Missing file in output folder of mmvec paired-omics command

[natalie-melendez]

Hi,

I recently starting using mmvec for microbial-metabolome integration and when performing the mmvec paired-omics command on qiime2-2020.6 it only generated two files: conditional_biplot.qza and conditionals.qza. However, it did not generate the model_stats.qza. How can I resolve this issue?

Thank you!

[mortonjt]

Hi @natalie-melendez its hard to see why the model_stats.qza wasn't generated. Do you run with the verbose flag? How long did it run? What command did you use? How many epoches was run? What summary interval was used?

[natalie-melendez]

Hi @mortonjt! Initially I did not run with the verbose flag. I ran it again using the verbose flag and this is what I got:

(qiime2-2020.6) megl5@x86_64-apple-darwin13 ~ % qiime mmvec paired-omics --i-microbes 16S-no-sings-filtered-rarified-N30-non-Bifido-glioma.qza --i-metabolites untargeted-metabolites-RGDOXDH-time-series-N30-glioma.qza --p-summary-interval 1 --output-dir mmvec-model-summary-N30-non-Bifido-glioma --verbose
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/biom/table.py:4068: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return constructor(mat, index=index, columns=columns)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/sparse/frame.py:257: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

sparse_index=BlockIndex(N, blocs, blens),
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/generic.py:4583: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_data).finalize(self)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/sparse/frame.py:854: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

default_kind=self._default_kind,
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/frame.py:3471: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return klass(values, index=self.index, name=items, fastpath=True)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/sparse/frame.py:785: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_arrays, index=index, columns=columns).finalize(
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/ops/init.py:1641: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_values, index=self.index, name=self.name)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/sparse/frame.py:339: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

default_fill_value=self.default_fill_value,
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/generic.py:6289: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_data).finalize(self)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/generic.py:5884: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_data).finalize(self)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/generic.py:3606: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

result = self._constructor(new_data).finalize(self)
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/q2/_method.py:49: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2024-11-22 14:39:48.905339: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
2024-11-22 14:39:48.905891: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 16. Tune using inter_op_parallelism_threads for best performance.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:90: The name tf.log is deprecated. Please use tf.math.log instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:91: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.random.categorical instead.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:102: The name tf.random_normal is deprecated. Please use tf.random.normal instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:118: Normal.init (from tensorflow.python.ops.distributions.normal) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use tfp.distributions instead of tf.distributions.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/tensorflow_core/python/ops/distributions/normal.py:160: Distribution.init (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use tfp.distributions instead of tf.distributions.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:135: Multinomial.init (from tensorflow.python.ops.distributions.multinomial) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use tfp.distributions instead of tf.distributions.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:183: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:185: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:189: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:191: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:196: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/tensorflow_core/python/ops/clip_ops.py:301: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:206: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:243: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

100%|███████████████████████████████████████████████████████████| 3662/3662 [00:04<00:00, 824.97it/s]
Saved FeatureData[Conditional] to: mmvec-model-summary-N30-non-Bifido-glioma/conditionals.qza
Saved PCoAResults % Properties('biplot') to: mmvec-model-summary-N30-non-Bifido-glioma/conditional_biplot.qza

I'm fairly new with mmvec. I don't understand your question in reference to epoches. What is epoches? Also, I used the summary interval at 1 (same value as in the original mmvec github), however truthfully I do not know what this is for.

Also, when I run the command, in addition to creating the output folder the command creates another folder:

[mortonjt]

Try running the following code with 10000 epochs -- it'll make it run longer (and potentially make it more accurate).

qiime mmvec paired-omics --i-microbes 16S-no-sings-filtered-rarified-N30-non-Bifido-glioma.qza --i-metabolites untargeted-metabolites-RGDOXDH-time-series-N30-glioma.qza --p-summary-interval 1 --output-dir mmvec-model-summary-N30-non-Bifido-glioma --epochs 10000 --verbose

[natalie-melendez]

I ran it with 10000 epochs, however, it still only generates 2 files.

(qiime2-2020.6) megl5@x86_64-apple-darwin13 ~ % qiime mmvec paired-omics --i-microbes 16S-no-sings-filtered-rarified-N30-non-Bifido-glioma.qza --i-metabolites untargeted-metabolites-RGDOXDH-time-series-N30-glioma.qza --p-summary-interval 1 --output-dir mmvec-model-summary-N30-non-Bifido-glioma --verbose --p-epochs 10000
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/biom/table.py:4068: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return constructor(mat, index=index, columns=columns)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/sparse/frame.py:257: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

sparse_index=BlockIndex(N, blocs, blens),
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/generic.py:4583: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_data).finalize(self)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/sparse/frame.py:854: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

default_kind=self._default_kind,
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/frame.py:3471: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return klass(values, index=self.index, name=items, fastpath=True)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/sparse/frame.py:785: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_arrays, index=index, columns=columns).finalize(
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/ops/init.py:1641: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_values, index=self.index, name=self.name)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/sparse/frame.py:339: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

default_fill_value=self.default_fill_value,
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/generic.py:6289: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_data).finalize(self)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/generic.py:5884: FutureWarning: SparseSeries is deprecated and will be removed in a future version.
Use a Series with sparse values instead.

