Adressed issues from previous commit, added functionality for complex numbers

.gitignore

@@ -16,4 +16,8 @@ _static
 _templates
 
 # ipython checkpoints
-docs/source/**/.ipynb_checkpoints
+docs/source/**/.ipynb_checkpoints
+docs/**/.ipynb_checkpoints
+docs/**/.ipynb_checkpoints
+docs/.ipynb_checkpoints
+docs/examples/.ipynb_checkpoints

Data/iron.yml → Data/fe.yaml

@@ -5,7 +5,7 @@ meta:
   comment: maybe there is something to say here
   last_updated: 2020-04-28
   references:
-    @article{curtiss2013unicorn,
+    '@article{curtiss2013unicorn,
     author = "Curtiss, Michael and Becker, Iain and Bosman, Tudor and Doroshenko, Sergey and Grijincu, Lucian and Jackson, Tom and Kunnatur, Sandhya and Lassen, Soren and Pronin, Philip and Sankar, Sriram and others",
     title = "Unicorn: A system for searching the social graph",
     journal = "Proceedings of the VLDB Endowment",
@@ -15,7 +15,7 @@ meta:
     year = "2013",
     publisher = "VLDB Endowment",
     doi = "10.14778/2536222.2536239"
-    }
+    }'
 data:
   thermal_conductivity:
     curtiss2013unicorn:
@@ -25,11 +25,9 @@ data:
       comment: some value comment here
   refractive_index:
     curtiss2013unicorn:
-      value:
-        wavelength:
-          values: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, ...]
-          arg_values: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, ...]
-          arg_unit: nm
-          comment: maybe one can write a fit of the data here
+      value: [1+2j, 2+4j]
+      arg_value: [800]
+      arg_unit: nm
+      comment: maybe one can write a fit of the data here
       unit: ''
       comment: n = n' + i k

Data/ni.yaml

@@ -0,0 +1,51 @@
+meta:
+  name: nickel
+  formula: Ni
+  last_updated: 7272181873150
+  description:
+  comment:
+  references:
+    '@article{wikipedia,
+      author = "Wikipedia",
+      title = "Nickel",
+      url = "https://de.wikipedia.org/wiki/Nickel"
+      }
+
+     @online{ri_info,
+      author = "refractiveindex.info",
+      title = "Nickel",
+      urls = "https://refractiveindex.info/?shelf=main&book=Ni&page=Johnson"
+      }'
+
+data:
+  thermal_conductivity:
+    wikipedia:
+      value: 91
+      units: W/(mK)
+    ri_info:
+      value: 90
+      units: W/(mK)
+
+  molar_volume:
+    wikipedia:
+      value: 6.59e-06
+      units: m^3/mol
+
+  boiling_point:
+    wikipedia:
+      value: 3003.0
+      units: K
+
+  melting_point:
+    wikipedia:
+      value: 1728.0
+      units: K
+
+  refractive_index:
+    ri_info:
+      value: 1+2j
+      arg_value: 0.188
+      arg_name: Wavelength
+      arg_unit: �m
+
+

Data/template.yaml

@@ -0,0 +1,12 @@
+Material name: ''
+References:
+- ''
+Comment: ''
+Data:
+- Parameter name: ''
+  Comment: ''
+  Reference number: 1
+  Units: []
+  Value names: []
+  Values:
+  - []

README.md

@@ -1,8 +1,8 @@
 # MBI Material Database Concept
 
-Due to the general demand for collecting, storing, and exchanging material parameters, we provide a text file based solution as well as several Python scripts and GUIs to read, change, and store material parameters.
+Due to the general demand for collecting, storing, and exchanging material parameters, we provide a text-file-based solution, as well as several Python scripts and GUIs to read, change, and store material parameters.
 
-Initially, we will only support [YAML](https://en.wikipedia.org/wiki/YAML) as data source, but the general design will allow also for other data sources, such as SQL databases or other file formats, e.g. XML or JSON.
+Initially, we will only support the [YAML](https://en.wikipedia.org/wiki/YAML) format as a data source, but the general design will allow also for other data sources, such as SQL databases or other file formats, e.g. XML or JSON.
 
