-#### Parameters:
-
-- `p` (`peppy.Project`):  a Project to run filter on
-
-
-
-
 ```python
 def validate_input_files(project, schemas, sample_name=None)
 ```

docs/geofetch/sra_convert.md

@@ -9,10 +9,10 @@ The basic advantages over just using prefetch are:
 - it can automatically delete sra data that has been already converted
 - it allows a more flexible specification of locations, using either environment variables or command-line arguments.
 
-This effectively makes it easier to interact with *project-level* management of sra and fastq data using [looper](http://looper.databio.org) and PEP-compatible projects.
+This effectively makes it easier to interact with *project-level* management of sra and fastq data using [looper](../looper/README.md) and PEP-compatible projects.
 
 
 
 ## Tutorial
 
-See the [how-to](how_to_convert_fastq_from_sra.md) for an example of how to use `sraconvert`.
+See the [how-to SRA to FASTQ](code/howto-sra-to-fastq.md) for an example of how to use `sraconvert`.

docs/looper/code/python-api.md

@@ -140,6 +140,61 @@ def description(self)
 
 
 
+```python
+def from_dict(cls, pep_dictionary: dict)
+```
+
+Init a peppy project instance from a dictionary representation of an already processed PEP.
+#### Parameters:
+
+- `pep_dictionary` (`Dict[Any]`):  dict,_samples: list | dict, _subsamples: list[list | dict]}
+
+
+
+
+```python
+def from_pandas(cls, samples_df: pandas.core.frame.DataFrame, sub_samples_df: List[pandas.core.frame.DataFrame]=None, config: dict=None)
+```
+
+Init a peppy project instance from a pandas Dataframe
+#### Parameters:
+
+- `samples_df` (``):  in-memory pandas DataFrame object of samples
+- `sub_samples_df` (``):  in-memory list of pandas DataFrame objects of sub-samples
+- `config` (``):  dict of yaml file
+
+
+
+
+```python
+def from_pep_config(cls, cfg: str=None, amendments: Union[str, Iterable[str]]=None, sample_table_index: Union[str, Iterable[str]]=None, subsample_table_index: Union[str, Iterable[str]]=None, defer_samples_creation: bool=False)
+```
+
+Init a peppy project instance from a yaml file
+#### Parameters:
+
+- `cfg` (`str`):  Project config file (YAML) or sample table (CSV/TSV)with one row per sample to constitute project
+- `sample_table_index` (`str | Iterable[str]`):  name of the columns to setthe sample_table index to
+- `subsample_table_index` (`str | Iterable[str]`):  name of the columns to setthe subsample_table index to
+- `amendments` (`str | Iterable[str]`):  names of the amendments to activate
+- `amendments` (`Iterable[str]`):  amendments to use within configuration file
+- `defer_samples_creation` (`bool`):  whether the sample creation should be skipped
+
+
+
+
+```python
+def from_sample_yaml(cls, yaml_file: str)
+```
+
+Init a peppy project instance from a yaml file
+#### Parameters:
+
+- `yaml_file` (`str`):  path to yaml file
+
+
+
+
 ```python
 def get_pipestat_managers(self, sample_name=None, project_level=False)
 ```