>>> series = pd.Series(pd.SparseArray(...))

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

return self._constructor(new_data).finalize(self)
/opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/pandas/core/generic.py:3606: FutureWarning: SparseDataFrame is deprecated and will be removed in a future version.
Use a regular DataFrame whose columns are SparseArrays instead.

See http://pandas.pydata.org/pandas-docs/stable/user_guide/sparse.html#migrating for more.

result = self._constructor(new_data).finalize(self)
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/q2/_method.py:49: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.

2024-11-22 15:08:14.223568: I tensorflow/core/platform/cpu_feature_guard.cc:145] This TensorFlow binary is optimized with Intel(R) MKL-DNN to use the following CPU instructions in performance critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA
To enable them in non-MKL-DNN operations, rebuild TensorFlow with the appropriate compiler flags.
2024-11-22 15:08:14.224320: I tensorflow/core/common_runtime/process_util.cc:115] Creating new thread pool with default inter op setting: 16. Tune using inter_op_parallelism_threads for best performance.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:90: The name tf.log is deprecated. Please use tf.math.log instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:91: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.random.categorical instead.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:102: The name tf.random_normal is deprecated. Please use tf.random.normal instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:118: Normal.init (from tensorflow.python.ops.distributions.normal) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use tfp.distributions instead of tf.distributions.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/tensorflow_core/python/ops/distributions/normal.py:160: Distribution.init (from tensorflow.python.ops.distributions.distribution) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use tfp.distributions instead of tf.distributions.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:135: Multinomial.init (from tensorflow.python.ops.distributions.multinomial) is deprecated and will be removed after 2019-01-01.
Instructions for updating:
The TensorFlow Distributions library has moved to TensorFlow Probability (https://github.com/tensorflow/probability). You should update all references to use tfp.distributions instead of tf.distributions.
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:183: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:185: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:189: The name tf.summary.merge_all is deprecated. Please use tf.compat.v1.summary.merge_all instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:191: The name tf.summary.FileWriter is deprecated. Please use tf.compat.v1.summary.FileWriter instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:196: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/tensorflow_core/python/ops/clip_ops.py:301: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use tf.where in 2.0, which has the same broadcast rule as np.where
WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:206: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.

WARNING:tensorflow:From /opt/miniconda3/envs/qiime2-2020.6/lib/python3.6/site-packages/mmvec/multimodal.py:243: The name tf.train.Saver is deprecated. Please use tf.compat.v1.train.Saver instead.

100%|███████████████████████████████████████████████████████| 363400/363400 [08:39<00:00, 699.26it/s]
Saved FeatureData[Conditional] to: mmvec-model-summary-N30-non-Bifido-glioma/conditionals.qza
Saved PCoAResults % Properties('biplot') to: mmvec-model-summary-N30-non-Bifido-glioma/conditional_biplot.qza

[mortonjt]

H @natalie-melendez I'm not sure why that file isn't being generated. But it is not the first time this error have been reported

https://forum.qiime2.org/t/no-model-stats-qza-file/29044/16

I'd be curious to hear if the non-qiime2 version generated tensor flow results. When you run tensorboard --logdir ., does it generate a visualization? If that works, that is the workaround for model_stats.qza.

[natalie-melendez]

Hi @mortonjt. Interestingly, yesterday I was looking at how to generate the heatmap with mmvec and I saw it needed the rank.qza file which I don't have and didn't know how to obtain it. Seeing the forum you have shared, I noticed that those files should have been generated inside the summary output folder during the paired-omics command?

I had a lot of issues downloading either qiime2-2020.6 or the mmvec standalone. Currently, I do not have the non-qiime2 version but I will try to download it and see if by using the non-qiime2 version I can generate those files.

When you say to run tensorboard --logdir ., were do I do that? I apologize if this is a simple question, I am new with this setup. Thank you!

[natalie-melendez]

Hi @mortonjt!

I tried installing the non-qiime2 mmvec version. However, even though it all seems like it installed properly and I am able to activate the environment, it gives me a traceback error. I'm seeing that the tensorflow-estimator package being installed is at version 2.6.0, while tensorflow and tensorflow-base is at 1.15.0. Should I downgrade the tensorflow-estimator?

(base) megl5@MEGLs-4-iMac ~ % conda create -n mmvec_env mamba python=3.7 -c conda-forge
Channels:

• conda-forge
• defaults
  Platform: osx-64
  Collecting package metadata (repodata.json): done
  Solving environment: done

