Commit 83ec5f44 authored by Elad Noor's avatar Elad Noor

updating code for sbtab v0.9.77

parent 2db0c4ac
Pipeline #218136017 failed with stages
in 16 minutes
......@@ -44,12 +44,10 @@ jupyter notebook
```
Then select the notebook called `equilibrator_cmd.ipynb` and follow the examples in it.
If you are using a Windows environment, that are a few dependencies that
work better under `conda`. Make sure to first run:
If you are using a Windows environment, we recommend using `conda` instead of `pip`:
```
conda install -c conda-forge python-levenshtein
conda install -c conda-forge equilibrator-api
```
and only then install the `pip` packages.
## Example Usage
......@@ -61,7 +59,7 @@ from equilibrator_api import ComponentContribution, Q_
cc = ComponentContribution()
```
- **IMPORTANT NOTE** - version 0.3.2 on PyPI introduces support for Magnesium ion concentration (pMg). Almost all Gibbs energy estimates are affected, and therefore current estimates are not backward-compatible. To revert to the previous estimates, initialize using: `cc = ComponentContribution.legacy()` instead of the default constructor.
- **IMPORTANT NOTE** - versions 0.3.2+ on PyPI introduces support for Magnesium ion concentration (pMg). Almost all Gibbs energy estimates are affected, and therefore current estimates are not backward-compatible. To revert to the previous estimates, initialize using: `cc = ComponentContribution.legacy()` instead of the default constructor.
You can parse a reaction formula that uses compound accessions from different
databases ([KEGG](https://www.kegg.jp/), [ChEBI](https://www.ebi.ac.uk/chebi/),
......
......@@ -53,7 +53,7 @@ test =
pytest
pytest-cov
pytest-raises
sbtab>=0.9.73
sbtab>=0.9.77
development =
black
isort
......
......@@ -693,7 +693,7 @@ class StoichiometricModel(object):
config_dict["stdev_factor"] = "0.0"
config_df = pd.DataFrame(
data=[(k, v, "") for k, v in config_dict.items()],
columns=["Option", "Value", "Comment"],
columns=["!Option", "!Value", "!Comment"],
)
config_sbtab = SBtabTable.from_data_frame(
df=config_df, table_id="Configuration", table_type="Config"
......@@ -704,7 +704,7 @@ class StoichiometricModel(object):
(rxn.rid, self._reaction_to_formula(rxn))
for rxn in self.reactions
],
columns=["ID", "ReactionFormula"],
columns=["!ID", "!ReactionFormula"],
)
reaction_sbtab = SBtabTable.from_data_frame(
df=reaction_df, table_id="Reaction", table_type="Reaction"
......@@ -719,7 +719,7 @@ class StoichiometricModel(object):
)
for cpd in self.S.index
],
columns=["ID", "Name", "Identifiers"],
columns=["!ID", "!Name", "!Identifiers"],
)
compound_sbtab = SBtabTable.from_data_frame(
df=compound_df, table_id="Compound", table_type="Compound"
......@@ -736,7 +736,7 @@ class StoichiometricModel(object):
)
for rxn, dg in zip(self.reactions, self.standard_dg_primes)
],
columns=["QuantityType", "Reaction", "Compound", "Value", "Unit"],
columns=["!QuantityType", "!Reaction", "!Compound", "!Value", "!Unit"],
)
thermo_sbtab = SBtabTable.from_data_frame(
df=thermo_df, table_id="Thermodynamics", table_type="Quantity"
......@@ -756,7 +756,7 @@ class StoichiometricModel(object):
)
for cpd, lb, ub in zip(self.S.index, lbs, ubs)
],
columns=["QuantityType", "Compound", "Min", "Max"],
columns=["!QuantityType", "!Compound", "!Min", "!Max"],
)
conc_sbtab = SBtabTable.from_data_frame(
df=conc_df,
......
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