 ## YAML Format
 
@@ -47,25 +47,25 @@ data:
 ```
 
 First, there is `meta` section which holds meta-data of the material.
-Most importantly, the must be references defined, in order to link paramters to them.
-The `references` entrie must be a plain `bibtex` entry which can hold any type of reference and as many as necessary.
+Most importantly, there must be `references` defined, in order to link paramters to them.
+The `references` entry must be in plain `bibtex` format, which can hold any type of reference and as many as necessary.
+Further, one must include a `last_updated` field.
 
 In the `data` section the actual physical parameters are declared.
-It is possible to have multiple values stored for a single parameter, by indexing each by a `citation_key` which must be defined above in the `meta` section.
-The actual parmaters, must have `value` and `unit` as well as optional  `uncertainty` and `comment`.
+It is possible to have multiple values stored for a single parameter, by indexing each by a `citation_key`, which must be defined above in the `meta` section.
+The actual parmaters, must have `value` and `unit` and can optionally have `uncertainty` and `comment`.
 
 In case of a functional dependence of a parameter, the name of the `argument` must be given as new index. The actual parameter `values`, the `arg_values` and `arg_unit` are mandatory.
 The `comment` is optional.
 With this format even multiple functional dependencies are possible from one and the same or multiple references.
 
 ## Python Scripts
 
-Although the YAML file format allows for human readability and does not require any programmatic access, we provide several Python modules in order to inteact with the material database.
+The YAML file format allows for human readability and does not require any programmatic access.
+However, we provide several Python (other languages such as MATLAB can be implemented in the future as well) modules in order to inteact with the material database:
 
-We first stick to Python but other languages such as MATLAB can be implemented in the future as well.
-
-We divide the modules into a `Parser` module which is dedicated for reading and writing data from and to e.g. a YAML file.
-The data must be provided as a `dict` in Python with a well defined structure.
+The `Parser` module is dedicated for reading and writing data from and to e.g. a YAML file.
+New data must be provided as a `dict` in Python with a well-defined structure.
 
 The `Material` module implements a class for `Material`, `Paramter`, and `References` and should allow for easy interaction in a Python script with the material database.
 
@@ -76,11 +76,11 @@ An example of programmatic access to the database could look like this:
 ```python
 import material_database as md
 
-parser = md.PyYamlParser('/my/path/to/yaml/files')
+parser = md.parser.PyYamlParser('/my/path/to/yaml/files')
 
-iron = md.Material(parser.read_from_file(iron.yaml))
+iron = md.Material(parser.load('fe'), log_level=10)
 
-print(iron.thermal_conductivity)
-.. 80
+print(iron.thermal_conductivity['curtisss2013unicorn'].value)
+..80
 
 ```

docs/examples/simple_test.ipynb

@@ -2,9 +2,18 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
    "source": [
     "# configure to autoload all extension\n",
     "%load_ext autoreload\n",
@@ -13,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,58 +32,68 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO :: base path of PyYamlParser set to: ./\n"
+     ]
+    }
+   ],
    "source": [
     "yaml_data_path = './'\n",
     "yaml_parser = md.parser.PyYamlParser(yaml_data_path, log_level=20)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "80"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "material_data = yaml_parser.load('test_material')"
+    "test_material.thermal_conductivity['curtiss2013unicorn'].value"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "82"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "material_data['meta']['references']['curtiss2013unicorn']"
+    "test_material.thermal_conductivity['aad2015combined'].value"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {},
    "outputs": [],
-   "source": [
-    "test_material = md.Material(material_data, log_level=20)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_material.references['curtiss2013unicorn'].dump()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "test_material.parameters['thermal_conductivity'].dump()"
-   ]
+   "source": []
   }
  ],
  "metadata": {
@@ -93,7 +112,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,

docs/examples/test_material.yml → docs/examples/test_material.yaml

@@ -4,7 +4,7 @@ meta:
   description: here is some text describing the material
   comment: some comments
   references: 
-   '@article{aad2015combined,
+      '@article{aad2015combined,
          author = {Aad, Georges and Abbott, B and Abdallah, J and Abdinov, O and Aben, R and Abolins, M and AbouZeid, OS and Abramowicz, H and Abreu, H and Abreu, R and others},
          journal = {Physical review letters},
          number = {19},