Package Plan

environment location: /opt/miniconda3/envs/mmvec_env

added / updated specs:
- mamba
- python=3.7

The following NEW packages will be INSTALLED:

bzip2 conda-forge/osx-64::bzip2-1.0.8-hfdf4475_7
c-ares conda-forge/osx-64::c-ares-1.34.3-hf13058a_1
ca-certificates conda-forge/osx-64::ca-certificates-2024.8.30-h8857fd0_0
cpp-expected conda-forge/osx-64::cpp-expected-1.1.0-hb8565cd_0
fmt conda-forge/osx-64::fmt-11.0.2-h3c5361c_0
icu conda-forge/osx-64::icu-75.1-h120a0e1_0
krb5 conda-forge/osx-64::krb5-1.21.3-h37d8d59_0
libarchive conda-forge/osx-64::libarchive-3.7.4-h20e244c_0
libcurl conda-forge/osx-64::libcurl-8.10.1-h58e7537_0
libcxx conda-forge/osx-64::libcxx-19.1.4-hf95d169_0
libedit conda-forge/osx-64::libedit-3.1.20191231-h0678c8f_2
libev conda-forge/osx-64::libev-4.33-h10d778d_2
libffi conda-forge/osx-64::libffi-3.4.2-h0d85af4_5
libiconv conda-forge/osx-64::libiconv-1.17-hd75f5a5_2
libmamba conda-forge/osx-64::libmamba-2.0.4-hd41e4cc_0
libnghttp2 conda-forge/osx-64::libnghttp2-1.64.0-hc7306c3_0
libsolv conda-forge/osx-64::libsolv-0.7.30-h69d5d9b_0
libsqlite conda-forge/osx-64::libsqlite-3.47.0-h2f8c449_1
libssh2 conda-forge/osx-64::libssh2-1.11.1-h3dc7d44_0
libxml2 conda-forge/osx-64::libxml2-2.13.5-h495214b_0
libzlib conda-forge/osx-64::libzlib-1.3.1-hd23fc13_2
lz4-c conda-forge/osx-64::lz4-c-1.9.4-hf0c8a7f_0
lzo conda-forge/osx-64::lzo-2.10-h10d778d_1001
mamba conda-forge/osx-64::mamba-2.0.4-hcd709ef_0
ncurses conda-forge/osx-64::ncurses-6.5-hf036a51_1
nlohmann_json conda-forge/osx-64::nlohmann_json-3.11.3-hf036a51_1
openssl conda-forge/osx-64::openssl-3.4.0-hd471939_0
pip conda-forge/noarch::pip-24.0-pyhd8ed1ab_0
python conda-forge/osx-64::python-3.7.12-hf3644f1_100_cpython
readline conda-forge/osx-64::readline-8.2-h9e318b2_1
reproc conda-forge/osx-64::reproc-14.2.5.post0-h6e16a3a_0
reproc-cpp conda-forge/osx-64::reproc-cpp-14.2.5.post0-h240833e_0
setuptools conda-forge/noarch::setuptools-69.0.3-pyhd8ed1ab_0
simdjson conda-forge/osx-64::simdjson-3.10.1-h37c8870_0
spdlog conda-forge/osx-64::spdlog-1.14.1-h325aa07_1
sqlite conda-forge/osx-64::sqlite-3.47.0-h6285a30_1
tk conda-forge/osx-64::tk-8.6.13-h1abcd95_1
wheel conda-forge/noarch::wheel-0.42.0-pyhd8ed1ab_0
xz conda-forge/osx-64::xz-5.2.6-h775f41a_0
yaml-cpp conda-forge/osx-64::yaml-cpp-0.8.0-he965462_0
zstd conda-forge/osx-64::zstd-1.5.6-h915ae27_0

Proceed ([y]/n)? y

Downloading and Extracting Packages:

Preparing transaction: done
Verifying transaction: done
Executing transaction: done

To activate this environment, use

$ conda activate mmvec_env

To deactivate an active environment, use

$ conda deactivate

(base) megl5@MEGLs-4-iMac ~ %
(base) megl5@MEGLs-4-iMac ~ % conda activate mmvec_env
(mmvec_env) megl5@MEGLs-4-iMac ~ %
(mmvec_env) megl5@MEGLs-4-iMac ~ % mamba install mmvec -c conda-forge
warning libmamba 'repo.anaconda.com', a commercial channel hosted by Anaconda.com, is used.

warning libmamba Please make sure you understand Anaconda Terms of Services.

warning libmamba See: https://legal.anaconda.com/policies/en/
warning libmamba 'repo.anaconda.com', a commercial channel hosted by Anaconda.com, is used.

warning libmamba Please make sure you understand Anaconda Terms of Services.

warning libmamba See: https://legal.anaconda.com/policies/en/
warning libmamba 'repo.anaconda.com', a commercial channel hosted by Anaconda.com, is used.

warning libmamba Please make sure you understand Anaconda Terms of Services.

warning libmamba See: https://legal.anaconda.com/policies/en/
warning libmamba 'repo.anaconda.com', a commercial channel hosted by Anaconda.com, is used.

warning libmamba Please make sure you understand Anaconda Terms of Services.

warning libmamba See: https://legal.anaconda.com/policies/en/
pkgs/main/noarch 728.8kB @ 1.8MB/s 0.4s
pkgs/r/osx-64 694.3kB @ 1.7MB/s 0.3s
pkgs/r/noarch 2.1MB @ 2.7MB/s 0.7s
pkgs/main/osx-64 6.4MB @ 1.7MB/s 3.6s
conda-forge/noarch 17.5MB @ 3.9MB/s 4.4s
conda-forge/osx-64 35.0MB @ 6.1MB/s 5.7s

Pinned packages:

• python=3.7

Transaction

Prefix: /opt/miniconda3/envs/mmvec_env

Updating specs:

• mmvec

Package Version Build Channel Size
──────────────────────────────────────────────────────────────────────────────────────────────
Install:
──────────────────────────────────────────────────────────────────────────────────────────────

• _tflow_select 2.3.0 mkl pkgs/main 3kB
• absl-py 0.15.0 pyhd8ed1ab_0 conda-forge 100kB
• appnope 0.1.4 pyhd8ed1ab_0 conda-forge 10kB
• astor 0.8.1 pyh9f0ad1d_0 conda-forge 26kB
• backcall 0.2.0 pyh9f0ad1d_0 conda-forge 14kB
• backports 1.0 pyhd8ed1ab_4 conda-forge 7kB
• backports.functools_lru_cache 2.0.0 pyhd8ed1ab_0 conda-forge 12kB
• biom-format 2.1.12 py37h49e79e5_1 conda-forge 11MB
• brotli 1.1.0 h00291cd_2 conda-forge 19kB
• brotli-bin 1.1.0 h00291cd_2 conda-forge 17kB
• brotli-python 1.0.9 py37h0582d14_7 conda-forge 392kB
• cachecontrol 0.14.0 pyhd8ed1ab_1 conda-forge 24kB
• cached-property 1.5.2 hd8ed1ab_1 conda-forge 4kB
• cached_property 1.5.2 pyha770c72_1 conda-forge 11kB
• certifi 2024.8.30 pyhd8ed1ab_0 conda-forge 164kB
• cffi 1.15.1 py37h7346b73_1 conda-forge 224kB
• charset-normalizer 3.4.0 pyhd8ed1ab_0 conda-forge 47kB
• click 8.1.3 py37hf985489_0 conda-forge 148kB
• colorama 0.4.6 pyhd8ed1ab_0 conda-forge 25kB
• conda 4.12.0 py37hf985489_0 conda-forge 1MB
• conda-package-handling 2.2.0 pyh38be061_0 conda-forge 255kB
• conda-package-streaming 0.11.0 pyhd8ed1ab_0 conda-forge 21kB
• cryptography 38.0.2 py37hbf3704f_1 conda-forge 1MB
• cycler 0.11.0 pyhd8ed1ab_0 conda-forge 10kB
• cython 0.29.32 py37hc568d09_0 conda-forge 2MB
• decorator 5.1.1 pyhd8ed1ab_0 conda-forge 12kB
• exceptiongroup 1.2.2 pyhd8ed1ab_0 conda-forge 20kB
• fonttools 4.38.0 py37h8052db5_0 conda-forge 2MB
• freetype 2.12.1 h60636b9_2 conda-forge 599kB
• future 0.18.2 py37hf985489_5 conda-forge 726kB
• gast 0.2.2 py_0 conda-forge 10kB
• google-pasta 0.2.0 pyhd8ed1ab_1 conda-forge 49kB
• grpc-cpp 1.48.1 h7df5b86_1 conda-forge 4MB
• grpcio 1.48.1 py37hd7d2073_1 conda-forge 757kB
• h5py 3.7.0 nompi_py37hdc5a9f1_101 conda-forge 1MB
• hdf5 1.12.2 nompi_h48135f9_101 conda-forge 3MB
• hdmedians 0.14.2 py37h49e79e5_2 conda-forge 134kB
• idna 3.10 pyhd8ed1ab_0 conda-forge 50kB
• importlib-metadata 4.11.4 py37hf985489_0 conda-forge 34kB
• importlib_metadata 4.11.4 hd8ed1ab_0 conda-forge 4kB
• iniconfig 2.0.0 pyhd8ed1ab_0 conda-forge 11kB
• ipython 7.33.0 py37hf985489_0 conda-forge 1MB
• jedi 0.19.1 pyhd8ed1ab_0 conda-forge 841kB
• joblib 1.3.2 pyhd8ed1ab_0 conda-forge 221kB
• jpeg 9e hb7f2c08_3 conda-forge 232kB
• keras-applications 1.0.8 py_1 conda-forge 31kB
• keras-preprocessing 1.1.2 pyhd8ed1ab_0 conda-forge 35kB
• kiwisolver 1.4.4 py37h229a17a_0 conda-forge 65kB
• lcms2 2.14 h90f4b2a_0 conda-forge 254kB
• lerc 4.0.0 hb486fe8_0 conda-forge 290kB
• libabseil 20220623.0 cxx17_h844d122_6 conda-forge 989kB
• libaec 1.1.3 h73e2aa4_0 conda-forge 29kB
• libblas 3.9.0 20_osx64_openblas conda-forge 15kB
• libbrotlicommon 1.1.0 h00291cd_2 conda-forge 67kB
• libbrotlidec 1.1.0 h00291cd_2 conda-forge 30kB
• libbrotlienc 1.1.0 h00291cd_2 conda-forge 296kB
• libcblas 3.9.0 20_osx64_openblas conda-forge 15kB
• libdeflate 1.14 hb7f2c08_0 conda-forge 87kB
• libgfortran 5.0.0 13_2_0_h97931a8_3 conda-forge 110kB
• libgfortran5 13.2.0 h2873a65_3 conda-forge 2MB
• liblapack 3.9.0 20_osx64_openblas conda-forge 15kB
• libopenblas 0.3.25 openmp_hfef2a42_0 conda-forge 6MB
• libpng 1.6.43 h92b6c6a_0 conda-forge 269kB
• libprotobuf 3.20.1 hbc0c0cd_4 conda-forge 2MB
• libtiff 4.4.0 h6268bbc_5 conda-forge 453kB
• libwebp-base 1.4.0 h10d778d_0 conda-forge 355kB
• libxcb 1.13 h0d85af4_1004 conda-forge 312kB
• llvm-openmp 19.1.4 ha54dae1_0 conda-forge 305kB
• lockfile 0.12.2 py_1 conda-forge 11kB
• markdown 3.6 pyhd8ed1ab_0 conda-forge 78kB
• matplotlib 3.5.3 py37hf985489_2 conda-forge 7kB
• matplotlib-base 3.5.3 py37h3748cd6_2 conda-forge 8MB
• matplotlib-inline 0.1.7 pyhd8ed1ab_0 conda-forge 15kB
• mmvec 1.0.5 py_0 conda-forge 28kB
• mock 5.1.0 pyhd8ed1ab_0 conda-forge 34kB
• msgpack-python 1.0.4 py37h229a17a_0 conda-forge 82kB
• munkres 1.1.4 pyh9f0ad1d_0 conda-forge 12kB
• natsort 8.4.0 pyhd8ed1ab_0 conda-forge 37kB
• numpy 1.21.6 py37h345d48f_0 conda-forge 6MB
• openjpeg 2.5.0 h5d0d7b0_1 conda-forge 516kB
• opt_einsum 3.3.0 pyhc1e730c_2 conda-forge 58kB
• packaging 23.2 pyhd8ed1ab_0 conda-forge 49kB
• pandas 0.25.3 py37h0a44026_0 pkgs/main 8MB
• parso 0.8.4 pyhd8ed1ab_0 conda-forge 75kB
• patsy 0.5.6 pyhd8ed1ab_0 conda-forge 187kB
• pexpect 4.9.0 pyhd8ed1ab_0 conda-forge 54kB
• pickleshare 0.7.5 py_1003 conda-forge 9kB
• pillow 9.2.0 py37ha6ba2b9_2 conda-forge 47MB
• pluggy 1.0.0 py37hf985489_3 conda-forge 26kB
• prompt-toolkit 3.0.48 pyha770c72_0 conda-forge 270kB
• protobuf 3.20.1 py37hc568d09_0 conda-forge 311kB
• pthread-stubs 0.4 h00291cd_1002 conda-forge 8kB
• ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge 17kB
• pycosat 0.6.4 py37h8052db5_0 conda-forge 114kB
• pycparser 2.21 pyhd8ed1ab_0 conda-forge 103kB
• pygments 2.17.2 pyhd8ed1ab_0 conda-forge 860kB
• pyopenssl 23.2.0 pyhd8ed1ab_1 conda-forge 129kB
• pyparsing 3.1.4 pyhd8ed1ab_0 conda-forge 90kB
• pysocks 1.7.1 py37hf985489_5 conda-forge 28kB
• pytest 7.4.4 pyhd8ed1ab_0 conda-forge 245kB
• python-dateutil 2.9.0 pyhd8ed1ab_0 conda-forge 223kB
• python_abi 3.7 4_cp37m conda-forge 6kB
• pytz 2024.2 pyhd8ed1ab_0 conda-forge 187kB
• re2 2022.06.01 hb486fe8_1 conda-forge 183kB
• requests 2.32.2 pyhd8ed1ab_0 conda-forge 58kB
• ruamel_yaml 0.15.80 py37h994c40b_1007 conda-forge 240kB
• scikit-bio 0.5.5 py37h917ab60_1000 conda-forge 1MB
• scikit-learn 1.0.2 py37h572704e_0 conda-forge 7MB
• scipy 1.7.3 py37h4e3cf02_0 conda-forge 21MB
• seaborn 0.12.2 hd8ed1ab_0 conda-forge 6kB
• seaborn-base 0.12.2 pyhd8ed1ab_0 conda-forge 232kB
• six 1.16.0 pyh6c4a22f_0 conda-forge 14kB
• statsmodels 0.12.2 py37h032687b_0 conda-forge 11MB
• tensorboard 1.15.0 py37_0 conda-forge 4MB
• tensorflow 1.15.0 mkl_py37hb249377_0 pkgs/main 4kB
• tensorflow-base 1.15.0 mkl_py37h032239d_0 pkgs/main 79MB
• tensorflow-estimator 2.6.0 py37hfc69ec5_0 conda-forge 658kB
• termcolor 1.1.0 pyhd8ed1ab_3 conda-forge 9kB
• threadpoolctl 3.1.0 pyh8a188c0_0 conda-forge 18kB
• tomli 2.0.2 pyhd8ed1ab_0 conda-forge 18kB
• tornado 6.2 py37h994c40b_0 conda-forge 663kB
• tqdm 4.67.1 pyhd8ed1ab_0 conda-forge 89kB
• traitlets 5.9.0 pyhd8ed1ab_0 conda-forge 98kB
• typing-extensions 4.7.1 hd8ed1ab_0 conda-forge 10kB
• typing_extensions 4.7.1 pyha770c72_0 conda-forge 36kB
• unicodedata2 14.0.0 py37h69ee0a8_1 conda-forge 509kB
• urllib3 2.2.1 pyhd8ed1ab_0 conda-forge 95kB
• wcwidth 0.2.10 pyhd8ed1ab_0 conda-forge 33kB
• werkzeug 0.16.1 py_0 conda-forge 261kB
• wrapt 1.14.1 py37h994c40b_0 conda-forge 47kB
• xorg-libxau 1.0.11 h00291cd_1 conda-forge 13kB
• xorg-libxdmcp 1.1.5 h00291cd_0 conda-forge 18kB
• yaml 0.2.5 h0d85af4_2 conda-forge 84kB
• zipp 3.15.0 pyhd8ed1ab_0 conda-forge 17kB
• zlib 1.2.13 h87427d6_6 conda-forge 89kB
• zstandard 0.18.0 py37h994c40b_0 conda-forge 793kB

Change:
──────────────────────────────────────────────────────────────────────────────────────────────

• spdlog 1.14.1 h325aa07_1 conda-forge 171kB

• spdlog 1.14.1 h0f84946_0 conda-forge 167kB

Downgrade:
──────────────────────────────────────────────────────────────────────────────────────────────

• fmt 11.0.2 h3c5361c_0 conda-forge 184kB

• fmt 10.2.1 h7728843_0 conda-forge 181kB

• icu 75.1 h120a0e1_0 conda-forge 12MB

• icu 73.2 hf5e326d_0 conda-forge 12MB

• libcurl 8.10.1 h58e7537_0 conda-forge 403kB

• libcurl 8.8.0 hf9fcc65_1 conda-forge 391kB

• libmamba 2.0.4 hd41e4cc_0 conda-forge 2MB

• libmamba 1.5.8 ha449628_0 conda-forge 1MB

• libnghttp2 1.64.0 hc7306c3_0 conda-forge 607kB

• libnghttp2 1.58.0 h64cf6d3_1 conda-forge 600kB

• libsolv 0.7.30 h69d5d9b_0 conda-forge 416kB

• libsolv 0.7.29 h4f92f52_0 conda-forge 416kB

• libsqlite 3.47.0 h2f8c449_1 conda-forge 915kB

• libsqlite 3.46.0 h1b8f9f3_0 conda-forge 909kB

• libssh2 1.11.1 h3dc7d44_0 conda-forge 284kB

• libssh2 1.11.0 hd019ec5_0 conda-forge 260kB

• libxml2 2.13.5 h495214b_0 conda-forge 609kB

• libxml2 2.12.7 h3e169fe_1 conda-forge 619kB

• libzlib 1.3.1 hd23fc13_2 conda-forge 57kB

• libzlib 1.2.13 h87427d6_6 conda-forge 57kB

• mamba 2.0.4 hcd709ef_0 conda-forge 396kB

• mamba 0.1.2 py37ha1cc60f_0 conda-forge 95kB

• sqlite 3.47.0 h6285a30_1 conda-forge 930kB

• sqlite 3.46.0 h28673e1_0 conda-forge 912kB

Summary:

Install: 136 packages
Change: 1 packages
Downgrade: 12 packages

Total download: 265MB

──────────────────────────────────────────────────────────────────────────────────────────────

Confirm changes: [Y/n] y

Transaction starting
icu 11.8MB @ 2.9MB/s 4.0s
biom-format 10.9MB @ 2.3MB/s 4.7s
scipy 20.6MB @ 1.5MB/s 13.3s
pillow 47.2MB @ 3.2MB/s 14.5s
scikit-learn 7.1MB @ 2.7MB/s 2.6s
pandas 8.2MB @ 968.7kB/s 8.5s
matplotlib-base 7.7MB @ 2.0MB/s 3.9s
tensorflow-base 79.4MB @ 4.5MB/s 17.6s
grpc-cpp 4.2MB @ 2.8MB/s 1.5s
numpy 6.3MB @ 3.3MB/s 1.9s
libprotobuf 2.4MB @ 3.2MB/s 0.7s
statsmodels 11.0MB @ 549.4kB/s 19.7s
tensorboard 4.0MB @ 1.8MB/s 2.2s
hdf5 3.2MB @ 2.0MB/s 1.6s
cython 2.1MB @ 2.9MB/s 0.7s
libgfortran5 1.6MB @ 2.3MB/s 0.7s
libopenblas 6.0MB @ 1.6MB/s 3.7s
libmamba 1.3MB @ 2.1MB/s 0.6s
cryptography 1.2MB @ 1.8MB/s 0.5s
scikit-bio 1.3MB @ 1.8MB/s 0.7s
fonttools 2.0MB @ 1.5MB/s 1.3s
ipython 1.2MB @ 1.7MB/s 0.6s
h5py 1.1MB @ 1.5MB/s 0.5s
libabseil 988.6kB @ 1.3MB/s 0.5s
conda 1.0MB @ 1.7MB/s 0.5s
sqlite 912.4kB @ 1.1MB/s 0.7s
jedi 841.3kB @ 2.6MB/s 0.3s
pygments 860.4kB @ 2.5MB/s 0.3s
libsqlite 908.6kB @ 2.2MB/s 0.4s
grpcio 756.9kB @ 1.8MB/s 0.4s
zstandard 792.7kB @ 1.1MB/s 0.6s
tornado 663.2kB @ 1.6MB/s 0.3s
future 726.5kB @ 1.9MB/s 0.4s
tensorflow-estimator 658.4kB @ 2.0MB/s 0.3s
libnghttp2 599.7kB @ 886.4kB/s 0.4s
libxml2 619.3kB @ 1.5MB/s 0.4s
openjpeg 515.5kB @ 1.4MB/s 0.3s
unicodedata2 508.7kB @ ??.?MB/s 0.3s
freetype 599.3kB @ 719.4kB/s 0.3s
libsolv 415.5kB @ 978.8kB/s 0.3s
libwebp-base 355.1kB @ 1.2MB/s 0.3s
libtiff 453.3kB @ 389.0kB/s 0.3s
libcurl 390.7kB @ 389.1kB/s 0.3s
brotli-python 392.2kB @ 433.5kB/s 0.3s
libxcb 312.4kB @ 568.3kB/s 0.2s
llvm-openmp 305.1kB @ 731.8kB/s 0.2s
libbrotlienc 296.4kB @ 1.2MB/s 0.2s
protobuf 311.0kB @ 245.7kB/s 0.2s
lerc 290.3kB @ 729.5kB/s 0.2s
prompt-toolkit 270.3kB @ 644.5kB/s 0.1s
werkzeug 261.3kB @ 732.0kB/s 0.2s
libssh2 259.6kB @ 802.8kB/s 0.2s
libpng 268.5kB @ 731.5kB/s 0.2s
conda-package-handling 255.1kB @ 732.1kB/s 0.2s
seaborn-base 231.9kB @ 852.3kB/s 0.1s
pytest 244.6kB @ 1.4MB/s 0.2s
jpeg 231.8kB @ 972.5kB/s 0.1s
lcms2 253.5kB @ 511.1kB/s 0.3s
patsy 187.2kB @ 539.1kB/s 0.2s
cffi 224.0kB @ 211.7kB/s 0.2s
ruamel_yaml 240.1kB @ 205.8kB/s 0.3s
python-dateutil 222.7kB @ 1.1MB/s 0.2s
joblib 221.2kB @ 977.8kB/s 0.2s
pytz 187.0kB @ 33.5kB/s 0.1s
certifi 163.8kB @ 644.4kB/s 0.1s
spdlog 167.2kB @ 742.8kB/s 0.2s
re2 182.9kB @ 109.2kB/s 0.2s
fmt 181.5kB @ 802.8kB/s 0.2s
click 148.1kB @ 852.0kB/s 0.2s
libgfortran 110.1kB @ 808.6kB/s 0.1s
pycparser 102.7kB @ 972.6kB/s 0.1s
hdmedians 133.5kB @ 275.4kB/s 0.3s
absl-py 100.3kB @ 539.3kB/s 0.2s
traitlets 98.4kB @ ??.?MB/s 0.1s
pycosat 114.0kB @ 231.1kB/s 0.4s
pyparsing 90.1kB @ ??.?MB/s 0.1s
urllib3 94.7kB @ 481.3kB/s 0.1s
mamba 95.3kB @ ??.?MB/s 0.2s
zlib 88.7kB @ ??.?MB/s 0.1s
tqdm 89.5kB @ ??.?MB/s 0.1s
yaml 84.2kB @ ??.?MB/s 0.1s
markdown 78.3kB @ 327.3kB/s 0.1s
libdeflate 86.6kB @ 322.4kB/s 0.2s
parso 75.2kB @ ??.?MB/s 0.1s
libbrotlicommon 67.3kB @ ??.?MB/s 0.1s
opt_einsum 58.0kB @ ??.?MB/s 0.1s
msgpack-python 82.4kB @ ??.?MB/s 0.3s
kiwisolver 64.8kB @ ??.?MB/s 0.2s
requests 57.9kB @ ??.?MB/s 0.1s
libzlib 57.4kB @ ??.?MB/s 0.1s
pexpect 53.6kB @ 154.4kB/s 0.1s
idna 49.8kB @ 317.5kB/s 0.1s
google-pasta 49.1kB @ 154.6kB/s 0.1s
packaging 49.5kB @ ??.?MB/s 0.1s
charset-normalizer 47.3kB @ ??.?MB/s 0.1s
wrapt 46.8kB @ ??.?MB/s 0.1s
natsort 37.3kB @ ??.?MB/s 0.1s
typing_extensions 36.3kB @ ??.?MB/s 0.1s
mock 33.6kB @ ??.?MB/s 0.1s
keras-preprocessing 35.2kB @ ??.?MB/s 0.1s
importlib-metadata 33.6kB @ ??.?MB/s 0.1s
libbrotlidec 29.9kB @ ??.?MB/s 0.1s
wcwidth 32.6kB @ 103.0kB/s 0.2s
keras-applications 30.6kB @ ??.?MB/s 0.1s
pyopenssl 129.0kB @ 77.8kB/s 1.3s
libaec 28.6kB @ 154.5kB/s 0.1s
astor 25.8kB @ ??.?MB/s 0.1s
mmvec 28.2kB @ ??.?MB/s 0.1s
pysocks 28.4kB @ ??.?MB/s 0.1s
pluggy 25.6kB @ ??.?MB/s 0.1s
conda-package-streaming 20.6kB @ ??.?MB/s 0.1s
colorama 25.2kB @ ??.?MB/s 0.1s
cachecontrol 23.7kB @ ??.?MB/s 0.1s
exceptiongroup 20.4kB @ ??.?MB/s 0.1s
xorg-libxdmcp 18.5kB @ ??.?MB/s 0.1s
brotli 19.4kB @ ??.?MB/s 0.1s
tomli 18.2kB @ ??.?MB/s 0.1s
zipp 17.2kB @ ??.?MB/s 0.1s
threadpoolctl 18.3kB @ ??.?MB/s 0.1s
ptyprocess 16.5kB @ ??.?MB/s 0.1s
brotli-bin 16.6kB @ ??.?MB/s 0.1s
libblas 14.7kB @ ??.?MB/s 0.1s
liblapack 14.7kB @ ??.?MB/s 0.1s
libcblas 14.6kB @ ??.?MB/s 0.1s
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Transaction finished

(mmvec_env) megl5@MEGLs-4-iMac ~ %
(mmvec_env) megl5@MEGLs-4-iMac ~ % mmvec --help
Traceback (most recent call last):
File "/opt/miniconda3/envs/mmvec_env/bin/mmvec", line 18, in
from tensorflow.contrib.distributions import Multinomial, Normal
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow/init.py", line 50, in getattr
module = self._load()
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow/init.py", line 44, in _load
module = _importlib.import_module(self.name)
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/importlib/init.py", line 127, in import_module
return _bootstrap._gcd_import(name[level:], package, level)
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_core/contrib/init.py", line 39, in
from tensorflow.contrib import compiler
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_core/contrib/compiler/init.py", line 21, in
from tensorflow.contrib.compiler import jit
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_core/contrib/compiler/init.py", line 22, in
from tensorflow.contrib.compiler import xla
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_core/contrib/compiler/xla.py", line 22, in
from tensorflow.python.estimator import model_fn as model_fn_lib
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_core/python/estimator/model_fn.py", line 26, in
from tensorflow_estimator.python.estimator import model_fn
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_estimator/init.py", line 10, in
from tensorflow_estimator._api.v1 import estimator
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_estimator/_api/v1/estimator/init.py", line 10, in
from tensorflow_estimator._api.v1.estimator import experimental
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_estimator/_api/v1/estimator/experimental/init.py", line 10, in
from tensorflow_estimator.python.estimator.canned.dnn import dnn_logit_fn_builder
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/canned/dnn.py", line 27, in
from tensorflow_estimator.python.estimator import estimator
File "/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_estimator/python/estimator/estimator.py", line 36, in
from tensorflow.python.profiler import trace
ImportError: cannot import name 'trace' from 'tensorflow.python.profiler' (/opt/miniconda3/envs/mmvec_env/lib/python3.7/site-packages/tensorflow_core/python/profiler/init.py)

[natalie-melendez]

Update: I downgraded tensorflow-estimator from 2.6.0 to 1.15.1. with the command pip install tensorflow-estimator==1.15.1

This resolved the tensorflow issue and no longer was I receiving the previous message. I proceeded to run the mmvec paired-omics command on the non-qiime2 version of mmvec and the output folder created had the following files:

However, I am not sure how to visualize these files. I tried running the tensoreboard --logdir . command however, I get this error message:

[minjiahaoKAI]

Update: I downgraded tensorflow-estimator from 2.6.0 to 1.15.1. with the command pip install tensorflow-estimator==1.15.1更新：我将 tensorflow-estimator 从 2.6.0 降级到 1.15.1。使用命令 pip install tensorflow-estimator==1.15.1

This resolved the tensorflow issue and no longer was I receiving the previous message. I proceeded to run the mmvec paired-omics command on the non-qiime2 version of mmvec and the output folder created had the following files:这解决了 tensorflow 问题，我不再收到之前的消息。我继续在非 qiime2 版本的 mmvec 上运行 mmvec paired-omics 命令，创建的输出文件夹包含以下文件：

However, I am not sure how to visualize these files. I tried running the tensoreboard --logdir . command however, I get this error message:但是，我不确定如何可视化这些文件。我尝试运行 tensoreboard --logdir 。但是，我收到以下错误消息：

Hi,
Recently, I'm running these codes to get the co-occurrence of the microorganisms and metabolites of interests to me. And I got the files in the Summary folder, which are exactly the same as the ones you showed. So, I am wondering that if you have already addressed these issues and how you visualize these results?